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from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
@dataclass
class GPTConfig:
name: str = "yuspec-gamedev-10m-v0.1"
vocab_size: int = 16000
block_size: int = 1024
n_layer: int = 8
n_head: int = 4
n_embd: int = 256
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
self.n_head = config.n_head
self.n_embd = config.n_embd
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)
mask = torch.tril(torch.ones(config.block_size, config.block_size))
self.register_buffer("mask", mask.view(1, 1, config.block_size, config.block_size))
def forward(self, x):
batch, seq_len, channels = x.size()
q, k, v = self.c_attn(x).split(channels, dim=2)
head_dim = channels // self.n_head
q = q.view(batch, seq_len, self.n_head, head_dim).transpose(1, 2)
k = k.view(batch, seq_len, self.n_head, head_dim).transpose(1, 2)
v = v.view(batch, seq_len, self.n_head, head_dim).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) / math.sqrt(head_dim)
att = att.masked_fill(self.mask[:, :, :seq_len, :seq_len] == 0, float("-inf"))
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(batch, seq_len, channels)
return self.resid_dropout(self.c_proj(y))
class MLP(nn.Module):
def __init__(self, config: GPTConfig):
super().__init__()
self.fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
self.proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
x = self.fc(x)
x = F.gelu(x)
x = self.proj(x)
return self.dropout(x)
class Block(nn.Module):
def __init__(self, config: GPTConfig):
super().__init__()
self.ln1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln2 = nn.LayerNorm(config.n_embd)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class GPT(nn.Module):
def __init__(self, config: GPTConfig):
super().__init__()
self.config = config
self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
self.position_embedding = nn.Embedding(config.block_size, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
self.ln_f = nn.LayerNorm(config.n_embd)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.lm_head.weight = self.token_embedding.weight
self.apply(self._init_weights)
total_params = sum(p.numel() for p in self.parameters())
print(f"Parameters: {total_params / 1e6:.2f}M")
def _init_weights(self, module):
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, idx, targets=None):
_, seq_len = idx.size()
if seq_len > self.config.block_size:
raise ValueError(f"Sequence length {seq_len} exceeds block_size {self.config.block_size}")
pos = torch.arange(0, seq_len, dtype=torch.long, device=idx.device)
tok_emb = self.token_embedding(idx)
pos_emb = self.position_embedding(pos)
x = self.dropout(tok_emb + pos_emb)
for block in self.blocks:
x = block(x)
x = self.ln_f(x)
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))
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens=256, temperature=0.8, top_k=50, eos_id=None, vocab_limit=None):
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.config.block_size :]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / max(temperature, 1e-6)
if vocab_limit is not None and vocab_limit < logits.size(-1):
logits[:, vocab_limit:] = -float("inf")
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_id = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, next_id), dim=1)
if eos_id is not None and torch.all(next_id == eos_id):
break
return idx
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