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293 lines
10 KiB
293 lines
10 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import six
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import paddle.fluid.core as core
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import paddle.fluid.proto.framework_pb2 as framework_pb2
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def get_all_op_protos():
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"""
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Get all registered op proto from PaddlePaddle C++ end.
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:return: A list of registered OpProto.
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"""
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protostrs = core.get_all_op_protos()
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ret_values = []
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for pbstr in protostrs:
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op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr))
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ret_values.append(op_proto)
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return ret_values
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def is_str(s):
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return isinstance(s, six.string_types)
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class OpDescCreationMethod(object):
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"""
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Convert the user's input(only keyword arguments are supported) to OpDesc
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based on the OpProto.
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:param op_proto: The OpProto object.
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:type op_proto: op_proto_pb2.OpProto
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"""
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def __init__(self, op_proto):
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if not isinstance(op_proto, framework_pb2.OpProto):
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raise TypeError(
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"Type of op_proto should be OpProto in PaddlePaddle.")
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self.__op_proto__ = op_proto
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def __call__(self, *args, **kwargs):
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"""
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Convert user's input to OpDesc. Only keyword arguments are supported.
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:return: The OpDesc based on user input.
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:rtype: op_desc_pb2.OpDesc
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"""
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if len(args) != 0:
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raise ValueError("Only keyword arguments are supported.")
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op_desc = framework_pb2.OpDesc()
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for input_parameter in self.__op_proto__.inputs:
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input_arguments = kwargs.get(input_parameter.name, [])
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if is_str(input_arguments):
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input_arguments = [input_arguments]
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if not input_parameter.duplicable and len(input_arguments) > 1:
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raise ValueError(
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"Input %s expects only one input, but %d are given." %
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(input_parameter.name, len(input_arguments)))
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ipt = op_desc.inputs.add()
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ipt.parameter = input_parameter.name
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ipt.arguments.extend(input_arguments)
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for output_parameter in self.__op_proto__.outputs:
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output_arguments = kwargs.get(output_parameter.name, [])
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if is_str(output_arguments):
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output_arguments = [output_arguments]
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if not output_parameter.duplicable and len(output_arguments) > 1:
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raise ValueError(
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"Output %s expects only one output, but %d are given." %
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(output_parameter.name, len(output_arguments)))
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out = op_desc.outputs.add()
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out.parameter = output_parameter.name
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out.arguments.extend(output_arguments)
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# Types
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op_desc.type = self.__op_proto__.type
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# Attrs
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for attr in self.__op_proto__.attrs:
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if attr.generated:
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continue
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user_defined_attr = kwargs.get(attr.name, None)
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if user_defined_attr is not None:
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new_attr = op_desc.attrs.add()
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new_attr.name = attr.name
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new_attr.type = attr.type
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if isinstance(user_defined_attr, np.ndarray):
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user_defined_attr = user_defined_attr.tolist()
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if attr.type == framework_pb2.INT:
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new_attr.i = user_defined_attr
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elif attr.type == framework_pb2.FLOAT:
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new_attr.f = user_defined_attr
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elif attr.type == framework_pb2.LONG:
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new_attr.l = user_defined_attr
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elif attr.type == framework_pb2.STRING:
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new_attr.s = user_defined_attr
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elif attr.type == framework_pb2.BOOLEAN:
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new_attr.b = user_defined_attr
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elif attr.type == framework_pb2.INTS:
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new_attr.ints.extend(user_defined_attr)
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elif attr.type == framework_pb2.FLOATS:
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new_attr.floats.extend(user_defined_attr)
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elif attr.type == framework_pb2.STRINGS:
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new_attr.strings.extend(user_defined_attr)
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elif attr.type == framework_pb2.BOOLEANS:
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new_attr.bools.extend(user_defined_attr)
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elif attr.type == framework_pb2.LONGS:
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new_attr.longs.extend(user_defined_attr)
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elif attr.type == framework_pb2.INT_PAIRS:
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for p in user_defined_attr:
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pair = new_attr.int_pairs.add()
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pair.first = p[0]
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pair.second = p[1]
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else:
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raise NotImplementedError(
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"A not supported attribute type: %s." % (
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str(attr.type)))
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return op_desc
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@staticmethod
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def any_is_true(generator):
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"""
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Reduce a boolean array to a single boolean parameter. If any element in
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the array is True, this function will return True, otherwise False.
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"""
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for flag in generator:
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if flag:
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return True
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return False
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class OpInfo(object):
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def __init__(self, name, method, inputs, outputs, attrs):
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self.name = name
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self.method = method
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self.inputs = inputs
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self.outputs = outputs
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self.attrs = attrs
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def create_op_creation_method(op_proto):
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"""
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Generate op creation method for an OpProto.
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"""
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method = OpDescCreationMethod(op_proto)
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def __impl__(*args, **kwargs):
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opdesc = method(*args, **kwargs)
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return core.Operator.create(opdesc.SerializeToString())
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return OpInfo(
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method=__impl__,
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name=op_proto.type,
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inputs=[(var.name, var.duplicable) for var in op_proto.inputs],
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outputs=[(var.name, var.duplicable) for var in op_proto.outputs],
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attrs=[attr.name for attr in op_proto.attrs])
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class OperatorFactory(object):
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def __init__(self):
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self.op_methods = dict()
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for op_proto in get_all_op_protos():
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method = create_op_creation_method(op_proto)
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self.op_methods[method.name] = method
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def __call__(self, *args, **kwargs):
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if "type" in kwargs:
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if len(args) != 0:
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raise ValueError(
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"Except the argument \"type\","
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"all of the other arguments should be keyword arguments.")
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t = kwargs.pop("type")
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else:
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if len(args) != 1:
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raise ValueError(
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"Except the argument \"type\","
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"all of the other arguments should be keyword arguments.")
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t = args[0]
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return self.get_op_info(t).method(**kwargs)
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def types(self):
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return list(self.op_methods.keys())
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def get_op_info(self, t):
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if t not in self.op_methods:
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raise ValueError("The operator: %s is not registered." % t)
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return self.op_methods.get(t)
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def get_op_input_names(self, type):
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return [x[0] for x in self.get_op_info(type).inputs]
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def get_op_inputs(self, type):
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return self.get_op_info(type).inputs
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def get_op_output_names(self, type):
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return [x[0] for x in self.get_op_info(type).outputs]
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def get_op_outputs(self, type):
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return self.get_op_info(type).outputs
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def get_op_attr_names(self, type):
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return self.get_op_info(type).attrs
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class __RecurrentOp__(object):
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__proto__ = None
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type = "recurrent"
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def __init__(self):
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# cache recurrent_op's proto
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if self.__proto__ is None:
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for op_proto in get_all_op_protos():
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if op_proto.type == self.type:
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self.__proto__ = op_proto
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def __call__(self, *args, **kwargs):
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if self.type not in args and "type" not in kwargs:
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kwargs["type"] = self.type
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# create proto
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create_method = OpDescCreationMethod(self.__proto__)
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proto = create_method(*args, **kwargs)
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# create rnnop
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return core.RecurrentOp.create(proto.SerializeToString())
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class __DynamicRecurrentOp__(object):
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__proto__ = None
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type = "dynamic_recurrent"
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def __init__(self):
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# cache recurrent_op's proto
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if self.__proto__ is None:
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for op_proto in get_all_op_protos():
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if op_proto.type == self.type:
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self.__proto__ = op_proto
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def __call__(self, *args, **kwargs):
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if self.type not in args and "type" not in kwargs:
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kwargs["type"] = self.type
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# create proto
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create_method = OpDescCreationMethod(self.__proto__)
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proto = create_method(*args, **kwargs)
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# create rnnop
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return core.DynamicRecurrentOp.create(proto.SerializeToString())
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class __CondOp__(object):
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__proto__ = None
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type = "cond"
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def __init__(self):
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# cache recurrent_op's proto
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if self.__proto__ is None:
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for op_proto in get_all_op_protos():
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if op_proto.type == self.type:
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self.__proto__ = op_proto
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def __call__(self, *args, **kwargs):
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if self.type not in args and "type" not in kwargs:
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kwargs["type"] = self.type
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# create proto
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create_method = OpDescCreationMethod(self.__proto__)
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proto = create_method(*args, **kwargs)
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# create condop
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return core.CondOp.create(proto.SerializeToString())
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Operator = OperatorFactory() # The default global factory
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RecurrentOp = __RecurrentOp__()
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DynamicRecurrentOp = __DynamicRecurrentOp__()
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CondOp = __CondOp__()
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