# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors. # Copyright 2020, Microsoft and the HuggingFace Inc. team. # # 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-v2 model configuration, mainly copied from :class:`~transformers.DeBERTaV2Config""" from transformers import PretrainedConfig from modelscope.utils import logger as logging logger = logging.get_logger() class DebertaV2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DebertaV2Model`]. It is used to instantiate a DeBERTa-v2 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 [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: vocab_size (`int`, *optional*, defaults to 128100): Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DebertaV2Model`]. hidden_size (`int`, *optional*, defaults to 1536): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 24): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 6144): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`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 (`int`, *optional*, defaults to 0): The vocabulary size of the `token_type_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-7): The epsilon used by the layer normalization layers. relative_attention (`bool`, *optional*, defaults to `True`): Whether use relative position encoding. max_relative_positions (`int`, *optional*, defaults to -1): The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value as `max_position_embeddings`. pad_token_id (`int`, *optional*, defaults to 0): The value used to pad input_ids. position_biased_input (`bool`, *optional*, defaults to `False`): Whether add absolute position embedding to content embedding. pos_att_type (`List[str]`, *optional*): The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`, `["p2c", "c2p"]`, `["p2c", "c2p"]`. layer_norm_eps (`float`, optional, defaults to 1e-12): The epsilon used by the layer normalization layers. """ model_type = 'deberta_v2' def __init__(self, vocab_size=128100, hidden_size=1536, num_hidden_layers=24, num_attention_heads=24, intermediate_size=6144, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=0, initializer_range=0.02, layer_norm_eps=1e-7, relative_attention=False, max_relative_positions=-1, pad_token_id=0, position_biased_input=True, pos_att_type=None, pooler_dropout=0, pooler_hidden_act='gelu', **kwargs): super().__init__(**kwargs) 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.relative_attention = relative_attention self.max_relative_positions = max_relative_positions self.pad_token_id = pad_token_id self.position_biased_input = position_biased_input # Backwards compatibility if type(pos_att_type) == str: pos_att_type = [x.strip() for x in pos_att_type.lower().split('|')] self.pos_att_type = pos_att_type self.vocab_size = vocab_size self.layer_norm_eps = layer_norm_eps self.pooler_hidden_size = kwargs.get('pooler_hidden_size', hidden_size) self.pooler_dropout = pooler_dropout self.pooler_hidden_act = pooler_hidden_act