| """GPT-2 style transformer (BPE). Pre-norm, GELU, weight-tied head, causal attention. |
| Trained fully from scratch.""" |
| import math, torch, torch.nn as nn |
| from torch.nn import functional as F |
|
|
| class CausalSelfAttention(nn.Module): |
| def __init__(self, cfg): |
| super().__init__() |
| assert cfg['n_embd'] % cfg['n_head'] == 0 |
| self.c_attn = nn.Linear(cfg['n_embd'], 3 * cfg['n_embd']) |
| self.c_proj = nn.Linear(cfg['n_embd'], cfg['n_embd']) |
| self.n_head = cfg['n_head']; self.n_embd = cfg['n_embd'] |
| self.drop = nn.Dropout(cfg['dropout']) |
| self.resid_drop = nn.Dropout(cfg['dropout']) |
| self.register_buffer('mask', torch.tril(torch.ones(cfg['block_size'], cfg['block_size'])) |
| .view(1, 1, cfg['block_size'], cfg['block_size'])) |
|
|
| def forward(self, x): |
| B, T, C = x.size() |
| q, k, v = self.c_attn(x).split(self.n_embd, dim=2) |
| hs = C // self.n_head |
| q = q.view(B, T, self.n_head, hs).transpose(1, 2) |
| k = k.view(B, T, self.n_head, hs).transpose(1, 2) |
| v = v.view(B, T, self.n_head, hs).transpose(1, 2) |
| att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(hs)) |
| att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float('-inf')) |
| att = self.drop(F.softmax(att, dim=-1)) |
| y = (att @ v).transpose(1, 2).contiguous().view(B, T, C) |
| return self.resid_drop(self.c_proj(y)) |
|
|
| class MLP(nn.Module): |
| def __init__(self, cfg): |
| super().__init__() |
| self.c_fc = nn.Linear(cfg['n_embd'], 4 * cfg['n_embd']) |
| self.c_proj = nn.Linear(4 * cfg['n_embd'], cfg['n_embd']) |
| self.drop = nn.Dropout(cfg['dropout']) |
| def forward(self, x): |
| return self.drop(self.c_proj(F.gelu(self.c_fc(x)))) |
|
|
| class Block(nn.Module): |
| def __init__(self, cfg): |
| super().__init__() |
| self.ln1 = nn.LayerNorm(cfg['n_embd']); self.attn = CausalSelfAttention(cfg) |
| self.ln2 = nn.LayerNorm(cfg['n_embd']); self.mlp = MLP(cfg) |
| def forward(self, x): |
| x = x + self.attn(self.ln1(x)) |
| x = x + self.mlp(self.ln2(x)) |
| return x |
|
|
| class GPT2(nn.Module): |
| def __init__(self, cfg): |
| super().__init__() |
| self.cfg = cfg; self.block_size = cfg['block_size'] |
| self.wte = nn.Embedding(cfg['vocab_size'], cfg['n_embd']) |
| self.wpe = nn.Embedding(cfg['block_size'], cfg['n_embd']) |
| self.drop = nn.Dropout(cfg['dropout']) |
| self.blocks = nn.ModuleList([Block(cfg) for _ in range(cfg['n_layer'])]) |
| self.ln_f = nn.LayerNorm(cfg['n_embd']) |
| self.head = nn.Linear(cfg['n_embd'], cfg['vocab_size'], bias=False) |
| self.wte.weight = self.head.weight |
| self.apply(self._init) |
| for pn, p in self.named_parameters(): |
| if pn.endswith('c_proj.weight'): |
| nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * cfg['n_layer'])) |
|
|
| def _init(self, m): |
| if isinstance(m, nn.Linear): |
| nn.init.normal_(m.weight, mean=0.0, std=0.02) |
| if m.bias is not None: nn.init.zeros_(m.bias) |
| elif isinstance(m, nn.Embedding): |
| nn.init.normal_(m.weight, mean=0.0, std=0.02) |
|
|
| def forward(self, idx, targets=None): |
| B, T = idx.size() |
| pos = torch.arange(0, T, dtype=torch.long, device=idx.device) |
| x = self.drop(self.wte(idx) + self.wpe(pos)) |
| for b in self.blocks: x = b(x) |
| x = self.ln_f(x) |
| logits = self.head(x) |
| loss = None |
| if targets is not None: |
| loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), |
| ignore_index=-100) |
| return logits, loss |
|
|
| @torch.no_grad() |
| def generate(self, idx, max_new_tokens, temperature=0.8, top_k=50, eot_id=None): |
| for _ in range(max_new_tokens): |
| idx_cond = idx[:, -self.block_size:] |
| logits, _ = self(idx_cond) |
| logits = logits[:, -1, :] / temperature |
| if top_k: |
| v, _ = torch.topk(logits, top_k) |
| logits[logits < v[:, [-1]]] = float('-inf') |
| probs = F.softmax(logits, dim=-1) |
| nxt = torch.multinomial(probs, 1) |
| if eot_id is not None and nxt.item() == eot_id: |
| break |
| idx = torch.cat((idx, nxt), dim=1) |
| return idx |
|
|
