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体验新版 GitCode,发现更多精彩内容 >>
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579449b0
编写于
1月 22, 2018
作者:
D
dangqingqing
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电子邮件补丁
差异文件
Update comments and revert pybind11.
上级
d2a70243
变更
4
隐藏空白更改
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并排
Showing
4 changed file
with
83 addition
and
26 deletion
+83
-26
cmake/external/pybind11.cmake
cmake/external/pybind11.cmake
+1
-1
paddle/pybind/protobuf.cc
paddle/pybind/protobuf.cc
+60
-10
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+3
-5
python/paddle/v2/fluid/profiler.py
python/paddle/v2/fluid/profiler.py
+19
-10
未找到文件。
cmake/external/pybind11.cmake
浏览文件 @
579449b0
...
...
@@ -26,7 +26,7 @@ ExternalProject_Add(
extern_pybind
${
EXTERNAL_PROJECT_LOG_ARGS
}
GIT_REPOSITORY
"https://github.com/pybind/pybind11.git"
GIT_TAG
"v2.
2
.1"
GIT_TAG
"v2.
1
.1"
PREFIX
${
PYBIND_SOURCE_DIR
}
UPDATE_COMMAND
""
CONFIGURE_COMMAND
""
...
...
paddle/pybind/protobuf.cc
浏览文件 @
579449b0
...
...
@@ -21,24 +21,74 @@ limitations under the License. */
#include "paddle/framework/program_desc.h"
#include "paddle/framework/var_desc.h"
using
boost
::
variant
;
// Cast boost::variant for PyBind.
// Copy from
// https://github.com/pybind/pybind11/issues/576#issuecomment-269563199
namespace
pybind11
{
namespace
detail
{
// Can be replaced by a generic lambda in C++14
struct
variant_caster_visitor
:
public
boost
::
static_visitor
<
handle
>
{
return_value_policy
policy
;
handle
parent
;
variant_caster_visitor
(
return_value_policy
policy
,
handle
parent
)
:
policy
(
policy
),
parent
(
parent
)
{}
template
<
class
T
>
handle
operator
()(
T
const
&
src
)
const
{
return
make_caster
<
T
>::
cast
(
src
,
policy
,
parent
);
}
};
template
<
class
Variant
>
struct
variant_caster
;
template
<
template
<
class
...
>
class
V
,
class
...
Ts
>
struct
variant_caster
<
V
<
Ts
...
>>
{
using
Type
=
V
<
Ts
...
>
;
template
<
typename
T
>
typename
std
::
enable_if
<
!
std
::
is_same
<
T
,
boost
::
detail
::
variant
::
void_
>::
value
,
bool
>::
type
try_load
(
handle
src
,
bool
convert
)
{
auto
caster
=
make_caster
<
T
>
();
if
(
!
load_success_
&&
caster
.
load
(
src
,
convert
))
{
load_success_
=
true
;
value
=
cast_op
<
T
>
(
caster
);
return
true
;
}
return
false
;
}
template
<
typename
T
>
typename
std
::
enable_if
<
std
::
is_same
<
T
,
boost
::
detail
::
variant
::
void_
>::
value
,
bool
>::
type
try_load
(
handle
src
,
bool
convert
)
{
return
false
;
}
bool
load
(
handle
src
,
bool
convert
)
{
auto
unused
=
{
false
,
try_load
<
Ts
>
(
src
,
convert
)...};
(
void
)(
unused
);
return
load_success_
;
}
static
handle
cast
(
Type
const
&
src
,
return_value_policy
policy
,
handle
parent
)
{
variant_caster_visitor
visitor
(
policy
,
parent
);
return
boost
::
apply_visitor
(
visitor
,
src
);
}
PYBIND11_TYPE_CASTER
(
Type
,
_
(
"Variant"
));
bool
load_success_
{
false
};
};
// Add specialization for concrete variant type
template
<
class
...
Args
>
struct
type_caster
<
boost
::
variant
<
Args
...
>>
:
variant_caster
<
boost
::
variant
<
Args
...
>>
{};
template
<
>
struct
visit_helper
<
boost
::
variant
>
{
template
<
typename
...
Args
>
static
auto
call
(
Args
&&
...
args
)
->
decltype
(
boost
::
apply_visitor
(
args
...))
{
return
boost
::
apply_visitor
(
args
...);
}
};
}
// namespace detail
}
// namespace pybind11
...
...
paddle/pybind/pybind.cc
浏览文件 @
579449b0
...
...
@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/pybind/protobuf.h"
#include "pybind11/iostream.h"
#include <mutex> // for call_once
#include <unordered_map>
...
...
@@ -62,8 +61,8 @@ bool IsCompileGPU() {
#endif
}
PYBIND11_
MODULE
(
core
,
m
)
{
m
.
doc
()
=
"C++ core of PaddlePaddle"
;
PYBIND11_
PLUGIN
(
core
)
{
py
::
module
m
(
"core"
,
"C++ core of PaddlePaddle"
)
;
// using framework in this function. Since it is inside a function, it will
// not cause namespace pollution.
...
...
@@ -501,8 +500,7 @@ All parameter, weight, gradient are variables in Paddle.
m
.
def
(
"enable_profiler"
,
platform
::
EnableProfiler
);
m
.
def
(
"disable_profiler"
,
platform
::
DisableProfiler
);
m
.
def
(
"reset_profiler"
,
platform
::
ResetProfiler
);
py
::
add_ostream_redirect
(
m
,
"ostream_redirect"
);
return
m
.
ptr
();
}
}
// namespace pybind
}
// namespace paddle
python/paddle/v2/fluid/profiler.py
浏览文件 @
579449b0
...
...
@@ -52,22 +52,30 @@ def cuda_profiler(output_file, output_mode=None, config=None):
def
reset_profiler
():
"""The profiler clear interface.
reset_profiler will clear the previous time record.
"""
core
.
reset_profiler
()
@
contextmanager
def
profiler
(
state
,
sorted_key
=
None
):
"""The profiler interface.
Different from cuda_profiler, this fuction can be used to profile both CPU
and GPU program.
Different from cuda_profiler, this profiler can be used to profile both CPU
and GPU program. By defalut, it records the CPU and GPU operator kernels,
if you want to profile other program, you can refer the profiling tutorial
to add more records.
Args:
state (string) : The profiler state, It should be 'CPU' or 'GPU'.
sorted_key (string) : If None, the profiler results will be printed
without sorting. Otherwise, the profiler results will be sorted
by the this flag. This flag should be one of 'calls', 'total',
'max', 'min' or 'ave'.
The `calls` means sorting by the calling counter.
state (string) : The profiling state, It should be 'CPU' or 'GPU'.
Although users may define CPUPlace or CUDAPlace when using Fluid,
the profiler doesn't get the state based on this Place. Since the
implementation is an independent part from the Fluid.
sorted_key (string) : If None, the profiling results will be printed
in the order of first end time of events. Otherwise, the profiling
results will be sorted by the this flag. This flag should be one
of 'calls', 'total', 'max', 'min' or 'ave'.
The `calls` means sorting by the number of calls.
The `total` means sorting by the total execution time.
The `max` means sorting by the maximum execution time.
The `min` means sorting by the minimum execution time.
...
...
@@ -92,5 +100,6 @@ def profiler(state, sorted_key=None):
'min'
:
core
.
EventSortingKey
.
kMin
,
'ave'
:
core
.
EventSortingKey
.
kAve
,
}
with
core
.
ostream_redirect
(
stdout
=
True
,
stderr
=
True
):
core
.
disable_profiler
(
key_map
[
sorted_key
])
# TODO(qingqing) : redirect C++ ostream to Python stream.
# with core.ostream_redirect(stdout=True, stderr=True):
core
.
disable_profiler
(
key_map
[
sorted_key
])
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