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.

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 BlockDescs. A BlockDesc refers to its parent block by its index in the array. For example, operators in the step block of an RNN operator needs to be able to access variables in its ancessor blocks.

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

class Program(objects):
    def __init__(self):
        self.proto = 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

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

class Block(objects):
    def __init__(self, program, parent_idx):
        self.proto = core.NewBlock(program.proto)
        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, those defined in the global block, but can be created in some sub-blocks, e.g., an FC layer in the step block of an RNN operator.

prepand_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 output shape from input shape.

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

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

Operator creates the OpDesc message in C++ space, so could it 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.proto = core.NewVarDesc(block.proto, 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 writes 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, s/he 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 Functions¶

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

Data Layer¶

def data_layer(name, type, column_name):
    block = the_current_program.glolal_block()
    var = block.create_global_var(
            name=name,
            shape=[None] + type.dims(),
            dtype=type.dtype)
    block.prepend_operator(block,
                           type="Feed",
                           inputs = None,
                           outputs = [var],
                           {column_name: column_name})
    return var

The input to the feed operator is a special variable in the global scope, which is the output of Python readers.

FC Layer¶

def fc_layer(input, size, ...):
    block = program.current_block()
    w = block.create_parameter(...)
    b = block.create_parameter(...)
    out = block.create_var()
    op = block.append_operator("FC", X=input, W=w, b=b, out=out)
    out.writer = op
    return out