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Transformer Block for FrawdLLM.
A transformer block combines:
1. Multi-head self-attention (tokens gather info from each other)
2. MLP (each token processes info independently)
With two important additions:
- LayerNorm: Keeps values stable during training
- Residual connections: Add input to output ("don't lose what you had")
Structure (Pre-LN, which is more stable):
Input
↓
┌─────────────┐
│ LayerNorm │
└─────────────┘
↓
┌─────────────┐
│ Attention │───────┐
└─────────────┘ │ (residual)
↓ │
+ ←─────────────────┘
↓
┌─────────────┐
│ LayerNorm │
└─────────────┘
↓
┌─────────────┐
│ MLP │───────┐
└─────────────┘ │ (residual)
↓ │
+ ←─────────────────┘
↓
Output
"""
import torch
import torch.nn as nn
from .config import ModelConfig
from .attention import CausalSelfAttention
from .mlp import MLP
class TransformerBlock(nn.Module):
"""
One transformer block = Attention + MLP with norms and residuals.
Input: [batch_size, seq_len, n_embd]
Output: [batch_size, seq_len, n_embd]
"""
def __init__(self, config: ModelConfig):
super().__init__()
self.config = config
# Layer norms (one before attention, one before MLP)
self.ln1 = nn.LayerNorm(config.n_embd)
self.ln2 = nn.LayerNorm(config.n_embd)
# Attention and MLP
self.attn = CausalSelfAttention(config)
self.mlp = MLP(config)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Apply transformer block.
Args:
x: [batch_size, seq_len, n_embd]
Returns:
[batch_size, seq_len, n_embd]
"""
# Attention with residual connection
# x + attention(norm(x))
# "Keep x, add attention's contribution"
x = x + self.attn(self.ln1(x))
# MLP with residual connection
# x + mlp(norm(x))
# "Keep x, add MLP's contribution"
x = x + self.mlp(self.ln2(x))
return x
if __name__ == "__main__":
# Test the transformer block
from .config import get_config
print("Testing TransformerBlock...")
print("=" * 50)
config = get_config("tiny")
print(f"Config: n_embd={config.n_embd}, n_head={config.n_head}, "
f"n_layer={config.n_layer}")
block = TransformerBlock(config)
# Count parameters
num_params = sum(p.numel() for p in block.parameters())
print(f"Block parameters: {num_params:,}")
# Test input: [batch=2, seq=8, n_embd=256]
x = torch.randn(2, 8, config.n_embd)
print(f"\nInput shape: {x.shape}")
# Forward pass
out = block(x)
print(f"Output shape: {out.shape}")
# Verify shapes match
assert x.shape == out.shape, "Input and output shapes should match!"
print("\nTransformerBlock working!")
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