Paddle/paddle/framework/backward.md

Operator/expression 's Backward

Motivation

In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation lineage, the operator/ expression's Backward feature will generate the backward pass respect to forward pass.

Implement : gradient operator registry

	forward operator	backward operator
Operator::inputs_	Inputs	Inputs, Outputs, OutputGradients
Operator::outputs_	Outputs	InputGradients

Inputs/Outputs means the input/output of the operator, InputGradients/OutputGradients is the gradient respect to forward opeartor. Forward operator and Backward operator are isomorphic, save their corresponding needs into member attribute.

We use a global hash map record the gradient operators available, follow the philosophy of minimum core, make operator pluggable unit. Each gradient is an operator and it needs to regist itself.

grad_op_builder(fengjiayi)

Implement : Backward network

given a forward network, it generates the backward network. We only care about the Gradients—OutputGradients,InputGradients.

1. bla bla bla (yuyang)

2. NetOp

   when the input forward network is a NetOp, it need to call the sub NetOp/Operators backward function recursively and ensure them done. During the process, we need to collect the OutputGradients name.

   We share variable in the same scope, as a result, duplicate operator OutputGradients will overwirte then duplicate variable.

   Share variable between operators or same input variable used in multiple operators lead to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively, and add a generic add operator instead.

​ Then collect the sub graph OutputGradients/InputGradients as the NetOp's and return it.