# Design Doc: Python API

Due to the refactorization of the PaddlePaddle core, we need Python classes to construct corresponding protobuf messages that describe a DL program.

| Python classes | Protobuf messages | | — | — | | Program | ProgramDesc | | Block | BlockDesc | | Operator | OpDesc | | Variable | VarDesc |

Please be aware that these Python classes need to maintain some construction-time information, which are not part of the protobuf messages.

## Core Concepts

### Program

A `ProgramDesc` describes a DL program, which is composed of an array of `BlockDesc`s. The `BlockDesc`s in a `ProgramDesc` can have a tree-like hierarchical structure. However, the `ProgramDesc` onlys stores a flattened array of `BlockDesc`s. A `BlockDesc` refers to its parent block by its index in the array. For example, operators in the step block of an RNN operator need to be able to access variables in its ancestor blocks.

Whenever we create a block, we need to set its parent block to the current block, hence the Python class `Program` needs to maintain a data member `current_block`.

    class Program(objects):
        def __init__(self):
            self.desc = core.NewProgram() # a C++ ProgramDesc pointer.
            self.blocks = vector<Block>()
            self.blocks.append(Block(self, -1)) # the global block
            self.current_block = 0          # initialized to the global block

        def global_block():
            return self.blocks[0]

        def current_block():
            return self.get_block(self.current_block)

        def rollback():
            self.current_block = self.current_block().parent_idx

        def create_block():
            new_block_idx = len(self.block)
            self.blocks.append(Block(self, self.current_block))
            self.current_block = new_block_idx
            return current_block()

`Program` is an accessor to the protobuf message `ProgramDesc`, which is created in C++ space, because the InferShape function is in C++, which manipulates `VarDesc` messages, which are in turn members of `BlockDesc`, which is a member of `ProgramDesc`.

`Program` creates the first block as the global block in its constructor. All parameters and their initializer operators are in the global block.

### Block

A Block includes

1. a map from variable names to an instance of the Python `Variable` class, and
2. a list of `Operator` instances.

    class Block(objects):
        def __init__(self, program, parent_idx):
            self.desc = core.NewBlock(program.desc)
            self.program = program
            self.vars = map<string, Variable>()
            self.ops = vector<Operator>()
            self.parent_idx = parent_idx

        def create_var(self, ...):
            return Variable(self, ...)

        def _create_global_var(self, ...):
            program.global_block().create_var(...)

        def create_parameter(self, name, ...):
            # Parameter is a subclass of variable. See Parameter section for details.
            self.vars[name] = Parameter(self._create_global_var(...), ...)
            return self.vars[name]

        def append_operator(self, ...):
            self.ops.append(Operator(self, ...))

        def prepend_operator(self, ...): # Parameter's ctor prepands initialize operators.
           self.ops.prepend(Operator(self, ...))

`create_parameter` is necessary because parameters are global variables, defined in the global block, but can be created in some sub-blocks. For example, an FC layer in the step block of an RNN operator.

`prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the preamble of the global block.

### Operator

The `Operator` class fills in the `OpDesc` message and calls the C++ function `InferShape` to infer the output shapes from the input shapes.

    class Operator(object):
        def __init__(self,
                     block,  # Block
                     type,   # string
                     inputs, # dict<string, Variable>
                     outputs,# dict<stirng, Variable>
                     attrs   # dict<string, Any>
                     ):
            self.desc = core.NewOpDesc(block.desc, type, inputs, outputs, attrs)
            core.infer_shape(self.desc, inputs, outputs)

        def type(self):
            return self.desc.type()

`Operator` creates the `OpDesc` message in C++ space, so that it can call the `InferShape` function, which is in C++.

### Variable

Operators take Variables as its inputs and outputs.

    class Variable(object):
        def __init__(self,
                     block=None,      # Block
                     name=None,       # string
                     shape,           # tuple
                     dtype="float32", # string
                     lod_level=None   # int
                     ):
            if name is None:
                name = unique_name_generator()
            self.name = name
            self.block = block
            self.desc = core.NewVarDesc(block.desc, name, shape, lod_level)
            self.writer = None

Please be aware of `self.writer`, that tracks operator who creates the variable. It possible that there are more than one operators who write a variable, but in Python space, each write to a variable is represented by a Variable class. This is guaranteed by the fact that `core.NewVarDesc` must NOT create a new `VarDesc` message if its name already exists in the specified block.

### Parameter

A parameter is a global variable with an initializer (or load) operator.

    class Parameter(Variable):
        def __init__(self,
                     block=None,      # Block
                     name=None,       # string
                     shape,           # tuple
                     dtype="float32", # string
                     lod_level=None   # int
                     trainable,       # bool
                     initialize_op_attrs,
                     optimize_op_attrs):
            super(Parameter, self).__init__(block, name, shape, dtype, lod_level)
            self.trainable = trainable
            self.optimize_op_attrs = optimize_op_attrs
            block.prepend(Operator(block,  # Block
                                   initialize_op_attrs['type'],   # string
                                   None,   # no inputs
                                   self,   # output is the parameter
                                   initialize_op_attrs)

When users create a parameter, they can call

    program.create_parameter(
      ...,
      init_attr={
        type: "uniform_random",
        min: -1.0,
        max: 1.0,
      })
    )

In above example, `init_attr.type` names an initialize operator. It can also name the load operator

    init_attr={
     type: "load",
     filename: "something.numpy",
    }

`optimize_op_attrs` is not in the `VarDesc` message, but kept in the Python instance, as it will be used in the Python space when creating the optimize operator’s `OpDesc`, and will be in the `OpDesc` message.

## Layer Function

A layer is a Python function that creates some operators and variables. Layers simplify the work of application programmers.

Layer functions take `Variable` and configuration parameters as its input and return the output variable(s).

For example, `FullyConnected` take one or more variable as its input. The input could be input data or another layer’s output. There are many configuration options for a `FullyConnected` layer, such as layer size, activation, parameter names, initialization strategies of parameters, and so on. The `FullyConnected` layer will return an output variable.

### Necessity for reusing code between layer functions

There are a lot of code that can be reused. Such as

- Give the default value of configuration. e.g., default initialize strategy for parameters is uniform random with `min = -1.0`, `max = 1.0`. and default initialize strategy for bias is to fill zero.
- Append the activation operator.
- Create a temporary variable.
- Create parameter.
- Generate a unique name.
- Add a bias.
- ...

A mechanism to reuse code between layer functions is necessary. It will be around 150 lines of code if we write a `FullyConnected` layer without any helper functions.

### Comparision between global functions and helper class

The `FullyConnected` layer will be as follow when we provide global functions:

    def fc_layer(input, size, param_attr=None, bias_attr=None, act=None, name=None):
      if name is None:
        name = unique_name("fc")
      input = multiple_input(input)
      param_attr = default_param_attr(param_attr)
      param_attr = multiple_param_attr(param_attr, len(input))

      # mul
      mul_results = []
      for ipt, attr in zip(input, param_attr):
        shape = ipt.shape[1:] + [size]
        w = g_program.global_block().create_parameter(shape, ipt.dtype, name, attr)
        tmp = create_tmp_var(name)
        g_program.current_block().append_op("mul", {ipt, w}, {tmp})
      mul_results.append(tmp)

      # add sum
      ...
      # add bias
      ...
      # add activation
      ...
      return out

We can provide many helpers functions for layer developers. However, there are several disadvantages for global helper functions:

1. We need a namespace for these methods, then layer developers can quickly figure out what method they can use.
2. Global functions will force layer developers to pass its parameter time by time.

So we provide a helper class, `LayerHelper`, to share code between layer functions. The `FullyConnected` Layer will be as follow.

    def fc_layer(input, size, param_attr=None, bias_attr=None, act=None, name=None):
      helper = LayerHelper(locals())  # pass all parameter to LayerHelper

      mul_results = []
      for ipt, param in helper.iter_multiple_input_and_param():
        w = helper.create_parameter(shape=ipt.shape[1:] + [size], dtype = ipt.dtype)
        tmp = helper.create_tmp_variable()
        helper.append_op('mul', {ipt, w}, {tmp})
        mul_results.append(tmp)

      pre_bias = helper.add_sum(mul_results)
      pre_activation = helper.add_bias(pre_bias)
      return helper.add_activation(pre_activation)

We not only use the fewer lines of code to write `fc_layer` but also make the code clearer to understand. At the same time, layer developers can figure out what function they can invoke by typing `helper.` in a python editor.

### Implementation of layer helper

We just keep all parameters of a layer function as a dictionary in layer helper as a private data member. Every method of layer helper will look up the dictionary after it is invoked. In that way, we can implement a layer helper for all layer functions even some layer does not contain some operator. For example, The `activation` is used by the FullyConnected layer or convolution layers, but a cross-entropy layer does not use it. The example code of `add_activation` are:

    class LayerHelper(object):
      def __init__(self, **kwargs):  # kwargs is short for `keyword arguments`
        self.kwargs = kwargs

      def add_activation(self, input_var):
        act = self.kwargs.get("act", None)  # default value is None
        if act is None:  # do nothing if no act
          return input_var

        tmp = self.create_tmp_var(self)
        self.append_op(type=act, input=input_var, output=tmp)
        return tmp

## Optimizer

Optimizer Design Doc