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00e596ed
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00e596ed
编写于
3月 02, 2018
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
get max threads of GPU
上级
60e7ee06
变更
7
显示空白变更内容
内联
并排
Showing
7 changed file
with
320 addition
and
55 deletion
+320
-55
paddle/fluid/operators/concat_op.h
paddle/fluid/operators/concat_op.h
+10
-9
paddle/fluid/operators/math/concat.cc
paddle/fluid/operators/math/concat.cc
+54
-21
paddle/fluid/operators/math/concat.cu
paddle/fluid/operators/math/concat.cu
+148
-22
paddle/fluid/operators/math/concat.h
paddle/fluid/operators/math/concat.h
+8
-3
paddle/fluid/operators/math/concat_test.cc
paddle/fluid/operators/math/concat_test.cc
+74
-0
paddle/fluid/platform/gpu_info.cc
paddle/fluid/platform/gpu_info.cc
+20
-0
paddle/fluid/platform/gpu_info.h
paddle/fluid/platform/gpu_info.h
+6
-0
未找到文件。
paddle/fluid/operators/concat_op.h
浏览文件 @
00e596ed
...
...
@@ -32,6 +32,7 @@ class ConcatKernel : public framework::OpKernel<T> {
int64_t
axis
=
static_cast
<
int64_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
auto
place
=
ctx
.
GetPlace
();
out
->
mutable_data
<
T
>
(
place
);
std
::
vector
<
framework
::
Tensor
>
inputs
(
ins
.
size
());
for
(
size_t
j
=
0
;
j
<
ins
.
size
();
++
j
)
{
inputs
[
j
]
=
*
ins
[
j
];
...
...
@@ -49,17 +50,17 @@ class ConcatGradKernel : public framework::OpKernel<T> {
auto
*
in
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
outs
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
int64_t
axis
=
static_cast
<
int64_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
size_t
input_offset
=
0
;
auto
in_stride
=
framework
::
stride_numel
(
in
->
dims
());
for
(
auto
&
out
:
outs
)
{
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
out_stride
=
framework
::
stride_numel
(
out
->
dims
());
StridedNumelCopyWithAxis
<
T
>
(
ctx
.
device_context
(),
axis
,
out
->
data
<
T
>
(),
out_stride
,
in
->
data
<
T
>
()
+
input_offset
,
in_stride
,
out_stride
[
axis
]);
input_offset
+=
out_stride
[
axis
];
std
::
vector
<
framework
::
Tensor
>
outputs
(
outs
.
size
());
for
(
size_t
j
=
0
;
j
<
outs
.
size
();
++
j
)
{
outs
[
j
]
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
outputs
[
j
]
=
*
outs
[
j
];
}
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
paddle
::
operators
::
math
::
ConcatGradFunctor
<
DeviceContext
,
T
>
concat_grad_functor
;
concat_grad_functor
(
dev_ctx
,
*
in
,
static_cast
<
int
>
(
axis
),
outputs
);
}
};
...
...
paddle/fluid/operators/math/concat.cc
浏览文件 @
00e596ed
...
...
@@ -25,16 +25,12 @@ template <typename T>
class
ConcatFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
std
::
vector
<
framework
::
Tensor
>&
input
,
const
int
axis
,
const
std
::
vector
<
framework
::
Tensor
>&
input
,
const
int
axis
,
framework
::
Tensor
*
output
)
{
// assume the the max size of input is less than 8 and see the performance
// save origin dim
int
num
=
input
.
size
();
std
::
vector
<
paddle
::
framework
::
DDim
>
origin_dim
(
num
);
// for (int j = 0; j < num; ++j) {
// origin_dim[j] = input[j].dims();
// }
auto
out_dim
=
output
->
dims
();
// get the matrix size
int
rows
=
1
;
...
...
@@ -42,40 +38,72 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
{
rows
*=
dim_0
[
i
];
}
int
cols
=
input
[
0
].
numel
()
/
rows
;
int
out_rows
=
rows
,
out_cols
=
0
;
bool
sameShape
=
true
;
// reshape to matrix
// get input's cols
std
::
vector
<
int64_t
>
input_cols
(
input
.
size
());
for
(
int
i
=
0
;
i
<
num
;
++
i
)
{
int
t_cols
=
input
[
i
].
numel
()
/
rows
;
if
(
sameShape
)
{
if
(
t_cols
!=
cols
)
sameShape
=
false
;
}
out_cols
+=
t_cols
;
input
[
i
].
Resize
({
rows
,
t_cols
})
;
input
_cols
[
i
]
=
t_cols
;
}
output
->
Resize
({
out_rows
,
out_cols
});
auto
&
cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
context
.
GetPlace
());
// computation
for
(
int
k
=
0
;
k
<
rows
;
++
k
)
{
// offset k * out_cols
for
(
int
k
=
0
;
k
<
out_rows
;
++
k
)
{
T
*
dst_ptr
=
output
->
data
<
T
>
()
+
k
*
out_cols
;
int
col_idx
=
0
;
for
(
int
j
=
0
;
j
<
num
;
++
j
)
{
int
col_len
=
input
[
j
].
dims
()[
1
];
int
col_len
=
input
_cols
[
j
];
const
T
*
src_prt
=
input
[
j
].
data
<
T
>
()
+
k
*
col_len
;
memory
::
Copy
(
cpu_place
,
dst_ptr
+
col_idx
,
cpu_place
,
src_prt
,
sizeof
(
T
)
*
col_len
);
col_idx
+=
col_len
;
}
}
}
};
template
<
typename
T
>
class
ConcatGradFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
int
axis
,
std
::
vector
<
framework
::
Tensor
>&
outputs
)
{
// assume the the max size of input is less than 8 and see the performance
// save origin dim
int
num
=
outputs
.
size
();
std
::
vector
<
paddle
::
framework
::
DDim
>
origin_dim
(
num
);
// recover origin dim
// for (int j = 0; j < num; ++j) {
// input[j]->Resize(origin_dim[j]);
// }
output
->
Resize
(
out_dim
);
// get the matrix size
int
input_rows
=
1
;
auto
dim_0
=
outputs
[
0
].
dims
();
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
{
input_rows
*=
dim_0
[
i
];
}
int
input_cols
=
0
;
// get outputs' cols
std
::
vector
<
int64_t
>
output_cols
(
outputs
.
size
());
for
(
int
i
=
0
;
i
<
num
;
++
i
)
{
int
t_cols
=
outputs
[
i
].
numel
()
/
input_rows
;
input_cols
+=
t_cols
;
output_cols
[
i
]
=
t_cols
;
}
auto
&
cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
context
.
GetPlace
());
// computation
for
(
int
k
=
0
;
k
<
input_rows
;
++
k
)
{
const
T
*
src_ptr
=
input
.
data
<
T
>
()
+
k
*
input_cols
;
int
col_idx
=
0
;
for
(
int
j
=
0
;
j
<
num
;
++
j
)
{
int
col_len
=
output_cols
[
j
];
T
*
dst_ptr
=
outputs
[
j
].
data
<
T
>
()
+
k
*
col_len
;
memory
::
Copy
(
cpu_place
,
dst_ptr
,
cpu_place
,
src_ptr
+
col_idx
,
sizeof
(
T
)
*
col_len
);
col_idx
+=
col_len
;
}
}
}
};
...
...
@@ -84,6 +112,11 @@ template class ConcatFunctor<platform::CPUDeviceContext, int64_t>;
template
class
ConcatFunctor
<
platform
::
CPUDeviceContext
,
float
>;
template
class
ConcatFunctor
<
platform
::
CPUDeviceContext
,
double
>;
template
class
ConcatGradFunctor
<
platform
::
CPUDeviceContext
,
int
>;
template
class
ConcatGradFunctor
<
platform
::
CPUDeviceContext
,
int64_t
>;
template
class
ConcatGradFunctor
<
platform
::
CPUDeviceContext
,
float
>;
template
class
ConcatGradFunctor
<
platform
::
CPUDeviceContext
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/concat.cu
浏览文件 @
00e596ed
...
...
@@ -22,7 +22,7 @@ namespace math {
// TODO(zcd): This can be replaced by tensor,
// if that, maybe we should add int8 to VarType::Type.
// Or replaced by tensorArray.
static
constexpr
int
MaxSize
=
32
;
static
constexpr
int
MaxSize
=
8
;
template
<
typename
T
>
struct
CUDADeviceArray
{
T
data
[
MaxSize
];
...
...
@@ -54,7 +54,6 @@ __global__ void KernelConcat(const CUDADeviceArray<const T*> inputs,
const
int
output_rows
,
const
int
output_cols
,
T
*
output
)
{
int
tid_x
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
tid_y
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
int
segment
=
upper_bound
<
int
>
(
input_cols
.
data
,
input_cols
.
size
,
tid_x
)
-
1
;
int
curr_offset
=
input_cols
.
data
[
segment
];
...
...
@@ -69,13 +68,73 @@ __global__ void KernelConcat(const CUDADeviceArray<const T*> inputs,
int
local_col
=
tid_x
-
curr_offset
;
int
segment_width
=
curr_col_offset
-
curr_offset
;
const
T
*
input_ptr
=
inputs
.
data
[
curr_segment
];
int
tid_y
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
for
(;
tid_y
<
output_rows
;
tid_y
+=
blockDim
.
y
*
gridDim
.
y
)
output
[
tid_y
*
output_cols
+
tid_x
]
=
input_ptr
[
tid_y
*
segment_width
+
local_col
];
}
}
template
<
typename
T
>
__global__
void
KernelConcat
(
const
CUDADeviceArray
<
const
T
*>
inputs
,
const
int
input_col
,
const
int
output_rows
,
const
int
output_cols
,
T
*
output
)
{
int
tid_x
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
float
inv_input_col
=
1.0
/
input_col
;
for
(;
tid_x
<
output_cols
;
tid_x
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
split
=
tid_x
*
inv_input_col
;
int
in_offset
=
tid_x
-
split
*
input_col
;
const
T
*
input_ptr
=
inputs
.
data
[
split
];
int
tid_y
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
for
(;
tid_y
<
output_rows
;
tid_y
+=
blockDim
.
y
*
gridDim
.
y
)
output
[
tid_y
*
output_cols
+
tid_x
]
=
input_ptr
[
tid_y
*
input_col
+
in_offset
];
}
}
template
<
typename
T
>
__global__
void
KernelConcatGrad
(
const
T
*
input
,
const
int
input_row
,
const
int
input_col
,
CUDADeviceArray
<
int
>
output_cols
,
CUDADeviceArray
<
T
*>
outputs
)
{
int
tid_x
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
segment
=
upper_bound
<
int
>
(
output_cols
.
data
,
output_cols
.
size
,
tid_x
)
-
1
;
int
curr_offset
=
output_cols
.
data
[
segment
];
int
curr_segment
=
segment
;
for
(;
tid_x
<
input_col
;
tid_x
+=
blockDim
.
x
*
gridDim
.
x
)
{
T
curr_col_offset
;
while
((
curr_col_offset
=
output_cols
.
data
[
curr_segment
+
1
])
<=
tid_x
)
{
curr_offset
=
curr_col_offset
;
++
curr_segment
;
}
int
local_col
=
tid_x
-
curr_offset
;
int
segment_width
=
curr_col_offset
-
curr_offset
;
T
*
output_ptr
=
outputs
.
data
[
curr_segment
];
int
tid_y
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
for
(;
tid_y
<
input_row
;
tid_y
+=
blockDim
.
y
*
gridDim
.
y
)
output_ptr
[
tid_y
*
segment_width
+
local_col
]
=
input
[
tid_y
*
input_col
+
tid_x
];
}
}
template
<
typename
T
>
__global__
void
KernelConcatGrad
(
const
T
*
input
,
const
int
input_row
,
const
int
input_col
,
const
int
output_cols
,
CUDADeviceArray
<
T
*>
outputs
)
{
int
tid_x
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
float
inv_input_col
=
1.0
/
input_col
;
for
(;
tid_x
<
input_col
;
tid_x
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
split
=
tid_x
*
inv_input_col
;
int
in_offset
=
tid_x
-
split
*
input_col
;
T
*
output_ptr
=
outputs
.
data
[
split
];
int
tid_y
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
for
(;
tid_y
<
input_row
;
tid_y
+=
blockDim
.
y
*
gridDim
.
y
)
output_ptr
[
tid_y
*
output_cols
+
in_offset
]
=
input
[
tid_y
*
input_col
+
tid_x
];
}
}
/*
* All tensors' dimension should be the same.
*/
...
...
@@ -83,17 +142,13 @@ template <typename T>
class
ConcatFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
std
::
vector
<
framework
::
Tensor
>&
input
,
const
int
axis
,
const
std
::
vector
<
framework
::
Tensor
>&
input
,
const
int
axis
,
framework
::
Tensor
*
output
)
{
// assume the the max size of input is less than 8 and see the performance
// save origin dim
int
num
=
input
.
size
();
// std::vector<paddle::framework::DDim> origin_dim(num);
// for (int j = 0; j < num; ++j) {
// origin_dim[j] = input[j].dims();
// }
auto
out_dim
=
output
->
dims
();
PADDLE_ENFORCE_LT
(
num
,
MaxSize
,
"input number should be less than %d"
,
MaxSize
);
// get the matrix size
int
rows
=
1
;
auto
dim_0
=
input
[
0
].
dims
();
...
...
@@ -117,30 +172,96 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
if
(
t_cols
!=
cols
)
sameShape
=
false
;
}
out_cols
+=
t_cols
;
input
[
i
].
Resize
({
rows
,
t_cols
});
inputs_cols
.
data
[
i
+
1
]
=
out_cols
;
inputs_data
.
data
[
i
]
=
input
[
i
].
data
<
T
>
();
}
output
->
Resize
({
out_rows
,
out_cols
});
// computation
const
int
kThreadsPerBlock
=
256
;
// set the thread block and grid according to CurrentDeviceId
const
int
kThreadsPerBlock
=
1024
;
int
block_cols
=
std
::
min
(
out_cols
,
kThreadsPerBlock
);
int
block_rows
=
std
::
max
(
kThreadsPerBlock
/
block_cols
,
1
);
dim3
block_size
=
dim3
(
block_cols
,
block_rows
,
1
);
int
grid_cols
=
(
out_cols
+
block_cols
-
1
)
/
block_cols
;
int
grid_rows
=
(
out_rows
+
block_rows
-
1
)
/
block_rows
;
int
dev_id
=
paddle
::
platform
::
GetCurrentDeviceId
();
int
multi_process
=
paddle
::
platform
::
GetCUDAMultiProcessors
(
dev_id
);
int
max_threads_per_mp
=
paddle
::
platform
::
GetCUDAMaxThreadsPerMultiProcessor
(
dev_id
);
int
max_threads
=
multi_process
*
max_threads_per_mp
;
int
max_blocks
=
std
::
max
(
max_threads
/
kThreadsPerBlock
,
1
);
int
grid_cols
=
std
::
min
((
out_cols
+
block_cols
-
1
)
/
block_cols
,
max_blocks
);
int
grid_rows
=
std
::
min
(
max_blocks
/
grid_cols
,
std
::
max
(
out_rows
/
block_rows
,
1
));
dim3
grid_size
=
dim3
(
grid_cols
,
grid_rows
,
1
);
if
(
sameShape
)
{
KernelConcat
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
inputs_data
,
cols
,
out_rows
,
out_cols
,
output
->
data
<
T
>
());
}
else
{
KernelConcat
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
inputs_data
,
inputs_cols
,
out_rows
,
out_cols
,
output
->
data
<
T
>
());
}
}
};
template
<
typename
T
>
class
ConcatGradFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
int
axis
,
std
::
vector
<
framework
::
Tensor
>&
outputs
)
{
// assume the the max size of input is less than 8 and see the performance
// save origin dim
int
num
=
outputs
.
size
();
PADDLE_ENFORCE_LT
(
num
,
MaxSize
,
"input number should be less than %d"
,
MaxSize
);
// recover origin dim
// for (int j = 0; j < num; ++j) {
// input[j].Resize(origin_dim[j]);
// }
output
->
Resize
(
out_dim
);
// get the matrix size
int
input_row
=
1
;
auto
dim_0
=
outputs
[
0
].
dims
();
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
{
input_row
*=
dim_0
[
i
];
}
int
output_col_0
=
outputs
[
0
].
numel
()
/
input_row
;
int
input_col
=
0
;
bool
sameShape
=
true
;
CUDADeviceArray
<
T
*>
outputs_data
;
CUDADeviceArray
<
int
>
outputs_cols
;
outputs_data
.
size
=
num
;
outputs_cols
.
size
=
num
+
1
;
outputs_cols
.
data
[
0
]
=
0
;
for
(
int
i
=
0
;
i
<
num
;
++
i
)
{
int
t_col
=
outputs
[
i
].
numel
()
/
input_row
;
if
(
sameShape
)
{
if
(
t_col
!=
output_col_0
)
sameShape
=
false
;
}
input_col
+=
t_col
;
outputs_cols
.
data
[
i
+
1
]
=
input_col
;
outputs_data
.
data
[
i
]
=
outputs
[
i
].
data
<
T
>
();
}
// computation
const
int
kThreadsPerBlock
=
256
;
int
block_cols
=
std
::
min
(
input_col
,
kThreadsPerBlock
);
int
block_rows
=
std
::
max
(
kThreadsPerBlock
/
block_cols
,
1
);
dim3
block_size
=
dim3
(
block_cols
,
block_rows
,
1
);
int
grid_cols
=
(
input_col
+
block_cols
-
1
)
/
block_cols
;
int
grid_rows
=
(
input_row
+
block_rows
-
1
)
/
block_rows
;
dim3
grid_size
=
dim3
(
grid_cols
,
grid_rows
,
1
);
if
(
sameShape
)
{
KernelConcatGrad
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
input
.
data
<
T
>
(),
input_row
,
input_col
,
output_col_0
,
outputs_data
);
}
else
{
KernelConcatGrad
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
input
.
data
<
T
>
(),
input_row
,
input_col
,
outputs_cols
,
outputs_data
);
}
}
};
...
...
@@ -149,6 +270,11 @@ template class ConcatFunctor<platform::CUDADeviceContext, int64_t>;
template
class
ConcatFunctor
<
platform
::
CUDADeviceContext
,
float
>;
template
class
ConcatFunctor
<
platform
::
CUDADeviceContext
,
double
>;
template
class
ConcatGradFunctor
<
platform
::
CUDADeviceContext
,
int
>;
template
class
ConcatGradFunctor
<
platform
::
CUDADeviceContext
,
int64_t
>;
template
class
ConcatGradFunctor
<
platform
::
CUDADeviceContext
,
float
>;
template
class
ConcatGradFunctor
<
platform
::
CUDADeviceContext
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/concat.h
浏览文件 @
00e596ed
...
...
@@ -20,18 +20,23 @@ namespace operators {
namespace
math
{
/*
* the tensor's shape of input will be changed,
* so the second parameter is not const.
*
*/
template
<
typename
DeviceContext
,
typename
T
>
class
ConcatFunctor
{
public:
void
operator
()(
const
DeviceContext
&
context
,
std
::
vector
<
framework
::
Tensor
>&
input
,
const
int
axis
,
const
std
::
vector
<
framework
::
Tensor
>&
input
,
const
int
axis
,
framework
::
Tensor
*
output
);
};
template
<
typename
DeviceContext
,
typename
T
>
class
ConcatGradFunctor
{
public:
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
int
axis
,
std
::
vector
<
framework
::
Tensor
>&
outputs
);
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/concat_test.cc
浏览文件 @
00e596ed
...
...
@@ -251,6 +251,80 @@ void testConcat() {
}
}
}
/**
* cast4:
* inputs:
* axis = 1
* t_a.shape: [2, 3, 4]
* t_b.shape: [2, 3, 4]
* output:
* out.shape: [2, 6, 4]
*/
dim_a
=
make_ddim
({
2
,
3
,
4
});
dim_b
=
make_ddim
({
2
,
3
,
4
});
dim_out
=
make_ddim
({
2
,
6
,
4
});
input_a
.
Resize
(
dim_a
);
input_b
.
Resize
(
dim_b
);
out
.
Resize
(
dim_out
);
if
(
paddle
::
platform
::
is_gpu_place
(
Place
()))
{
input_a_cpu
.
Resize
(
dim_a
);
input_b_cpu
.
Resize
(
dim_b
);
out_cpu
.
Resize
(
dim_out
);
}
if
(
paddle
::
platform
::
is_gpu_place
(
Place
()))
{
a_ptr
=
input_a_cpu
.
data
<
int
>
();
b_ptr
=
input_b_cpu
.
data
<
int
>
();
}
else
{
a_ptr
=
input_a
.
data
<
int
>
();
b_ptr
=
input_b
.
data
<
int
>
();
}
for
(
int
i
=
0
;
i
<
2
*
3
*
4
;
++
i
)
{
a_ptr
[
i
]
=
i
;
}
for
(
int
i
=
0
;
i
<
2
*
3
*
4
;
++
i
)
{
b_ptr
[
i
]
=
i
;
}
if
(
paddle
::
platform
::
is_gpu_place
(
Place
()))
{
TensorCopy
(
input_a_cpu
,
Place
(),
*
context
,
&
input_a
);
TensorCopy
(
input_b_cpu
,
Place
(),
*
context
,
&
input_b
);
}
input
.
clear
();
input
.
push_back
(
input_a
);
input
.
push_back
(
input_b
);
concat_functor
(
*
context
,
input
,
1
,
&
out
);
// check the dim of input_a, input_b
PADDLE_ENFORCE_EQ
(
input_a
.
dims
(),
dim_a
);
PADDLE_ENFORCE_EQ
(
input_b
.
dims
(),
dim_b
);
if
(
paddle
::
platform
::
is_gpu_place
(
Place
()))
{
TensorCopy
(
out
,
CPUPlace
(),
*
context
,
&
out_cpu
);
out_ptr
=
out_cpu
.
data
<
int
>
();
}
else
{
out_ptr
=
out
.
data
<
int
>
();
}
// check the data
cols
=
12
;
idx_a
=
0
,
idx_b
=
0
;
for
(
int
i
=
0
;
i
<
2
;
++
i
)
{
for
(
int
j
=
0
;
j
<
24
;
++
j
)
{
if
(
j
>=
cols
)
{
PADDLE_ENFORCE_EQ
(
out_ptr
[
i
*
24
+
j
],
b_ptr
[
idx_b
]);
++
idx_b
;
}
else
{
PADDLE_ENFORCE_EQ
(
out_ptr
[
i
*
24
+
j
],
a_ptr
[
idx_a
]);
++
idx_a
;
}
}
}
}
TEST
(
math
,
concat
)
{
...
...
paddle/fluid/platform/gpu_info.cc
浏览文件 @
00e596ed
...
...
@@ -33,6 +33,26 @@ int GetCUDADeviceCount() {
return
count
;
}
int
GetCUDAMultiProcessors
(
int
id
)
{
PADDLE_ENFORCE_LT
(
id
,
GetCUDADeviceCount
(),
"id must less than GPU count"
);
int
count
;
PADDLE_ENFORCE
(
cudaDeviceGetAttribute
(
&
count
,
cudaDevAttrMultiProcessorCount
,
id
),
"cudaDeviceGetAttribute failed in "
"paddle::platform::GetCUDAMultiProcessors"
);
return
count
;
}
int
GetCUDAMaxThreadsPerMultiProcessor
(
int
id
)
{
PADDLE_ENFORCE_LT
(
id
,
GetCUDADeviceCount
(),
"id must less than GPU count"
);
int
count
;
PADDLE_ENFORCE
(
cudaDeviceGetAttribute
(
&
count
,
cudaDevAttrMaxThreadsPerMultiProcessor
,
id
),
"cudaDeviceGetAttribute failed in "
"paddle::platform::GetCUDAMaxThreadsPerMultiProcessor"
);
return
count
;
}
int
GetCurrentDeviceId
()
{
int
device_id
;
PADDLE_ENFORCE
(
...
...
paddle/fluid/platform/gpu_info.h
浏览文件 @
00e596ed
...
...
@@ -30,6 +30,12 @@ const std::string kEnvFractionGpuMemoryToUse =
//! Get the total number of GPU devices in system.
int
GetCUDADeviceCount
();
//! Get the MultiProcessors of the ith GPU.
int
GetCUDAMultiProcessors
(
int
i
);
//! Get the MaxThreads of each MultiProcessor of the ith GPU.
int
GetCUDAMaxThreadsPerMultiProcessor
(
int
i
);
//! Get the current GPU device id in system.
int
GetCurrentDeviceId
();
...
...
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