[TensorFlow] Adding GroupViT (#18020)

* chore: initial commit

* chore: adding util methods

yet to work on the nn.functional.interpolate port with align_corener=True

* chore: refactor the utils

* used tf.compat.v1.image.resize to align the F.interpolate function
* added type hints to the method signatures
* added references to the gists where one 2 one alignment of torch and tf has been shown

* chore: adding the layers

* chore: porting all the layers from torch to tf

This is the initial draft, nothing is tested yet.

* chore: aligning the layers with reference to tf clip

* chore: aligning the modules

* added demaraction comments
* added copied and adapted from comments

* chore: aligning with CLIP

* chore: wrangling the layers to keep it tf compatible

* chore: aligning the names of the layers for porting

* chore: style changes

* chore: adding docs and inits

* chore: adding tfp dependencis

the code is taken from TAPAS

* chore: initial commit for testing

* chore: aligning the vision embeddings with the vit implementatino

* chore: changing model prefix

* chore: fixing the name of the model and the layer normalization test case

* chore: every test passes but the slow ones

* chore: fix style and integration test

* chore: moving comments below decorators

* chore: make fixup and fix-copies changes

* chore: adding the Vision and Text Model to check_repo

* chore: modifying the prefix name to align it with the torch implementation

* chore: fix typo in configuration

* choer: changing the name of the model variable

* chore: adding segmentation flag

* chore: gante's review

* chore: style refactor

* chore: amy review

* chore: adding shape_list to parts that have been copied from other snippets

* chore: init batchnorm with torch defaults

* chore: adding shape_list to pass the tests

* test fix: adding seed as 0

* set seed

* chore: changing the straight through trick to fix -ve dimensinos

* chore: adding a dimension to the loss

* chore: adding reviewers and contributors names to the docs

* chore: added changes after review

* chore: code quality fixup

* chore: fixing the segmentation snippet

* chore: adding  to the layer calls

* chore: changing int32 to int64 for inputs of serving

* chore: review changes

* chore: style changes

* chore: remove from_pt=True

* fix: repo consistency

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

docs/source/en/index.mdx

@@ -248,7 +248,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          GPT NeoX           |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
 |      GPT NeoX Japanese      |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            GPT-J            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|          GroupViT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          GroupViT           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/groupvit.mdx

@@ -26,7 +26,7 @@ Tips:
 - You may specify `output_segmentation=True` in the forward of `GroupViTModel` to get the segmentation logits of input texts. 
 - The quickest way to get started with GroupViT is by checking the [example notebooks](https://github.com/xvjiarui/GroupViT/blob/main/demo/GroupViT_hf_inference_notebook.ipynb) (which showcase zero-shot segmentation inference). One can also check out the [HuggingFace Spaces demo](https://huggingface.co/spaces/xvjiarui/GroupViT) to play with GroupViT. 
 
-This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui).
+This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui). The TensorFlow version was contributed by [ariG23498](https://huggingface.co/ariG23498) with the help of [Yih-Dar SHIEH](https://huggingface.co/ydshieh), [Amy Roberts](https://huggingface.co/amyeroberts), and [Joao Gante](https://huggingface.co/joaogante).
 The original code can be found [here](https://github.com/NVlabs/GroupViT).
 
 
@@ -59,3 +59,20 @@ The original code can be found [here](https://github.com/NVlabs/GroupViT).
 
 [[autodoc]] GroupViTVisionModel
     - forward
+
+## TFGroupViTModel
+
+[[autodoc]] TFGroupViTModel
+    - call
+    - get_text_features
+    - get_image_features
+
+## TFGroupViTTextModel
+
+[[autodoc]] TFGroupViTTextModel
+    - call
+
+## TFGroupViTVisionModel
+
+[[autodoc]] TFGroupViTVisionModel
+    - call

src/transformers/__init__.py

@@ -2417,6 +2417,15 @@ else:
             "TFGPTJPreTrainedModel",
         ]
     )
+    _import_structure["models.groupvit"].extend(
+        [
+            "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFGroupViTModel",
+            "TFGroupViTPreTrainedModel",
+            "TFGroupViTTextModel",
+            "TFGroupViTVisionModel",
+        ]
+    )
     _import_structure["models.hubert"].extend(
         [
             "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -4986,6 +4995,13 @@ if TYPE_CHECKING:
             TFGPTJModel,
             TFGPTJPreTrainedModel,
         )
+        from .models.groupvit import (
+            TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFGroupViTModel,
+            TFGroupViTPreTrainedModel,
+            TFGroupViTTextModel,
+            TFGroupViTVisionModel,
+        )
         from .models.hubert import (
             TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFHubertForCTC,

src/transformers/models/auto/modeling_tf_auto.py

@@ -50,6 +50,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("funnel", ("TFFunnelModel", "TFFunnelBaseModel")),
         ("gpt2", "TFGPT2Model"),
         ("gptj", "TFGPTJModel"),
+        ("groupvit", "TFGroupViTModel"),
         ("hubert", "TFHubertModel"),
         ("layoutlm", "TFLayoutLMModel"),
         ("layoutlmv3", "TFLayoutLMv3Model"),

src/transformers/models/groupvit/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -44,6 +44,20 @@ else:
         "GroupViTVisionModel",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_groupvit"] = [
+        "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFGroupViTModel",
+        "TFGroupViTPreTrainedModel",
+        "TFGroupViTTextModel",
+        "TFGroupViTVisionModel",
+    ]
+
 if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -67,6 +81,20 @@ if TYPE_CHECKING:
             GroupViTVisionModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_groupvit import (
+            TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFGroupViTModel,
+            TFGroupViTPreTrainedModel,
+            TFGroupViTTextModel,
+            TFGroupViTVisionModel,
+        )
+
 else:
     import sys
 

src/transformers/models/groupvit/configuration_groupvit.py

@@ -162,7 +162,7 @@ class GroupViTVisionConfig(PretrainedConfig):
             The number of layers in each encoder block.
         num_group_tokens (`List[int]`, *optional*, defaults to [64, 8, 0]):
             The number of group tokens for each stage.
-        num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 0]):
+        num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 8]):
             The number of output groups for each stage, 0 means no group.
         num_attention_heads (`int`, *optional*, defaults to 6):
             Number of attention heads for each attention layer in the Transformer encoder.

src/transformers/models/groupvit/modeling_groupvit.py

@@ -1300,7 +1300,7 @@ class GroupViTVisionModel(GroupViTPreTrainedModel):
         >>> import requests
         >>> from transformers import AutoProcessor, GroupViTVisionModel
 
-        >>> processor = AutoPProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
+        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
         >>> model = GroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"

src/transformers/utils/dummy_tf_objects.py

@@ -1309,6 +1309,37 @@ class TFGPTJPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFGroupViTModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGroupViTPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGroupViTTextModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGroupViTVisionModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/models/groupvit/test_modeling_groupvit.py

@@ -17,6 +17,7 @@
 
 import inspect
 import os
+import random
 import tempfile
 import unittest
 
@@ -24,7 +25,7 @@ import numpy as np
 
 import requests
 from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
-from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+from transformers.testing_utils import is_pt_tf_cross_test, require_torch, require_vision, slow, torch_device
 from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
@@ -95,7 +96,8 @@ class GroupViTVisionModelTester:
         self.seq_length = num_patches
 
     def prepare_config_and_inputs(self):
-        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        rng = random.Random(0)
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], rng=rng)
         config = self.get_config()
 
         return config, pixel_values
@@ -161,6 +163,18 @@ class GroupViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
     def test_inputs_embeds(self):
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        import tensorflow as tf
+
+        seed = 338
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
     def test_model_common_attributes(self):
         config, _ = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -368,7 +382,8 @@ class GroupViTTextModelTester:
         self.scope = scope
 
     def prepare_config_and_inputs(self):
-        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        rng = random.Random(0)
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, rng=rng)
 
         input_mask = None
         if self.use_input_mask:
@@ -532,6 +547,18 @@ class GroupViTModelTest(ModelTesterMixin, unittest.TestCase):
     def test_model_common_attributes(self):
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        import tensorflow as tf
+
+        seed = 163
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
     # override as the `logit_scale` parameter initilization is different for GROUPVIT
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

tests/models/groupvit/test_modeling_tf_groupvit.py

@@ -0,0 +1,715 @@
+# 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 TensorFlow GroupViT model. """
+
+
+import inspect
+import os
+import random
+import tempfile
+import unittest
+from importlib import import_module
+
+import numpy as np
+
+import requests
+from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
+from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_vision, slow
+from transformers.utils import is_tf_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import TFGroupViTModel, TFGroupViTTextModel, TFGroupViTVisionModel, TFSharedEmbeddings
+    from transformers.models.groupvit.modeling_tf_groupvit import TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import CLIPProcessor
+
+
+class TFGroupViTVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        depths=[6, 3, 3],
+        num_group_tokens=[64, 8, 0],
+        num_output_groups=[64, 8, 8],
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.depths = depths
+        self.num_hidden_layers = sum(depths)
+        self.expected_num_hidden_layers = len(depths) + 1
+        self.num_group_tokens = num_group_tokens
+        self.num_output_groups = num_output_groups
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+        num_patches = (image_size // patch_size) ** 2
+        # no [CLS] token for GroupViT
+        self.seq_length = num_patches
+
+    def prepare_config_and_inputs(self):
+
+        rng = random.Random(0)
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], rng=rng)
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return GroupViTVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            depths=self.depths,
+            num_group_tokens=self.num_group_tokens,
+            num_output_groups=self.num_output_groups,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = TFGroupViTVisionModel(config=config)
+        result = model(pixel_values, training=False)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.num_output_groups[-1], self.hidden_size)
+        )
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_tf
+class TFGroupViTVisionModelTest(TFModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as GroupViT does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (TFGroupViTVisionModel,) if is_tf_available() else ()
+
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFGroupViTVisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=GroupViTVisionConfig, has_text_modality=False, hidden_size=37
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="GroupViT does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="GroupViT does not use inputs_embeds")
+    def test_graph_mode_with_inputs_embeds(self):
+        pass
+
+    def test_model_common_attributes(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.call)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    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_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, "seq_length", None)
+
+        expected_num_attention_outputs = sum(g > 0 for g in self.model_tester.num_group_tokens)
+
+        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)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+            attentions = outputs.attentions
+            # GroupViT returns attention grouping of each stage
+            self.assertEqual(len(attentions), sum(g > 0 for g in self.model_tester.num_group_tokens))
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+            attentions = outputs.attentions
+            # GroupViT returns attention grouping of each stage
+            self.assertEqual(len(attentions), expected_num_attention_outputs)
+
+            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)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+
+            added_hidden_states = 1
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = outputs.attentions
+
+            # GroupViT returns attention grouping of each stage
+            self.assertEqual(len(self_attentions), expected_num_attention_outputs)
+            for i, self_attn in enumerate(self_attentions):
+                if self_attn is None:
+                    continue
+
+                self.assertListEqual(
+                    list(self_attentions[i].shape[-2:]),
+                    [
+                        self.model_tester.num_output_groups[i],
+                        self.model_tester.num_output_groups[i - 1] if i > 0 else seq_len,
+                    ],
+                )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+
+            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
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            seq_length = getattr(self.model_tester, "seq_length", None)
+
+            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:
+            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)
+
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        # `GroupViT` computes some indices using argmax, uses them as
+        # one-hot encoding for further computation. The problem is
+        # while PT/TF have very small difference in `y_soft` (~ 1e-9),
+        # the argmax could be totally different, if there are at least
+        # 2 indices with almost identical values. This leads to very
+        # large difference in the outputs. We need specific seeds to
+        # avoid almost identical values happening in `y_soft`.
+        import torch
+
+        seed = 338
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGroupViTVisionModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    @slow
+    def test_saved_model_creation_extended(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        if hasattr(config, "use_cache"):
+            config.use_cache = True
+
+        seq_len = getattr(self.model_tester, "seq_length", None)
+
+        for model_class in self.all_model_classes:
+            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            model = model_class(config)
+            num_out = len(model(class_inputs_dict))
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname, saved_model=True)
+                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
+                model = tf.keras.models.load_model(saved_model_dir)
+                outputs = model(class_inputs_dict)
+                output_hidden_states = outputs["hidden_states"]
+                output_attentions = outputs["attentions"]
+
+                # Check num outputs
+                self.assertEqual(len(outputs), num_out)
+
+                # Check num layers
+                expected_num_layers = getattr(
+                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+                )
+
+                self.assertEqual(len(output_hidden_states), expected_num_layers)
+                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
+
+                # Check attention outputs
+                image_size = (self.model_tester.image_size, self.model_tester.image_size)
+                patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
+                num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+                seq_len = num_patches + 1
+
+                self.assertListEqual(
+                    list(output_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+
+                # Check hidden states
+                self.assertListEqual(
+                    list(output_hidden_states[0].shape[-2:]),
+                    [seq_len, self.model_tester.hidden_size],
+                )
+
+
+class TFGroupViTTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        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.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        rng = random.Random(0)
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, rng=rng)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+            # make sure the first token has attention mask `1` to ensure that, after combining the causal mask, there
+            # is still at least one token being attended to for each batch.
+            # TODO: Change `random_attention_mask` in PT/TF/Flax common test file, after a discussion with the team.
+            input_mask = tf.concat(
+                [tf.ones_like(input_mask[:, :1], dtype=input_mask.dtype), input_mask[:, 1:]], axis=-1
+            )
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return GroupViTTextConfig(
+            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,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = TFGroupViTTextModel(config=config)
+        result = model(input_ids, attention_mask=input_mask, training=False)
+        result = model(input_ids, training=False)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_tf
+class TFGroupViTTextModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (TFGroupViTTextModel,) if is_tf_available() else ()
+    test_pruning = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFGroupViTTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=GroupViTTextConfig, 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)
+
+    @unittest.skip(reason="GroupViTTextModel does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGroupViTTextModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    @slow
+    def test_saved_model_creation_extended(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        if hasattr(config, "use_cache"):
+            config.use_cache = True
+
+        for model_class in self.all_model_classes:
+            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            model = model_class(config)
+            num_out = len(model(class_inputs_dict))
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname, saved_model=True)
+                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
+                model = tf.keras.models.load_model(saved_model_dir)
+                outputs = model(class_inputs_dict)
+                output_hidden_states = outputs["hidden_states"]
+                output_attentions = outputs["attentions"]
+
+                # Check number of outputs
+                self.assertEqual(len(outputs), num_out)
+
+                # Check number of layers
+                expected_num_layers = getattr(
+                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+                )
+
+                # Check hidden states
+                self.assertEqual(len(output_hidden_states), expected_num_layers)
+                self.assertListEqual(
+                    list(output_hidden_states[0].shape[-2:]),
+                    [self.model_tester.seq_length, self.model_tester.hidden_size],
+                )
+
+                # Check attention outputs
+                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
+
+                seq_length = self.model_tester.seq_length
+                key_length = getattr(self.model_tester, "key_length", seq_length)
+
+                self.assertListEqual(
+                    list(output_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_length, key_length],
+                )
+
+
+class TFGroupViTModelTester:
+    def __init__(self, parent, is_training=True):
+        self.parent = parent
+        self.text_model_tester = TFGroupViTTextModelTester(parent)
+        self.vision_model_tester = TFGroupViTVisionModelTester(parent)
+        self.is_training = is_training
+
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, pixel_values
+
+    def get_config(self):
+        return GroupViTConfig.from_text_vision_configs(
+            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
+        model = TFGroupViTModel(config)
+        result = model(input_ids, pixel_values, attention_mask, training=False)
+        self.parent.assertEqual(
+            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
+        )
+        self.parent.assertEqual(
+            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
+        )
+
+    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_loss": True,
+        }
+        return config, inputs_dict
+
+
+@require_tf
+class TFGroupViTModelTest(TFModelTesterMixin, unittest.TestCase):
+    all_model_classes = (TFGroupViTModel,) if is_tf_available() else ()
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFGroupViTModelTester(self)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="hidden_states are tested in individual model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="input_embeds are tested in individual model tests")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="CLIPModel does not have input/output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        # `GroupViT` computes some indices using argmax, uses them as
+        # one-hot encoding for further computation. The problem is
+        # while PT/TF have very small difference in `y_soft` (~ 1e-9),
+        # the argmax could be totally different, if there are at least
+        # 2 indices with almost identical values. This leads to very
+        # large difference in the outputs. We need specific seeds to
+        # avoid almost identical values happening in `y_soft`.
+        import torch
+
+        seed = 158
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
+    # overwrite from common since `TFGroupViTModelTester` set `return_loss` to `True` and causes the preparation of
+    # `symbolic_inputs` failed.
+    def test_keras_save_load(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # remove `return_loss` to make code work
+        if self.__class__.__name__ == "TFGroupViTModelTest":
+            inputs_dict.pop("return_loss", None)
+
+        tf_main_layer_classes = set(
+            module_member
+            for model_class in self.all_model_classes
+            for module in (import_module(model_class.__module__),)
+            for module_member_name in dir(module)
+            if module_member_name.endswith("MainLayer")
+            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
+            and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
+            for module_member in (getattr(module, module_member_name),)
+            if isinstance(module_member, type)
+            and tf.keras.layers.Layer in module_member.__bases__
+            and getattr(module_member, "_keras_serializable", False)
+        )
+        for main_layer_class in tf_main_layer_classes:
+            # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
+            if "T5" in main_layer_class.__name__:
+                # Take the same values than in TFT5ModelTester for this shared layer
+                shared = TFSharedEmbeddings(99, 32, name="shared")
+                config.use_cache = inputs_dict.pop("use_cache", None)
+                main_layer = main_layer_class(config, embed_tokens=shared)
+            else:
+                main_layer = main_layer_class(config)
+
+            symbolic_inputs = {
+                name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
+            }
+
+            model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
+            outputs = model(inputs_dict)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                filepath = os.path.join(tmpdirname, "keras_model.h5")
+                model.save(filepath)
+                if "T5" in main_layer_class.__name__:
+                    model = tf.keras.models.load_model(
+                        filepath,
+                        custom_objects={
+                            main_layer_class.__name__: main_layer_class,
+                            "TFSharedEmbeddings": TFSharedEmbeddings,
+                        },
+                    )
+                else:
+                    model = tf.keras.models.load_model(
+                        filepath, custom_objects={main_layer_class.__name__: main_layer_class}
+                    )
+                assert isinstance(model, tf.keras.Model)
+                after_outputs = model(inputs_dict)
+                self.assert_outputs_same(after_outputs, outputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGroupViTModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    @unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
+    @slow
+    def test_saved_model_creation(self):
+        pass
+
+    @unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
+    @slow
+    def test_saved_model_creation_extended(self):
+        pass
+
+    @unittest.skip(reason="`saved_model` doesn't work with nested outputs so no preparation happens.")
+    @slow
+    def test_prepare_serving_output(self):
+        pass
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@require_vision
+@require_tf
+class TFGroupViTModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "nvidia/groupvit-gcc-yfcc"
+        model = TFGroupViTModel.from_pretrained(model_name)
+        processor = CLIPProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        inputs = processor(
+            text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="tf"
+        )
+
+        outputs = model(**inputs, training=False)
+
+        # verify the logits
+        self.assertEqual(
+            outputs.logits_per_image.shape,
+            tf.TensorShape((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            outputs.logits_per_text.shape,
+            tf.TensorShape((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
+        )
+
+        expected_logits = tf.constant([[13.3523, 6.3629]])
+
+        tf.debugging.assert_near(outputs.logits_per_image, expected_logits, atol=1e-3)

tests/test_modeling_tf_common.py

@@ -757,7 +757,7 @@ class TFModelTesterMixin:
                     name="pixel_values",
                     dtype="float32",
                 )
-            elif model_class.__name__ in ["TFCLIPModel"]:
+            elif model_class.__name__ in ["TFCLIPModel", "TFGroupViTModel"]:
                 inputs = {
                     "input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"),
                     "pixel_values": tf.keras.Input(

utils/check_repo.py

@@ -163,6 +163,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "GroupViTVisionModel",
     "TFCLIPTextModel",
     "TFCLIPVisionModel",
+    "TFGroupViTTextModel",
+    "TFGroupViTVisionModel",
     "FlaxCLIPTextModel",
     "FlaxCLIPVisionModel",
     "FlaxWav2Vec2ForCTC",

utils/documentation_tests.txt

@@ -39,6 +39,8 @@ src/transformers/models/electra/modeling_tf_electra.py
 src/transformers/models/glpn/modeling_glpn.py
 src/transformers/models/gpt2/modeling_gpt2.py
 src/transformers/models/gptj/modeling_gptj.py
+src/transformers/models/groupvit/modeling_groupvit.py
+src/transformers/models/groupvit/modeling_tf_groupvit.py
 src/transformers/models/hubert/modeling_hubert.py
 src/transformers/models/layoutlm/modeling_layoutlm.py
 src/transformers/models/layoutlm/modeling_tf_layoutlm.py