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397de907
编写于
11月 12, 2018
作者:
N
nhzlx
浏览文件
操作
浏览文件
下载
差异文件
merge develops
test=develop
上级
d6ff0069
792bf0b7
变更
28
隐藏空白更改
内联
并排
Showing
28 changed file
with
1325 addition
and
124 deletion
+1325
-124
cmake/external/protobuf.cmake
cmake/external/protobuf.cmake
+49
-53
paddle/fluid/API.spec
paddle/fluid/API.spec
+2
-0
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+2
-0
paddle/fluid/framework/var_type_inference.h
paddle/fluid/framework/var_type_inference.h
+25
-0
paddle/fluid/inference/analysis/analyzer.cc
paddle/fluid/inference/analysis/analyzer.cc
+3
-1
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+1
-0
paddle/fluid/operators/activation_op.cc
paddle/fluid/operators/activation_op.cc
+6
-10
paddle/fluid/operators/batch_norm_op.cc
paddle/fluid/operators/batch_norm_op.cc
+10
-1
paddle/fluid/operators/conv_op.cc
paddle/fluid/operators/conv_op.cc
+12
-0
paddle/fluid/operators/cross_entropy_op.cc
paddle/fluid/operators/cross_entropy_op.cc
+11
-0
paddle/fluid/operators/elementwise_op.h
paddle/fluid/operators/elementwise_op.h
+6
-10
paddle/fluid/operators/mean_op.cc
paddle/fluid/operators/mean_op.cc
+19
-2
paddle/fluid/operators/mul_op.cc
paddle/fluid/operators/mul_op.cc
+10
-1
paddle/fluid/operators/pool_op.cc
paddle/fluid/operators/pool_op.cc
+14
-4
paddle/fluid/operators/similarity_focus_op.cc
paddle/fluid/operators/similarity_focus_op.cc
+87
-0
paddle/fluid/operators/similarity_focus_op.h
paddle/fluid/operators/similarity_focus_op.h
+168
-0
paddle/fluid/operators/softmax_op.cc
paddle/fluid/operators/softmax_op.cc
+9
-1
paddle/fluid/operators/tensor_array_to_tensor_op.cc
paddle/fluid/operators/tensor_array_to_tensor_op.cc
+246
-0
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+1
-0
python/paddle/fluid/distribute_lookup_table.py
python/paddle/fluid/distribute_lookup_table.py
+39
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+113
-0
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+58
-4
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+59
-7
python/paddle/fluid/tests/book/test_label_semantic_roles.py
python/paddle/fluid/tests/book/test_label_semantic_roles.py
+1
-1
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
+11
-2
python/paddle/fluid/tests/unittests/test_similarity_focus_op.py
.../paddle/fluid/tests/unittests/test_similarity_focus_op.py
+217
-0
python/paddle/fluid/tests/unittests/test_tensor_array_to_tensor.py
...ddle/fluid/tests/unittests/test_tensor_array_to_tensor.py
+142
-0
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+4
-27
未找到文件。
cmake/external/protobuf.cmake
浏览文件 @
397de907
...
...
@@ -30,66 +30,61 @@ UNSET_VAR(PROTOBUF_LITE_LIBRARY)
UNSET_VAR
(
PROTOBUF_LIBRARY
)
UNSET_VAR
(
PROTOBUF_INCLUDE_DIR
)
UNSET_VAR
(
Protobuf_PROTOC_EXECUTABLE
)
function
(
protobuf_generate_python SRCS
)
# shameless copy from https://github.com/Kitware/CMake/blob/master/Modules/FindProtobuf.cmake
if
(
NOT ARGN
)
message
(
SEND_ERROR
"Error: PROTOBUF_GENERATE_PYTHON() called without any proto files"
)
return
()
endif
()
if
(
NOT COMMAND protobuf_generate_python
)
# before cmake 3.4, protobuf_genrerate_python is not defined.
function
(
protobuf_generate_python SRCS
)
# shameless copy from https://github.com/Kitware/CMake/blob/master/Modules/FindProtobuf.cmake
if
(
NOT ARGN
)
message
(
SEND_ERROR
"Error: PROTOBUF_GENERATE_PYTHON() called without any proto files"
)
return
()
endif
()
if
(
PROTOBUF_GENERATE_CPP_APPEND_PATH
)
# Create an include path for each file specified
foreach
(
FIL
${
ARGN
}
)
get_filename_component
(
ABS_FIL
${
FIL
}
ABSOLUTE
)
get_filename_component
(
ABS_PATH
${
ABS_FIL
}
PATH
)
list
(
FIND _protobuf_include_path
${
ABS_PATH
}
_contains_already
)
if
(
${
_contains_already
}
EQUAL -1
)
list
(
APPEND _protobuf_include_path -I
${
ABS_PATH
}
)
endif
()
endforeach
()
else
()
set
(
_protobuf_include_path -I
${
CMAKE_CURRENT_SOURCE_DIR
}
)
endif
()
if
(
DEFINED PROTOBUF_IMPORT_DIRS AND NOT DEFINED Protobuf_IMPORT_DIRS
)
set
(
Protobuf_IMPORT_DIRS
"
${
PROTOBUF_IMPORT_DIRS
}
"
)
endif
()
if
(
DEFINED Protobuf_IMPORT_DIRS
)
foreach
(
DIR
${
Protobuf_IMPORT_DIRS
}
)
get_filename_component
(
ABS_PATH
${
DIR
}
ABSOLUTE
)
list
(
FIND _protobuf_include_path
${
ABS_PATH
}
_contains_already
)
if
(
${
_contains_already
}
EQUAL -1
)
list
(
APPEND _protobuf_include_path -I
${
ABS_PATH
}
)
endif
()
endforeach
()
endif
()
set
(
${
SRCS
}
)
if
(
PROTOBUF_GENERATE_CPP_APPEND_PATH
)
# Create an include path for each file specified
foreach
(
FIL
${
ARGN
}
)
get_filename_component
(
ABS_FIL
${
FIL
}
ABSOLUTE
)
get_filename_component
(
FIL_WE
${
FIL
}
NAME_WE
)
if
(
NOT PROTOBUF_GENERATE_CPP_APPEND_PATH
)
get_filename_component
(
FIL_DIR
${
FIL
}
DIRECTORY
)
if
(
FIL_DIR
)
set
(
FIL_WE
"
${
FIL_DIR
}
/
${
FIL_WE
}
"
)
endif
()
get_filename_component
(
ABS_PATH
${
ABS_FIL
}
PATH
)
list
(
FIND _protobuf_include_path
${
ABS_PATH
}
_contains_already
)
if
(
${
_contains_already
}
EQUAL -1
)
list
(
APPEND _protobuf_include_path -I
${
ABS_PATH
}
)
endif
()
endforeach
()
else
()
set
(
_protobuf_include_path -I
${
CMAKE_CURRENT_SOURCE_DIR
}
)
endif
()
if
(
DEFINED PROTOBUF_IMPORT_DIRS AND NOT DEFINED Protobuf_IMPORT_DIRS
)
set
(
Protobuf_IMPORT_DIRS
"
${
PROTOBUF_IMPORT_DIRS
}
"
)
endif
()
list
(
APPEND
${
SRCS
}
"
${
CMAKE_CURRENT_BINARY_DIR
}
/
${
FIL_WE
}
_pb2.py"
)
add_custom_command
(
OUTPUT
"
${
CMAKE_CURRENT_BINARY_DIR
}
/
${
FIL_WE
}
_pb2.py"
COMMAND
${
Protobuf_PROTOC_EXECUTABLE
}
--python_out
${
CMAKE_CURRENT_BINARY_DIR
}
${
_protobuf_include_path
}
${
ABS_FIL
}
DEPENDS
${
ABS_FIL
}
${
Protobuf_PROTOC_EXECUTABLE
}
COMMENT
"Running Python protocol buffer compiler on
${
FIL
}
"
VERBATIM
)
if
(
DEFINED Protobuf_IMPORT_DIRS
)
foreach
(
DIR
${
Protobuf_IMPORT_DIRS
}
)
get_filename_component
(
ABS_PATH
${
DIR
}
ABSOLUTE
)
list
(
FIND _protobuf_include_path
${
ABS_PATH
}
_contains_already
)
if
(
${
_contains_already
}
EQUAL -1
)
list
(
APPEND _protobuf_include_path -I
${
ABS_PATH
}
)
endif
(
)
endforeach
()
endif
()
set
(
${
SRCS
}
${${
SRCS
}}
PARENT_SCOPE
)
endfunction
()
endif
()
set
(
${
SRCS
}
)
foreach
(
FIL
${
ARGN
}
)
get_filename_component
(
ABS_FIL
${
FIL
}
ABSOLUTE
)
get_filename_component
(
FIL_WE
${
FIL
}
NAME_WE
)
if
(
NOT PROTOBUF_GENERATE_CPP_APPEND_PATH
)
get_filename_component
(
FIL_DIR
${
FIL
}
DIRECTORY
)
if
(
FIL_DIR
)
set
(
FIL_WE
"
${
FIL_DIR
}
/
${
FIL_WE
}
"
)
endif
()
endif
()
list
(
APPEND
${
SRCS
}
"
${
CMAKE_CURRENT_BINARY_DIR
}
/
${
FIL_WE
}
_pb2.py"
)
add_custom_command
(
OUTPUT
"
${
CMAKE_CURRENT_BINARY_DIR
}
/
${
FIL_WE
}
_pb2.py"
COMMAND
${
PROTOBUF_PROTOC_EXECUTABLE
}
--python_out
${
CMAKE_CURRENT_BINARY_DIR
}
${
_protobuf_include_path
}
${
ABS_FIL
}
DEPENDS
${
ABS_FIL
}
${
PROTOBUF_PROTOC_EXECUTABLE
}
COMMENT
"Running Python protocol buffer compiler on
${
FIL
}
"
VERBATIM
)
endforeach
()
set
(
${
SRCS
}
${${
SRCS
}}
PARENT_SCOPE
)
endfunction
()
# Print and set the protobuf library information,
# finish this cmake process and exit from this file.
...
...
@@ -126,6 +121,7 @@ macro(PROMPT_PROTOBUF_LIB)
# FIND_Protobuf.cmake uses `Protobuf_PROTOC_EXECUTABLE`.
# make `protobuf_generate_cpp` happy.
SET
(
Protobuf_PROTOC_EXECUTABLE
${
PROTOBUF_PROTOC_EXECUTABLE
}
)
FOREACH
(
dep
${
protobuf_DEPS
}
)
ADD_DEPENDENCIES
(
protobuf
${
dep
}
)
ADD_DEPENDENCIES
(
protobuf_lite
${
dep
}
)
...
...
paddle/fluid/API.spec
浏览文件 @
397de907
...
...
@@ -179,6 +179,7 @@ paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], vara
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.similarity_focus ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
...
...
@@ -201,6 +202,7 @@ paddle.fluid.layers.create_tensor ArgSpec(args=['dtype', 'name', 'persistable'],
paddle.fluid.layers.create_parameter ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.layers.create_global_var ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None))
paddle.fluid.layers.cast ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.tensor_array_to_tensor ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.concat ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.sums ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.assign ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,))
...
...
paddle/fluid/framework/operator.cc
浏览文件 @
397de907
...
...
@@ -259,6 +259,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
if
(
row_size
>=
0
)
{
ss
<<
"[row_size="
<<
row_size
<<
"]"
;
}
std
::
string
dtype
=
GetDtype
(
*
scope
,
output
.
second
[
i
]);
ss
<<
":"
<<
dtype
;
ss
<<
"["
<<
GetDims
(
*
scope
,
var_name
,
true
)
<<
"]"
;
ss
<<
"("
<<
GetLoD
(
*
scope
,
var_name
)
<<
")"
;
}
...
...
paddle/fluid/framework/var_type_inference.h
浏览文件 @
397de907
...
...
@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/type_defs.h"
namespace
paddle
{
...
...
@@ -24,5 +27,27 @@ class VarTypeInference {
virtual
void
operator
()(
const
OpDesc
&
op_desc
,
BlockDesc
*
block
)
const
=
0
;
};
class
PassInDtypeAndVarTypeToOutput
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
final
{
auto
in_out_var_names
=
this
->
GetInputOutputWithSameType
();
for
(
auto
&
i_o_n
:
in_out_var_names
)
{
auto
&
x_name
=
op_desc
.
Input
(
i_o_n
.
first
).
at
(
0
);
auto
&
out_name
=
op_desc
.
Output
(
i_o_n
.
second
).
at
(
0
);
auto
&
x
=
block
->
FindRecursiveOrCreateVar
(
x_name
);
auto
&
out
=
block
->
FindRecursiveOrCreateVar
(
out_name
);
out
.
SetType
(
x
.
GetType
());
out
.
SetDataType
(
x
.
GetDataType
());
}
}
protected:
virtual
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
=
0
;
};
}
// namespace framework
}
// namespace paddle
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @
397de907
...
...
@@ -113,7 +113,9 @@ void Analyzer::Run(Argument* argument) {
passes
.
push_back
(
"infer_clean_graph_pass"
);
passes
.
push_back
(
"graph_viz_pass"
);
// add graphviz for debug.
for
(
auto
&
pass
:
ir_passes_
)
{
if
(
!
disabled_ir_passes_
.
count
(
pass
))
{
// skip mkldnn pass when use_mkldnn_ = false;
bool
skip_pass
=
(
!
use_mkldnn_
)
&&
pass
.
find
(
"mkldnn"
)
!=
std
::
string
::
npos
;
if
(
!
disabled_ir_passes_
.
count
(
pass
)
&&
!
skip_pass
)
{
passes
.
push_back
(
pass
);
passes
.
push_back
(
"graph_viz_pass"
);
// add graphviz for debug.
}
...
...
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
397de907
...
...
@@ -317,6 +317,7 @@ op_library(save_op DEPS lod_tensor)
op_library
(
load_op DEPS lod_tensor
)
op_library
(
save_combine_op DEPS lod_tensor
)
op_library
(
load_combine_op DEPS lod_tensor
)
op_library
(
tensor_array_to_tensor_op DEPS concat_op
)
op_library
(
concat_op DEPS concat_and_split
)
list
(
REMOVE_ITEM GENERAL_OPS
${
DEPS_OPS
}
)
...
...
paddle/fluid/operators/activation_op.cc
浏览文件 @
397de907
...
...
@@ -91,16 +91,12 @@ class ActivationOp : public framework::OperatorWithKernel {
}
};
class
ActivationOpInferVarType
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
auto
x_name
=
op_desc
.
Input
(
"X"
)[
0
];
auto
out_name
=
op_desc
.
Output
(
"Out"
)[
0
];
auto
&
x
=
block
->
FindRecursiveOrCreateVar
(
x_name
);
auto
&
out
=
block
->
FindRecursiveOrCreateVar
(
out_name
);
out
.
SetType
(
x
.
GetType
());
out
.
SetDataType
(
x
.
GetDataType
());
class
ActivationOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Out"
}};
}
};
...
...
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @
397de907
...
...
@@ -170,6 +170,15 @@ The required data format for this layer is one of the following:
}
};
class
BatchNormOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Y"
}};
}
};
template
<
typename
T
>
class
BatchNormKernel
<
platform
::
CPUDeviceContext
,
T
>
:
public
framework
::
OpKernel
<
T
>
{
...
...
@@ -525,7 +534,7 @@ class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
batch_norm
,
ops
::
BatchNormOp
,
ops
::
BatchNormOpMaker
,
ops
::
BatchNormGradMaker
);
ops
::
BatchNorm
OpInferVarType
,
ops
::
BatchNorm
GradMaker
);
REGISTER_OPERATOR
(
batch_norm_grad
,
ops
::
BatchNormGradOp
);
REGISTER_OP_CPU_KERNEL
(
...
...
paddle/fluid/operators/conv_op.cc
浏览文件 @
397de907
...
...
@@ -224,6 +224,15 @@ $$
)DOC"
);
}
class
ConvOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{
{
"Input"
,
/*->*/
"Output"
}};
}
};
void
Conv3DOpMaker
::
Make
()
{
AddInput
(
"Input"
,
...
...
@@ -365,6 +374,7 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
conv2d
,
ops
::
ConvOp
,
ops
::
Conv2DOpMaker
,
ops
::
ConvOpInferVarType
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
conv2d_grad
,
ops
::
ConvOpGrad
);
...
...
@@ -372,7 +382,9 @@ REGISTER_OPERATOR(conv2d_grad, ops::ConvOpGrad);
REGISTER_OPERATOR
(
depthwise_conv2d
,
ops
::
ConvOp
,
ops
::
Conv2DOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
depthwise_conv2d_grad
,
ops
::
ConvOpGrad
);
REGISTER_OPERATOR
(
conv3d
,
ops
::
ConvOp
,
ops
::
Conv3DOpMaker
,
ops
::
ConvOpInferVarType
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
conv3d_grad
,
ops
::
ConvOpGrad
);
...
...
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @
397de907
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/cross_entropy_op.h"
#include <string>
namespace
paddle
{
namespace
operators
{
...
...
@@ -179,6 +180,15 @@ or not. But the output only shares the LoD information with input X.
)DOC"
);
}
};
class
CrossEntropyOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Y"
}};
}
};
}
// namespace operators
}
// namespace paddle
...
...
@@ -186,6 +196,7 @@ namespace ops = paddle::operators;
using
CPUCtx
=
paddle
::
platform
::
CPUDeviceContext
;
REGISTER_OPERATOR
(
cross_entropy
,
ops
::
CrossEntropyOp
,
ops
::
CrossEntropyOpMaker
,
ops
::
CrossEntropyOpInferVarType
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
cross_entropy_grad
,
ops
::
CrossEntropyGradientOp
);
REGISTER_OP_CPU_KERNEL
(
cross_entropy
,
ops
::
CrossEntropyOpKernel
<
CPUCtx
,
float
>
,
...
...
paddle/fluid/operators/elementwise_op.h
浏览文件 @
397de907
...
...
@@ -75,16 +75,12 @@ class ElementwiseOp : public framework::OperatorWithKernel {
}
};
class
ElementwiseOpInferVarType
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
auto
x_name
=
op_desc
.
Input
(
"X"
)[
0
];
auto
out_name
=
op_desc
.
Output
(
"Out"
)[
0
];
auto
&
x
=
block
->
FindRecursiveOrCreateVar
(
x_name
);
auto
&
out
=
block
->
FindRecursiveOrCreateVar
(
out_name
);
out
.
SetType
(
x
.
GetType
());
out
.
SetDataType
(
x
.
GetDataType
());
class
ElementwiseOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Out"
}};
}
};
...
...
paddle/fluid/operators/mean_op.cc
浏览文件 @
397de907
...
...
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/mean_op.h"
#include <string>
namespace
paddle
{
namespace
operators
{
...
...
@@ -42,6 +42,14 @@ Mean Operator calculates the mean of all elements in X.
}
};
class
MeanOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Out"
}};
}
};
class
MeanGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -50,6 +58,14 @@ class MeanGradOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
framework
::
GradVarName
(
"X"
));
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
input_data_type
=
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
());
return
framework
::
OpKernelType
(
input_data_type
,
ctx
.
GetPlace
());
}
};
class
MeanGradMaker
:
public
framework
::
SingleGradOpDescMaker
{
...
...
@@ -71,7 +87,8 @@ class MeanGradMaker : public framework::SingleGradOpDescMaker {
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
mean
,
ops
::
MeanOp
,
ops
::
MeanOpMaker
,
ops
::
MeanGradMaker
);
REGISTER_OPERATOR
(
mean
,
ops
::
MeanOp
,
ops
::
MeanOpMaker
,
ops
::
MeanOpInferVarType
,
ops
::
MeanGradMaker
);
REGISTER_OPERATOR
(
mean_grad
,
ops
::
MeanGradOp
);
REGISTER_OP_CPU_KERNEL
(
mean
,
ops
::
MeanKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
...
...
paddle/fluid/operators/mul_op.cc
浏览文件 @
397de907
...
...
@@ -126,6 +126,14 @@ or not. But the output only shares the LoD information with input $X$.
}
};
class
MulOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Out"
}};
}
};
class
MulGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -178,7 +186,8 @@ class MulOpGradMaker : public framework::SingleGradOpDescMaker {
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
mul
,
ops
::
MulOp
,
ops
::
MulOpMaker
,
ops
::
MulOpGradMaker
);
REGISTER_OPERATOR
(
mul
,
ops
::
MulOp
,
ops
::
MulOpMaker
,
ops
::
MulOpInferVarType
,
ops
::
MulOpGradMaker
);
REGISTER_OPERATOR
(
mul_grad
,
ops
::
MulGradOp
);
REGISTER_OP_CPU_KERNEL
(
mul
,
ops
::
MulKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
...
...
paddle/fluid/operators/pool_op.cc
浏览文件 @
397de907
...
...
@@ -40,7 +40,7 @@ int PoolOutputSize(int input_size, int filter_size, int padding, int stride,
return
output_size
;
}
void
PoolOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
void
PoolOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"X(Input) of Pooling should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Out(Output) of Pooling should not be null."
);
...
...
@@ -81,7 +81,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
}
framework
::
OpKernelType
PoolOp
::
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
const
framework
::
ExecutionContext
&
ctx
)
const
{
framework
::
LibraryType
library_
{
framework
::
LibraryType
::
kPlain
};
std
::
string
data_format
=
ctx
.
Attr
<
std
::
string
>
(
"data_format"
);
framework
::
DataLayout
layout_
=
framework
::
StringToDataLayout
(
data_format
);
...
...
@@ -104,7 +104,7 @@ framework::OpKernelType PoolOp::GetExpectedKernelType(
layout_
,
library_
);
}
void
PoolOpGrad
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
void
PoolOpGrad
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input(X@GRAD) should not be null."
);
...
...
@@ -112,7 +112,7 @@ void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
}
framework
::
OpKernelType
PoolOpGrad
::
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
const
framework
::
ExecutionContext
&
ctx
)
const
{
framework
::
LibraryType
library_
{
framework
::
LibraryType
::
kPlain
};
std
::
string
data_format
=
ctx
.
Attr
<
std
::
string
>
(
"data_format"
);
framework
::
DataLayout
layout_
=
framework
::
StringToDataLayout
(
data_format
);
...
...
@@ -262,6 +262,14 @@ Example:
)DOC"
);
}
class
PoolOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Out"
}};
}
};
void
Pool3dOpMaker
::
Make
()
{
AddInput
(
"X"
,
"(Tensor) The input tensor of pooling operator. "
...
...
@@ -372,6 +380,7 @@ Example:
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
pool2d
,
ops
::
PoolOp
,
ops
::
Pool2dOpMaker
,
ops
::
PoolOpInferVarType
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
pool2d_grad
,
ops
::
PoolOpGrad
);
...
...
@@ -383,6 +392,7 @@ REGISTER_OP_CPU_KERNEL(
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OPERATOR
(
pool3d
,
ops
::
PoolOp
,
ops
::
Pool3dOpMaker
,
ops
::
PoolOpInferVarType
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
pool3d_grad
,
ops
::
PoolOpGrad
);
...
...
paddle/fluid/operators/similarity_focus_op.cc
0 → 100644
浏览文件 @
397de907
/* Copyright (c) 2016 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/similarity_focus_op.h"
namespace
paddle
{
namespace
operators
{
class
SimilarityFocusOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), a 4-D tensor with shape,"
" [BatchSize, X, Y, Z]"
);
AddOutput
(
"Out"
,
"(Tensor, default Tensor<float>), the similarity focus mask"
" with the same shape of input X."
);
AddAttr
<
int
>
(
"axis"
,
"(int32), indicating the dimension to be select. It can"
" only be 1, 2, or 3."
);
AddAttr
<
std
::
vector
<
int
>>
(
"indexes"
,
"(std::vector<int32>), indicating the indexes"
" of the selected dimension."
);
AddComment
(
R"DOC(
SimilarityFocus Operator.
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th row or j-th column will be skipped. And then the
next largest number will be selected from the remaining numbers. Obviously
there will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
)DOC"
);
}
};
class
SimilarityFocusOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) should be not null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
4
,
"Input(X)'s rank should be 4."
);
ctx
->
SetOutputDim
(
"Out"
,
x_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
platform
::
CPUPlace
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
similarity_focus
,
ops
::
SimilarityFocusOp
,
ops
::
SimilarityFocusOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
similarity_focus
,
ops
::
SimilarityFocusKernel
<
float
>
,
ops
::
SimilarityFocusKernel
<
double
>
);
paddle/fluid/operators/similarity_focus_op.h
0 → 100644
浏览文件 @
397de907
/* Copyright (c) 2016 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 <algorithm>
#include <cstring>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
SimilarityFocusKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
Tensor
*
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
const
Tensor
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
T
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
x_data
=
x
->
data
<
T
>
();
int
axis
=
context
.
Attr
<
int
>
(
"axis"
);
std
::
vector
<
int
>
indexes
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"indexes"
);
int64_t
batch_size
=
x
->
dims
()[
0
];
int64_t
dim
[
4
];
for
(
int
i
=
1
;
i
<=
3
;
++
i
)
{
dim
[
i
]
=
x
->
dims
()[
i
];
}
if
(
indexes
.
size
()
<
1
)
{
PADDLE_THROW
(
"Indexes' size can not be 0."
);
}
for
(
auto
index
:
indexes
)
{
if
(
dim
[
axis
]
<
index
)
{
PADDLE_THROW
(
"Index exceeds tensor shape limit."
);
}
}
int64_t
array_size
=
1
;
for
(
int
i
=
1
;
i
<=
3
;
++
i
)
{
if
(
i
!=
axis
)
{
array_size
*=
dim
[
i
];
}
}
std
::
vector
<
std
::
pair
<
T
,
int64_t
>>
array
(
array_size
);
bool
(
*
cmp
)(
std
::
pair
<
T
,
int64_t
>
,
std
::
pair
<
T
,
int64_t
>
)
=
[](
std
::
pair
<
T
,
int64_t
>
x
,
std
::
pair
<
T
,
int64_t
>
y
)
{
return
x
.
first
>
y
.
first
;
};
int64_t
(
*
compute_index
)(
int64_t
*
,
int
,
int
,
int
,
int
)
=
[](
int64_t
*
dim
,
int
d1
,
int
d2
,
int
d3
,
int
d4
)
{
return
d1
*
dim
[
1
]
*
dim
[
2
]
*
dim
[
3
]
+
d2
*
dim
[
2
]
*
dim
[
3
]
+
d3
*
dim
[
3
]
+
d4
;
};
memset
(
out_data
,
0
,
sizeof
(
T
)
*
batch_size
*
dim
[
1
]
*
dim
[
2
]
*
dim
[
3
]);
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
auto
index
:
indexes
)
{
if
(
axis
==
1
)
{
for
(
int
j
=
0
;
j
<
dim
[
2
];
++
j
)
{
for
(
int
k
=
0
;
k
<
dim
[
3
];
++
k
)
{
array
[
j
*
dim
[
3
]
+
k
]
=
std
::
make_pair
(
x_data
[
compute_index
(
dim
,
i
,
index
,
j
,
k
)],
j
*
dim
[
3
]
+
k
);
}
}
std
::
sort
(
array
.
begin
(),
array
.
end
(),
cmp
);
int
tag_num
=
0
;
std
::
vector
<
bool
>
tag2
(
dim
[
2
]),
tag3
(
dim
[
3
]);
for
(
auto
x
:
array
)
{
int
idx2
=
x
.
second
/
dim
[
3
];
int
idx3
=
x
.
second
%
dim
[
3
];
if
(
tag2
[
idx2
]
||
tag3
[
idx3
])
{
continue
;
}
tag_num
++
;
tag2
[
idx2
]
=
true
;
tag3
[
idx3
]
=
true
;
for
(
int
j
=
0
;
j
<
dim
[
1
];
++
j
)
{
out_data
[
compute_index
(
dim
,
i
,
j
,
idx2
,
idx3
)]
=
1
;
}
if
(
tag_num
==
std
::
min
(
dim
[
2
],
dim
[
3
]))
{
break
;
}
}
}
else
if
(
axis
==
2
)
{
for
(
int
j
=
0
;
j
<
dim
[
1
];
++
j
)
{
for
(
int
k
=
0
;
k
<
dim
[
3
];
++
k
)
{
array
[
j
*
dim
[
3
]
+
k
]
=
std
::
make_pair
(
x_data
[
compute_index
(
dim
,
i
,
j
,
index
,
k
)],
j
*
dim
[
3
]
+
k
);
}
}
std
::
sort
(
array
.
begin
(),
array
.
end
(),
cmp
);
int
tag_num
=
0
;
std
::
vector
<
bool
>
tag1
(
dim
[
1
]),
tag3
(
dim
[
3
]);
for
(
auto
x
:
array
)
{
int
idx1
=
x
.
second
/
dim
[
3
];
int
idx3
=
x
.
second
%
dim
[
3
];
if
(
tag1
[
idx1
]
||
tag3
[
idx3
])
{
continue
;
}
tag_num
++
;
tag1
[
idx1
]
=
true
;
tag3
[
idx3
]
=
true
;
for
(
int
j
=
0
;
j
<
dim
[
2
];
++
j
)
{
out_data
[
compute_index
(
dim
,
i
,
idx1
,
j
,
idx3
)]
=
1
;
}
if
(
tag_num
==
std
::
min
(
dim
[
1
],
dim
[
3
]))
{
break
;
}
}
}
else
if
(
axis
==
3
)
{
for
(
int
j
=
0
;
j
<
dim
[
1
];
++
j
)
{
for
(
int
k
=
0
;
k
<
dim
[
2
];
++
k
)
{
array
[
j
*
dim
[
2
]
+
k
]
=
std
::
make_pair
(
x_data
[
compute_index
(
dim
,
i
,
j
,
k
,
index
)],
j
*
dim
[
2
]
+
k
);
}
}
std
::
sort
(
array
.
begin
(),
array
.
end
(),
cmp
);
int
tag_num
=
0
;
std
::
vector
<
bool
>
tag1
(
dim
[
1
]),
tag2
(
dim
[
2
]);
for
(
auto
x
:
array
)
{
int
idx1
=
x
.
second
/
dim
[
2
];
int
idx2
=
x
.
second
%
dim
[
2
];
if
(
tag1
[
idx1
]
||
tag2
[
idx2
])
{
continue
;
}
tag_num
++
;
tag1
[
idx1
]
=
true
;
tag2
[
idx2
]
=
true
;
for
(
int
j
=
0
;
j
<
dim
[
3
];
++
j
)
{
out_data
[
compute_index
(
dim
,
i
,
idx1
,
idx2
,
j
)]
=
1
;
}
if
(
tag_num
==
std
::
min
(
dim
[
1
],
dim
[
2
]))
{
break
;
}
}
}
else
{
PADDLE_THROW
(
"Axis must be 1 or 2 or 3"
);
}
}
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/softmax_op.cc
浏览文件 @
397de907
...
...
@@ -124,6 +124,14 @@ For each row $i$ and each column $j$ in the matrix, we have:
}
};
class
SoftmaxOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>
GetInputOutputWithSameType
()
const
override
{
return
std
::
unordered_map
<
std
::
string
,
std
::
string
>
{{
"X"
,
/*->*/
"Out"
}};
}
};
class
SoftmaxOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -196,7 +204,7 @@ class SoftmaxOpGradMaker : public framework::SingleGradOpDescMaker {
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
softmax
,
ops
::
SoftmaxOp
,
ops
::
SoftmaxOpMaker
,
ops
::
SoftmaxOpGradMaker
);
ops
::
SoftmaxOp
InferVarType
,
ops
::
SoftmaxOp
GradMaker
);
REGISTER_OPERATOR
(
softmax_grad
,
ops
::
SoftmaxOpGrad
);
REGISTER_OP_CPU_KERNEL
(
softmax
,
ops
::
SoftmaxKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
...
...
paddle/fluid/operators/tensor_array_to_tensor_op.cc
0 → 100644
浏览文件 @
397de907
/* 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 <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/variable.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
void
LodTensorArray2LodTensorVector
(
const
framework
::
Scope
&
scope
,
const
std
::
string
&
base_name
,
const
std
::
string
&
lod_tensor_array_name
,
std
::
vector
<
std
::
string
>
*
res_names
)
{
auto
&
inx
=
scope
.
FindVar
(
lod_tensor_array_name
)
->
Get
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
0
;
i
<
inx
.
size
();
i
++
)
{
std
::
string
var_name
=
base_name
+
std
::
to_string
(
i
);
framework
::
Variable
*
g_feed_value
=
const_cast
<
framework
::
Scope
&>
(
scope
).
Var
(
var_name
);
auto
&
feed_input
=
*
(
g_feed_value
->
GetMutable
<
paddle
::
framework
::
LoDTensor
>
());
feed_input
.
ShareDataWith
(
inx
[
i
]);
res_names
->
push_back
(
var_name
);
}
}
void
LodTensorVectorResizeFromLodTensorArray
(
const
framework
::
Scope
&
scope
,
const
std
::
string
&
base_name
,
const
std
::
string
&
lod_tensor_array_name
,
std
::
vector
<
std
::
string
>
*
res_names
)
{
auto
&
inx
=
scope
.
FindVar
(
lod_tensor_array_name
)
->
Get
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
0
;
i
<
inx
.
size
();
i
++
)
{
std
::
string
var_name
=
base_name
+
std
::
to_string
(
i
);
framework
::
Variable
*
g_feed_value
=
const_cast
<
framework
::
Scope
&>
(
scope
).
Var
(
var_name
);
auto
&
feed_input
=
*
(
g_feed_value
->
GetMutable
<
paddle
::
framework
::
LoDTensor
>
());
auto
dims
=
inx
[
i
].
dims
();
feed_input
.
Resize
(
dims
);
res_names
->
push_back
(
var_name
);
}
}
void
LodTensorArrayCreateFromLodTensorArray
(
const
framework
::
Scope
&
scope
,
const
std
::
string
&
input_lod_tensor_array_name
,
const
std
::
string
&
output_lod_tensor_array_name
)
{
auto
&
inx
=
scope
.
FindVar
(
input_lod_tensor_array_name
)
->
Get
<
framework
::
LoDTensorArray
>
();
auto
&
grad_inx
=
*
scope
.
FindVar
(
output_lod_tensor_array_name
)
->
GetMutable
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
0
;
i
<
inx
.
size
();
i
++
)
{
std
::
string
var_name
=
output_lod_tensor_array_name
+
std
::
to_string
(
i
);
framework
::
Variable
*
g_feed_value
=
const_cast
<
framework
::
Scope
&>
(
scope
).
Var
(
var_name
);
auto
&
feed_input
=
*
(
g_feed_value
->
GetMutable
<
paddle
::
framework
::
LoDTensor
>
());
grad_inx
.
push_back
(
feed_input
);
}
}
class
LoDTensorArray2TensorOp
:
public
framework
::
OperatorBase
{
public:
using
OperatorBase
::
OperatorBase
;
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
auto
axis
=
Attr
<
int
>
(
"axis"
);
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
axis
;
auto
&
inx
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensorArray
>
();
auto
&
out
=
*
scope
.
FindVar
(
Output
(
"Out"
))
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
&
out_inx
=
*
scope
.
FindVar
(
Output
(
"OutIndex"
))
->
GetMutable
<
framework
::
LoDTensor
>
();
const
size_t
n
=
inx
.
size
();
PADDLE_ENFORCE_GT
(
n
,
0
,
"Input tensorarray size should > 0."
);
std
::
string
base_name
=
Inputs
(
"X"
)[
0
];
std
::
vector
<
std
::
string
>
names
;
// get the input tensorarray items' dim in out_inx
auto
out_inx_dim
=
out_inx
.
dims
();
out_inx_dim
[
0
]
=
inx
.
size
();
out_inx
.
Resize
(
out_inx_dim
);
std
::
string
var_name
=
"out_index"
;
framework
::
Variable
*
tmp_index_var
=
const_cast
<
framework
::
Scope
&>
(
scope
).
Var
(
var_name
);
auto
&
tmp_index_tensor
=
*
(
tmp_index_var
->
GetMutable
<
paddle
::
framework
::
LoDTensor
>
());
tmp_index_tensor
.
Resize
(
out_inx_dim
);
int
*
tmp_index_data
=
tmp_index_tensor
.
mutable_data
<
int
>
(
platform
::
CPUPlace
());
auto
out_dims
=
inx
[
0
].
dims
();
size_t
out_dim_sum
=
0
;
for
(
size_t
index
=
0
;
index
<
inx
.
size
();
index
++
)
{
auto
inx_dims
=
inx
[
index
].
dims
();
out_dim_sum
+=
inx_dims
[
axis
];
tmp_index_data
[
index
]
=
inx_dims
[
axis
];
}
out_inx
.
ShareDataWith
(
tmp_index_tensor
);
// get input array items' dims
out_dims
[
axis
]
=
out_dim_sum
;
out
.
Resize
(
out_dims
);
LodTensorArray2LodTensorVector
(
scope
,
base_name
,
Input
(
"X"
),
&
names
);
// Invoke Reshape Op
auto
concat_op
=
framework
::
OpRegistry
::
CreateOp
(
"concat"
,
{{
"X"
,
names
}},
{{
"Out"
,
{
Output
(
"Out"
)}}},
attrs
);
concat_op
->
Run
(
scope
,
place
);
}
};
class
LoDTensorArray2TensorOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"Input LoDTensorArray of tensor_array_to_tensor operator."
);
AddOutput
(
"Out"
,
"Output tensor of tensor_array_to_tensor operator."
);
AddOutput
(
"OutIndex"
,
"Output input LoDTensorArray items' dims of "
"tensor_array_to_tensor operator."
);
AddAttr
<
int
>
(
"axis"
,
"The axis along which the input tensors will be concatenated."
)
.
SetDefault
(
0
);
AddComment
(
R"DOC(
tensor_array_to_tensor Operator.
Concatenate the input LoDTensorArray along dimension axis to the output Tensor.
Examples:
Input = {[1,2], [3,4], [5,6]}
axis = 0
Output = [[1,2],
[3,4],
[5,6]]
OutputIndex = [1,1,1]
)DOC"
);
}
};
class
LoDTensorArray2TensorOpInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{}
};
class
LoDTensorArray2TensorGradInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{}
};
class
LoDTensorArray2TensorGradInferVarType
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
for
(
auto
&
out_var
:
op_desc
.
Output
(
framework
::
GradVarName
(
"X"
)))
{
block
->
Var
(
out_var
)
->
SetType
(
framework
::
proto
::
VarType
::
LOD_TENSOR_ARRAY
);
}
}
};
class
LoDTensorArray2TensorGradOp
:
public
framework
::
OperatorBase
{
public:
using
OperatorBase
::
OperatorBase
;
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
auto
axis
=
Attr
<
int
>
(
"axis"
);
framework
::
AttributeMap
attrs
;
attrs
[
"axis"
]
=
axis
;
auto
&
inx
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensorArray
>
();
const
size_t
n
=
inx
.
size
();
PADDLE_ENFORCE_GT
(
n
,
0
,
"Input tensorarray size should > 0."
);
std
::
string
base_name
=
Inputs
(
"X"
)[
0
];
std
::
vector
<
std
::
string
>
names
;
LodTensorArray2LodTensorVector
(
scope
,
base_name
,
Input
(
"X"
),
&
names
);
// grad
auto
dx_name
=
Output
(
framework
::
GradVarName
(
"X"
));
auto
dout_name
=
Input
(
framework
::
GradVarName
(
"Out"
));
std
::
vector
<
std
::
string
>
grad_names
;
LodTensorVectorResizeFromLodTensorArray
(
scope
,
"grad_name"
,
Input
(
"X"
),
&
grad_names
);
auto
concat_grad_op
=
framework
::
OpRegistry
::
CreateOp
(
"concat_grad"
,
{{
"X"
,
names
},
{
"Out@GRAD"
,
{
dout_name
}}},
{{
"X@GRAD"
,
grad_names
}},
attrs
);
concat_grad_op
->
Run
(
scope
,
place
);
LodTensorArrayCreateFromLodTensorArray
(
scope
,
Input
(
"X"
),
dx_name
);
auto
&
grad_inx
=
*
scope
.
FindVar
(
dx_name
)
->
GetMutable
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
0
;
i
<
grad_names
.
size
();
i
++
)
{
std
::
string
var_name
=
grad_names
[
i
];
auto
&
feed_input
=
scope
.
FindVar
(
var_name
)
->
Get
<
framework
::
LoDTensor
>
();
grad_inx
[
i
].
ShareDataWith
(
feed_input
);
}
}
};
}
// namespace operators
}
// namespace paddle
USE_OP
(
concat
);
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
tensor_array_to_tensor
,
ops
::
LoDTensorArray2TensorOp
,
ops
::
LoDTensorArray2TensorOpMaker
,
ops
::
LoDTensorArray2TensorOpInferShape
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
tensor_array_to_tensor_grad
,
ops
::
LoDTensorArray2TensorGradOp
,
ops
::
LoDTensorArray2TensorGradInferShape
,
ops
::
LoDTensorArray2TensorGradInferVarType
);
python/paddle/fluid/__init__.py
浏览文件 @
397de907
...
...
@@ -34,6 +34,7 @@ from . import regularizer
from
.
import
average
from
.
import
metrics
from
.
import
transpiler
from
.
import
distribute_lookup_table
from
.param_attr
import
ParamAttr
,
WeightNormParamAttr
from
.data_feeder
import
DataFeeder
from
.core
import
LoDTensor
,
LoDTensorArray
,
CPUPlace
,
CUDAPlace
,
CUDAPinnedPlace
,
Scope
...
...
python/paddle/fluid/distribute_lookup_table.py
0 → 100644
浏览文件 @
397de907
# 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.
LOOKUP_TABLE_TYPE
=
"lookup_table"
def
find_distributed_lookup_table
(
program
):
"""
Find distribute lookup table in program.
We only support one distribute table now.
:param program:
:return: table_name or None
"""
table_name
=
None
for
op
in
program
.
global_block
().
ops
:
if
op
.
type
==
LOOKUP_TABLE_TYPE
:
if
op
.
attr
(
'is_distributed'
)
is
True
:
if
table_name
is
None
:
table_name
=
op
.
input
(
"W"
)[
0
]
if
table_name
!=
op
.
input
(
"W"
)[
0
]:
raise
RuntimeError
(
"all distributed lookup_table_ops"
" should have only one table"
)
else
:
if
table_name
is
not
None
:
assert
op
.
input
(
"W"
)[
0
]
!=
table_name
return
table_name
python/paddle/fluid/layers/nn.py
浏览文件 @
397de907
...
...
@@ -160,6 +160,7 @@ __all__ = [
'affine_grid'
,
'sequence_reverse'
,
'affine_channel'
,
'similarity_focus'
,
'hash'
,
'grid_sampler'
,
'log_loss'
,
...
...
@@ -7933,6 +7934,118 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
return
out
def
similarity_focus
(
input
,
axis
,
indexes
,
name
=
None
):
"""
SimilarityFocus Operator
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th row or j-th column will be skipped. And then the
next largest number will be selected from the remaining numbers. Obviously
there will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
.. code-block:: text
* Example :
Given a 4-D tensor x with the shape (BatchSize, C, A, B), where C is
the number of channels and the shape of feature map is (A, B):
x.shape = (2, 3, 2, 2)
x.data = [[[[0.8, 0.1],
[0.4, 0.5]],
[[0.9, 0.7],
[0.9, 0.9]],
[[0.8, 0.9],
[0.1, 0.2]]],
[[[0.2, 0.5],
[0.3, 0.4]],
[[0.9, 0.7],
[0.8, 0.4]],
[[0.0, 0.2],
[0.4, 0.7]]]]
Given axis: 1 (the axis of the channel)
Given indexes: [0]
then we get a 4-D tensor out with the same shape of input x:
out.shape = (2, 3, 2, 2)
out.data = [[[[1.0, 0.0],
[0.0, 1.0]],
[[1.0, 0.0],
[0.0, 1.0]],
[[1.0, 0.0],
[0.0, 1.0]]],
[[[0.0, 1.0],
[1.0, 0.0]],
[[0.0, 1.0],
[1.0, 0.0]],
[[0.0, 1.0],
[1.0, 0.0]]]]
Args:
input(Variable): The input tensor variable(default float). It should
be a 4-D tensor with shape [BatchSize, A, B, C].
axis(int): Indicating the dimension to be selected. It can only be
1, 2 or 3.
indexes(list): Indicating the indexes of the selected dimension.
Returns:
Variable: A tensor variable with the same shape and same type
as the input.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[2, 3, 2, 2], dtype='float32')
x = fluid.layers.layer_norm(input=data, axis=1, indexes=[0])
"""
helper
=
LayerHelper
(
'similarity_focus'
,
**
locals
())
# check attrs
if
isinstance
(
axis
,
int
)
is
False
:
raise
TypeError
(
"axis must be int type."
)
if
isinstance
(
indexes
,
list
)
is
False
:
raise
TypeError
(
"indexes must be list type."
)
if
axis
!=
1
and
axis
!=
2
and
axis
!=
3
:
raise
ValueError
(
"axis must be 1, 2 or 3."
)
if
len
(
indexes
)
==
0
:
raise
ValueError
(
"indexes can not be empty."
)
if
name
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
input
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
'similarity_focus'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
"axis"
:
axis
,
"indexes"
:
indexes
})
return
out
def
hash
(
input
,
hash_size
,
num_hash
=
1
,
name
=
None
):
"""
Hash the input to an integer whose value is less than the given hash size.
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
397de907
...
...
@@ -24,10 +24,10 @@ from .layer_function_generator import templatedoc
import
numpy
__all__
=
[
'create_tensor'
,
'create_parameter'
,
'create_global_var'
,
'cast'
,
'concat'
,
'
sums'
,
'assign'
,
'fill_constant_batch_size_like'
,
'fill_constant
'
,
'
argmin'
,
'argmax'
,
'argsort'
,
'ones'
,
'zeros'
,
'reverse'
,
'has_inf
'
,
'has_nan'
,
'isfinite'
'create_tensor'
,
'create_parameter'
,
'create_global_var'
,
'cast'
,
'
tensor_array_to_tensor'
,
'concat'
,
'sums'
,
'assign
'
,
'
fill_constant_batch_size_like'
,
'fill_constant'
,
'argmin'
,
'argmax
'
,
'
argsort'
,
'ones'
,
'zeros'
,
'reverse'
,
'has_inf'
,
'
has_nan'
,
'isfinite'
]
...
...
@@ -193,6 +193,60 @@ def concat(input, axis=0, name=None):
return
out
def
tensor_array_to_tensor
(
input
,
axis
=
1
,
name
=
None
):
"""
This function concatenates the input LodTensorArray along the axis mentioned
and returns that as the output.
A simple example as below:
.. code-block:: text
Given:
input.data = {[[0.6, 0.1, 0.3],
[0.5, 0.3, 0.2]],
[[1.3],
[1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1
Then:
output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
[0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
output_index.data = [3, 1, 2]
Args:
input(list): Input LodTensorArray
axis(int): Integer axis along which the tensors will be concatenated
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: Output variable of the concatenation
Variable: The input LodTensorArray items' dims along the axis
Examples:
.. code-block:: python
output, output_index = fluid.layers.tensor_array_to_tensor(input=tensor_array)
"""
helper
=
LayerHelper
(
'tensor_array_concat'
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
helper
.
input_dtype
())
out_index
=
helper
.
create_variable_for_type_inference
(
dtype
=
"int32"
)
helper
.
append_op
(
type
=
'tensor_array_concat'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
[
out
],
'OutIndex'
:
[
out_index
]},
attrs
=
{
'axis'
:
axis
})
return
out
,
out_index
def
sums
(
input
,
out
=
None
):
"""
This function performs the sum operation on the input and returns the
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
397de907
...
...
@@ -13,21 +13,23 @@
# limitations under the License.
from
__future__
import
print_function
import
re
import
sys
from
collections
import
defaultdict
from
contextlib
import
contextmanager
from
paddle.fluid.framework
import
Program
,
Variable
,
name_scope
,
default_main_program
from
paddle.fluid.distribute_lookup_table
import
find_distributed_lookup_table
from
.
import
framework
from
.
import
layers
from
.
import
unique_name
from
.backward
import
append_backward
from
.clip
import
append_gradient_clip_ops
,
error_clip_callback
from
.framework
import
program_guard
from
.
import
unique_name
from
.initializer
import
Constant
from
.layer_helper
import
LayerHelper
from
.regularizer
import
append_regularization_ops
from
.clip
import
append_gradient_clip_ops
,
error_clip_callback
from
contextlib
import
contextmanager
from
.layers
import
ops
from
.regularizer
import
append_regularization_ops
__all__
=
[
'SGD'
,
'Momentum'
,
'Adagrad'
,
'Adam'
,
'Adamax'
,
'DecayedAdagrad'
,
'Ftrl'
,
...
...
@@ -85,7 +87,7 @@ class Optimizer(object):
name
=
unique_name
.
generate
(
"learning_rate"
),
shape
=
[
1
],
value
=
float
(
self
.
_learning_rate
),
dtype
=
'float32'
if
self
.
_dtype
==
None
else
self
.
_dtype
,
dtype
=
'float32'
if
self
.
_dtype
is
None
else
self
.
_dtype
,
persistable
=
True
)
def
_global_learning_rate
(
self
,
program
=
None
):
...
...
@@ -245,6 +247,50 @@ class Optimizer(object):
end
=
len
(
global_block
.
ops
)
return
global_block
.
_slice_ops
(
start
,
end
)
def
_process_distribute_lookuptable
(
self
,
param_grads
,
loss
,
startup_program
):
"""
Because distribute lookup table only support SGD optimizer for now, not support
other optimizer and regularization, so we should find the table parameter out,
and avoid to add regularization and other op for it, and add sgd optimize op
for it independently.
:param param_grads(list((Var, Var))): list of (param, grad) pair.
:param loss: the loss variable.
:param startup_program: the startup program
"""
program
=
loss
.
block
.
program
table_name
=
find_distributed_lookup_table
(
program
)
table_param
=
None
table_grad
=
None
new_param_grads
=
[]
for
p
,
g
in
param_grads
:
if
p
.
name
==
table_name
:
if
table_param
is
not
None
:
raise
RuntimeError
(
"multi dist table var found, only support one now!"
)
table_param
=
p
table_grad
=
g
else
:
new_param_grads
.
append
((
p
,
g
))
sgd_op
=
None
if
table_param
is
not
None
:
with
program_guard
(
program
,
startup_program
):
param_and_grad
=
[
table_param
,
table_grad
]
with
table_param
.
block
.
program
.
_optimized_guard
(
param_and_grad
),
\
framework
.
name_scope
(
"optimizer"
):
self
.
_create_global_learning_rate
()
# create the optimize op
sgd_op
=
loss
.
block
.
append_op
(
type
=
'sgd'
,
inputs
=
{
"Param"
:
table_param
,
"Grad"
:
table_grad
,
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
)
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
]})
return
new_param_grads
,
(
table_param
,
table_grad
),
sgd_op
def
minimize
(
self
,
loss
,
startup_program
=
None
,
...
...
@@ -260,6 +306,9 @@ class Optimizer(object):
params_grads
=
sorted
(
params_grads
,
key
=
lambda
x
:
x
[
0
].
name
)
params_grads
,
table_param_and_grad
,
table_optimize_op
=
\
self
.
_process_distribute_lookuptable
(
params_grads
,
loss
,
startup_program
)
params_grads
=
append_gradient_clip_ops
(
params_grads
)
# Add regularization if any
...
...
@@ -268,6 +317,9 @@ class Optimizer(object):
optimize_ops
=
self
.
_create_optimization_pass
(
params_grads
,
loss
,
startup_program
)
if
table_optimize_op
is
not
None
:
optimize_ops
.
append
(
table_optimize_op
)
params_grads
.
append
(
table_param_and_grad
)
return
optimize_ops
,
params_grads
...
...
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @
397de907
...
...
@@ -38,7 +38,7 @@ depth = 8
mix_hidden_lr
=
1e-3
IS_SPARSE
=
True
PASS_NUM
=
1
0
PASS_NUM
=
1
BATCH_SIZE
=
10
embedding_name
=
'emb'
...
...
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @
397de907
...
...
@@ -567,7 +567,6 @@ class TestDistLookupTable(TestDistLookupTableBase):
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'uniform_random'
,
'uniform_random'
,
'recv'
,
'recv'
,
'recv'
,
'fetch_barrier'
,
'concat'
,
'fake_init'
...
...
@@ -639,7 +638,7 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
# 5 save table
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
5
].
ops
],
[
"save"
])
trainer
,
_
=
self
.
get_trainer
(
config
)
trainer
,
trainer_startup
=
self
.
get_trainer
(
config
)
self
.
assertEqual
(
len
(
trainer
.
blocks
),
1
)
ops
=
[
'split_ids'
,
'prefetch'
,
'merge_ids'
,
'sequence_pool'
,
...
...
@@ -653,6 +652,16 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
'recv'
,
'concat'
]
self
.
assertEqual
([
op
.
type
for
op
in
trainer
.
blocks
[
0
].
ops
],
ops
)
startup_ops
=
[
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'fill_constant'
,
'uniform_random'
,
'uniform_random'
,
'recv'
,
'recv'
,
'recv'
,
'fetch_barrier'
,
'concat'
,
'fake_init'
]
self
.
assertEqual
([
op
.
type
for
op
in
trainer_startup
.
blocks
[
0
].
ops
],
startup_ops
)
class
TestDistLookupTableSliceSize
(
TestDistLookupTableBase
):
...
...
python/paddle/fluid/tests/unittests/test_similarity_focus_op.py
0 → 100755
浏览文件 @
397de907
# 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
class
TestSimilarityFocusOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
2
x_dim
,
y_dim
,
z_dim
=
3
,
2
,
2
self
.
inputs
=
{
'X'
:
np
.
array
([[[[
0.8
,
0.1
],
[
0.4
,
0.5
]],
[[
0.9
,
0.7
],
[
0.9
,
0.9
]],
[[
0.8
,
0.9
],
[
0.1
,
0.2
]]],
[[[
0.2
,
0.5
],
[
0.3
,
0.4
]],
[[
0.9
,
0.7
],
[
0.8
,
0.4
]],
[[
0.0
,
0.2
],
[
0.4
,
0.7
]]]]),
}
self
.
attrs
=
{
'axis'
:
1
,
'indexes'
:
[
0
],
}
output
=
None
for
batch
in
range
(
batch_size
):
res
=
np
.
zeros
((
1
,
y_dim
,
z_dim
)).
astype
(
"float32"
).
reshape
(
-
1
)
for
index
in
self
.
attrs
[
'indexes'
]:
channel
=
self
.
inputs
[
'X'
][
batch
,
index
,
:,
:].
reshape
(
-
1
).
copy
(
)
tag1
=
[
0
for
i
in
range
(
y_dim
)]
tag2
=
[
0
for
i
in
range
(
z_dim
)]
cnt
=
0
for
i
in
range
(
channel
.
size
):
index
=
channel
.
argmax
()
idx1
=
index
//
z_dim
idx2
=
index
%
z_dim
if
tag1
[
idx1
]
+
tag2
[
idx2
]
==
0
:
tag1
[
idx1
]
=
1
tag2
[
idx2
]
=
1
res
[
index
]
=
1
cnt
+=
1
if
cnt
==
min
(
y_dim
,
z_dim
):
break
channel
[
index
]
=
-
1
res
=
res
.
reshape
(
1
,
y_dim
,
z_dim
).
repeat
([
x_dim
],
axis
=
0
)
res
=
res
.
reshape
(
1
,
x_dim
,
y_dim
,
z_dim
)
if
output
is
not
None
:
output
=
np
.
concatenate
((
output
,
res
),
axis
=
0
)
else
:
output
=
res
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestSimilarityFocusOp_axis1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
3
x_dim
,
y_dim
,
z_dim
=
4
,
5
,
6
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
(
batch_size
,
x_dim
,
y_dim
,
z_dim
)).
astype
(
"float32"
),
}
self
.
attrs
=
{
'axis'
:
1
,
'indexes'
:
[
0
,
3
],
}
output
=
None
for
batch
in
range
(
batch_size
):
res
=
np
.
zeros
((
1
,
y_dim
,
z_dim
)).
astype
(
"float32"
).
reshape
(
-
1
)
for
index
in
self
.
attrs
[
'indexes'
]:
channel
=
self
.
inputs
[
'X'
][
batch
,
index
,
:,
:].
reshape
(
-
1
).
copy
(
)
tag1
=
[
0
for
i
in
range
(
y_dim
)]
tag2
=
[
0
for
i
in
range
(
z_dim
)]
cnt
=
0
for
i
in
range
(
channel
.
size
):
index
=
channel
.
argmax
()
idx1
=
index
//
z_dim
idx2
=
index
%
z_dim
if
tag1
[
idx1
]
+
tag2
[
idx2
]
==
0
:
tag1
[
idx1
]
=
1
tag2
[
idx2
]
=
1
res
[
index
]
=
1
cnt
+=
1
if
cnt
==
min
(
y_dim
,
z_dim
):
break
channel
[
index
]
=
-
1
res
=
res
.
reshape
(
1
,
y_dim
,
z_dim
)
res
=
res
.
repeat
([
x_dim
],
axis
=
0
)
res
=
res
.
reshape
(
1
,
x_dim
,
y_dim
,
z_dim
)
if
output
is
not
None
:
output
=
np
.
concatenate
((
output
,
res
),
axis
=
0
)
else
:
output
=
res
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestSimilarityFocusOp_axis2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
6
x_dim
,
y_dim
,
z_dim
=
7
,
8
,
9
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
(
batch_size
,
x_dim
,
y_dim
,
z_dim
)).
astype
(
"float32"
),
}
self
.
attrs
=
{
'axis'
:
2
,
'indexes'
:
[
0
,
3
,
5
],
}
output
=
None
for
batch
in
range
(
batch_size
):
res
=
np
.
zeros
((
x_dim
,
1
,
z_dim
)).
astype
(
"float32"
).
reshape
(
-
1
)
for
index
in
self
.
attrs
[
'indexes'
]:
channel
=
self
.
inputs
[
'X'
][
batch
,
:,
index
,
:].
reshape
(
-
1
).
copy
(
)
tag1
=
[
0
for
i
in
range
(
x_dim
)]
tag2
=
[
0
for
i
in
range
(
z_dim
)]
cnt
=
0
for
i
in
range
(
channel
.
size
):
index
=
channel
.
argmax
()
idx1
=
index
//
z_dim
idx2
=
index
%
z_dim
if
tag1
[
idx1
]
+
tag2
[
idx2
]
==
0
:
tag1
[
idx1
]
=
1
tag2
[
idx2
]
=
1
res
[
index
]
=
1
cnt
+=
1
if
cnt
==
min
(
x_dim
,
z_dim
):
break
channel
[
index
]
=
-
1
res
=
res
.
reshape
(
x_dim
,
1
,
z_dim
)
res
=
res
.
repeat
([
y_dim
],
axis
=
1
)
res
=
res
.
reshape
(
1
,
x_dim
,
y_dim
,
z_dim
)
if
output
is
not
None
:
output
=
np
.
concatenate
((
output
,
res
),
axis
=
0
)
else
:
output
=
res
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestSimilarityFocusOp_axis3
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
64
x_dim
,
y_dim
,
z_dim
=
48
,
48
,
13
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
(
batch_size
,
x_dim
,
y_dim
,
z_dim
)).
astype
(
"float32"
),
}
self
.
attrs
=
{
'axis'
:
3
,
'indexes'
:
[
0
,
2
,
7
,
9
],
}
output
=
None
for
batch
in
range
(
batch_size
):
res
=
np
.
zeros
((
x_dim
,
y_dim
,
1
)).
astype
(
"float32"
).
reshape
(
-
1
)
for
index
in
self
.
attrs
[
'indexes'
]:
channel
=
self
.
inputs
[
'X'
][
batch
,
:,
:,
index
].
reshape
(
-
1
).
copy
(
)
tag1
=
[
0
for
i
in
range
(
x_dim
)]
tag2
=
[
0
for
i
in
range
(
y_dim
)]
cnt
=
0
for
i
in
range
(
channel
.
size
):
index
=
channel
.
argmax
()
idx1
=
index
//
y_dim
idx2
=
index
%
y_dim
if
tag1
[
idx1
]
+
tag2
[
idx2
]
==
0
:
tag1
[
idx1
]
=
1
tag2
[
idx2
]
=
1
res
[
index
]
=
1
cnt
+=
1
if
cnt
==
min
(
x_dim
,
y_dim
):
break
channel
[
index
]
=
-
1
res
=
res
.
reshape
(
x_dim
,
y_dim
,
1
)
res
=
res
.
repeat
([
z_dim
],
axis
=
2
)
res
=
res
.
reshape
(
1
,
x_dim
,
y_dim
,
z_dim
)
if
output
is
not
None
:
output
=
np
.
concatenate
((
output
,
res
),
axis
=
0
)
else
:
output
=
res
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_tensor_array_to_tensor.py
0 → 100644
浏览文件 @
397de907
# 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.
from
__future__
import
print_function
import
unittest
import
numpy
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
paddle.fluid.executor
import
Executor
class
TestLoDTensorArrayConcat
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
op_type
=
"tensor_array_to_tensor"
self
.
attrs
=
{
"axis"
:
0
}
self
.
outputs
=
[
"Out"
]
def
test_get_set
(
self
):
scope
=
core
.
Scope
()
program
=
fluid
.
Program
()
block
=
program
.
global_block
()
input_arr
=
block
.
create_var
(
name
=
"tmp_lod_tensor_array"
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
)
input_arr
.
persistable
=
True
input_arr_var
=
scope
.
var
(
'tmp_lod_tensor_array'
)
input_tensor_array
=
input_arr_var
.
get_lod_tensor_array
()
self
.
assertEqual
(
0
,
len
(
input_tensor_array
))
cpu
=
core
.
CPUPlace
()
for
i
in
range
(
10
):
t
=
core
.
LoDTensor
()
if
i
==
0
:
t
.
set
(
numpy
.
array
([[
i
],
[
i
]],
dtype
=
'float32'
),
cpu
)
else
:
t
.
set
(
numpy
.
array
([[
i
]],
dtype
=
'float32'
),
cpu
)
input_tensor_array
.
append
(
t
)
self
.
assertEqual
(
10
,
len
(
input_tensor_array
))
random_grad
=
numpy
.
random
.
random_sample
([
11
]).
astype
(
numpy
.
float32
)
y_out
=
block
.
create_var
(
name
=
"Out"
)
y_out
.
persistable
=
True
y_out_index
=
block
.
create_var
(
name
=
"OutIndex"
)
y_out_index
.
persistable
=
True
y_grad_arr
=
block
.
create_var
(
name
=
'Out@GRAD'
,
dtype
=
'float32'
,
shape
=
[
11
])
y_grad_arr
.
persistable
=
True
y_grad
=
scope
.
var
(
'Out@GRAD'
)
y_grad_tensor
=
y_grad
.
get_tensor
()
y_grad_tensor
.
set
(
random_grad
,
cpu
)
op
=
block
.
append_op
(
type
=
self
.
op_type
,
inputs
=
{
"X"
:
input_arr
},
outputs
=
{
"Out"
:
y_out
,
"OutIndex"
:
y_out_index
},
attrs
=
self
.
attrs
)
out_grad
=
block
.
create_var
(
name
=
"tmp_lod_tensor_array@GRAD"
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
)
out_grad
.
persistable
=
True
grad_op_desc_list
,
op_grad_to_var
=
core
.
get_grad_op_desc
(
op
.
desc
,
set
(),
[])
grad_op_desc
=
grad_op_desc_list
[
0
]
new_op_desc
=
block
.
desc
.
append_op
()
new_op_desc
.
copy_from
(
grad_op_desc
)
for
var_name
in
grad_op_desc
.
output_arg_names
():
block
.
desc
.
var
(
var_name
.
encode
(
"ascii"
))
grad_op_desc
.
infer_var_type
(
block
.
desc
)
grad_op_desc
.
infer_shape
(
block
.
desc
)
for
arg
in
grad_op_desc
.
output_arg_names
():
grad_var
=
block
.
desc
.
find_var
(
arg
.
encode
(
"ascii"
))
grad_var
.
set_dtype
(
core
.
VarDesc
.
VarType
.
FP32
)
fetch_list
=
[]
fetch_list
.
append
(
block
.
var
(
'Out'
))
fetch_list
.
append
(
block
.
var
(
'OutIndex'
))
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
out
=
exe
.
run
(
program
,
fetch_list
=
fetch_list
,
scope
=
scope
)
#print ("index: ", numpy.array(out[1]))
# test forward
tensor_res
=
numpy
.
array
(
out
[
0
])
tensor_res_out_idx
=
numpy
.
array
(
out
[
1
])
tensor_gt
=
numpy
.
array
(
[
0
]
+
[
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
],
dtype
=
'float32'
)
self
.
assertEqual
(
len
(
tensor_res
),
len
(
tensor_gt
))
self
.
assertEqual
(
len
(
tensor_res_out_idx
),
10
)
for
i
in
range
(
len
(
tensor_res
)):
self
.
assertEqual
(
tensor_res
[
i
],
tensor_gt
[
i
])
for
i
in
range
(
len
(
tensor_res_out_idx
)):
if
i
==
0
:
self
.
assertEqual
(
tensor_res_out_idx
[
i
],
2
)
else
:
self
.
assertEqual
(
tensor_res_out_idx
[
i
],
1
)
# test backward
grad_tensor
=
scope
.
var
(
'tmp_lod_tensor_array@GRAD'
)
grad_tensor_array
=
grad_tensor
.
get_lod_tensor_array
()
self
.
assertEqual
(
10
,
len
(
grad_tensor_array
))
for
i
in
range
(
len
(
grad_tensor_array
)):
if
i
==
0
:
self
.
assertEqual
(
numpy
.
array
(
grad_tensor_array
[
i
])[
0
],
numpy
.
array
(
random_grad
[
i
]))
self
.
assertEqual
(
numpy
.
array
(
grad_tensor_array
[
i
])[
1
],
numpy
.
array
(
random_grad
[
i
+
1
]))
if
i
==
1
:
self
.
assertEqual
(
numpy
.
array
(
grad_tensor_array
[
i
]),
numpy
.
array
(
random_grad
[
i
+
1
]))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
397de907
...
...
@@ -31,18 +31,17 @@ Steps to transpile pserver:
"""
import
math
import
sys
import
numpy
as
np
import
collections
import
six
import
logging
from
.ps_dispatcher
import
RoundRobin
,
HashName
,
PSDispatcher
from
.ps_dispatcher
import
RoundRobin
,
PSDispatcher
from
..
import
core
,
framework
,
unique_name
from
..framework
import
Program
,
default_main_program
,
\
default_startup_program
,
Block
,
\
Parameter
,
grad_var_name
from
.details
import
*
from
..distribute_lookup_table
import
find_distributed_lookup_table
from
functools
import
reduce
LOOKUP_TABLE_TYPE
=
"lookup_table"
...
...
@@ -292,7 +291,8 @@ class DistributeTranspiler(object):
self
.
optimize_ops
,
self
.
params_grads
=
self
.
_get_optimize_pass
()
ps_dispatcher
=
self
.
config
.
split_method
(
self
.
pserver_endpoints
)
self
.
has_distributed_lookup_table
=
self
.
_has_distributed_lookup_table
()
self
.
table_name
=
find_distributed_lookup_table
(
self
.
origin_program
)
self
.
has_distributed_lookup_table
=
self
.
table_name
!=
None
self
.
param_name_to_grad_name
=
dict
()
self
.
grad_name_to_param_name
=
dict
()
for
param_var
,
grad_var
in
self
.
params_grads
:
...
...
@@ -966,28 +966,6 @@ to transpile() call.")
# ====================== private transpiler functions =====================
def
_has_distributed_lookup_table
(
self
):
# process lookup_table_op
# 1. check all lookup_table_op is distributed
# 2. check all lookup_table_op share the same table.
distributed_lookup_table_ops
=
[]
# support only one distributed_lookup_table now
self
.
table_name
=
None
for
op
in
self
.
origin_program
.
global_block
().
ops
:
if
op
.
type
==
LOOKUP_TABLE_TYPE
:
if
op
.
attr
(
'is_distributed'
)
is
True
:
if
self
.
table_name
is
None
:
self
.
table_name
=
op
.
input
(
"W"
)[
0
]
if
self
.
table_name
!=
op
.
input
(
"W"
)[
0
]:
raise
RuntimeError
(
"all distributed lookup_table_ops"
" should have only one table"
)
distributed_lookup_table_ops
.
append
(
op
)
else
:
if
self
.
table_name
is
not
None
:
assert
op
.
input
(
"W"
)[
0
]
!=
self
.
table_name
return
len
(
distributed_lookup_table_ops
)
>
0
def
_update_dist_lookup_table_vars
(
self
,
param_list
,
grad_list
,
params_grads
):
# TODO(wuyi): put find a way to put dist lookup table stuff all together.
...
...
@@ -1341,7 +1319,6 @@ to transpile() call.")
"""
create a new block to handle save checkpoint.
"""
import
os
pserver_program
.
global_block
().
create_var
(
name
=
"kLookupTablePath"
,
...
...
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