wehub-resource-sync
316 lines
12 KiB
Python
316 lines
12 KiB
Python
# 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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import numpy as np
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from paddle.base import core
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from paddle.base.proto import framework_pb2
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# NOTE: this is added to support creating a Scalar message
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# from a python number
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def make_scalar_proto(value):
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s = framework_pb2.Scalar()
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if isinstance(value, bool):
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s.type = framework_pb2.Scalar.Type.BOOLEAN
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s.b = value
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elif isinstance(value, int):
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s.type = framework_pb2.Scalar.Type.LONG
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s.i = value
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elif isinstance(value, float):
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s.type = framework_pb2.Scalar.Type.FLOAT64
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s.r = value
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elif isinstance(value, complex):
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s.type = framework_pb2.Scalar.Type.COMPLEX128
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complex_value = framework_pb2.Complex()
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complex_value.r = value.real
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complex_value.i = value.imag
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s.c.CopyFrom(complex_value)
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return s
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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(bytes(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, str)
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class OpDescCreationMethod:
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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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)
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self.__op_proto__ = op_proto
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self.__extra_attrs__ = core.get_op_extra_attrs(op_proto.type)
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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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f"Input {input_parameter.name} expects only one input, but {len(input_arguments)} are given."
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)
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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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f"Output {output_parameter.name} expects only one output, but {len(output_arguments)} are given."
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)
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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.FLOAT64:
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new_attr.float64 = user_defined_attr
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elif attr.type == framework_pb2.FLOAT64S:
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new_attr.float64s.extend(user_defined_attr)
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# the code below manipulates protobuf directly
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elif attr.type == framework_pb2.SCALAR:
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scalar = make_scalar_proto(user_defined_attr)
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new_attr.scalar.CopyFrom(scalar)
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elif attr.type == framework_pb2.SCALARS:
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scalars = [
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make_scalar_proto(item) for item in user_defined_attr
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]
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for item in scalars:
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new_attr.scalars.MergeFrom(item)
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else:
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raise NotImplementedError(
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f"A not supported attribute type: {attr.type}."
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)
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for attr_name, default_val in self.__extra_attrs__.items():
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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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attr_type = int(
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core.get_attribute_type(op_desc.type, attr_name)
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)
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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.FLOAT64:
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new_attr.float64 = user_defined_attr
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elif attr.type == framework_pb2.FLOAT64S:
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new_attr.float64s.extend(user_defined_attr)
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# the code below manipulates protobuf directly
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elif attr.type == framework_pb2.SCALAR:
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scalar = make_scalar_proto(user_defined_attr)
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new_attr.scalar.CopyFrom(scalar)
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elif attr.type == framework_pb2.SCALARS:
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scalars = [
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make_scalar_proto(item) for item in user_defined_attr
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]
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for item in scalars:
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new_attr.scalars.MergeFrom(item)
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else:
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raise NotImplementedError(
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f"A not supported attribute type: {attr_type}."
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)
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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:
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def __init__(self, name, method, inputs, outputs, attrs, extra_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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self.extra_attrs = extra_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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extra_attrs_map = core.get_op_extra_attrs(op_proto.type)
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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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extra_attrs=list(extra_attrs_map.keys()),
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)
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class OperatorFactory:
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def __init__(self):
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self.op_methods = {}
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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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)
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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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)
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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(f"The operator: {t} is not registered.")
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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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def get_op_extra_attr_names(self, type):
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return self.get_op_info(type).extra_attrs
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Operator = OperatorFactory() # The default global factory
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