transformer_model.py

import torch.nn as nn
import torch

class TransformerBlock(nn.Module):
    def __init__(self, d_model, n_heads, ff_dim):
        super().__init__()
        self.attention = nn.MultiheadAttention(d_model, n_heads, batch_first=True)
        self.ff = nn.Sequential(
            nn.Linear(d_model, ff_dim),
            nn.ReLU(),
            nn.Linear(ff_dim, d_model),
        )
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)

    def forward(self, x):
        attn_output, _ = self.attention(x, x, x)
        x = self.norm1(x + attn_output)
        x = self.norm2(x + self.ff(x))
        return x

class TransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model, n_heads, n_layers, max_len):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_embedding = nn.Parameter(torch.randn(1, max_len, d_model))
        self.transformer_blocks = nn.ModuleList([
            TransformerBlock(d_model, n_heads, ff_dim=4*d_model)
            for _ in range(n_layers)
        ])
        self.output = nn.Linear(d_model, vocab_size)

    def forward(self, x):
        x = self.embedding(x) + self.pos_embedding[:, :x.size(1), :]
        for block in self.transformer_blocks:
            x = block(x)
        return self.output(x)