A study of K-interv examines how quantizing the softmax in Transformer attention during pretraining changes both the forward computation and the gradients that train the model, as low-precision Transformer systems increasingly quantize attention matrix multiplications while leaving softmax at higher precision. The research focuses on the interaction between an approximate softmax and pretraining dynamics.
Low-precision Transformer systems increasingly quantize attention matrix multiplications, while softmax often remains at higher precision. During pretraining, an approximate softmax changes the gradients that train the model as well as its forward computation. We study this interaction with K-interv
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