NanoSocrates/Project_Model/Libs/Transformer/Models/NanoSocraDecoder.py

import torch
import Project_Model.Libs.Embedder as Embedder
from ..Classes import DeToken

class NanoSocraDecoder(torch.nn.Module):

    def __init__(
        self,
        decoder_embedder: Embedder.NanoSocratesEmbedder,
        decoder_layers: torch.nn.Sequential,
        detokener: DeToken

    ) -> None:
        super().__init__()


        self.__decoder_embedder = decoder_embedder
        self.__decoder = decoder_layers
        self.__detokener = detokener

    def forward(self, args: tuple[torch.Tensor,torch.Tensor, torch.Tensor]):

        decoder_embedder_input, prefix_mask, tgt_padding = args

        decoder_tensor = self.__decoder_embedder(decoder_embedder_input)

        decoder_output, _, _, _, _, _ = self.__decoder(
            (decoder_tensor, decoder_tensor, decoder_tensor, prefix_mask, tgt_padding, True)
        )

        logits: torch.Tensor = self.__detokener(decoder_output)

        return logits