wehub-resource-sync
170 lines
7.8 KiB
Python
170 lines
7.8 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" DeBERTa model configuration"""
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from __future__ import annotations
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from typing import Dict, List
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from paddlenlp.transformers.configuration_utils import PretrainedConfig
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__all__ = ["DEBERTA_PRETRAINED_INIT_CONFIGURATION", "DebertaConfig", "DEBERTA_PRETRAINED_RESOURCE_FILES_MAP"]
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DEBERTA_PRETRAINED_INIT_CONFIGURATION = {
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"deberta-base": {
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"attention_probs_dropout_prob": 0.1,
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"embedding_size": 768,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-07,
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"max_position_embeddings": 512,
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"max_relative_positions": -1,
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"model_type": "deberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_hidden_states": True,
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"pad_token_id": 0,
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"pos_att_type": ["c2p", "p2c"],
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"position_biased_input": False,
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"relative_attention": True,
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"type_vocab_size": 0,
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"vocab_size": 50265,
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},
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}
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DEBERTA_PRETRAINED_RESOURCE_FILES_MAP = {
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"model_state": {
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"microsoft/deberta-base": "https://paddlenlp.bj.bcebos.com/models/community/microsoft/deberta-base/model_state.pdparams"
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}
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}
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class DebertaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DeBERTaModel`] . It is used to
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instantiate a DeBERTa model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the DeBERTa
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DeBERTa-v1-base architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (:obj:`int`, `optional`, defaults to 50265):
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Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the
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:obj:`inputs_ids` passed when calling [`DeBERTaModel`].
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hidden_size (:obj:`int`, `optional`, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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embedding_size (:obj:`int`, `optional`, defaults to 768):
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Dimensionality of the embedding layer.
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num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (:obj:`int`, `optional`, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (:obj:`int`, `optional`, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string,
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:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
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hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (:obj:`int`, `optional`, defaults to 0):
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The vocabulary size of the :obj:`token_type_ids` passed when calling [`DeBERTaModel`].
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initializer_range (:obj:`float`, `optional`, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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pad_token_id (:obj:`int`, `optional`, defaults to 0):
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The value used to pad input_ids.
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position_biased_input (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether add position bias to the input embeddings.
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pos_att_type (:obj:`List[str]`, `optional`, defaults to :obj:`["p2c", "c2p"]`):
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The type of relative position attention. It should be a subset of `["p2c", "c2p", "p2p"]`.
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output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether the model returns attentions weights.
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output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether the model returns all hidden-states.
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relative_attention (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether use relative position encoding.
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Examples:
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```python
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>>> from paddlenlp.transformers import DeBERTaModel, DeBERTaConfig
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>>> # Initializing a DeBERTa DeBERTa-base style configuration
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>>> configuration = DeBERTaConfig()
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>>> # Initializing a model from the DeBERTa-base-uncased style configuration
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>>> model = DeBERTaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "deberta"
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attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
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pretrained_init_configuration = DEBERTA_PRETRAINED_INIT_CONFIGURATION
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def __init__(
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self,
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vocab_size: int = 50265,
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hidden_size: int = 768,
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num_hidden_layers: int = 12,
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num_attention_heads: int = 12,
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intermediate_size: int = 3072,
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hidden_act: str = "gelu",
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hidden_dropout_prob: float = 0.1,
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attention_probs_dropout_prob: float = 0.1,
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max_position_embeddings: int = 512,
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type_vocab_size: int = 0,
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initializer_range: float = 0.02,
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layer_norm_eps: float = 1e-7,
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pad_token_id: int = 0,
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position_biased_input: bool = False,
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pos_att_type: List[str] = ["p2c", "c2p"],
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output_attentions: bool = False,
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output_hidden_states: bool = True,
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relative_attention: bool = True,
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**kwargs
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.embedding_size = kwargs.get("embedding_size", hidden_size)
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.position_biased_input = position_biased_input
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self.pos_att_type = pos_att_type
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self.output_attentions = output_attentions
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self.output_hidden_states = output_hidden_states
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self.relative_attention = relative_attention
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self.pad_token_id = pad_token_id
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