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f59a7c1d
编写于
10月 14, 2017
作者:
Q
qijun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add gpu functor for SelectedRows
上级
4741266d
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
405 addition
and
195 deletion
+405
-195
paddle/framework/lod_tensor.h
paddle/framework/lod_tensor.h
+0
-3
paddle/framework/selected_rows.h
paddle/framework/selected_rows.h
+4
-3
paddle/framework/selected_rows_test.cc
paddle/framework/selected_rows_test.cc
+1
-1
paddle/framework/type_defs.h
paddle/framework/type_defs.h
+2
-4
paddle/operators/math/CMakeLists.txt
paddle/operators/math/CMakeLists.txt
+1
-1
paddle/operators/math/math_function.cc
paddle/operators/math/math_function.cc
+13
-4
paddle/operators/math/math_function.cu
paddle/operators/math/math_function.cu
+107
-0
paddle/operators/math/math_function_test.cc
paddle/operators/math/math_function_test.cc
+0
-179
paddle/operators/math/math_function_test.cu
paddle/operators/math/math_function_test.cu
+277
-0
未找到文件。
paddle/framework/lod_tensor.h
浏览文件 @
f59a7c1d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
...
...
paddle/framework/selected_rows.h
浏览文件 @
f59a7c1d
...
@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
...
@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#pragma once
#pragma once
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/framework/tensor.h"
namespace
paddle
{
namespace
paddle
{
...
@@ -34,9 +35,9 @@ class SelectedRows {
...
@@ -34,9 +35,9 @@ class SelectedRows {
void
set_height
(
int64_t
height
)
{
height_
=
height
;
}
void
set_height
(
int64_t
height
)
{
height_
=
height
;
}
const
std
::
v
ector
<
int64_t
>&
rows
()
const
{
return
rows_
;
}
const
V
ector
<
int64_t
>&
rows
()
const
{
return
rows_
;
}
void
set_rows
(
const
std
::
v
ector
<
int64_t
>&
rows
)
{
rows_
=
rows
;
}
void
set_rows
(
const
V
ector
<
int64_t
>&
rows
)
{
rows_
=
rows
;
}
DDim
GetCompleteDims
()
const
{
DDim
GetCompleteDims
()
const
{
std
::
vector
<
int64_t
>
dims
=
vectorize
(
value_
->
dims
());
std
::
vector
<
int64_t
>
dims
=
vectorize
(
value_
->
dims
());
...
@@ -48,7 +49,7 @@ class SelectedRows {
...
@@ -48,7 +49,7 @@ class SelectedRows {
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
// SelectedRows are simplely concated when adding together. Until a
// SelectedRows are simplely concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
// SelectedRows add a Tensor, will the duplicate rows be handled.
std
::
v
ector
<
int64_t
>
rows_
;
V
ector
<
int64_t
>
rows_
;
std
::
unique_ptr
<
Tensor
>
value_
{
nullptr
};
std
::
unique_ptr
<
Tensor
>
value_
{
nullptr
};
int64_t
height_
;
int64_t
height_
;
};
};
...
...
paddle/framework/selected_rows_test.cc
浏览文件 @
f59a7c1d
...
@@ -18,7 +18,7 @@ namespace framework {
...
@@ -18,7 +18,7 @@ namespace framework {
class
SelectedRowsTester
:
public
::
testing
::
Test
{
class
SelectedRowsTester
:
public
::
testing
::
Test
{
public:
public:
virtual
void
SetUp
()
override
{
virtual
void
SetUp
()
override
{
std
::
v
ector
<
int64_t
>
rows
{
0
,
4
,
7
};
V
ector
<
int64_t
>
rows
{
0
,
4
,
7
};
int64_t
height
=
10
;
int64_t
height
=
10
;
int64_t
row_numel
=
100
;
int64_t
row_numel
=
100
;
selected_rows_
.
reset
(
new
SelectedRows
(
rows
,
height
));
selected_rows_
.
reset
(
new
SelectedRows
(
rows
,
height
));
...
...
paddle/framework/type_defs.h
浏览文件 @
f59a7c1d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
...
@@ -37,7 +34,8 @@ using OpCreator = std::function<OperatorBase*(
...
@@ -37,7 +34,8 @@ using OpCreator = std::function<OperatorBase*(
const
VariableNameMap
&
/*outputs*/
,
const
AttributeMap
&
/*attrs*/
)
>
;
const
VariableNameMap
&
/*outputs*/
,
const
AttributeMap
&
/*attrs*/
)
>
;
using
GradOpMakerFN
=
std
::
function
<
std
::
vector
<
std
::
unique_ptr
<
OpDescBind
>>
(
using
GradOpMakerFN
=
std
::
function
<
std
::
vector
<
std
::
unique_ptr
<
OpDescBind
>>
(
const
OpDescBind
&
,
const
std
::
unordered_set
<
std
::
string
>&
/*no_grad_set*/
)
>
;
const
OpDescBind
&
,
const
std
::
unordered_set
<
std
::
string
>&
/*no_grad_set*/
,
std
::
unordered_map
<
std
::
string
,
std
::
string
>*
/*grad_to_var*/
)
>
;
}
// namespace framework
}
// namespace framework
}
// namespace paddle
}
// namespace paddle
paddle/operators/math/CMakeLists.txt
浏览文件 @
f59a7c1d
if
(
WITH_GPU
)
if
(
WITH_GPU
)
nv_library
(
math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator
)
nv_library
(
math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator
)
nv_test
(
math_function_
test SRCS math_function_test.cc
DEPS math_function tensor
)
nv_test
(
math_function_
gpu_test SRCS math_function_test.cu
DEPS math_function tensor
)
nv_library
(
softmax SRCS softmax.cc softmax.cu DEPS operator
)
nv_library
(
softmax SRCS softmax.cc softmax.cu DEPS operator
)
nv_library
(
cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator
)
nv_library
(
cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator
)
nv_library
(
pooling SRCS pooling.cc pooling.cu DEPS device_context
)
nv_library
(
pooling SRCS pooling.cc pooling.cu DEPS device_context
)
...
...
paddle/operators/math/math_function.cc
浏览文件 @
f59a7c1d
...
@@ -162,15 +162,24 @@ struct SelectedRowsAdd<platform::CPUPlace, T> {
...
@@ -162,15 +162,24 @@ struct SelectedRowsAdd<platform::CPUPlace, T> {
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
in2_value
.
numel
()
/
in2_rows
.
size
());
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
in2_value
.
numel
()
/
in2_rows
.
size
());
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
out_value
->
numel
()
/
out_rows
.
size
());
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
out_value
->
numel
()
/
out_rows
.
size
());
auto
*
out_data
=
out_value
->
data
<
T
>
();
auto
in1_place
=
input1
.
place
();
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
in1_place
));
auto
in2_place
=
input2
.
place
();
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
in2_place
));
auto
out_place
=
context
.
GetPlace
();
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
out_place
));
auto
*
out_data
=
out_value
->
data
<
T
>
();
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
memory
::
Copy
(
platform
::
CPUPlace
(),
out_data
,
platform
::
CPUPlace
(),
in1_data
,
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
out_place
),
out_data
,
boost
::
get
<
platform
::
CPUPlace
>
(
in1_place
),
in1_data
,
in1_value
.
numel
()
*
sizeof
(
T
));
in1_value
.
numel
()
*
sizeof
(
T
));
auto
*
in2_data
=
in2_value
.
data
<
T
>
();
auto
*
in2_data
=
in2_value
.
data
<
T
>
();
memory
::
Copy
(
platform
::
CPUPlace
(),
out_data
+
in1_value
.
numel
(),
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
out_place
),
platform
::
CPUPlace
(),
in2_data
,
in2_value
.
numel
()
*
sizeof
(
T
));
out_data
+
in1_value
.
numel
(),
boost
::
get
<
platform
::
CPUPlace
>
(
in2_place
),
in2_data
,
in2_value
.
numel
()
*
sizeof
(
T
));
}
}
};
};
...
...
paddle/operators/math/math_function.cu
浏览文件 @
f59a7c1d
...
@@ -155,6 +155,113 @@ void matmul<platform::GPUPlace, double>(
...
@@ -155,6 +155,113 @@ void matmul<platform::GPUPlace, double>(
matrix_b
.
data
<
double
>
(),
beta
,
matrix_out
->
data
<
double
>
());
matrix_b
.
data
<
double
>
(),
beta
,
matrix_out
->
data
<
double
>
());
}
}
template
<
typename
T
>
struct
SelectedRowsAdd
<
platform
::
GPUPlace
,
T
>
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
SelectedRows
&
input2
,
framework
::
SelectedRows
*
output
)
{
auto
in1_height
=
input1
.
height
();
PADDLE_ENFORCE_EQ
(
in1_height
,
input2
.
height
());
output
->
set_height
(
in1_height
);
auto
&
in1_rows
=
input1
.
rows
();
auto
&
in2_rows
=
input2
.
rows
();
std
::
vector
<
int64_t
>
out_rows
;
out_rows
.
reserve
(
in1_rows
.
size
()
+
in2_rows
.
size
());
// concat rows
out_rows
.
insert
(
out_rows
.
end
(),
in1_rows
.
begin
(),
in1_rows
.
end
());
out_rows
.
insert
(
out_rows
.
end
(),
in2_rows
.
begin
(),
in2_rows
.
end
());
output
->
set_rows
(
out_rows
);
auto
*
out_value
=
output
->
mutable_value
();
auto
&
in1_value
=
input1
.
value
();
auto
&
in2_value
=
input2
.
value
();
auto
in1_row_numel
=
in1_value
.
numel
()
/
in1_rows
.
size
();
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
in2_value
.
numel
()
/
in2_rows
.
size
());
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
out_value
->
numel
()
/
out_rows
.
size
());
auto
*
out_data
=
out_value
->
data
<
T
>
();
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
auto
in1_place
=
input1
.
place
();
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
in1_place
));
auto
in2_place
=
input2
.
place
();
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
in2_place
));
auto
out_place
=
context
.
GetPlace
();
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
out_place
))
memory
::
Copy
(
boost
::
get
<
platform
::
GPUPlace
>
(
out_place
),
out_data
,
boost
::
get
<
platform
::
GPUPlace
>
(
in1_place
),
in1_data
,
in1_value
.
numel
()
*
sizeof
(
T
),
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
).
stream
());
auto
*
in2_data
=
in2_value
.
data
<
T
>
();
memory
::
Copy
(
boost
::
get
<
platform
::
GPUPlace
>
(
out_place
),
out_data
+
in1_value
.
numel
(),
boost
::
get
<
platform
::
GPUPlace
>
(
in2_place
),
in2_data
,
in2_value
.
numel
()
*
sizeof
(
T
),
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
).
stream
());
}
};
template
struct
SelectedRowsAdd
<
platform
::
GPUPlace
,
float
>;
namespace
{
template
<
int
block_size
,
typename
T
>
__global__
void
SelectedRowsAddTensorKernel
(
T
*
selected_rows
,
int64_t
*
rows
,
T
*
tensor_in
,
T
*
tensor_out
,
const
int64_t
row_numel
)
{
const
ty
=
blockIdx
.
y
;
int
tid
=
threadIdx
.
x
;
selected_rows
+=
ty
*
row_numel
;
tensor_in
+=
rows
[
ty
]
*
row_numel
;
tensor_out
+=
rows
[
ty
]
*
row_numel
;
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
=
tensor_in
[
index
]
+
selected_rows
[
index
];
}
}
}
template
<
typename
T
>
struct
SelectedRowsAddTensor
<
platform
::
GPUPlace
,
T
>
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
Tensor
&
input2
,
framework
::
Tensor
*
output
)
{
auto
in1_height
=
input1
.
height
();
auto
in2_dims
=
input2
.
dims
();
auto
out_dims
=
output
->
dims
();
PADDLE_ENFORCE_EQ
(
in1_height
,
in2_dims
[
0
]);
PADDLE_ENFORCE_EQ
(
in1_height
,
out_dims
[
0
]);
auto
&
in1_value
=
input1
.
value
();
auto
&
in1_rows
=
input1
.
rows
();
int64_t
in1_row_numel
=
in1_value
.
numel
()
/
in1_rows
.
size
();
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
input2
.
numel
()
/
in1_height
);
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
output
->
numel
()
/
in1_height
);
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
auto
*
in2_data
=
input2
.
data
<
T
>
();
auto
*
out_data
=
output
->
data
<
T
>
();
const
int
block_size
=
256
;
dim3
threads
(
block_size
,
1
);
dim3
grid
(
1
,
in1_height
);
SelectedRowsAddTensorKernel
<
block_size
,
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
).
stream
()
>>>
(
in1_data
,
in1_rows
.
data
(),
in2_data
,
out_data
,
in1_row_numel
);
}
};
template
struct
SelectedRowsAddTensor
<
platform
::
GPUPlace
,
float
>;
}
// namespace math
}
// namespace math
}
// namespace operators
}
// namespace operators
}
// namespace paddle
}
// namespace paddle
paddle/operators/math/math_function_test.cc
浏览文件 @
f59a7c1d
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/math_function.h"
#include "gtest/gtest.h"
#include "gtest/gtest.h"
#ifdef PADDLE_WITH_CUDA
TEST
(
math_function
,
notrans_mul_trans
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
out_gpu
;
paddle
::
framework
::
Tensor
out
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr
,
6
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
out_gpu
.
mutable_data
<
float
>
({
2
,
2
},
*
gpu_place
);
paddle
::
operators
::
math
::
matmul
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
input1_gpu
,
false
,
input2_gpu
,
true
,
1
,
&
out_gpu
,
0
);
out
.
CopyFrom
<
float
>
(
out_gpu
,
*
cpu_place
,
context
);
float
*
out_ptr
=
out
.
data
<
float
>
();
context
.
Wait
();
EXPECT_EQ
(
out_ptr
[
0
],
5
);
EXPECT_EQ
(
out_ptr
[
1
],
14
);
EXPECT_EQ
(
out_ptr
[
2
],
14
);
EXPECT_EQ
(
out_ptr
[
3
],
50
);
delete
gpu_place
;
}
TEST
(
math_function
,
trans_mul_notrans
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
out_gpu
;
paddle
::
framework
::
Tensor
out
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr
,
6
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
out_gpu
.
mutable_data
<
float
>
({
3
,
3
},
*
gpu_place
);
paddle
::
operators
::
math
::
matmul
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
input1_gpu
,
true
,
input2_gpu
,
false
,
1
,
&
out_gpu
,
0
);
out
.
CopyFrom
<
float
>
(
out_gpu
,
*
cpu_place
,
context
);
float
*
out_ptr
=
out
.
data
<
float
>
();
context
.
Wait
();
EXPECT_EQ
(
out_ptr
[
0
],
9
);
EXPECT_EQ
(
out_ptr
[
1
],
12
);
EXPECT_EQ
(
out_ptr
[
2
],
15
);
EXPECT_EQ
(
out_ptr
[
3
],
12
);
EXPECT_EQ
(
out_ptr
[
4
],
17
);
EXPECT_EQ
(
out_ptr
[
5
],
22
);
EXPECT_EQ
(
out_ptr
[
6
],
15
);
EXPECT_EQ
(
out_ptr
[
7
],
22
);
EXPECT_EQ
(
out_ptr
[
8
],
29
);
delete
gpu_place
;
}
TEST
(
math_function
,
gemm_notrans_cublas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
input3_gpu
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
3
,
4
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
,
10
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input2
,
*
gpu_place
,
context
);
input3_gpu
.
CopyFrom
<
float
>
(
input3
,
*
gpu_place
,
context
);
float
*
a
=
input1_gpu
.
data
<
float
>
();
float
*
b
=
input2_gpu
.
data
<
float
>
();
float
*
c
=
input3_gpu
.
mutable_data
<
float
>
(
*
gpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
false
,
false
,
m
,
n
,
k
,
1
,
a
,
3
,
b
+
1
,
4
,
1
,
c
+
1
,
4
);
input3
.
CopyFrom
<
float
>
(
input3_gpu
,
*
cpu_place
,
context
);
// numpy code:
// a = np.arange(6).reshape(2, 3)
// b = np.arange(12).reshape(3, 4)[:, 1:]
// c = np.arange(8).reshape(2, 4)[:, 1:]
// out = np.arange(8).reshape(2, 4)
// out[:, 1:] = np.dot(a, b) + c
context
.
Wait
();
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
delete
gpu_place
;
}
TEST
(
math_function
,
gemm_trans_cublas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
input3_gpu
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
4
,
3
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
4
,
8
,
1
,
5
,
9
,
2
,
6
,
10
,
3
,
7
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input2
,
*
gpu_place
,
context
);
input3_gpu
.
CopyFrom
<
float
>
(
input3
,
*
gpu_place
,
context
);
float
*
a
=
input1_gpu
.
data
<
float
>
();
float
*
b
=
input2_gpu
.
data
<
float
>
();
float
*
c
=
input3_gpu
.
mutable_data
<
float
>
(
*
gpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
false
,
true
,
m
,
n
,
k
,
1
,
a
,
3
,
b
+
3
,
3
,
1
,
c
+
1
,
4
);
input3
.
CopyFrom
<
float
>
(
input3_gpu
,
*
cpu_place
,
context
);
context
.
Wait
();
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
delete
gpu_place
;
}
#endif
TEST
(
math_function
,
gemm_notrans_cblas
)
{
TEST
(
math_function
,
gemm_notrans_cblas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input2
;
...
...
paddle/operators/math/math_function_test.cu
0 → 100644
浏览文件 @
f59a7c1d
#include "gtest/gtest.h"
#include "paddle/operators/math/math_function.h"
TEST
(
math_function
,
notrans_mul_trans
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
out_gpu
;
paddle
::
framework
::
Tensor
out
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr
,
6
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
out_gpu
.
mutable_data
<
float
>
({
2
,
2
},
*
gpu_place
);
paddle
::
operators
::
math
::
matmul
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
input1_gpu
,
false
,
input2_gpu
,
true
,
1
,
&
out_gpu
,
0
);
out
.
CopyFrom
<
float
>
(
out_gpu
,
*
cpu_place
,
context
);
float
*
out_ptr
=
out
.
data
<
float
>
();
context
.
Wait
();
EXPECT_EQ
(
out_ptr
[
0
],
5
);
EXPECT_EQ
(
out_ptr
[
1
],
14
);
EXPECT_EQ
(
out_ptr
[
2
],
14
);
EXPECT_EQ
(
out_ptr
[
3
],
50
);
delete
gpu_place
;
}
TEST
(
math_function
,
trans_mul_notrans
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
out_gpu
;
paddle
::
framework
::
Tensor
out
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr
,
6
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
out_gpu
.
mutable_data
<
float
>
({
3
,
3
},
*
gpu_place
);
paddle
::
operators
::
math
::
matmul
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
input1_gpu
,
true
,
input2_gpu
,
false
,
1
,
&
out_gpu
,
0
);
out
.
CopyFrom
<
float
>
(
out_gpu
,
*
cpu_place
,
context
);
float
*
out_ptr
=
out
.
data
<
float
>
();
context
.
Wait
();
EXPECT_EQ
(
out_ptr
[
0
],
9
);
EXPECT_EQ
(
out_ptr
[
1
],
12
);
EXPECT_EQ
(
out_ptr
[
2
],
15
);
EXPECT_EQ
(
out_ptr
[
3
],
12
);
EXPECT_EQ
(
out_ptr
[
4
],
17
);
EXPECT_EQ
(
out_ptr
[
5
],
22
);
EXPECT_EQ
(
out_ptr
[
6
],
15
);
EXPECT_EQ
(
out_ptr
[
7
],
22
);
EXPECT_EQ
(
out_ptr
[
8
],
29
);
delete
gpu_place
;
}
TEST
(
math_function
,
gemm_notrans_cublas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
input3_gpu
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
3
,
4
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
,
10
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input2
,
*
gpu_place
,
context
);
input3_gpu
.
CopyFrom
<
float
>
(
input3
,
*
gpu_place
,
context
);
float
*
a
=
input1_gpu
.
data
<
float
>
();
float
*
b
=
input2_gpu
.
data
<
float
>
();
float
*
c
=
input3_gpu
.
mutable_data
<
float
>
(
*
gpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
false
,
false
,
m
,
n
,
k
,
1
,
a
,
3
,
b
+
1
,
4
,
1
,
c
+
1
,
4
);
input3
.
CopyFrom
<
float
>
(
input3_gpu
,
*
cpu_place
,
context
);
// numpy code:
// a = np.arange(6).reshape(2, 3)
// b = np.arange(12).reshape(3, 4)[:, 1:]
// c = np.arange(8).reshape(2, 4)[:, 1:]
// out = np.arange(8).reshape(2, 4)
// out[:, 1:] = np.dot(a, b) + c
context
.
Wait
();
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
delete
gpu_place
;
}
TEST
(
math_function
,
gemm_trans_cublas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
input3_gpu
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
4
,
3
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
4
,
8
,
1
,
5
,
9
,
2
,
6
,
10
,
3
,
7
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
,
context
);
input2_gpu
.
CopyFrom
<
float
>
(
input2
,
*
gpu_place
,
context
);
input3_gpu
.
CopyFrom
<
float
>
(
input3
,
*
gpu_place
,
context
);
float
*
a
=
input1_gpu
.
data
<
float
>
();
float
*
b
=
input2_gpu
.
data
<
float
>
();
float
*
c
=
input3_gpu
.
mutable_data
<
float
>
(
*
gpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
false
,
true
,
m
,
n
,
k
,
1
,
a
,
3
,
b
+
3
,
3
,
1
,
c
+
1
,
4
);
input3
.
CopyFrom
<
float
>
(
input3_gpu
,
*
cpu_place
,
context
);
context
.
Wait
();
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
delete
gpu_place
;
}
TEST
(
math_function
,
selected_rows_add
)
{
using
namespace
paddle
::
framework
;
using
namespace
paddle
::
platform
;
using
namespace
paddle
::
operators
::
math
;
CPUPlace
gpu_place
(
0
);
CUDADeviceContext
ctx
(
gpu_place
);
SetConstant
<
GPUPlace
,
float
>
functor
;
int64_t
height
=
10
;
int64_t
row_numel
=
10
;
Vector
<
int64_t
>
rows1
{
0
,
4
,
7
};
std
::
unique_ptr
<
SelectedRows
>
selected_rows1
{
new
SelectedRows
(
rows1
,
height
)};
auto
*
in1_value
=
selected_rows1
->
mutable_value
();
in1_value
->
mutable_data
<
float
>
(
make_ddim
({
static_cast
<
int64_t
>
(
rows1
.
size
()),
row_numel
}),
gpu_place
);
functor
(
ctx
,
in1_value
,
1.0
);
Vector
<
int64_t
>
rows2
{
0
,
5
,
7
,
9
};
std
::
unique_ptr
<
SelectedRows
>
selected_rows2
{
new
SelectedRows
(
rows2
,
height
)};
auto
*
in2_value
=
selected_rows2
->
mutable_value
();
in2_value
->
mutable_data
<
float
>
(
make_ddim
({
static_cast
<
int64_t
>
(
rows2
.
size
()),
row_numel
}),
gpu_place
);
functor
(
ctx
,
in2_value
,
2.0
);
std
::
unique_ptr
<
SelectedRows
>
output
{
new
SelectedRows
()};
auto
*
out_value
=
output
->
mutable_value
();
// simplely concat two SelectedRows
out_value
->
mutable_data
<
float
>
(
make_ddim
({
7
,
10
}),
gpu_place
);
SelectedRowsAdd
<
GPUPlace
,
float
>
add_functor
;
add_functor
(
ctx
,
*
selected_rows1
,
*
selected_rows2
,
output
.
get
());
auto
out_height
=
output
->
height
();
EXPECT_EQ
(
out_height
,
height
);
auto
&
out_rows
=
output
->
rows
();
// input1 rows
EXPECT_EQ
(
out_rows
[
0
],
0
);
EXPECT_EQ
(
out_rows
[
1
],
4
);
EXPECT_EQ
(
out_rows
[
2
],
7
);
// input2 rows
EXPECT_EQ
(
out_rows
[
3
],
0
);
EXPECT_EQ
(
out_rows
[
4
],
5
);
EXPECT_EQ
(
out_rows
[
5
],
7
);
EXPECT_EQ
(
out_rows
[
6
],
9
);
Tensor
out_cpu
;
out_cpu
.
CopyFrom
<
float
>
(
*
out_value
,
platform
::
CPUPlace
(),
ctx
);
ctx
.
Wait
();
auto
*
out_cpu_data
=
out_cpu
.
data
<
float
>
();
// input1 value
EXPECT_EQ
(
out_cpu_data
[
0
*
row_numel
+
0
],
1.0
);
EXPECT_EQ
(
out_cpu_data
[
0
*
row_numel
+
8
],
1.0
);
EXPECT_EQ
(
out_cpu_data
[
1
*
row_numel
+
1
],
1.0
);
EXPECT_EQ
(
out_cpu_data
[
2
*
row_numel
+
6
],
1.0
);
// input2 value
EXPECT_EQ
(
out_cpu_data
[
3
*
row_numel
+
3
],
2.0
);
EXPECT_EQ
(
out_cpu_data
[
3
*
row_numel
+
8
],
2.0
);
EXPECT_EQ
(
out_cpu_data
[
4
*
row_numel
+
4
],
2.0
);
EXPECT_EQ
(
out_cpu_data
[
5
*
row_numel
+
7
],
2.0
);
EXPECT_EQ
(
out_cpu_data
[
6
*
row_numel
+
9
],
2.0
);
std
::
unique_ptr
<
Tensor
>
tensor1
{
new
Tensor
()};
tensor1
->
mutable_data
<
float
>
(
make_ddim
({
height
,
row_numel
}),
gpu_place
);
SetConstant
<
GPUPlace
,
float
>
constant_functor
;
constant_functor
(
ctx
,
tensor1
.
get
(),
3.0
);
std
::
unique_ptr
<
Tensor
>
tensor2
{
new
Tensor
()};
tensor2
->
mutable_data
<
float
>
(
make_ddim
({
height
,
row_numel
}),
gpu_place
);
SelectedRowsAddTensor
<
GPUPlace
,
float
>
add_tensor_functor
;
add_tensor_functor
(
ctx
,
*
output
,
*
tensor1
,
tensor2
.
get
());
Tensor
tensor2_cpu
;
tensor2_cpu
.
CopyFrom
<
float
>
(
*
tensor2
,
platform
::
CPUPlace
(),
ctx
);
ctx
.
Wait
();
auto
*
tensor2_cpu_data
=
tensor2_cpu
->
data
<
float
>
();
// row0: 1.0 + 2.0 + 3.0
EXPECT_EQ
(
tensor2_cpu_data
[
0
*
row_numel
+
0
],
6.0
);
// row1: 3.0
EXPECT_EQ
(
tensor2_cpu_data
[
1
*
row_numel
+
1
],
3.0
);
// row4 : 1.0 + 3.0
EXPECT_EQ
(
tensor2_cpu_data
[
4
*
row_numel
+
6
],
4.0
);
// row5: 2.0 + 3.0
EXPECT_EQ
(
tensor2_cpu_data
[
5
*
row_numel
+
7
],
5.0
);
// row6: 3.0
EXPECT_EQ
(
tensor2_cpu_data
[
6
*
row_numel
+
1
],
3.0
);
// row7: 1.0 + 2.0 + 3.0
EXPECT_EQ
(
tensor2_cpu_data
[
7
*
row_numel
+
3
],
6.0
);
// row9: 2.0 + 3.0
EXPECT_EQ
(
tensor2_cpu_data
[
9
*
row_numel
+
6
],
5.0
);
}
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