Design Doc: Functions, Operators, and Layers

In a DL system, we can compose one or more fine grained operators into a coarse grained one. For example, the FC layer can be composed of a multiplication operator and an add operator.

Historically, some fine grained operations are known as operators, and some coarse level ones are known as layers. But we need a well-defined separation.

In general, operators are those very fine grained operations, e.g., mul and add. In the implementation, we can write them as C++ functions:

template <typename T> T add(T x, T y) { return x + y; }
template <typename T> T mul(T x, T y) { return x * y; }

Then we can wrap them into operators which are C++ classes and can be created from Python bindings by name. A C macro can do this. For example, the following macro invocation



template <typename T> class mulOp : public OperatorBase {...};
REGISTER_OP(mulOp<float32>, "mul");

so that in Python we can create operator mul by:

X1 = Var()
X2 = Var()
Y = Var()
paddle.cpp.create_operator("mul", input=[X1, X2], output=Y)

Also, at the same time, we can compose a coarse level C++ operator class by composing functions mul and add:

template <typename T>
class FCOp : public OperatorBase {
  void Run(...) {
    add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b"));

We need to support such composition in Python as well. To do so, we need a higher level Python wrapping of operator creation than paddle.cpp.create_operator. This higher level operator API should be compatible with the layer API.

Let's explain using an example. Suppose that we are going to compose the FC using mul and add in Python, we'd like to have Python functions mul and add defined in module operator:

def operator.mul(X1, X2):
    O = Var()
    paddle.cpp.create_operator("mul", input={X1, Y1}, output=O)
    return O

def operator.add(X1, X2):
    O = Var()
    paddle.cpp.create_operator("add", input={X1, X2}, output=O)
    return O

Above code snippets are automatically generated. Given them, users can define

def layer.fc(X):
    W = Var()
    b = Var()
    return operator.add(operator.mul(X, W), b)

If we don't have operator.mul and operator.add, the definiton of layer.fc would be complicated:

def layer.fc(X):
    W = Var()
    b = Var()
    O1 = Var()
    paddle.cpp.create_operator("mul", input=[X, W], output=O1)
    O2 = Var()
    paddle.cpp.create_operator("add", input=[O1, b], output=O2)
    return O2

We'd like to have Python bindings to operators in package paddle.operator, and Python compositions of operators in package paddle.layer. So we have the following concepts in above illustrative example:

C++ functions/functors mul add
C++ operator class mulOp addOp FCOp
Python binding operator.mul operator.add operator.fc
Python function layer.fc

This is how we differentiate layer and operators in PaddlePaddle:

  • those defined in C++ and have a lightweighted Python wrapper in module operators are operators; whereas
  • those who don't have C++ implementations but a Python implementation that compose C++ operators are known as layers.