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framework.py
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framework.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import contextlib
import re
import numpy as np
import proto.framework_pb2 as framework_pb2
from . import core
import unique_name
__all__ = [
'Block',
'Variable',
'Program',
'Operator',
'default_startup_program',
'default_main_program',
'program_guard',
'switch_startup_program',
'switch_main_program',
'get_var',
]
EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
def grad_var_name(var_name):
"""
return gradient name for a certain var name
"""
return var_name + GRAD_VAR_SUFFIX
def convert_np_dtype_to_dtype_(np_dtype):
"""
Convert the data type in numpy to the data type in Paddle
Args:
np_dtype(np.dtype): the data type in numpy
Returns(core.VarDesc.VarType): the data type in Paddle
"""
dtype = np.dtype(np_dtype)
if dtype == np.float32:
return core.VarDesc.VarType.FP32
elif dtype == np.float64:
return core.VarDesc.VarType.FP64
elif dtype == np.float16:
return core.VarDesc.VarType.FP16
elif dtype == np.int32:
return core.VarDesc.VarType.INT32
elif dtype == np.int16:
return core.VarDesc.VarType.INT16
elif dtype == np.int64:
return core.VarDesc.VarType.INT64
elif dtype == np.bool:
return core.VarDesc.VarType.BOOL
else:
raise ValueError("Not supported numpy dtype " + str(dtype))
def dtype_is_floating(dtype):
"""
Check the data type is floating or not.
Args:
dtype(np.dtype|core.VarDesc.VarType): data type.
Could be numpy format or Paddle format
Returns(bool): True if data type is a float value
"""
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
return dtype in [
core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32,
core.VarDesc.VarType.FP64
]
def _debug_string_(proto, throw_on_error=True):
"""
Get the debug string of a protobuf message. The message could be not
initialized.
Args:
proto(google.protobuf.message.Message): The protobuf message
throw_on_error(bool): True if raise an error when the protobuf message
is not initialized.
Returns(str): The debug string of the protobuf message
"""
error_fields = list()
if not proto.IsInitialized(error_fields) and throw_on_error:
raise ValueError("{0} are not initialized.\nThe message is {1}:\n".
format(error_fields, proto))
return proto.__str__()
class Variable(object):
"""
Python variable. Every input and output of an operator is a variable. Every
variable belongs to a block. The variable has a name and two variables in
different blocks could have the same name.
There are many kinds of variables. Please reference the framework.proto for
details.
Notes: The constructor of Variable should not be invoked directly. Please
use `Block.create_var` to create a variable.
>>> cur_program = Program()
>>> cur_block = cur_program.current_block()
>>> new_variable = cur_block.create_var(
>>> name="X", shape=[-1, 23, 48], dtype='float32')
Args:
block(Block): The associated block. It will be passed by
`Block.create_var` automatically.
type(core.VarDesc.VarType): Variable type. Please reference the
framework.proto for details.
shape(tuple|list|None): The shape of variable. -1 means the batch size.
Some kinds of variable do not contain shape, just set it to None.
dtype(np.dtype|core.VarDesc.VarType|str): The data type of variable.
lod_level(int): The level of lod tensor. 0 means it is not a time
series data.
capacity(int): The capacity of Channel variable. Ignored
for other types.
persistable(bool): True if the variable should be saved as check point.
Defaults to False.
stop_gradient(bool): True if the variable will stop to calculate
gradients when backward. Defaults to False.
"""
def __init__(self,
block,
type=core.VarDesc.VarType.LOD_TENSOR,
name=None,
shape=None,
dtype=None,
lod_level=None,
capacity=None,
persistable=None,
error_clip=None,
stop_gradient=False,
**kwargs):
self.block = block
self.error_clip = error_clip
if name is None:
name = unique_name.generate('_generated_var')
is_new_var = False
self.desc = self.block.desc.find_var(name)
if self.desc is None:
self.desc = self.block.desc.var(name)
is_new_var = True
if is_new_var:
self.desc.set_type(type)
elif self.desc.type() != type:
raise ValueError("Variable {0} has been created before. The "
"previous type is {1}; the new type is {2}. They"
" are not matched".format(self.name,
self.desc.type(), type))
if shape is not None:
if is_new_var:
self.desc.set_shape(shape)
else:
old_shape = self.shape
shape = tuple(shape)
if shape != old_shape:
raise ValueError(
"Variable {0} has been created before. the previous "
"shape is {1}; the new shape is {2}. They are not "
"matched.".format(self.name, old_shape, shape))
if dtype is not None:
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if is_new_var:
self.desc.set_dtype(dtype)
else:
old_dtype = self.dtype
if dtype != old_dtype:
raise ValueError("Variable {0} has been created before. "
"The previous data type is {1}; the new "
"data type is {2}. They are not "
"matched.".format(self.name, old_dtype,
dtype))
if lod_level is not None:
if is_new_var:
self.desc.set_lod_level(lod_level)
else:
if lod_level != self.lod_level:
raise ValueError("Variable {0} has been created before. "
"The previous lod_level is {1}; the new "
"lod_level is {2}. They are not "
"matched".format(self.name, self.lod_level,
lod_level))
if persistable is not None:
if is_new_var:
self.desc.set_persistable(persistable)
else:
if persistable != self.persistable:
raise ValueError(
"Variable {0} has been created before."
"The previous persistable is {1}; the new "
"persistable is {2}. They are not matched".format(
self.name, self.persistable, persistable))
if capacity is not None:
if is_new_var:
self.desc.set_capacity(capacity)
else:
# TODO(abhinavarora) : Compare with set capacity once,
# get_capacity is implemented
pass
self.block.vars[name] = self
self.op = None
self.stop_gradient = stop_gradient
def __str__(self):
return self.to_string(True)
def to_string(self, throw_on_error, with_details=False):
"""
Get debug string.
Args:
throw_on_error(bool): True if raise an exception when self is not
intialized.
with_details(bool): more details about variables and parameters
(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
Returns(str): The debug string.
"""
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
protostr = self.desc.serialize_to_string()
proto = framework_pb2.VarDesc.FromString(str(protostr))
res_str = _debug_string_(proto, throw_on_error)
if with_details:
additional_attr = ("error_clip", "stop_gradient")
for attr_name in additional_attr:
res_str += "%s: %s\n" % (attr_name,
str(getattr(self, attr_name)))
return res_str
__repr__ = __str__
def set_desc(self, input):
self.desc = input
@property
def persistable(self):
return self.desc.persistable()
@persistable.setter
def persistable(self, p):
self.desc.set_persistable(p)
@property
def name(self):
return self.desc.name()
@name.setter
def name(self, new_name):
self.desc.set_name(new_name)
@property
def shape(self):
# convert to tuple, make it as same as numpy API.
return tuple(self.desc.shape())
@property
def dtype(self):
return self.desc.dtype()
@property
def lod_level(self):
return self.desc.lod_level()
@property
def type(self):
return self.desc.type()
def set_error_clip(self, error_clip):
self.error_clip = error_clip
def get_all_op_protos():
"""
Get all registered op proto from PaddlePaddle C++ end.
Returns(list): 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
class OpProtoHolder(object):
"""
A global variable to hold all OpProtos from C++ as a map
"""
@classmethod
def instance(cls):
if not hasattr(cls, '_instance'):
cls._instance = cls()
return cls._instance
def __init__(self):
assert not hasattr(
self.__class__,
'_instance'), 'Please use `instance()` to get OpProtoHolder object!'
op_protos = get_all_op_protos()
self.op_proto_map = {}
for proto in op_protos:
self.op_proto_map[proto.type] = proto
def get_op_proto(self, type):
"""
Get OpProto by a type string.
Args:
type(str): The type that operator registered in C++ side.
Returns(framework_pb2.OpProto): The OpProto
"""
if type not in self.op_proto_map:
raise ValueError("Operator \"%s\" has not been registered." % type)
return self.op_proto_map[type]
class Operator(object):
"""
Python Operator class. The operator represents the build in instructions in a
Block. Users can use the build in instructions to describe their neural
network.
"""
def __init__(self,
block,
desc,
type=None,
inputs=None,
outputs=None,
attrs=None):
"""
Constructor.
Notes: The constructor of operator should not be invoked directly. Use
Block.append_op or Block.prepend_op instead.
>>> cur_program = Program()
>>> cur_block = cur_program.current_block()
>>> # var1 += var2 + var3
>>> cur_block.append_op(type="sum",
>>> inputs={"X": [var1, var2, var3]},
>>> outputs={"Out": [var1]})
Args:
block(Block): The block has the current operator.
desc(core.OpDesc): The protobuf description.
type(str): The type of operator.
inputs(dict): The input dictionary. Key is the input parameter name.
Value is a list of variables.
outputs(dict): The output dictionary which has the same format with
inputs.
attrs(dict): The attributes dictionary. Key is attribute name. Value
is the attribute value. The attribute type should be as same as
the type registered in C++
"""
self.block = block
self.desc = desc
self.attrs = attrs
if len(self.desc.type()) != 0:
return
if type is None:
raise ValueError(
"`type` to initilized an Operator can not be None.")
self.desc.set_type(type)
proto = OpProtoHolder.instance().get_op_proto(type)
def find_name(var_list, name):
for var_name in var_list:
if var_list[var_name] is not None and var_name == name:
return True
return False
if inputs is not None:
for in_proto in proto.inputs:
found = find_name(inputs, in_proto.name)
assert found or in_proto.dispensable, "Input {} not found".format(
in_proto.name)
if found:
in_args = inputs[in_proto.name]
if not isinstance(in_args, list):
in_args = [in_args]
if not in_proto.duplicable and len(in_args) > 1:
raise ValueError(
"Input %s expects only one input, but %d are given."
% (in_proto.name, len(in_args)))
in_arg_names = []
for arg in in_args:
if isinstance(arg, basestring):
in_arg_names.append(arg)
else:
in_arg_names.append(arg.name)
self.desc.set_input(in_proto.name, in_arg_names)
else:
self.desc.set_input(in_proto.name, [])
if outputs is not None:
given = set()
need = set()
for n in outputs:
given.add(n)
for m in proto.outputs:
need.add(m.name)
if not given == need:
raise ValueError(("Incorrect setting for output(s) of "
"operator \"%s\". Need: [%s] Given: [%s]") %
(type, ", ".join(str(e) for e in need),
", ".join(str(e) for e in given)))
for out_proto in proto.outputs:
out_args = outputs[out_proto.name]
if not isinstance(out_args, list):
out_args = [out_args]
if not out_proto.duplicable and len(out_args) > 1:
raise ValueError(
"Output %s expects only one output, but %d are given." %
(out_proto.name, len(out_args)))
out_arg_names = []
for arg in out_args:
out_arg_names.append(arg.name)
arg.op = self
self.desc.set_output(out_proto.name, out_arg_names)
if attrs is not None:
if not isinstance(attrs, dict):
raise TypeError("'attrs' should be a dict.")
for attr in proto.attrs:
attr_name = attr.name
if (attr_name not in attrs) or (attrs[attr_name] is None):
continue
if isinstance(attrs[attr_name], Block):
self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
elif isinstance(attrs[attr_name], core.BlockDesc) or \
isinstance(attrs[attr_name], core.ProgramDesc):
self.desc.set_serialized_attr(
attr_name, attrs[attr_name].serialize_to_string())
else:
self.desc.set_attr(attr_name, attrs[attr_name])
self.desc.check_attrs()
no_kernel_op_set = {
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send',
'recv', 'listen_and_serv', 'parallel_do', 'save_combine',
'load_combine', 'ncclInit', 'channel_create', 'channel_close',
'channel_send', 'channel_recv', 'select'
}
if type not in no_kernel_op_set:
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)
def to_string(self, throw_on_error):
"""
To debug string.
Args:
throw_on_error(bool): raise exception when self is not initialized
when throw_on_error is True
Returns(str): The debug string.
"""
protostr = self.desc.serialize_to_string()
proto = framework_pb2.OpDesc.FromString(str(protostr))
return _debug_string_(proto, throw_on_error)
def __str__(self):
return self.to_string(True)
__repr__ = __str__
@property
def type(self):
return self.desc.type()
def input(self, name):
"""
Get input arguments by the input parameter name
Args:
name(str): The input parameter name
Returns(list): return the list of argument names associated with the
specific parameter name.
"""
return self.desc.input(name)
def rename_input(self, old_name, new_name):
self.desc.rename_input(old_name, new_name)
def rename_output(self, old_name, new_name):
self.desc.rename_output(old_name, new_name)
@property
def input_names(self):
"""
Get all input parameter names
Returns(list): return a list of input parameter names
"""
return self.desc.input_names()
@property
def input_arg_names(self):
return self.desc.input_arg_names()
@property
def output_arg_names(self):
return self.desc.output_arg_names()
def output(self, name):
"""
Get output arguments by the output parameter name
Args:
name(str): The output parameter name
Returns(list): return the list of argument names associated with the
specific parameter name.
"""
return self.desc.output(name)
@property
def output_names(self):
"""
Get all output parameter names
Returns(list): return a list of output parameter names
"""
return self.desc.output_names()
@property
def idx(self):
"""
Return the array index of current operator.
Returns(int): The array index in block.ops array
Raises:
ValueError: when the operator is not found.
"""
for i, op in enumerate(self.block.ops):
if op == self:
return i
raise ValueError(
"Can't find op itself in it's block. It could be a bug of Paddle.")
def has_attr(self, name):
"""
operator has the attribute with name or not.
Args:
name(str): the attribute name
Returns(bool): True if has this attribute.
"""
return self.desc.has_attr(name)
def attr_type(self, name):
"""
Get the type of attribute by attribute name
Args:
name(str): the attribute name
Returns(core.AttrType): the attribute type
"""
return self.desc.attr_type(name)
@property
def attr_names(self):
"""
Get all attribute names
Returns(list): The list of attribute name
"""
return self.desc.attr_names()
def attr(self, name):
"""
Get attribute by name
Args:
name(str): the attribute name
Returns(bool|int|str|float|list): The attribute value. The return value
can be any valid attribute type.
"""
return self.desc.attr(name)
def block_attr(self, name):
"""
Get the block attribute by name
Args:
name(str): the attribute name
Returns(int): the block index
"""
return self.desc.block_attr(name)
def all_attrs(self):
"""
Get the attribute dict
Returns(dict): The Operator's attribute dict
"""
attr_names = self.attr_names
attr_map = {}
for n in attr_names:
if n == 'sub_block':
attr_map[n] = self.block_attr(n)
else:
attr_map[n] = self.attr(n)
return attr_map
class Block(object):
def __init__(self, program, idx):
self.desc = program.desc.block(idx)
self.vars = collections.OrderedDict() # var_name --> var
self.ops = list() # operator list
self.program = program
self.removed_vars = collections.OrderedDict()
def __str__(self):
return self.to_string(True)
def to_string(self, throw_on_error, with_details=False):
"""
To debug string.
Args:
throw_on_error(bool): raise exception when self is not initialized
when throw_on_error is True
with_details(bool): more details about variables and parameters
(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
Returns(str): The debug string.
"""
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
if with_details:
re_add_indent = re.compile(r"\n(.)")
res_str = "blocks {\n idx: %d\n parent_idx: %d" % (
self.idx, self.parent_idx)
for var in self.vars.itervalues():
res_str += "\n vars {\n %s }" % re_add_indent.sub(
r"\n \1", var.to_string(throw_on_error, with_details))
for op in self.ops:
res_str += "\n ops {\n %s }" % re_add_indent.sub(
r"\n \1", op.to_string(throw_on_error))
res_str += "\n}"
else:
protostr = self.desc.serialize_to_string()
proto = framework_pb2.BlockDesc.FromString(str(protostr))
res_str = _debug_string_(proto, throw_on_error)
return res_str
__repr__ = __str__
@property
def parent_idx(self):
return self.desc.parent
@property
def forward_block_idx(self):
return self.desc.get_forward_block_idx()
def set_forward_block_idx(self, idx):
self.desc.set_forward_block_idx(idx)
@property
def idx(self):
return self.desc.id
def var(self, name):
if not isinstance(name, basestring):
raise TypeError()
v = self.vars.get(name, None)
if v is None:
raise ValueError("var %s not in this block" % name)
return v
def var_recursive(self, name):
frontier = list()
visited = set()
frontier.append(self)
prog = self.program
while len(frontier) != 0: # BFS
cur = frontier[0]
frontier = frontier[1:]
if id(cur) in visited:
continue
if cur.has_var(name):
return cur.var(name)
if cur.parent_idx != -1:
frontier.append(prog.block(cur.parent_idx))
if cur.forward_block_idx != -1:
frontier.append(prog.block(cur.forward_block_idx))
visited.add(id(cur))
raise ValueError("Var {0} is not found recursively".format(name))
def all_parameters(self):
return list(self.iter_parameters())
def iter_parameters(self):
return (item[1] for item in self.vars.iteritems()
if isinstance(item[1], Parameter))
def create_var(self, *args, **kwargs):
var = Variable(block=self, *args, **kwargs)
if 'initializer' in kwargs:
kwargs['initializer'](var, self)
return var
def has_var(self, name):
return name in self.vars
def rename_var(self, name, new_name):
"""
Rename variable in vars and ops' inputs and outputs
"""
if not self.has_var(name):
raise ValueError("var %s is not in current" % name)
v = self.var(name)
if type(v) == Parameter:
var_type = "Parameter"
stop_gradient = v.stop_gradient
trainable = v.trainable
optimize_attr = v.optimize_attr
regularizer = v.regularizer
gradient_clip_attr = v.gradient_clip_attr
error_clip = v.error_clip
elif type(v) == Variable:
var_type = "Variable"
error_clip = v.error_clip
stop_gradient = v.stop_gradient
else:
raise ValueError("unsupported var type: %s", type(v))
orig_var_type = v.type
self.desc.rename_var(name, new_name)
# NOTE: v is destroyed by C++ after calling rename_var.
d = self.desc.find_var(new_name)
if var_type == "Parameter":
var = Parameter(
self,
d.shape(),
d.dtype(),
type=orig_var_type,
name=new_name,
stop_gradient=stop_gradient,
trainable=trainable,
optimize_attr=optimize_attr,
regularizer=regularizer,
gradient_clip_attr=gradient_clip_attr,
error_clip=error_clip)
elif var_type == "Variable":
var = Variable(
self,
type=orig_var_type,
name=new_name,
error_clip=error_clip,
stop_gradient=stop_gradient)
# rename the python side, sync_with_cpp will only add
# new vars/ops to python side.
self.vars[new_name] = var
del self.vars[name]
self.sync_with_cpp()
def remove_var(self, name):
self.sync_with_cpp()
self.desc.remove_var(name)
del self.vars[name]
def create_parameter(self, *args, **kwargs):
global_block = self.program.global_block()
param = Parameter(global_block, *args, **kwargs)
if 'initializer' in kwargs:
kwargs['initializer'](param, self)
return param
def append_op(self, *args, **kwargs):
op_desc = self.desc.append_op()
op = Operator(block=self, desc=op_desc, *args, **kwargs)
self.ops.append(op)
return op
def insert_op(self, index, *args, **kwargs):
self.sync_with_cpp()
op_desc = self.desc.insert_op(index)
op = Operator(block=self, desc=op_desc, *args, **kwargs)
self.ops.insert(index, op)
return op
def remove_op(self, index):
self.sync_with_cpp()
self.desc.remove_op(index, index + 1)
del self.ops[index]
def slice_ops(self, start, end):
return self.ops[start:end]
def prepend_op(self, *args, **kwargs):
op_desc = self.desc.prepend_op()
op = Operator(self, op_desc, *args, **kwargs)
self.ops.insert(0, op)
return op
def sync_with_cpp(self):
"""
Sync from the desc on the c++ end.
This method is used to synchronize the c++ desc instance generated by backward.
"""
# sync variables from cpp
for var in self.desc.all_vars():
if not self.has_var(var.name()):
self.create_var(name=var.name(), desc=var, type=var.type())
# sync variables removed from c++ end
for var in self.vars.keys():
if not self.desc.find_var(var):
self.vars.pop(var)
# sync operators from cpp
ops_in_cpp = []
for op_idx in range(0, self.desc.op_size()):
ops_in_cpp.append(self.desc.op(op_idx))
if len(self.ops) != 0:
first_op_in_python = self.ops[0].desc
last_op_in_python = self.ops[len(self.ops) - 1].desc
start_index = None
end_index = None
for index in range(len(ops_in_cpp)):
if first_op_in_python == ops_in_cpp[index]:
start_index = index
if last_op_in_python == ops_in_cpp[index]:
end_index = index
assert start_index is not None
assert end_index is not None
assert start_index <= end_index
else:
start_index = 0
end_index = -1
# sync ops append to the head of cpp_ops
for index in range((start_index - 1 - 1), -1, -1):
op_desc = ops_in_cpp[index]
op = Operator(self, op_desc)
self.ops.insert(0, op)
# sync ops append to the end of cpp_ops
for index in range((end_index + 1), len(ops_in_cpp)):
op_desc = ops_in_cpp[index]
op = Operator(self, op_desc)
self.ops.append(op)
# sync ops removed from c++ end
if end_index != -1 and end_index < len(self.ops):
ops_in_cpp_index = 0
ops_in_python_index = 0
while ops_in_python_index < len(
self.ops) and ops_in_cpp_index < len(ops_in_cpp):
if self.ops[ops_in_python_index].desc != ops_in_cpp[
ops_in_cpp_index]:
del self.ops[ops_in_python_index]
else:
ops_in_cpp_index += 1
ops_in_python_index += 1
assert len(self.ops) == len(ops_in_cpp)
for index in range(len(self.ops)):
assert self.ops[index].desc == ops_in_cpp[index]
def copy_param_info_from(self, other):
"""
Copy the information of parameters from the other block
Args:
other(Block): the other block
Returns:
None
"""
if not isinstance(other, Block):
raise TypeError("copy_param_info_from should be invoked with Block")
for p in other.iter_parameters():
assert isinstance(p, Parameter)
v = self.vars.get(p.name, None)
if v is None:
raise ValueError("copy_param_info_from should be invoked with "
"same topology")
assert isinstance(v, Variable)
new_p = Parameter(
block=self,
shape=v.shape,
dtype=v.dtype,
type=v.type,
lod_level=v.lod_level,
stop_gradient=p.stop_gradient,
trainable=p.trainable,
optimize_attr=p.optimize_attr,
regularizer=p.regularizer,
gradient_clip_attr=p.gradient_clip_attr,
error_clip=p.error_clip,
name=v.name)
self.vars[new_p.name] = new_p
def clone_variable(self, var):
"""
Clone a variable into current block.
Args:
var: the variable to be cloned.
Returns:
The new variable cloned from 'var' in current block.
"""
assert isinstance(var, Variable)
ret_var = None
# make STEP_SCOPES var can be safely cloned.
if var.type == core.VarDesc.VarType.STEP_SCOPES:
ret_var = self.create_var(
name=var.name, persistable=var.persistable, type=var.type)
elif var.type == core.VarDesc.VarType.SELECTED_ROWS:
ret_var = self.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
persistable=True)
else:
ret_var = self.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
return ret_var
class Program(object):
def __init__(self):
self.desc = core.ProgramDesc()
self.blocks = [Block(self, 0)]
self.current_block_idx = 0
self._seed = 0
def __str__(self):