tensor.device(dev) = in your code.thrust::tranform and std::transform.thrust has the same API as C++ standard library. Using transform, one can quickly implement customized elementwise kernels.thrust::transform and std::transform.thrust has the same API as C++ standard library. Using transform, one can quickly implement customized element-wise kernels.thrust also has more complex APIs, like scan, reduce, reduce_by_key.We need a method to build mappings between Op type names and Op classes.
It’s constructor takes proto and checker. They are compeleted during Op_Maker’s construction. (ScaleOpMaker)
It’s constructor takes proto and checker. They are completed during Op_Maker’s construction. (ScaleOpMaker)
Add operator to combine gradientsRNNOp,Eval(targets) to deduce the minimal block to Run,Parameters","Design Doc: ProgramDesc","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Refactorization Overview","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Interaction between C++ and Python","Design for TensorArray","Background","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","GET STARTED","RNN Models","RNN Configuration","Build PaddlePaddle from Source Code and Run Unit Test","Contribute Code","Write New Layers","How to write a new operator","How to use Eigen in Paddle","HOW TO","Tune GPU Performance","Run Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no 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\ No newline at end of file
diff --git a/develop/doc_cn/_sources/design/refactorization.md.txt b/develop/doc_cn/_sources/design/refactorization.md.txt
index a07675b3e0494e189321cb638599bdd6ce31c0b4..629422e7743af666b42fd69fbff442ce15bef596 100644
--- a/develop/doc_cn/_sources/design/refactorization.md.txt
+++ b/develop/doc_cn/_sources/design/refactorization.md.txt
@@ -17,7 +17,7 @@ The goals of refactoring include:
1. A graph is composed of *variables* and *operators*.
-1. The description of graphs must be capable of being serialized/deserialized, so that
+1. The description of graphs must be capable of being serialized/deserialized, so that:
1. It can to be sent to the cloud for distributed execution, and
1. It can be sent to clients for mobile or enterprise deployment.
@@ -137,19 +137,18 @@ Compile Time -> IR -> Runtime
* `Eigen::Tensor` contains basic math and element-wise functions.
* Note that `Eigen::Tensor` has broadcast implementation.
* Limit the number of `tensor.device(dev) = ` in your code.
-* `thrust::tranform` and `std::transform`.
- * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized elementwise kernels.
+* `thrust::transform` and `std::transform`.
+ * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized element-wise kernels.
* `thrust` also has more complex APIs, like `scan`, `reduce`, `reduce_by_key`.
* Hand-writing `GPUKernel` and `CPU` code
* Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
---
# Operator Registration
-## Why registration is necessary?
+## Why is registration necessary?
We need a method to build mappings between Op type names and Op classes.
## How is registration implemented?
-
Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
---
@@ -170,7 +169,7 @@ Maintaining a map, whose key is the type name and the value is the corresponding
# Related Concepts
### Op_Maker
-It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
+It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
### Register Macros
```cpp
@@ -200,7 +199,7 @@ Make sure the registration process is executed and linked.
---
# Backward Module (2/2)
### Build Backward Network
-- **Input**: graph of forwarding operators
+- **Input**: graph of forward operators
- **Output**: graph of backward operators
- **Corner cases in construction**
- Shared Variables => insert an `Add` operator to combine gradients
@@ -224,7 +223,7 @@ Make sure the registration process is executed and linked.
---
# Block (in design)
-## the difference with original RNNOp
+## the difference between original RNNOp and Block
- As an operator is more intuitive than `RNNOp`,
- Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
- Fits the compile-time/ runtime separation design paradigm.
diff --git a/develop/doc_cn/design/refactorization.html b/develop/doc_cn/design/refactorization.html
index 457abdd17422492ed299eea52b747a14352a7619..20af163b044c674b6c9bfff39a90ebf839b881a8 100644
--- a/develop/doc_cn/design/refactorization.html
+++ b/develop/doc_cn/design/refactorization.html
@@ -207,7 +207,7 @@
tensor.device(dev) = in your code.thrust::tranform and std::transform.thrust has the same API as C++ standard library. Using transform, one can quickly implement customized elementwise kernels.thrust::transform and std::transform.thrust has the same API as C++ standard library. Using transform, one can quickly implement customized element-wise kernels.thrust also has more complex APIs, like scan, reduce, reduce_by_key.We need a method to build mappings between Op type names and Op classes.
It’s constructor takes proto and checker. They are compeleted during Op_Maker’s construction. (ScaleOpMaker)
It’s constructor takes proto and checker. They are completed during Op_Maker’s construction. (ScaleOpMaker)
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