This project provides insight into the two central roles of the SwiGLU network in Llama3:
Technology Innovation Points::
Compared to the traditional FFN layer, SwiGLU uses a gating mechanism to realize more complex nonlinear transformations. Key Code Segment:output = torch.matmul(F.silu(w1(x)) * w3(x), w2.T)
included among theseF.silu(Sigmoid Linear Unit) as an activation function, with thew3(x)Element-level multiplication is performed to form a gated structure, which significantly enhances the model representation.
Realization details::
1. The project provides a detailed annotation of the role of the three sets of weighting matrices (w1/w2/w3).
2. Demonstrate through dimensional tracking, for examplew1.shape=[11008,4096]The intermediate extension process of
3. Provide experimental recommendations: replace SwiGLU with ReLU to compare output quality differences
This implementation has increased but better FFN parameters compared to the original LLaMA, and is one of the key components of the Llama3 performance improvement. The project suggests understanding with a focus on grasping the impact of the gating mechanism on the gradient flow.
This answer comes from the articleDeepdive Llama3 From Scratch: Teaching You to Implement Llama3 Models From ScratchThe
