from __future__ import annotations from dataclasses import dataclass from typing import Optional import torch import torch.nn as nn import torch.nn.functional as F @dataclass class GPTConfig: vocab_size: int = 32000 block_size: int = 256 n_layer: int = 6 n_head: int = 8 n_embd: int = 384 dropout: float = 0.1 bias: bool = False class CausalSelfAttention(nn.Module): def __init__(self, config: GPTConfig): super().__init__() assert config.n_embd % config.n_head == 0, "n_embd must be divisible by n_head" self.n_head = config.n_head self.n_embd = config.n_embd self.dropout = config.dropout self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias) self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) self.attn_dropout = nn.Dropout(config.dropout) self.resid_dropout = nn.Dropout(config.dropout) self.register_buffer( "bias", torch.tril(torch.ones(config.block_size, config.block_size)).view(1, 1, config.block_size, config.block_size), persistent=False, ) def forward(self, x: torch.Tensor) -> torch.Tensor: b, t, c = x.size() q, k, v = self.c_attn(x).split(self.n_embd, dim=2) q = q.view(b, t, self.n_head, c // self.n_head).transpose(1, 2) k = k.view(b, t, self.n_head, c // self.n_head).transpose(1, 2) v = v.view(b, t, self.n_head, c // self.n_head).transpose(1, 2) # Prefer PyTorch's fused scaled-dot-product attention when available. if hasattr(F, "scaled_dot_product_attention"): y = F.scaled_dot_product_attention( q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0.0, is_causal=True, ) else: att = (q @ k.transpose(-2, -1)) * (1.0 / (k.size(-1) ** 0.5)) att = att.masked_fill(self.bias[:, :, :t, :t] == 0, float("-inf")) att = F.softmax(att, dim=-1) att = self.attn_dropout(att) y = att @ v y = y.transpose(1, 2).contiguous().view(b, t, c) return self.resid_dropout(self.c_proj(y)) class MLP(nn.Module): def __init__(self, config: GPTConfig): super().__init__() self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) self.gelu = nn.GELU() self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) self.dropout = nn.Dropout(config.dropout) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.dropout(self.c_proj(self.gelu(self.c_fc(x)))) class Block(nn.Module): def __init__(self, config: GPTConfig): super().__init__() self.ln_1 = nn.LayerNorm(config.n_embd, bias=config.bias) self.attn = CausalSelfAttention(config) self.ln_2 = nn.LayerNorm(config.n_embd, bias=config.bias) self.mlp = MLP(config) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x + self.attn(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class GPTLanguageModel(nn.Module): def __init__(self, config: GPTConfig): super().__init__() self.config = config self.transformer = nn.ModuleDict( dict( wte=nn.Embedding(config.vocab_size, config.n_embd), wpe=nn.Embedding(config.block_size, config.n_embd), drop=nn.Dropout(config.dropout), h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ln_f=nn.LayerNorm(config.n_embd, bias=config.bias), ) ) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Weight tying saves parameters and is common in GPT-style models. self.transformer.wte.weight = self.lm_head.weight self.apply(self._init_weights) for name, param in self.named_parameters(): if name.endswith("c_proj.weight"): nn.init.normal_(param, mean=0.0, std=0.02 / (2 * config.n_layer) ** 0.5) def _init_weights(self, module: nn.Module) -> None: if isinstance(module, nn.Linear): nn.init.normal_(module.weight, mean=0.0, std=0.02) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, mean=0.0, std=0.02) def forward_hidden(self, idx: torch.Tensor) -> torch.Tensor: b, t = idx.size() if t > self.config.block_size: raise ValueError(f"Sequence length {t} exceeds block_size {self.config.block_size}") pos = torch.arange(0, t, dtype=torch.long, device=idx.device) tok_emb = self.transformer.wte(idx) pos_emb = self.transformer.wpe(pos) x = self.transformer.drop(tok_emb + pos_emb) for block in self.transformer.h: x = block(x) return self.transformer.ln_f(x) def forward(self, idx: torch.Tensor, targets: Optional[torch.Tensor] = None): x = self.forward_hidden(idx) logits = self.lm_head(x) loss = None if targets is not None: loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) return logits, loss @torch.no_grad() def generate( self, idx: torch.Tensor, max_new_tokens: int, temperature: float = 1.0, top_k: Optional[int] = None, top_p: float = 1.0, repetition_penalty: float = 1.0, ) -> torch.Tensor: for _ in range(max_new_tokens): idx_cond = idx[:, -self.config.block_size :] logits, _ = self(idx_cond) logits = logits[:, -1, :] / max(temperature, 1e-8) if repetition_penalty > 1.0: # Downweight tokens already seen in the current context to reduce loops. for batch_idx in range(idx.size(0)): seen_tokens = torch.unique(idx[batch_idx]) logits[batch_idx, seen_tokens] = logits[batch_idx, seen_tokens] / repetition_penalty if top_k is not None and top_k > 0: v, _ = torch.topk(logits, min(top_k, logits.size(-1))) logits[logits < v[:, [-1]]] = -float("inf") if 0.0 < top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) sorted_probs = F.softmax(sorted_logits, dim=-1) cumulative_probs = torch.cumsum(sorted_probs, dim=-1) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = False indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) logits = logits.masked_fill(indices_to_remove, -float("inf")) probs = F.softmax(logits, dim=-1) idx_next = torch.multinomial(probs, num_samples=1) idx = torch.cat((idx, idx_next), dim=1) return idx def config_from_dict(cfg: dict) -> GPTConfig: return GPTConfig( vocab_size=int(cfg["vocab_size"]), block_size=int(cfg["block_size"]), n_layer=int(cfg["n_layer"]), n_head=int(cfg["n_head"]), n_embd=int(cfg["n_embd"]), dropout=float(cfg.get("dropout", 0.1)), bias=bool(cfg.get("bias", False)), )