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体验新版 GitCode,发现更多精彩内容 >>
提交
09409bad
编写于
10月 26, 2018
作者:
D
dzhwinter
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
staged. test speed=49ms in 1080.
上级
468467f3
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
310 addition
and
216 deletion
+310
-216
paddle/fluid/framework/executor.cc
paddle/fluid/framework/executor.cc
+62
-62
paddle/fluid/inference/api/api_impl.cc
paddle/fluid/inference/api/api_impl.cc
+30
-2
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
+2
-3
paddle/fluid/inference/api/demo_ci/real_data_icnet_tester.cc
paddle/fluid/inference/api/demo_ci/real_data_icnet_tester.cc
+55
-49
paddle/fluid/inference/api/demo_ci/thread_icnet_test.cc
paddle/fluid/inference/api/demo_ci/thread_icnet_test.cc
+77
-50
paddle/fluid/operators/conv_cudnn_op.cu.cc
paddle/fluid/operators/conv_cudnn_op.cu.cc
+2
-2
paddle/fluid/operators/load_combine_op.cc
paddle/fluid/operators/load_combine_op.cc
+12
-12
paddle/fluid/operators/top_k_op.cc
paddle/fluid/operators/top_k_op.cc
+1
-1
paddle/fluid/operators/top_k_op.cu
paddle/fluid/operators/top_k_op.cu
+68
-31
paddle/fluid/operators/top_k_op.h
paddle/fluid/operators/top_k_op.h
+1
-4
未找到文件。
paddle/fluid/framework/executor.cc
浏览文件 @
09409bad
...
...
@@ -397,72 +397,72 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
}
platform
::
DeviceContextPool
::
Instance
().
Get
(
place_
)
->
Wait
();
VLOG
(
3
)
<<
"start checking"
;
auto
&
dev_ctx
=
*
platform
::
DeviceContextPool
::
Instance
().
Get
(
place_
);
std
::
vector
<
std
::
string
>
outputs
;
auto
&
block
=
ctx
->
prog_
.
Block
(
0
);
for
(
auto
&
op
:
block
.
AllOps
())
{
if
(
op
->
Type
()
==
"load_combine"
||
op
->
Type
()
==
"fetch"
||
op
->
Type
()
==
"feed"
)
continue
;
// for(auto& real_op : ctx->ops_) {
// if(real_op->Type() == op->Type()) {
// VLOG(3) << real_op->Type() << " " <<place_ << " " << real_op->DebugStringEx(local_scope);
// }
// }
//
VLOG(3) << "start checking";
//
auto& dev_ctx = *platform::DeviceContextPool::Instance().Get(place_);
//
std::vector<std::string> outputs;
//
auto& block = ctx->prog_.Block(0);
//
for(auto& op : block.AllOps()) {
//
if(op->Type() == "load_combine" || op->Type() == "fetch" || op->Type() == "feed") continue;
//
// for(auto& real_op : ctx->ops_) {
//
// if(real_op->Type() == op->Type()) {
//
// VLOG(3) << real_op->Type() << " " <<place_ << " " << real_op->DebugStringEx(local_scope);
//
// }
//
// }
//VLOG(3) << "start op output" << op->Type();
for
(
auto
var_name
:
op
->
InputArgumentNames
())
{
auto
*
var
=
local_scope
->
Var
(
var_name
);
auto
*
var_desc
=
block
.
FindVar
(
var_name
);
if
(
var_desc
->
Persistable
())
continue
;
auto
*
tensor
=
var
->
GetMutable
<
framework
::
LoDTensor
>
();
framework
::
Tensor
check
;
VLOG
(
3
)
<<
"before tensor copy"
;
//
//VLOG(3) << "start op output" << op->Type();
//
for(auto var_name: op->InputArgumentNames()) {
//
auto* var = local_scope->Var(var_name);
//
auto* var_desc = block.FindVar(var_name);
//
if (var_desc->Persistable()) continue;
//
auto* tensor = var->GetMutable<framework::LoDTensor>();
//
framework::Tensor check;
//
VLOG(3) << "before tensor copy";
framework
::
TensorCopy
(
*
tensor
,
platform
::
CPUPlace
(),
dev_ctx
,
&
check
);
//
framework::TensorCopy(*tensor, platform::CPUPlace(), dev_ctx, &check);
VLOG
(
3
)
<<
"after tensor copy"
;
float
sum
=
.0
;
for
(
size_t
i
=
0
;
i
<
check
.
numel
();
++
i
)
{
if
(
std
::
type_index
(
check
.
type
())
==
std
::
type_index
(
typeid
(
int64_t
)))
{
sum
+=
static_cast
<
float
>
(
check
.
data
<
int64_t
>
()[
i
]);
}
else
{
sum
+=
check
.
data
<
float
>
()[
i
];
}
}
VLOG
(
3
)
<<
"op "
<<
op
->
Type
()
<<
" input var "
<<
var_name
<<
" sum "
<<
sum
;
}
VLOG
(
3
)
<<
"op "
<<
op
->
Type
()
<<
"input finished"
;
for
(
auto
var_name
:
op
->
OutputArgumentNames
())
{
auto
*
var
=
local_scope
->
Var
(
var_name
);
auto
*
var_desc
=
block
.
FindVar
(
var_name
);
if
(
var_desc
->
Persistable
())
continue
;
auto
*
tensor
=
var
->
GetMutable
<
framework
::
LoDTensor
>
();
framework
::
Tensor
check
;
VLOG
(
3
)
<<
"before tensor copy"
;
if
(
op
->
Type
()
==
"batch_norm"
&&
platform
::
is_gpu_place
(
place_
))
{
VLOG
(
3
)
<<
"op "
<<
op
->
Type
()
<<
" output var "
<<
var_name
<<
" "
<<
tensor
->
numel
();
tensor
->
mutable_data
<
float
>
(
place_
);
framework
::
TensorCopy
(
*
tensor
,
platform
::
CPUPlace
(),
dev_ctx
,
&
check
);
}
else
{
framework
::
TensorCopy
(
*
tensor
,
platform
::
CPUPlace
(),
dev_ctx
,
&
check
);
}
//
VLOG(3) << "after tensor copy";
//
float sum = .0;
//
for(size_t i=0; i < check.numel(); ++i) {
//
if(std::type_index(check.type()) == std::type_index(typeid(int64_t))) {
//
sum += static_cast<float>(check.data<int64_t>()[i]);
//
} else {
//
sum += check.data<float>()[i];
//
}
//
}
//
VLOG(3) << "op " << op->Type() << " input var " << var_name << " sum " << sum;
//
}
//
VLOG(3) << "op " << op->Type() << "input finished";
//
for(auto var_name: op->OutputArgumentNames()) {
//
auto* var = local_scope->Var(var_name);
//
auto* var_desc = block.FindVar(var_name);
//
if (var_desc->Persistable()) continue;
//
auto* tensor = var->GetMutable<framework::LoDTensor>();
//
framework::Tensor check;
//
VLOG(3) << "before tensor copy";
//
if(op->Type() == "batch_norm" && platform::is_gpu_place(place_)) {
//
VLOG(3) << "op " << op->Type() << " output var " << var_name << " " << tensor->numel();
//
tensor->mutable_data<float>(place_);
//
framework::TensorCopy(*tensor, platform::CPUPlace(), dev_ctx, &check);
//
} else {
//
framework::TensorCopy(*tensor, platform::CPUPlace(), dev_ctx, &check);
//
}
VLOG
(
3
)
<<
"after tensor copy"
;
float
sum
=
.0
;
for
(
size_t
i
=
0
;
i
<
check
.
numel
();
++
i
)
{
if
(
std
::
type_index
(
check
.
type
())
==
std
::
type_index
(
typeid
(
int64_t
)))
{
sum
+=
static_cast
<
float
>
(
check
.
data
<
int64_t
>
()[
i
]);
}
else
{
sum
+=
check
.
data
<
float
>
()[
i
];
}
}
VLOG
(
3
)
<<
"op "
<<
op
->
Type
()
<<
" output var "
<<
var_name
<<
" sum "
<<
sum
;
}
}
VLOG
(
3
)
<<
"after checking result"
;
//
VLOG(3) << "after tensor copy";
//
float sum = .0;
//
for(size_t i=0; i < check.numel(); ++i) {
//
if(std::type_index(check.type()) == std::type_index(typeid(int64_t))) {
//
sum += static_cast<float>(check.data<int64_t>()[i]);
//
} else {
//
sum += check.data<float>()[i];
//
}
//
}
//
VLOG(3) << "op " << op->Type() << " output var " << var_name << " sum " << sum;
//
}
//
}
//
VLOG(3) << "after checking result";
if
(
local_scope
!=
scope
)
{
scope
->
DeleteScope
(
local_scope
);
...
...
paddle/fluid/inference/api/api_impl.cc
浏览文件 @
09409bad
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <fstream>
#include <map>
#include <set>
#include <sstream>
...
...
@@ -88,6 +89,7 @@ bool NativePaddlePredictor::Init(
VLOG
(
3
)
<<
config_
.
model_dir
;
inference_program_
=
paddle
::
inference
::
Load
(
executor_
.
get
(),
scope_
.
get
(),
config_
.
model_dir
);
VLOG
(
3
)
<<
"load model finish"
;
}
else
if
(
!
config_
.
prog_file
.
empty
()
&&
!
config_
.
param_file
.
empty
())
{
// All parameters are saved in a single file.
...
...
@@ -100,6 +102,31 @@ bool NativePaddlePredictor::Init(
VLOG
(
3
)
<<
"scope_"
;
inference_program_
=
paddle
::
inference
::
Load
(
executor_
.
get
(),
scope_
.
get
(),
config_
.
prog_file
,
config_
.
param_file
);
// VLOG(3) << "modify the program!";
// {
// std::ofstream ofs("program.txt", std::ios::out);
// std::string s = inference_program_->Proto()->SerializeAsString();
// ofs.write(s.data(), s.size());
// ofs.close();
// }
auto
&
block
=
inference_program_
->
Block
(
0
);
for
(
auto
*
op_desc
:
block
.
AllOps
())
{
if
(
op_desc
->
HasAttr
(
"use_cudnn"
))
{
op_desc
->
SetAttr
(
"use_cudnn"
,
false
);
}
if
(
op_desc
->
HasAttr
(
"workspace_size_MB"
))
{
op_desc
->
SetAttr
(
"workspace_size_MB"
,
0
);
}
}
// {
// std::ofstream ofs("after_program.txt", std::ios::out);
// std::string s = inference_program_->Proto()->SerializeAsString();
// ofs.write(s.data(), s.size());
// ofs.close();
// }
VLOG
(
3
)
<<
"load program finish"
;
}
else
{
LOG
(
ERROR
)
<<
"fail to load inference model."
;
...
...
@@ -306,9 +333,10 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
if
(
config
.
use_gpu
)
{
// 1. GPU memeroy
VLOG
(
3
)
<<
"before check"
;
// PADDLE_ENFORCE_GT(
// PADDLE_ENFORCE_GT(
// config.fraction_of_gpu_memory, 0.f,
// "fraction_of_gpu_memory in the config should be set to range (0., 1.]");
// "fraction_of_gpu_memory in the config should be set to range (0.,
// 1.]");
VLOG
(
3
)
<<
"failed on first"
;
PADDLE_ENFORCE_GE
(
config
.
device
,
0
,
"Invalid device id %d"
,
config
.
device
);
VLOG
(
3
)
<<
"after flags"
;
...
...
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @
09409bad
...
...
@@ -77,7 +77,7 @@ add_executable(real_data_icnet_tester real_data_icnet_tester.cc)
# add_library(${DEMO_NAME} SHARED ${DEMO_NAME}.cc)
# add_executable(test test.cc)
#
add_executable(thread_icnet_test thread_icnet_test.cc)
add_executable
(
thread_icnet_test thread_icnet_test.cc
)
if
(
WITH_MKL
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/mklml/include"
)
...
...
@@ -130,6 +130,5 @@ target_link_libraries(real_data_icnet_tester ${DEPS})
# target_link_libraries(${DEMO_NAME} ${DEPS})
# target_link_libraries(test ${DEMO_NAME} )
#
target_link_libraries(thread_icnet_test ${DEPS})
target_link_libraries
(
thread_icnet_test
${
DEPS
}
)
# target_compile_definitions(${DEMO_NAME} PRIVATE "API_DEFINITION")
paddle/fluid/inference/api/demo_ci/real_data_icnet_tester.cc
浏览文件 @
09409bad
...
...
@@ -25,10 +25,13 @@ namespace paddle {
NativeConfig
GetConfig
()
{
NativeConfig
config
;
// config.model_dir = FLAGS_dirname;
config
.
prog_file
=
"hs_lb_without_bn/__model__"
;
config
.
param_file
=
"hs_lb_without_bn/__params__"
;
config
.
fraction_of_gpu_memory
=
0.8
;
config
.
prog_file
=
"hs_lb_without_bn/__model__"
;
config
.
param_file
=
"hs_lb_without_bn/__params__"
;
// config.prog_file = "hs_lb_without_bn_cuda/__model__";
// config.param_file = "hs_lb_without_bn_cuda/__params__";
config
.
fraction_of_gpu_memory
=
0.0
;
config
.
use_gpu
=
true
;
config
.
device
=
0
;
return
config
;
...
...
@@ -43,13 +46,12 @@ double time_diff(Time t1, Time t2) {
return
counter
.
count
()
/
1000.0
;
}
void
test_naive
(
int
batch_size
){
void
test_naive
(
int
batch_size
)
{
NativeConfig
config
=
GetConfig
();
auto
predictor
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
);
int
height
=
449
;
int
width
=
581
;
// =============read file list =============
std
::
ifstream
infile
(
"new_file.list"
);
std
::
string
temp_s
;
...
...
@@ -62,61 +64,65 @@ void test_naive(int batch_size){
// size_t file_num = all_files.size();
infile
.
close
();
// =============read file list =============
for
(
size_t
f_k
=
0
;
f_k
<
1
;
f_k
++
)
{
std
::
ifstream
in_img
(
all_files
[
f_k
]);
std
::
cout
<<
all_files
[
f_k
]
<<
std
::
endl
;
float
temp_v
;
for
(
size_t
f_k
=
0
;
f_k
<
1
;
f_k
++
)
{
std
::
ifstream
in_img
(
all_files
[
f_k
]);
std
::
cout
<<
all_files
[
f_k
]
<<
std
::
endl
;
float
temp_v
;
float
sum_n
=
0.0
;
std
::
vector
<
float
>
data
;
while
(
!
in_img
.
eof
())
{
in_img
>>
temp_v
;
data
.
push_back
(
float
(
temp_v
));
// std::cout << temp_v << " ";
sum_n
+=
temp_v
;
}
float
sum_n
=
0.0
;
std
::
vector
<
float
>
data
;
while
(
!
in_img
.
eof
())
{
in_img
>>
temp_v
;
data
.
push_back
(
float
(
temp_v
));
// std::cout << temp_v << " ";
sum_n
+=
temp_v
;
}
in_img
.
close
();
std
::
cout
<<
"sum: "
<<
sum_n
<<
std
::
endl
;
PaddleTensor
tensor
;
tensor
.
shape
=
std
::
vector
<
int
>
({
batch_size
,
3
,
height
,
width
});
tensor
.
data
.
Resize
(
sizeof
(
float
)
*
batch_size
*
3
*
height
*
width
);
std
::
copy
(
data
.
begin
(),
data
.
end
(),
static_cast
<
float
*>
(
tensor
.
data
.
data
()));
tensor
.
dtype
=
PaddleDType
::
FLOAT32
;
std
::
vector
<
PaddleTensor
>
paddle_tensor_feeds
(
1
,
tensor
);
PaddleTensor
tensor_out
;
in_img
.
close
();
std
::
cout
<<
"sum: "
<<
sum_n
<<
std
::
endl
;
std
::
vector
<
PaddleTensor
>
outputs
(
1
,
tensor_out
)
;
// predictor->Run(paddle_tensor_feeds, &outputs, batch_size
);
std
::
cout
<<
"start predict123:"
<<
std
::
endl
;
auto
time1
=
time
();
for
(
size_t
i
=
0
;
i
<
1
;
i
++
)
{
predictor
->
Run
(
paddle_tensor_feeds
,
&
outputs
,
batch_size
);
}
PaddleTensor
tensor
;
tensor
.
shape
=
std
::
vector
<
int
>
({
batch_size
,
3
,
height
,
width
}
);
tensor
.
data
.
Resize
(
sizeof
(
float
)
*
batch_size
*
3
*
height
*
width
)
;
std
::
copy
(
data
.
begin
(),
data
.
end
(),
static_cast
<
float
*>
(
tensor
.
data
.
data
()));
tensor
.
dtype
=
PaddleDType
::
FLOAT32
;
std
::
vector
<
PaddleTensor
>
paddle_tensor_feeds
(
1
,
tensor
);
PaddleTensor
tensor_out
;
auto
time2
=
time
();
std
::
ofstream
ofresult
(
"naive_test_result.txt"
,
std
::
ios
::
app
);
std
::
vector
<
PaddleTensor
>
outputs
(
1
,
tensor_out
);
// predictor->Run(paddle_tensor_feeds, &outputs, batch_size);
std
::
cout
<<
"start predict123:"
<<
std
::
endl
;
auto
time1
=
time
();
int
steps
=
100
;
for
(
size_t
i
=
0
;
i
<
steps
;
i
++
)
{
if
(
i
==
5
)
time1
=
time
();
predictor
->
Run
(
paddle_tensor_feeds
,
&
outputs
,
batch_size
);
}
std
::
cout
<<
"batch: "
<<
batch_size
<<
" predict cost: "
<<
time_diff
(
time1
,
time2
)
/
1000.0
<<
"ms"
<<
std
::
endl
;
std
::
cout
<<
outputs
.
size
()
<<
std
::
endl
;
int64_t
*
data_o
=
static_cast
<
int64_t
*>
(
outputs
[
0
].
data
.
data
());
auto
time2
=
time
();
std
::
ofstream
ofresult
(
"naive_test_result.txt"
,
std
::
ios
::
app
);
std
::
cout
<<
"batch: "
<<
batch_size
<<
" predict cost: "
<<
time_diff
(
time1
,
time2
)
/
steps
<<
"ms"
<<
std
::
endl
;
std
::
cout
<<
outputs
.
size
()
<<
std
::
endl
;
int64_t
*
data_o
=
static_cast
<
int64_t
*>
(
outputs
[
0
].
data
.
data
());
int64_t
sum_out
=
0
;
for
(
size_t
j
=
0
;
j
<
outputs
[
0
].
data
.
length
()
/
sizeof
(
int64_t
);
++
j
)
{
ofresult
<<
std
::
to_string
(
data_o
[
j
])
<<
" "
;
for
(
size_t
j
=
0
;
j
<
outputs
[
0
].
data
.
length
()
/
sizeof
(
int64_t
);
++
j
)
{
ofresult
<<
std
::
to_string
(
data_o
[
j
])
<<
" "
;
sum_out
+=
data_o
[
j
];
}
}
std
::
cout
<<
"sum_out "
<<
sum_out
<<
std
::
endl
;
ofresult
<<
std
::
endl
;
ofresult
.
close
();
}
ofresult
<<
std
::
endl
;
ofresult
.
close
();
}
}
}
// namespace paddle
int
main
(
int
argc
,
char
**
argv
)
{
// google::ParseCommandLineFlags(&argc, &argv, true);
paddle
::
test_naive
(
1
<<
0
);
// google::ParseCommandLineFlags(&argc, &argv, true);
paddle
::
test_naive
(
1
<<
0
);
return
0
;
}
paddle/fluid/inference/api/demo_ci/thread_icnet_test.cc
浏览文件 @
09409bad
...
...
@@ -20,22 +20,21 @@
#include <chrono>
#include <fstream>
#include <iostream>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include <thread> // NOLINT
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#define ASSERT_TRUE(x) x
#define ASSERT_EQ(x, y) assert(x == y)
namespace
paddle
{
// DEFINE_string(dirname, "./LB_icnet_model",
// "Directory of the inference model.");
namespace
paddle
{
NativeConfig
GetConfig
()
{
NativeConfig
config
;
config
.
prog_file
=
"./dzh_lb
/__model__"
;
config
.
param_file
=
"./dzh_lb
/__params__"
;
config
.
fraction_of_gpu_memory
=
0.
08
;
config
.
prog_file
=
"./hs_lb_without_bn_cuda
/__model__"
;
config
.
param_file
=
"./hs_lb_without_bn_cuda
/__params__"
;
config
.
fraction_of_gpu_memory
=
0.
5
;
config
.
use_gpu
=
true
;
config
.
device
=
0
;
return
config
;
...
...
@@ -50,56 +49,84 @@ double time_diff(Time t1, Time t2) {
return
counter
.
count
()
/
1000.0
;
}
void
test_naive
(
int
batch_size
,
std
::
string
model_path
){
PaddlePredictor
*
pres
[
2
];
void
test_naive
(
int
batch_size
,
std
::
string
model_path
)
{
NativeConfig
config
=
GetConfig
();
// config.model_dir = model_path;
auto
predictor0
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
);
auto
predictor1
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
);
pres
[
0
]
=
predictor0
.
get
();
pres
[
1
]
=
predictor1
.
get
();
int
height
=
449
;
int
width
=
581
;
std
::
vector
<
float
>
data
;
for
(
int
i
=
0
;
i
<
3
*
height
*
width
;
i
++
)
{
data
.
push_back
(
0
);
}
PaddleTensor
tensor
;
tensor
.
shape
=
std
::
vector
<
int
>
({
batch_size
,
3
,
height
,
width
});
tensor
.
data
.
Resize
(
sizeof
(
float
)
*
batch_size
*
3
*
height
*
width
);
std
::
copy
(
data
.
begin
(),
data
.
end
(),
static_cast
<
float
*>
(
tensor
.
data
.
data
()));
tensor
.
dtype
=
PaddleDType
::
FLOAT32
;
std
::
vector
<
PaddleTensor
>
paddle_tensor_feeds
(
1
,
tensor
);
constexpr
int
num_jobs
=
5
;
// each job run 1 batch
std
::
vector
<
std
::
thread
>
threads
;
for
(
int
tid
=
0
;
tid
<
num_jobs
;
++
tid
)
{
threads
.
emplace_back
([
&
,
tid
]()
{
auto
predictor
=
pres
[
tid
];
std
::
vector
<
PaddleTensor
>
local_outputs
;
for
(
size_t
i
=
0
;
i
<
1000
;
i
++
)
{
ASSERT_TRUE
(
predictor
->
Run
(
paddle_tensor_feeds
,
&
local_outputs
));
std
::
cout
<<
"run: "
<<
tid
<<
std
::
endl
;
}
ASSERT_EQ
(
local_outputs
.
size
(),
1UL
);
});
for
(
int
i
=
0
;
i
<
3
*
height
*
width
;
++
i
)
{
data
.
push_back
(
0.0
);
}
for
(
int
i
=
0
;
i
<
num_jobs
;
++
i
)
{
threads
[
i
].
join
();
}
}
//TEST(alexnet, naive) {
// test_naive(1 << 0, "./trt_models/vgg19");
//}
// read data
// std::ifstream infile("new_file.list");
// std::string temp_s;
// std::vector<std::string> all_files;
// while (!infile.eof()) {
// infile >> temp_s;
// all_files.push_back(temp_s);
// }
}
// namespace paddle
// // size_t file_num = all_files.size();
// infile.close();
// // =============read file list =============
// for (size_t f_k = 0; f_k < 1; f_k++) {
// std::ifstream in_img(all_files[f_k]);
// std::cout << all_files[f_k] << std::endl;
// float temp_v;
int
main
(
int
argc
,
char
**
argv
)
{
paddle
::
test_naive
(
1
<<
0
,
""
);
}
// float sum_n = 0.0;
// std::vector<float> data;
// while (!in_img.eof()) {
// in_img >> temp_v;
// data.push_back(float(temp_v));
// sum_n += temp_v;
// }
// in_img.close();
// std::cout << "sum: " << sum_n << std::endl;
PaddleTensor
tensor
;
tensor
.
shape
=
std
::
vector
<
int
>
({
batch_size
,
3
,
height
,
width
});
tensor
.
data
.
Resize
(
sizeof
(
float
)
*
batch_size
*
3
*
height
*
width
);
std
::
copy
(
data
.
begin
(),
data
.
end
(),
static_cast
<
float
*>
(
tensor
.
data
.
data
()));
tensor
.
dtype
=
PaddleDType
::
FLOAT32
;
std
::
vector
<
PaddleTensor
>
paddle_tensor_feeds
(
1
,
tensor
);
constexpr
int
num_jobs
=
2
;
// each job run 1 batch
std
::
vector
<
std
::
thread
>
threads
;
for
(
int
tid
=
0
;
tid
<
num_jobs
;
++
tid
)
{
threads
.
emplace_back
([
&
,
tid
]()
{
PaddleTensor
tensor_out
;
std
::
vector
<
PaddleTensor
>
outputs
(
1
,
tensor_out
);
auto
predictor
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
);
for
(
size_t
i
=
0
;
i
<
1000
;
i
++
)
{
ASSERT_TRUE
(
predictor
->
Run
(
paddle_tensor_feeds
,
&
outputs
));
VLOG
(
0
)
<<
"tid : "
<<
tid
<<
" run: "
<<
i
<<
"finished"
;
//std::cout <<"tid : " << tid << " run: " << i << "finished" << std::endl;
ASSERT_EQ
(
outputs
.
size
(),
1UL
);
// int64_t* data_o = static_cast<int64_t*>(outputs[0].data.data());
// int64_t sum_out = 0;
// for (size_t j = 0; j < outputs[0].data.length() / sizeof(int64_t);
// ++j) {
// sum_out += data_o[j];
// }
// std::cout << "tid : " << tid << "pass : " << i << " " << sum_out
// << std::endl;
}
});
}
for
(
int
i
=
0
;
i
<
num_jobs
;
++
i
)
{
threads
[
i
].
join
();
}
}
// }
}
// namespace paddle
int
main
(
int
argc
,
char
**
argv
)
{
paddle
::
test_naive
(
1
<<
0
,
""
);
return
0
;
}
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @
09409bad
...
...
@@ -163,7 +163,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
VLOG
(
3
)
<<
"after get workspace"
;
// Allocate on GPU memory
platform
::
CUDAPlace
gpu
=
boost
::
get
<
platform
::
CUDAPlace
>
(
ctx
.
GetPlace
());
workspace_size_in_bytes
=
1024
;
//
workspace_size_in_bytes = 1024;
cudnn_workspace
=
paddle
::
memory
::
Alloc
(
gpu
,
workspace_size_in_bytes
);
VLOG
(
3
)
<<
"allocate memory"
;
// ------------------- cudnn conv forward ---------------------
...
...
@@ -324,7 +324,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// Already on GPU
void
*
cudnn_workspace
=
nullptr
;
platform
::
CUDAPlace
gpu
=
boost
::
get
<
platform
::
CUDAPlace
>
(
ctx
.
GetPlace
());
workspace_size_in_bytes
=
1024
;
//
workspace_size_in_bytes = 1024;
cudnn_workspace
=
paddle
::
memory
::
Alloc
(
gpu
,
workspace_size_in_bytes
);
// ------------------- cudnn conv backward data ---------------------
ScalingParamType
<
T
>
alpha
=
1.0
f
,
beta
=
0.0
f
;
...
...
paddle/fluid/operators/load_combine_op.cc
浏览文件 @
09409bad
...
...
@@ -62,18 +62,18 @@ class LoadCombineOp : public framework::OperatorBase {
VLOG
(
3
)
<<
"before deserialization"
;
// Get data from fin to tensor
DeserializeFromStream
(
fin
,
tensor
,
dev_ctx
);
VLOG
(
3
)
<<
"after deserialization"
;
framework
::
Tensor
check
;
framework
::
TensorCopy
(
*
tensor
,
platform
::
CPUPlace
(),
dev_ctx
,
&
check
);
float
sum
=
.0
;
for
(
size_t
i
=
0
;
i
<
check
.
numel
();
++
i
)
{
if
(
std
::
type_index
(
check
.
type
())
==
std
::
type_index
(
typeid
(
int64_t
)))
{
sum
+=
static_cast
<
float
>
(
check
.
data
<
int64_t
>
()[
i
]);
}
else
{
sum
+=
check
.
data
<
float
>
()[
i
];
}
}
VLOG
(
3
)
<<
"sum result"
<<
sum
;
//
VLOG(3) << "after deserialization";
//
framework::Tensor check;
//
framework::TensorCopy(*tensor, platform::CPUPlace(), dev_ctx, &check);
//
float sum = .0;
//
for(size_t i=0; i < check.numel(); ++i) {
//
if(std::type_index(check.type()) == std::type_index(typeid(int64_t))) {
//
sum += static_cast<float>(check.data<int64_t>()[i]);
//
} else {
//
sum += check.data<float>()[i];
//
}
//
}
//
VLOG(3) << "sum result" << sum;
auto
in_dtype
=
framework
::
ToDataType
(
tensor
->
type
());
auto
out_dtype
=
load_as_fp16
?
framework
::
proto
::
VarType
::
FP16
:
in_dtype
;
...
...
paddle/fluid/operators/top_k_op.cc
浏览文件 @
09409bad
...
...
@@ -50,7 +50,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor) The input of Topk op"
);
AddOutput
(
"Out"
,
"(Tensor) The output tensor of Topk op"
)
.
Reuse
(
"X"
)
;
AddOutput
(
"Out"
,
"(Tensor) The output tensor of Topk op"
);
AddOutput
(
"Indices"
,
"(Tensor) The indices of Topk elements of input"
);
AddComment
(
R"DOC(
Top K operator
...
...
paddle/fluid/operators/top_k_op.cu
浏览文件 @
09409bad
...
...
@@ -256,36 +256,65 @@ __device__ __forceinline__ void BlockReduce(Pair<T>* sh_topk, int* maxid,
* 3. go to the second setp, until one thread's topk value is null;
* 4. go to the first setp, until get the topk value.
*/
template
<
typename
T
,
int
MaxLength
,
int
BlockSize
>
__global__
void
KeMatrixTopK
(
T
*
output
,
int
output_stride
,
int64_t
*
indices
,
const
T
*
src
,
int
lds
,
int
dim
,
int
k
)
{
const
T
*
src
,
int
lds
,
int
dim
,
int
k
,
int
grid_dim
,
int
num
)
{
__shared__
Pair
<
T
>
sh_topk
[
BlockSize
];
__shared__
int
maxid
[
BlockSize
/
2
];
const
int
tid
=
threadIdx
.
x
;
const
int
warp
=
threadIdx
.
x
/
32
;
output
+=
blockIdx
.
x
*
output_stride
;
indices
+=
blockIdx
.
x
*
k
;
Pair
<
T
>
topk
[
MaxLength
];
int
beam
=
MaxLength
;
Pair
<
T
>
max
;
bool
is_empty
=
false
;
bool
firststep
=
true
;
const
int
bid
=
blockIdx
.
x
;
for
(
int
i
=
bid
;
i
<
num
;
i
+=
grid_dim
)
{
int
top_num
=
k
;
__shared__
int
maxid
[
BlockSize
/
2
];
T
*
out
=
output
+
i
*
output_stride
;
int64_t
*
inds
=
indices
+
i
*
k
;
Pair
<
T
>
topk
[
MaxLength
];
int
beam
=
MaxLength
;
Pair
<
T
>
max
;
bool
is_empty
=
false
;
bool
firststep
=
true
;
for
(
int
j
=
0
;
j
<
MaxLength
;
j
++
)
{
topk
[
j
].
set
(
-
INFINITY
,
-
1
);
}
while
(
top_num
)
{
ThreadGetTopK
<
T
,
MaxLength
,
BlockSize
>
(
topk
,
&
beam
,
k
,
src
+
i
*
lds
,
&
firststep
,
&
is_empty
,
&
max
,
dim
,
tid
);
for
(
int
k
=
0
;
k
<
MaxLength
;
k
++
)
{
topk
[
k
].
set
(
-
INFINITY
,
-
1
);
sh_topk
[
tid
]
=
topk
[
0
];
BlockReduce
<
T
,
MaxLength
,
BlockSize
>
(
sh_topk
,
maxid
,
topk
,
&
out
,
&
inds
,
&
beam
,
&
top_num
,
tid
,
warp
);
}
}
while
(
k
)
{
ThreadGetTopK
<
T
,
MaxLength
,
BlockSize
>
(
topk
,
&
beam
,
k
,
src
+
blockIdx
.
x
*
lds
,
&
firststep
,
&
is_empty
,
&
max
,
dim
,
tid
);
sh_topk
[
tid
]
=
topk
[
0
];
BlockReduce
<
T
,
MaxLength
,
BlockSize
>
(
sh_topk
,
maxid
,
topk
,
&
output
,
&
indices
,
&
beam
,
&
k
,
tid
,
warp
);
}
inline
static
int
GetDesiredBlockDim
(
int
dim
)
{
if
(
dim
>
128
)
{
return
256
;
}
else
if
(
dim
>
64
)
{
return
128
;
}
else
if
(
dim
>
32
)
{
return
64
;
}
else
{
return
32
;
}
}
#define FIXED_BLOCK_DIM_BASE(dim, ...) \
case (dim): { \
constexpr auto kBlockDim = (dim); \
__VA_ARGS__; \
} break
#define FIXED_BLOCK_DIM(...) \
FIXED_BLOCK_DIM_BASE(256, ##__VA_ARGS__); \
FIXED_BLOCK_DIM_BASE(128, ##__VA_ARGS__); \
FIXED_BLOCK_DIM_BASE(64, ##__VA_ARGS__); \
FIXED_BLOCK_DIM_BASE(32, ##__VA_ARGS__)
template
<
typename
T
>
class
TopkOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -298,30 +327,38 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
size_t
k
=
static_cast
<
int
>
(
ctx
.
Attr
<
int
>
(
"k"
));
const
T
*
input_data
=
input
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// FIXME(typhoonzero): data is always converted to type T?
int64_t
*
indices_data
=
indices
->
mutable_data
<
int64_t
>
(
ctx
.
GetPlace
());
size_t
input_height
=
input
->
dims
()[
0
];
size_t
input_width
=
input
->
dims
()[
1
];
framework
::
DDim
inputdims
=
input
->
dims
();
const
size_t
input_height
=
framework
::
product
(
framework
::
slice_ddim
(
inputdims
,
0
,
inputdims
.
size
()
-
1
));
const
size_t
input_width
=
inputdims
[
inputdims
.
size
()
-
1
];
if
(
k
>
input_width
)
k
=
input_width
;
// NOTE: pass lds and dim same to input width.
// NOTE: old matrix implementation of stride is different to eigen.
// TODO(typhoonzero): refine this kernel.
dim3
threads
(
256
,
1
);
dim3
grid
(
input_height
,
1
);
KeMatrixTopK
<
T
,
5
,
256
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
output_data
,
output
->
dims
()[
1
],
indices_data
,
input_data
,
input_width
,
input_width
,
static_cast
<
int
>
(
k
));
const
int
kMaxHeight
=
2048
;
int
gridx
=
input_height
<
kMaxHeight
?
input_height
:
kMaxHeight
;
auto
&
dev_ctx
=
ctx
.
cuda_device_context
();
switch
(
GetDesiredBlockDim
(
input_width
))
{
FIXED_BLOCK_DIM
(
KeMatrixTopK
<
T
,
5
,
kBlockDim
><<<
gridx
,
kBlockDim
,
0
,
dev_ctx
.
stream
()
>>>
(
output_data
,
k
,
indices_data
,
input_data
,
input_width
,
input_width
,
static_cast
<
int
>
(
k
),
gridx
,
input_height
));
default:
PADDLE_THROW
(
"Error"
);
}
}
};
#undef FIXED_BLOCK_DIM_BASE
#undef FIXED_BLOCK_DIM
}
// namespace operators
}
// namespace paddle
...
...
paddle/fluid/operators/top_k_op.h
浏览文件 @
09409bad
...
...
@@ -34,7 +34,6 @@ class TopkKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
// Get the top k elements of each row of input tensor
// FIXME: only deal with matrix(2d tensor).
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
*
indices
=
ctx
.
Output
<
Tensor
>
(
"Indices"
);
...
...
@@ -44,8 +43,6 @@ class TopkKernel : public framework::OpKernel<T> {
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int64_t
*
indices_data
=
indices
->
mutable_data
<
int64_t
>
(
ctx
.
GetPlace
());
auto
eg_input
=
EigenMatrix
<
T
>::
From
(
*
input
);
// reshape input to a flattern matrix(like flat_inner_dims)
framework
::
DDim
inputdims
=
input
->
dims
();
const
size_t
row
=
framework
::
product
(
...
...
@@ -53,7 +50,7 @@ class TopkKernel : public framework::OpKernel<T> {
const
size_t
col
=
inputdims
[
inputdims
.
size
()
-
1
];
Eigen
::
DSizes
<
int
,
2
>
flat2dims
(
row
,
col
);
// NOTE: eigen shape doesn't affect paddle tensor.
eg_input
.
reshape
(
flat2dims
);
auto
eg_input
=
EigenMatrix
<
T
>::
Reshape
(
*
input
,
inputdims
.
size
()
-
1
);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
...
...
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