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Self-contained 63.8M Parameter GPT-Style Decoder Transformer
- Pre-LayerNorm Architecture
- Flash Attention via PyTorch F.scaled_dot_product_attention
- Tied Token Embedding & LM Head Weights
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import GPTConfig
class CausalSelfAttention(nn.Module):
def __init__(self, config: GPTConfig):
super().__init__()
assert config.d_model % config.n_head == 0, "d_model must be divisible by n_head"
self.n_head = config.n_head
self.d_model = config.d_model
self.head_dim = config.d_model // config.n_head
self.dropout_p = config.dropout
self.c_attn = nn.Linear(config.d_model, 3 * config.d_model, bias=config.bias)
self.c_proj = nn.Linear(config.d_model, config.d_model, bias=config.bias)
self.resid_dropout = nn.Dropout(config.dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, T, C = x.size()
qkv = self.c_attn(x)
q, k, v = qkv.split(self.d_model, dim=2)
k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
dropout_p = self.dropout_p if self.training else 0.0
y = F.scaled_dot_product_attention(
q, k, v, attn_mask=None, dropout_p=dropout_p, is_causal=True
)
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.d_model, config.d_ffn, bias=config.bias)
self.gelu = nn.GELU()
self.c_proj = nn.Linear(config.d_ffn, config.d_model, 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 TransformerBlock(nn.Module):
def __init__(self, config: GPTConfig):
super().__init__()
self.ln_1 = nn.LayerNorm(config.d_model, elementwise_affine=config.bias)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.d_model, elementwise_affine=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 SmallGPT(nn.Module):
def __init__(self, config: GPTConfig):
super().__init__()
self.config = config
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size, config.d_model),
wpe = nn.Embedding(config.context_length, config.d_model),
drop = nn.Dropout(config.dropout),
h = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layer)]),
ln_f = nn.LayerNorm(config.d_model, elementwise_affine=config.bias),
))
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
# Weight tying: token embeddings and LM head share weights
self.transformer.wte.weight = self.lm_head.weight
# Weight initialization
self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith('c_proj.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
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: torch.Tensor, targets: torch.Tensor = None):
b, t = idx.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)
x = self.transformer.ln_f(x)
if targets is not None:
logits = self.lm_head(x)
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
logits = self.lm_head(x[:, [-1], :])
loss = None
return logits, loss
def get_num_params(self) -> int:
return sum(p.numel() for p in self.parameters())
def configure_optimizers(self, weight_decay: float, learning_rate: float, betas: tuple, device_type: str):
decay_params = [p for n, p in self.named_parameters() if p.requires_grad and p.dim() >= 2]
nodecay_params = [p for n, p in self.named_parameters() if p.requires_grad and p.dim() < 2]
optim_groups = [
{'params': decay_params, 'weight_decay': weight_decay},
{'params': nodecay_params, 'weight_decay': 0.0}
]
fused_available = 'fused' in torch.optim.AdamW.__init__.__code__.co_varnames
use_fused = fused_available and device_type == 'cuda'
extra_args = dict(fused=True) if use_fused else dict()
return torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, eps=1e-8, **extra_args)
@torch.no_grad()
def generate(self, idx: torch.Tensor, max_new_tokens: int = 80, temperature: float = 0.8, top_k: int = 40) -> torch.Tensor:
self.eval()
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1) <= self.config.context_length else idx[:, -self.config.context_length:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / max(temperature, 1e-5)
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)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
# Alias
GPT = SmallGPT
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