configuration.py

from transformers import PretrainedConfig


class QiDeBERTaConfig(PretrainedConfig):
    model_type = "QiDeBERTa"
    attribute_map = {
        "hidden_size": "d_model",
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
        "intermediate_size": "d_ff",
    }

    def __init__(
        self,
        vocab_size=25500,
        d_model=1024,
        num_layers=24,
        num_heads=16,
        d_ff=4096,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        initializer_range=0.02,
        layer_norm_eps=1e-7,
        relative_attention=True,
        max_relative_positions=-1,
        classifier_num_labels=-1,
        unk_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        pad_token_id=3,
        mask_token_id=4,
        position_biased_input=False,
        position_buckets=256,
        pos_att_type="p2c|c2p",
        share_att_key=True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.d_model = d_model
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.d_ff = d_ff
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.relative_attention = relative_attention
        self.max_relative_positions = max_relative_positions
        self.classifier_num_labels = classifier_num_labels
        self.unk_token_id = unk_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.mask_token_id = mask_token_id
        self.position_biased_input = position_biased_input
        self.share_att_key = share_att_key
        self.position_buckets = position_buckets

        # Backwards compatibility
        if isinstance(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", d_model)