diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md
index b4205fed2e6e98282d60a25a83e5b2e2c5d0cfce..133b17c7be811f26a63b245816f382432b2af0f5 100644
--- a/paddle/framework/backward.md
+++ b/paddle/framework/backward.md
@@ -2,32 +2,24 @@
 
 ## 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.
- 
+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 pass will be generated respect to forward pass.
+  
 ## Backward Operator Registry
 
-A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences, which is quite similar with operator registry itself.
+A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences.
 
 For example, we have got a `add_two_op`, and is registered by the following code:
 
 ```cpp
-REGISTER_OP(add_two, AddTwoOp, AddTwoOpMaker);
+REGISTER_OP(add_two, AddTwoOp, AddTwoOpMaker, add_two_grad, AddTwoGradOp);
 ```
 
 `add_two` is the operator's type. `AddTwoOp` and `AddTwoOpMaker` are the operator class and the operator maker class respectively.
 
-Assume that we have also got the backward operator of `add_two_op`, which calculating the gradients of `add_two_op`'s inputs. Then we register it by the following way:
-
-```cpp
-REGISTER_GRADIENT_OP(add_two, add_two_grad, AddTwoGradOp);
-```
-
 `add_two_grad` is the type of backward operator, and `AddTwoGradOp` is its class name.
 
 ## Backward Opeartor Creating
 
-### Usage
-
 Given a certain forward operator, we can get its corresponding backward opeartor by calling:
 
 ```cpp
@@ -36,13 +28,13 @@ OperatorBase* bwd_op = BuildGradOp(const OperatorBase* fwd_op);
 
 The function `BuildGradOp` will sequentially execute following processes:
 
-1. Getting the `type_` of given forward operator, and then creating the corresponding backward operator.
+1. Get the `type_` of given forward operator, and then get the corresponding backward operator's type by looking up the `OpInfoMap`.
 
-2. Copying all the attributes of forward operator expect `input_format` and `output_format`(if it has), for their elements differ between forward and backward operators.
+2. Build two maps named `inputs` and `outputs` to temporary storage backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these are not necessary for gradient computing.
 
-3. Copying forward operator's `inputs_` and `outputs_` to backward operator's `inputs_`. And adding forward inputs' gradient variables into backward `output_`, adding forward outputs' gradient variables into backward `input_`.
+3. Add forward inputs' gradient variables into map `output`, adding forward outputs' gradient variables into map `input`.
 
-4. Building backward operator's `input_format`, `output_format` (if necessary) and `in_out_idxs_` according to its `inputs_` and `outputs_` just created.
+4. Building backward operator with `inputs`, `outputs` and forward operator's attributes.
 
 ## Backward Network Building