import paddle.v2.framework.core as core import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2 import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2 import paddle.v2.framework.proto.attribute_pb2 as attribute_pb2 def get_all_op_protos(): """ Get all registered op proto from Paddle C++ :return: list of OpProto """ protostrs = core.get_all_op_protos() ret_values = [] for pbstr in protostrs: op_proto = op_proto_pb2.OpProto.FromString(str(pbstr)) ret_values.append(op_proto) return ret_values class OpDescCreationMethod(object): """ A Functor object to convert user input(use key word args) to OpDesc based on OpProto. :param op_proto: The OpProto object. :type op_proto: op_proto_pb2.OpProto """ def __init__(self, op_proto): if not isinstance(op_proto, op_proto_pb2.OpProto): raise TypeError("Argument should be OpProto") self.__op_proto__ = op_proto def __call__(self, *args, **kwargs): """ Convert user input to OpDesc. Only key-word args are supported. :return: OpDesc based on user input :rtype: op_desc_pb2.OpDesc """ if len(args) != 0: raise ValueError("Only keyword arguments is supported by Paddle") op_desc = op_desc_pb2.OpDesc() # Inputs ipts, ipt_format, _ = OpDescCreationMethod.extract_input_or_output( "input", kwargs, self.__op_proto__.inputs) op_desc.inputs.extend(ipts) if ipt_format is not None: op_desc.attrs.extend([ipt_format]) # Outputs outs, out_format, tmp_index = OpDescCreationMethod.extract_input_or_output( "output", kwargs, self.__op_proto__.outputs) op_desc.outputs.extend(outs) if out_format is not None: op_desc.attrs.extend([out_format]) if len(tmp_index) != 0: tmp_index_attr = op_desc.attrs.add() tmp_index_attr.type = attribute_pb2.INTS tmp_index_attr.name = "temporary_index" tmp_index_attr.ints.extend(tmp_index) # Types op_desc.type = self.__op_proto__.type # Attrs for attr in self.__op_proto__.attrs: if attr.generated: continue user_defined_attr = kwargs.get(attr.name, None) if user_defined_attr is not None: new_attr = op_desc.attrs.add() new_attr.name = attr.name new_attr.type = attr.type if attr.type == attribute_pb2.INT: new_attr.i = user_defined_attr elif attr.type == attribute_pb2.FLOAT: new_attr.f = user_defined_attr elif attr.type == attribute_pb2.STRING: new_attr.s = user_defined_attr elif attr.type == attribute_pb2.INTS: new_attr.ints.extend(user_defined_attr) elif attr.type == attribute_pb2.FLOATS: new_attr.floats.extend(user_defined_attr) elif attr.type == attribute_pb2.STRINGS: new_attr.strings.extend(user_defined_attr) else: raise NotImplementedError("Not support attribute type " + attr.type) return op_desc @staticmethod def extract_input_or_output(in_out, kwargs, meta): """ Extract input variable names or output variable names from key-word arguments, which base on VarProtos. :param in_out: "input" or "output" :param kwargs: key-word arguments that user inputted. :param meta: a list of VarProto :return: The three object will be return. The variable names. The input_format or output_format attribute(None if the input or output is not multiple). The temporary variable index list. """ multiple = OpDescCreationMethod.any_is_true((m.multiple for m in meta)) tmp_index = [] retv = [] if multiple: var_format = op_desc_pb2.AttrDesc() var_format.type = attribute_pb2.INTS var_format.name = "%s_format" % in_out var_format.ints.append(0) for var in meta: var_name = var.name if var.temporary: var_name = [core.var_names.temp()] tmp_index.append(len(retv)) else: var_name = kwargs.get(var_name, []) if not isinstance(var_name, list): var_name = [var_name] retv.extend(var_name) var_format.ints.append(len(var_name) + var_format.ints[-1]) return retv, var_format, tmp_index else: for var in meta: if var.temporary: retv.append(kwargs.get(var.name, core.var_names.temp())) tmp_index.append(len(retv)) else: retv.append(kwargs.get(var.name, core.var_names.empty())) return retv, None, tmp_index @staticmethod def any_is_true(generator): """ Reduce a bool array to one. If any of them is True, then return True. """ for flag in generator: if flag: return True return False class OpInfo(object): def __init__(self, name, method, inputs, outputs, attrs, no_temp_outputs): self.name = name self.method = method self.inputs = inputs self.outputs = outputs self.attrs = attrs self.no_temp_outputs = no_temp_outputs def create_op_creation_method(op_proto): """ Generate op creation method for an OpProto """ method = OpDescCreationMethod(op_proto) def __impl__(*args, **kwargs): opdesc = method(*args, **kwargs) return core.Operator.create(opdesc.SerializeToString()) return OpInfo( method=__impl__, name=op_proto.type, inputs=[var.name for var in op_proto.inputs], outputs=[var.name for var in op_proto.outputs], attrs=[attr.name for attr in op_proto.attrs], no_temp_outputs=[ var.name for var in op_proto.outputs if not var.temporary ]) class OperatorFactory(object): def __init__(self): self.op_methods = dict() for op_proto in get_all_op_protos(): method = create_op_creation_method(op_proto) self.op_methods[method.name] = method def __call__(self, *args, **kwargs): if 'type' in kwargs: if len(args) != 0: raise ValueError("All Paddle argument should be key-word " "argument except type") t = kwargs.pop('type') else: if len(args) != 1: raise ValueError("All Paddle argument should be key-word " "argument except type") t = args[0] return self.get_op_info(t).method(**kwargs) def types(self): return self.op_methods.keys() def get_op_info(self, t): if t not in self.op_methods: raise ValueError("operator %s is not registered", t) return self.op_methods.get(t) def get_op_input_names(self, type): return self.get_op_info(type).inputs def get_op_output_names(self, type): return self.get_op_info(type).outputs def get_op_attr_names(self, type): return self.get_op_info(type).attrs def get_op_no_temp_output_names(self, type): return self.get_op_info(type).no_temp_outputs Operator = OperatorFactory() # Default global factory