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体验新版 GitCode,发现更多精彩内容 >>
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187bf412
编写于
4月 06, 2021
作者:
W
wuhuanzhou
提交者:
GitHub
4月 06, 2021
浏览文件
操作
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电子邮件补丁
差异文件
optimize compilation of operators using eigen (#31851)
上级
78af100c
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
309 addition
and
60 deletion
+309
-60
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+3
-1
paddle/fluid/operators/addmm_op.h
paddle/fluid/operators/addmm_op.h
+5
-3
paddle/fluid/operators/eigen/CMakeLists.txt
paddle/fluid/operators/eigen/CMakeLists.txt
+10
-0
paddle/fluid/operators/eigen/broadcast.cc
paddle/fluid/operators/eigen/broadcast.cc
+86
-0
paddle/fluid/operators/eigen/broadcast.cu
paddle/fluid/operators/eigen/broadcast.cu
+87
-0
paddle/fluid/operators/eigen/eigen_function.h
paddle/fluid/operators/eigen/eigen_function.h
+52
-0
paddle/fluid/operators/expand_as_op.h
paddle/fluid/operators/expand_as_op.h
+10
-9
paddle/fluid/operators/expand_as_v2_op.h
paddle/fluid/operators/expand_as_v2_op.h
+10
-9
paddle/fluid/operators/expand_op.h
paddle/fluid/operators/expand_op.h
+12
-10
paddle/fluid/operators/expand_v2_op.h
paddle/fluid/operators/expand_v2_op.h
+12
-10
paddle/fluid/operators/meshgrid_op.h
paddle/fluid/operators/meshgrid_op.h
+10
-8
paddle/fluid/operators/tile_op.h
paddle/fluid/operators/tile_op.h
+12
-10
未找到文件。
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
187bf412
...
...
@@ -10,6 +10,7 @@ file(WRITE ${pybind_file} "// Generated by the paddle/fluid/operators/CMakeLists
copy_if_different
(
${
pybind_file
}
${
pybind_file_final
}
)
add_subdirectory
(
math
)
add_subdirectory
(
eigen
)
add_subdirectory
(
controlflow
)
add_subdirectory
(
detection
)
add_subdirectory
(
elementwise
)
...
...
@@ -110,8 +111,9 @@ set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_fun
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions beam_search fc matrix_inverse
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
box_wrapper boost ps_gpu_wrapper
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
common_infer_shape_functions
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
eigen_cc_function
)
if
(
WITH_GPU OR WITH_ROCM
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
depthwise_conv prelu bert_encoder_functor
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
depthwise_conv prelu bert_encoder_functor
eigen_cu_function
)
endif
()
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
device_memory_aligment
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
layer
)
...
...
paddle/fluid/operators/addmm_op.h
浏览文件 @
187bf412
...
...
@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
...
...
@@ -32,8 +33,8 @@ template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
using
Array1
=
Eigen
::
DSizes
<
int64_t
,
1
>
;
using
Array2
=
Eigen
::
DSizes
<
int64_t
,
2
>
;
using
Array1
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
1
>
;
using
Array2
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
2
>
;
using
Tensor
=
framework
::
Tensor
;
...
...
@@ -105,7 +106,8 @@ class AddMMKernel : public framework::OpKernel<T> {
auto
eigen_out
=
EigenTensor
<
T
,
2
>::
From
(
*
out
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
eigen_out
.
device
(
place
)
=
eigen_input
.
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
2
>::
Eval
(
place
,
eigen_out
,
eigen_input
,
bcast_dims
);
blas
.
GEMM
(
false
,
false
,
x_dims
[
0
],
y_dims
[
1
],
x_dims
[
1
],
alpha
,
x
->
data
<
T
>
(),
x_dims
[
1
],
y
->
data
<
T
>
(),
y_dims
[
1
],
beta
,
...
...
paddle/fluid/operators/eigen/CMakeLists.txt
0 → 100644
浏览文件 @
187bf412
file
(
GLOB EIGEN_CC_SOURCES RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"*.cc"
)
cc_library
(
eigen_cc_function SRCS
${
EIGEN_CC_SOURCES
}
DEPS eigen3
)
if
(
WITH_GPU OR WITH_ROCM
)
file
(
GLOB EIGEN_CU_SOURCES RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"*.cu"
)
if
(
WITH_GPU
)
nv_library
(
eigen_cu_function SRCS
${
EIGEN_CU_SOURCES
}
DEPS eigen3
)
elseif
(
WITH_ROCM
)
hip_library
(
eigen_cu_function SRCS
${
EIGEN_CU_SOURCES
}
DEPS eigen3
)
endif
()
endif
()
paddle/fluid/operators/eigen/broadcast.cc
0 → 100644
浏览文件 @
187bf412
/* Copyright (c) 2021 PaddlePaddle Authors. 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. */
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/platform/float16.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
,
int
Rank
>
struct
EigenBroadcast
<
Eigen
::
DefaultDevice
,
T
,
Rank
>
{
using
Array
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
;
using
InType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
Rank
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
InType32BitIndex
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
Rank
,
Eigen
::
RowMajor
,
int
>
,
Eigen
::
Aligned
>
;
using
OutType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
Rank
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
OutType32BitIndex
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
Rank
,
Eigen
::
RowMajor
,
int
>
,
Eigen
::
Aligned
>
;
static
void
Eval
(
const
Eigen
::
DefaultDevice
&
dev
,
OutType
out
,
InType
in
,
const
Array
&
bcast
)
{
out
.
device
(
dev
)
=
in
.
broadcast
(
bcast
);
}
static
void
Eval
(
const
Eigen
::
DefaultDevice
&
dev
,
OutType32BitIndex
out
,
InType32BitIndex
in
,
const
Array
&
bcast
)
{
out
.
device
(
dev
)
=
in
.
broadcast
(
bcast
);
}
};
template
<
typename
T
,
int
Rank
>
struct
EigenBroadcastGrad
<
Eigen
::
DefaultDevice
,
T
,
Rank
>
{
using
Array
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
;
using
Array2
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
*
2
>
;
using
InType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
OutType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
static
void
Eval
(
const
Eigen
::
DefaultDevice
&
dev
,
OutType
out
,
InType
in
,
const
Array
&
reduce_dims
,
const
Array2
&
reshape_dims
)
{
out
.
device
(
dev
)
=
in
.
reshape
(
reshape_dims
).
sum
(
reduce_dims
).
reshape
(
out
.
dimensions
());
}
};
#define INSTANTIATION(FUNCTOR, T) \
template struct FUNCTOR<Eigen::DefaultDevice, T, 1>; \
template struct FUNCTOR<Eigen::DefaultDevice, T, 2>; \
template struct FUNCTOR<Eigen::DefaultDevice, T, 3>; \
template struct FUNCTOR<Eigen::DefaultDevice, T, 4>; \
template struct FUNCTOR<Eigen::DefaultDevice, T, 5>; \
template struct FUNCTOR<Eigen::DefaultDevice, T, 6>
INSTANTIATION
(
EigenBroadcast
,
bool
);
INSTANTIATION
(
EigenBroadcast
,
platform
::
float16
);
INSTANTIATION
(
EigenBroadcast
,
float
);
INSTANTIATION
(
EigenBroadcast
,
double
);
INSTANTIATION
(
EigenBroadcast
,
int
);
INSTANTIATION
(
EigenBroadcast
,
int64_t
);
INSTANTIATION
(
EigenBroadcastGrad
,
bool
);
INSTANTIATION
(
EigenBroadcastGrad
,
float
);
INSTANTIATION
(
EigenBroadcastGrad
,
platform
::
float16
);
INSTANTIATION
(
EigenBroadcastGrad
,
double
);
INSTANTIATION
(
EigenBroadcastGrad
,
int
);
INSTANTIATION
(
EigenBroadcastGrad
,
int64_t
);
template
struct
EigenBroadcastGrad
<
Eigen
::
DefaultDevice
,
float
,
0
>;
template
struct
EigenBroadcastGrad
<
Eigen
::
DefaultDevice
,
double
,
0
>;
template
struct
EigenBroadcastGrad
<
Eigen
::
DefaultDevice
,
int
,
0
>;
template
struct
EigenBroadcastGrad
<
Eigen
::
DefaultDevice
,
int64_t
,
0
>;
#undef INSTANTIATION
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/eigen/broadcast.cu
0 → 100644
浏览文件 @
187bf412
/* Copyright (c) 2021 PaddlePaddle Authors. 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. */
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/platform/float16.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
,
int
Rank
>
struct
EigenBroadcast
<
Eigen
::
GpuDevice
,
T
,
Rank
>
{
using
Array
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
;
using
InType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
Rank
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
InType32BitIndex
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
Rank
,
Eigen
::
RowMajor
,
int
>
,
Eigen
::
Aligned
>
;
using
OutType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
Rank
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
OutType32BitIndex
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
Rank
,
Eigen
::
RowMajor
,
int
>
,
Eigen
::
Aligned
>
;
static
void
Eval
(
const
Eigen
::
GpuDevice
&
dev
,
OutType
out
,
InType
in
,
const
Array
&
bcast
)
{
out
.
device
(
dev
)
=
in
.
broadcast
(
bcast
);
}
static
void
Eval
(
const
Eigen
::
GpuDevice
&
dev
,
OutType32BitIndex
out
,
InType32BitIndex
in
,
const
Array
&
bcast
)
{
out
.
device
(
dev
)
=
in
.
broadcast
(
bcast
);
}
};
template
<
typename
T
,
int
Rank
>
struct
EigenBroadcastGrad
<
Eigen
::
GpuDevice
,
T
,
Rank
>
{
using
Array
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
;
using
Array2
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
*
2
>
;
using
InType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
OutType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
static
void
Eval
(
const
Eigen
::
GpuDevice
&
dev
,
OutType
out
,
InType
in
,
const
Array
&
reduce_dims
,
const
Array2
&
reshape_dims
)
{
out
.
device
(
dev
)
=
in
.
reshape
(
reshape_dims
).
sum
(
reduce_dims
).
reshape
(
out
.
dimensions
());
}
};
#define INSTANTIATION(FUNCTOR, T) \
template struct FUNCTOR<Eigen::GpuDevice, T, 1>; \
template struct FUNCTOR<Eigen::GpuDevice, T, 2>; \
template struct FUNCTOR<Eigen::GpuDevice, T, 3>; \
template struct FUNCTOR<Eigen::GpuDevice, T, 4>; \
template struct FUNCTOR<Eigen::GpuDevice, T, 5>; \
template struct FUNCTOR<Eigen::GpuDevice, T, 6>
INSTANTIATION
(
EigenBroadcast
,
bool
);
INSTANTIATION
(
EigenBroadcast
,
platform
::
float16
);
INSTANTIATION
(
EigenBroadcast
,
float
);
INSTANTIATION
(
EigenBroadcast
,
double
);
INSTANTIATION
(
EigenBroadcast
,
int
);
INSTANTIATION
(
EigenBroadcast
,
int64_t
);
INSTANTIATION
(
EigenBroadcastGrad
,
bool
);
INSTANTIATION
(
EigenBroadcastGrad
,
float
);
INSTANTIATION
(
EigenBroadcastGrad
,
platform
::
float16
);
INSTANTIATION
(
EigenBroadcastGrad
,
double
);
INSTANTIATION
(
EigenBroadcastGrad
,
int
);
INSTANTIATION
(
EigenBroadcastGrad
,
int64_t
);
template
struct
EigenBroadcastGrad
<
Eigen
::
GpuDevice
,
float
,
0
>;
template
struct
EigenBroadcastGrad
<
Eigen
::
GpuDevice
,
platform
::
float16
,
0
>;
template
struct
EigenBroadcastGrad
<
Eigen
::
GpuDevice
,
double
,
0
>;
template
struct
EigenBroadcastGrad
<
Eigen
::
GpuDevice
,
int
,
0
>;
template
struct
EigenBroadcastGrad
<
Eigen
::
GpuDevice
,
int64_t
,
0
>;
#undef INSTANTIATION
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/eigen/eigen_function.h
0 → 100644
浏览文件 @
187bf412
/* Copyright (c) 2021 PaddlePaddle Authors. 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. */
#pragma once
#include "unsupported/Eigen/CXX11/Tensor"
namespace
paddle
{
namespace
operators
{
template
<
typename
EigenDevice
,
typename
T
,
int
Rank
>
struct
EigenBroadcast
{
using
Array
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
;
using
InType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
Rank
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
InType32BitIndex
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
Rank
,
Eigen
::
RowMajor
,
int
>
,
Eigen
::
Aligned
>
;
using
OutType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
Rank
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
OutType32BitIndex
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
Rank
,
Eigen
::
RowMajor
,
int
>
,
Eigen
::
Aligned
>
;
static
void
Eval
(
const
EigenDevice
&
dev
,
OutType
out
,
InType
in
,
const
Array
&
bcast
);
static
void
Eval
(
const
EigenDevice
&
dev
,
OutType32BitIndex
out
,
InType32BitIndex
in
,
const
Array
&
bcast
);
};
template
<
typename
EigenDevice
,
typename
T
,
int
Rank
>
struct
EigenBroadcastGrad
{
using
Array
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
;
using
Array2
=
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
*
2
>
;
using
InType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
using
OutType
=
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
;
static
void
Eval
(
const
EigenDevice
&
dev
,
OutType
out
,
InType
in
,
const
Array
&
reduce_dims
,
const
Array2
&
reshape_dims
);
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/expand_as_op.h
浏览文件 @
187bf412
...
...
@@ -22,6 +22,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#define MAX_RANK_SUPPORTED 6
...
...
@@ -75,7 +76,7 @@ class ExpandAsKernel : public framework::OpKernel<T> {
auto
in_dims
=
in0
->
dims
();
auto
*
target_tensor
=
context
.
Input
<
Tensor
>
(
"target_tensor"
);
auto
*
out0
=
context
.
Output
<
Tensor
>
(
"Out"
);
Eigen
::
DSizes
<
int
,
Rank
>
bcast_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
bcast_dims
;
int
bcast_dims_remainder
=
0
;
auto
x_dims
=
in0
->
dims
();
auto
y_dims
=
target_tensor
->
dims
();
...
...
@@ -104,7 +105,8 @@ class ExpandAsKernel : public framework::OpKernel<T> {
auto
y
=
EigenTensor
<
T
,
Rank
>::
From
(
*
out0
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
y
.
device
(
place
)
=
x
.
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
y
,
x
,
bcast_dims
);
}
};
...
...
@@ -165,20 +167,19 @@ class ExpandAsGradKernel : public framework::OpKernel<T> {
auto
*
out0
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_grad
=
EigenVector
<
T
>::
Flatten
(
*
out0
);
Eigen
::
DSizes
<
int
,
Dims
*
2
>
reshape_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
*
2
>
reshape_dims
;
for
(
size_t
i
=
0
;
i
<
reshape_size
;
++
i
)
{
reshape_dims
[
i
]
=
reshape_dims_vec
[
i
];
}
Eigen
::
DSizes
<
int
,
Dims
>
reduce_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
>
reduce_dims
;
for
(
size_t
i
=
0
;
i
<
reduce_size
;
++
i
)
{
reduce_dims
[
i
]
=
reduce_dims_vec
[
i
];
}
auto
out_grad
=
EigenVector
<
T
>::
Flatten
(
*
in0
);
x_grad
.
device
(
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
())
=
out_grad
.
reshape
(
reshape_dims
)
.
sum
(
reduce_dims
)
.
reshape
(
x_grad
.
dimensions
());
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
EigenBroadcastGrad
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Dims
>::
Eval
(
place
,
x_grad
,
out_grad
,
reduce_dims
,
reshape_dims
);
}
};
...
...
paddle/fluid/operators/expand_as_v2_op.h
浏览文件 @
187bf412
...
...
@@ -23,6 +23,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#define MAX_RANK_SUPPORTED 6
...
...
@@ -108,7 +109,7 @@ class ExpandAsV2Kernel : public framework::OpKernel<T> {
}
}
auto
*
out0
=
context
.
Output
<
Tensor
>
(
"Out"
);
Eigen
::
DSizes
<
int
,
Rank
>
bcast_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
bcast_dims
;
for
(
size_t
i
=
0
;
i
<
repeat_times
.
size
();
++
i
)
{
bcast_dims
[
i
]
=
repeat_times
[
i
];
}
...
...
@@ -122,7 +123,8 @@ class ExpandAsV2Kernel : public framework::OpKernel<T> {
auto
y
=
EigenTensor
<
T
,
Rank
>::
From
(
*
out0
,
out_dims
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
y
.
device
(
place
)
=
x
.
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
y
,
x
,
bcast_dims
);
}
};
...
...
@@ -191,20 +193,19 @@ class ExpandAsV2GradKernel : public framework::OpKernel<T> {
auto
*
out0
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_grad
=
EigenVector
<
T
>::
Flatten
(
*
out0
);
Eigen
::
DSizes
<
int
,
Dims
*
2
>
reshape_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
*
2
>
reshape_dims
;
for
(
size_t
i
=
0
;
i
<
reshape_size
;
++
i
)
{
reshape_dims
[
i
]
=
reshape_dims_vec
[
i
];
}
Eigen
::
DSizes
<
int
,
Dims
>
reduce_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
>
reduce_dims
;
for
(
size_t
i
=
0
;
i
<
reduce_size
;
++
i
)
{
reduce_dims
[
i
]
=
reduce_dims_vec
[
i
];
}
auto
out_grad
=
EigenVector
<
T
>::
Flatten
(
*
in0
);
x_grad
.
device
(
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
())
=
out_grad
.
reshape
(
reshape_dims
)
.
sum
(
reduce_dims
)
.
reshape
(
x_grad
.
dimensions
());
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
EigenBroadcastGrad
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Dims
>::
Eval
(
place
,
x_grad
,
out_grad
,
reduce_dims
,
reshape_dims
);
}
};
...
...
paddle/fluid/operators/expand_op.h
浏览文件 @
187bf412
...
...
@@ -25,6 +25,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#define MAX_RANK_SUPPORTED 6
...
...
@@ -141,7 +142,7 @@ class ExpandKernel : public framework::OpKernel<T> {
"of dimensions (%d) of the input."
,
expand_times
.
size
(),
static_cast
<
size_t
>
(
in_dims
.
size
())));
auto
*
out0
=
context
.
Output
<
Tensor
>
(
"Out"
);
Eigen
::
DSizes
<
int
,
Rank
>
bcast_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
bcast_dims
;
for
(
size_t
i
=
0
;
i
<
expand_times
.
size
();
++
i
)
{
bcast_dims
[
i
]
=
expand_times
[
i
];
}
...
...
@@ -160,9 +161,11 @@ class ExpandKernel : public framework::OpKernel<T> {
// use 32-bit index to speed up
bool
use_32bit_index
=
y
.
size
()
<
Eigen
::
NumTraits
<
int
>::
highest
();
if
(
use_32bit_index
)
{
To32BitIndex
(
y
).
device
(
place
)
=
To32BitIndex
(
x
).
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
To32BitIndex
(
y
),
To32BitIndex
(
x
),
bcast_dims
);
}
else
{
y
.
device
(
place
)
=
x
.
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
y
,
x
,
bcast_dims
);
}
}
};
...
...
@@ -241,20 +244,19 @@ class ExpandGradKernel : public framework::OpKernel<T> {
auto
*
out0
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_grad
=
EigenVector
<
T
>::
Flatten
(
*
out0
);
Eigen
::
DSizes
<
int
,
Dims
*
2
>
reshape_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
*
2
>
reshape_dims
;
for
(
size_t
i
=
0
;
i
<
reshape_size
;
++
i
)
{
reshape_dims
[
i
]
=
reshape_dims_vec
[
i
];
}
Eigen
::
DSizes
<
int
,
Dims
>
reduce_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
>
reduce_dims
;
for
(
size_t
i
=
0
;
i
<
reduce_size
;
++
i
)
{
reduce_dims
[
i
]
=
reduce_dims_vec
[
i
];
}
auto
out_grad
=
EigenVector
<
T
>::
Flatten
(
*
in0
);
x_grad
.
device
(
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
())
=
out_grad
.
reshape
(
reshape_dims
)
.
sum
(
reduce_dims
)
.
reshape
(
x_grad
.
dimensions
());
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
EigenBroadcastGrad
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Dims
>::
Eval
(
place
,
x_grad
,
out_grad
,
reduce_dims
,
reshape_dims
);
}
};
...
...
paddle/fluid/operators/expand_v2_op.h
浏览文件 @
187bf412
...
...
@@ -26,6 +26,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#define MAX_RANK_SUPPORTED 6
...
...
@@ -174,7 +175,7 @@ class ExpandV2Kernel : public framework::OpKernel<T> {
}
auto
*
out0
=
context
.
Output
<
Tensor
>
(
"Out"
);
Eigen
::
DSizes
<
int
,
Rank
>
bcast_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
bcast_dims
;
for
(
size_t
i
=
0
;
i
<
repeat_times
.
size
();
++
i
)
{
bcast_dims
[
i
]
=
repeat_times
[
i
];
}
...
...
@@ -194,9 +195,11 @@ class ExpandV2Kernel : public framework::OpKernel<T> {
// use 32-bit index to speed up
bool
use_32bit_index
=
y
.
size
()
<
Eigen
::
NumTraits
<
int
>::
highest
();
if
(
use_32bit_index
)
{
To32BitIndex
(
y
).
device
(
place
)
=
To32BitIndex
(
x
).
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
To32BitIndex
(
y
),
To32BitIndex
(
x
),
bcast_dims
);
}
else
{
y
.
device
(
place
)
=
x
.
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
y
,
x
,
bcast_dims
);
}
}
};
...
...
@@ -275,20 +278,19 @@ class ExpandV2GradKernel : public framework::OpKernel<T> {
auto
*
out0
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_grad
=
EigenVector
<
T
>::
Flatten
(
*
out0
);
Eigen
::
DSizes
<
int
,
Dims
*
2
>
reshape_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
*
2
>
reshape_dims
;
for
(
size_t
i
=
0
;
i
<
reshape_size
;
++
i
)
{
reshape_dims
[
i
]
=
reshape_dims_vec
[
i
];
}
Eigen
::
DSizes
<
int
,
Dims
>
reduce_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
>
reduce_dims
;
for
(
size_t
i
=
0
;
i
<
reduce_size
;
++
i
)
{
reduce_dims
[
i
]
=
reduce_dims_vec
[
i
];
}
auto
out_grad
=
EigenVector
<
T
>::
Flatten
(
*
in0
);
x_grad
.
device
(
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
())
=
out_grad
.
reshape
(
reshape_dims
)
.
sum
(
reduce_dims
)
.
reshape
(
x_grad
.
dimensions
());
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
EigenBroadcastGrad
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Dims
>::
Eval
(
place
,
x_grad
,
out_grad
,
reduce_dims
,
reshape_dims
);
}
};
...
...
paddle/fluid/operators/meshgrid_op.h
浏览文件 @
187bf412
...
...
@@ -25,6 +25,7 @@
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/platform/errors.h"
#define MAX_RANK_SUPPORTED 6
...
...
@@ -106,19 +107,21 @@ class MeshgridKernel : public framework::OpKernel<T> {
reshape_ins_tensor
.
Resize
(
out_dims_reshape
);
framework
::
DDim
out_dims
=
framework
::
make_ddim
(
shape
);
Eigen
::
DSizes
<
int
,
Rank
>
bcast_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
bcast_dims
;
for
(
int64_t
j
=
0
;
j
<
size
;
j
++
)
{
bcast_dims
[
j
]
=
shape
[
j
];
}
bcast_dims
[
i
]
=
1
;
outs
[
i
]
->
Resize
(
out_dims
);
auto
x
=
framework
::
EigenTensor
<
T
,
Rank
>::
From
(
reshape_ins_tensor
);
auto
x
=
framework
::
EigenTensor
<
T
,
Rank
>::
From
(
static_cast
<
const
framework
::
Tensor
>
(
reshape_ins_tensor
));
outs
[
i
]
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
y
=
framework
::
EigenTensor
<
T
,
Rank
>::
From
(
*
outs
[
i
]);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
y
.
device
(
place
)
=
x
.
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
y
,
x
,
bcast_dims
);
}
}
};
...
...
@@ -169,21 +172,20 @@ class MeshgridGradKernel : public framework::OpKernel<T> {
}
}
Eigen
::
DSizes
<
int
,
Rank
>
reduce_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
reduce_dims
;
for
(
int
k
=
0
;
k
<
n
;
k
++
)
{
reduce_dims
[
k
]
=
reduce_dims_vec
[
k
];
}
Eigen
::
DSizes
<
int
,
Rank
*
2
>
reshape_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
*
2
>
reshape_dims
;
for
(
int
k
=
0
;
k
<
n
*
2
;
k
++
)
{
reshape_dims
[
k
]
=
reshape_dims_vec
[
k
];
}
auto
tensor_reduce_tmp
=
out_grad_tmp
.
reshape
(
reshape_dims
).
sum
(
reduce_dims
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
in_grad
.
device
(
place
)
=
tensor_reduce_tmp
.
reshape
(
in_grad
.
dimensions
());
EigenBroadcastGrad
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
in_grad
,
out_grad_tmp
,
reduce_dims
,
reshape_dims
);
}
}
};
...
...
paddle/fluid/operators/tile_op.h
浏览文件 @
187bf412
...
...
@@ -26,6 +26,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#define MAX_RANK_SUPPORTED 6
...
...
@@ -155,7 +156,7 @@ class TileKernel : public framework::OpKernel<T> {
"'repeat_times' for tile op must match after promotion."
,
vec_in_dims
.
size
(),
repeat_times
.
size
()));
auto
*
out0
=
context
.
Output
<
Tensor
>
(
"Out"
);
Eigen
::
DSizes
<
int
,
Rank
>
bcast_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Rank
>
bcast_dims
;
for
(
size_t
i
=
0
;
i
<
repeat_times
.
size
();
++
i
)
{
bcast_dims
[
i
]
=
repeat_times
[
i
];
}
...
...
@@ -175,9 +176,11 @@ class TileKernel : public framework::OpKernel<T> {
// use 32-bit index to speed up
bool
use_32bit_index
=
y
.
size
()
<
Eigen
::
NumTraits
<
int
>::
highest
();
if
(
use_32bit_index
)
{
To32BitIndex
(
y
).
device
(
place
)
=
To32BitIndex
(
x
).
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
To32BitIndex
(
y
),
To32BitIndex
(
x
),
bcast_dims
);
}
else
{
y
.
device
(
place
)
=
x
.
broadcast
(
bcast_dims
);
EigenBroadcast
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Rank
>::
Eval
(
place
,
y
,
x
,
bcast_dims
);
}
}
};
...
...
@@ -255,21 +258,20 @@ class TileGradKernel : public framework::OpKernel<T> {
auto
*
out0
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_grad
=
EigenVector
<
T
>::
Flatten
(
*
out0
);
Eigen
::
DSizes
<
int
,
Dims
*
2
>
reshape_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
*
2
>
reshape_dims
;
for
(
size_t
i
=
0
;
i
<
reshape_size
;
++
i
)
{
reshape_dims
[
i
]
=
reshape_dims_vec
[
i
];
}
Eigen
::
DSizes
<
int
,
Dims
>
reduce_dims
;
Eigen
::
DSizes
<
Eigen
::
DenseIndex
,
Dims
>
reduce_dims
;
for
(
size_t
i
=
0
;
i
<
reduce_size
;
++
i
)
{
reduce_dims
[
i
]
=
reduce_dims_vec
[
i
];
}
auto
out_grad
=
EigenVector
<
T
>::
Flatten
(
*
in0
);
x_grad
.
device
(
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
())
=
out_grad
.
reshape
(
reshape_dims
)
.
sum
(
reduce_dims
)
.
reshape
(
x_grad
.
dimensions
());
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
EigenBroadcastGrad
<
std
::
decay_t
<
decltype
(
place
)
>
,
T
,
Dims
>::
Eval
(
place
,
x_grad
,
out_grad
,
reduce_dims
,
reshape_dims
);
}
};
...
...
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