The Full Transformer — Encoder + Decoder

The Problem

A single attention layer is a feature extractor, not a model. One matmul per layer is not enough capacity for language. You need depth — and depth breaks without the right plumbing.

The 2017 Vaswani paper packaged six design decisions that turned one attention layer into a stackable block. Every transformer since — encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5) — inherits the same skeleton. In 2026 the blocks have been refined (RMSNorm, SwiGLU, pre-norm, RoPE) but the skeleton is identical.

This lesson is the skeleton. Next lessons specialize it — 06 for encoders, 07 for decoders, 08 for encoder-decoder.

The Concept

The six pieces

1. Embedding + positional signal. Tokens → vectors. Position injected via RoPE (modern) or sinusoidal (classic).
2. Self-attention. Every position attends to every other. Masked in decoders.
3. Feed-forward network (FFN). Position-wise two-layer MLP: W_2 · activation(W_1 · x). Expansion ratio 4× by default.
4. Residual connection. x + sublayer(x). Without this, gradients vanish past ~6 layers.
5. Layer normalization. LayerNorm or RMSNorm (modern). Stabilizes the residual stream.
6. Cross-attention (decoder only). Queries come from the decoder, keys and values from the encoder output.

Watch a vector flow through one block: attention mixes across positions, the residual carries it forward, the FFN transforms it, and norm keeps the stream stable.

transformer-block

Encoder block (used by BERT, T5 encoder)

x → LN → MHA(self) → + → LN → FFN → + → out
                     ^              ^
                     |              |
                     └── residual ──┘

Encoder is bidirectional. No masking. All positions see all positions.

Decoder block (used by GPT, T5 decoder)

x → LN → MHA(masked self) → + → LN → MHA(cross to encoder) → + → LN → FFN → + → out

Decoder has three sublayers per block. The middle one — cross-attention — is the only place information flows from encoder to decoder. In a pure decoder-only architecture (GPT), cross-attention is omitted and you just have masked self-attention + FFN.

Pre-norm vs post-norm

Original paper: x + sublayer(LN(x)) vs LN(x + sublayer(x)). Post-norm lost favor around 2019 — it is harder to train deeply without careful warmup. Pre-norm (LN before sublayer) is the 2026 default: Llama, Qwen, GPT-3+, Mistral all use it.

The 2026 modernized block

Vaswani 2017 shipped LayerNorm + ReLU. Modern stacks replaced both. What production blocks actually look like:

RMSNorm drops the mean-centering of LayerNorm (one fewer subtraction), which saves compute and is empirically at least as stable. SwiGLU (Swish(W1 x) ⊙ W3 x) consistently outperforms ReLU/GELU FFN by ~0.5 point ppl in the Llama, PaLM and Qwen papers.

Parameter count

For one block with d_model = d and FFN expansion r:

• MHA: 4 · d² (Q, K, V, O projections)
• FFN (SwiGLU): 3 · d · (r · d) ≈ 3rd²
• Norms: negligible

At d = 4096, r = 2.6, layers = 32 (roughly Llama 3 8B), total: 32 · (4·4096² + 3·2.6·4096²) ≈ 32 · (67M + 131M) ≈ 32 · 198M ≈ 6.3B parameters in the blocks, plus about 1B for the embeddings and output head (128K vocab × 4096, untied). That lands near the published 8B. The real model uses GQA (smaller K and V projections) and a wider FFN (r = 3.5), which moves parameters from attention to the FFN but keeps the total close to 8B.

Build It

Step 1: the building blocks

Using the tiny Matrix class from Lesson 03 (copied to this file for independence):

• layer_norm(x, eps=1e-5) — subtract mean, divide by std.
• rms_norm(x, eps=1e-6) — divide by RMS. No mean subtraction.
• gelu(x) and silu(x) * W3 x (SwiGLU).
• ffn_swiglu(x, W1, W2, W3).
• encoder_block(x, params) and decoder_block(x, enc_out, params).

See code/main.py for the full wiring.

Step 2: wire a 2-layer encoder and a 2-layer decoder

Stack them. Pass the encoder output into every decoder cross-attention. Add a final LN before the output projection.

def encode(tokens, params):
    x = embed(tokens, params.emb) + sinusoidal(len(tokens), params.d)
    for block in params.encoder_blocks:
        x = encoder_block(x, block)
    return x

def decode(target_tokens, encoder_out, params):
    x = embed(target_tokens, params.emb) + sinusoidal(len(target_tokens), params.d)
    for block in params.decoder_blocks:
        x = decoder_block(x, encoder_out, block)
    return x

Step 3: run forward on a toy example

Feed a 6-token source and a 5-token target through. Verify the output shape is (5, vocab). No training — this lesson is about the architecture, not the loss.

Step 4: swap in RMSNorm + SwiGLU

Replace LayerNorm and ReLU-FFN with RMSNorm and SwiGLU. Confirm shapes still match. This is the 2026 modernization with one function substitution.

Use It

The PyTorch/TF reference implementations: nn.TransformerEncoderLayer, nn.TransformerDecoderLayer. But most 2026 production code rolls its own block because:

• Flash Attention is called inside attention, not via nn.MultiheadAttention.
• GQA / MLA are not in the stdlib reference.
• RoPE, RMSNorm, SwiGLU are not the PyTorch defaults.

HF transformers has clean reference blocks you should read: modeling_llama.py is the canonical 2026 decoder-only block. It's ~500 lines and worth walking through once.

Encoder vs decoder vs encoder-decoder — when to pick:

Decoder-only won language because it scales cleanest and handles both comprehension and generation. Encoder-decoder is still best when the input has a clear "source sequence" identity (translation, speech recognition, structured tasks).

Ship It

See outputs/skill-transformer-block-reviewer.md. The skill reviews a new transformer block implementation against the 2026 defaults and flags missing pieces (pre-norm, RoPE, RMSNorm, GQA, FFN expansion ratio).

Exercises

1. Easy. Count the parameters in your encoder_block at d_model=512, n_heads=8, ffn_expansion=4, swiglu=True. Validate by implementing the block and using sum(p.numel() for p in block.parameters()).
2. Medium. Switch from post-norm to pre-norm. Initialize both and measure the activation norm after 12 stacked layers on random input. Post-norm's activations should explode; pre-norm's should stay bounded.
3. Hard. Implement a 4-layer encoder-decoder on a toy copy task (copy x reversed). Train 100 steps. Report loss. Swap in RMSNorm + SwiGLU + RoPE — does loss drop?

Key Terms

Further Reading

• Vaswani et al. (2017). Attention Is All You Need — original block spec.
• Xiong et al. (2020). On Layer Normalization in the Transformer Architecture — why pre-norm beats post-norm deeply.
• Zhang, Sennrich (2019). Root Mean Square Layer Normalization — RMSNorm.
• Shazeer (2020). GLU Variants Improve Transformer — the SwiGLU paper.
• HuggingFace modeling_llama.py — canonical 2026 decoder-only block.