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体验新版 GitCode,发现更多精彩内容 >>
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8b9d33fa
编写于
12月 12, 2018
作者:
S
sneaxiy
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add unittest and fix bug
add API.spec test=develop
上级
e240ba29
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
374 addition
and
137 deletion
+374
-137
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/py_func_op.cc
paddle/fluid/operators/py_func_op.cc
+81
-42
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+147
-95
python/paddle/fluid/tests/unittests/test_py_func_op.py
python/paddle/fluid/tests/unittests/test_py_func_op.py
+145
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
8b9d33fa
...
@@ -197,6 +197,7 @@ paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act
...
@@ -197,6 +197,7 @@ paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act
paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
paddle.fluid.layers.py_func ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
...
...
paddle/fluid/operators/py_func_op.cc
浏览文件 @
8b9d33fa
...
@@ -26,26 +26,35 @@ namespace py = pybind11;
...
@@ -26,26 +26,35 @@ namespace py = pybind11;
static
std
::
vector
<
py
::
object
>
g_py_callables
;
static
std
::
vector
<
py
::
object
>
g_py_callables
;
const
char
kForwardPythonCallableId
[]
=
"forward_callable_id"
;
const
char
kBackwardPythonCallableId
[]
=
"backward_callable_id"
;
const
char
kPyFuncBackwardSkipVars
[]
=
"backward_skip_vars"
;
size_t
AppendPythonCallableObjectAndReturnId
(
py
::
object
py_obj
)
{
size_t
AppendPythonCallableObjectAndReturnId
(
py
::
object
py_obj
)
{
g_py_callables
.
emplace_back
(
py_obj
);
g_py_callables
.
emplace_back
(
py_obj
);
return
g_py_callables
.
size
()
-
1
;
return
g_py_callables
.
size
()
-
1
;
}
}
static
py
::
object
*
GetPythonCallableObject
(
size_t
i
)
{
static
py
::
object
*
GetPythonCallableObject
(
size_t
i
)
{
PADDLE_ENFORCE_LT
(
i
,
g_py_callables
.
size
());
PADDLE_ENFORCE_LT
(
i
,
g_py_callables
.
size
()
,
"Invalid python callable id"
);
return
&
g_py_callables
[
i
];
return
&
g_py_callables
[
i
];
}
}
void
CallPythonFunc
(
py
::
object
*
callable
,
const
std
::
string
&
func_token
,
std
::
string
PythonObjectToString
(
const
py
::
object
&
py_callable
)
{
py
::
gil_scoped_acquire
guard
;
return
py
::
str
(
*
py_callable
);
}
void
CallPythonFunc
(
py
::
object
*
callable
,
const
std
::
vector
<
framework
::
LoDTensor
>
&
ins
,
const
std
::
vector
<
framework
::
LoDTensor
>
&
ins
,
std
::
vector
<
framework
::
LoDTensor
*>
*
out
)
{
std
::
vector
<
framework
::
LoDTensor
*>
*
out
)
{
py
::
gil_scoped_acquire
guard
{}
;
py
::
gil_scoped_acquire
guard
;
py
::
tuple
in_args
(
ins
.
size
());
py
::
tuple
in_args
(
ins
.
size
());
for
(
size_t
i
=
0
;
i
<
ins
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
ins
.
size
();
++
i
)
{
in_args
[
i
]
=
ins
[
i
].
IsInitialized
()
?
py
::
cast
(
ins
[
i
])
:
py
::
cast
(
nullptr
);
in_args
[
i
]
=
ins
[
i
].
IsInitialized
()
?
py
::
cast
(
ins
[
i
])
:
py
::
cast
(
nullptr
);
}
}
auto
ret
=
(
*
callable
)(
func_token
,
*
in_args
);
auto
ret
=
(
*
callable
)(
*
in_args
);
auto
ret_tuple
=
py
::
cast
<
py
::
tuple
>
(
ret
);
auto
ret_tuple
=
py
::
cast
<
py
::
tuple
>
(
ret
);
PADDLE_ENFORCE_EQ
(
py
::
len
(
ret_tuple
),
out
->
size
(),
"Output number not match"
);
PADDLE_ENFORCE_EQ
(
py
::
len
(
ret_tuple
),
out
->
size
(),
"Output number not match"
);
for
(
size_t
i
=
0
;
i
<
out
->
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
out
->
size
();
++
i
)
{
...
@@ -55,7 +64,7 @@ void CallPythonFunc(py::object *callable, const std::string &func_token,
...
@@ -55,7 +64,7 @@ void CallPythonFunc(py::object *callable, const std::string &func_token,
try
{
try
{
auto
*
out_tensor
=
py
::
cast
<
framework
::
LoDTensor
*>
(
ret_tuple
[
i
]);
auto
*
out_tensor
=
py
::
cast
<
framework
::
LoDTensor
*>
(
ret_tuple
[
i
]);
PADDLE_ENFORCE_NOT_NULL
(
out_tensor
,
PADDLE_ENFORCE_NOT_NULL
(
out_tensor
,
"Output tensor
should not be nullptr"
);
"Output tensor
%d should not be nullptr"
,
i
);
(
*
out
)[
i
]
->
set_lod
(
out_tensor
->
lod
());
(
*
out
)[
i
]
->
set_lod
(
out_tensor
->
lod
());
(
*
out
)[
i
]
->
ShareDataWith
(
*
out_tensor
);
(
*
out
)[
i
]
->
ShareDataWith
(
*
out_tensor
);
}
catch
(
py
::
cast_error
&
)
{
}
catch
(
py
::
cast_error
&
)
{
...
@@ -69,26 +78,23 @@ class PyFuncOpShapeInference : public framework::InferShapeBase {
...
@@ -69,26 +78,23 @@ class PyFuncOpShapeInference : public framework::InferShapeBase {
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
!
ctx
->
IsRuntime
(),
PADDLE_ENFORCE
(
!
ctx
->
IsRuntime
(),
"Infer shape cannot be called in runtime."
);
"Infer shape cannot be called in runtime."
);
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
"X"
),
"Input(X) must exist"
);
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
"X"
)
||
ctx
->
HasOutputs
(
"Out"
),
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
"Out"
),
"Output(Out) must exist"
);
"Input(X) or Output(Out) must exist"
);
PADDLE_ENFORCE_GE
(
ctx
->
Attrs
().
Get
<
int
>
(
kForwardPythonCallableId
),
0
,
"Function id cannot be less than 0"
);
auto
*
op
=
boost
::
get
<
const
framework
::
OpDesc
*>
(
ctx
->
GetOp
());
auto
*
op
=
boost
::
get
<
const
framework
::
OpDesc
*>
(
ctx
->
GetOp
());
auto
*
block
=
op
->
Block
();
auto
*
block
=
op
->
Block
();
// No need to infer shape in forward part
if
(
block
->
ForwardBlockID
()
<
0
)
{
return
;
}
PADDLE_ENFORCE
(
!
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"token"
).
empty
(),
"Function token cannot be empty"
);
const
std
::
string
kGradVarSuffix
=
framework
::
kGradVarSuffix
;
const
std
::
string
kGradVarSuffix
=
framework
::
kGradVarSuffix
;
auto
out_vars
=
ctx
->
GetOutputVarPtrs
(
"Out"
);
auto
out_vars
=
ctx
->
GetOutputVarPtrs
(
"Out"
);
for
(
auto
&
out_var
:
out_vars
)
{
for
(
auto
&
out_var
:
out_vars
)
{
auto
*
out_var_desc
=
boost
::
get
<
framework
::
VarDesc
*>
(
out_var
);
auto
*
out_var_desc
=
boost
::
get
<
framework
::
VarDesc
*>
(
out_var
);
if
(
out_var_desc
==
nullptr
)
{
continue
;
}
auto
out_name
=
out_var_desc
->
Name
();
auto
out_name
=
out_var_desc
->
Name
();
if
(
out_name
==
framework
::
kEmptyVarName
||
if
(
out_name
==
framework
::
kEmptyVarName
||
out_name
.
size
()
<
kGradVarSuffix
.
size
())
{
out_name
.
size
()
<
=
kGradVarSuffix
.
size
())
{
continue
;
continue
;
}
}
...
@@ -98,6 +104,8 @@ class PyFuncOpShapeInference : public framework::InferShapeBase {
...
@@ -98,6 +104,8 @@ class PyFuncOpShapeInference : public framework::InferShapeBase {
auto
*
in_var_desc
=
block
->
FindVarRecursive
(
fwd_var_name
);
auto
*
in_var_desc
=
block
->
FindVarRecursive
(
fwd_var_name
);
PADDLE_ENFORCE_NOT_NULL
(
in_var_desc
,
"Forward variable %s not found"
,
PADDLE_ENFORCE_NOT_NULL
(
in_var_desc
,
"Forward variable %s not found"
,
fwd_var_name
);
fwd_var_name
);
VLOG
(
10
)
<<
"Infer shape of Out("
<<
out_name
<<
") as Input("
<<
in_var_desc
->
Name
()
<<
")"
;
out_var_desc
->
SetShape
(
in_var_desc
->
GetShape
());
out_var_desc
->
SetShape
(
in_var_desc
->
GetShape
());
out_var_desc
->
SetDataType
(
in_var_desc
->
GetDataType
());
out_var_desc
->
SetDataType
(
in_var_desc
->
GetDataType
());
out_var_desc
->
SetLoDLevel
(
in_var_desc
->
GetLoDLevel
());
out_var_desc
->
SetLoDLevel
(
in_var_desc
->
GetLoDLevel
());
...
@@ -112,13 +120,15 @@ class PyFuncOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -112,13 +120,15 @@ class PyFuncOpMaker : public framework::OpProtoAndCheckerMaker {
void
Make
()
override
{
void
Make
()
override
{
AddInput
(
"X"
,
"Inputs of py_func op."
).
AsDuplicable
();
AddInput
(
"X"
,
"Inputs of py_func op."
).
AsDuplicable
();
AddOutput
(
"Out"
,
"Outputs of py_func op"
).
AsDuplicable
();
AddOutput
(
"Out"
,
"Outputs of py_func op"
).
AsDuplicable
();
AddAttr
<
int
>
(
"handle_idx"
,
"Index of the registered py_func handle"
)
AddAttr
<
int
>
(
kForwardPythonCallableId
,
"Index of registered forward Python function."
)
.
SetDefault
(
0
);
.
SetDefault
(
0
);
AddAttr
<
std
::
string
>
(
"token"
,
"Token of function token to be called"
)
AddAttr
<
int
>
(
kBackwardPythonCallableId
,
.
SetDefault
(
""
);
"Index of registered backward Python function"
)
AddAttr
<
std
::
string
>
(
"backward_token"
,
.
SetDefault
(
-
1
);
"Token of backward function to be called"
)
AddAttr
<
std
::
vector
<
std
::
string
>>
(
kPyFuncBackwardSkipVars
,
.
SetDefault
(
""
);
"Unused forward in/out in backward op"
)
.
SetDefault
(
std
::
vector
<
std
::
string
>
());
AddComment
(
R"DOC("PyFunc Op")DOC"
);
AddComment
(
R"DOC("PyFunc Op")DOC"
);
}
}
};
};
...
@@ -129,7 +139,8 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
...
@@ -129,7 +139,8 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
std
::
vector
<
std
::
unique_ptr
<
framework
::
OpDesc
>>
operator
()()
const
override
{
std
::
vector
<
std
::
unique_ptr
<
framework
::
OpDesc
>>
operator
()()
const
override
{
auto
&
fwd_attrs
=
Attrs
();
auto
&
fwd_attrs
=
Attrs
();
if
(
fwd_attrs
.
at
(
"backward_token"
).
empty
())
{
// no backward op when backward_id is less than 0
if
(
boost
::
get
<
int
>
(
fwd_attrs
.
at
(
kBackwardPythonCallableId
))
<
0
)
{
return
{};
return
{};
}
}
...
@@ -137,36 +148,65 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
...
@@ -137,36 +148,65 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
grad_op
->
SetType
(
"py_func"
);
grad_op
->
SetType
(
"py_func"
);
framework
::
AttributeMap
bwd_attrs
;
framework
::
AttributeMap
bwd_attrs
;
bwd_attrs
[
"token"
]
=
fwd_attrs
.
at
(
"backward_token"
);
bwd_attrs
[
kForwardPythonCallableId
]
=
bwd_attrs
[
"backward_token"
]
=
std
::
string
(
""
);
fwd_attrs
.
at
(
kBackwardPythonCallableId
);
bwd_attrs
[
kBackwardPythonCallableId
]
=
-
1
;
grad_op
->
SetAttrMap
(
bwd_attrs
);
grad_op
->
SetAttrMap
(
bwd_attrs
);
auto
bwd_in
=
Input
(
"X"
);
// All forward inputs
auto
fwd_out
=
Output
(
"Out"
);
auto
fwd_ins
=
Input
(
"X"
);
auto
fwd_out_grad
=
OutputGrad
(
"Out"
);
// All forward outputs
bwd_in
.
insert
(
bwd_in
.
end
(),
fwd_out
.
begin
(),
fwd_out
.
end
());
auto
fwd_outs
=
Output
(
"Out"
);
bwd_in
.
insert
(
bwd_in
.
end
(),
fwd_out_grad
.
begin
(),
fwd_out_grad
.
end
());
// For memory reused, some inputs/output in forward part may be not needed
// in backward part
// Just skip these vars
auto
&
backward_skip_var_list
=
boost
::
get
<
std
::
vector
<
std
::
string
>>
(
fwd_attrs
.
at
(
kPyFuncBackwardSkipVars
));
std
::
unordered_set
<
std
::
string
>
backward_skip_var_set
(
backward_skip_var_list
.
begin
(),
backward_skip_var_list
.
end
());
std
::
vector
<
std
::
string
>
bwd_ins
;
bwd_ins
.
reserve
(
fwd_ins
.
size
()
+
fwd_outs
.
size
());
for
(
auto
&
fwd_in
:
fwd_ins
)
{
if
(
backward_skip_var_set
.
count
(
fwd_in
)
==
0
)
{
bwd_ins
.
emplace_back
(
fwd_in
);
}
}
for
(
auto
&
fwd_out
:
fwd_outs
)
{
if
(
backward_skip_var_set
.
count
(
fwd_out
)
==
0
)
{
bwd_ins
.
emplace_back
(
fwd_out
);
}
}
// Backward OG cannot be skipped
// But in Python side, if OG is kEmptyVarName, input tensor would be None
auto
fwd_out_grads
=
OutputGrad
(
"Out"
);
bwd_ins
.
reserve
(
bwd_ins
.
size
()
+
fwd_out_grads
.
size
());
bwd_ins
.
insert
(
bwd_ins
.
end
(),
fwd_out_grads
.
begin
(),
fwd_out_grads
.
end
());
auto
bwd_out
=
InputGrad
(
"X"
,
false
);
// Backward IG cannot be skipped
// But in Python side, if IG is not needed, users can just return None
auto
bwd_outs
=
InputGrad
(
"X"
,
false
);
if
(
VLOG_IS_ON
(
10
))
{
if
(
VLOG_IS_ON
(
10
))
{
std
::
string
in_str
=
"PyFunc Grad Input: "
;
std
::
string
in_str
=
"PyFunc Grad Input: "
;
for
(
auto
&
in
:
bwd_in
)
{
for
(
auto
&
in
:
bwd_in
s
)
{
in_str
+=
in
;
in_str
+=
in
;
in_str
+=
" "
;
in_str
+=
" "
;
}
}
VLOG
(
10
)
<<
in_str
;
VLOG
(
10
)
<<
in_str
;
std
::
string
out_str
=
"PyFunc Grad Output: "
;
std
::
string
out_str
=
"PyFunc Grad Output: "
;
for
(
auto
&
out
:
bwd_out
)
{
for
(
auto
&
out
:
bwd_out
s
)
{
out_str
+=
out
;
out_str
+=
out
;
out
+=
" "
;
out
_str
+=
" "
;
}
}
VLOG
(
10
)
<<
out_str
;
VLOG
(
10
)
<<
out_str
;
}
}
grad_op
->
SetInput
(
"X"
,
bwd_in
);
grad_op
->
SetInput
(
"X"
,
bwd_in
s
);
grad_op
->
SetOutput
(
"Out"
,
InputGrad
(
"X"
,
false
)
);
grad_op
->
SetOutput
(
"Out"
,
bwd_outs
);
std
::
vector
<
std
::
unique_ptr
<
framework
::
OpDesc
>>
ret
(
1
);
std
::
vector
<
std
::
unique_ptr
<
framework
::
OpDesc
>>
ret
(
1
);
ret
[
0
]
=
std
::
move
(
grad_op
);
ret
[
0
]
=
std
::
move
(
grad_op
);
...
@@ -210,12 +250,11 @@ class PyFuncOp : public framework::OperatorBase {
...
@@ -210,12 +250,11 @@ class PyFuncOp : public framework::OperatorBase {
outputs
[
i
]
=
out_tensor
;
outputs
[
i
]
=
out_tensor
;
}
}
auto
&
token
=
Attr
<
std
::
string
>
(
"token"
);
auto
callable_id
=
static_cast
<
size_t
>
(
Attr
<
int
>
(
kForwardPythonCallableId
));
auto
handle_idx
=
static_cast
<
size_t
>
(
Attr
<
int
>
(
"handle_idx"
));
auto
*
py_callable
=
GetPythonCallableObject
(
callable_id
);
auto
*
py_callable
=
GetPythonCallableObject
(
handle_idx
);
VLOG
(
10
)
<<
"Call py_func_op with id "
<<
callable_id
<<
": "
VLOG
(
10
)
<<
"Call py_func_op with token "
<<
token
<<
", and handle_idx "
<<
PythonObjectToString
(
*
py_callable
);
<<
handle_idx
;
CallPythonFunc
(
py_callable
,
inputs
,
&
outputs
);
CallPythonFunc
(
py_callable
,
token
,
inputs
,
&
outputs
);
}
}
};
};
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
8b9d33fa
...
@@ -9087,104 +9087,140 @@ def get_tensor_from_selected_rows(x, name=None):
...
@@ -9087,104 +9087,140 @@ def get_tensor_from_selected_rows(x, name=None):
return
out
return
out
@
templatedoc
()
class
PyFuncWrapper
(
object
):
def
py_func
(
func
,
x
,
out
,
backward_func
=
None
):
_register_funcs
=
[]
"""
"""
def
__init__
(
self
,
func
):
if
func
is
None
or
not
hasattr
(
func
,
'__call__'
):
class
PyFuncRegister
(
object
):
raise
TypeError
(
'func must be a Python function'
)
_main_program_to_register
=
dict
()
self
.
_func
=
func
@
classmethod
# find named args using reflection
def
get_instance
(
cls
,
prog
):
self
.
_named_args
=
inspect
.
getargspec
(
self
.
_func
)[
0
]
if
not
isinstance
(
prog
,
Program
):
self
.
_id
=
core
.
append_python_callable_object_and_return_id
(
self
)
raise
TypeError
(
"prog must be type of Program"
)
'''
Why record self here?
ret
=
cls
.
_main_program_to_register
.
get
(
prog
,
None
)
if
ret
is
None
:
1. For debug usage. Users can call
ret
=
PyFuncRegister
()
:code:`py_func.registered_func(idx)` method
ret
.
_idx
=
core
.
append_python_callable_object_and_return_id
(
ret
)
to find the registered function coresponding
ret
.
_token_func_dict
=
dict
()
to :code:`idx`.
ret
.
_func_token_dict
=
dict
()
cls
.
_main_program_to_register
[
prog
]
=
ret
2. For increasing reference count of self.
It seems that to release Python object
return
ret
whose reference count is 1 would cause
segmentation fault error in C++ side.
@
property
May be lack of Python GC in C++ side?
def
handle_idx
(
self
):
'''
return
self
.
_idx
PyFuncWrapper
.
_register_funcs
.
append
(
self
)
def
unique_token
(
self
,
func
):
@
classmethod
return
self
.
_register_func
(
func
)
def
registered_func
(
cls
,
idx
):
return
cls
.
_register_funcs
[
idx
].
_func
def
_register_func
(
self
,
func
):
if
func
is
None
:
@
classmethod
raise
ValueError
(
"func cannot be None"
)
def
registered_func_num
(
cls
):
return
len
(
cls
.
_register_funcs
)
token
=
self
.
_func_token_dict
.
get
(
func
,
None
)
if
token
is
not
None
:
@
property
return
token
def
id
(
self
):
return
self
.
_id
token
=
unique_name
.
generate
(
'py_func_op_token'
)
self
.
_token_func_dict
[
token
]
=
func
def
__call__
(
self
,
*
args
):
self
.
_func_token_dict
[
func
]
=
token
kwargs
=
dict
()
return
token
idx
=
0
for
arg
in
self
.
_named_args
:
def
__call__
(
self
,
token
,
*
args
):
kwargs
[
arg
]
=
args
[
idx
]
func
=
self
.
_token_func_dict
.
get
(
token
,
None
)
idx
+=
1
if
func
is
None
:
raise
ValueError
(
"func has not been registered"
)
ret0
=
self
.
_func
(
*
args
[
idx
:],
**
kwargs
)
if
ret0
is
None
:
arg_list
=
inspect
.
getargspec
(
func
)
return
None
kwargs
=
dict
()
idx
=
0
if
not
isinstance
(
ret0
,
(
list
,
tuple
)):
for
arg
in
arg_list
[
0
]:
ret0
=
(
ret0
,
)
kwargs
[
arg
]
=
args
[
idx
]
idx
+=
1
ret
=
[]
for
i
in
six
.
moves
.
range
(
len
(
ret0
)):
args
=
args
[
idx
:]
if
ret0
[
i
]
is
None
:
ret0
=
func
(
*
args
,
**
kwargs
)
ret
.
append
(
None
)
if
ret0
is
None
:
continue
return
None
if
isinstance
(
ret0
[
i
],
core
.
LoDTensor
):
if
not
isinstance
(
ret0
,
(
list
,
tuple
)):
ret
.
append
(
ret0
[
i
])
ret0
=
(
ret0
,
)
continue
ret
=
[]
if
isinstance
(
ret0
[
i
],
np
.
ndarray
):
for
i
in
six
.
moves
.
range
(
len
(
ret0
)):
r
=
ret0
[
i
]
if
ret0
[
i
]
is
None
:
else
:
ret
.
append
(
None
)
r
=
np
.
array
(
ret0
[
i
])
continue
if
isinstance
(
ret0
[
i
],
core
.
LoDTensor
):
ret
.
append
(
ret0
[
i
])
continue
if
isinstance
(
ret0
[
i
],
np
.
ndarray
):
t
=
core
.
LoDTensor
()
r
=
ret0
[
i
]
t
.
set
(
r
,
core
.
CPUPlace
())
else
:
ret
.
append
(
t
)
r
=
np
.
array
(
ret0
[
i
])
t
=
core
.
LoDTensor
()
return
tuple
(
ret
)
t
.
set
(
r
,
core
.
CPUPlace
())
ret
.
append
(
t
)
return
tuple
(
ret
)
@
templatedoc
()
def
py_func
(
func
,
x
,
out
,
backward_func
=
None
,
skip_vars_in_backward_input
=
None
):
"""
PyFunc Operator.
User can use :code:`py_func` to register operators in Python side.
The inputs of :code:`func` is :code:`LoDTensor` and outputs can be
numpy array or :code:`LoDTensor`. Paddle would call the registered
:code:`func` in forward part, and call :code:`backward_func` in
backward part (if :code:`backward_func` is not None).
User should set the right data type and shape of :code:`out` before
calling this function. However, data types and shapes of gradients of
:code:`out` and :code:`x` would be infered automatically.
The orders of inputs of :code:`backward_func` would be: forward input
:code:`x`, forward output :code:`out` and backward input gradient of
:code:`out`. If some variables of :code:`out` have no gradient, the input
tensor would be None in Python side. If some variables of :code:`in` have
no gradient, users should return None.
Args:
func (callable): forward Python function.
x (Variable|list(Variable)|tuple(Variable)): inputs of :code:`func`.
out (Variable|list(Variable)|tuple(Variable)): outputs of :code:`func`.
Paddle cannot infer shapes and data types of :code:`out`. Users
should create :code:`out` beforehand.
backward_func (callable|None): backward Python function.
None means no backward. Default None.
skip_vars_in_backward_input (Variable|list(Variable)|tuple(Variable)):
Variables that are not needed in :code:`backward_func` inputs.
These variables must be any of :code:`x` and :code:`out`.
If set, these vars would not be inputs of :code:`backward_func`,
Only useful when :code:`backward_func` is not None. Default None.
Returns:
out (Variable|list(Variable)|tuple(Variable)): input :code:`out`
"""
helper
=
LayerHelper
(
'py_func'
,
**
locals
())
helper
=
LayerHelper
(
'py_func'
,
**
locals
())
if
isinstance
(
x
,
Variable
):
if
x
is
None
:
x
=
[]
elif
isinstance
(
x
,
Variable
):
x
=
[
x
]
x
=
[
x
]
elif
not
isinstance
(
x
,
(
list
,
tuple
)):
raise
TypeError
(
'Input must be Variable/list(Variable)/tuple(Variable)'
)
if
isinstance
(
out
,
Variable
):
if
out
is
None
:
out_list
=
[]
elif
isinstance
(
out
,
Variable
):
out_list
=
[
out
]
out_list
=
[
out
]
el
se
:
el
if
isinstance
(
out
,
(
list
,
tuple
))
:
out_list
=
out
out_list
=
out
else
:
raise
TypeError
(
'Output must be Variable/list(Variable)/tuple(Variable)'
)
if
func
is
None
or
not
hasattr
(
func
,
'__call__'
):
fwd_func_id
=
PyFuncWrapper
(
func
).
id
raise
TypeError
(
'Input func must be a function'
)
bwd_func_id
=
PyFuncWrapper
(
backward_func
).
id
if
backward_func
is
not
None
else
-
1
if
backward_func
is
not
None
and
not
hasattr
(
backward_func
,
'__call__'
):
raise
TypeError
(
'Input backward_func must be a function'
)
for
each_out
in
out_list
:
for
each_out
in
out_list
:
if
len
(
each_out
.
shape
)
==
0
:
if
len
(
each_out
.
shape
)
==
0
:
...
@@ -9192,18 +9228,34 @@ def py_func(func, x, out, backward_func=None):
...
@@ -9192,18 +9228,34 @@ def py_func(func, x, out, backward_func=None):
'Output shapes of py_func op should be provided by users manually'
'Output shapes of py_func op should be provided by users manually'
)
)
py_func_reg
=
PyFuncRegister
.
get_instance
(
helper
.
main_program
)
backward_skip_vars
=
set
()
forward_token
=
py_func_reg
.
unique_token
(
func
)
if
backward_func
is
not
None
and
skip_vars_in_backward_input
is
not
None
:
backward_token
=
py_func_reg
.
unique_token
(
if
isinstance
(
skip_vars_in_backward_input
,
Variable
):
backward_func
)
if
backward_func
is
not
None
else
''
skip_vars_in_backward_input
=
[
skip_vars_in_backward_input
]
fwd_in_out
=
[
v
.
name
for
v
in
x
]
fwd_in_out
.
extend
([
v
.
name
for
v
in
out_list
])
fwd_in_out
=
set
(
fwd_in_out
)
backward_skip_vars
=
set
()
for
v
in
skip_vars_in_backward_input
:
if
not
v
.
name
in
fwd_in_out
:
raise
ValueError
(
'Variable {} is not found in forward inputs and outputs'
.
format
(
v
.
name
))
backward_skip_vars
.
add
(
v
.
name
)
helper
.
append_op
(
helper
.
append_op
(
type
=
'py_func'
,
type
=
'py_func'
,
inputs
=
{
'X'
:
x
},
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out_list
},
outputs
=
{
'Out'
:
out_list
},
attrs
=
{
attrs
=
{
'
handle_idx'
:
py_func_reg
.
handle_idx
,
'
forward_callable_id'
:
fwd_func_id
,
'
token'
:
forward_token
,
'
backward_callable_id'
:
bwd_func_id
,
'backward_
token'
:
backward_token
'backward_
skip_vars'
:
list
(
backward_skip_vars
)
})
})
return
out
return
out
# For debug usage
py_func
.
registered_func
=
PyFuncWrapper
.
registered_func
py_func
.
registered_func_num
=
PyFuncWrapper
.
registered_func_num
python/paddle/fluid/tests/unittests/test_py_func_op.py
0 → 100644
浏览文件 @
8b9d33fa
# 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
paddle.fluid
as
fluid
import
paddle
import
unittest
import
six
import
numpy
as
np
def
tanh
(
x
):
return
np
.
tanh
(
x
)
def
tanh_grad
(
y
,
dy
):
return
np
.
array
(
dy
)
*
(
1
-
np
.
square
(
np
.
array
(
y
)))
def
cross_entropy
(
logits
,
labels
):
logits
=
np
.
array
(
logits
)
labels
=
np
.
array
(
labels
)
M
=
logits
.
shape
[
0
]
N
=
logits
.
shape
[
1
]
ret
=
np
.
ndarray
([
M
,
1
]).
astype
(
logits
.
dtype
)
for
idx
in
six
.
moves
.
range
(
M
):
ret
[
idx
][
0
]
=
-
np
.
log
(
logits
[
idx
][
labels
[
idx
][
0
]])
return
ret
def
cross_entropy_grad
(
logits
,
labels
,
bwd_dout
):
logits
=
np
.
array
(
logits
)
labels
=
np
.
array
(
labels
)
bwd_dout
=
np
.
array
(
bwd_dout
)
M
=
logits
.
shape
[
0
]
N
=
logits
.
shape
[
1
]
dlogits
=
np
.
zeros
([
M
,
N
]).
astype
(
logits
.
dtype
)
for
idx
in
six
.
moves
.
range
(
M
):
dlogits
[
idx
][
labels
[
idx
][
0
]]
=
-
bwd_dout
[
idx
]
/
logits
[
idx
][
labels
[
idx
][
0
]]
return
dlogits
,
None
def
simple_fc_net
(
img
,
label
,
use_py_func_op
):
hidden
=
img
for
idx
in
range
(
4
):
hidden
=
fluid
.
layers
.
fc
(
hidden
,
size
=
200
,
bias_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
1.0
)))
if
use_py_func_op
:
hidden
=
fluid
.
layers
.
tanh
(
hidden
)
else
:
new_hidden
=
fluid
.
default_main_program
().
current_block
(
).
create_var
(
name
=
'hidden_{}'
.
format
(
idx
),
dtype
=
'float32'
,
shape
=
hidden
.
shape
)
hidden
=
fluid
.
layers
.
py_func
(
func
=
tanh
,
x
=
hidden
,
out
=
new_hidden
,
backward_func
=
tanh_grad
,
skip_vars_in_backward_input
=
hidden
)
prediction
=
fluid
.
layers
.
fc
(
hidden
,
size
=
10
,
act
=
'softmax'
)
if
not
use_py_func_op
:
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
else
:
loss
=
fluid
.
default_main_program
().
current_block
().
create_var
(
name
=
'loss'
,
dtype
=
'float32'
,
shape
=
[
-
1
,
1
])
fluid
.
layers
.
py_func
(
func
=
cross_entropy
,
x
=
[
prediction
,
label
],
out
=
loss
,
backward_func
=
cross_entropy_grad
,
skip_vars_in_backward_input
=
loss
)
loss
=
fluid
.
layers
.
mean
(
loss
)
return
loss
def
reader
():
for
_
in
six
.
moves
.
range
(
100
):
yield
np
.
random
.
random
([
784
]),
np
.
random
.
random_integers
(
size
=
[
1
],
low
=
0
,
high
=
9
)
def
test_main
(
use_cuda
,
use_py_func_op
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
None
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
with
fluid
.
scope_guard
(
fluid
.
core
.
Scope
()):
fluid
.
default_main_program
().
random_seed
=
1
fluid
.
default_startup_program
().
random_seed
=
1
np
.
random
.
seed
(
1
)
img
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
784
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
loss
=
simple_fc_net
(
img
,
label
,
use_py_func_op
)
optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
1e-3
)
optimizer
.
minimize
(
loss
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
img
,
label
],
place
=
place
)
r
=
paddle
.
batch
(
reader
,
batch_size
=
10
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
ret
=
[]
for
epoch_id
in
six
.
moves
.
range
(
2
):
for
d
in
r
():
L
,
=
exe
.
run
(
feed
=
feeder
.
feed
(
d
),
fetch_list
=
[
loss
])
ret
.
append
(
L
[
0
])
return
np
.
array
(
ret
)
class
TestPyFuncOp
(
unittest
.
TestCase
):
def
test_loss_diff
(
self
):
losses
=
[]
for
use_cuda
in
[
True
,
False
]:
for
use_py_func_op
in
[
True
,
False
]:
L
=
test_main
(
use_cuda
,
use_py_func_op
)
if
L
is
not
None
:
losses
.
append
(
L
)
for
idx
in
six
.
moves
.
range
(
len
(
losses
)
-
1
):
max_diff
=
np
.
max
(
np
.
abs
(
losses
[
idx
]
-
losses
[
0
]))
self
.
assertAlmostEqual
(
max_diff
,
0
,
delta
=
1e-3
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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