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406 lines
13 KiB
406 lines
13 KiB
import paddle.v2.framework.core as core
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import paddle.v2.framework.proto.framework_pb2 as framework_pb2
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import collections
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import numpy as np
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import copy
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__all__ = ['Block', 'Variable', 'Program', 'Operator']
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class Variable(object):
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def __init__(self,
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block,
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type=core.VarDesc.VarType.LOD_TENSOR,
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name=None,
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shape=None,
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dtype=None,
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lod_level=None,
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**kwargs):
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self.block = block
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if name is None:
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name = Variable._unique_var_name_()
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is_new_var = False
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self.desc = self.block.desc.find_var(name)
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if self.desc is None:
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self.desc = self.block.desc.var(name)
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is_new_var = True
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if is_new_var:
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self.desc.set_type(type)
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elif self.desc.type() != type:
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raise ValueError("Variable {0} has been created before. The "
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"previous type is {1}; the new type is {2}. They"
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" are not matched".format(self.name,
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self.desc.type(), type))
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if shape is not None:
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if is_new_var:
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self.desc.set_shape(shape)
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else:
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old_shape = self.shape
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shape = tuple(shape)
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if shape != old_shape:
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raise ValueError(
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"Variable {0} has been created before. the previous "
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"shape is {1}; the new shape is {2}. They are not "
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"matched.".format(self.name, old_shape, shape))
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if dtype is not None:
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if not isinstance(dtype, core.DataType):
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dtype = Variable._convert_np_dtype_to_dtype_(dtype)
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if is_new_var:
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self.desc.set_data_type(dtype)
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else:
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old_dtype = self.data_type()
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if dtype != old_shape:
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raise ValueError("Variable {0} has been created before. "
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"The previous data type is {1}; the new "
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"data type is {2}. They are not "
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"matched.".format(self.name, old_dtype,
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dtype))
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if lod_level is not None:
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if is_new_var:
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self.desc.set_lod_level(lod_level)
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else:
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if lod_level != self.lod_level:
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raise ValueError("Variable {0} has been created before. "
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"The previous lod_level is {1}; the new "
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"lod_level is {2}. They are not "
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"matched".format(self.name, self.lod_level,
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lod_level))
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self.block.vars[name] = self
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self.op = None
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def __str__(self):
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protostr = self.desc.serialize_to_string()
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proto = framework_pb2.VarDesc.FromString(str(protostr))
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return proto.__str__()
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__repr__ = __str__
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@property
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def name(self):
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return self.desc.name()
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@property
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def shape(self):
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# convert to tuple, make it as same as numpy API.
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return tuple(self.desc.shape())
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@property
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def data_type(self):
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return self.desc.data_type()
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@property
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def lod_level(self):
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return self.desc.lod_level()
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@staticmethod
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def _unique_var_name_():
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uid = core.unique_integer() # unique during whole process.
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return "_generated_var_%d" % uid
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@staticmethod
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def _convert_np_dtype_to_dtype_(np_dtype):
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dtype = np.dtype(np_dtype)
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if dtype == np.float32:
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return core.DataType.FP32
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elif dtype == np.float64:
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return core.DataType.FP64
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elif dtype == np.float16:
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return core.DataType.FP16
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elif dtype == np.int32:
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return core.DataType.INT32
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elif dtype == np.int16:
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return core.DataType.INT16
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elif dtype == np.int64:
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return core.DataType.INT64
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elif dtype == np.bool:
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return core.DataType.BOOL
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else:
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raise ValueError("Not supported numpy dtype " + str(dtype))
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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(str(pbstr))
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ret_values.append(op_proto)
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return ret_values
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class OpProtoHolder(object):
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@classmethod
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def instance(cls):
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if not hasattr(cls, '_instance'):
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cls._instance = cls()
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return cls._instance
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def __init__(self):
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assert not hasattr(
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self.__class__,
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'_instance'), 'Please use `instance()` to get OpProtoHolder opject!'
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op_protos = get_all_op_protos()
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self.op_proto_map = {}
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for proto in op_protos:
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self.op_proto_map[proto.type] = proto
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def get_op_proto(self, type):
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assert type in self.op_proto_map, "Operator \"%s\" has not been registered." % type
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return self.op_proto_map[type]
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class Operator(object):
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def __init__(self,
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block,
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desc,
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type=None,
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inputs=None,
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outputs=None,
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attrs=None):
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self.block = block
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self.desc = desc
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if len(self.desc.type()) != 0:
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return
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if type is None:
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raise ValueError(
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"`type` to initilized an Operator can not be None.")
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self.desc.set_type(type)
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proto = OpProtoHolder.instance().get_op_proto(type)
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if inputs is not None:
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given = set()
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need = set()
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for n in inputs:
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given.add(n)
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for m in proto.inputs:
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need.add(m.name)
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if not given == need:
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raise ValueError(
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"Incorrect setting for input(s) of operator \"%s\". Need: [%s] Given: [%s]"
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% (type, ", ".join(str(e) for e in need), ", ".join(
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str(e) for e in given)))
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for in_proto in proto.inputs:
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in_argus = inputs[in_proto.name]
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if not isinstance(in_argus, list):
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in_argus = [in_argus]
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if not in_proto.duplicable and len(in_argus) > 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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(in_proto.name, len(in_argus)))
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in_argu_names = []
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for argu in in_argus:
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in_argu_names.append(argu.name)
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self.desc.set_input(in_proto.name, in_argu_names)
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if outputs is not None:
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given = set()
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need = set()
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for n in outputs:
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given.add(n)
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for m in proto.outputs:
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need.add(m.name)
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if not given == need:
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raise ValueError(
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"Incorrect setting for output(s) of operator \"%s\". Need: [%s] Given: [%s]"
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% (type, ", ".join(str(e) for e in need), ", ".join(
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str(e) for e in given)))
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for out_proto in proto.outputs:
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out_argus = outputs[out_proto.name]
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if not isinstance(out_argus, list):
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out_argus = [out_argus]
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if not out_proto.duplicable and len(out_argus) > 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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(out_proto.name, len(out_argus)))
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out_argu_names = []
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for argu in out_argus:
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out_argu_names.append(argu.name)
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argu.op = self
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self.desc.set_output(out_proto.name, out_argu_names)
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if attrs is not None:
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for attr in proto.attrs:
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attr_name = attr.name
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if not attr_name in attrs:
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continue
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if not isinstance(attrs[attr_name], Block):
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self.desc.set_attr(attr_name, attrs[attr_name])
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else:
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self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
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self.desc.check_attrs()
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self.desc.infer_shape(self.block.desc)
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def __str__(self):
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protostr = self.desc.serialize_to_string()
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proto = framework_pb2.OpDesc.FromString(str(protostr))
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return proto.__str__()
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__repr__ = __str__
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@property
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def type(self):
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return self.desc.type()
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def input(self, name):
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return self.desc.input(name)
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@property
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def input_names(self):
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return self.desc.input_names()
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def output(self, name):
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return self.desc.output(name)
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@property
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def output_names(self):
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return self.desc.output_names()
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def has_attr(self, name):
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return self.desc.has_attr(name)
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def attr_type(self, name):
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return self.desc.attr_type(name)
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@property
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def attr_names(self):
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return self.desc.attr_names()
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def attr(self, name):
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return self.desc.attr(name)
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def block_attr(self, name):
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return self.desc.block_attr(name)
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class Block(object):
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def __init__(self, program, idx):
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self.desc = program.desc.block(idx)
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self.vars = dict() # var_name --> var
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self.ops = collections.deque() # operator list
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self.program = program
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def __str__(self):
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protostr = self.desc.serialize_to_string()
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proto = framework_pb2.BlockDesc.FromString(str(protostr))
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return proto.__str__()
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__repr__ = __str__
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@property
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def parent_idx(self):
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return self.desc.parent
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@property
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def idx(self):
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return self.desc.id
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def create_var(self, *args, **kwargs):
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return Variable(self, *args, **kwargs)
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def create_parameter(self, *args, **kwargs):
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global_block = self.program.global_block()
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return Parameter(global_block, *args, **kwargs)
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def append_op(self, *args, **kwargs):
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op_desc = self.desc.append_op()
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op = Operator(self, op_desc, *args, **kwargs)
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self.ops.append(op)
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return op
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def prepend_op(self, *args, **kwargs):
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op_desc = self.desc.prepend_op()
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op = Operator(self, op_desc, *args, **kwargs)
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self.ops.appendleft(op)
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return op
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class Program(object):
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@classmethod
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def instance(cls):
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# From https://stackoverflow.com/questions/8212053
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# Making Program as a Singleton class.
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if not hasattr(cls, '_instance'):
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cls._instance = cls()
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return cls._instance
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def __init__(self):
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assert not hasattr(self.__class__,
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'_instance'), 'Do not call constructor directly!'
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self.desc = core.ProgramDesc.instance()
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self.blocks = [Block(self, 0)]
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self.current_block_idx = 0
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def __str__(self):
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protostr = self.desc.serialize_to_string()
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proto = framework_pb2.ProgramDesc.FromString(str(protostr))
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return proto.__str__()
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__repr__ = __str__
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def global_block(self):
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return self.blocks[0]
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def current_block(self):
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return self.blocks[self.current_block_idx]
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def create_block(self):
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new_block_idx = len(self.blocks)
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self.desc.append_block(self.current_block().desc)
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self.current_block_idx = new_block_idx
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self.blocks.append(Block(self, self.current_block_idx))
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return self.current_block()
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def rollback(self):
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self.current_block_idx = self.current_block().parent_idx
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class Parameter(Variable):
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def __init__(self, block, shape, dtype, **kwargs):
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if shape is None or dtype is None:
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raise ValueError("Parameter must set shape and dtype")
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if len(shape) == 0:
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raise ValueError("Parameter shape cannot be empty")
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for each in shape:
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if each < 0:
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raise ValueError("Parameter shape should not be related with "
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"batch-size")
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Variable.__init__(self, block, shape=shape, dtype=dtype, **kwargs)
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self.trainable = kwargs.get('trainable', True)
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self.init_attr = kwargs.get('initialize_attr', {
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'type': 'uniform_random',
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'min': -1.0,
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'max': 1.0
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})
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self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
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self._append_initialize_ops_()
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def _append_initialize_ops_(self):
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attr = copy.deepcopy(self.init_attr)
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op_type = attr.pop('type', None)
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block = self.block
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assert isinstance(block, Block)
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shape = self.shape
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attr['dims'] = shape
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attr['data_type'] = int(self.data_type)
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op = block.prepend_op(
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type=op_type, inputs=None, outputs={'Out': [self]}, attrs=attr)
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self.op = op
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# program is a global instance.
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g_program = Program.instance()
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