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eb6d9e3b
编写于
10月 15, 2018
作者:
Q
Qiao Longfei
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into optimize-sum-seq-pooling-op
上级
0170d36c
e37c9e67
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
529 addition
and
79 deletion
+529
-79
paddle/fluid/inference/analysis/analyzer.cc
paddle/fluid/inference/analysis/analyzer.cc
+1
-1
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
+1
-0
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
+4
-2
paddle/fluid/inference/tensorrt/convert/pad_op.cc
paddle/fluid/inference/tensorrt/convert/pad_op.cc
+68
-0
paddle/fluid/inference/tensorrt/convert/test_pad_op.cc
paddle/fluid/inference/tensorrt/convert/test_pad_op.cc
+52
-0
paddle/fluid/operators/math/CMakeLists.txt
paddle/fluid/operators/math/CMakeLists.txt
+3
-3
paddle/fluid/operators/math/selected_rows_functor.cc
paddle/fluid/operators/math/selected_rows_functor.cc
+39
-3
paddle/fluid/operators/math/selected_rows_functor.h
paddle/fluid/operators/math/selected_rows_functor.h
+114
-0
paddle/fluid/operators/math/selected_rows_functor_test.cc
paddle/fluid/operators/math/selected_rows_functor_test.cc
+144
-29
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+84
-39
python/paddle/fluid/parallel_executor.py
python/paddle/fluid/parallel_executor.py
+19
-2
未找到文件。
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @
eb6d9e3b
...
...
@@ -70,7 +70,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
auto
trt_teller
=
[
&
](
const
Node
*
node
)
{
std
::
unordered_set
<
std
::
string
>
teller_set
(
{
"mul"
,
"conv2d"
,
"pool2d"
,
"relu"
,
"softmax"
,
"sigmoid"
,
"depthwise_conv2d"
,
"batch_norm"
,
"concat"
,
"tanh"
,
"depthwise_conv2d"
,
"batch_norm"
,
"concat"
,
"tanh"
,
"pad"
,
"elementwise_add"
,
"dropout"
});
if
(
!
node
->
IsFunction
())
return
false
;
...
...
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @
eb6d9e3b
...
...
@@ -185,3 +185,4 @@ USE_TRT_CONVERTER(softmax);
USE_TRT_CONVERTER
(
batch_norm
);
USE_TRT_CONVERTER
(
concat
);
USE_TRT_CONVERTER
(
dropout
);
USE_TRT_CONVERTER
(
pad
);
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @
eb6d9e3b
# Add TRT tests
nv_library
(
tensorrt_converter
SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc
pad_op.cc
DEPS tensorrt_engine operator scope framework_proto op_registry
)
nv_test
(
test_op_converter SRCS test_op_converter.cc DEPS
...
...
@@ -26,6 +26,8 @@ nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine batch_norm_op SERIAL
)
nv_test
(
test_trt_concat_op SRCS test_concat_op.cc concat_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine concat_op SERIAL
)
nv_test
(
test_trt_dropout_op SRCS test_dropout_op.cc dropout_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine dropout_op SERIAL
)
nv_test
(
test_trt_pad_op SRCS test_pad_op.cc pad_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine pad_op SERIAL
)
paddle/fluid/inference/tensorrt/convert/pad_op.cc
0 → 100644
浏览文件 @
eb6d9e3b
/* Copyright (c) 2018 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/inference/tensorrt/convert/op_converter.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
/*
* PadOp.
*/
class
PadOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
4
)
<<
"convert a fluid transpose op to tensorrt tranpose layer"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
// Declare inputs
auto
*
input
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
const
std
::
vector
<
int
>
paddings
=
boost
::
get
<
std
::
vector
<
int
>>
(
op_desc
.
GetAttr
(
"paddings"
));
const
float
pad_value
=
boost
::
get
<
float
>
(
op_desc
.
GetAttr
(
"pad_value"
));
nvinfer1
::
Dims
input_shape
=
input
->
getDimensions
();
int
nbDims
=
input_shape
.
nbDims
;
int
pad_size
=
static_cast
<
int
>
(
paddings
.
size
());
PADDLE_ENFORCE_GE
(
nbDims
,
2
);
PADDLE_ENFORCE_EQ
((
nbDims
+
1
)
*
2
,
pad_size
);
PADDLE_ENFORCE
(
pad_value
==
0.0
,
"The pad layer of TRT only support zero."
);
nvinfer1
::
DimsHW
pre_pad
(
paddings
[
pad_size
-
4
],
paddings
[
pad_size
-
2
]);
nvinfer1
::
DimsHW
post_pad
(
paddings
[
pad_size
-
3
],
paddings
[
pad_size
-
1
]);
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Padding
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input
),
pre_pad
,
post_pad
);
PADDLE_ENFORCE
(
layer
!=
nullptr
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
engine_
->
SetITensor
(
output_name
,
layer
->
getOutput
(
0
));
layer
->
setName
((
"scale (Output: "
+
output_name
+
")"
).
c_str
());
layer
->
getOutput
(
0
)
->
setName
(
output_name
.
c_str
());
if
(
test_mode
)
{
// the test framework can not determine which is the
// output, so place the declaration inside.
engine_
->
DeclareOutput
(
output_name
);
}
}
};
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
REGISTER_TRT_OP_CONVERTER
(
pad
,
PadOpConverter
);
paddle/fluid/inference/tensorrt/convert/test_pad_op.cc
0 → 100644
浏览文件 @
eb6d9e3b
/* Copyright (c) 2018 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 <gtest/gtest.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
TEST
(
PadConverter
,
main
)
{
framework
::
Scope
scope
;
std
::
unordered_set
<
std
::
string
>
parameters
;
TRTConvertValidation
validator
(
10
,
parameters
,
scope
,
1000
);
validator
.
DeclInputVar
(
"pad-X"
,
nvinfer1
::
Dims3
(
3
,
2
,
2
));
validator
.
DeclOutputVar
(
"pad-Out"
,
nvinfer1
::
Dims3
(
3
,
3
,
5
));
// Prepare Op description
framework
::
OpDesc
desc
;
desc
.
SetType
(
"pad"
);
desc
.
SetInput
(
"X"
,
{
"pad-X"
});
desc
.
SetOutput
(
"Out"
,
{
"pad-Out"
});
std
::
vector
<
int
>
paddings
=
{
0
,
0
,
0
,
0
,
0
,
1
,
1
,
2
};
float
pad_value
=
0.0
;
desc
.
SetAttr
(
"paddings"
,
paddings
);
desc
.
SetAttr
(
"pad_value"
,
pad_value
);
LOG
(
INFO
)
<<
"set OP"
;
validator
.
SetOp
(
*
desc
.
Proto
());
LOG
(
INFO
)
<<
"execute"
;
validator
.
Execute
(
2
);
}
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
USE_OP
(
pad
);
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @
eb6d9e3b
...
...
@@ -3,8 +3,8 @@ add_subdirectory(detail)
endif
(
NOT WIN32
)
function
(
math_library TARGET
)
# math_library is a function to create math library.
# The interface is the same as cc_library.
# math_library is a function to create math library.
# The interface is the same as cc_library.
# But it handle split GPU/CPU code and link some common library.
set
(
cc_srcs
)
set
(
cu_srcs
)
...
...
@@ -53,7 +53,7 @@ cc_library(blas SRCS blas.cc DEPS cblas framework_proto device_context)
math_library
(
math_function DEPS blas
)
math_library
(
maxouting
)
math_library
(
pooling
)
math_library
(
selected_rows_functor DEPS selected_rows math_function
)
math_library
(
selected_rows_functor DEPS selected_rows math_function
blas
)
math_library
(
sequence2batch
)
math_library
(
sequence_padding
)
math_library
(
sequence_pooling DEPS math_function
)
...
...
paddle/fluid/operators/math/selected_rows_functor.cc
浏览文件 @
eb6d9e3b
...
...
@@ -15,7 +15,6 @@ limitations under the License. */
#include <set>
#include <unordered_map>
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
namespace
paddle
{
...
...
@@ -150,6 +149,45 @@ template struct SelectedRowsAddTo<platform::CPUDeviceContext, double>;
template
struct
SelectedRowsAddTo
<
platform
::
CPUDeviceContext
,
int
>;
template
struct
SelectedRowsAddTo
<
platform
::
CPUDeviceContext
,
int64_t
>;
template
<
typename
T
>
struct
SelectedRowsSumTo
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
std
::
vector
<
framework
::
SelectedRows
*>&
input1
,
const
std
::
vector
<
int64_t
>&
input2_offsets
,
framework
::
SelectedRows
*
input2
)
{
// Ensure all selected rows have the same height
size_t
size
=
0u
;
for
(
auto
iter
=
input1
.
begin
();
iter
!=
input1
.
end
();
++
iter
)
{
auto
&
in_rows
=
(
*
iter
)
->
rows
();
size
+=
in_rows
.
end
()
-
in_rows
.
begin
();
auto
in1_height
=
(
*
iter
)
->
height
();
PADDLE_ENFORCE_EQ
(
in1_height
,
input2
->
height
());
}
// concat rows
std
::
vector
<
int64_t
>
in2_rows
;
in2_rows
.
reserve
(
in2_rows
.
size
()
+
size
);
for
(
auto
iter
=
input1
.
begin
();
iter
!=
input1
.
end
();
++
iter
)
{
const
framework
::
Vector
<
int64_t
>&
in_rows
=
(
*
iter
)
->
rows
();
in2_rows
.
insert
(
in2_rows
.
end
(),
in_rows
.
begin
(),
in_rows
.
end
());
}
input2
->
set_rows
(
in2_rows
);
auto
*
in2_value
=
input2
->
mutable_value
();
auto
*
in2_data
=
in2_value
->
data
<
T
>
();
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
context
);
size_t
offset
=
0u
;
for
(
size_t
i
=
0u
;
i
!=
input1
.
size
();
++
i
)
{
auto
&
in_value
=
input1
[
i
]
->
value
();
const
auto
*
in_data
=
in_value
.
data
<
T
>
();
offset
+=
input2_offsets
[
i
];
blas
.
VCOPY
(
in_value
.
numel
(),
in_data
,
in2_data
+
offset
);
}
}
};
template
struct
SelectedRowsSumTo
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
SelectedRowsSumTo
<
platform
::
CPUDeviceContext
,
double
>;
template
<
typename
T
>
struct
SelectedRowsAddToTensor
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
...
...
@@ -260,8 +298,6 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
}
};
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
double
>;
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
int
>;
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
int64_t
>;
...
...
paddle/fluid/operators/math/selected_rows_functor.h
浏览文件 @
eb6d9e3b
...
...
@@ -12,10 +12,13 @@ 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 <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/device_context.h"
#define INLINE_FOR2(sizei, sizej) \
...
...
@@ -51,6 +54,15 @@ struct SelectedRowsAddTo {
const
int64_t
input2_offset
,
framework
::
SelectedRows
*
input2
);
};
// input2 = [all input in input1] + input2
template
<
typename
DeviceContext
,
typename
T
>
struct
SelectedRowsSumTo
{
void
operator
()(
const
DeviceContext
&
context
,
const
std
::
vector
<
framework
::
SelectedRows
*>&
input1
,
const
std
::
vector
<
int64_t
>&
input2_offsets
,
framework
::
SelectedRows
*
input2
);
};
// input2 = input1 + input2
template
<
typename
DeviceContext
,
typename
T
>
struct
SelectedRowsAddToTensor
{
...
...
@@ -75,6 +87,108 @@ struct MergeAdd {
framework
::
SelectedRows
*
output
);
};
template
<
>
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
float
>
{
framework
::
SelectedRows
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
)
{
framework
::
SelectedRows
out
;
(
*
this
)(
context
,
input
,
&
out
);
return
out
;
}
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
,
framework
::
SelectedRows
*
output
)
{
framework
::
SelectedRows
&
out
=
*
output
;
auto
input_rows
=
input
.
rows
();
std
::
vector
<
int64_t
>
merge_rows
;
merge_rows
.
reserve
(
input_rows
.
size
());
std
::
unordered_map
<
int64_t
,
size_t
>
rows_pos_map
;
rows_pos_map
.
reserve
(
input_rows
.
size
());
size_t
idx
=
0u
;
for
(
std
::
vector
<
int64_t
>::
iterator
iter
=
input_rows
.
begin
();
iter
!=
input_rows
.
end
();
++
iter
)
{
if
(
rows_pos_map
.
find
(
*
iter
)
==
rows_pos_map
.
end
())
{
rows_pos_map
[
*
iter
]
=
idx
++
;
merge_rows
.
emplace_back
(
*
iter
);
}
}
auto
input_width
=
input
.
value
().
dims
()[
1
];
out
.
set_rows
(
merge_rows
);
out
.
set_height
(
input
.
height
());
out
.
mutable_value
()
->
mutable_data
<
float
>
(
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
merge_rows
.
size
()),
input_width
}),
context
.
GetPlace
());
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
float
>
constant_functor
;
constant_functor
(
context
,
out
.
mutable_value
(),
0.0
);
auto
*
out_data
=
out
.
mutable_value
()
->
data
<
float
>
();
auto
*
input_data
=
input
.
value
().
data
<
float
>
();
auto
blas
=
GetBlas
<
platform
::
CPUDeviceContext
,
float
>
(
context
);
for
(
size_t
i
=
0
;
i
<
input_rows
.
size
();
i
++
)
{
size_t
out_i
=
rows_pos_map
[
input_rows
[
i
]];
float
*
y
=
out_data
+
out_i
*
input_width
;
const
float
*
x
=
input_data
+
i
*
input_width
;
blas
.
AXPY
(
input_width
,
1.
,
x
,
y
);
}
}
};
template
<
>
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
double
>
{
framework
::
SelectedRows
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
)
{
framework
::
SelectedRows
out
;
(
*
this
)(
context
,
input
,
&
out
);
return
out
;
}
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
,
framework
::
SelectedRows
*
output
)
{
framework
::
SelectedRows
&
out
=
*
output
;
auto
input_rows
=
input
.
rows
();
std
::
vector
<
int64_t
>
merge_rows
;
merge_rows
.
reserve
(
input_rows
.
size
());
std
::
unordered_map
<
int64_t
,
size_t
>
rows_pos_map
;
rows_pos_map
.
reserve
(
input_rows
.
size
());
size_t
idx
=
0u
;
for
(
std
::
vector
<
int64_t
>::
iterator
iter
=
input_rows
.
begin
();
iter
!=
input_rows
.
end
();
++
iter
)
{
if
(
rows_pos_map
.
find
(
*
iter
)
==
rows_pos_map
.
end
())
{
rows_pos_map
[
*
iter
]
=
idx
++
;
merge_rows
.
emplace_back
(
*
iter
);
}
}
auto
input_width
=
input
.
value
().
dims
()[
1
];
out
.
set_rows
(
merge_rows
);
out
.
set_height
(
input
.
height
());
out
.
mutable_value
()
->
mutable_data
<
double
>
(
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
merge_rows
.
size
()),
input_width
}),
context
.
GetPlace
());
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
double
>
constant_functor
;
constant_functor
(
context
,
out
.
mutable_value
(),
0.0
);
auto
*
out_data
=
out
.
mutable_value
()
->
data
<
double
>
();
auto
*
input_data
=
input
.
value
().
data
<
double
>
();
auto
blas
=
GetBlas
<
platform
::
CPUDeviceContext
,
double
>
(
context
);
for
(
size_t
i
=
0
;
i
<
input_rows
.
size
();
i
++
)
{
size_t
out_i
=
rows_pos_map
[
input_rows
[
i
]];
double
*
y
=
out_data
+
out_i
*
input_width
;
const
double
*
x
=
input_data
+
i
*
input_width
;
blas
.
AXPY
(
input_width
,
1.
,
x
,
y
);
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
struct
Add
{
framework
::
SelectedRows
operator
()(
const
DeviceContext
&
context
,
...
...
paddle/fluid/operators/math/selected_rows_functor_test.cc
浏览文件 @
eb6d9e3b
...
...
@@ -220,17 +220,98 @@ TEST(selected_rows_functor, cpu_add_to) {
EXPECT_EQ
(
tensor1_data
[
9
*
row_numel
+
6
],
5.0
);
}
TEST
(
selected_rows_functor
,
cpu_merge_add
)
{
TEST
(
selected_rows_functor
,
cpu_merge_add
_float
)
{
paddle
::
platform
::
CPUPlace
cpu_place
;
paddle
::
platform
::
CPUDeviceContext
ctx
(
cpu_place
);
paddle
::
operators
::
math
::
SetConstant
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
set_const
;
functor
;
int64_t
height
=
10
;
int64_t
row_numel
=
10
;
std
::
vector
<
int64_t
>
rows
{
0
,
4
,
4
,
7
};
std
::
unique_ptr
<
paddle
::
framework
::
SelectedRows
>
selected_rows
{
new
paddle
::
framework
::
SelectedRows
(
rows
,
height
)};
auto
*
in_value
=
selected_rows
->
mutable_value
();
in_value
->
mutable_data
<
float
>
(
paddle
::
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
rows
.
size
()),
row_numel
}),
cpu_place
);
functor
(
ctx
,
in_value
,
1.0
);
std
::
unique_ptr
<
paddle
::
framework
::
SelectedRows
>
output
{
new
paddle
::
framework
::
SelectedRows
()};
paddle
::
operators
::
math
::
scatter
::
MergeAdd
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
merge_add_functor
;
merge_add_functor
(
ctx
,
*
selected_rows
,
output
.
get
());
auto
out_height
=
output
->
height
();
EXPECT_EQ
(
out_height
,
height
);
auto
&
out_rows
=
output
->
rows
();
EXPECT_EQ
(
out_rows
[
0
],
0
);
EXPECT_EQ
(
out_rows
[
1
],
4
);
EXPECT_EQ
(
out_rows
[
2
],
7
);
auto
*
out_data
=
output
->
value
().
data
<
float
>
();
EXPECT_EQ
(
out_data
[
0
*
row_numel
],
1.0
);
EXPECT_EQ
(
out_data
[
1
*
row_numel
],
2.0
);
EXPECT_EQ
(
out_data
[
2
*
row_numel
],
1.0
);
}
TEST
(
selected_rows_functor
,
cpu_merge_add_int
)
{
paddle
::
platform
::
CPUPlace
cpu_place
;
paddle
::
platform
::
CPUDeviceContext
ctx
(
cpu_place
);
paddle
::
operators
::
math
::
SetConstant
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
functor
;
int64_t
height
=
10
;
int64_t
row_numel
=
8
;
int64_t
row_numel
=
10
;
std
::
vector
<
int64_t
>
rows1
{
5
,
2
,
5
,
3
,
5
};
std
::
vector
<
int64_t
>
rows
{
0
,
4
,
4
,
7
};
std
::
unique_ptr
<
paddle
::
framework
::
SelectedRows
>
selected_rows
{
new
paddle
::
framework
::
SelectedRows
(
rows
,
height
)};
auto
*
in_value
=
selected_rows
->
mutable_value
();
in_value
->
mutable_data
<
int
>
(
paddle
::
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
rows
.
size
()),
row_numel
}),
cpu_place
);
functor
(
ctx
,
in_value
,
1
);
std
::
unique_ptr
<
paddle
::
framework
::
SelectedRows
>
output
{
new
paddle
::
framework
::
SelectedRows
()};
paddle
::
operators
::
math
::
scatter
::
MergeAdd
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
merge_add_functor
;
merge_add_functor
(
ctx
,
*
selected_rows
,
output
.
get
());
auto
out_height
=
output
->
height
();
EXPECT_EQ
(
out_height
,
height
);
auto
&
out_rows
=
output
->
rows
();
EXPECT_EQ
(
out_rows
[
0
],
0
);
EXPECT_EQ
(
out_rows
[
1
],
4
);
EXPECT_EQ
(
out_rows
[
2
],
7
);
auto
*
out_data
=
output
->
value
().
data
<
int
>
();
EXPECT_EQ
(
out_data
[
0
*
row_numel
],
1
);
EXPECT_EQ
(
out_data
[
1
*
row_numel
],
2
);
EXPECT_EQ
(
out_data
[
2
*
row_numel
],
1
);
}
TEST
(
selected_rows_functor
,
cpu_sum_to
)
{
paddle
::
platform
::
CPUPlace
cpu_place
;
paddle
::
platform
::
CPUDeviceContext
ctx
(
cpu_place
);
paddle
::
operators
::
math
::
SetConstant
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
functor
;
int64_t
height
=
10
;
int64_t
row_numel
=
10
;
std
::
vector
<
int64_t
>
rows1
{
0
,
4
,
7
};
std
::
unique_ptr
<
paddle
::
framework
::
SelectedRows
>
selected_rows1
{
new
paddle
::
framework
::
SelectedRows
(
rows1
,
height
)};
auto
*
in1_value
=
selected_rows1
->
mutable_value
();
...
...
@@ -238,9 +319,9 @@ TEST(selected_rows_functor, cpu_merge_add) {
paddle
::
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
rows1
.
size
()),
row_numel
}),
cpu_place
);
set_const
(
ctx
,
in1_value
,
1.0
);
std
::
vector
<
int64_t
>
rows2
{
2
,
5
,
3
,
5
,
3
};
functor
(
ctx
,
in1_value
,
1.0
);
std
::
vector
<
int64_t
>
rows2
{
0
,
5
,
7
,
9
};
std
::
unique_ptr
<
paddle
::
framework
::
SelectedRows
>
selected_rows2
{
new
paddle
::
framework
::
SelectedRows
(
rows2
,
height
)};
auto
*
in2_value
=
selected_rows2
->
mutable_value
();
...
...
@@ -248,33 +329,67 @@ TEST(selected_rows_functor, cpu_merge_add) {
paddle
::
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
rows2
.
size
()),
row_numel
}),
cpu_place
);
set_const
(
ctx
,
in2_value
,
1.0
);
functor
(
ctx
,
in2_value
,
2.0
);
std
::
unique_ptr
<
paddle
::
framework
::
SelectedRows
>
output
{
new
paddle
::
framework
::
SelectedRows
()};
output
->
set_height
(
height
);
paddle
::
operators
::
math
::
scatter
::
MergeAdd
<
paddle
::
platform
::
CPUDeviceContext
,
auto
*
out_value
=
output
->
mutable_value
();
// simplely concat two SelectedRows
out_value
->
mutable_data
<
float
>
(
paddle
::
framework
::
make_ddim
({
7
,
10
}),
cpu_place
);
paddle
::
operators
::
math
::
SelectedRowsSumTo
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
merge_add_functor
;
std
::
vector
<
const
paddle
::
framework
::
SelectedRows
*>
inputs
;
inputs
.
push_back
(
selected_rows1
.
get
());
inputs
.
push_back
(
selected_rows2
.
get
());
merge_add_functor
(
ctx
,
inputs
,
output
.
get
());
EXPECT_EQ
(
output
->
height
(),
height
);
EXPECT_EQ
(
output
->
value
().
dims
(),
paddle
::
framework
::
make_ddim
({
3
,
row_numel
}));
std
::
vector
<
int64_t
>
ret_rows
{
2
,
3
,
5
};
EXPECT_EQ
(
output
->
rows
(),
ret_rows
);
sum_to_functor
;
sum_to_functor
(
ctx
,
std
::
vector
<
paddle
::
framework
::
SelectedRows
*>
(
{
selected_rows1
.
get
(),
selected_rows2
.
get
()}),
std
::
vector
<
int64_t
>
({
0
,
in1_value
->
numel
()}),
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
);
auto
*
out_data
=
output
->
value
().
data
<
float
>
();
for
(
size_t
i
=
0
;
i
<
ret_rows
.
size
();
++
i
)
{
for
(
size_t
j
=
0
;
j
<
row_numel
;
++
j
)
{
EXPECT_EQ
(
out_data
[
i
*
row_numel
+
j
],
ret_rows
[
i
]);
std
::
cout
<<
out_data
[
i
*
row_numel
+
j
]
<<
" "
;
}
std
::
cout
<<
"
\n
"
;
}
// input1 value
EXPECT_EQ
(
out_data
[
0
*
row_numel
+
0
],
1.0
);
EXPECT_EQ
(
out_data
[
0
*
row_numel
+
8
],
1.0
);
EXPECT_EQ
(
out_data
[
1
*
row_numel
+
1
],
1.0
);
EXPECT_EQ
(
out_data
[
2
*
row_numel
+
6
],
1.0
);
// input2 value
EXPECT_EQ
(
out_data
[
3
*
row_numel
+
3
],
2.0
);
EXPECT_EQ
(
out_data
[
3
*
row_numel
+
8
],
2.0
);
EXPECT_EQ
(
out_data
[
4
*
row_numel
+
4
],
2.0
);
EXPECT_EQ
(
out_data
[
5
*
row_numel
+
7
],
2.0
);
EXPECT_EQ
(
out_data
[
6
*
row_numel
+
9
],
2.0
);
std
::
unique_ptr
<
paddle
::
framework
::
Tensor
>
tensor1
{
new
paddle
::
framework
::
Tensor
()};
tensor1
->
mutable_data
<
float
>
(
paddle
::
framework
::
make_ddim
({
height
,
row_numel
}),
cpu_place
);
functor
(
ctx
,
tensor1
.
get
(),
3.0
);
paddle
::
operators
::
math
::
SelectedRowsAddToTensor
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
add_to_tensor_functor
;
add_to_tensor_functor
(
ctx
,
*
output
,
tensor1
.
get
());
auto
*
tensor1_data
=
tensor1
->
data
<
float
>
();
// row0: 1.0 + 2.0 + 3.0
EXPECT_EQ
(
tensor1_data
[
0
*
row_numel
+
0
],
6.0
);
// row1: 3.0
EXPECT_EQ
(
tensor1_data
[
1
*
row_numel
+
1
],
3.0
);
// row4 : 1.0 + 3.0
EXPECT_EQ
(
tensor1_data
[
4
*
row_numel
+
6
],
4.0
);
// row5: 2.0 + 3.0
EXPECT_EQ
(
tensor1_data
[
5
*
row_numel
+
7
],
5.0
);
// row6: 3.0
EXPECT_EQ
(
tensor1_data
[
6
*
row_numel
+
1
],
3.0
);
// row7: 1.0 + 2.0 + 3.0
EXPECT_EQ
(
tensor1_data
[
7
*
row_numel
+
3
],
6.0
);
// row9: 2.0 + 3.0
EXPECT_EQ
(
tensor1_data
[
9
*
row_numel
+
6
],
5.0
);
}
paddle/fluid/pybind/pybind.cc
浏览文件 @
eb6d9e3b
...
...
@@ -667,16 +667,17 @@ All parameter, weight, gradient are variables in Paddle.
ExecutionStrategy allows the user to more preciously control how to run
the program in ParallelExecutor by setting the property.
The available properties include:
use_cuda (bool): Whether to use CUDA or not. Default True.
num_threads (int): The number of threads that used to run the
operators in ParallelExecutor. If it is not set, it will be
set in ParallelExecutor according to the device count.
Default 0.
allow_op_delay (bool): Whether to delay the communication operators
to run. Default False.
num_iteration_per_drop_scope (int): how many iterations between
the two dropping local scopes. Default 100.
Examples:
.. code-block:: python
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 4
train_exe = fluid.ParallelExecutor(use_cuda=True,
loss_name=loss.name,
exec_strategy=exec_strategy)
train_loss, = train_exe.run([loss.name], feed=feed_dict)
)DOC"
);
...
...
@@ -686,19 +687,34 @@ All parameter, weight, gradient are variables in Paddle.
[](
const
ExecutionStrategy
&
self
)
{
return
self
.
num_threads_
;
},
[](
ExecutionStrategy
&
self
,
size_t
num_threads
)
{
self
.
num_threads_
=
num_threads
;
})
},
R"DOC(The type is INT, num_threads represents the size of thread pool that
used to run the operators of the current program in ParallelExecutor.
If :math:`num\_threads=1`, all the operators will execute one by one,
but the order maybe difference between iterations.
If it is not set, it will be set in ParallelExecutor according to the
device type and device count, for GPU, :math:`num\_threads=device\_count*4`, for CPU,
:math:`num\_threads=CPU\_NUM*4`, the explanation of:math:`CPU\_NUM` is in ParallelExecutor.
if it is not set, ParallelExecutor will get the cpu count by calling
`multiprocessing.cpu_count()`. Default 0.)DOC"
)
.
def_property
(
"use_cuda"
,
[](
const
ExecutionStrategy
&
self
)
{
return
self
.
use_cuda_
;
},
[](
ExecutionStrategy
&
self
,
bool
use_cuda
)
{
self
.
use_cuda_
=
use_cuda
;
})
})
// FIXME(chengduo): Doesn't add doc for 'use_cuda', use_cuda may
// make user confuse, because ParallelExecutor has a parameter named
// 'use_cuda' too, in current implementation, ParallelExecutor's
// 'use_cuda' will rewrite ExecutionStrategy's 'use_cuda'.
.
def_property
(
"allow_op_delay"
,
[](
const
ExecutionStrategy
&
self
)
{
return
self
.
allow_op_delay_
;
},
[](
ExecutionStrategy
&
self
,
bool
allow_op_delay
)
{
self
.
allow_op_delay_
=
allow_op_delay
;
})
},
R"DOC(The type is BOOL, allow_op_delay represents whether to delay the
communication operators to run, it may make the execution faster.
Note that in some models, allow_op_delay may cause program hang. Default False.)DOC"
)
.
def_property
(
"num_iteration_per_drop_scope"
,
[](
const
ExecutionStrategy
&
self
)
{
...
...
@@ -706,7 +722,19 @@ All parameter, weight, gradient are variables in Paddle.
},
[](
ExecutionStrategy
&
self
,
size_t
num_iteration_per_drop_scope
)
{
self
.
num_iteration_per_drop_scope_
=
num_iteration_per_drop_scope
;
});
},
R"DOC(The type is INT, num_iteration_per_drop_scope indicates how
many iterations to clean up the temp variables which
is generated during execution. It may make the execution faster,
because the temp variable's shape maybe the same between two iterations. Default 100.
NOTES:
1. If you fetch data when calling the 'run', the ParallelExecutor
will clean up the temp variables at the end of the current iteration.
2. In some NLP model, it may cause the GPU memory is insufficient,
in this case, you should reduce `num_iteration_per_drop_scope`.
)DOC"
);
exec_strategy
.
def_property
(
"use_experimental_executor"
,
[](
const
ExecutionStrategy
&
self
)
{
...
...
@@ -721,20 +749,17 @@ All parameter, weight, gradient are variables in Paddle.
BuildStrategy allows the user to more preciously control how to
build the SSA Graph in ParallelExecutor by setting the property.
The available properties include:
reduce_strategy (str): There are two reduce strategies, 'AllReduce'
and 'Reduce'. If you want that all parameters will be optimized
on all devices, you can choose 'AllReduce'; if you choose
'Reduce', all parameters will be evenly allocated to different
devices for optimization, and then broadcast the optimized
parameter to other devices. Default 'AllReduce'.
gradient_scale_strategy (str): There are two ways of defining loss@grad,
'CoeffNumDevice' and 'Customized'. By default, ParallelExecutor
sets the loss@grad according to the number of devices. If you want
to customize loss@grad, you can choose 'Customized'.
Default 'CoeffNumDevice'.
debug_graphviz_path (str): Whether to write the SSA Graph to file in the
form of graphviz. It is useful for debugging. Default "".
Examples:
.. code-block:: python
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
train_exe = fluid.ParallelExecutor(use_cuda=True,
loss_name=loss.name,
build_strategy=build_strategy)
train_loss, = train_exe.run([loss.name], feed=feed_dict)
)DOC"
);
py
::
enum_
<
BuildStrategy
::
ReduceStrategy
>
(
build_strategy
,
"ReduceStrategy"
)
...
...
@@ -753,31 +778,51 @@ All parameter, weight, gradient are variables in Paddle.
[](
const
BuildStrategy
&
self
)
{
return
self
.
reduce_
;
},
[](
BuildStrategy
&
self
,
BuildStrategy
::
ReduceStrategy
strategy
)
{
self
.
reduce_
=
strategy
;
})
},
R"DOC(The type is STR, there are two reduce strategies in ParallelExecutor,
'AllReduce' and 'Reduce'. If you want that all the parameters'
optimization are done on all devices independently, you should choose 'AllReduce';
if you choose 'Reduce', all the parameters' optimization will be evenly distributed
to different devices, and then broadcast the optimized parameter to other devices.
In some models, `Reduce` is faster. Default 'AllReduce'. )DOC"
)
.
def_property
(
"gradient_scale_strategy"
,
[](
const
BuildStrategy
&
self
)
{
return
self
.
gradient_scale_
;
},
[](
BuildStrategy
&
self
,
BuildStrategy
::
GradientScaleStrategy
strategy
)
{
self
.
gradient_scale_
=
strategy
;
})
},
R"DOC(The type is STR, there are three ways of defining :math:`loss@grad` in
ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default,
ParallelExecutor sets the :math:`loss@grad` according to the number of devices.
If you want to customize :math:`loss@grad`, you can choose 'Customized'.
Default 'CoeffNumDevice'.)DOC"
)
.
def_property
(
"debug_graphviz_path"
,
[](
const
BuildStrategy
&
self
)
{
return
self
.
debug_graphviz_path_
;
},
[](
BuildStrategy
&
self
,
const
std
::
string
&
path
)
{
self
.
debug_graphviz_path_
=
path
;
})
},
R"DOC(The type is STR, debug_graphviz_path indicate the path that
writing the SSA Graph to file in the form of graphviz, you.
It is useful for debugging. Default "")DOC"
)
.
def_property
(
"enable_data_balance"
,
[](
const
BuildStrategy
&
self
)
{
return
self
.
enable_data_balance_
;
},
[](
BuildStrategy
&
self
,
bool
b
)
{
self
.
enable_data_balance_
=
b
;
})
.
def_property
(
"fuse_elewise_add_act_ops"
,
[](
const
BuildStrategy
&
self
)
{
return
self
.
fuse_elewise_add_act_ops_
;
},
[](
BuildStrategy
&
self
,
bool
b
)
{
self
.
fuse_elewise_add_act_ops_
=
b
;
})
[](
BuildStrategy
&
self
,
bool
b
)
{
self
.
enable_data_balance_
=
b
;
})
// FIXME(chengudo): enable_data_balance seems not important
.
def_property
(
"fuse_elewise_add_act_ops"
,
[](
const
BuildStrategy
&
self
)
{
return
self
.
fuse_elewise_add_act_ops_
;
},
[](
BuildStrategy
&
self
,
bool
b
)
{
self
.
fuse_elewise_add_act_ops_
=
b
;
},
R"DOC(The type is BOOL, fuse_elewise_add_act_ops indicate whether
to fuse elementwise_add_op and activation_op,
it may make the execution faster. Default False)DOC"
)
.
def
(
"_create_passes_from_strategy"
,
[](
BuildStrategy
&
self
)
->
std
::
shared_ptr
<
ir
::
PassBuilder
>
{
return
self
.
CreatePassesFromStrategy
();
...
...
python/paddle/fluid/parallel_executor.py
浏览文件 @
eb6d9e3b
...
...
@@ -31,15 +31,32 @@ BuildStrategy = core.ParallelExecutor.BuildStrategy
class
ParallelExecutor
(
object
):
"""
ParallelExecutor can run program in parallel.
ParallelExecutor is designed for data parallelism, which focuses on distributing
the data across different nodes and every node operates on the data in parallel.
If you use ParallelExecutor to run the current program on GPU, the node means GPU
device, and ParallelExecutor will get the available GPU device automatically on
the current machine. If you use ParallelExecutor to run the current program on CPU,
the node means the CPU device, and you can specify the CPU device number by adding
'CPU_NUM' environment variable, for example 'CPU_NUM=4', if the environment variable
is not found, ParallelExecutor will call `multiprocessing.cpu_count` to get the number
of CPUs in the system.
Args:
use_cuda (bool): Whether to use CUDA or not.
loss_name (str): The loss name must set in training. Default None.
main_program (Program): The program that need to run, if not provided,
then default_main_program will be used. Default None.
share_vars_from(ParallelExecutor): If provi
ed
, it will share variables
share_vars_from(ParallelExecutor): If provi
de
, it will share variables
from the specified ParallelExecutor. Default None.
exec_strategy(ExecutionStrategy): exec_strategy is used to control how to run
the program in ParallelExecutor, for example how many threads are used to
execute the program, how many iterations to clean up the temp variables
which is generated during execution. For more information, please refer
to fluid.ExecutionStrategy. Default None.
build_strategy(BuildStrategy): build_strategy is used to control how to
build the SSA Graph in ParallelExecutor by setting the property,
for example reduce_strategy, gradient_scale_strategy. For more information,
please refer to fluid.BuildStrategy. Default None.
num_trainers(int): If greater than 1, NCCL will be initialized with
multiple rank of nodes, each node should have same number of GPUs.
Distributed training will be enabled then. Default 1.
...
...
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