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%20theory%2C%20which%20provides%20the%20theoretical%20feasibility%20of%20decoupling%20the%20forward%20and%20backward%20passes.%20We%20apply%20our%20Approx-BP%20theory%20to%20backpropagation%20training%20and%20derive%20memory-efficient%20alternatives%20of%20GELU%20and%20SiLU%20activation%20functions%2C%20which%20use%20derivative%20functions%20of%20ReLUs%20in%20the%20backward%20pass%20while%20keeping%20their%20forward%20pass%20unchanged.%20In%20addition%2C%20we%20introduce%20a%20Memory-Sharing%20Backpropagation%20strategy%2C%20which%20enables%20the%20activation%20memory%20to%20be%20shared%20by%20two%20adjacent%20layers%2C%20thereby%20removing%20activation%20memory%20usage%20redundancy.%20Our%20method%20neither%20induces%20extra%20computation%20nor%20reduces%20training%20efficiency.%20We%20conduct%20extensive%20experiments%20with%20pretrained%20vision%20and%20language%20models%2C%20and%20the%20results%20demonstrate%20that%20our%20proposal%20can%20reduce%20up%20to%20%24sim%24%2430%25%24%20of%20the%20peak%20memory%20usage.%20Our%20code%20is%20released%20at%20https:%2F%2Fgithub.com%2Fyyyyychen%2FLowMemoryBP.)
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