import paddle.v2.framework.core as core import paddle.v2.framework.proto.framework_pb2 as framework_pb2 import collections import numpy as np import copy __all__ = ['Block', 'Variable', 'Program', 'Operator'] class Variable(object): def __init__(self, block, type=core.VarDesc.VarType.LOD_TENSOR, name=None, shape=None, dtype=None, lod_level=None, **kwargs): self.block = block if name is None: name = Variable._unique_var_name_() try: self.desc = self.block.desc.var(name) is_new_var = False except core.EnforceNotMet: self.desc = self.block.desc.new_var(name) is_new_var = True if is_new_var: self.desc.set_type(type) elif self.desc.type() != type: raise ValueError("Variable {0} has been created before. The " "previous type is {1}; the new type is {2}. They" " are not matched".format(self.name, self.desc.type(), type)) if shape is not None: if is_new_var: self.desc.set_shape(shape) else: old_shape = self.shape shape = tuple(shape) if shape != old_shape: raise ValueError( "Variable {0} has been created before. the previous " "shape is {1}; the new shape is {2}. They are not " "matched.".format(self.name, old_shape, shape)) if dtype is not None: if not isinstance(dtype, core.DataType): dtype = Variable._convert_np_dtype_to_dtype_(dtype) if is_new_var: self.desc.set_data_type(dtype) else: old_dtype = self.data_type() if dtype != old_shape: raise ValueError("Variable {0} has been created before. " "The previous data type is {1}; the new " "data type is {2}. They are not " "matched.".format(self.name, old_dtype, dtype)) if lod_level is not None: if is_new_var: self.desc.set_lod_level(lod_level) else: if lod_level != self.lod_level: raise ValueError("Variable {0} has been created before. " "The previous lod_level is {1}; the new " "lod_level is {2}. They are not " "matched".format(self.name, self.lod_level, lod_level)) self.block.vars[name] = self self.op = None def __str__(self): protostr = self.desc.serialize_to_string() proto = framework_pb2.VarDesc.FromString(str(protostr)) return proto.__str__() __repr__ = __str__ @property def name(self): return self.desc.name() @property def shape(self): # convert to tuple, make it as same as numpy API. return tuple(self.desc.shape()) @property def data_type(self): return self.desc.data_type() @property def lod_level(self): return self.desc.lod_level() @staticmethod def _unique_var_name_(): uid = core.unique_integer() # unique during whole process. return "_generated_var_%d" % uid @staticmethod def _convert_np_dtype_to_dtype_(np_dtype): dtype = np.dtype(np_dtype) if dtype == np.float32: return core.DataType.FP32 elif dtype == np.float64: return core.DataType.FP64 elif dtype == np.float16: return core.DataType.FP16 elif dtype == np.int32: return core.DataType.INT32 elif dtype == np.int16: return core.DataType.INT16 elif dtype == np.int64: return core.DataType.INT64 elif dtype == np.bool: return core.DataType.BOOL else: raise ValueError("Not supported numpy dtype " + str(dtype)) def get_all_op_protos(): """ Get all registered op proto from PaddlePaddle C++ end. :return: A list of registered OpProto. """ protostrs = core.get_all_op_protos() ret_values = [] for pbstr in protostrs: op_proto = framework_pb2.OpProto.FromString(str(pbstr)) ret_values.append(op_proto) return ret_values class OpProtoHolder(object): @classmethod def instance(cls): if not hasattr(cls, '_instance'): cls._instance = cls() return cls._instance def __init__(self): assert not hasattr( self.__class__, '_instance'), 'Please use `instance()` to get OpProtoHolder opject!' op_protos = get_all_op_protos() self.op_proto_map = {} for proto in op_protos: self.op_proto_map[proto.type] = proto def get_op_proto(self, type): assert type in self.op_proto_map, "Operator \"%s\" has not been registered." % type return self.op_proto_map[type] class Operator(object): def __init__(self, block, desc, type=None, inputs=None, outputs=None, attrs=None): self.block = block self.desc = desc if len(self.desc.type()) != 0: return if type is None: raise ValueError( "`type` to initilized an Operator can not be None.") self.desc.set_type(type) proto = OpProtoHolder.instance().get_op_proto(type) if inputs is not None: given = set() need = set() for n in inputs: given.add(n) for m in proto.inputs: need.add(m.name) if not given == need: raise ValueError( "Incorrect setting for input(s) of operator \"%s\". Need: [%s] Given: [%s]" % (type, ", ".join(str(e) for e in need), ", ".join( str(e) for e in given))) for in_proto in proto.inputs: in_argus = inputs[in_proto.name] if not isinstance(in_argus, list): in_argus = [in_argus] if not in_proto.duplicable and len(in_argus) > 1: raise ValueError( "Input %s expects only one input, but %d are given." % (in_proto.name, len(in_argus))) in_argu_names = [] for argu in in_argus: in_argu_names.append(argu.name) self.desc.set_input(in_proto.name, in_argu_names) if outputs is not None: given = set() need = set() for n in outputs: given.add(n) for m in proto.outputs: need.add(m.name) if not given == need: raise ValueError( "Incorrect setting for output(s) of operator \"%s\". Need: [%s] Given: [%s]" % (type, ", ".join(str(e) for e in need), ", ".join( str(e) for e in given))) for out_proto in proto.outputs: out_argus = outputs[out_proto.name] if not isinstance(out_argus, list): out_argus = [out_argus] if not out_proto.duplicable and len(out_argus) > 1: raise ValueError( "Output %s expects only one output, but %d are given." % (out_proto.name, len(out_argus))) out_argu_names = [] for argu in out_argus: out_argu_names.append(argu.name) argu.op = self self.desc.set_output(out_proto.name, out_argu_names) if attrs is not None: for attr in proto.attrs: attr_name = attr.name if not attr_name in attrs: continue if not isinstance(attrs[attr_name], Block): self.desc.set_attr(attr_name, attrs[attr_name]) else: self.desc.set_block_attr(attr_name, attrs[attr_name].desc) self.desc.check_attrs() self.desc.infer_shape(self.block.desc) def __str__(self): protostr = self.desc.serialize_to_string() proto = framework_pb2.OpDesc.FromString(str(protostr)) return proto.__str__() __repr__ = __str__ @property def type(self): return self.desc.type() def input(self, name): return self.desc.input(name) @property def input_names(self): return self.desc.input_names() def output(self, name): return self.desc.output(name) @property def output_names(self): return self.desc.output_names() def has_attr(self, name): return self.desc.has_attr(name) def attr_type(self, name): return self.desc.attr_type(name) @property def attr_names(self): return self.desc.attr_names() def attr(self, name): return self.desc.attr(name) def block_attr(self, name): return self.desc.block_attr(name) class Block(object): def __init__(self, program, idx): self.desc = program.desc.block(idx) self.vars = dict() # var_name --> var self.ops = collections.deque() # operator list self.program = program def __str__(self): protostr = self.desc.serialize_to_string() proto = framework_pb2.BlockDesc.FromString(str(protostr)) return proto.__str__() __repr__ = __str__ @property def parent_idx(self): return self.desc.parent @property def idx(self): return self.desc.id def create_var(self, *args, **kwargs): return Variable(self, *args, **kwargs) def create_parameter(self, *args, **kwargs): global_block = self.program.global_block() return Parameter(global_block, *args, **kwargs) def append_op(self, *args, **kwargs): op_desc = self.desc.append_op() op = Operator(self, op_desc, *args, **kwargs) self.ops.append(op) return op def prepend_op(self, *args, **kwargs): op_desc = self.desc.prepend_op() op = Operator(self, op_desc, *args, **kwargs) self.ops.appendleft(op) return op class Program(object): @classmethod def instance(cls): # From https://stackoverflow.com/questions/8212053 # Making Program as a Singleton class. if not hasattr(cls, '_instance'): cls._instance = cls() return cls._instance def __init__(self): assert not hasattr(self.__class__, '_instance'), 'Do not call constructor directly!' self.desc = core.ProgramDesc.instance() self.blocks = [Block(self, 0)] self.current_block_idx = 0 def __str__(self): protostr = self.desc.serialize_to_string() proto = framework_pb2.ProgramDesc.FromString(str(protostr)) return proto.__str__() __repr__ = __str__ def global_block(self): return self.blocks[0] def current_block(self): return self.blocks[self.current_block_idx] def create_block(self): new_block_idx = len(self.blocks) self.desc.append_block(self.current_block().desc) self.current_block_idx = new_block_idx self.blocks.append(Block(self, self.current_block_idx)) return self.current_block() def rollback(self): self.current_block_idx = self.current_block().parent_idx class Parameter(Variable): def __init__(self, block, shape, dtype, **kwargs): if shape is None or dtype is None: raise ValueError("Parameter must set shape and dtype") if len(shape) == 0: raise ValueError("Parameter shape cannot be empty") for each in shape: if each < 0: raise ValueError("Parameter shape should not be related with " "batch-size") Variable.__init__(self, block, shape=shape, dtype=dtype, **kwargs) self.trainable = kwargs.get('trainable', True) self.init_attr = kwargs.get('initialize_attr', { 'type': 'uniform_random', 'min': -1.0, 'max': 1.0 }) self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0}) self._append_initialize_ops_() def _append_initialize_ops_(self): attr = copy.deepcopy(self.init_attr) op_type = attr.pop('type', None) block = self.block assert isinstance(block, Block) shape = self.shape attr['dims'] = shape attr['data_type'] = int(self.data_type) op = block.prepend_op( type=op_type, inputs=None, outputs={'Out': [self]}, attrs=attr) self.op = op # program is a global instance. g_program = Program.instance()