# coding=utf-8 # # 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. """ VGCN-BERT model configuration""" from collections import OrderedDict from typing import Mapping from transformers.configuration_utils import PretrainedConfig from transformers.onnx import OnnxConfig from transformers.utils import logging logger = logging.get_logger(__name__) VGCNBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "zhibinlu/vgcn-distilbert-base-uncased": "https://huggingface.co/zhibinlu/vgcn-distilbert-base-uncased/resolve/main/config.json", } class VGCNBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`VGCNBertModel`] or a [`TFVGCNBertModel`]. It is used to instantiate a VGCN-BERT 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 VGCN-BERT [zhibinlu/vgcn-distilbert-base-uncased](https://huggingface.co/zhibinlu/vgcn-distilbert-base-uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vgcn_graph_embedding_dim (`int`, *optional*, defaults to 16): Dimensionality of the number of output embedding from VGCN graph embedding module. vgcn_hidden_dim (`int`, *optional*, defaults to 128): Dimensionality of the graph convolutional hidden layer in VGCN. vgcn_activation (`str` or `Callable`, *optional*, defaults to `"None"`): The non-linear activation function (function or string) for graph convolutional layer in VGCN. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. vgcn_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for VGCN graph embedding module. vgcn_weight_init_mode (`str`, defaults to `"transparent"`): The weight initialization mode for VGCN graph embedding module, `"transparent"`, `"normal"`, `"uniform"` are supported. vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the VGCN-BERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`VGCNBertModel`] or [`TFVGCNBertModel`]. 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). sinusoidal_pos_embds (`boolean`, *optional*, defaults to `False`): Whether to use sinusoidal positional embeddings. n_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. n_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. dim (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. hidden_dim (`int`, *optional*, defaults to 3072): The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. activation (`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"` and `"gelu_new"` are supported. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. qa_dropout (`float`, *optional*, defaults to 0.1): The dropout probabilities used in the question answering model [`VGCNBertForQuestionAnswering`]. seq_classif_dropout (`float`, *optional*, defaults to 0.2): The dropout probabilities used in the sequence classification and the multiple choice model [`VGCNBertForSequenceClassification`]. Examples: ```python >>> from transformers import VGCNBertConfig, VGCNBertModel >>> # Initializing a VGCN-BERT configuration >>> configuration = VGCNBertConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = VGCNBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "vgcn-bert" attribute_map = { "hidden_size": "dim", "num_attention_heads": "n_heads", "num_hidden_layers": "n_layers", } def __init__( self, vgcn_graph_embds_dim=16, vgcn_hidden_dim=128, vgcn_activation=None, vgcn_dropout=0.1, vgcn_weight_init_mode="transparent", vocab_size=30522, max_position_embeddings=512, sinusoidal_pos_embds=False, n_layers=6, n_heads=12, dim=768, hidden_dim=4 * 768, dropout=0.1, attention_dropout=0.1, activation="gelu", initializer_range=0.02, qa_dropout=0.1, seq_classif_dropout=0.2, pad_token_id=0, **kwargs, ): self.vgcn_graph_embds_dim = vgcn_graph_embds_dim self.vgcn_hidden_dim = vgcn_hidden_dim self.vgcn_activation = vgcn_activation self.vgcn_dropout = vgcn_dropout self.vgcn_weight_init_mode = vgcn_weight_init_mode self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.sinusoidal_pos_embds = sinusoidal_pos_embds self.n_layers = n_layers self.n_heads = n_heads self.dim = dim self.hidden_dim = hidden_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation = activation self.initializer_range = initializer_range self.qa_dropout = qa_dropout self.seq_classif_dropout = seq_classif_dropout super().__init__(**kwargs, pad_token_id=pad_token_id) class VGCNBertOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} else: dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ] )