# How to write a new operator

 - [Background](#background)
 - [Implementing C++ Types](#implementing-c++-types)
   - [Defining ProtoMaker](#defining-protoMaker)
   - [Defining Operator](#defining-operator)
   - [Registering Operator](#registering-operator)
   - [Compilation](#compilation)
 - [Python Binding](#python-binding)
 - [Unit Tests](#unit-tests)
   - [Testing Forward Operators](#testing-forward-operators)
   - [Testing Backward Operators](#testing-backward-operators)
   - [Compiling and Running](#compiling-and-running)
 - [Remarks](#remarks)
## Background

Here are the base types needed. For details, please refer to the design docs.

- `framework::OperatorBase`: Operator (Op)base class.
- `framework::OpKernel`: Base class for Op computation.
- `framework::OperatorWithKernel`: Inherited from OperatorBase, describing an operator with computation.
- `class OpProtoAndCheckerMaker`: Describes an Operator's input, output, attributes and description, mainly used to interface with Python API.

An operator can be differentiated by whether in has kernel methods. An operator with kernel inherits from `OperatorWithKernel` while the ones without inherit from `OperatorBase`. This tutorial focuses on implementing operators with kernels. In short, an operator includes the following information:


 Information           | Where is it defined
--------------  | :----------------------
OpProtoMake definition  | `.cc`files, Backward Op does not need an OpProtoMake interface.
Op definition           | `.cc` files
Kernel implementation       | The kernel methods shared between CPU and GPU are defined in `.h` files. CPU-specific kernels live in `.cc` files, while GPU-specific kernels are implemented in `.cu`files.
Registering the Op           | Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the GPU implementation.


New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions. **


Let's take matrix multiplication operator, [MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc), as an example to introduce the writing of an Operator with Kernel.


## Implementing C++ Types


### 1. Defining Class ProtoMaker

Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and pne output.

First, define `ProtoMaker` to describe the Operator's input, output, and additional comments:

```cpp
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X", "(Tensor), 2D tensor of size (M x K)");
    AddInput("Y", "(Tensor), 2D tensor of size (K x N)");
    AddOutput("Out", "(Tensor), 2D tensor of size (M x N)");
    AddComment(R"DOC(
Two Element Mul Operator.
The equation is: Out = X * Y
)DOC");
  }
};
```

[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)is inherited from`framework::OpProtoAndCheckerMaker`, consisting of 2 variables in the constructor：

   - `framework::OpProto` stores Operator input and variable attribute, used for generating Python API interfaces.
   - `framework::OpAttrChecker` is used to validate variable attributes.

The constructor utilizes `AddInput`, `AddOutput`, and `AddComment`, so that the corresponding information will be added to `OpProto`.

The code above adds two inputs `X` and `Y` to `MulOp`, an output `Out`, and their corresponding descriptions, in accordance to Paddle's [naming convention](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/name_convention.md).


An additional example [`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37) is implemented as follows:

```cpp
template <typename AttrType>
class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X", "The input tensor of scale operator.").NotInGradient();
    AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
    AddComment(R"DOC(Scale operator
The equation is: Out = scale*X
)DOC");
    AddAttr<AttrType>("scale", "scale of scale operator.").SetDefault(1.0);
  }
};
```

There are two changes in this example:

- `AddInput("X","...").NotInGradient()` expresses that input `X` is not involved in `ScaleOp`'s corresponding computation. If an input to an operator is not participating in back-propagation, please explicitly set `.NotInGradient()`.

- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);`  adds `scale`constant as an attribute, and sets the default value to 1.0.


### 2. Defining Operator

The following code defines the interface for MulOp:

```cpp
class MulOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  void InferShape(const framework::InferShapeContext &ctx) const override {
    auto dim0 = ctx.Input<Tensor>("X")->dims();
    auto dim1 = ctx.Input<Tensor>("Y")->dims();
    PADDLE_ENFORCE_EQ(dim0.size(), 2,
                      "input X(%s) should be a tensor with 2 dims, a matrix",
                      ctx.op_.Input("X"));
    PADDLE_ENFORCE_EQ(dim1.size(), 2,
                      "input Y(%s) should be a tensor with 2 dims, a matrix",
                      ctx.op_.Input("Y"));
    PADDLE_ENFORCE_EQ(
        dim0[1], dim1[0],
        "First matrix's width must be equal with second matrix's height.");
    ctx.Output<Tensor>("Out")->Resize({dim0[0], dim1[1]});
  }
};
```

[`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22) is inherited from `OperatorWithKernel`. Its `public` member

```cpp
using framework::OperatorWithKernel::OperatorWithKernel;
```

expresses an operator constructor using base class `OperatorWithKernel`, alternatively written as

```cpp
MulOp(const std::string &type, const framework::VariableNameMap &inputs,
      const framework::VariableNameMap &outputs,
      const framework::AttributeMap &attrs)
  : OperatorWithKernel(type, inputs, outputs, attrs) {}
```

`InferShape` interface needs to be re-written.`InferShape` is a constant method and cannot modify Op's member variables, its constant member `const framework::InferShapeContext &ctx` can be used to extract input, output, and attributes. It functions to

  - 1). validate and error out early: it checks input data dimensions and types.
  - 2). configures the tensor shape in the output.

Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, which also include the registration methods introduced later.

### 3. Defining OpKernel

`MulKernel` inherits `framework::OpKernel`, which includes the following templates:

- `typename  Place` denotes device type. When different devices, namely the CPU and the GPU, share the same kernel, this template needs to be added. If they don't share kernels, this must not be added. An example of a non-sharing kernel is [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).

- `typename T` denotes data type, such as `float` or `double`.

`MulKernel` types need to rewrite the interface for `Compute`.
- `Compute` takes one input variable `const framework::ExecutionContext& context`.
- Compared with `InferShapeContext`, `ExecutionContext` includes device types, and can similarly extract input, output, and attribute variables.
- `Compute` implements the computation logics of an `OpKernel`.

`MulKernel`'s implementation of `Compute` is as follows:

  ```cpp
  template <typename Place, typename T>
  class MulKernel : public framework::OpKernel {
  public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* X = context.Input<Tensor>("X");
    auto* Y = context.Input<Tensor>("Y");
    auto* Z = context.Output<Tensor>("Out");
    Z->mutable_data<T>(context.GetPlace());
    auto* device_context =
        const_cast<platform::DeviceContext*>(context.device_context_);
    math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
  }
  };
  ```

Note that **different devices (CPU, GPU)share an Op definition; whether or not they share the same `OpKernel` depends on whether `Compute` calls functions that support both devices.**

`MulOp`'s CPU and GPU share the same `Kernel`. A non-sharing  `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).

To ease the writing of `OpKernel` compute, and for reusing code cross-device, `Eigen unsupported Tensor` module is used to implement `Compute` interface. To learn about how the Eigen library is used in PaddlePaddle, please see [usage document](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md).


This concludes the forward implementation of an operator. Next its operation and kernel need to be registered in a `.cc` file.

The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. **Note that a backward operator does not include a `ProtoMaker`**.

### 4. Registering Operator

- In `.cc` files, register forward and backward operator classes and the CPU kernel.

    ```cpp
    namespace ops = paddle::operators;
    REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
    REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
    REGISTER_OP_CPU_KERNEL(mul_grad,
                  ops::MulGradKernel<paddle::platform::CPUPlace, float>);
    ```

   In that code block,

    - `REGISTER_OP` registers the `ops::MulOp` class, type named `mul`, its type `ProtoMaker` is `ops::MulOpMaker`, registering `ops::MulOpGrad` as `mul_grad`.
    - `REGISTER_OP_WITHOUT_GRADIENT` registers an operator without gradient.
    - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulKernel`.


- Registering GPU Kernel in `.cu` files
    - Note that if GPU Kernel is implemented using the `Eigen unsupported` module, then on top of `.cu`, a macro definition `#define EIGEN_USE_GPU` is needed, such as

    ```cpp
    // if use Eigen unsupported module before include head files
    #define EIGEN_USE_GPU

    namespace ops = paddle::operators;
    REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
    REGISTER_OP_GPU_KERNEL(mul_grad,
                           ops::MulGradKernel<paddle::platform::GPUPlace, float>);
    ```

### 5. Compilation

Run the following commands to compile.

```
make mul_op
```

## Python Binding

The system will automatically bind to Python and link it to a generated library.

## Unit Tests

Unit tests for an operator include

1. comparing a forward operator's implementations on different devices,

2. comparing a backward operator's implementation on different devices, and

3. a scaling test for the backward operator.

Here, we introduce the [unit tests for `MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py).

### Testing Forward Operators

A forward operator unit test inherits `unittest.TestCase` and defines metaclass `__metaclass__ = OpTestMeta`. More concrete tests are performed in `OpTestMeta`. Testing a forward operator requires the following:

1. Defining input, output and relevant attributes in `setUp` method.

2. Generating random input data.

3. Implementing the same computation logic in a Python script:

  ```python
  import unittest
  import numpy as np
  from gradient_checker import GradientChecker, create_op
  from op_test_util import OpTestMeta

  class TestMulOp(unittest.TestCase):
      __metaclass__ = OpTestMeta

      def setUp(self):
          self.type = "mul"
          self.inputs = {
              'X': np.random.random((32, 84)).astype("float32"),
              'Y': np.random.random((84, 100)).astype("float32")
          }
          self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
  ```
Get its output, and compare it with the forward operator's own output.

The code above first loads required packages. In addition, we have

- `self.type = "mul" ` defines the type that is identical to what the operator's registered type.
- `self.inputs` defines input, with type `numpy.array` and initializes it.
- `self.outputs` defines output and completes the same operator computation in the Python script, and returns its result from the Python script.

### Testing Backward Operators

A backward operator unit test inherits `GradientChecker`, which inherits `unittest.TestCase`. As a result, **a backward operator unit test needs to be have the prefix `test_`**.

```python
class TestMulGradOp(GradientChecker):
    def setUp(self):
        self.op = create_op("mul")
        self.inputs = {
            'X': np.random.random((32, 84)).astype("float32"),
            'Y': np.random.random((84, 100)).astype("float32")
        }

    def test_cpu_gpu_compare(self):
        self.compare_grad(self.op, self.inputs)

    def test_normal(self):
        # mul op will enlarge the relative error
        self.check_grad(
            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)

    def test_ignore_x(self):
        self.check_grad(
            self.op,
            self.inputs, ["Y"],
            "Out",
            max_relative_error=0.5,
            no_grad_set={"X"})

    def test_ignore_y(self):
        self.check_grad(
            self.op,
            self.inputs, ["X"],
            "Out",
            max_relative_error=0.5,
            no_grad_set={"Y"})
```

Some key points in the code above include:

- `create_op("mul")` creates the backward operator's corresponding forward operator.
- `compare_grad` compares results between utilizing the CPU and the GPU.
- `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
  - The first variable `self.op` denotes the forward operator.
  - The second variable `self.inputs` denotes the input dictionary, which has its key value identical to its `ProtoMaker` definitions.
  - The third variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
  - The fourth variable `"Out"` points to the network's final output target `Out`.
- `test_ignore_x` and `test_ignore_y`branches test the cases where there is only one scaling input.

### Compiling and Running


Any new unit testing file of the format `test_*.py`  added to the director `python/paddle/v2/framework/tests` is automatically added to the project to compile.

Note that **unlike the compile test for Ops, running unit tests requires compiling the entire project** and requires compiling with flag `WITH_TESTING` on i.e. `cmake paddle_dir -DWITH_TESTING=ON`.

After successfully compiling the project, run the following command to run unit tests:

```bash
make test ARGS="-R test_mul_op -V"
```

Or,

```bash
ctest -R test_mul_op
```

## Remarks

- Every `*_op.h` (if applicable), `*_op.cc`, and `*_op.cu` (if applicable) must be created for a unique Op. Compiling will fail if multiple operators are included per file.
- The type with which an operator is registered needs to be identical to the Op's name. Registering `REGISTER_OP(B, ...)` in `A_op.cc` will cause unit testing failures.
- If the operator does not implement a GPU kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail.
- If multiple operators rely on some shared methods, a file NOT named `*_op.*` can be created to store them, such as `gather.h`.
