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Eli Bendersky: Sparsely-gated Mixture Of Experts (MoE)
- In transformer models, the attention block is typically followed by a feed forward layer (FF) with a hidden layer and ReLU activation.
- The feed forward layer holds most of the weights in the transformer due to its large hidden dimension, playing a major role in token reasoning.
- Transformers repeat blocks multiple times, leading to challenges in total layer size, paving the way for efficiency improvements like sparsely-gated mixture of experts (MoE).
- MoE architecture involves experts, a router or gate, and selecting top experts based on scores to process tokens effectively through weighted averaging.
- MoE aims to increase model capacity without significantly raising computational costs by using a subset of parameters for each token.
- The experts not among the top K for a token are disregarded, enhancing computational efficiency by eschewing unnecessary computation.
- MoE allows for an overall model size increase without a proportional rise in computational expense.
- Efficient MoE implementation may pose challenges related to load balancing among experts, addressing through methods like adding noise or special losses.
- The MoE architecture offers specialization among experts, with each focusing on different model capabilities, optimizing token flow through the model.
- Considerations like the Shrinking Batch Problem in MoE papers emphasize challenges associated with expert batch sizes and computational efficiency.
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