import paddle.v2.framework.core as core import paddle.v2.framework.proto.framework_pb2 as framework_pb2 def get_all_op_protos(): """ Get all registered op proto from PaddlePaddle C++ end. :return: list of 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 def is_str(s): return isinstance(s, str) or isinstance(s, unicode) class OpDescCreationMethod(object): """ A Functor object converting the user's input(only keyword arguments are supported) to OpDesc based on the OpProto. :param op_proto: The OpProto object. :type op_proto: op_proto_pb2.OpProto """ def __init__(self, op_proto): if not isinstance(op_proto, framework_pb2.OpProto): raise TypeError( "Type of op_proto should be OpProto in PaddlePaddle.") self.__op_proto__ = op_proto def __call__(self, *args, **kwargs): """ Convert user's input to OpDesc. Only keyword arguments are supported. :return: OpDesc based on user input :rtype: op_desc_pb2.OpDesc """ if len(args) != 0: raise ValueError("Only keyword arguments are supported.") op_desc = framework_pb2.OpDesc() for input_parameter in self.__op_proto__.inputs: input_arguments = kwargs.get(input_parameter.name, []) if is_str(input_arguments): input_arguments = [input_arguments] if not input_parameter.duplicable and len(input_arguments) > 1: raise ValueError( "Input %s expects only one input, but %d are given." % (input_parameter.name, len(input_arguments))) ipt = op_desc.inputs.add() ipt.parameter = input_parameter.name ipt.arguments.extend(input_arguments) for output_parameter in self.__op_proto__.outputs: output_arguments = kwargs.get(output_parameter.name, []) if is_str(output_arguments): output_arguments = [output_arguments] if not output_parameter.duplicable and len(output_arguments) > 1: raise ValueError( "Output %s expects only one output, but %d are given." % (output_parameter.name, len(output_arguments))) out = op_desc.outputs.add() out.parameter = output_parameter.name out.arguments.extend(output_arguments) # 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 == framework_pb2.INT: new_attr.i = user_defined_attr elif attr.type == framework_pb2.FLOAT: new_attr.f = user_defined_attr elif attr.type == framework_pb2.STRING: new_attr.s = user_defined_attr elif attr.type == framework_pb2.INTS: new_attr.ints.extend(user_defined_attr) elif attr.type == framework_pb2.FLOATS: new_attr.floats.extend(user_defined_attr) elif attr.type == framework_pb2.STRINGS: new_attr.strings.extend(user_defined_attr) elif attr.type == framework_pb2.INT_PAIRS: for p in user_defined_attr: pair = new_attr.pairs.add() pair.first = p[0] pair.second = p[1] else: raise NotImplementedError( "A not supported attribute type: %s." % ( str(attr.type))) return op_desc @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): self.name = name self.method = method self.inputs = inputs self.outputs = outputs self.attrs = attrs 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]) 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 PaddlePaddle arguments should be keyword " "arguments except the argument \"type\".")) t = kwargs.pop('type') else: if len(args) != 1: raise ValueError( ("All PaddlePaddle arguments should be keyword " "arguments except the argument \"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("The 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 class __RecurrentOp__(object): __proto__ = None type = 'recurrent' def __init__(self): # cache recurrent_op's proto if self.__proto__ is None: for op_proto in get_all_op_protos(): if op_proto.type == self.type: self.__proto__ = op_proto def __call__(self, *args, **kwargs): if self.type not in args and 'type' not in kwargs: kwargs['type'] = self.type # create proto create_method = OpDescCreationMethod(self.__proto__) proto = create_method(*args, **kwargs) # create rnnop return core.RecurrentOp.create(proto.SerializeToString()) Operator = OperatorFactory() # Default global factory RecurrentOp = __RecurrentOp__()