# Anakin 使用教程 ##
本教程将会简略的介绍Anakin的工作原理，一些基本的Anakin API，以及如何调用这些API。
## 内容 ###
- [Anakin的工作原理](#principle)
- [Anakin APIs](#api)
- [示例代码](#example)
## Anakin的工作原理 ###
用Anakin来进行前向计算主要分为三个步骤：
- 将外部模型通过[Anakin Parser](Converter_ch.md)解析为Anakin模型
在使用Anakin之前，用户必须将所有其他模型转换成Anakin模型，我们提供了转换脚本，用户可通过[Anakin Parser](Converter_ch.md)进行模型转换。
- 生成Anakin计算图
加载Anakin模型生成原始计算图，然后需要对原始计算图进行优化。你只需要调用相应的API优化即可。
- 执行计算图
Anakin会选择不同硬件平台执行计算图。
## Anakin APIs ###
### Tensor ####
`Tensor`提供基础的数据操作和管理，为ops提供统一的数据接口。`Tensor`包含以下几个属性：
- Buffer
数据存储区
- Shape
数据的维度信息
- Event
用于异步计算的同步
`Tensor` 类包含三个`Shape`对象， 分别是`_shape`, `_valid_shape`和 `offset`。 `_shape`为`tensor`真正空间信息，`_valid_shape`表示当前`tensor`使用的空间信息， `_offset`表示当前`tensor`数据指针相对于真正数据空间的信息。 `Tensor`不同维度与分别与数学中的向量、矩阵等相对应如下表所示。
Dimentions | Math entity |
:----: | :----:
1 | vector
2 | matrix
3 | 3-tensor
n | n-tensor
#### 声明tensor对象
`Tensor`接受三个模板参数:
```c++
template
class Tensor .../* Inherit other class */{
//some implements
...
};
```
TargetType是平台类型，如X86，GPU等等，在Anakin内部有相应的标识与之对应；datatype是普通的数据类型，在Anakin内部也有相应的标志与之对应；[LayOutType](#layout)是数据分布类型，如batch x channel x height x width [NxCxHxW], 在Anakin内部用一个struct来标识。 Anakin中数据类型与基本数据类型的对应如下:
1. TargetType
Anakin TargetType | platform
:----: | :----:|
NV | NVIDIA GPU
ARM | ARM
AMD | AMD GPU
X86 | X86
NVHX86 | NVIDIA GPU with Pinned Memory
2. DataType
Anakin DataType | C++ | Description
:---: | :---: | :---: |
AK_HALF | short | fp16
AK_FLOAT | float | fp32
AK_DOUBLE | double | fp64
AK_INT8 | char | int8
AK_INT16 | short | int16
AK_INT32 | int | int32
AK_INT64 | long | int64
AK_UINT8 | unsigned char | uint8
AK_UINT16 | unsigned short | uint8
AK_UINT32 | unsigned int | uint32
AK_STRING | std::string | /
AK_BOOL | bool | /
AK_SHAPE | / | Anakin Shape
AK_TENSOR | / | Anakin Tensor
3. LayOutType
Anakin LayOutType ( Tensor LayOut ) | Tensor Dimention | Tensor Support | Op Support
:---: | :---: | :---: | :---: |
W | 1-D | YES | NO
HW | 2-D | YES | NO
WH | 2-D | YES | NO
NW | 2-D | YES | YES
NHW | 3-D | YES |YES
NCHW ( default ) | 4-D | YES | YES
NHWC | 4-D | YES | NO
NCHW_C4 | 5-D | YES | YES
理论上，Anakin支持申明1维以上的tensor，但是对于Anakin中的Op来说，只支持NW、NHW、NCHW、NCHW_C4这四种LayOut，其中NCHW是默认的LayOutType，NCHW_C4是专门针对于int8这种数据类型的。
例子
> 下面的代码将展示如何使用tensor， 我们建议先看看这些示例。
> 要想获得更多关于tensor的信息， 请参考 *soure_path/core/tensor.h*
> 1. 使用shape对象初始化tensor
``` c++
//create a null tensor. A null tensor holds for nothing.
//tensor's buffer is resident at CPU and its datatype is AK_FLOAT.
//tensor's Layout is NCHW(default)
Tensor mytensor;
//1. using shape object to create a tensor.
Shape shape1(NUM); //1-D shape. NUM is the number of dimention.
Tensor mytensor1(shape1); //1-D tensor.
// A 4-D shape
Shape shape2(N, C, H, W); // batch x channel x height x width
```
>`注意：Shape的维度必须和tensor的`[LayoutType](#layout)`相同，比如Shape(N,C,H,W), 那么Tensor的 LayoutType必须是NCHW，否则会出错。如下列代码所示`
```c++
// A 4-D tensor.
Tensor mytensor2(shape2); //right
//A 4-D tensor which is resident at GPU and its datatype is AK_INT8
Tensor mytensor3(shape2); //right
Tensor mytensor4(shape2); //wrong!! shape's dimetion must be equal to tensor's Layout.
Tensor mytensor5(shape2); //wrong!!!!
```
> 2. 使用现有的数据和shape初始化tensor
```c++
/**
* A construtor of Tensor.
* data_ptr is a pointer to any data type of data
* TargetType is type of a platform [Anakin TargetType]
* id : device id
* shape: a Anakin shape
*/
Tensor(Dtype* data_ptr, TargetType_t target, int id, Shape shape);
//using existing data feed to a tensor
Tensor mytensor(data_ptr, TargetType, device_id, shape); //shape must has dimention (N, C, H, W).
```
> 3. 使用tensor初始化tensor
```c++
Tensor tensor(exist_tensor);
```
> 提示： 你可以用` typedef Tensor Tensor4d_X86 `方便定义tensor
#### 填充tensor数据区
填充数据区得看你申明tensor的方式， 下面展示了如何填充tensor的数据区。
```c++
首先来看看tensor的四种声明方式：
1. Tensor mytensor;
2. Tensor mytensor1(shape1);
3. Tensor mytensor(data_ptr, TargetType, device_id, shape);
4. Tensor tensor(exist_tensor);
相关的声明方式的数据填充方法如下：
1：声明一个空的tensor，此时没有为其分配内存，所以，我们需要手动的为其分配内存。
//parama shape
mytensor.re_alloc(Shape shape);
//Get writable pointer to mytensor.
//parama index (int): where you start to write.
//Dtype is your data type such int, float or double.
Dtype *p = mytensor.mutable_data(index/*=0*/);
//write data to mytensor
for(int i = 0; i < mytensor.size(); i++){
p[i] = 1.0f;
}
//do something ...
2: 这种声明方式会自动分配内存
//Get writable pointer to mytensor.
//parama index (int): where you start to write.
//Dtype is your data type such int, float or double.
Dtype *p = mytensor1.mutable_data(index/*=0*/);
//write data to mytensor
for(int i = 0; i < mytensor.size(); i++){
p[i] = 1.0f;
}
//do something ...
3：在该种声明方式中，我们仍不需要手动为其分配内存。但在构造函数内部是否为其分配内存，得依情况而定。如果data_ptr和申明的
tensor都在都一个目标平台上，那么该tensor就会与data_ptr共享内存空间，相反，如果他们不在同一个平台上（如data_ptr在X86上，而
tensor在GPU上），那么此时tensor就会开辟一个新的内存空间，并将data_ptr所指向的数据拷贝到tensor的buffer中。
//Get writable pointer to mytensor.
//parama index (int): where you start to write.
//Dtype is your data type such int, float or double.
Dtype *p = mytensor.mutable_data(index/*=0*/);
//write data to mytensor
for(int i = 0; i < mytensor.size(); i++){
p[i] = 1.0f;
}
//do something ...
4：该种方式仍不需要手动分配内存
//Get writable pointer to mytensor.
//parama index (int): where you start to write.
//Dtype is your data type such int, float or double.
Dtype *p = mytensor.mutable_data(index/*=0*/);
//write data to mytensor
for(int i = 0; i < mytensor.size(); i++){
p[i] = 1.0f;
}
//do something ...
另外，你还可以获取一个tensor的可读指针，示例如下：
//Get read-only pointer to mytensor.
//parama index (int): where you start to read.
//Dtype is your data type such int, float or double.
Dtype *p = mytensor.data(index/*=0*/);
//do something ...
```
如果想更详细的了解tensor，请查阅*soure_path/saber/core/tensor.h*
#### 获取tensor的shape
```c++
//some declarations
// ...
Shape shape = mytensor.shape();
//Get a first dimetion size of tesor, if it has.
int d1 = shape[0];
//Get a second dimention size of tensor, if it has.
int d2 = shape[1];
...
//Get a n-th dimention size of tensor, if it has.
int dn = shape[n-1];
//Get a tensor's dimention
int dims = mytensor.dims();
//Get the size of tensor.
//size = d1 x d2 x ... x dn.
int size = mytensor.size();
//Get the size of tensor at interval [Di, Dj)
// form i-th dimention to j-th dimention, but not including the j-th dimention.
// which means di x (di+1) x ... x (dj -1)
int size = mytensor.count(start, end);
```
#### 设置tensor的shape
我们可以用tensor的成员函数set_shape来设置tensor的shape。 下面是set_shape的定义
```c++
/**
* \brief set a tensor's shape
* \param valid_shape [a Shape object]
* \param shape [a Shape object]
* \param offset [a Shape object]
* \return the status of this operation, that means whether it success * or not.
*/
SaberStatus set_shape(Shape valid_shape, Shape shape = Shape::zero(TensorAPI::layout_dims::value), Shape offset = Shape::minusone(TensorAPI::layout_dims::value));
```
这个成员函数只设置tensor的shape。这些shape对象(valid_shape, shape, offset)的[LayOutType](#layout)必须和当前的tensor的相应三个shape对象的LayOutType相同，如果不同就会出错，返回SaberInvalidValue。 如果相同，那么将成功设置tensor的shape。
```c++
// some declarations
// ...
//valid_shape, shape , offset are Shape object;
//All these Shape object's LayOutType must be equal to mytensor's.
mytensor.set_shape(valid_shape, shape, offset);
```
#### 重置 tensor的shape
```c++
//some declarations
Shape shape, valid_shape, offset;
//do some initializations
...
mytensor.reshape(valid_shape, shape, offset);
```
注意： Reshape操作仍然需要shape的[LayOutType](#layout) 与tensor的相同
### Graph ###
`Graph`类负责加载Anakin模型生成计算图、对图进行优化、存储模型等操作。
#### 图的声明
与`Tensor`一样，graph也接受三个模板参数。
```c++
template
class Graph ... /* inherit other class*/{
//some implements
...
};
```
前面已经介绍过[TargetType](#target)和[DataType](#datatype)是Anakin内部自定义数据类型。[TargetType](#target)表示平台类型 (如NV、X86), [DataType](#datatype)是Anakin基本数据类型与C++/C中的基本数据类型相对应。 [Precision](#precision)为op所支持的精度类型, 稍后我们在介绍它。
```c++
//Create a empty graph object.
Graph graph = Graph tmp();
//Create a pointer to a empty graph.
Graph *graph = new Graph();
//Create a pointer to a empty graph.
auto graph = new Graph();
```
#### 加载 Anakin 模型
```c++
//some declarations
...
auto graph = new Graph();
std::string model_path = "the/path/to/where/your/models/are";
const char *model_path1 = "the/path/to/where/your/models/are";
//Loading Anakin model to generate a compute graph.
auto status = graph->load(model_path);
//Or this way.
auto status = graph->load(model_path1);
//Check whether load operation success.
if(!status){
std::cout << "error" << endl;
//do something...
}
```
#### 优化计算图
```c++
//some declarations
...
//Load graph.
...
//According to the ops of loaded graph, optimize compute graph.
graph->Optimize();
```
> 注意： 第一次加载原始图，必须要优化。
#### 保存模型
你可以在任何时候保存模型， 特别的， 你可以保存一个优化的模型，这样，下次再加载模型时，就不必进行优化操作。
```c++
//some declarations
...
//Load graph.
...
// save a model
//save_model_path: the path to where your model is.
auto status = graph->save(save_model_path);
//Checking
if(!status){
cout << "error" << endl;
//do somethin...
}
```
#### 重新设置计算图里的tensor的shape
```c++
//some declarations
...
//Load graph.
...
vector shape{10, 256, 256, 10};
//input_name : std::string.
//Reshape a tensor named input_name.
graph->Reshape(input_name, shape);//Note: shape is a vector, not a Shape object.
```
#### 设置 batch size
`Graph` 支持重新设置batch size的大小。
```c++
//some declarations
...
//Load graph.
...
//input_name : std::string.
//Reset a tensor named input_name.
int new_batch_size = 4;
graph->ResetBatchSize(input_name, new_batch_size);
```
### Net ###
`Net` 是计算图的执行器。你可以通过Net对象获得输入和输出
#### Creating a graph executor
`Net`接受四个模板参数。
```c++
template
class Net{
//some implements
...
};
```
由于有些Op可能支持多种精度，我们可以通过Precision来指定。OpRunType表示同步或异步类型，异步是默认类型。OpRunType::SYNC表示同步，在GPU上只有单个流；OpRunType::ASYNC表示异步，在GPU上有多个流并以异步方式执行。实际上，Precision和OpRunType都是enum class, 详细设计请参考*source_root/framework/core/types.h*.
1. Precision
Precision | Op support
:---: | :---:
Precision::INT4 | NO
Precision::INT8 | NO
Precision::FP16 | NO
Precision::FP32 | YES
Precision::FP64 | NO
现在Op的精度只支持FP32， 但在将来我们会支持剩下的Precision.
2. OpRunType
OpRunType | Sync/Aync |Description
:---: | :---: | :---:
OpRunType::SYNC | Synchronization | single-stream on GPU
OpRunType::ASYNC | Asynchronization | multi-stream on GPU
用graph对象创建一个执行器。
```c++
//some declarations
...
//Create a pointer to a graph.
auto graph = new Graph();
//do something...
...
//create a executor
Net executor(*graph);
```
#### 获取输入输出tensor
获取输入输出tensor，并填充输入tensor的buffer。如果想要获取输入和输出tensor，那么必须指定输入的名字，如"input_0", "input_1", "input_2", ..., 必须传入如上字符串才能够获得输入tensor。另外，如果想知道input_i对应哪个输入，你需要去dash board查看，如何使用dash board请看[Anakin Parser](Converter_ch.md)。请看如下示例代码
```c++
//some declaratinos
...
//create a executor
//TargetType is NV [NVIDIA GPU]
Net executor(*graph);
//Get the first input tensor.
//The following tensors(tensor_in0, tensor_in2 ...) are resident at GPU.
//Note: Member function get_in returns an pointer to tensor.
Tensor* tensor_in0 = executor.get_in("input_0");
//If you have multiple input tensors
//You just type this code below.
Tensor* tensor_in1 = executor.get_in("input_1");
...
auto tensor_inn = executor.get_in("input_n");
```
当得到输入tensor之后，就可以填充它的数据区了。
```c++
//This tensor is resident at GPU.
auto tensor_d_in = executor.get_in("input_0");
//If we want to feed above tensor, we must feed the tensor which is resident at host. And then copy the host tensor to the device's one.
//using Tensor4d = Tensor;
Tensor4d tensor_h_in; //host tensor;
//Tensor tensor_h_in;
//Allocate memory for host tensor.
tensor_h_in.re_alloc(tensor_d_in->valid_shape());
//Get a writable pointer to tensor.
float *h_data = tensor_h_in.mutable_data();
//Feed your tensor.
/** example
for(int i = 0; i < tensor_h_in.size(); i++){
h_data[i] = 1.0f;
}
*/
//Copy host tensor's data to device tensor.
tensor_d_in->copy_from(tensor_h_in);
// And then
```
类似的，我们可以利用成员函数get_out来获得输出tensor。但与获得输入tensor不同的是， 我们需要指定输入tensor结点的名字，这个可以从dash board中看到，请从[Anakin Parser](Converter_ch.md)中查看dash board的使用方法。假如有个输出结点叫pred_out, 那么我们可以通过如下代码获得相应的输出tensor：
```c++
//Note: this tensor are resident at GPU.
Tensor* tensor_out_d = executor.get_out("pred_out");
```
#### Executing graph
当一切准备就绪后，我们就可以执行真正的计算了！
```c++
executor.prediction();
```
## 示例代码 ##
下面的例子展示了如何调用Anakin。
在这儿之前， 请确保你已经有了Anakin模型。如果还没有，那么请使用[Anakin Parser](Converter_ch.md)转换你的模型。
### Single-thread
单线程例子在 *source_root/test/framework/net/net_exec_test.cpp`*
```c++
std::string model_path = "your_Anakin_models/xxxxx.anakin.bin";
// Create an empty graph object.
auto graph = new Graph();
// Load Anakin model.
auto status = graph->load(model_path);
if(!status ) {
LOG(FATAL) << " [ERROR] " << status.info();
}
// Reshape
graph->Reshape("input_0", {10, 384, 960, 10});
// You must optimize graph for the first time.
graph->Optimize();
// Create a executer.
Net net_executer(*graph);
//Get your input tensors through some specific string such as "input_0", "input_1", and
//so on.
//And then, feed the input tensor.
//If you don't know Which input do these specific string ("input_0", "input_1") correspond with, you can launch dash board to find out.
auto d_tensor_in_p = net_executer.get_in("input_0");
Tensor4d h_tensor_in;
auto valid_shape_in = d_tensor_in_p->valid_shape();
for (int i=0; icopy_from(h_tensor_in);
//Do inference.
net_executer.prediction();
//Get result tensor through the name of output node.
//And also, you need to see the dash board again to find out how many output nodes are and remember their name.
//For example, you've got a output node named obj_pre_out
//Then, you can get an output tensor.
auto d_tensor_out_0_p = net_executer.get_out("obj_pred_out"); //get_out returns a pointer to output tensor.
auto d_tensor_out_1_p = net_executer.get_out("lc_pred_out"); //get_out returns a pointer to output tensor.
//......
// do something else ...
//...
//save model.
//You might not optimize the graph when you load the saved model again.
std::string save_model_path = model_path + std::string(".saved");
auto status = graph->save(save_model_path);
if (!status ) {
LOG(FATAL) << " [ERROR] " << status.info();
}
```