Add TFCLIPModel (#13967)

* Start the work for TFCLIPModel

* Convert to TF code (TODO: loss + doc)

* Clean up

* Fix pooled_output for TFCLIPTextTransformer - using tf.gather_nd

* assert -> raise error

* Expose TFCLIPModel

* Deal with dummy_inputs

* Add tests

* Fix all tests. TODO: manual check weight loading + add more comments

* Fix pt tf equivalence test

* fixes

* update TFCLIPVisionEmbeddings's Conv2D

* Fix loss + overwrite test_pt_tf_model_equivalence from common

* Add a comment about the change about MainLayer in test_keras_save_load

* Set return_loss=True in TFCLIPModelTester + make tests pass

* overwrite test_pt_tf_model_equivalence from tf common

* fix base_model_prefix

* Fix examples

* remove unused

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* apply review suggestions

* change self.pre_layrnorm to self.pre_layernorm

* apply more review suggestions

* return attention probs before dropout (to align with PT)

* fix weight init

* fix

* build doc

* fix missing doc

* fix for test

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/index.mdx

@@ -202,7 +202,7 @@ Flax), PyTorch, and/or TensorFlow.
 |       BlenderbotSmall       |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           Canine            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            CLIP             |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
+|            CLIP             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |

docs/source/model_doc/clip.mdx

@@ -125,6 +125,23 @@ This model was contributed by [valhalla](https://huggingface.co/valhalla). The o
 [[autodoc]] CLIPVisionModel
     - forward
 
+## TFCLIPModel
+
+[[autodoc]] TFCLIPModel
+    - call
+    - get_text_features
+    - get_image_features
+
+## TFCLIPTextModel
+
+[[autodoc]] TFCLIPTextModel
+    - call
+
+## TFCLIPVisionModel
+
+[[autodoc]] TFCLIPVisionModel
+    - call
+
 ## FlaxCLIPModel
 
 [[autodoc]] FlaxCLIPModel

src/transformers/__init__.py

@@ -1549,6 +1549,15 @@ if is_tf_available():
             "TFCamembertModel",
         ]
     )
+    _import_structure["models.clip"].extend(
+        [
+            "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFCLIPModel",
+            "TFCLIPPreTrainedModel",
+            "TFCLIPTextModel",
+            "TFCLIPVisionModel",
+        ]
+    )
     _import_structure["models.convbert"].extend(
         [
             "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3394,6 +3403,13 @@ if TYPE_CHECKING:
             TFCamembertForTokenClassification,
             TFCamembertModel,
         )
+        from .models.clip import (
+            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFCLIPModel,
+            TFCLIPPreTrainedModel,
+            TFCLIPTextModel,
+            TFCLIPVisionModel,
+        )
         from .models.convbert import (
             TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFConvBertForMaskedLM,

src/transformers/activations_tf.py

@@ -63,6 +63,12 @@ def gelu_fast(x):
     return 0.5 * x * (1.0 + tf.tanh(x * coeff2 * (1.0 + coeff1 * x * x)))
 
 
+def quick_gelu(x):
+    x = tf.convert_to_tensor(x)
+    coeff = tf.cast(1.702, x.dtype)
+    return x * tf.math.sigmoid(coeff * x)
+
+
 if version.parse(tf.version.VERSION) >= version.parse("2.4"):
 
     def approximate_gelu_wrap(x):
@@ -84,6 +90,7 @@ ACT2FN = {
     "mish": mish,
     "tanh": tf.keras.activations.tanh,
     "gelu_fast": gelu_fast,
+    "quick_gelu": quick_gelu,
 }
 
 

src/transformers/models/auto/modeling_tf_auto.py

@@ -29,6 +29,7 @@ logger = logging.get_logger(__name__)
 TF_MODEL_MAPPING_NAMES = OrderedDict(
     [
         # Base model mapping
+        ("clip", "TFCLIPModel"),
         ("deberta-v2", "TFDebertaV2Model"),
         ("deberta", "TFDebertaModel"),
         ("rembert", "TFRemBertModel"),

src/transformers/models/clip/__init__.py

@@ -20,6 +20,7 @@ from typing import TYPE_CHECKING
 from ...file_utils import (
     _LazyModule,
     is_flax_available,
+    is_tf_available,
     is_tokenizers_available,
     is_torch_available,
     is_vision_available,
@@ -47,6 +48,15 @@ if is_torch_available():
         "CLIPVisionModel",
     ]
 
+if is_tf_available():
+    _import_structure["modeling_tf_clip"] = [
+        "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFCLIPModel",
+        "TFCLIPPreTrainedModel",
+        "TFCLIPTextModel",
+        "TFCLIPVisionModel",
+    ]
+
 if is_flax_available():
     _import_structure["modeling_flax_clip"] = [
         "FlaxCLIPModel",
@@ -78,6 +88,15 @@ if TYPE_CHECKING:
             CLIPVisionModel,
         )
 
+    if is_tf_available():
+        from .modeling_tf_clip import (
+            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFCLIPModel,
+            TFCLIPPreTrainedModel,
+            TFCLIPTextModel,
+            TFCLIPVisionModel,
+        )
+
     if is_flax_available():
         from .modeling_flax_clip import (
             FlaxCLIPModel,

src/transformers/utils/dummy_tf_objects.py

@@ -704,6 +704,57 @@ class TFCamembertModel:
         requires_backends(self, ["tf"])
 
 
+TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFCLIPModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+    def call(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFCLIPPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+    def call(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFCLIPTextModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+    def call(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFCLIPVisionModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+    def call(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/test_modeling_clip.py

@@ -29,6 +29,7 @@ from transformers.file_utils import is_torch_available, is_vision_available
 from transformers.testing_utils import (
     is_flax_available,
     is_pt_flax_cross_test,
+    is_pt_tf_cross_test,
     require_torch,
     require_vision,
     slow,
@@ -581,6 +582,148 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
 
             self.assertTrue(models_equal)
 
+    # overwrite from common since CLIPModel/TFCLIPModel return CLIPOutput/TFCLIPOutput
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        import numpy as np
+        import tensorflow as tf
+
+        import transformers
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            tf_model_class_name = "TF" + model_class.__name__  # Add the "TF" at the beginning
+
+            if not hasattr(transformers, tf_model_class_name):
+                # transformers does not have TF version yet
+                return
+
+            tf_model_class = getattr(transformers, tf_model_class_name)
+
+            config.output_hidden_states = True
+
+            tf_model = tf_model_class(config)
+            pt_model = model_class(config)
+
+            # make sure only tf inputs are forward that actually exist in function args
+            tf_input_keys = set(inspect.signature(tf_model.call).parameters.keys())
+
+            # remove all head masks
+            tf_input_keys.discard("head_mask")
+            tf_input_keys.discard("cross_attn_head_mask")
+            tf_input_keys.discard("decoder_head_mask")
+
+            pt_inputs = self._prepare_for_class(inputs_dict, model_class)
+            pt_inputs = {k: v for k, v in pt_inputs.items() if k in tf_input_keys}
+
+            # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
+            pt_model.eval()
+            tf_inputs_dict = {}
+            for key, tensor in pt_inputs.items():
+                # skip key that does not exist in tf
+                if type(tensor) == bool:
+                    tf_inputs_dict[key] = tensor
+                elif key == "input_values":
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                elif key == "pixel_values":
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                else:
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
+
+            # Check we can load pt model in tf and vice-versa with model => model functions
+            tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
+            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+
+            # need to rename encoder-decoder "inputs" for PyTorch
+            #            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
+            #                pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
+
+            with torch.no_grad():
+                pto = pt_model(**pt_inputs)
+            tfo = tf_model(tf_inputs_dict, training=False)
+
+            self.assertEqual(len(tfo), len(pto), "Output lengths differ between TF and PyTorch")
+            for tf_output, pt_output in zip(tfo.to_tuple(), pto.to_tuple()):
+
+                if not (isinstance(tf_output, tf.Tensor) and isinstance(pt_output, torch.Tensor)):
+                    continue
+
+                tf_out = tf_output.numpy()
+                pt_out = pt_output.numpy()
+
+                self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
+
+                if len(tf_out.shape) > 0:
+
+                    tf_nans = np.copy(np.isnan(tf_out))
+                    pt_nans = np.copy(np.isnan(pt_out))
+
+                    pt_out[tf_nans] = 0
+                    tf_out[tf_nans] = 0
+                    pt_out[pt_nans] = 0
+                    tf_out[pt_nans] = 0
+
+                max_diff = np.amax(np.abs(tf_out - pt_out))
+                self.assertLessEqual(max_diff, 4e-2)
+
+            # Check we can load pt model in tf and vice-versa with checkpoint => model functions
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
+                torch.save(pt_model.state_dict(), pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
+
+                tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
+                tf_model.save_weights(tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
+
+            # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
+            pt_model.eval()
+            tf_inputs_dict = {}
+            for key, tensor in pt_inputs.items():
+                # skip key that does not exist in tf
+                if type(tensor) == bool:
+                    tensor = np.array(tensor, dtype=bool)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor, dtype=tf.int32)
+                elif key == "input_values":
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                elif key == "pixel_values":
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                else:
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
+
+            # need to rename encoder-decoder "inputs" for PyTorch
+            #            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
+            #                pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
+
+            with torch.no_grad():
+                pto = pt_model(**pt_inputs)
+
+            tfo = tf_model(tf_inputs_dict)
+
+            self.assertEqual(len(tfo), len(pto), "Output lengths differ between TF and PyTorch")
+            for tf_output, pt_output in zip(tfo.to_tuple(), pto.to_tuple()):
+
+                if not (isinstance(tf_output, tf.Tensor) and isinstance(pt_output, torch.Tensor)):
+                    continue
+
+                tf_out = tf_output.numpy()
+                pt_out = pt_output.numpy()
+
+                self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
+
+                if len(tf_out.shape) > 0:
+                    tf_nans = np.copy(np.isnan(tf_out))
+                    pt_nans = np.copy(np.isnan(pt_out))
+
+                    pt_out[tf_nans] = 0
+                    tf_out[tf_nans] = 0
+                    pt_out[pt_nans] = 0
+                    tf_out[pt_nans] = 0
+
+                max_diff = np.amax(np.abs(tf_out - pt_out))
+                self.assertLessEqual(max_diff, 4e-2)
+
     # overwrite from common since FlaxCLIPModel returns nested output
     # which is not supported in the common test
     @is_pt_flax_cross_test

tests/test_modeling_tf_clip.py

@@ -0,0 +1,659 @@
+# coding=utf-8
+# Copyright 2021 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 CLIP model. """
+
+
+import inspect
+import os
+import tempfile
+import unittest
+from importlib import import_module
+
+import requests
+from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
+from transformers.file_utils import is_tf_available, is_vision_available
+from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_vision, slow
+
+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 numpy as np
+    import tensorflow as tf
+
+    from transformers import TFCLIPModel, TFCLIPTextModel, TFCLIPVisionModel, TFSharedEmbeddings
+    from transformers.models.clip.modeling_tf_clip import TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import CLIPProcessor
+
+
+class TFCLIPVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        num_hidden_layers=5,
+        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.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.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return CLIPVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            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,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = TFCLIPVisionModel(config=config)
+        result = model(pixel_values, training=False)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.patch_size, self.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 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 TFCLIPVisionModelTest(TFModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as CLIP does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (TFCLIPVisionModel,) 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 = TFCLIPVisionModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=CLIPVisionConfig, has_text_modality=False, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_inputs_embeds(self):
+        # CLIP does not use inputs_embeds
+        pass
+
+    def test_graph_mode_with_inputs_embeds(self):
+        # CLIP does not use inputs_embeds
+        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
+
+        # in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
+        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
+
+        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
+            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)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            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
+
+            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)
+
+            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)
+
+            # CLIP has a different seq_length
+            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_length = num_patches + 1
+
+            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)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFCLIPVisionModel.from_pretrained(model_name, from_pt=True)
+            self.assertIsNotNone(model)
+
+
+class TFCLIPTextModelTester:
+    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):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return CLIPTextConfig(
+            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 = TFCLIPTextModel(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 TFCLIPTextModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (TFCLIPTextModel,) if is_tf_available() else ()
+    test_pruning = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFCLIPTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=CLIPTextConfig, 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_inputs_embeds(self):
+        # CLIP does not use inputs_embeds
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFCLIPTextModel.from_pretrained(model_name, from_pt=True)
+            self.assertIsNotNone(model)
+
+
+class TFCLIPModelTester:
+    def __init__(self, parent, is_training=True):
+        self.parent = parent
+        self.text_model_tester = TFCLIPTextModelTester(parent)
+        self.vision_model_tester = TFCLIPVisionModelTester(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 CLIPConfig.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 = TFCLIPModel(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 TFCLIPModelTest(TFModelTesterMixin, unittest.TestCase):
+    all_model_classes = (TFCLIPModel,) 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 = TFCLIPModelTester(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)
+
+    # hidden_states are tested in individual model tests
+    def test_hidden_states_output(self):
+        pass
+
+    # input_embeds are tested in individual model tests
+    def test_inputs_embeds(self):
+        pass
+
+    # CLIPModel does not have input/output embeddings
+    def test_model_common_attributes(self):
+        pass
+
+    # overwrite from common since `TFCLIPModelTester` 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__ == "TFCLIPModelTest":
+            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)
+
+    # overwrite from common since CLIPModel/TFCLIPModel return CLIPOutput/TFCLIPOutput
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        import torch
+
+        import transformers
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beginning
+            pt_model_class = getattr(transformers, pt_model_class_name)
+
+            config.output_hidden_states = True
+
+            tf_model = model_class(config)
+            pt_model = pt_model_class(config)
+
+            # Check we can load pt model in tf and vice-versa with model => model functions
+
+            tf_model = transformers.load_pytorch_model_in_tf2_model(
+                tf_model, pt_model, tf_inputs=self._prepare_for_class(inputs_dict, model_class)
+            )
+            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+
+            # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
+            pt_model.eval()
+            pt_inputs_dict = {}
+            for name, key in self._prepare_for_class(inputs_dict, model_class).items():
+                if type(key) == bool:
+                    pt_inputs_dict[name] = key
+                elif name == "input_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
+                elif name == "pixel_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
+                else:
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
+
+            # need to rename encoder-decoder "inputs" for PyTorch
+            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
+                pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
+
+            with torch.no_grad():
+                pto = pt_model(**pt_inputs_dict)
+            tfo = tf_model(self._prepare_for_class(inputs_dict, model_class), training=False)
+
+            self.assertEqual(len(tfo), len(pto), "Output lengths differ between TF and PyTorch")
+            for tf_output, pt_output in zip(tfo.to_tuple(), pto.to_tuple()):
+
+                if not (isinstance(tf_output, tf.Tensor) and isinstance(pt_output, torch.Tensor)):
+                    continue
+
+                tf_out = tf_output.numpy()
+                pt_out = pt_output.numpy()
+
+                self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
+
+                if len(tf_out.shape) > 0:
+
+                    tf_nans = np.copy(np.isnan(tf_out))
+                    pt_nans = np.copy(np.isnan(pt_out))
+
+                    pt_out[tf_nans] = 0
+                    tf_out[tf_nans] = 0
+                    pt_out[pt_nans] = 0
+                    tf_out[pt_nans] = 0
+
+                max_diff = np.amax(np.abs(tf_out - pt_out))
+                self.assertLessEqual(max_diff, 4e-2)
+
+            # Check we can load pt model in tf and vice-versa with checkpoint => model functions
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
+                torch.save(pt_model.state_dict(), pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
+
+                tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
+                tf_model.save_weights(tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
+
+            # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
+            pt_model.eval()
+            pt_inputs_dict = {}
+            for name, key in self._prepare_for_class(inputs_dict, model_class).items():
+                if type(key) == bool:
+                    key = np.array(key, dtype=bool)
+                    pt_inputs_dict[name] = torch.from_numpy(key).to(torch.long)
+                elif name == "input_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
+                elif name == "pixel_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
+                else:
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
+            # need to rename encoder-decoder "inputs" for PyTorch
+            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
+                pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
+
+            with torch.no_grad():
+                pto = pt_model(**pt_inputs_dict)
+            tfo = tf_model(self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertEqual(len(tfo), len(pto), "Output lengths differ between TF and PyTorch")
+            for tf_output, pt_output in zip(tfo.to_tuple(), pto.to_tuple()):
+
+                if not (isinstance(tf_output, tf.Tensor) and isinstance(pt_output, torch.Tensor)):
+                    continue
+
+                tf_out = tf_output.numpy()
+                pt_out = pt_output.numpy()
+
+                self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
+
+                if len(tf_out.shape) > 0:
+                    tf_nans = np.copy(np.isnan(tf_out))
+                    pt_nans = np.copy(np.isnan(pt_out))
+
+                    pt_out[tf_nans] = 0
+                    tf_out[tf_nans] = 0
+                    pt_out[pt_nans] = 0
+                    tf_out[pt_nans] = 0
+
+                max_diff = np.amax(np.abs(tf_out - pt_out))
+                self.assertLessEqual(max_diff, 4e-2)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFCLIPModel.from_pretrained(model_name, from_pt=True)
+            self.assertIsNotNone(model)
+
+
+# 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
+class TFCLIPModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "openai/clip-vit-base-patch32"
+        model = TFCLIPModel.from_pretrained(model_name, from_pt=True)
+        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([[24.5701, 19.3049]])
+
+        tf.debugging.assert_near(outputs.logits_per_image, expected_logits, atol=1e-3)

tests/test_modeling_tf_common.py

@@ -282,6 +282,8 @@ class TFModelTesterMixin:
             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__
@@ -458,7 +460,7 @@ class TFModelTesterMixin:
                     "input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"),
                 }
             # TODO: A better way to handle vision models
-            elif model_class.__name__ in ["TFViTModel", "TFViTForImageClassification"]:
+            elif model_class.__name__ in ["TFViTModel", "TFViTForImageClassification", "TFCLIPVisionModel"]:
                 inputs = tf.keras.Input(
                     batch_shape=(
                         3,
@@ -469,6 +471,20 @@ class TFModelTesterMixin:
                     name="pixel_values",
                     dtype="float32",
                 )
+            elif model_class.__name__ in ["TFCLIPModel"]:
+                inputs = {
+                    "input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"),
+                    "pixel_values": tf.keras.Input(
+                        batch_shape=(
+                            3,
+                            self.model_tester.vision_model_tester.num_channels,
+                            self.model_tester.vision_model_tester.image_size,
+                            self.model_tester.vision_model_tester.image_size,
+                        ),
+                        name="pixel_values",
+                        dtype="float32",
+                    ),
+                }
             elif model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                 inputs = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32")
             else:
@@ -1244,6 +1260,13 @@ def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
     return output
 
 
+def random_attention_mask(shape, rng=None, name=None, dtype=None):
+    attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None, dtype=dtype)
+    # make sure that at least one token is attended to for each batch
+    attn_mask = tf.concat([tf.constant(value=1, shape=(shape[0], 1), dtype=dtype), attn_mask[:, 1:]], axis=1)
+    return attn_mask
+
+
 def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
     """Creates a random float32 tensor"""
     if rng is None:

tests/test_modeling_tf_vit.py

@@ -22,7 +22,7 @@ import unittest
 
 from transformers import ViTConfig
 from transformers.file_utils import cached_property, is_tf_available, is_vision_available
-from transformers.testing_utils import require_tf, require_vision, slow
+from transformers.testing_utils import require_tf, require_vision, slow, tooslow
 
 from .test_configuration_common import ConfigTester
 from .test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
@@ -200,7 +200,7 @@ class TFViTModelTest(TFModelTesterMixin, unittest.TestCase):
 
     # overwrite from common since `encoder_seq_length` and `encoder_key_length` are calculated
     # in a different way than in text models.
-    @slow
+    @tooslow
     def test_saved_model_creation_extended(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         config.output_hidden_states = True

utils/check_repo.py

@@ -111,6 +111,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "BeitForMaskedImageModeling",
     "CLIPTextModel",
     "CLIPVisionModel",
+    "TFCLIPTextModel",
+    "TFCLIPVisionModel",
     "FlaxCLIPTextModel",
     "FlaxCLIPVisionModel",
     "FlaxWav2Vec2ForCTC",