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3c38b94 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | """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 # weight tying
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
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