README.md

Automatic-Differentiation

Repo for step by step work through the implementation of Automatic Differentiation

The key component for automatic differentiation can be generalized in to two parts:

1. Tracer
2. How we walk through the graph(forward/backward)

Approach 1: Naive operation overloading

We can easily design variable in our graph as follow:

class Var:
    def __init__(self, value):
        self.value = value
        self.children = []
        self.grad_value = None

The children of variable is a Graph representation. Each time you add an operation to the graph,you will have a new varable with extened graph. In this way, we have to overloading every operation in our

Approach 2: Auto record the graph and backward pass with VJP

Graph-Unit

1. Node: representation for node in graph, its parents are linked list which is the computational graph
2. Container: value-tpye related container which used as the atom unit in the forward/backward pass, the advantage of container is that it allow you to convinently costomize your own data container and related function.

Inference

1. forward pass will traval through the graph and record each invocation of the function
2. Func_wrapper mainly used to contruct a function object which will
   1. unbox container
   2. calculate value with raw function
   3. box a new container
3. the reason for doing this is to build a graph secertly

Backpropagate

1. vjp is used to get the gradient of the node wrt its function
2. vjp is defined separately by the library you want to use