A step-by-step approach to dismantling the reasoning process
To systematically understand the Llama3 reasoning process, it is recommended that the following steps be followed:
- Get Project Code: Download the Deepdive-llama3-from-scratch project via GitHub, and we recommend running it in a Jupyter Notebook environment.
- Learning in modules: Focus on
llama3_inference.pyThe 6 core stages in: input embedding → attention computation → feedforward network → residual connectivity → output layer → prediction - Dimensional tracking techniques: Using PyTorch's
.shapeMethod validation matrix dimensionality changes (e.g., [17×4096] → [17×128]), hand-drawn data flow transformation diagrams are recommended - Comparison realization: Add in key computational nodes (e.g. RMSNorm, RoPE positional coding)
print()statement outputs the intermediate result
Advanced Tip: Combine the project'sattention.pydocument, paying particular attention to the details of the Grouped Query Attention (GQA) implementation by modifying thenum_kv_headsparameter to observe the change in the amount of computation.
This answer comes from the articleDeepdive Llama3 From Scratch: Teaching You to Implement Llama3 Models From ScratchThe
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