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deepx改为 py侧的函数，尽可能前向传播inplace化，从而让推理，训练的前向最省内存 autograd交给中层去做，autograder先接受init序列，再接收forward的整组IR序列 和pytorch不同，deepx相信autograder能把forward的IR序列，先修改为非inplace的IR序列，以此根据autograder内IRmap注册的反向算子序列，生成反向计算图及IR序列，再根据IR序列的关系，尽可能尝试inplace化，从而达到反向传播也最省内存 |
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