Writing a Decoder-Only Transformer for LLM from Scratch in Python
Google/DeepMind
OpenAI
Meta
The author of a series of articles describes in detail the implementation of a transformer block for a small decoder-only LLM using the PyTorch framework. Components covered include: masked Multi-Head Attention, Feed Forward Network with GELU, normalization layer, and residual connections. The article explains the difference from the original architecture: pre-normalization (Pre-LN) is used instead of post-normalization for training stability.
Author Vladimir, using the example of a watermarking task for licensed text, implements a transformer block for a decoder-only LLM. He explains that the transformer is an architecture from the Google paper "Attention Is All You Need," where a sequence is processed in parallel using an attention mechanism. Multi-Head Attention is based on three projection matrices: Q, K, V. In the first step, token similarity is computed (attention matrix), and in the second step, token content is mixed according to the found weights. For efficiency, all attention heads are implemented through a single large linear projection. After attention, a Feed Forward Network with GELU activation and dropout is applied. Building the transformer includes normalization (Pre-LN) and residual connections. Additionally, a causal mask is used for the decoder. Overall, the block is ready for use in assembling an LLM, training, and text generation.
Source: Habr — хаб NLP —
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