import torch import torch.nn as nn from torch.nn import functional as F import config as cfg class Head(nn.Module): def __init__(self, head_size): super().__init__() self.key = nn.Linear(cfg.n_embd, head_size, bias=False) self.query = nn.Linear(cfg.n_embd, head_size, bias=False) self.value = nn.Linear(cfg.n_embd, head_size, bias=False) self.register_buffer('tril', torch.tril(torch.ones(cfg.block_size, cfg.block_size))) self.dropout = nn.Dropout(cfg.dropout) def forward(self, x): B,T,C = x.shape k = self.key(x) # (B,T,hs) q = self.query(x) # (B,T,hs) wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T) wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T) wei = F.softmax(wei, dim=-1) # (B, T, T) wei = self.dropout(wei) v = self.value(x) out = wei @ v return out class MultiHeadAttention(nn.Module): """ multiple heads of self-attention in parallel """ def __init__(self, num_heads, head_size): super().__init__() self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) self.proj = nn.Linear(head_size * num_heads, cfg.n_embd) self.dropout = nn.Dropout(cfg.dropout) def forward(self, x): out = torch.cat([h(x) for h in self.heads], dim=-1) out = self.dropout(self.proj(out)) return out class FeedFoward(nn.Module): """ a simple linear layer followed by a non-linearity """ 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), nn.Dropout(cfg.dropout), ) def forward(self, x): return self.net(x) class Block(nn.Module): """ Transformer block: communication followed by computation """ def __init__(self, n_embd, n_head): # n_embd: embedding dimension, n_head: the number of heads we'd like super().__init__() head_size = n_embd // n_head self.sa = MultiHeadAttention(n_head, head_size) self.ffwd = FeedFoward(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 GPTLanguageModel(nn.Module): def __init__(self, vocab_size): super().__init__() # each token directly reads off the logits for the next token from a lookup table self.token_embedding_table = nn.Embedding(vocab_size, cfg.n_embd) self.position_embedding_table = nn.Embedding(cfg.block_size, cfg.n_embd) self.blocks = nn.Sequential(*[Block(cfg.n_embd, n_head=cfg.n_head) for _ in range(cfg.n_layer)]) self.ln_f = nn.LayerNorm(cfg.n_embd) self.lm_head = nn.Linear(cfg.n_embd, vocab_size) self.apply(self._init_weights) def _init_weights(self, module): if isinstance(module, nn.Linear): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) if module.bias is not None: torch.nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) def forward(self, idx, targets=None): B, T = idx.shape # idx and targets are both (B,T) tensor of integers tok_emb = self.token_embedding_table(idx) # (B,T,C) pos_emb = self.position_embedding_table(torch.arange(T, device=cfg.device)) # (T,C) x = tok_emb + pos_emb # (B,T,C) x = self.blocks(x) # (B,T,C) x = self.ln_f(x) # (B,T,C) logits = self.lm_head(x) # (B,T,vocab_size) if targets is None: loss = None else: B, T, C = logits.shape logits = logits.view(B*T, C) targets = targets.view(B*T) loss = F.cross_entropy(logits, targets) return logits, loss def generate(self, idx, max_new_tokens): # idx is (B, T) array of indices in the current context for _ in range(max_new_tokens): idx_cond = idx[:, -cfg.block_size:] logits, loss = self(idx_cond) logits = logits[:, -1, :] probs = F.softmax(logits, dim=-1) idx_next = torch.multinomial(probs, num_samples=1) idx = torch.cat((idx, idx_next), dim=1) return idx