Deberta tf (#12972)

* TFDeberta

moved weights to build and fixed name scope

added missing ,

bug fixes to enable graph mode execution

updated setup.py

fixing typo

fix imports

embedding mask fix

added layer names avoid autmatic incremental names

+XSoftmax

cleanup

added names to layer

disable keras_serializable
Distangled attention output shape hidden_size==None
using symbolic inputs

test for Deberta tf

make style

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

removed tensorflow-probability

removed blank line

* removed tf experimental api
+torch_gather tf implementation from @Rocketknight1

* layername DeBERTa --> deberta

* copyright fix

* added docs for TFDeberta & make style

* layer_name change to fix load from pt model

* layer_name change as pt model

* SequenceClassification layername change,
to same as pt model

* switched to keras built-in LayerNormalization

* added `TFDeberta` prefix most layer classes

* updated to tf.Tensor in the docstring

docs/source/index.rst

@@ -343,7 +343,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           DeBERTa           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/deberta.rst

@@ -38,7 +38,8 @@ the training data performs consistently better on a wide range of NLP tasks, ach
 pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*
 
 
-This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. The original code can be found `here
+This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. This model TF 2.0 implementation was
+contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__ . The original code can be found `here
 <https://github.com/microsoft/DeBERTa>`__.
 
 
@@ -103,3 +104,45 @@ DebertaForQuestionAnswering
 
 .. autoclass:: transformers.DebertaForQuestionAnswering
     :members: forward
+
+
+TFDebertaModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDebertaModel
+    :members: call
+
+
+TFDebertaPreTrainedModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDebertaPreTrainedModel
+    :members: call
+
+
+TFDebertaForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDebertaForMaskedLM
+    :members: call
+
+
+TFDebertaForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDebertaForSequenceClassification
+    :members: call
+
+
+TFDebertaForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDebertaForTokenClassification
+    :members: call
+
+
+TFDebertaForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDebertaForQuestionAnswering
+    :members: call

src/transformers/__init__.py

@@ -1297,6 +1297,17 @@ if is_tf_available():
             "TFCTRLPreTrainedModel",
         ]
     )
+    _import_structure["models.deberta"].extend(
+        [
+            "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFDebertaForMaskedLM",
+            "TFDebertaForQuestionAnswering",
+            "TFDebertaForSequenceClassification",
+            "TFDebertaForTokenClassification",
+            "TFDebertaModel",
+            "TFDebertaPreTrainedModel",
+        ]
+    )
     _import_structure["models.distilbert"].extend(
         [
             "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -2820,6 +2831,15 @@ if TYPE_CHECKING:
             TFCTRLModel,
             TFCTRLPreTrainedModel,
         )
+        from .models.deberta import (
+            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFDebertaForMaskedLM,
+            TFDebertaForQuestionAnswering,
+            TFDebertaForSequenceClassification,
+            TFDebertaForTokenClassification,
+            TFDebertaModel,
+            TFDebertaPreTrainedModel,
+        )
         from .models.distilbert import (
             TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFDistilBertForMaskedLM,

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
+        ("deberta", "TFDebertaModel"),
         ("rembert", "TFRemBertModel"),
         ("roformer", "TFRoFormerModel"),
         ("convbert", "TFConvBertModel"),
@@ -144,6 +145,7 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
 TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
     [
         # Model for Masked LM mapping
+        ("deberta", "TFDebertaForMaskedLM"),
         ("rembert", "TFRemBertForMaskedLM"),
         ("roformer", "TFRoFormerForMaskedLM"),
         ("convbert", "TFConvBertForMaskedLM"),
@@ -183,6 +185,7 @@ TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
 TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Sequence Classification mapping
+        ("deberta", "TFDebertaForSequenceClassification"),
         ("rembert", "TFRemBertForSequenceClassification"),
         ("roformer", "TFRoFormerForSequenceClassification"),
         ("convbert", "TFConvBertForSequenceClassification"),
@@ -211,6 +214,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
 TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
     [
         # Model for Question Answering mapping
+        ("deberta", "TFDebertaForQuestionAnswering"),
         ("rembert", "TFRemBertForQuestionAnswering"),
         ("roformer", "TFRoFormerForQuestionAnswering"),
         ("convbert", "TFConvBertForQuestionAnswering"),
@@ -234,6 +238,7 @@ TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
 TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Token Classification mapping
+        ("deberta", "TFDebertaForTokenClassification"),
         ("rembert", "TFRemBertForTokenClassification"),
         ("roformer", "TFRoFormerForTokenClassification"),
         ("convbert", "TFConvBertForTokenClassification"),

src/transformers/models/deberta/__init__.py

@@ -18,7 +18,7 @@
 
 from typing import TYPE_CHECKING
 
-from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
+from ...file_utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
 
 
 _import_structure = {
@@ -40,6 +40,17 @@ if is_torch_available():
         "DebertaPreTrainedModel",
     ]
 
+if is_tf_available():
+    _import_structure["modeling_tf_deberta"] = [
+        "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFDebertaForMaskedLM",
+        "TFDebertaForQuestionAnswering",
+        "TFDebertaForSequenceClassification",
+        "TFDebertaForTokenClassification",
+        "TFDebertaModel",
+        "TFDebertaPreTrainedModel",
+    ]
+
 
 if TYPE_CHECKING:
     from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig
@@ -59,6 +70,18 @@ if TYPE_CHECKING:
             DebertaPreTrainedModel,
         )
 
+    if is_tf_available():
+        from .modeling_tf_deberta import (
+            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFDebertaForMaskedLM,
+            TFDebertaForQuestionAnswering,
+            TFDebertaForSequenceClassification,
+            TFDebertaForTokenClassification,
+            TFDebertaModel,
+            TFDebertaPreTrainedModel,
+        )
+
+
 else:
     import sys
 

src/transformers/utils/dummy_tf_objects.py

@@ -634,6 +634,63 @@ class TFCTRLPreTrainedModel:
         requires_backends(cls, ["tf"])
 
 
+TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFDebertaForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFDebertaForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFDebertaForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFDebertaForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFDebertaModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFDebertaPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
 TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/test_modeling_tf_deberta.py

+# 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.
+
+
+import unittest
+
+from transformers import DebertaConfig, is_tf_available
+from transformers.testing_utils import require_tf, slow
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import (
+        TFDebertaForMaskedLM,
+        TFDebertaForQuestionAnswering,
+        TFDebertaForSequenceClassification,
+        TFDebertaForTokenClassification,
+        TFDebertaModel,
+    )
+
+
+class TFDebertaModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = 13
+        self.seq_length = 7
+        self.is_training = True
+        self.use_input_mask = True
+        self.use_token_type_ids = True
+        self.use_labels = True
+        self.vocab_size = 99
+        self.hidden_size = 32
+        self.num_hidden_layers = 5
+        self.num_attention_heads = 4
+        self.intermediate_size = 37
+        self.hidden_act = "gelu"
+        self.hidden_dropout_prob = 0.1
+        self.attention_probs_dropout_prob = 0.1
+        self.max_position_embeddings = 512
+        self.type_vocab_size = 16
+        self.relative_attention = False
+        self.max_relative_positions = -1
+        self.position_biased_input = True
+        self.type_sequence_label_size = 2
+        self.initializer_range = 0.02
+        self.num_labels = 3
+        self.num_choices = 4
+        self.scope = None
+
+    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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+
+        config = DebertaConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            relative_attention=self.relative_attention,
+            max_relative_positions=self.max_relative_positions,
+            position_biased_input=self.position_biased_input,
+            initializer_range=self.initializer_range,
+            return_dict=True,
+        )
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFDebertaModel(config=config)
+        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
+
+        inputs = [input_ids, input_mask]
+        result = model(inputs)
+
+        result = model(input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_masked_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFDebertaForMaskedLM(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFDebertaForSequenceClassification(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFDebertaForTokenClassification(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def create_and_check_for_question_answering(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFDebertaForQuestionAnswering(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+        result = model(inputs)
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_tf
+class TFDebertaModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            TFDebertaModel,
+            TFDebertaForMaskedLM,
+            TFDebertaForQuestionAnswering,
+            TFDebertaForSequenceClassification,
+            TFDebertaForTokenClassification,
+        )
+        if is_tf_available()
+        else ()
+    )
+
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFDebertaModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=DebertaConfig, 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_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model = TFDebertaModel.from_pretrained("kamalkraj/deberta-base")
+        self.assertIsNotNone(model)
+
+
+@require_tf
+class TFDeBERTaModelIntegrationTest(unittest.TestCase):
+    @unittest.skip(reason="Model not available yet")
+    def test_inference_masked_lm(self):
+        pass
+
+    @slow
+    def test_inference_no_head(self):
+        model = TFDebertaModel.from_pretrained("kamalkraj/deberta-base")
+        input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
+        attention_mask = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
+        output = model(input_ids, attention_mask=attention_mask)[0]
+
+        expected_slice = tf.constant(
+            [
+                [
+                    [-0.59855896, -0.80552566, -0.8462135],
+                    [1.4484025, -0.93483794, -0.80593085],
+                    [0.3122741, 0.00316059, -1.4131377],
+                ]
+            ]
+        )
+        tf.debugging.assert_near(output[:, 1:4, 1:4], expected_slice, atol=1e-4)