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体验新版 GitCode,发现更多精彩内容 >>
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21053c16
编写于
11月 29, 2017
作者:
Q
qingqing01
提交者:
GitHub
11月 29, 2017
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差异文件
Merge pull request #5954 from qingqing01/nvprof
Add CUDA profiler tools in new framework.
上级
e5198e17
696b0253
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
132 addition
and
0 deletion
+132
-0
paddle/platform/cuda_profiler.h
paddle/platform/cuda_profiler.h
+53
-0
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+5
-0
python/paddle/v2/fluid/profiler.py
python/paddle/v2/fluid/profiler.py
+46
-0
python/paddle/v2/fluid/tests/test_profiler.py
python/paddle/v2/fluid/tests/test_profiler.py
+28
-0
未找到文件。
paddle/platform/cuda_profiler.h
0 → 100644
浏览文件 @
21053c16
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include <cuda_profiler_api.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
namespace
paddle
{
namespace
platform
{
void
CudaProfilerInit
(
std
::
string
output_file
,
std
::
string
output_mode
,
std
::
vector
<
std
::
string
>
config_flags
)
{
std
::
array
<
char
,
128
>
buf
;
std
::
string
tmpl
=
"/tmp/cuda_profile_config.XXXXXX"
;
PADDLE_ENFORCE_LT
(
tmpl
.
size
(),
buf
.
size
());
memcpy
(
buf
.
data
(),
tmpl
.
data
(),
tmpl
.
size
());
auto
result
=
mktemp
(
buf
.
data
());
PADDLE_ENFORCE
(
strlen
(
result
)
!=
0
);
std
::
string
config_file
=
result
;
{
std
::
ofstream
ofs
(
config_file
,
std
::
ios
::
out
|
std
::
ios
::
trunc
);
PADDLE_ENFORCE
(
ofs
.
is_open
(),
"ofstream: "
,
ofs
.
rdstate
());
for
(
const
auto
&
line
:
config_flags
)
{
ofs
<<
line
<<
std
::
endl
;
}
}
PADDLE_ENFORCE
(
output_mode
==
"kvp"
||
output_mode
==
"csv"
);
cudaOutputMode_t
mode
=
output_mode
==
"csv"
?
cudaCSV
:
cudaKeyValuePair
;
PADDLE_ENFORCE
(
cudaProfilerInitialize
(
config_file
.
c_str
(),
output_file
.
c_str
(),
mode
));
}
void
CudaProfilerStart
()
{
PADDLE_ENFORCE
(
cudaProfilerStart
());
}
void
CudaProfilerStop
()
{
PADDLE_ENFORCE
(
cudaProfilerStop
());
}
}
// namespace platform
}
// namespace paddle
paddle/pybind/pybind.cc
浏览文件 @
21053c16
...
...
@@ -37,6 +37,7 @@ limitations under the License. */
#ifdef PADDLE_WITH_CUDA
#include "paddle/operators/nccl/nccl_gpu_common.h"
#include "paddle/platform/cuda_profiler.h"
#include "paddle/platform/gpu_info.h"
#endif
...
...
@@ -460,6 +461,10 @@ All parameter, weight, gradient are variables in Paddle.
m
.
def
(
"op_support_gpu"
,
OpSupportGPU
);
#ifdef PADDLE_WITH_CUDA
m
.
def
(
"get_cuda_device_count"
,
platform
::
GetCUDADeviceCount
);
m
.
def
(
"nvprof_init"
,
platform
::
CudaProfilerInit
);
m
.
def
(
"nvprof_start"
,
platform
::
CudaProfilerStart
);
m
.
def
(
"nvprof_stop"
,
platform
::
CudaProfilerStop
);
#endif
return
m
.
ptr
();
...
...
python/paddle/v2/fluid/profiler.py
0 → 100644
浏览文件 @
21053c16
import
paddle.v2.fluid.core
as
core
from
contextlib
import
contextmanager
__all__
=
[
'CudaProfiler'
]
NVPROF_CONFIG
=
[
"gpustarttimestamp"
,
"gpuendtimestamp"
,
"gridsize3d"
,
"threadblocksize"
,
"streamid"
,
"enableonstart 0"
,
"conckerneltrace"
,
]
@
contextmanager
def
cuda_profiler
(
output_file
,
output_mode
=
None
,
config
=
None
):
"""The CUDA profiler.
This fuctions is used to profile CUDA program by CUDA runtime application
programming interface. The profiling result will be written into
`output_file` with Key-Value pair format or Comma separated values format.
The user can set the output mode by `output_mode` argument and set the
counters/options for profiling by `config` argument. The default config
caontains 'gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'.
Args:
output_file (string) : The output file name, the result will be
written into this file.
output_mode (string) : The output mode has Key-Value pair format and
Comma separated values format. It should be 'kv' or 'csv'.
config (string) : The profiler options and counters can refer to
"Compute Command Line Profiler User Guide".
"""
if
output_mode
is
None
:
output_mode
=
'csv'
if
output_mode
not
in
[
'kv'
,
'csv'
]:
raise
ValueError
(
"The output mode must be 'key-value' or 'csv'."
)
config
=
NVPROF_CONFIG
if
config
is
None
else
config
core
.
nvprof_init
(
output_file
,
output_mode
,
config
)
# Enables profiler collection by the active CUDA profiling tool.
core
.
nvprof_start
()
yield
# Disables profiler collection.
core
.
nvprof_stop
()
python/paddle/v2/fluid/tests/test_profiler.py
0 → 100644
浏览文件 @
21053c16
import
unittest
import
numpy
as
np
import
paddle.v2.fluid
as
fluid
import
paddle.v2.fluid.profiler
as
profiler
import
paddle.v2.fluid.layers
as
layers
class
TestProfiler
(
unittest
.
TestCase
):
def
test_nvprof
(
self
):
if
not
fluid
.
core
.
is_compile_gpu
():
return
epoc
=
8
dshape
=
[
4
,
3
,
28
,
28
]
data
=
layers
.
data
(
name
=
'data'
,
shape
=
[
3
,
28
,
28
],
dtype
=
'float32'
)
conv
=
layers
.
conv2d
(
data
,
20
,
3
,
stride
=
[
1
,
1
],
padding
=
[
1
,
1
])
place
=
fluid
.
GPUPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
with
profiler
.
cuda_profiler
(
"cuda_profiler.txt"
,
'csv'
)
as
nvprof
:
for
i
in
range
(
epoc
):
input
=
np
.
random
.
random
(
dshape
).
astype
(
"float32"
)
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
'data'
:
input
})
if
__name__
==
'__main__'
:
unittest
.
main
()
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