nanoGPT Architecture Overview | Generated by AI
That’s a very good and accurate high-level description of the architecture for a simple, decoder-only Transformer model like the one implemented in nanoGPT (which is based on the original GPT architecture).
Here is a slightly more detailed breakdown confirming your points:
🧠 nanoGPT/GPT Architecture Overview
The core components you mentioned are all present and form the backbone of the model:
1. 🔍 Input Processing
- Token Embedding: Converts the input token IDs (the indices representing words/characters) into dense, high-dimensional vector representations (\(n_{emb}\)).
- Positional Embedding: Adds information about the order or position of the tokens in the sequence.
- The final input to the main transformer block is the sum of the token embedding and the positional embedding.
2. 🧱 Transformer Block (n_layer’s block)
The entire model consists of a stack of \(n_{layer}\) identical Transformer Blocks. Each block primarily contains two main sub-components:
A. Causal Self-Attention
- Query (Q), Key (K), Value (V) Calculation: The input \(x\) is projected into three separate matrices: \(\mathbf{Q}\), \(\mathbf{K}\), and \(\mathbf{V}\), usually through three separate
nn.Linearlayers. - Attention Calculation: The attention score is calculated as: \(\)\text{Attention}(\mathbf{Q}, \mathbf{K}, \mathbf{V}) = \text{Softmax}\left(\frac{\mathbf{Q}\mathbf{K}^T}{\sqrt{d_k}} + \mathbf{M}\right)\mathbf{V}\(\) Where \(d_k\) is the dimension of the keys, and \(\mathbf{M}\) is the causal mask (a triangular matrix of \(-\infty\) or a very large negative number) that ensures a token can only attend to tokens that came before it in the sequence. This is the critical part that makes it “causal” or “decoder-only.”
B. Multi-Layer Perceptron (MLP)
- This is the Feed-Forward Network (FFN). It typically involves two
nn.Linearlayers:- Expansion: A projection from \(n_{emb}\) to a larger dimension (commonly \(4 \times n_{emb}\)), followed by a non-linearity like GELU.
nn.Linear(n_emb, 4 * n_emb)followed by \(\text{GELU}\) (Gaussian Error Linear Unit).
- Projection/Contraction: A projection back from the large dimension to the original \(n_{emb}\) dimension.
nn.Linear(4 * n_emb, n_emb).
- Expansion: A projection from \(n_{emb}\) to a larger dimension (commonly \(4 \times n_{emb}\)), followed by a non-linearity like GELU.
C. Norms and Skip Connections
- Layer Normalization: Applied before both the Attention and the MLP sub-components (this is the Pre-Norm style, common in modern GPT-like models).
- Residual/Skip Connections: The input to each sub-component (Attention and MLP) is added to its output before normalization, preventing information loss and stabilizing training: \(\text{Output} = \text{Input} + \text{SubComponent}(\text{Input})\).
3. 📉 Output Head
- After the final Transformer Block, there’s often a final Layer Normalization.
- The output is passed through a final
nn.Linearlayer (the unembedding or projection layer) that maps the \(n_{emb}\) vector back to a vector of size \(V\) (the vocabulary size), which represents the logits (prediction scores) for the next token.
In summary, your understanding is spot-on! It is the sequence of Embedding \(\rightarrow\) (LayerNorm \(\rightarrow\) Causal Attention \(\rightarrow\) Skip Connection) \(\rightarrow\) (LayerNorm \(\rightarrow\) MLP \(\rightarrow\) Skip Connection) repeated \(N\) times.
Would you like a more detailed explanation of a specific part, such as how the Multi-Head Attention works, or the role of the Layer Normalization?