| import math
|
| from dataclasses import dataclass
|
|
|
| import torch
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
|
|
|
|
| @dataclass
|
| class GPTConfig:
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| vocab_size: int = 50257
|
| block_size: int = 256
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| n_layer: int = 6
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| n_head: int = 6
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| n_embd: int = 384
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| dropout: float = 0.1
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| bias: bool = False
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|
|
|
|
| class CausalSelfAttention(nn.Module):
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| def __init__(self, cfg: GPTConfig):
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| super().__init__()
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| assert cfg.n_embd % cfg.n_head == 0
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| self.n_head = cfg.n_head
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| self.qkv = nn.Linear(cfg.n_embd, 3 * cfg.n_embd, bias=cfg.bias)
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| self.proj = nn.Linear(cfg.n_embd, cfg.n_embd, bias=cfg.bias)
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| self.attn_drop = cfg.dropout
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| self.resid_drop = nn.Dropout(cfg.dropout)
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|
|
| def forward(self, x):
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| B, T, C = x.shape
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| q, k, v = self.qkv(x).split(C, dim=2)
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| q, k, v = (t.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) for t in (q, k, v))
|
| y = F.scaled_dot_product_attention(
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| q, k, v,
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| dropout_p=self.attn_drop if self.training else 0.0,
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| is_causal=True,
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| )
|
| y = y.transpose(1, 2).contiguous().view(B, T, C)
|
| return self.resid_drop(self.proj(y))
|
|
|
|
|
| class MLP(nn.Module):
|
| def __init__(self, cfg: GPTConfig):
|
| super().__init__()
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| self.fc = nn.Linear(cfg.n_embd, 4 * cfg.n_embd, bias=cfg.bias)
|
| self.proj = nn.Linear(4 * cfg.n_embd, cfg.n_embd, bias=cfg.bias)
|
| self.drop = nn.Dropout(cfg.dropout)
|
|
|
| def forward(self, x):
|
| return self.drop(self.proj(F.gelu(self.fc(x))))
|
|
|
|
|
| class Block(nn.Module):
|
| """Pre-LayerNorm transformer block."""
|
|
|
| def __init__(self, cfg: GPTConfig):
|
| super().__init__()
|
| self.ln1 = nn.LayerNorm(cfg.n_embd, bias=cfg.bias)
|
| self.attn = CausalSelfAttention(cfg)
|
| self.ln2 = nn.LayerNorm(cfg.n_embd, bias=cfg.bias)
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| self.mlp = MLP(cfg)
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|
|
| def forward(self, x):
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| x = x + self.attn(self.ln1(x))
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| x = x + self.mlp(self.ln2(x))
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| return x
|
|
|
|
|
| class GPT(nn.Module):
|
| def __init__(self, cfg: GPTConfig):
|
| super().__init__()
|
| self.cfg = cfg
|
| self.tok_emb = nn.Embedding(cfg.vocab_size, cfg.n_embd)
|
| self.pos_emb = 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))
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| self.ln_f = nn.LayerNorm(cfg.n_embd, bias=cfg.bias)
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| self.lm_head = nn.Linear(cfg.n_embd, cfg.vocab_size, bias=False)
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| self.lm_head.weight = self.tok_emb.weight
|
|
|
| self.apply(self._init_weights)
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| for name, p in self.named_parameters():
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| if name.endswith("proj.weight"):
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| nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * cfg.n_layer))
|
|
|
| def _init_weights(self, m):
|
| if isinstance(m, nn.Linear):
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| 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 num_params(self):
|
| return sum(p.numel() for p in self.parameters())
|
|
|
| def forward(self, idx, targets=None):
|
| B, T = idx.shape
|
| assert T <= self.cfg.block_size, f"Sequence length {T} > block_size {self.cfg.block_size}"
|
| pos = torch.arange(T, device=idx.device)
|
| x = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
|
| for block in self.blocks:
|
| x = block(x)
|
| x = self.ln_f(x)
|
|
|
| if targets is not None:
|
| logits = self.lm_head(x)
|
| loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
|
| return logits, loss
|
| return self.lm_head(x[:, [-1], :]), None
|
|
|
| def configure_optimizer(self, lr=3e-4, weight_decay=0.1, betas=(0.9, 0.95)):
|
| params = [p for p in self.parameters() if p.requires_grad]
|
| decay = [p for p in params if p.dim() >= 2]
|
| no_decay = [p for p in params if p.dim() < 2]
|
| groups = [
|
| {"params": decay, "weight_decay": weight_decay},
|
| {"params": no_decay, "weight_decay": 0.0},
|
| ]
|
| return torch.optim.AdamW(groups, lr=lr, betas=betas)
|
|
|
| @torch.no_grad()
|
| def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
|
| self.eval()
|
| eos_token_id = 50256
|
|
|
| for _ in range(max_new_tokens):
|
| idx_cond = idx[:, -self.cfg.block_size:]
|
| logits, _ = self(idx_cond)
|
| logits = logits[:, -1, :] / max(temperature, 1e-8)
|
|
|
| if top_k is not None:
|
| v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
|
| logits[logits < v[:, [-1]]] = float("-inf")
|
|
|
| probs = F.softmax(logits, dim=-1)
|
| next_token = torch.multinomial(probs, 1)
|
| idx = torch.cat([idx, next_token], dim=1)
|
|
|
| if torch.all(next_token == eos_token_id):
|
| break
|
|
|
| return idx
|
|
|
|
|
| if __name__ == "__main__":
|
| cfg = GPTConfig(vocab_size=1000, block_size=64, n_layer=2, n_head=2, n_embd=64)
|
| model = GPT(cfg)
|
| print(f"params: {model.num_params():,}")
|
| x = torch.randint(0, cfg.vocab_size, (4, 64))
|
| logits, loss = model(x, targets=x)
|
| print("loss:", loss.item())
|
| print(model.generate(x[:1, :8], max_new_tokens=10).shape) |
