model/transformer_block.py

import torch
import torch.nn as nn
from .multi_head_attention import MultiHeadAttention
from .feed_forward import FeedForward

class TransformerBlock(nn.Module):
    def __init__(self, Config):
        super().__init__()
        self.attn = MultiHeadAttention(Config)
        self.ff = FeedForward(Config)
        self.ln1 = nn.LayerNorm(Config.n_embed)
        self.ln2 = nn.LayerNorm(Config.n_embed)

    def forward(self,x):
        x = x + self.attn(self.ln1(x))
        x = x + self.ff(self.ln2(x))
        
        return x