Add DeBERTa model

docs/model_zoo/index.rst

@@ -84,6 +84,8 @@ PaddleNLP的Transformer预训练模型包含从 `huggingface.co`_ 直接转换
    ChineseBert <transformers/ChineseBert/contents>
    ConvBert <transformers/ConvBert/contents>
    CTRL <transformers/CTRL/contents>
+   Deberta <transformers/Deberta/contents>
+   DebertaV2 <transformers/DebertaV2/contents>
    DistilBert <transformers/DistilBert/contents>
    ELECTRA <transformers/ELECTRA/contents>
    ERNIE <transformers/ERNIE/contents>
@@ -145,6 +147,10 @@ Transformer预训练模型适用任务汇总
 +--------------------+-------------------------+----------------------+--------------------+-----------------+-----------------+
 |CTRL_               | ✅                      | ❌                   | ❌                 | ❌              | ❌              |
 +--------------------+-------------------------+----------------------+--------------------+-----------------+-----------------+
+|Deberta_            | ✅                      | ✅                   | ✅                 | ❌              | ✅              |
++--------------------+-------------------------+----------------------+--------------------+-----------------+-----------------+
+|DebertaV2_          | ✅                      | ✅                   | ✅                 | ❌              | ✅              |
++--------------------+-------------------------+----------------------+--------------------+-----------------+-----------------+
 |DistilBert_         | ✅                      | ✅                   | ✅                 | ❌              | ❌              |
 +--------------------+-------------------------+----------------------+--------------------+-----------------+-----------------+
 |ELECTRA_            | ✅                      | ✅                   | ✅                 | ❌              | ✅              |
@@ -220,6 +226,8 @@ Transformer预训练模型适用任务汇总
 .. _ChineseBert: https://arxiv.org/abs/2106.16038
 .. _ConvBert: https://arxiv.org/abs/2008.02496
 .. _CTRL: https://arxiv.org/abs/1909.05858
+.. _DeBERTa: https://arxiv.org/abs/2006.03654
+.. _DebertaV2: https://arxiv.org/abs/2111.09543
 .. _DistilBert: https://arxiv.org/abs/1910.01108
 .. _ELECTRA: https://arxiv.org/abs/2003.10555
 .. _ERNIE: https://arxiv.org/abs/1904.09223

docs/model_zoo/transformers/Deberta/contents.rst

@@ -0,0 +1,17 @@
+
+
+------------------------------------
+Deberta模型汇总
+------------------------------------
+
+
+
+下表汇总介绍了目前PaddleNLP支持的Deberta模型对应预训练权重。
+
++----------------------------------------------------------------------------------+--------------+----------------------------------------------------------------------------------+
+| Pretrained Weight                                                                | Language     | Details of the model                                                             |
++==================================================================================+==============+==================================================================================+
+|``microsoft/deberta-base``                                                        | English      | 12-layer, 768-hidden,                                                            |
+|                                                                                  |              | 12-heads, 100M parameters.                                                       |
+|                                                                                  |              | It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.|
++----------------------------------------------------------------------------------+--------------+----------------------------------------------------------------------------------+

docs/model_zoo/transformers/DebertaV2/contents.rst

@@ -0,0 +1,30 @@
+
+
+------------------------------------
+DebertaV2模型汇总
+------------------------------------
+
+
+
+下表汇总介绍了目前PaddleNLP支持的DebertaV2模型对应预训练权重。
+
++----------------------------------------------------------------------------------+--------------+----------------------------------------------------------------------------------+
+| Pretrained Weight                                                                | Language     | Details of the model                                                             |
++==================================================================================+==============+==================================================================================+
+|``deepset/deberta-v3-large-squad2``                                               | English      | 24-layer, 1024-hidden,                                                           |
+|                                                                                  |              | 16-heads, 304M parameters.                                                       |
+|                                                                                  |              | The deberta-v3-large model fine-tuned using the SQuAD2.0 dataset.                |
++----------------------------------------------------------------------------------+--------------+----------------------------------------------------------------------------------+
+|``microsoft/deberta-v2-xlarge``                                                   | English      | 24-layer, 1536-hidden,                                                           |
+|                                                                                  |              | 24-heads, 900M parameters.                                                       |
+|                                                                                  |              | The deberta-v2 model.                                                            |
++----------------------------------------------------------------------------------+--------------+----------------------------------------------------------------------------------+
+|``microsoft/deberta-v3-base``                                                     | English      | 12-layer, 768-hidden,                                                            |
+|                                                                                  |              | 12-heads, 86M parameters.                                                        |
+|                                                                                  |              | The deberta-v2 model.                                                            |
++----------------------------------------------------------------------------------+--------------+----------------------------------------------------------------------------------+
+|``microsoft/deberta-v3-large``                                                    | English      | 24-layer, 1024-hidden,                                                           |
+|                                                                                  |              | 16-heads, 304M parameters.                                                       |
+|                                                                                  |              | The deberta-v2 model.                                                            |
++----------------------------------------------------------------------------------+--------------+----------------------------------------------------------------------------------+
+

docs/source/paddlenlp.transformers.deberta.modeling.rst

@@ -0,0 +1,7 @@
+modeling
+===========================================
+
+.. automodule:: paddlenlp.transformers.deberta.modeling
+   :members:
+   :no-undoc-members:
+   :show-inheritance:

docs/source/paddlenlp.transformers.deberta.rst

@@ -0,0 +1,14 @@
+deberta
+========================================
+
+.. automodule:: paddlenlp.transformers.deberta
+   :members:
+   :no-undoc-members:
+   :show-inheritance:
+
+
+.. toctree::
+   :maxdepth: 4
+
+   paddlenlp.transformers.deberta.modeling
+   paddlenlp.transformers.deberta.tokenizer

docs/source/paddlenlp.transformers.deberta.tokenizer.rst

@@ -0,0 +1,7 @@
+tokenizer
+=================================================
+
+.. automodule:: paddlenlp.transformers.deberta.tokenizer
+   :members:
+   :no-undoc-members:
+   :show-inheritance:

docs/source/paddlenlp.transformers.deberta_v2.modeling.rst

@@ -0,0 +1,7 @@
+modeling
+===========================================
+
+.. automodule:: paddlenlp.transformers.deberta_v2.modeling
+   :members:
+   :no-undoc-members:
+   :show-inheritance:

docs/source/paddlenlp.transformers.deberta_v2.rst

@@ -0,0 +1,14 @@
+deberta_v2
+========================================
+
+.. automodule:: paddlenlp.transformers.deberta_v2
+   :members:
+   :no-undoc-members:
+   :show-inheritance:
+
+
+.. toctree::
+   :maxdepth: 4
+
+   paddlenlp.transformers.deberta_v2.modeling
+   paddlenlp.transformers.deberta_v2.tokenizer

docs/source/paddlenlp.transformers.deberta_v2.tokenizer.rst

@@ -0,0 +1,7 @@
+tokenizer
+=================================================
+
+.. automodule:: paddlenlp.transformers.deberta_v2.tokenizer
+   :members:
+   :no-undoc-members:
+   :show-inheritance:

docs/source/paddlenlp.transformers.rst

@@ -24,6 +24,8 @@ paddlenlp.transformers
    paddlenlp.transformers.convbert
    paddlenlp.transformers.ctrl
    paddlenlp.transformers.dallebart
+   paddlenlp.transformers.deberta
+   paddlenlp.transformers.deberta_v2
    paddlenlp.transformers.distilbert
    paddlenlp.transformers.electra
    paddlenlp.transformers.ernie

paddlenlp/transformers/__init__.py

@@ -283,6 +283,12 @@
 from .qwen import *
 from .mixtral.modeling import *
 from .mixtral.configuration import *
+from .deberta.modeling import *
+from .deberta.tokenizer import *
+from .deberta.configuration import *
+from .deberta_v2.modeling import *
+from .deberta_v2.tokenizer import *
+from .deberta_v2.configuration import *
 
 # For faster tokenizer
 from ..utils.import_utils import is_fast_tokenizer_available

paddlenlp/transformers/deberta/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2024 PaddlePaddle Authors. 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.

paddlenlp/transformers/deberta/configuration.py

@@ -0,0 +1,170 @@
+# Copyright (c) 2023 PaddlePaddle Authors. 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.
+
+""" DeBERTa model configuration"""
+from __future__ import annotations
+
+from typing import Dict, List
+
+from paddlenlp.transformers.configuration_utils import PretrainedConfig
+
+__all__ = ["DEBERTA_PRETRAINED_INIT_CONFIGURATION", "DebertaConfig", "DEBERTA_PRETRAINED_RESOURCE_FILES_MAP"]
+
+DEBERTA_PRETRAINED_INIT_CONFIGURATION = {
+    "deberta-base": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 768,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 768,
+        "initializer_range": 0.02,
+        "intermediate_size": 3072,
+        "layer_norm_eps": 1e-07,
+        "max_position_embeddings": 512,
+        "max_relative_positions": -1,
+        "model_type": "deberta",
+        "num_attention_heads": 12,
+        "num_hidden_layers": 12,
+        "output_hidden_states": True,
+        "pad_token_id": 0,
+        "paddlenlp_version": None,
+        "pos_att_type": ["c2p", "p2c"],
+        "position_biased_input": False,
+        "relative_attention": True,
+        "type_vocab_size": 0,
+        "vocab_size": 50265,
+    },
+}
+
+DEBERTA_PRETRAINED_RESOURCE_FILES_MAP = {
+    "model_state": {
+        "microsoft/deberta-base": "https://paddlenlp.bj.bcebos.com/models/community/microsoft/deberta-base/model_state.pdparams"
+    }
+}
+
+
+class DebertaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeBERTaModel`] . It is used to
+    instantiate a DeBERTa model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the DeBERTa
+    DeBERTa-v1-base architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (:obj:`int`, `optional`, defaults to 50265):
+            Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling [`DeBERTaModel`].
+        hidden_size (:obj:`int`, `optional`, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        embedding_size (:obj:`int`, `optional`, defaults to 768):
+            Dimensionality of the embedding layer.
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string,
+            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (:obj:`int`, `optional`, defaults to 0):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling [`DeBERTaModel`].
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        pad_token_id (:obj:`int`, `optional`, defaults to 0):
+            The value used to pad input_ids.
+        position_biased_input (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether add position bias to the input embeddings.
+        pos_att_type (:obj:`List[str]`, `optional`, defaults to :obj:`["p2c", "c2p"]`):
+            The type of relative position attention. It should be a subset of `["p2c", "c2p", "p2p"]`.
+        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether the model returns attentions weights.
+        output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether the model returns all hidden-states.
+        relative_attention (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether use relative position encoding.
+
+    Examples:
+
+    ```python
+    >>> from paddlenlp.transformers import DeBERTaModel, DeBERTaConfig
+
+    >>> # Initializing a DeBERTa DeBERTa-base style configuration
+    >>> configuration = DeBERTaConfig()
+
+    >>> # Initializing a model from the DeBERTa-base-uncased style configuration
+    >>> model = DeBERTaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "deberta"
+    attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
+    pretrained_init_configuration = DEBERTA_PRETRAINED_INIT_CONFIGURATION
+
+    def __init__(
+        self,
+        vocab_size: int = 50265,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_act: str = "gelu",
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 512,
+        type_vocab_size: int = 0,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-7,
+        pad_token_id: int = 0,
+        position_biased_input: bool = False,
+        pos_att_type: List[str] = ["p2c", "c2p"],
+        output_attentions: bool = False,
+        output_hidden_states: bool = True,
+        relative_attention: bool = True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.embedding_size = kwargs.get("embedding_size", hidden_size)
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_biased_input = position_biased_input
+        self.pos_att_type = pos_att_type
+        self.output_attentions = output_attentions
+        self.output_hidden_states = output_hidden_states
+        self.relative_attention = relative_attention
+        self.pad_token_id = pad_token_id