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Stage 2: MODEL ARCHITECTURE.
A from-scratch GPT-style decoder-only transformer, built directly in PyTorch
(no pretrained weights, no transformers.AutoModel -- this is the part that
makes "built an LLM from scratch" an honest claim):
- token + positional embeddings
- N transformer blocks, each with: multi-head causal self-attention,
a feed-forward MLP, and layer norm (pre-norm, like GPT-2)
- a final layer norm + linear head tied to the token embedding weights
Sizes (see PRESETS below):
- "tiny" ~ a few M params -> for a fast CPU dry-run / sanity check
- "small" ~ 30M params -> trains in well under an hour on an RTX 3060
- "medium" ~ 125M params -> GPT-1 scale, still fits an RTX 3060 12GB
"""
import math
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
PRESETS = {
"tiny": dict(n_layer=4, n_head=4, n_embd=128, block_size=128), # CPU dry-run
"small": dict(n_layer=6, n_head=6, n_embd=384, block_size=256), # ~30M params
"medium": dict(n_layer=12, n_head=12, n_embd=768, block_size=512), # ~125M params
}
@dataclass
class GPTConfig:
vocab_size: int = 8000
block_size: int = 256
n_layer: int = 6
n_head: int = 6
n_embd: int = 384
dropout: float = 0.1
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.head_dim = config.n_embd // config.n_head
self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd)
self.proj = nn.Linear(config.n_embd, config.n_embd)
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):
B, T, C = x.shape
qkv = self.qkv(x)
q, k, v = qkv.split(C, dim=2)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
k = k.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)
att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
att = att.masked_fill(self.mask[:, :, :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.proj(y))
class MLP(nn.Module):
def __init__(self, config: GPTConfig):
super().__init__()
self.fc = nn.Linear(config.n_embd, 4 * config.n_embd)
self.proj = nn.Linear(4 * config.n_embd, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
return self.dropout(self.proj(F.gelu(self.fc(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.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
self.pos_emb = nn.Embedding(config.block_size, config.n_embd)
self.drop = 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.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.head.weight = self.tok_emb.weight # weight tying
self.apply(self._init_weights)
n_params = sum(p.numel() for p in self.parameters())
print(f"GPT initialized: {n_params/1e6:.2f}M parameters "
f"(n_layer={config.n_layer}, n_head={config.n_head}, "
f"n_embd={config.n_embd}, vocab_size={config.vocab_size}, "
f"block_size={config.block_size})")
def _init_weights(self, module):
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(self, idx, targets=None):
B, T = idx.shape
assert T <= self.config.block_size, "sequence longer than block_size"
pos = torch.arange(0, T, device=idx.device).unsqueeze(0)
x = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
for block in self.blocks:
x = block(x)
x = self.ln_f(x)
logits = self.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, max_new_tokens, temperature=1.0, top_k=None, top_p=None):
self.eval()
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.config.block_size:]
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")
if top_p is not None:
sorted_logits, sorted_idx = torch.sort(logits, descending=True)
probs = F.softmax(sorted_logits, dim=-1)
cum_probs = torch.cumsum(probs, dim=-1)
remove = cum_probs > top_p
remove[:, 1:] = remove[:, :-1].clone()
remove[:, 0] = False
sorted_logits[remove] = float("-inf")
logits = torch.full_like(logits, float("-inf")).scatter(1, sorted_idx, sorted_logits)
probs = F.softmax(logits, dim=-1)
next_id = torch.multinomial(probs, num_samples=1)
idx = torch.cat([idx, next_id], dim=1)
return idx
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