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7cd4d0ac
编写于
6月 19, 2018
作者:
C
chengduoZH
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电子邮件补丁
差异文件
add Doc fluid.Parameter, program and block
上级
527b22f2
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
313 addition
and
152 deletion
+313
-152
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+313
-152
未找到文件。
python/paddle/fluid/framework.py
浏览文件 @
7cd4d0ac
...
...
@@ -43,7 +43,8 @@ ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
def
grad_var_name
(
var_name
):
"""
return gradient name for a certain var name
Returns:
str: gradient name for a certain var name
"""
return
var_name
+
GRAD_VAR_SUFFIX
...
...
@@ -51,10 +52,12 @@ def grad_var_name(var_name):
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
np_dtype(np.dtype): the data type in numpy
.
Returns(core.VarDesc.VarType): the data type in Paddle
Returns:
core.VarDesc.VarType: the data type in Paddle.
"""
dtype
=
np
.
dtype
(
np_dtype
)
...
...
@@ -129,46 +132,44 @@ class Variable(object):
and usages. Please reference the framework.proto for details.
Most of a Variable's member variables can be setted to be None. It mean
it is not avaiable or will be specified later.
it is not avai
l
able or will be specified later.
Notes: The constructor of Variable should not be invoked directly. Please
use `Block.create_var` to create a variable.
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
new_variable = cur_block.create_var(
name="X", shape=[-1, 23, 48], dtype='float32')
Member variables:
Args:
block(Block): The block that the variable belongs to.
type(core.VarDesc.VarType): Variable type. Please reference the
framework.proto for details.
name(str|None): The name of the variable. If setted None, it will be
generated automatically.
Default: None
name(str|None): The name of the variable. If setted None, it will be
generated automatically. Default: None
shape(tuple|list|None): The shape of the variable. -1 means the batch size.
Some kinds of variable do not contain shape, just set it to None.
Default: None
dtype(np.dtype|core.VarDesc.VarType|str|None): The data type of variable.
Default: None
lod_level(int|None): The level of lod tensor. 0 means it is not a time
lod_level
(int|None): The level of lod tensor. 0 means it is not a time
series data.
Default: None
capacity(int|None): The capacity of Channel variable. Ignored
for other types.
Default: None
persistable(bool|None): True if the variable is persistable. A persistable
variable will not be deleted after an iteration ending.
Defaults: None.
error_clip(BaseErrorClipAttr|None): The error clip attributes of the
corresponding gradient variable.
Default: None
stop_gradient(bool): True if the variable will stop to calculate its
gradients when backward.
Default: False.
is_data(bool): True is the variable is an input data.
Default: False
capacity (int|None): The capacity of Channel variable. Ignored for other
types. Default: None
persistable (bool|None): True if the variable is persistable. A persistable
variable will not be deleted after an iteration ending. Defaults: None.
error_clip (BaseErrorClipAttr|None): The error clip attributes of the
corresponding gradient variable. Default: None
stop_gradient (bool): True if the variable will stop to calculate its
gradients when backward. Default: False.
is_data (bool): True if the variable is an input data. Default: False
Notes:
The constructor of Variable should not be invoked directly. Please
use `Block.create_var` to create a variable.
Examples:
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
new_variable = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
"""
def
__init__
(
self
,
...
...
@@ -271,13 +272,14 @@ class Variable(object):
Get debug string.
Args:
throw_on_error(bool): True if raise an exception when self is
not
in
tialized.
throw_on_error(bool): True if raise an exception when self is
not ini
tialized.
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.
(e.g. trainable, optimize_attr, ...) will be printed when
with_details is True. Default False;
Returns:
str: The debug string.
"""
assert
isinstance
(
throw_on_error
,
bool
)
and
isinstance
(
with_details
,
bool
)
...
...
@@ -294,6 +296,15 @@ class Variable(object):
__repr__
=
__str__
def
set_desc
(
self
,
input
):
"""
Set the variable description.
Args:
input(core.VarDesc): The new VarDesc.
Returns:
None
"""
self
.
desc
=
input
@
property
...
...
@@ -330,6 +341,15 @@ class Variable(object):
return
self
.
desc
.
type
()
def
set_error_clip
(
self
,
error_clip
):
"""
Set the error_clip.
Args:
error_clip(BaseErrorClipAttr) : The new error_clip.
Returns:
None
"""
self
.
error_clip
=
error_clip
...
...
@@ -337,8 +357,8 @@ def get_all_op_protos():
"""
Get all registered op proto from PaddlePaddle C++ end.
Returns
(list): list of OpProto
Returns
:
list: list of OpProto.
"""
protostrs
=
core
.
get_all_op_protos
()
ret_values
=
[]
...
...
@@ -391,9 +411,45 @@ class OpProtoHolder(object):
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.
In Fluid, all the operation are represented by Operator, and Operator
is regarded as a build in an instruction of a Block. Users can use the
build in instructions to describe their neural network.
Args:
block(Block): The block has the current operator.
desc(core.OpDesc): The protobuf description of Operator.
type(str): The type of operator.
inputs(dict): The input of this Operator. it is a dictionary, for every
element, key is the input parameter name, and value is a list of
variables. Default None.
outputs(dict): The output of this Operator. it is a dictionary, for
every element, key is the input parameter name, and value is a list
of variables. Default None.
attrs(dict): The attributes of this Operator. it is a dictionary, for
every element, key is attribute name, and value is the attribute value.
The attribute type should be as same as the type registered in C++ side.
Default None.
Returns:
Operator: The initialized Operator.
Raises:
ValueError: If the passed input, output and attrs doesn't match the
initializing Operator's that registered in C++ side.
Notes:
The constructor of operator should not be invoked directly. Use
Block.append_op or Block.prepend_op instead.
Examples:
.. code-block:: python
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]})
"""
OP_WITHOUT_KERNEL_SET
=
{
'feed'
,
'fetch'
,
'save'
,
'load'
,
'recurrent'
,
'go'
,
...
...
@@ -403,38 +459,9 @@ class Operator(object):
'channel_recv'
,
'select'
,
'gen_nccl_id'
}
def
__init__
(
self
,
block
,
desc
,
type
=
None
,
inputs
=
None
,
outputs
=
None
,
def
__init__
(
self
,
block
,
desc
,
type
,
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
...
...
@@ -457,9 +484,7 @@ class Operator(object):
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
)
...
...
@@ -547,12 +572,14 @@ class Operator(object):
def
to_string
(
self
,
throw_on_error
):
"""
To debug string.
Get debug string.
Args:
throw_on_error(bool):
raise exception when self is not initialized
when throw_on_error is True
throw_on_error(bool):
Whether to raise exception if self is not
initialized.
Returns(str): The debug string.
Returns:
str: The debug string.
"""
protostr
=
self
.
desc
.
serialize_to_string
()
...
...
@@ -570,29 +597,45 @@ class Operator(object):
def
input
(
self
,
name
):
"""
Get input arguments by the input parameter name
Args:
name(str): The input parameter name
Get the input arguments according to the input parameter name.
Returns(list): return the list of argument names associated with the
specific
parameter name.
Args:
name(str): The input
parameter name.
Returns:
list: return the list of argument names that associated with
\
the specific parameter name.
"""
return
self
.
desc
.
input
(
name
)
def
rename_input
(
self
,
old_name
,
new_name
):
"""
Rename the `old_name` to `new_name`.
Args:
old_name(str): The old name of the Operator's input.
new_name(str): The new name of the Operator's input.
Returns:
None
"""
self
.
desc
.
rename_input
(
old_name
,
new_name
)
def
rename_output
(
self
,
old_name
,
new_name
):
"""
Rename the `old_name` to `new_name`.
Args:
old_name(str): The old name of the Operator's output.
new_name(str): The new name of the Operator's output.
Returns:
None
"""
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
...
...
@@ -605,33 +648,23 @@ class Operator(object):
def
output
(
self
,
name
):
"""
Get output arguments by the output parameter name
Args:
name(str): The output parameter name
Get output arguments by the output parameter name.
Returns(list): return the list of argument names associated with the
specific
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
...
...
@@ -640,66 +673,78 @@ class Operator(object):
def
has_attr
(
self
,
name
):
"""
operator has the attribute with name or not.
Whether this Operator has the attribute with name or not.
Args:
name(str): the attribute name
name(str): the attribute name
.
Returns(bool): True if has this attribute.
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
Get the type of attribute by attribute's name.
Returns(core.AttrType): the attribute type
Args:
name(str): the attribute name.
Returns:
core.AttrType: the attribute type.
"""
return
self
.
desc
.
attr_type
(
name
)
def
set_attr
(
self
,
name
,
val
):
"""
Set the value of attribute by attribute's name.
Args:
name(str): the attribute name.
val(bool|int|str|float|list): the value of the attribute.
Raises:
ValueError: If the type of value doesn't match with desc.attr_type(name).
"""
self
.
attrs
[
name
]
=
val
self
.
desc
.
set_attr
(
name
,
val
)
@
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
Get the attribute by name.
Args:
name(str): the attribute name
name(str): the attribute name
.
Returns(bool|int|str|float|list): The attribute value. The return value
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
Get the block attribute by name.
Returns(int): the block index
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
Get the attribute dict.
Returns:
dict: The Operator's attribute dict.
"""
attr_names
=
self
.
attr_names
attr_map
=
{}
...
...
@@ -712,6 +757,35 @@ class Operator(object):
class
Block
(
object
):
"""
In Fluid, a Program is consistence of multi-Block, and Block stores
VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name.
One block could have some child blocks, and child block's name scopes
should inherit the parent's so that OpDesc in child block can reference
a VarDesc that is stored in the parent block.
Please reference the framework.proto for details.
Args:
program(Program): The Program that the Block belongs to.
idx(int): The block's id in the Program.
Notes:
The constructor of Block should not be invoked directly. Please
use `Program.create_block()` to create a block.
Examples:
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
var = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
cur_block.append_op(type="abs",
inputs={"X": [var]},
outputs={"Out": [var]})
"""
def
__init__
(
self
,
program
,
idx
):
self
.
desc
=
program
.
desc
.
block
(
idx
)
self
.
vars
=
collections
.
OrderedDict
()
# var_name --> var
...
...
@@ -724,15 +798,17 @@ class Block(object):
def
to_string
(
self
,
throw_on_error
,
with_details
=
False
):
"""
To debug string.
Get debug string.
Args:
throw_on_error(bool): raise exception when self is not initialized
when throw_on_error is True
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.
(e.g. trainable, optimize_attr, ...) will be printed when
with_details is True. Default False.
Returns:
str: The debug string.
"""
assert
isinstance
(
throw_on_error
,
bool
)
and
isinstance
(
with_details
,
bool
)
...
...
@@ -764,6 +840,15 @@ class Block(object):
return
self
.
desc
.
get_forward_block_idx
()
def
set_forward_block_idx
(
self
,
idx
):
"""
Set the forward block Idx.
Args:
idx(int): the block index.
Returns:
None
"""
self
.
desc
.
set_forward_block_idx
(
idx
)
@
property
...
...
@@ -771,6 +856,19 @@ class Block(object):
return
self
.
desc
.
id
def
var
(
self
,
name
):
"""
Get a Variable by name from this block.
Args:
name(str): the Variable's name.
Raises:
ValueError: The If input's type is not str, or this block
doesn't have a Variable with the giving name.
Returns:
Variable: the Variable with the giving name.
"""
if
not
isinstance
(
name
,
basestring
):
raise
TypeError
(
"var require string as parameter, but get %s instead."
%
...
...
@@ -781,6 +879,19 @@ class Block(object):
return
v
def
var_recursive
(
self
,
name
):
"""
Get a Variable by name from this block recursively.
Args:
name(str): the Variable's name.
Raises:
ValueError: this block and this parent block doesn't
have a Variable with the giving name.
Returns:
Variable: the Variable with the giving name.
"""
frontier
=
list
()
visited
=
set
()
...
...
@@ -827,6 +938,18 @@ class Block(object):
def
rename_var
(
self
,
name
,
new_name
):
"""
Rename variable in vars and ops' inputs and outputs
Args:
name(str): the name that need to be renamed.
new_name(str): the name that need to rename to.
Raises:
ValueError: If this block doesn't have this the giving name,
or the type of the var with the giving name is not Parameter
or Variable.
Returns:
Variable: the Variable with the giving name.
"""
if
not
self
.
has_var
(
name
):
raise
ValueError
(
"var %s is not in current block"
%
name
)
...
...
@@ -890,12 +1013,27 @@ class Block(object):
return
param
def
append_op
(
self
,
*
args
,
**
kwargs
):
"""
Appends a new Operator according to the giving arguments.
Returns:
Operator: the append Operator.
"""
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
):
"""
Insert a Operator according to the giving arguments.
Args:
index(int): the place that the operator to insert.
Returns:
Operator: the insert Operator.
"""
self
.
sync_with_cpp
()
op_desc
=
self
.
desc
.
insert_op
(
index
)
op
=
Operator
(
block
=
self
,
desc
=
op_desc
,
*
args
,
**
kwargs
)
...
...
@@ -903,11 +1041,30 @@ class Block(object):
return
op
def
remove_op
(
self
,
index
):
"""
Remove the specific position operator.
Args:
index(int): the position that the operator to insert.
Returns:
None
"""
self
.
sync_with_cpp
()
self
.
desc
.
remove_op
(
index
,
index
+
1
)
del
self
.
ops
[
index
]
def
slice_ops
(
self
,
start
,
end
):
"""
Return the Operator between start and end.
Args:
start(int): the start position.
end(int): the end position.
Returns:
list: the Operators between start and end.
"""
return
self
.
ops
[
start
:
end
]
def
prepend_op
(
self
,
*
args
,
**
kwargs
):
...
...
@@ -918,9 +1075,8 @@ class Block(object):
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 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
():
...
...
@@ -985,9 +1141,14 @@ class Block(object):
def
copy_param_info_from
(
self
,
other
):
"""
Copy the information of parameters from the other block
Copy the information of parameters from the other block.
Args:
other(Block): the other block
other(Block): the other block.
Raises:
ValueError: If type of input is not Block, or the `other` and this
block is not in the same topology.
Returns:
None
...
...
@@ -1019,11 +1180,12 @@ class Block(object):
def
clone_variable
(
self
,
var
):
"""
Clone a variable into current block.
Args:
var: the variable to be cloned.
Returns:
T
he new variable cloned from 'var' in current block.
Variable: t
he new variable cloned from 'var' in current block.
"""
assert
isinstance
(
var
,
Variable
)
ret_var
=
None
...
...
@@ -1330,22 +1492,21 @@ class Parameter(Variable):
The training of a neural network is essentially the updating of
its parameters.
Relative to a general V
riable, a Parameter has several its own
Relative to a general V
ariable, a Parameter has several its own
member variables:
trainable(bool): True if the parameter need to be updated after
iterations.
optimize_attr(map): Parameter attributes related with optimizing.
Currently, it only contains 'learning_rate'.
Default: {'learning_rate': 1.0}
regularizer(WeightDecayRegularizer): The Regularizer which will
be applied on the parameter.
Default: None
gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy
which will be applied on the parameter.
Default: None
do_model_average(bool): True if the model average strategy will
be applied on this parameter.
Args:
trainable(bool): True if the parameter need to be updated after
iterations.
optimize_attr(map): Parameter attributes related with optimizing.
Currently, it only contains 'learning_rate'.
Default: {'learning_rate': 1.0}
regularizer(WeightDecayRegularizer): The Regularizer which will
be applied on the parameter. Default: None
gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy
which will be applied on the parameter. Default: None
do_model_average(bool): True if the model average strategy will
be applied on this parameter.
"""
def
__init__
(
self
,
block
,
shape
,
dtype
,
**
kwargs
):
...
...
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