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体验新版 GitCode,发现更多精彩内容 >>
提交
c7762da3
编写于
9月 14, 2016
作者:
Y
Yu Yang
提交者:
GitHub
9月 14, 2016
浏览文件
操作
浏览文件
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差异文件
Merge branch 'master' into merge_icode
上级
9a9de924
487dc670
变更
20
隐藏空白更改
内联
并排
Showing
20 changed file
with
483 addition
and
127 deletion
+483
-127
doc/ui/api/trainer_config_helpers/layers.rst
doc/ui/api/trainer_config_helpers/layers.rst
+9
-9
paddle/cuda/src/hl_cuda_cudnn.cc
paddle/cuda/src/hl_cuda_cudnn.cc
+7
-9
paddle/gserver/gradientmachines/NeuralNetwork.cpp
paddle/gserver/gradientmachines/NeuralNetwork.cpp
+1
-0
paddle/gserver/layers/ConvexCombinationLayer.cpp
paddle/gserver/layers/ConvexCombinationLayer.cpp
+9
-7
paddle/gserver/layers/CosSimLayer.cpp
paddle/gserver/layers/CosSimLayer.cpp
+2
-2
paddle/gserver/layers/CosSimLayer.h
paddle/gserver/layers/CosSimLayer.h
+1
-3
paddle/gserver/layers/CudnnBatchNormLayer.cpp
paddle/gserver/layers/CudnnBatchNormLayer.cpp
+0
-18
paddle/utils/CustomStackTrace.cpp
paddle/utils/CustomStackTrace.cpp
+35
-0
paddle/utils/CustomStackTrace.h
paddle/utils/CustomStackTrace.h
+128
-36
paddle/utils/Util.cpp
paddle/utils/Util.cpp
+1
-7
paddle/utils/tests/CMakeLists.txt
paddle/utils/tests/CMakeLists.txt
+10
-0
paddle/utils/tests/test_CustomStackTrace.cpp
paddle/utils/tests/test_CustomStackTrace.cpp
+95
-0
paddle/utils/tests/test_CustomStackTracePrint.cpp
paddle/utils/tests/test_CustomStackTracePrint.cpp
+29
-0
paddle/utils/tests/test_CustomStackTracePrint.sh
paddle/utils/tests/test_CustomStackTracePrint.sh
+15
-0
python/CMakeLists.txt
python/CMakeLists.txt
+2
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+9
-1
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+63
-35
python/paddle/trainer_config_helpers/tests/CMakeLists.txt
python/paddle/trainer_config_helpers/tests/CMakeLists.txt
+5
-0
python/paddle/trainer_config_helpers/tests/layers_test.py
python/paddle/trainer_config_helpers/tests/layers_test.py
+19
-0
python/paddle/trainer_config_helpers/tests/layers_test_config.py
...paddle/trainer_config_helpers/tests/layers_test_config.py
+43
-0
未找到文件。
doc/ui/api/trainer_config_helpers/layers.rst
浏览文件 @
c7762da3
...
...
@@ -245,10 +245,10 @@ addto_layer
:members: addto_layer
:noindex:
convex
_comb_layer
linear
_comb_layer
-----------------
.. automodule:: paddle.trainer_config_helpers.layers
:members:
convex
_comb_layer
:members:
linear
_comb_layer
:noindex:
interpolation_layer
...
...
@@ -280,7 +280,13 @@ tensor_layer
.. automodule:: paddle.trainer_config_helpers.layers
:members: tensor_layer
:noindex:
cos_sim
-------
.. automodule:: paddle.trainer_config_helpers.layers
:members: cos_sim
:noindex:
trans_layer
------------
.. automodule:: paddle.trainer_config_helpers.layers
...
...
@@ -341,12 +347,6 @@ rank_cost
:members: rank_cost
:noindex:
cos_sim
-------
.. automodule:: paddle.trainer_config_helpers.layers
:members: cos_sim
:noindex:
crf_layer
-----------------
.. automodule:: paddle.trainer_config_helpers.layers
...
...
paddle/cuda/src/hl_cuda_cudnn.cc
浏览文件 @
c7762da3
...
...
@@ -150,7 +150,7 @@ CUDNN_DNN_ROUTINE_EACH_AFTER_R3(DYNAMIC_LOAD_CUDNN_WRAP)
// APIs available after R4:
#if CUDNN_VERSION >= 400
0
#if CUDNN_VERSION >= 400
7
#define CUDNN_DNN_ROUTINE_EACH_AFTER_R4(__macro) \
__macro(cudnnBatchNormalizationForwardTraining) \
__macro(cudnnBatchNormalizationForwardInference) \
...
...
@@ -999,7 +999,7 @@ void hl_batch_norm_forward_training(hl_tensor_descriptor inputDesc,
double
epsilon
,
real
*
savedMean
,
real
*
savedVar
)
{
#if CUDNN_VERSION >= 400
0
#if CUDNN_VERSION >= 400
7
if
((
NULL
!=
runningMean
&&
NULL
==
runningInvVar
)
||
(
NULL
==
runningMean
&&
NULL
!=
runningInvVar
))
{
LOG
(
FATAL
)
<<
"runningMean and runningInvVar can be NULL "
...
...
@@ -1024,7 +1024,7 @@ void hl_batch_norm_forward_training(hl_tensor_descriptor inputDesc,
CHECK_SYNC
(
"hl_batch_norm_forward_training failed"
);
#else
LOG
(
FATAL
)
<<
"CudnnBatchNorm requires cudnn version >= 400
0
. "
LOG
(
FATAL
)
<<
"CudnnBatchNorm requires cudnn version >= 400
7
. "
<<
"But cudnn lib version is "
<<
g_cudnn_lib_version
;
#endif
}
...
...
@@ -1039,7 +1039,7 @@ void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
real
*
estimatedMean
,
real
*
estimatedInvVar
,
double
epsilon
)
{
#if CUDNN_VERSION >= 400
0
#if CUDNN_VERSION >= 400
7
cudnnTensorDescriptor_t
xDesc
=
GET_TENSOR_DESCRIPTOR
(
inputDesc
);
cudnnTensorDescriptor_t
yDesc
=
GET_TENSOR_DESCRIPTOR
(
outputDesc
);
cudnnTensorDescriptor_t
bnDesc
=
GET_TENSOR_DESCRIPTOR
(
bnParamDesc
);
...
...
@@ -1053,7 +1053,7 @@ void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
CHECK_SYNC
(
"hl_batch_norm_forward_inference failed"
);
#else
LOG
(
FATAL
)
<<
"CudnnBatchNorm requires cudnn version >= 400
0
. "
LOG
(
FATAL
)
<<
"CudnnBatchNorm requires cudnn version >= 400
7
. "
<<
"But cudnn lib version is "
<<
g_cudnn_lib_version
;
#endif
}
...
...
@@ -1071,7 +1071,7 @@ void hl_batch_norm_backward(hl_tensor_descriptor inputDesc,
double
epsilon
,
real
*
savedMean
,
real
*
savedInvVar
)
{
#if CUDNN_VERSION >= 400
0
#if CUDNN_VERSION >= 400
7
if
((
NULL
!=
savedMean
&&
NULL
==
savedInvVar
)
||
(
NULL
==
savedMean
&&
NULL
!=
savedInvVar
))
{
LOG
(
FATAL
)
<<
"savedMean and savedVar can be NULL "
...
...
@@ -1087,16 +1087,14 @@ void hl_batch_norm_backward(hl_tensor_descriptor inputDesc,
cudnnBatchNormMode_t
mode
=
CUDNN_BATCHNORM_SPATIAL
;
CHECK_CUDNN
(
dynload
::
cudnnBatchNormalizationBackward
(
t_resource
.
cudnn_handle
,
mode
,
&
alpha
,
&
beta
,
#if CUDNN_VERSION >= 5000
&
alpha
,
&
beta
,
#endif
xDesc
,
input
,
dyDesc
,
outGrad
,
dxDesc
,
inGrad
,
bnDesc
,
scale
,
scaleGrad
,
biasGrad
,
epsilon
,
savedMean
,
savedInvVar
));
CHECK_SYNC
(
"hl_batch_norm_backward failed"
);
#else
LOG
(
FATAL
)
<<
"CudnnBatchNorm requires cudnn version >= 400
0
. "
LOG
(
FATAL
)
<<
"CudnnBatchNorm requires cudnn version >= 400
7
. "
<<
"But cudnn lib version is "
<<
g_cudnn_lib_version
;
#endif
}
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @
c7762da3
...
...
@@ -277,6 +277,7 @@ void NeuralNetwork::getState(MachineState& machineState) {
}
void
NeuralNetwork
::
backward
(
const
UpdateCallback
&
callback
)
{
gLayerStackTrace
.
pop
(
""
);
// tell layer trace is during backward.
FOR_EACH_R
(
layer
,
layers_
)
{
REGISTER_TIMER_INFO
(
"BackwardTimer"
,
(
*
layer
)
->
getName
().
c_str
());
if
((
*
layer
)
->
needGradient
())
{
...
...
paddle/gserver/layers/ConvexCombinationLayer.cpp
浏览文件 @
c7762da3
...
...
@@ -21,18 +21,20 @@ limitations under the License. */
namespace
paddle
{
/**
* @brief A layer for
convex weighted average
of vectors,
* @brief A layer for
weighted sum
of vectors,
* which is used in NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND
* TRANSLATE
* - Input: the
first input contains the convex weights (batchSize x weightDim)
,
* and the s
hape of second input is (batchSize x (weightdim*dataDim))
.
* - Output: the s
hape of output is (batchSize x dataDim).
* - Input: the
the size of the first input is weightDim
,
* and the s
ize of the second input is weightdim * dataDim
.
* - Output: the s
izeof the output is dataDim
* \f[
* out
[i][j] = \sum_{j}(in0(i, j
) * in1(i,j + i * dataDim)),
* i = 0,1,...,(
batchSize
-1); j = 0, 1,...,(dataDim-1)
* out
(j) = \sum_{i}(in0(i
) * in1(i,j + i * dataDim)),
* i = 0,1,...,(
weightDim
-1); j = 0, 1,...,(dataDim-1)
* \f]
* Note that the above computation is for one sample. Multiple samples are
* processed in one batch.
*
* The config file api is
convex
_comb_layer.
* The config file api is
linear
_comb_layer.
*/
class
ConvexCombinationLayer
:
public
Layer
{
protected:
...
...
paddle/gserver/layers/CosSimLayer.cpp
浏览文件 @
c7762da3
...
...
@@ -48,7 +48,7 @@ void CosSimLayer::forward(PassType passType) {
REGISTER_TIMER_INFO
(
"CosFwAtvTimer"
,
getName
().
c_str
());
MatrixPtr
prevOut1
=
getInputValue
(
0
);
MatrixPtr
prevOut2
=
getInputValue
(
1
);
outV
->
cosSim
(
*
prevOut1
,
*
prevOut2
,
kCosSimScale_
);
outV
->
cosSim
(
*
prevOut1
,
*
prevOut2
,
config_
.
cos_scale
()
);
}
}
...
...
@@ -59,7 +59,7 @@ void CosSimLayer::backward(const UpdateCallback& callback) {
outG
->
cosSimDerivative
(
*
this
->
getOutputValue
(),
*
getInputValue
(
0
),
*
getInputValue
(
1
),
*
getInputGrad
(
0
),
*
getInputGrad
(
1
),
kCosSimScale_
);
*
getInputGrad
(
1
),
config_
.
cos_scale
()
);
}
}
...
...
paddle/gserver/layers/CosSimLayer.h
浏览文件 @
c7762da3
...
...
@@ -36,7 +36,7 @@ namespace paddle {
class
CosSimLayer
:
public
Layer
{
public:
explicit
CosSimLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
,
kCosSimScale_
(
5.0
f
)
{}
:
Layer
(
config
)
{}
~
CosSimLayer
()
{}
...
...
@@ -44,8 +44,6 @@ public:
void
forward
(
PassType
passType
);
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
);
const
real
kCosSimScale_
;
};
}
// namespace paddle
paddle/gserver/layers/CudnnBatchNormLayer.cpp
浏览文件 @
c7762da3
...
...
@@ -115,29 +115,11 @@ void CudnnBatchNormLayer::backward(const UpdateCallback& callback) {
create
(
tmpBiasGrad_
,
1
,
channels_
,
&
betaGrad
);
}
// because of the different api of cudnn v4 and v5.
if
(
hl_get_cudnn_lib_version
()
<
5000
)
{
if
(
weight_
->
getWGrad
())
{
create
(
tmpWGrad_
,
1
,
channels_
,
&
gammaGrad
);
}
if
(
biases_
&&
biases_
->
getWGrad
())
{
create
(
tmpBiasGrad_
,
1
,
channels_
,
&
betaGrad
);
}
}
hl_batch_norm_backward
(
ioDesc_
,
input
,
ioDesc_
,
outGrad
,
ioDesc_
,
inGrad
,
bnParamDesc_
,
gamma
,
gammaGrad
,
betaGrad
,
EPS
,
savedMean
,
savedInvVar
);
// because of the different api of cudnn v4 and v5.
if
(
hl_get_cudnn_lib_version
()
<
5000
)
{
if
(
weight_
->
getWGrad
()
&&
biases_
->
getWGrad
())
{
weight_
->
getWGrad
()
->
add
(
*
tmpWGrad_
);
biases_
->
getWGrad
()
->
add
(
*
tmpBiasGrad_
);
}
}
{
REGISTER_TIMER_INFO
(
"WeightUpdate"
,
getName
().
c_str
());
biases_
->
getParameterPtr
()
->
incUpdate
(
callback
);
...
...
paddle/utils/CustomStackTrace.cpp
浏览文件 @
c7762da3
...
...
@@ -14,9 +14,44 @@ limitations under the License. */
#include "CustomStackTrace.h"
#include "CommandLineParser.h"
#include <iostream>
P_DEFINE_bool
(
layer_stack_error_only_current_thread
,
true
,
"Dump current thread or whole process layer stack when signal error "
"occurred. true means only dump current thread layer stack"
);
namespace
paddle
{
CustomStackTrace
<
std
::
string
>
gLayerStackTrace
;
static
std
::
mutex
gLayerStackTraceMtx
;
void
installLayerStackTracer
()
{
logging
::
installFailureWriter
([](
const
char
*
data
,
int
sz
)
{
std
::
lock_guard
<
std
::
mutex
>
guard
(
gLayerStackTraceMtx
);
if
(
!
gLayerStackTrace
.
empty
())
{
size_t
curTid
=
-
1UL
;
std
::
hash
<
std
::
thread
::
id
>
hasher
;
gLayerStackTrace
.
dump
([
&
curTid
,
&
hasher
](
std
::
thread
::
id
tid
,
bool
*
isForwarding
,
const
std
::
string
&
layerName
)
{
if
(
curTid
!=
hasher
(
tid
))
{
if
(
curTid
!=
-
1UL
)
{
std
::
cerr
<<
std
::
endl
;
}
curTid
=
hasher
(
tid
);
std
::
cerr
<<
"Thread ["
<<
tid
<<
"] "
;
if
(
isForwarding
)
{
std
::
cerr
<<
(
*
isForwarding
?
"Forwarding "
:
"Backwarding "
);
}
}
std
::
cerr
<<
layerName
<<
", "
;
},
FLAGS_layer_stack_error_only_current_thread
);
std
::
cerr
<<
std
::
endl
;
}
std
::
cerr
.
write
(
data
,
sz
);
});
}
}
// namespace paddle
paddle/utils/CustomStackTrace.h
浏览文件 @
c7762da3
...
...
@@ -15,6 +15,9 @@ limitations under the License. */
#pragma once
#include <stack>
#include <thread>
#include <unordered_map>
#include <functional>
#include "ThreadLocal.h"
...
...
@@ -29,25 +32,18 @@ namespace paddle {
* @code{.cpp}
*
* paddle::CustomStackTrace<std::string> stack;
* PASS_TEST=0;
* for (auto& layer : layers){
* stack.push(layer->getName());
* layer->forward(
passType
);
* layer->forward();
* }
* for (auto& layer : layers){
*
* stack.pop(""); // mark under pop stage.
*
* for (auto it = layers.rbegin(); it != layers.rend(); ++it){
* auto& layer = *it;
* layer->backward(passType);
* stack.pop(layer->getName());
* }
*
* if(passType == PASS_TEST) {
* stack.clear();
* }
* else {
* stack.dump([](const std::string& layername){
* LOG(INFO) << "LayerName: " << layername;
* })
* }
*
*
* @endcode
*/
...
...
@@ -55,45 +51,141 @@ template <typename T>
class
CustomStackTrace
{
public:
/**
* @brief Pop out an item from the top of the stack
. For safety the item
*
will be poped should equal to ip
.
* @brief Pop out an item from the top of the stack
if item == top.
*
Else, just set status to popping
.
*/
void
pop
(
const
T
&
ip
)
{
auto
&
p
=
*
logstack_
;
CHECK_EQ
(
ip
,
p
.
top
());
p
.
pop
();
void
pop
(
const
T
&
item
)
{
pushing
()
=
false
;
auto
&
s
=
this
->
stack
();
if
(
item
==
s
.
top
())
{
s
.
pop
();
}
}
/**
* @brief Empty the stack by sequence from top to button.
* @param[in] callback A function deal with each item while dumping.
* It must have and only have a in parameter which is the stack item.
* @brief clear current thread stack.
*/
template
<
typename
Callback
>
void
dump
(
Callback
callback
)
{
auto
&
p
=
*
logstack_
;
while
(
!
p
.
empty
())
{
callback
(
p
.
top
());
p
.
pop
();
void
clear
()
{
auto
&
s
=
stack
();
while
(
!
s
.
empty
())
{
s
.
pop
();
}
}
/**
* @brief Only empty the stack.
* @brief return true if all thread's stack is empty.
* @return true if empty
*/
void
clear
()
{
dump
([](
const
T
&
ip
){});
bool
empty
()
const
{
std
::
lock_guard
<
std
::
mutex
>
g
(
this
->
mtx_
);
for
(
auto
p
:
this
->
stackBuffers_
)
{
std
::
stack
<
T
>&
s
=
*
p
.
second
;
if
(
!
s
.
empty
())
{
return
false
;
}
}
return
true
;
}
/**
* @brief DumpCallback Type. It will be invoked many times by dump method.
*
* The first parameter is stack thread id.
* The second parameter is the last action of stack is push or not.
* The third parameter is the item in stack.
*/
typedef
std
::
function
<
void
(
const
std
::
thread
::
id
&
/*threadId*/
,
bool
*
/*isPushing*/
,
const
T
&
/*item*/
)
>
DumpCallback
;
/**
* Dump all thread stack, and all stack will be cleared.
*/
void
dump
(
const
DumpCallback
&
callback
,
bool
onlyCurrentThread
=
false
)
{
std
::
lock_guard
<
std
::
mutex
>
g
(
this
->
mtx_
);
for
(
auto
p
:
this
->
stackBuffers_
)
{
std
::
thread
::
id
tid
=
p
.
first
;
if
(
onlyCurrentThread
&&
tid
!=
std
::
this_thread
::
get_id
())
{
continue
;
}
std
::
stack
<
T
>&
s
=
*
p
.
second
;
bool
*
isPush
=
nullptr
;
auto
it
=
this
->
pushingBuffers_
.
find
(
tid
);
if
(
it
!=
this
->
pushingBuffers_
.
end
())
{
isPush
=
it
->
second
;
}
while
(
!
s
.
empty
())
{
callback
(
tid
,
isPush
,
s
.
top
());
s
.
pop
();
}
}
}
/**
* @brief Push item
ip to the top of the
stack.
* @brief Push item
to current thread
stack.
*/
void
push
(
const
T
&
ip
)
{
auto
&
p
=
*
logstack_
;
p
.
push
(
ip
);
void
push
(
const
T
&
item
)
{
pushing
()
=
true
;
auto
&
p
=
this
->
stack
();
p
.
push
(
item
);
}
private:
ThreadLocalD
<
std
::
stack
<
T
>
>
logstack_
;
/**
* Get thread local attribute, and save them into a map (threadId => TYPE*)
*
* @tparam TYPE thread local attribute type.
* @param threadLocal Thread Local object.
* @param buffers a map from threadId to TYPE*
*/
template
<
typename
TYPE
>
inline
TYPE
&
getThreadLocal
(
ThreadLocal
<
TYPE
>&
threadLocal
,
std
::
unordered_map
<
std
::
thread
::
id
,
TYPE
*>&
buffers
)
{
TYPE
*
retv
=
threadLocal
.
get
(
false
);
if
(
retv
)
{
return
*
retv
;
}
else
{
std
::
lock_guard
<
std
::
mutex
>
guard
(
this
->
mtx_
);
retv
=
threadLocal
.
get
();
auto
id
=
std
::
this_thread
::
get_id
();
buffers
.
insert
({
id
,
retv
});
return
*
retv
;
}
}
/**
* @brief Get thread local stack reference.
*/
std
::
stack
<
T
>&
stack
()
{
return
this
->
getThreadLocal
(
this
->
logStack_
,
this
->
stackBuffers_
);
}
/**
* @brief Get thread local pushing flag.
*/
bool
&
pushing
()
{
return
this
->
getThreadLocal
(
this
->
isPushing_
,
this
->
pushingBuffers_
);
}
private:
mutable
std
::
mutex
mtx_
;
std
::
unordered_map
<
std
::
thread
::
id
,
std
::
stack
<
T
>*
>
stackBuffers_
;
std
::
unordered_map
<
std
::
thread
::
id
,
bool
*
>
pushingBuffers_
;
ThreadLocal
<
bool
>
isPushing_
;
ThreadLocal
<
std
::
stack
<
T
>
>
logStack_
;
};
extern
CustomStackTrace
<
std
::
string
>
gLayerStackTrace
;
/**
* @brief Install a failure handler to print layer stack when error.
*/
extern
void
installLayerStackTracer
();
}
// namespace paddle
paddle/utils/Util.cpp
浏览文件 @
c7762da3
...
...
@@ -129,13 +129,7 @@ void runInitFunctions() {
void
initMain
(
int
argc
,
char
**
argv
)
{
initializeLogging
(
argc
,
argv
);
logging
::
installFailureWriter
([](
const
char
*
data
,
int
sz
)
{
std
::
cerr
<<
"Current Layer forward/backward stack is "
<<
std
::
endl
;
gLayerStackTrace
.
dump
([](
const
std
::
string
&
layername
){
std
::
cerr
<<
"LayerName: "
<<
layername
<<
std
::
endl
;
});
std
::
cerr
.
write
(
data
,
sz
);
});
installLayerStackTracer
();
std
::
string
line
;
for
(
int
i
=
0
;
i
<
argc
;
++
i
)
{
line
+=
argv
[
i
];
...
...
paddle/utils/tests/CMakeLists.txt
浏览文件 @
c7762da3
...
...
@@ -2,3 +2,13 @@ add_simple_unittest(test_CommandLineParser)
add_simple_unittest
(
test_Logging
)
add_simple_unittest
(
test_Thread
)
add_simple_unittest
(
test_StringUtils
)
add_simple_unittest
(
test_CustomStackTrace
)
add_executable
(
test_CustomStackTracePrint
test_CustomStackTracePrint.cpp
)
link_paddle_exe
(
test_CustomStackTracePrint
)
add_test
(
NAME test_CustomStackTracePrint
COMMAND
${
PROJ_ROOT
}
/paddle/utils/tests/test_CustomStackTracePrint.sh
WORKING_DIRECTORY
${
CMAKE_CURRENT_BINARY_DIR
}
)
paddle/utils/tests/test_CustomStackTrace.cpp
0 → 100644
浏览文件 @
c7762da3
/* Copyright (c) 2016 Baidu, Inc. 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. */
#include <gtest/gtest.h>
#include <chrono>
#include "paddle/utils/CustomStackTrace.h"
#include "paddle/utils/CommandLineParser.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/Locks.h"
P_DEFINE_int32
(
test_thread_num
,
10
,
"testing thread number"
);
void
testNormalImpl
(
const
std
::
function
<
void
(
paddle
::
CustomStackTrace
<
std
::
string
>&
,
size_t
,
size_t
,
paddle
::
ThreadBarrier
&
,
paddle
::
ThreadBarrier
&
)
>&
callback
)
{
paddle
::
CustomStackTrace
<
std
::
string
>
tracer
;
paddle
::
ThreadBarrier
doneBarrier
(
FLAGS_test_thread_num
+
1
);
paddle
::
ThreadBarrier
startBarrier
(
FLAGS_test_thread_num
+
1
);
constexpr
size_t
countDown
=
10
;
constexpr
size_t
layerSize
=
1000
;
std
::
vector
<
std
::
unique_ptr
<
std
::
thread
>>
threads
;
threads
.
reserve
(
FLAGS_test_thread_num
);
for
(
int32_t
i
=
0
;
i
<
FLAGS_test_thread_num
;
++
i
)
{
threads
.
emplace_back
(
new
std
::
thread
([
&
tracer
,
&
countDown
,
&
layerSize
,
&
startBarrier
,
&
doneBarrier
,
&
callback
]{
callback
(
tracer
,
countDown
,
layerSize
,
startBarrier
,
doneBarrier
);
}));
}
size_t
cntDown
=
countDown
;
while
(
cntDown
--
>
0
)
{
startBarrier
.
wait
();
doneBarrier
.
wait
();
ASSERT_TRUE
(
tracer
.
empty
());
}
for
(
auto
&
thread
:
threads
)
{
thread
->
join
();
}
}
TEST
(
CustomStackTrace
,
normalTrain
)
{
testNormalImpl
([](
paddle
::
CustomStackTrace
<
std
::
string
>&
tracer
,
size_t
countDown
,
size_t
layerSize
,
paddle
::
ThreadBarrier
&
start
,
paddle
::
ThreadBarrier
&
finish
){
while
(
countDown
--
>
0
)
{
start
.
wait
();
for
(
size_t
i
=
0
;
i
<
layerSize
;
++
i
)
{
tracer
.
push
(
"layer_"
+
std
::
to_string
(
i
));
}
tracer
.
pop
(
""
);
for
(
size_t
i
=
0
;
i
<
layerSize
;
++
i
)
{
tracer
.
pop
(
"layer_"
+
std
::
to_string
(
layerSize
-
1
-
i
));
}
finish
.
wait
();
}
});
}
TEST
(
CustomStackTrace
,
normalTest
)
{
testNormalImpl
([]
(
paddle
::
CustomStackTrace
<
std
::
string
>&
tracer
,
size_t
countDown
,
size_t
layerSize
,
paddle
::
ThreadBarrier
&
start
,
paddle
::
ThreadBarrier
&
finish
){
while
(
countDown
--
>
0
)
{
start
.
wait
();
for
(
size_t
i
=
0
;
i
<
layerSize
;
++
i
)
{
tracer
.
push
(
"layer_"
+
std
::
to_string
(
i
));
}
tracer
.
clear
();
// in forward test, tracer will clear after forward.
finish
.
wait
();
}
});
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
paddle
::
initMain
(
argc
,
argv
);
return
RUN_ALL_TESTS
();
}
paddle/utils/tests/test_CustomStackTracePrint.cpp
0 → 100644
浏览文件 @
c7762da3
/* Copyright (c) 2016 Baidu, Inc. 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. */
#include "paddle/utils/Util.h"
#include "paddle/utils/CustomStackTrace.h"
int
main
(
int
argc
,
char
**
argv
)
{
paddle
::
initMain
(
argc
,
argv
);
for
(
size_t
i
=
0
;
i
<
1000
;
++
i
)
{
paddle
::
gLayerStackTrace
.
push
(
"layer_"
+
std
::
to_string
(
i
));
if
(
i
==
998
)
{
throw
"Unhandle exception"
;
}
}
return
0
;
}
paddle/utils/tests/test_CustomStackTracePrint.sh
0 → 100755
浏览文件 @
c7762da3
#!/bin/bash
echo
"Test Custom Stack Trace print correct result when fail"
./test_CustomStackTracePrint
>
customStackTraceLog 2>&1
if
[
$?
-eq
0
]
;
then
exit
1
else
set
-e
TEXT
=
""
for
((
i
=
0
;
i<
=
998
;
i++
))
do
TEXT
=
"layer_
$i
, "
$TEXT
done
TEXT
=
"Forwarding "
$TEXT
grep
-q
"
$TEXT
"
customStackTraceLog
fi
python/CMakeLists.txt
浏览文件 @
c7762da3
...
...
@@ -22,6 +22,8 @@ find_python_module(pip REQUIRED)
find_python_module
(
wheel REQUIRED
)
find_python_module
(
google.protobuf REQUIRED
)
add_subdirectory
(
paddle/trainer_config_helpers/tests
)
install
(
DIRECTORY
${
CMAKE_CURRENT_BINARY_DIR
}
/dist/
DESTINATION opt/paddle/share/wheels
)
python/paddle/trainer/config_parser.py
浏览文件 @
c7762da3
...
...
@@ -1623,7 +1623,7 @@ class BatchNormLayer(LayerBase):
# Also based on cudnn version.
use_cudnn
=
use_gpu
and
batch_norm_type
!=
"batch_norm"
and
\
((
not
parallel_nn
)
or
self
.
config
.
device
>
-
1
)
and
\
cudnn_version
>=
400
0
cudnn_version
>=
400
7
self
.
layer_type
=
"cudnn_batch_norm"
if
use_cudnn
else
"batch_norm"
super
(
BatchNormLayer
,
self
).
__init__
(
name
,
self
.
layer_type
,
0
,
active_type
=
active_type
,
...
...
@@ -2273,6 +2273,9 @@ class ConvexCombinationLayer(LayerBase):
name
,
'convex_comb'
,
size
,
inputs
=
inputs
,
device
=
device
)
config_assert
(
len
(
self
.
inputs
)
==
2
,
'ConvexCombinationLayer must have 2 inputs'
)
config_assert
(
size
*
self
.
get_input_layer
(
0
).
size
==
self
.
get_input_layer
(
1
).
size
,
'Wrong input size for ConvexCombinationLayer'
)
self
.
set_layer_size
(
size
)
@
config_layer
(
'interpolation'
)
...
...
@@ -2322,6 +2325,9 @@ class CosSimVecMatLayer(LayerBase):
self
.
config
.
cos_scale
=
cos_scale
config_assert
(
len
(
self
.
inputs
)
==
2
,
'CosSimVecMatLayer must have 2 inputs'
)
config_assert
(
size
*
self
.
get_input_layer
(
0
).
size
==
self
.
get_input_layer
(
1
).
size
,
'Wrong input size for CosSimVecMatLayer'
)
@
config_layer
(
'sampling_id'
)
class
SamplingIdLayer
(
LayerBase
):
...
...
@@ -2370,6 +2376,7 @@ class CosSimLayer(LayerBase):
self
,
name
,
inputs
,
cos_scale
=
5
,
device
=
None
):
super
(
CosSimLayer
,
self
).
__init__
(
name
,
'cos'
,
1
,
inputs
=
inputs
,
device
=
device
)
...
...
@@ -2377,6 +2384,7 @@ class CosSimLayer(LayerBase):
config_assert
(
self
.
get_input_layer
(
0
).
size
==
self
.
get_input_layer
(
1
).
size
,
'inputs of CosSimLayer must have same dim'
)
self
.
config
.
cos_scale
=
cos_scale
@
config_layer
(
'tensor'
)
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
c7762da3
...
...
@@ -47,6 +47,7 @@ __all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
'BaseGeneratedInput'
,
'conv_operator'
,
'conv_shift_layer'
,
'tensor_layer'
,
'selective_fc_layer'
,
'sampling_id_layer'
,
'slope_intercept_layer'
,
'trans_full_matrix_projection'
,
'linear_comb_layer'
,
'convex_comb_layer'
,
'ctc_layer'
,
'crf_layer'
,
'crf_decoding_layer'
,
'cross_entropy_with_selfnorm'
,
'cross_entropy'
,
'multi_binary_label_cross_entropy'
,
...
...
@@ -70,7 +71,8 @@ class LayerType(object):
POOLING_AVG
=
'average'
FC_LAYER
=
"fc"
COST
=
'cost'
COSINE_SIM
=
'cos_vm'
COSINE_SIM_VEC
=
'cos_vm'
COSINE_SIM
=
'cos'
HSIGMOID
=
'hsigmoid'
CONV_LAYER
=
"conv"
POOL_LAYER
=
"pool"
...
...
@@ -102,7 +104,7 @@ class LayerType(object):
SEL_FC_LAYER
=
"selective_fc"
SAMPLING_ID_LAYER
=
"sampling_id"
SLOPE_INTERCEPT_LAYER
=
"slope_intercept"
CONVEX
_COMBINATION_LAYER
=
"convex_comb"
LINEAR
_COMBINATION_LAYER
=
"convex_comb"
BLOCK_EXPAND
=
"blockexpand"
CTC_LAYER
=
"ctc"
...
...
@@ -171,6 +173,8 @@ class LayerOutput(object):
assert
LayerType
.
is_layer_type
(
layer_type
)
self
.
name
=
name
self
.
layer_type
=
layer_type
if
parents
is
not
None
and
type
(
parents
)
!=
list
:
parents
=
[
parents
]
self
.
parents
=
[]
if
parents
is
None
else
parents
self
.
activation
=
activation
self
.
num_filters
=
num_filters
...
...
@@ -512,7 +516,7 @@ class MixedLayerType(LayerOutput):
:rtype: MixedLayerType
"""
if
not
self
.
finalized
:
assert
isinstance
(
other
,
Projection
)
assert
isinstance
(
other
,
Projection
)
or
isinstance
(
other
,
Operator
)
self
.
inputs
.
append
(
other
)
self
.
parents
.
append
(
other
.
origin
)
return
self
...
...
@@ -1169,13 +1173,16 @@ def power_layer(input, weight, name=None, layer_attr=None):
@
layer_support
()
def
scaling_layer
(
input
,
weight
,
name
=
None
,
layer_attr
=
None
):
"""
A layer for
each row of a matrix, multiplying with a element of a vecto
r.
A layer for
multiplying input vector by weight scala
r.
.. math::
y
.row[i] = w[i] * x.row[i]
y
= w x
where :math:`x` is (batchSize x dataDim) input, :math:`w` is
(batchSize x 1) weight vector, and :math:`y` is (batchSize x dataDim) output.
where :math:`x` is size=dataDim input, :math:`w` is size=1 weight,
and :math:`y` is size=dataDim output.
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The example usage is:
...
...
@@ -1249,11 +1256,14 @@ def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None):
.. math::
similarity = cos(
\\
theta) = {
\\
mathbf{a}
\\
cdot
\\
mathbf{b}
\\
over
\\
|
\\
mathbf{b}
\\
|
\\
|
\\
mathbf{b}
\\
|}
\\
over
\\
|
\\
mathbf{a}
\\
|
\\
|
\\
mathbf{b}
\\
|}
The size of a is M, size of b is M*N,
Similarity will be calculated N times by step M. The output size is
N. The scale will be multiplied to similarity.
And the input dimension is :math:`a \in R^M`, :math:`b \in R^{MN}`. The
similarity will be calculated N times by step M. The output dimension is
:math:`R^N`. The scale will be multiplied to similarity.
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
:param name: layer name
:type name: basestring
...
...
@@ -1270,14 +1280,23 @@ def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None):
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer
(
name
=
name
,
type
=
LayerType
.
COSINE_SIM
,
size
=
size
,
cos_scale
=
scale
,
inputs
=
[
a
.
name
,
b
.
name
],
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
)
)
if
size
==
1
:
Layer
(
name
=
name
,
type
=
LayerType
.
COSINE_SIM
,
cos_scale
=
scale
,
inputs
=
[
a
.
name
,
b
.
name
],
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
)
)
else
:
Layer
(
name
=
name
,
type
=
LayerType
.
COSINE_SIM_VEC
,
size
=
size
,
cos_scale
=
scale
,
inputs
=
[
a
.
name
,
b
.
name
],
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
)
)
return
LayerOutput
(
name
,
LayerType
.
COSINE_SIM
,
parents
=
[
a
,
b
])
@
wrap_name_default
()
...
...
@@ -2909,29 +2928,37 @@ def slope_intercept_layer(input, name=None, slope=1.0, intercept=0.0):
@
wrap_name_default
()
def
convex_comb_layer
(
input
,
size
,
name
=
None
):
def
linear_comb_layer
(
weights
,
vectors
,
size
,
name
=
None
):
"""
A layer for
convex weighted average
of vectors takes two inputs.
- Input:
a vector containing the convex weights (batchSize x weightdim),
and a matrix in a vector form (batchSize x (weightdim * datadim)).
- Output: a vector
(batchSize * datadim).
A layer for
weighted sum
of vectors takes two inputs.
- Input:
size of weights is M
size of vectors is M*N
- Output: a vector
of size=N
.. math::
y[i][j] = \sum_{j}(x_{1}(i, j) * x_{2}(i,j + i * dataDim)),
z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)
where :math:`0 \le i \le N-1`
Or in the matrix notation:
.. math::
i = 0,1,...,(batchSize-1); j = 0, 1,...,(dataDim-1)
z = x^T Y
In this formular:
- :math:`x_{1}`: the first input.
- :math:`x_{2}`: the second input.
- :math:`y`: the output.
- :math:`x`: weights
- :math:`y`: vectors.
- :math:`z`: the output.
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The simple usage is:
.. code-block:: python
convex_comb = convex_comb_layer(input=input
s,
linear_comb = linear_comb_layer(weighs=weight, vectors=vector
s,
size=elem_dim)
:param input: The input layers.
...
...
@@ -2944,15 +2971,16 @@ def convex_comb_layer(input, size, name=None):
:rtype: LayerOutput
"""
assert
isinstance
(
input
,
list
)
or
isinstance
(
input
,
tuple
)
assert
len
(
input
)
==
2
Layer
(
name
=
name
,
type
=
LayerType
.
CONVEX
_COMBINATION_LAYER
,
type
=
LayerType
.
LINEAR
_COMBINATION_LAYER
,
size
=
size
,
inputs
=
[
Input
(
input
[
0
].
name
),
Input
(
input
[
1
]
.
name
)],
inputs
=
[
Input
(
weights
.
name
),
Input
(
vectors
.
name
)],
)
return
LayerOutput
(
name
,
LayerType
.
CONVEX_COMBINATION_LAYER
,
input
,
size
=
size
)
return
LayerOutput
(
name
,
LayerType
.
LINEAR_COMBINATION_LAYER
,
[
weights
,
vectors
],
size
=
size
)
convex_comb_layer
=
linear_comb_layer
@
wrap_name_default
()
def
block_expand_layer
(
input
,
...
...
python/paddle/trainer_config_helpers/tests/CMakeLists.txt
0 → 100644
浏览文件 @
c7762da3
#################### test_config_parser #########################
add_test
(
NAME layers_test
COMMAND
${
PROJ_ROOT
}
/paddle/.set_python_path.sh -d
${
PROJ_ROOT
}
/python/
python
${
PROJ_ROOT
}
/python/paddle/trainer_config_helpers/tests/layers_test.py
WORKING_DIRECTORY
${
PROJ_ROOT
}
/python/paddle
)
python/paddle/trainer_config_helpers/tests/layers_test.py
0 → 100644
浏览文件 @
c7762da3
# Copyright (c) 2016 Baidu, Inc. 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.
from
paddle.trainer.config_parser
import
parse_config_and_serialize
if
__name__
==
'__main__'
:
parse_config_and_serialize
(
'trainer_config_helpers/tests/layers_test_config.py'
,
''
)
python/paddle/trainer_config_helpers/tests/layers_test_config.py
0 → 100644
浏览文件 @
c7762da3
# Copyright (c) 2016 Baidu, Inc. 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.
from
paddle.trainer_config_helpers
import
*
num_classes
=
5
x
=
data_layer
(
name
=
"input1"
,
size
=
3
)
y
=
data_layer
(
name
=
"input2"
,
size
=
5
)
x1
=
fc_layer
(
input
=
x
,
size
=
5
)
y1
=
fc_layer
(
input
=
y
,
size
=
5
)
y2
=
fc_layer
(
input
=
y
,
size
=
15
)
cos1
=
cos_sim
(
a
=
x1
,
b
=
y1
)
cos3
=
cos_sim
(
a
=
x1
,
b
=
y2
,
size
=
3
)
linear_comb
=
linear_comb_layer
(
weights
=
x1
,
vectors
=
y2
,
size
=
3
)
out
=
fc_layer
(
input
=
[
cos1
,
cos3
,
linear_comb
],
size
=
num_classes
,
act
=
SoftmaxActivation
())
outputs
(
classification_cost
(
out
,
data_layer
(
name
=
"label"
,
size
=
num_classes
)))
settings
(
batch_size
=
10
,
learning_rate
=
2e-3
,
learning_method
=
AdamOptimizer
(),
regularization
=
L2Regularization
(
8e-4
),
gradient_clipping_threshold
=
25
)
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