model.py

import torch
import torch.nn as nn
from torch.nn import functional as F

class Head(nn.Module):
    """ One head of self-attention """
    def __init__(self, head_size, n_embd, block_size):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))

    def forward(self, x):
        B, T, C = x.shape
        k = self.key(x)
        q = self.query(x)
        # Compute attention scores ("affinities")
        wei = q @ k.transpose(-2, -1) * k.shape[-1]**-0.5
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
        wei = F.softmax(wei, dim=-1)
        v = self.value(x)
        out = wei @ v
        return out

class MultiHeadAttention(nn.Module):
    def __init__(self, num_heads, head_size, n_embd, block_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size, n_embd, block_size) for _ in range(num_heads)])
        self.proj = nn.Linear(num_heads * head_size, n_embd)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        return self.proj(out)

class FeedForward(nn.Module):
    def __init__(self, n_embd):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_embd, 4 * n_embd),
            nn.ReLU(),
            nn.Linear(4 * n_embd, n_embd),
        )

    def forward(self, x):
        return self.net(x)

class Block(nn.Module):
    def __init__(self, n_embd, n_head, block_size):
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size, n_embd, block_size)
        self.ffwd = FeedForward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        x = x + self.ffwd(self.ln2(x))
        return x

class MiniGPT(nn.Module):
    def __init__(self, vocab_size, n_embd=32, n_head=4, n_layer=3, block_size=64):
        super().__init__()
        self.block_size = block_size
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head, block_size) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx):
        B, T = idx.shape
        tok_emb = self.token_embedding_table(idx)
        pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))
        x = tok_emb + pos_emb
        x = self.blocks(x)
        x = self.ln_f(x)
        logits = self.lm_head(x)
        return logits

    @torch.no_grad()
    def generate(self, idx, max_new_tokens):
        # Generate text token by token
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.block_size:] # Crop context
            logits = self(idx_cond)
            logits = logits[:, -1, :] # Focus only on the last time step
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx