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""" 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),
]
)