# Copyright (c) 2022 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. """Albert model configuration""" from __future__ import annotations from paddlenlp.transformers.configuration_utils import PretrainedConfig __all__ = ["PRETRAINED_INIT_CONFIGURATION", "RobertaConfig"] PRETRAINED_INIT_CONFIGURATION = { "hfl/roberta-wwm-ext": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 21128, "pad_token_id": 0, }, "hfl/roberta-wwm-ext-large": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 2, "vocab_size": 21128, "pad_token_id": 0, }, "hfl/rbt6": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 6, "type_vocab_size": 2, "vocab_size": 21128, "pad_token_id": 0, }, "hfl/rbt4": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 4, "type_vocab_size": 2, "vocab_size": 21128, "pad_token_id": 0, }, "hfl/rbt3": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 3, "type_vocab_size": 2, "vocab_size": 21128, "pad_token_id": 0, }, "hfl/rbtl3": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 3, "type_vocab_size": 2, "vocab_size": 21128, "pad_token_id": 0, }, } class RobertaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`RobertaModel`]. It is used to instantiate a ALBERT 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 ALBERT albert-base-v1 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 (int): Vocabulary size of `inputs_ids` in `RobertaModel`. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `RobertaModel`. hidden_size (int, optional): Dimensionality of the embedding layer, encoder layers and pooler layer. Defaults to `768`. num_hidden_layers (int, optional): Number of hidden layers in the Transformer encoder. Defaults to `12`. num_attention_heads (int, optional): Number of attention heads for each attention layer in the Transformer encoder. Defaults to `12`. intermediate_size (int, optional): Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to ff layers are firstly projected from `hidden_size` to `intermediate_size`, and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`. Defaults to `3072`. hidden_act (str, optional): The non-linear activation function in the feed-forward layer. ``"gelu"``, ``"relu"`` and any other paddle supported activation functions are supported. Defaults to ``"gelu"``. hidden_dropout_prob (float, optional): The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to `0.1`. attention_probs_dropout_prob (float, optional): The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to `0.1`. max_position_embeddings (int, optional): The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. Defaults to `512`. type_vocab_size (int, optional): The vocabulary size of the `token_type_ids` passed when calling `~transformers.RobertaModel`. Defaults to `2`. initializer_range (float, optional): The standard deviation of the normal initializer. Defaults to 0.02. .. note:: A normal_initializer initializes weight matrices as normal distributions. See :meth:`RobertaPretrainedModel._init_weights()` for how weights are initialized in `RobertaModel`. pad_token_id(int, optional): The index of padding token in the token vocabulary. Defaults to `0`. cls_token_id(int, optional): The index of cls token in the token vocabulary. Defaults to `101`. Examples: ```python >>> from paddlenlp.transformers import RobertaModel, AlbertConfig >>> # Initializing a ALBERT albert-base-v1 style configuration >>> configuration = AlbertConfig() >>> # Initializing a model from the albert-base-v1 style configuration >>> model = RobertaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "roberta" pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION def __init__( self, vocab_size: int = 21128, 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 = 16, initializer_range: float = 0.02, pad_token_id: int = 0, layer_norm_eps: float = 1e-12, cls_token_id: int = 101, **kwargs ): super().__init__(pad_token_id=pad_token_id, cls_token_id=cls_token_id, **kwargs) 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.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.pad_token_id = pad_token_id self.layer_norm_eps = layer_norm_eps self.cls_token_id = cls_token_id