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Conceptually, the residual stream is like shared memory. It is used much like the DRAM on your computer. Different components of the model (attention, MLPs, etc) perform loads and stores from that memory. The loads and stores occur sequentially through the forward pass, one layer at a time. However each component in a given layer loads in parallel and stores in parallel with the others. The model learns to carve out subspaces in this vector space. This helps prevent components from clobbering over what previous components have written. The residual stream itself doesn’t do any computation, but serves as a shared medium through which layers communicate with each other.,详情可参考Replica Rolex
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