[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
\ No newline at end of file

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/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