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src/transformer.py

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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.")