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Milestone 3: MultiHeadAttention, FeedForward, and Transformer Block.
Architecture uses pre-norm (LayerNorm before attention/FFN, not after).
This is what modern models like LLaMA/Qwen do — it trains more stably.
Block layout:
x -> LayerNorm -> MultiHeadAttention -> + (residual) -> LayerNorm -> FeedForward -> + (residual)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from attention import Head
class MultiHeadAttention(nn.Module):
"""Multiple attention heads running in parallel, outputs concatenated and projected."""
def __init__(self, n_heads: int, head_size: int, n_embd: int, block_size: int, dropout: float):
super().__init__()
self.heads = nn.ModuleList([
Head(head_size=head_size, n_embd=n_embd, block_size=block_size, dropout=dropout)
for _ in range(n_heads)
])
# Project concatenated heads back to n_embd
self.proj = nn.Linear(n_heads * head_size, n_embd)
self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Run all heads, concatenate along the last dim
out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, n_heads * head_size)
out = self.dropout(self.proj(out)) # (B, T, n_embd)
return out
class FeedForward(nn.Module):
"""Position-wise feed-forward network: Linear -> ReLU -> Linear.
Standard GPT uses a 4x expansion of n_embd in the hidden layer.
We'll swap ReLU for SwiGLU in the modernization phase.
"""
def __init__(self, n_embd: int, dropout: float):
super().__init__()
self.net = nn.Sequential(
nn.Linear(n_embd, 4 * n_embd),
nn.ReLU(),
nn.Linear(4 * n_embd, n_embd),
nn.Dropout(dropout),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.net(x)
class Block(nn.Module):
"""One transformer block with pre-norm architecture.
Pre-norm applies LayerNorm *before* attention/FFN (not after).
This is more stable to train than post-norm (the original Transformer).
"""
def __init__(self, n_embd: int, n_heads: int, block_size: int, dropout: float):
super().__init__()
head_size = n_embd // n_heads
self.attn = MultiHeadAttention(
n_heads=n_heads,
head_size=head_size,
n_embd=n_embd,
block_size=block_size,
dropout=dropout,
)
self.ffn = FeedForward(n_embd=n_embd, dropout=dropout)
self.ln1 = nn.LayerNorm(n_embd)
self.ln2 = nn.LayerNorm(n_embd)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Pre-norm + residual for attention
x = x + self.attn(self.ln1(x))
# Pre-norm + residual for feed-forward
x = x + self.ffn(self.ln2(x))
return x
# ── Quick sanity check ────────────────────────────────────────────────────────
if __name__ == "__main__":
from tokenizer import DEVICE, BLOCK_SIZE
n_embd = 384
n_heads = 6
dropout = 0.1
batch_size = 4
block = Block(n_embd=n_embd, n_heads=n_heads, block_size=BLOCK_SIZE, dropout=dropout).to(DEVICE)
x = torch.randn(batch_size, BLOCK_SIZE, n_embd, device=DEVICE)
out = block(x)
print(f"Input shape : {x.shape}")
print(f"Output shape : {out.shape} (expected [4, {BLOCK_SIZE}, {n_embd}])")
# Count parameters
n_params = sum(p.numel() for p in block.parameters())
print(f"Block params : {n_params:,}")
print("\nMilestone 3 OK: transformer block works.")
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