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