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Stage 2 — ARCHITECTURE (lecture: Architecture, covered briefly because it is "not the hardest part").
Llama-style decoder-only Transformer:
- RMSNorm (pre-norm) : cheaper than LayerNorm, stable
- RoPE : position via query/key rotation instead of absolute position embeddings
- SwiGLU FFN : gated MLP, standard in modern models
- Causal self-attention via F.scaled_dot_product_attention (uses FlashAttention when available)
- Weight tying: embedding <-> output head
"""
from __future__ import annotations
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import ModelConfig
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
x_f = x.float()
x_f = x_f * torch.rsqrt(x_f.pow(2).mean(-1, keepdim=True) + self.eps)
return x_f.type_as(x) * self.weight
def rope_cache(seq_len: int, head_dim: int, base: float = 10000.0):
inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
t = torch.arange(seq_len).float()
freqs = torch.outer(t, inv_freq) # (T, hd/2)
return freqs.cos(), freqs.sin()
def apply_rope(x, cos, sin):
# x: (B, H, T, hd) — rotate (even, odd) dimension pairs
x1, x2 = x[..., 0::2], x[..., 1::2]
cos, sin = cos[None, None], sin[None, None]
out = torch.stack((x1 * cos - x2 * sin, x1 * sin + x2 * cos), dim=-1)
return out.flatten(-2).type_as(x)
class Attention(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
assert cfg.d_model % cfg.n_heads == 0
self.n_heads = cfg.n_heads
self.head_dim = cfg.d_model // cfg.n_heads
self.qkv = nn.Linear(cfg.d_model, 3 * cfg.d_model, bias=False)
self.proj = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
self.dropout = cfg.dropout
def forward(self, x, cos, sin):
B, T, C = x.shape
q, k, v = self.qkv(x).split(C, dim=-1)
q, k, v = (t.view(B, T, self.n_heads, self.head_dim).transpose(1, 2) for t in (q, k, v))
q, k = apply_rope(q, cos, sin), apply_rope(k, cos, sin)
y = F.scaled_dot_product_attention(
q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0.0
)
return self.proj(y.transpose(1, 2).contiguous().view(B, T, C))
class SwiGLU(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
hidden = int(cfg.ffn_mult * cfg.d_model)
hidden = 64 * ((hidden + 63) // 64)
self.w_gate = nn.Linear(cfg.d_model, hidden, bias=False)
self.w_up = nn.Linear(cfg.d_model, hidden, bias=False)
self.w_down = nn.Linear(hidden, cfg.d_model, bias=False)
def forward(self, x):
return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
class Block(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.norm1 = RMSNorm(cfg.d_model)
self.attn = Attention(cfg)
self.norm2 = RMSNorm(cfg.d_model)
self.ffn = SwiGLU(cfg)
self.drop = nn.Dropout(cfg.dropout)
def forward(self, x, cos, sin):
x = x + self.drop(self.attn(self.norm1(x), cos, sin))
x = x + self.drop(self.ffn(self.norm2(x)))
return x
class GPT(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.cfg = cfg
self.embed = nn.Embedding(cfg.vocab_size, cfg.d_model)
self.blocks = nn.ModuleList(Block(cfg) for _ in range(cfg.n_layers))
self.norm = RMSNorm(cfg.d_model)
self.head = nn.Linear(cfg.d_model, cfg.vocab_size, bias=False)
if cfg.tie_embeddings:
self.head.weight = self.embed.weight
cos, sin = rope_cache(cfg.context_len, cfg.d_model // cfg.n_heads)
self.register_buffer("rope_cos", cos, persistent=False)
self.register_buffer("rope_sin", sin, persistent=False)
self.apply(self._init)
# scaled init for residual projections (GPT-2): keeps residual-stream variance in check
for n, p in self.named_parameters():
if n.endswith("proj.weight") or n.endswith("w_down.weight"):
nn.init.normal_(p, 0.0, 0.02 / math.sqrt(2 * cfg.n_layers))
@staticmethod
def _init(m):
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0.0, 0.02)
elif isinstance(m, nn.Embedding):
nn.init.normal_(m.weight, 0.0, 0.02)
def num_params(self, non_embedding: bool = True) -> int:
n = sum(p.numel() for p in self.parameters())
if non_embedding:
n -= self.embed.weight.numel()
return n
def forward(self, idx, targets=None, loss_mask=None):
B, T = idx.shape
assert T <= self.cfg.context_len, f"length {T} > context_len {self.cfg.context_len}"
x = self.embed(idx)
cos, sin = self.rope_cos[:T], self.rope_sin[:T]
for blk in self.blocks:
x = blk(x, cos, sin)
logits = self.head(self.norm(x))
if targets is None:
return logits, None
loss = F.cross_entropy(logits.float().view(-1, logits.size(-1)), targets.view(-1), reduction="none")
if loss_mask is not None:
m = loss_mask.view(-1).float()
loss = (loss * m).sum() / m.sum().clamp(min=1)
else:
loss = loss.mean()
return logits, loss
def configure_optimizer(self, lr: float, weight_decay: float, betas=(0.9, 0.95)):
# weight decay only on matrices (>=2D), not norms/biases
decay = [p for p in self.parameters() if p.requires_grad and p.dim() >= 2]
no_decay = [p for p in self.parameters() if p.requires_grad and p.dim() < 2]
groups = [{"params": decay, "weight_decay": weight_decay}, {"params": no_decay, "weight_decay": 0.0}]
fused = torch.cuda.is_available()
return torch.optim.AdamW(groups, lr=lr, betas=betas, fused=fused)
@torch.no_grad()
def generate(self, idx, max_new_tokens: int, temperature: float = 1.0, top_k: int | None = 50,
eos_id: int | None = None, valid_vocab: int | None = None):
"""
Autoregressive sampling for a batch of equal-length prompts.
valid_vocab: tokenizer vocab size — ids above it (spare embedding rows) are never sampled.
Returns (new_tokens [B, <=max_new], finished [B]) — tokens after EOS are filled with eos_id.
"""
B = idx.size(0)
finished = torch.zeros(B, dtype=torch.bool, device=idx.device)
new = []
for _ in range(max_new_tokens):
ctx = idx[:, -self.cfg.context_len:]
logits, _ = self(ctx)
logits = logits[:, -1, :].float() / max(temperature, 1e-5)
if valid_vocab is not None and valid_vocab < logits.size(-1):
logits[:, valid_vocab:] = -float("inf")
if top_k:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float("inf")
nxt = torch.multinomial(F.softmax(logits, dim=-1), 1)
if eos_id is not None:
nxt = torch.where(finished[:, None], torch.full_like(nxt, eos_id), nxt)
finished |= nxt.squeeze(1) == eos_id
idx = torch.cat([idx, nxt], dim=1)
new.append(nxt)
if eos_id is not None and finished.all():
break
return torch.cat(new, dim=1), finished
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