File size: 6,619 Bytes
c5f49b9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import lru_cache
from dataclasses import dataclass, asdict
from typing import Any, Dict, Optional
# ================= DEVICE =================
device = "cpu"
torch.set_float32_matmul_precision("high")
# ================= MODEL CONFIG =================
@dataclass(frozen=True)
class GPTConfig:
n_embd: int = 192
n_head: int = 6
n_layer: int = 6
block_size: int = 256
dropout: float = 0.1
def validate(self) -> None:
if self.n_embd <= 0 or self.n_head <= 0 or self.n_layer <= 0:
raise ValueError("Invalid config: n_embd/n_head/n_layer must be > 0")
if self.block_size <= 8:
raise ValueError("Invalid config: block_size must be > 8")
if self.n_embd % self.n_head != 0:
raise ValueError("Invalid config: n_embd must be divisible by n_head")
if not (0.0 <= float(self.dropout) <= 0.5):
raise ValueError("Invalid config: dropout must be in [0, 0.5]")
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
DEFAULT_CONFIG = GPTConfig()
DEFAULT_CONFIG.validate()
# Back-compat exports (older scripts import these symbols).
n_embd = DEFAULT_CONFIG.n_embd
n_head = DEFAULT_CONFIG.n_head
n_layer = DEFAULT_CONFIG.n_layer
block_size = DEFAULT_CONFIG.block_size
dropout = DEFAULT_CONFIG.dropout
# ================= RMSNorm =================
class RMSNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
return self.weight * x * torch.rsqrt(
x.pow(2).mean(-1, keepdim=True) + 1e-6
)
# ================= SELF ATTENTION =================
class SelfAttention(nn.Module):
def __init__(self, cfg: GPTConfig):
super().__init__()
self.cfg = cfg
self.qkv = nn.Linear(cfg.n_embd, 3 * cfg.n_embd, bias=False)
self.proj = nn.Linear(cfg.n_embd, cfg.n_embd)
self.dropout = nn.Dropout(cfg.dropout)
def forward(self, x):
bsz, tsz, channels = x.size()
qkv = self.qkv(x)
q, k, v = qkv.chunk(3, dim=-1)
q = q.view(bsz, tsz, self.cfg.n_head, channels // self.cfg.n_head).transpose(1, 2)
k = k.view(bsz, tsz, self.cfg.n_head, channels // self.cfg.n_head).transpose(1, 2)
v = v.view(bsz, tsz, self.cfg.n_head, channels // self.cfg.n_head).transpose(1, 2)
out = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=None,
is_causal=True,
dropout_p=self.cfg.dropout if self.training else 0.0,
)
out = out.transpose(1, 2).contiguous().view(bsz, tsz, channels)
return self.dropout(self.proj(out))
# ================= FEED FORWARD =================
class FeedForward(nn.Module):
def __init__(self, cfg: GPTConfig):
super().__init__()
self.cfg = cfg
self.net = nn.Sequential(
nn.Linear(cfg.n_embd, 4 * cfg.n_embd),
nn.GELU(),
nn.Linear(4 * cfg.n_embd, cfg.n_embd),
nn.Dropout(cfg.dropout),
)
def forward(self, x):
return self.net(x)
# ================= TRANSFORMER BLOCK =================
class Block(nn.Module):
def __init__(self, cfg: GPTConfig):
super().__init__()
self.cfg = cfg
self.ln1 = RMSNorm(cfg.n_embd)
self.ln2 = RMSNorm(cfg.n_embd)
self.attn = SelfAttention(cfg)
self.ff = FeedForward(cfg)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.ff(self.ln2(x))
return x
# ================= GPT MODEL =================
class GPT(nn.Module):
def __init__(self, vocab_size: int, cfg: Optional[GPTConfig] = None):
super().__init__()
cfg = cfg or DEFAULT_CONFIG
cfg.validate()
self.cfg = cfg
self.token_emb = nn.Embedding(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.Sequential(*[Block(cfg) for _ in range(cfg.n_layer)])
self.ln_f = RMSNorm(cfg.n_embd)
self.head = nn.Linear(cfg.n_embd, vocab_size)
def forward(self, idx, targets=None):
bsz, tsz = idx.shape
if tsz > self.cfg.block_size:
raise ValueError(
f"Sequence length {tsz} exceeds block_size {self.cfg.block_size}."
)
pos = torch.arange(0, tsz, device=idx.device)
x = self.token_emb(idx) + self.pos_emb(pos)[None, :, :]
x = self.drop(x)
x = self.blocks(x)
logits = self.head(self.ln_f(x))
loss = None
if targets is not None:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)),
targets.view(-1),
)
return logits, loss
# ================= SIMPLE BPE TOKENIZER =================
class SimpleBPETokenizer:
def __init__(self):
self.vocab = {} # {int: bytes}
self.merges = {} # {(int, int): int}
@lru_cache(maxsize=32768)
def _encode_cached(self, text: str):
tokens = list(text.encode("utf-8", errors="ignore"))
while len(tokens) >= 2:
best_i = None
best_rank = None
for i in range(len(tokens) - 1):
rank = self.merges.get((tokens[i], tokens[i + 1]))
if rank is None:
continue
if best_rank is None or rank < best_rank:
best_rank = rank
best_i = i
if best_i is None:
break
merged = self.merges[(tokens[best_i], tokens[best_i + 1])]
tokens = tokens[:best_i] + [merged] + tokens[best_i + 2 :]
return tuple(tokens)
def encode(self, text: str):
return list(self._encode_cached(text))
def decode(self, tokens):
byte_stream = b"".join(self.vocab.get(t, b"") for t in tokens)
return byte_stream.decode("utf-8", errors="ignore")
def config_from_dict(d: Optional[Dict[str, Any]]) -> GPTConfig:
if not d:
return DEFAULT_CONFIG
cfg = GPTConfig(
n_embd=int(d.get("n_embd", DEFAULT_CONFIG.n_embd)),
n_head=int(d.get("n_head", DEFAULT_CONFIG.n_head)),
n_layer=int(d.get("n_layer", DEFAULT_CONFIG.n_layer)),
block_size=int(d.get("block_size", DEFAULT_CONFIG.block_size)),
dropout=float(d.get("dropout", DEFAULT_CONFIG.dropout)),
)
cfg.validate()
return cfg
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