NanoSocrates/Project_Model/Libs/Embedder/Classes/NanoSocratesEmbedder.py

import torch
from ..Utils import fixed_positional_encoding


# WIP FOR BATCHING
class NanoSocratesEmbedder(torch.nn.Module):

    def __init__(self, vocabulary_size: int, embedding_size: int) -> None:
        super().__init__()
        self.__embedder = torch.nn.Embedding(vocabulary_size, embedding_size)

    def forward(self, tokenized_sentence: list[list[int]]) -> torch.Tensor:

        TOKENIZED_TENSOR = torch.tensor(tokenized_sentence)

        computed_embeddings: torch.Tensor = self.__embedder(TOKENIZED_TENSOR)

        _, SENTENCE_LENGHT, EMBEDDING_SIZE = computed_embeddings.shape  # for batching

        POSITIONAL_ENCODINGS = fixed_positional_encoding(
            SENTENCE_LENGHT, EMBEDDING_SIZE
        )

        computed_embeddings = computed_embeddings + POSITIONAL_ENCODINGS  # for batching
        return computed_embeddings