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4f43093 | 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 | from __future__ import annotations
import math
from dataclasses import asdict, dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
@dataclass
class GPTConfig:
block_size: int = 1024
vocab_size: int = 8192
n_layer: int = 12
n_head: int = 12
n_embd: int = 768
dropout: float = 0.0
bias: bool = False
class CausalSelfAttention(nn.Module):
def __init__(self, config: GPTConfig) -> None:
super().__init__()
if config.n_embd % config.n_head:
raise ValueError("embedding dimension must be divisible by number of heads")
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.n_head = config.n_head
self.n_embd = config.n_embd
self.dropout = config.dropout
def forward(self, value: torch.Tensor) -> torch.Tensor:
batch, time, channels = value.size()
query, key, val = self.c_attn(value).split(self.n_embd, dim=2)
head_size = channels // self.n_head
query = query.view(batch, time, self.n_head, head_size).transpose(1, 2)
key = key.view(batch, time, self.n_head, head_size).transpose(1, 2)
val = val.view(batch, time, self.n_head, head_size).transpose(1, 2)
attended = F.scaled_dot_product_attention(
query,
key,
val,
attn_mask=None,
dropout_p=self.dropout if self.training else 0,
is_causal=True,
)
attended = attended.transpose(1, 2).contiguous().view(batch, time, channels)
return self.resid_dropout(self.c_proj(attended))
class MLP(nn.Module):
def __init__(self, config: GPTConfig) -> None:
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, value: torch.Tensor) -> torch.Tensor:
return self.dropout(self.c_proj(self.gelu(self.c_fc(value))))
class Block(nn.Module):
def __init__(self, config: GPTConfig) -> None:
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, value: torch.Tensor) -> torch.Tensor:
value = value + self.attn(self.ln_1(value))
return value + self.mlp(self.ln_2(value))
class GPT(nn.Module):
def __init__(self, config: GPTConfig) -> None:
super().__init__()
self.config = config
self.transformer = nn.ModuleDict(
{
"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)
self.transformer.wte.weight = self.lm_head.weight
self.apply(self._init_weights)
for name, parameter in self.named_parameters():
if name.endswith("c_proj.weight"):
torch.nn.init.normal_(
parameter, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer)
)
@staticmethod
def _init_weights(module: nn.Module) -> None:
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(
self, index: torch.Tensor, targets: torch.Tensor | None = None
) -> tuple[torch.Tensor, torch.Tensor | None]:
_, time = index.shape
if time > self.config.block_size:
raise ValueError("sequence exceeds model block size")
positions = torch.arange(0, time, dtype=torch.long, device=index.device)
value = self.transformer.drop(self.transformer.wte(index) + self.transformer.wpe(positions))
for block in self.transformer.h:
value = block(value)
value = self.transformer.ln_f(value)
logits = self.lm_head(value)
loss = (
F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
if targets is not None
else None
)
return logits, loss
@torch.no_grad()
def generate(
self,
index: torch.Tensor,
max_new_tokens: int,
temperature: float = 0.8,
top_k: int | None = 200,
) -> torch.Tensor:
for _ in range(max_new_tokens):
cropped = index[:, -self.config.block_size :]
logits, _ = self(cropped)
logits = logits[:, -1, :] / temperature
if top_k is not None:
values, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < values[:, [-1]]] = -float("Inf")
probabilities = F.softmax(logits, dim=-1)
index = torch.cat((index, torch.multinomial(probabilities, num_samples=1)), dim=1)
return index
def parameter_count(self, non_embedding: bool = False) -> int:
count = sum(parameter.numel() for parameter in self.parameters())
if non_embedding:
count -= self.transformer.wpe.weight.numel()
return count
def config_dict(self) -> dict[str, object]:
return asdict(self.config)
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