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18be545 | 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 | 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)),
)
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