NanoGPT Architecture and Token Processing | Generated by AI
Yes, you’re on the right track with understanding the NanoGPT architecture (as implemented in Andrej Karpathy’s popular repository), but let’s clarify and structure it properly — especially the relationship between sequence length (512 tokens), heads, embeddings, and the transformer blocks.
NanoGPT Overview (Typical Config)
NanoGPT is a from-scratch GPT-2-like model trained on Shakespeare (or tiny datasets). The default config is roughly GPT-2 small scaled down.
| Parameter | Typical Value in NanoGPT |
|---|---|
block_size (max sequence length) |
512 |
n_embd (embedding dimension) |
768 (for 124M model) |
n_head |
12 |
n_layer |
12 |
But for smaller demos, it uses even tinier configs (e.g., 64M or less).
Your Question Breakdown:
“for every 512 tokens, they have GPT model”
No.
The entire input sequence is 512 tokens, and one GPT model processes all 512 tokens at once (in parallel during training, autoregressively during inference).
So:
- Input: batch of sequences, each up to 512 tokens
- One single GPT model processes all 512 positions in parallel (thanks to attention masking)
“512 will be like 8 head 64 tokens”
Close, but not quite.
Let’s clarify multi-head attention:
n_embd= total embedding dimension (e.g., 768)n_head= number of attention heads (e.g., 12)- Head dimension =
n_embd // n_head=768 // 12 = 64
So:
- Each head operates on 64-dimensional vectors
- There are 12 heads, each looking at all 512 tokens
- Total: 12 heads × 64 dim = 768 dim
So yes — each head processes 512 tokens with 64-dim queries/keys/values
Input: [512 tokens] → each token has 768-dim embedding
↓ split into 12 heads
→ each head: 512 × 64 matrix (Q, K, V)
→ self-attention over 512 positions
Inside the Transformer Block (Forward Pass)
Each transformer block does:
def forward(x):
# x: [batch, seq_len=512, n_embd=768]
# 1. LayerNorm + Multi-Head Self-Attention
attn_out = self.attn(self.ln1(x)) # 12 heads, each 64-dim
x = x + attn_out # residual
# 2. LayerNorm + Feed-Forward (MLP)
ff_out = self.mlp(self.ln2(x)) # usually 4× expansion (3072 dim)
x = x + ff_out # residual
return x
This block is repeated n_layer times (e.g., 12).
Full Forward Pass Summary
Input tokens → [B, 512]
→ Token + Position Embeddings → [B, 512, 768]
→ For each of 12 layers:
→ Multi-Head Attention (12 heads × 64 dim) over 512 tokens
→ Feed-Forward (768 → 3072 → 768)
→ Final LayerNorm
→ Linear head → logits [B, 512, vocab_size]
Key Points
| Concept | Value |
|---|---|
| Max sequence length | 512 tokens |
| Model processes | All 512 tokens at once |
| Attention heads | 12 (for 124M model) |
| Per head dim | 64 |
| Attention computes | softmax(QK^T / √64) over 512×512 matrix |
| Causal masking | Ensures token only attends to past |
Visual
[Token 1] → emb → [768]
[Token 2] → emb → [768]
...
[Token 512] → emb → [768]
↓
[512 × 768] → 12 Transformer Blocks
↓
[512 × 768] → Linear → [512 × 50257] logits
Each block has 12 attention heads, each doing attention over all 512 positions.
Reference (NanoGPT code)
From model.py:
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(vocab_size, n_embd),
wpe = nn.Embedding(block_size, n_embd),
h = nn.ModuleList([Block(n_embd, n_head) for _ in range(n_layer)]),
ln_f = nn.LayerNorm(n_embd),
))
And in Block:
self.attn = CausalSelfAttention(n_embd, n_head) # n_head=12, head_size=64
Final Answer to Your Question
Yes, in NanoGPT:
- The model takes up to 512 tokens as input.
- It uses multi-head attention (e.g., 12 heads).
- Each head works with 64-dimensional vectors.
- Inside each transformer block, there is a forward pass with:
- Self-attention over all 512 tokens (causal)
- Feed-forward network
- The entire GPT model (all layers) processes all 512 tokens in parallel during training.
So your intuition is mostly correct — just remember: one model, 512 tokens, 12 heads × 64 dim, repeated in layers.
Let me know if you want the exact config for 124M vs smaller models!