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体验新版 GitCode,发现更多精彩内容 >>
提交
e2be6dd2
编写于
12月 28, 2017
作者:
Y
Yang Yu
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of github.com:baidu/Paddle into feature/is_nan
上级
71157b33
ba4322f4
变更
48
隐藏空白更改
内联
并排
Showing
48 changed file
with
791 addition
and
264 deletion
+791
-264
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+5
-0
doc/api/v2/fluid/layers.rst
doc/api/v2/fluid/layers.rst
+0
-6
doc/design/optimizer.md
doc/design/optimizer.md
+1
-1
paddle/framework/CMakeLists.txt
paddle/framework/CMakeLists.txt
+1
-1
paddle/framework/data_transform.cc
paddle/framework/data_transform.cc
+1
-0
paddle/framework/data_transform.h
paddle/framework/data_transform.h
+7
-7
paddle/framework/data_transform_test.cc
paddle/framework/data_transform_test.cc
+47
-27
paddle/framework/executor.cc
paddle/framework/executor.cc
+1
-1
paddle/framework/library_type.h
paddle/framework/library_type.h
+25
-2
paddle/framework/op_desc.cc
paddle/framework/op_desc.cc
+8
-0
paddle/framework/op_desc.h
paddle/framework/op_desc.h
+2
-0
paddle/framework/op_kernel_type.h
paddle/framework/op_kernel_type.h
+8
-0
paddle/framework/op_kernel_type_test.cc
paddle/framework/op_kernel_type_test.cc
+2
-4
paddle/framework/op_registry.h
paddle/framework/op_registry.h
+9
-7
paddle/framework/op_registry_test.cc
paddle/framework/op_registry_test.cc
+82
-0
paddle/framework/operator.cc
paddle/framework/operator.cc
+39
-25
paddle/framework/tensor.h
paddle/framework/tensor.h
+1
-1
paddle/framework/var_desc.cc
paddle/framework/var_desc.cc
+1
-1
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+28
-57
paddle/operators/batch_norm_op.cc
paddle/operators/batch_norm_op.cc
+6
-5
paddle/operators/conv_cudnn_op.cu.cc
paddle/operators/conv_cudnn_op.cu.cc
+4
-0
paddle/operators/cross_entropy_op.cc
paddle/operators/cross_entropy_op.cc
+3
-3
paddle/operators/dropout_op.cc
paddle/operators/dropout_op.cc
+5
-5
paddle/operators/dropout_op.cu
paddle/operators/dropout_op.cu
+2
-3
paddle/operators/math/math_function.cc
paddle/operators/math/math_function.cc
+21
-0
paddle/operators/math/math_function.cu
paddle/operators/math/math_function.cu
+29
-0
paddle/operators/math/math_function_impl.h
paddle/operators/math/math_function_impl.h
+0
-19
paddle/operators/send_op.cc
paddle/operators/send_op.cc
+1
-1
paddle/pybind/protobuf.cc
paddle/pybind/protobuf.cc
+22
-3
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+18
-17
python/paddle/v2/fluid/backward.py
python/paddle/v2/fluid/backward.py
+224
-14
python/paddle/v2/fluid/distribute_transpiler.py
python/paddle/v2/fluid/distribute_transpiler.py
+5
-3
python/paddle/v2/fluid/framework.py
python/paddle/v2/fluid/framework.py
+5
-3
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+130
-8
python/paddle/v2/fluid/optimizer.py
python/paddle/v2/fluid/optimizer.py
+3
-3
python/paddle/v2/fluid/tests/book_distribute/notest_recognize_digits_conv_dist.py
...ests/book_distribute/notest_recognize_digits_conv_dist.py
+17
-9
python/paddle/v2/fluid/tests/op_test.py
python/paddle/v2/fluid/tests/op_test.py
+2
-2
python/paddle/v2/fluid/tests/test_array_read_write_op.py
python/paddle/v2/fluid/tests/test_array_read_write_op.py
+2
-2
python/paddle/v2/fluid/tests/test_conditional_block.py
python/paddle/v2/fluid/tests/test_conditional_block.py
+2
-2
python/paddle/v2/fluid/tests/test_lod_tensor_array_ops.py
python/paddle/v2/fluid/tests/test_lod_tensor_array_ops.py
+2
-2
python/paddle/v2/fluid/tests/test_optimizer.py
python/paddle/v2/fluid/tests/test_optimizer.py
+7
-7
python/paddle/v2/fluid/tests/test_recurrent_op.py
python/paddle/v2/fluid/tests/test_recurrent_op.py
+2
-2
python/paddle/v2/fluid/tests/test_regularizer.py
python/paddle/v2/fluid/tests/test_regularizer.py
+3
-3
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
+1
-1
python/paddle/v2/fluid/tests/test_rnn_memory_helper_op.py
python/paddle/v2/fluid/tests/test_rnn_memory_helper_op.py
+1
-1
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
+2
-2
python/paddle/v2/fluid/tests/test_split_and_merge_lod_tensor_op.py
...ddle/v2/fluid/tests/test_split_and_merge_lod_tensor_op.py
+2
-2
python/paddle/v2/fluid/tests/test_while_op.py
python/paddle/v2/fluid/tests/test_while_op.py
+2
-2
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
e2be6dd2
...
...
@@ -252,6 +252,11 @@ first_seq
.. autoclass:: paddle.v2.layer.first_seq
:noindex:
sub_seq
---------
.. autoclass:: paddle.v2.layer.sub_seq
:noindex:
concat
------
.. autoclass:: paddle.v2.layer.concat
...
...
doc/api/v2/fluid/layers.rst
浏览文件 @
e2be6dd2
...
...
@@ -68,12 +68,6 @@ scale
:noindex:
reshape
---------
.. autofunction:: paddle.v2.fluid.layers.reshape
:noindex:
transpose
---------
.. autofunction:: paddle.v2.fluid.layers.transpose
...
...
doc/design/optimizer.md
浏览文件 @
e2be6dd2
...
...
@@ -79,7 +79,7 @@ class Optimizer(object):
def
minimize
(
self
,
loss
,
parameter_list
):
"""Add operations to minimize `loss` by updating `parameter_list`.
This method combines interface `append_backward
_ops
()` and
This method combines interface `append_backward()` and
`create_optimization_pass()` into one.
"""
params_grads
=
self
.
create_backward_pass
(
loss
,
parameter_list
)
...
...
paddle/framework/CMakeLists.txt
浏览文件 @
e2be6dd2
...
...
@@ -37,7 +37,7 @@ cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry init)
cc_library
(
proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog
)
cc_library
(
op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc
)
cc
_test
(
op_registry_test SRCS op_registry_test.cc DEPS op_registry
)
nv
_test
(
op_registry_test SRCS op_registry_test.cc DEPS op_registry
)
py_proto_compile
(
framework_py_proto SRCS framework.proto
)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
...
...
paddle/framework/data_transform.cc
浏览文件 @
e2be6dd2
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/data_transform.h"
#include "paddle/framework/lod_tensor.h"
namespace
paddle
{
namespace
framework
{
...
...
paddle/framework/data_transform.h
浏览文件 @
e2be6dd2
...
...
@@ -27,7 +27,7 @@ limitations under the License. */
namespace
paddle
{
namespace
framework
{
using
DataTransformF
N
=
using
DataTransformF
n
=
std
::
function
<
void
(
const
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
>
;
using
KernelTypePair
=
std
::
pair
<
OpKernelType
,
OpKernelType
>
;
...
...
@@ -47,7 +47,7 @@ struct KernelTypePairHash {
};
using
DataTransformMap
=
std
::
unordered_map
<
KernelTypePair
,
DataTransformF
N
,
KernelTypePairHash
>
;
std
::
unordered_map
<
KernelTypePair
,
DataTransformF
n
,
KernelTypePairHash
>
;
class
DataTransformFnMap
{
public:
...
...
@@ -58,25 +58,25 @@ class DataTransformFnMap {
}
void
Insert
(
const
OpKernelType
&
left
,
const
OpKernelType
&
right
,
const
DataTransformF
N
&
data_tranform_fn
)
{
const
DataTransformF
n
&
data_tranform_fn
)
{
Insert
(
std
::
make_pair
(
left
,
right
),
data_tranform_fn
);
}
void
Insert
(
const
KernelTypePair
&
kernel_type_pair
,
const
DataTransformF
N
&
data_tranform_fn
)
{
const
DataTransformF
n
&
data_tranform_fn
)
{
PADDLE_ENFORCE
(
!
Has
(
kernel_type_pair
),
"KernelTypePair %s has been registered"
,
""
);
map_
.
insert
({
kernel_type_pair
,
data_tranform_fn
});
}
const
DataTransformF
N
&
Get
(
const
KernelTypePair
&
key_pair
)
const
{
const
DataTransformF
n
&
Get
(
const
KernelTypePair
&
key_pair
)
const
{
auto
data_transformer
=
GetNullable
(
key_pair
);
PADDLE_ENFORCE_NOT_NULL
(
data_transformer
,
"DataTransformF
N
should not be NULL"
);
"DataTransformF
n
should not be NULL"
);
return
*
data_transformer
;
}
const
DataTransformF
N
*
GetNullable
(
const
KernelTypePair
&
key_pair
)
const
{
const
DataTransformF
n
*
GetNullable
(
const
KernelTypePair
&
key_pair
)
const
{
auto
it
=
map_
.
find
(
key_pair
);
if
(
it
==
map_
.
end
())
{
return
nullptr
;
...
...
paddle/framework/data_transform_test.cc
浏览文件 @
e2be6dd2
...
...
@@ -11,36 +11,61 @@ 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 <array>
#include <vector>
#include "paddle/framework/data_transform.h"
#include <gtest/gtest.h>
#include "paddle/framework/data_transform.h"
namespace
paddle
{
namespace
framework
{
using
namespace
platform
;
/**
* @brief cross validation of different kernel type transform
* We use four bit map represent different combination.
* If the field has multiple possible value, only choose two of them.
* For DataType, only test the FP32(float), FP64(double).
* e.g. 0000 -> FP32, CPUPlace, kNHWC, kPlain
* 1111 -> FP64, GPUPlace, kNCHW, kMKLDNN
*/
std
::
array
<
proto
::
DataType
,
2
>
kDataType
=
{
{
proto
::
DataType
::
FP32
,
proto
::
DataType
::
FP64
}};
std
::
array
<
Place
,
2
>
kPlace
=
{{
CPUPlace
(),
CUDAPlace
(
0
)}};
std
::
array
<
DataLayout
,
2
>
kDataLayout
=
{
{
DataLayout
::
kNHWC
,
DataLayout
::
kNCHW
}};
std
::
array
<
LibraryType
,
2
>
kLibraryType
=
{
{
LibraryType
::
kPlain
,
LibraryType
::
kMKLDNN
}};
OpKernelType
GenFromBit
(
const
std
::
vector
<
bool
>
bits
)
{
return
OpKernelType
(
kDataType
[
bits
[
0
]],
kPlace
[
bits
[
1
]],
kDataLayout
[
bits
[
2
]],
kLibraryType
[
bits
[
3
]]);
}
int
test_value
=
0
;
OpKernelType
kernel_type_1
(
proto
::
DataType
::
FP32
,
CPUPlace
(),
DataLayout
::
kNCHW
,
LibraryType
::
kCUDNN
);
OpKernelType
kernel_type_2
(
proto
::
DataType
::
FP32
,
CUDAPlace
(
0
),
DataLayout
::
kNCHW
,
LibraryType
::
kCUDNN
);
OpKernelType
kernel_type_3
(
proto
::
DataType
::
FP16
,
CUDAPlace
(
0
),
DataLayout
::
kNCHW
,
LibraryType
::
kCUDNN
);
auto
kernel0
=
GenFromBit
({
0
,
0
,
0
,
0
});
auto
kernel1
=
GenFromBit
({
0
,
0
,
0
,
1
});
auto
kernel2
=
GenFromBit
({
0
,
0
,
1
,
0
});
auto
kernel3
=
GenFromBit
({
0
,
0
,
1
,
1
});
void
type1_to_type2
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
void
TransDataType_t
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
test_value
++
;
}
void
type2_to_type3
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
void
TransDataLayout_t
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
test_value
--
;
}
void
type1_to_type3
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
void
TransLibraryType_t
(
std
::
vector
<
platform
::
DeviceContext
*>
ctx
,
const
Variable
&
in
,
Variable
*
out
)
{
test_value
+=
2
;
}
...
...
@@ -49,30 +74,25 @@ void type1_to_type3(std::vector<platform::DeviceContext*> ctx,
namespace
frw
=
paddle
::
framework
;
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel_type_1
,
frw
::
kernel_type_2
,
frw
::
type1_to_type2
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel_type_2
,
frw
::
kernel_type_3
,
frw
::
type2_to_type3
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel_type_1
,
frw
::
kernel_type_3
,
frw
::
type1_to_type3
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel0
,
frw
::
kernel1
,
frw
::
TransDataType_t
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel1
,
frw
::
kernel2
,
frw
::
TransDataLayout_t
);
REGISTER_DATA_TRANSFORM_FN
(
frw
::
kernel0
,
frw
::
kernel2
,
frw
::
TransLibraryType_t
);
TEST
(
DataTransform
,
Register
)
{
using
namespace
paddle
::
framework
;
using
namespace
paddle
::
platform
;
auto
&
instance
=
DataTransformFnMap
::
Instance
();
ASSERT_EQ
(
instance
.
Map
().
size
(),
3UL
);
std
::
vector
<
DeviceContext
*>
ctx
;
paddle
::
framework
::
Variable
in
;
paddle
::
framework
::
Variable
out
;
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel_type_1
,
frw
::
kernel_type_2
))(
ctx
,
in
,
&
out
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel0
,
frw
::
kernel1
))(
ctx
,
in
,
&
out
);
ASSERT_EQ
(
test_value
,
1
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel_type_2
,
frw
::
kernel_type_3
))(
ctx
,
in
,
&
out
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel1
,
frw
::
kernel2
))(
ctx
,
in
,
&
out
);
ASSERT_EQ
(
test_value
,
0
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel_type_1
,
frw
::
kernel_type_3
))(
ctx
,
in
,
&
out
);
instance
.
Get
(
std
::
make_pair
(
frw
::
kernel0
,
frw
::
kernel2
))(
ctx
,
in
,
&
out
);
ASSERT_EQ
(
test_value
,
2
);
}
paddle/framework/executor.cc
浏览文件 @
e2be6dd2
...
...
@@ -102,7 +102,7 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
VLOG
(
3
)
<<
op
->
DebugString
();
op
->
Run
(
*
local_scope
,
place_
);
}
if
(
create_local_scope
)
{
if
(
create_
vars
&&
create_
local_scope
)
{
scope
->
DeleteScope
(
local_scope
);
}
}
...
...
paddle/framework/library_type.h
浏览文件 @
e2be6dd2
...
...
@@ -20,7 +20,11 @@ namespace framework {
// For more details about the design of LibraryType, Please refer to
// https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/operator_kernel_type.md#library
enum
class
LibraryType
{
kPlain
=
0
,
kMKLDNN
=
1
,
kCUDNN
=
2
};
enum
class
LibraryType
{
kPlain
=
0
,
kMKLDNN
=
1
,
kCUDNN
=
2
,
};
inline
std
::
string
LibraryTypeToString
(
const
LibraryType
&
library_type
)
{
switch
(
library_type
)
{
...
...
@@ -31,7 +35,26 @@ inline std::string LibraryTypeToString(const LibraryType& library_type) {
case
LibraryType
::
kCUDNN
:
return
"CUDNN"
;
default:
PADDLE_THROW
(
"unknown LibraryType %d"
,
library_type
);
PADDLE_THROW
(
"unknown LibraryType %d"
,
static_cast
<
int
>
(
library_type
));
}
}
inline
LibraryType
StringToLibraryType
(
const
char
*
ctype
)
{
std
::
string
s
(
ctype
);
if
(
s
==
std
::
string
(
"PLAIN"
))
{
return
LibraryType
::
kPlain
;
}
else
if
(
s
==
std
::
string
(
"MKLDNN"
))
{
return
LibraryType
::
kMKLDNN
;
}
else
if
(
s
==
std
::
string
(
"CUDNN"
))
{
return
LibraryType
::
kCUDNN
;
// To be compatible with register macro.
// CPU, CUDA, PLAIN are same library type.
}
else
if
(
s
==
std
::
string
(
"CPU"
))
{
return
LibraryType
::
kPlain
;
}
else
if
(
s
==
std
::
string
(
"CUDA"
))
{
return
LibraryType
::
kPlain
;
}
else
{
PADDLE_THROW
(
"Unknown LibraryType %s"
,
s
.
c_str
());
}
}
...
...
paddle/framework/op_desc.cc
浏览文件 @
e2be6dd2
...
...
@@ -88,6 +88,14 @@ OpDesc::OpDesc(const std::string &type, const VariableNameMap &inputs,
need_update_
=
true
;
}
void
OpDesc
::
CopyFrom
(
const
OpDesc
&
op_desc
)
{
desc_
.
set_type
(
op_desc
.
Type
());
inputs_
=
op_desc
.
inputs_
;
outputs_
=
op_desc
.
outputs_
;
attrs_
=
op_desc
.
attrs_
;
need_update_
=
true
;
}
OpDesc
::
OpDesc
(
const
proto
::
OpDesc
&
desc
,
ProgramDesc
*
prog
)
:
desc_
(
desc
),
need_update_
(
false
)
{
// restore inputs_
...
...
paddle/framework/op_desc.h
浏览文件 @
e2be6dd2
...
...
@@ -35,6 +35,8 @@ class OpDesc {
OpDesc
(
const
proto
::
OpDesc
&
desc
,
ProgramDesc
*
prog
);
void
CopyFrom
(
const
OpDesc
&
op_desc
);
proto
::
OpDesc
*
Proto
();
std
::
string
Type
()
const
{
return
desc_
.
type
();
}
...
...
paddle/framework/op_kernel_type.h
浏览文件 @
e2be6dd2
...
...
@@ -68,6 +68,8 @@ struct OpKernelType {
data_type_
==
o
.
data_type_
&&
data_layout_
==
o
.
data_layout_
&&
library_type_
==
o
.
library_type_
;
}
bool
operator
!=
(
const
OpKernelType
&
o
)
const
{
return
!
(
*
this
==
o
);
}
};
inline
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
...
...
@@ -78,5 +80,11 @@ inline std::ostream& operator<<(std::ostream& os,
return
os
;
}
inline
std
::
string
KernelTypeToString
(
const
OpKernelType
&
kernel_key
)
{
std
::
ostringstream
stream
;
stream
<<
kernel_key
;
return
stream
.
str
();
}
}
// namespace framework
}
// namespace paddle
paddle/framework/op_kernel_type_test.cc
浏览文件 @
e2be6dd2
...
...
@@ -26,10 +26,8 @@ TEST(OpKernelType, ToString) {
OpKernelType
op_kernel_type
(
DataType
::
FP32
,
CPUPlace
(),
DataLayout
::
kNCHW
,
LibraryType
::
kCUDNN
);
std
::
ostringstream
stream
;
stream
<<
op_kernel_type
;
ASSERT_EQ
(
stream
.
str
(
),
paddle
::
framework
::
KernelTypeToString
(
op_kernel_type
),
"data_type[5]:data_layout[NCHW]:place[CPUPlace]:library_type[CUDNN]"
);
}
...
...
@@ -48,4 +46,4 @@ TEST(OpKernelType, Hash) {
OpKernelType
::
Hash
hasher
;
ASSERT_NE
(
hasher
(
op_kernel_type_1
),
hasher
(
op_kernel_type_2
));
}
\ No newline at end of file
}
paddle/framework/op_registry.h
浏览文件 @
e2be6dd2
...
...
@@ -79,30 +79,31 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
using
KERNEL_TYPE
=
typename
std
::
tuple_element
<
I
,
std
::
tuple
<
KernelTypes
...
>>::
type
;
void
operator
()(
const
char
*
op_type
)
const
{
void
operator
()(
const
char
*
op_type
,
const
char
*
library_type
)
const
{
using
T
=
typename
KERNEL_TYPE
::
ELEMENT_TYPE
;
OpKernelType
key
(
ToDataType
(
std
::
type_index
(
typeid
(
T
))),
PlaceType
());
OpKernelType
key
(
ToDataType
(
std
::
type_index
(
typeid
(
T
))),
PlaceType
(),
DataLayout
::
kAnyLayout
,
StringToLibraryType
(
library_type
));
OperatorWithKernel
::
AllOpKernels
()[
op_type
][
key
].
reset
(
new
KERNEL_TYPE
);
constexpr
auto
size
=
std
::
tuple_size
<
std
::
tuple
<
KernelTypes
...
>>::
value
;
OpKernelRegistrarFunctor
<
PlaceType
,
I
+
1
==
size
,
I
+
1
,
KernelTypes
...
>
func
;
func
(
op_type
);
func
(
op_type
,
library_type
);
}
};
template
<
typename
PlaceType
,
size_t
I
,
typename
...
KernelType
>
struct
OpKernelRegistrarFunctor
<
PlaceType
,
true
,
I
,
KernelType
...
>
{
void
operator
()(
const
char
*
op_type
)
const
{}
void
operator
()(
const
char
*
op_type
,
const
char
*
library_type
)
const
{}
};
// User can register many kernel in one place. The data type could be different.
template
<
typename
PlaceType
,
typename
...
KernelType
>
class
OpKernelRegistrar
:
public
Registrar
{
public:
explicit
OpKernelRegistrar
(
const
char
*
op_type
)
{
explicit
OpKernelRegistrar
(
const
char
*
op_type
,
const
char
*
library_type
)
{
OpKernelRegistrarFunctor
<
PlaceType
,
false
,
0
,
KernelType
...
>
func
;
func
(
op_type
);
func
(
op_type
,
library_type
);
}
};
...
...
@@ -181,7 +182,8 @@ class OpKernelRegistrar : public Registrar {
__reg_op_kernel_##op_type##_##DEVICE_TYPE##__, \
"REGISTER_OP_KERNEL must be called in global namespace"); \
static ::paddle::framework::OpKernelRegistrar<place_class, __VA_ARGS__> \
__op_kernel_registrar_##op_type##_##DEVICE_TYPE##__(#op_type); \
__op_kernel_registrar_##op_type##_##DEVICE_TYPE##__(#op_type, \
#DEVICE_TYPE); \
int TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE() { \
__op_kernel_registrar_##op_type##_##DEVICE_TYPE##__.Touch(); \
return 0; \
...
...
paddle/framework/op_registry_test.cc
浏览文件 @
e2be6dd2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/op_registry.h"
#include <gtest/gtest.h>
...
...
@@ -182,3 +196,71 @@ TEST(OperatorRegistrar, Test) {
using
namespace
paddle
::
framework
;
OperatorRegistrar
<
CosineOpComplete
,
CosineOpProtoAndCheckerMaker
>
reg
(
"cos"
);
}
namespace
paddle
{
namespace
framework
{
class
OpKernelTestMaker
:
public
OpProtoAndCheckerMaker
{
public:
OpKernelTestMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddComment
(
"NoGradOp, same input output. no Grad"
);
}
};
class
OpWithKernelTest
:
public
OperatorWithKernel
{
public:
using
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
InferShapeContext
*
ctx
)
const
override
{}
framework
::
OpKernelType
GetActualKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
proto
::
DataType
::
FP32
,
ctx
.
device_context
());
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
OpKernelTest
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
{}
};
}
// namespace framework
}
// namespace paddle
REGISTER_OP_WITHOUT_GRADIENT
(
op_with_kernel
,
paddle
::
framework
::
OpWithKernelTest
,
paddle
::
framework
::
OpKernelTestMaker
);
REGISTER_OP_CPU_KERNEL
(
op_with_kernel
,
paddle
::
framework
::
OpKernelTest
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
op_with_kernel
,
paddle
::
framework
::
OpKernelTest
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
TEST
(
OperatorRegistrar
,
CPU
)
{
paddle
::
framework
::
proto
::
OpDesc
op_desc
;
paddle
::
platform
::
CPUPlace
cpu_place
;
paddle
::
framework
::
Scope
scope
;
op_desc
.
set_type
(
"op_with_kernel"
);
auto
op
=
paddle
::
framework
::
OpRegistry
::
CreateOp
(
op_desc
);
op
->
Run
(
scope
,
cpu_place
);
}
#ifdef PADDLE_WITH_CUDA
TEST
(
OperatorRegistrar
,
CUDA
)
{
paddle
::
framework
::
proto
::
OpDesc
op_desc
;
paddle
::
platform
::
CUDAPlace
cuda_place
(
0
);
paddle
::
framework
::
Scope
scope
;
op_desc
.
set_type
(
"op_with_kernel"
);
auto
op
=
paddle
::
framework
::
OpRegistry
::
CreateOp
(
op_desc
);
op
->
Run
(
scope
,
cuda_place
);
}
#endif
paddle/framework/operator.cc
浏览文件 @
e2be6dd2
...
...
@@ -413,37 +413,51 @@ void OperatorWithKernel::Run(const Scope& scope,
}
if
(
actual_kernel_key
==
expected_kernel_key
)
{
kernel_iter
->
second
->
Compute
(
ctx
);
PADDLE_ENFORCE_EQ
(
actual_kernel_key
.
place_
,
expected_kernel_key
.
place_
,
"Currently, model parallelism is only supported between "
"CPU and other devices. For example, multi-GPU model "
"parallelism will failed."
);
}
else
{
Scope
&
op_scope
=
scope
.
NewScope
();
auto
input_vars
=
this
->
InputVars
();
for
(
auto
var_name
:
input_vars
)
{
op_scope
.
Var
(
var_name
);
}
// TODO(qijun) get appropriate DeviceContext from DeviceContext pool
platform
::
DeviceContext
*
trans_dev_ctx
=
nullptr
;
std
::
vector
<
platform
::
DeviceContext
*>
trans_dev_ctx_vec
{
trans_dev_ctx
};
const
DataTransformFn
*
trans_fun
=
DataTransformFnMap
::
Instance
().
GetNullable
(
std
::
make_pair
(
actual_kernel_key
,
expected_kernel_key
));
if
(
trans_fun
)
{
auto
input_vars
=
this
->
InputVars
();
// TODO(qijun) filter the input vars that do not need to be transformed
// filter vars that has been transformed
std
::
vector
<
std
::
string
>
need_trans
;
for
(
auto
var_name
:
input_vars
)
{
auto
var_name_trans
=
var_name
+
framework
::
KernelTypeToString
(
expected_kernel_key
);
if
(
!
scope
.
FindVar
(
var_name_trans
))
{
const_cast
<
Scope
&>
(
scope
).
Var
(
var_name_trans
);
need_trans
.
push_back
(
var_name
);
}
}
// TODO(qijun) get appropriate DataTransformFN from global map
framework
::
DataTransformFN
trans_fun
=
nullptr
;
if
(
!
need_trans
.
empty
())
{
// TODO(qijun) get appropriate DeviceContext from DeviceContext pool
platform
::
DeviceContext
*
trans_dev_ctx
=
nullptr
;
std
::
vector
<
platform
::
DeviceContext
*>
trans_dev_ctx_vec
{
trans_dev_ctx
};
// Wait for transform starting
dev_ctx
->
Wait
();
// Wait for transform starting
dev_ctx
->
Wait
();
for
(
auto
var_name
:
input_vars
)
{
trans_fun
(
trans_dev_ctx_vec
,
*
(
scope
.
FindVar
(
var_name
)),
op_scope
.
FindVar
(
var_name
));
}
// Wait for data transform finishing
for
(
auto
ctx
:
trans_dev_ctx_vec
)
{
ctx
->
Wait
();
for
(
auto
var_name
:
need_trans
)
{
(
*
trans_fun
)(
trans_dev_ctx_vec
,
*
(
scope
.
FindVar
(
var_name
)),
scope
.
FindVar
(
var_name
+
framework
::
KernelTypeToString
(
expected_kernel_key
)));
}
// Wait for data transform finishing
for
(
auto
ctx
:
trans_dev_ctx_vec
)
{
ctx
->
Wait
();
}
}
}
// Create a new ExecutionContext
ExecutionContext
op_ctx
(
*
this
,
op_scope
,
*
dev_ctx
);
kernel_iter
->
second
->
Compute
(
op_ctx
);
}
kernel_iter
->
second
->
Compute
(
ctx
);
}
OpKernelType
OperatorWithKernel
::
GetActualKernelType
(
...
...
paddle/framework/tensor.h
浏览文件 @
e2be6dd2
...
...
@@ -178,7 +178,7 @@ class Tensor {
DDim
dims_
;
/**
* @brief the layout of memory block, default is N
CHW
.
* @brief the layout of memory block, default is N
HWC
.
*
* @note the memory allocation order, describe how weight/data is stored
* For example, in 4-D Tensor(rank=4), there are three commonly
...
...
paddle/framework/var_desc.cc
浏览文件 @
e2be6dd2
...
...
@@ -74,7 +74,7 @@ const proto::TensorDesc &VarDesc::tensor_desc() const {
case
proto
::
VarDesc
::
LOD_TENSOR_ARRAY
:
return
desc_
.
tensor_array
().
tensor
();
default:
PADDLE_THROW
(
"
Unexpected branch
."
);
PADDLE_THROW
(
"
The type of var '"
,
this
->
Name
(),
"' is unsupported
."
);
}
}
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
e2be6dd2
file
(
GLOB GENERAL_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"*_op.cc"
)
string
(
REPLACE
".cc"
""
GENERAL_OPS
"
${
GENERAL_OPS
}
"
)
set
(
DEPS_OPS
""
)
set
(
pybind_file
${
PADDLE_SOURCE_DIR
}
/paddle/pybind/pybind.h
)
file
(
WRITE
${
pybind_file
}
"// Generated by the paddle/operator/CMakeLists.txt. DO NOT EDIT!
\n\n
"
)
function
(
op_library TARGET
)
...
...
@@ -48,6 +49,11 @@ function(op_library TARGET)
message
(
FATAL_ERROR
"The op library
${
TARGET
}
should contains at least one .cc file"
)
endif
()
list
(
LENGTH op_library_DEPS op_library_DEPS_len
)
if
(
${
op_library_DEPS_len
}
GREATER 0
)
set
(
DEPS_OPS
${
TARGET
}
${
DEPS_OPS
}
PARENT_SCOPE
)
endif
()
if
(
WITH_GPU
)
nv_library
(
${
TARGET
}
SRCS
${
cc_srcs
}
${
cu_cc_srcs
}
${
cu_srcs
}
DEPS
${
op_library_DEPS
}
${
op_common_deps
}
)
...
...
@@ -181,55 +187,26 @@ endfunction()
add_subdirectory
(
math
)
add_subdirectory
(
nccl
)
set
(
DEPS_OPS
cond_op
cross_entropy_op
recurrent_op
softmax_with_cross_entropy_op
softmax_op
sequence_softmax_op
sum_op
pool_op
maxout_op
unpool_op
pool_with_index_op
conv_op
conv_transpose_op
nccl_op
sequence_conv_op
sequence_pool_op
lod_rank_table_op
lod_tensor_to_array_op
array_to_lod_tensor_op
max_sequence_len_op
lstm_op
tensor_array_read_write_op
gru_op
adagrad_op
sgd_op
save_op
load_op
send_op
recv_op
)
if
(
WITH_GPU
)
op_library
(
nccl_op DEPS nccl_common
)
else
()
set
(
DEPS_OPS
${
DEPS_OPS
}
nccl_op
)
endif
()
if
(
WITH_DISTRIBUTE
)
add_subdirectory
(
detail
)
op_library
(
send_op SRCS send_op.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib_target protobuf
)
set_source_files_properties
(
send_op.cc
PROPERTIES
COMPILE_FLAGS
"-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor"
)
op_library
(
recv_op SRCS recv_op.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib_target protobuf
)
set_source_files_properties
(
recv_op.cc
PROPERTIES
COMPILE_FLAGS
"-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor"
)
cc_test
(
test_send_recv SRCS send_recv_op_test.cc DEPS send_op recv_op sum_op executor
)
add_subdirectory
(
detail
)
set
(
DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib_target protobuf
)
set
(
DISTRIBUTE_COMPILE_FLAGS
"-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor"
)
op_library
(
send_op DEPS
${
DISTRIBUTE_DEPS
}
)
set_source_files_properties
(
send_op.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
op_library
(
recv_op DEPS
${
DISTRIBUTE_DEPS
}
)
set_source_files_properties
(
recv_op.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
cc_test
(
test_send_recv SRCS send_recv_op_test.cc DEPS send_op recv_op sum_op executor
)
else
()
set
(
DEPS_OPS
${
DEPS_OPS
}
send_op recv_op
)
endif
()
op_library
(
cond_op
SRCS cond_op.cc DEPS framework_proto tensor operat
or net_op
)
op_library
(
cond_op
DEPS framework_proto tens
or net_op
)
op_library
(
cross_entropy_op DEPS cross_entropy
)
op_library
(
softmax_with_cross_entropy_op DEPS cross_entropy softmax
)
op_library
(
softmax_op DEPS softmax
)
...
...
@@ -242,21 +219,16 @@ op_library(pool_op DEPS pooling)
op_library
(
maxout_op DEPS maxouting
)
op_library
(
unpool_op DEPS unpooling
)
op_library
(
pool_with_index_op DEPS pooling
)
op_library
(
lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table
)
op_library
(
lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op
)
op_library
(
array_to_lod_tensor_op SRCS array_to_lod_tensor_op.cc DEPS lod_rank_table_op
)
op_library
(
max_sequence_len_op SRCS max_sequence_len_op.cc DEPS lod_rank_table
)
op_library
(
tensor_array_read_write_op SRCS tensor_array_read_write_op.cc
)
if
(
WITH_GPU
)
op_library
(
nccl_op DEPS nccl_common
)
endif
()
op_library
(
lod_rank_table_op DEPS lod_rank_table
)
op_library
(
lod_tensor_to_array_op DEPS lod_rank_table_op
)
op_library
(
array_to_lod_tensor_op DEPS lod_rank_table_op
)
op_library
(
max_sequence_len_op DEPS lod_rank_table
)
op_library
(
sequence_conv_op DEPS context_project
)
op_library
(
sequence_pool_op DEPS sequence_pooling
)
op_library
(
lstm_op DEPS sequence2batch lstm_compute
)
op_library
(
conv_transpose_op DEPS vol2col
)
op_library
(
gru_op DEPS sequence2batch gru_compute
)
op_library
(
recurrent_op SRCS recurrent_op.cc DEPS executor
)
op_library
(
recurrent_op DEPS executor
)
# FIXME(typhoonzero): save/load depends lodtensor serialization functions
op_library
(
save_op DEPS lod_tensor
)
op_library
(
load_op DEPS lod_tensor
)
...
...
@@ -269,13 +241,12 @@ endforeach()
set
(
GLOB_OP_LIB
${
OP_LIBRARY
}
CACHE INTERNAL
"Global OP library"
)
cc_test
(
gather_test SRCS gather_test.cc DEPS tensor
)
cc_test
(
net_op_test SRCS net_op_test.cc DEPS net_op
)
cc_test
(
scatter_test SRCS scatter_test.cc DEPS tensor
)
cc_test
(
beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor
)
cc_test
(
strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory
)
if
(
WITH_GPU
)
cc_test
(
nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context
)
cc_test
(
nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context
)
endif
()
cc_test
(
save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op
)
paddle/operators/batch_norm_op.cc
浏览文件 @
e2be6dd2
...
...
@@ -50,10 +50,6 @@ class BatchNormOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"SavedMean"
),
""
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"SavedVariance"
),
""
);
const
float
epsilon
=
ctx
->
Attrs
().
Get
<
float
>
(
"epsilon"
);
PADDLE_ENFORCE_GE
(
epsilon
,
0.0
,
"epsilon should be larger than 0"
);
PADDLE_ENFORCE_LE
(
epsilon
,
0.001
,
"epsilon should not be too large"
);
// make sure Mean/MeanOut and Variance/VarianceOut share memory in Python
PADDLE_ENFORCE_EQ
(
ctx
->
Inputs
(
"Mean"
)[
0
],
ctx
->
Outputs
(
"MeanOut"
)[
0
],
"Mean and MeanOut should share the same memory"
);
...
...
@@ -91,7 +87,12 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddAttr
<
bool
>
(
"is_test"
,
""
).
SetDefault
(
false
);
AddAttr
<
float
>
(
"momentum"
,
""
).
SetDefault
(
0.9
);
AddAttr
<
float
>
(
"epsilon"
,
""
).
SetDefault
(
1e-5
);
AddAttr
<
float
>
(
"epsilon"
,
""
)
.
SetDefault
(
1e-5
)
.
AddCustomChecker
([](
const
float
&
epsilon
)
{
PADDLE_ENFORCE
(
epsilon
>=
0.0
f
&&
epsilon
<=
0.001
f
,
"'epsilon' should be between 0.0 and 0.001."
);
});
AddAttr
<
std
::
string
>
(
"data_layout"
,
""
).
SetDefault
(
"NCHW"
);
AddInput
(
"X"
,
"The input tensor"
);
AddInput
(
"Scale"
,
...
...
paddle/operators/conv_cudnn_op.cu.cc
浏览文件 @
e2be6dd2
...
...
@@ -315,6 +315,10 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
}
// namespace operators
}
// namespace paddle
REGISTER_OP_KERNEL
(
conv2d
,
CUDNN
,
paddle
::
platform
::
CUDAPlace
,
paddle
::
operators
::
CudnnConvOpKernel
<
float
>
,
paddle
::
operators
::
CudnnConvOpKernel
<
double
>
);
REGISTER_OP_CUDA_KERNEL
(
conv2d_cudnn
,
paddle
::
operators
::
CudnnConvOpKernel
<
float
>
,
paddle
::
operators
::
CudnnConvOpKernel
<
double
>
);
...
...
paddle/operators/cross_entropy_op.cc
浏览文件 @
e2be6dd2
...
...
@@ -114,15 +114,15 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
CrossEntropyOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape
N x D,
"
"where N is the batch size and D is the number of classes. "
"(Tensor, default Tensor<float>), a 2-D tensor with shape
[N x D],
"
"
where N is the batch size and D is the number of classes. "
"This input is a probability computed by the previous operator, "
"which is almost always the result of a softmax operator."
);
AddInput
(
"Label"
,
"(Tensor), the ground truth which is a 2-D tensor. When "
"soft_label is set to false, Label is a Tensor<int64> with shape "
"[N x 1]. When soft_label is set to true, Label is a "
"Tensor<float/double> with shape [N x
K
]."
);
"Tensor<float/double> with shape [N x
D
]."
);
AddOutput
(
"Y"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape "
"[N x 1]. The cross entropy loss."
);
...
...
paddle/operators/dropout_op.cc
浏览文件 @
e2be6dd2
...
...
@@ -25,8 +25,6 @@ class DropoutOp : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE_GE
(
ctx
->
Attrs
().
Get
<
float
>
(
"dropout_prob"
),
0
);
PADDLE_ENFORCE_LE
(
ctx
->
Attrs
().
Get
<
float
>
(
"dropout_prob"
),
1
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
ctx
->
SetOutputDim
(
"Out"
,
x_dims
);
...
...
@@ -47,7 +45,11 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput
(
"Mask"
,
"The random sampled dropout mask."
).
AsIntermediate
();
AddAttr
<
float
>
(
"dropout_prob"
,
"Probability of setting units to zero."
)
.
SetDefault
(
.5
f
);
.
SetDefault
(
.5
f
)
.
AddCustomChecker
([](
const
float
&
drop_p
)
{
PADDLE_ENFORCE
(
drop_p
>=
0.0
f
&&
drop_p
<=
1.0
f
,
"'dropout_prob' must be between 0.0 and 1.0."
);
});
AddAttr
<
bool
>
(
"is_test"
,
"True if in test phase."
).
SetDefault
(
false
);
AddAttr
<
int
>
(
"seed"
,
"Dropout random seed."
).
SetDefault
(
0
);
...
...
@@ -78,8 +80,6 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) must not be null."
);
PADDLE_ENFORCE_GE
(
ctx
->
Attrs
().
Get
<
float
>
(
"dropout_prob"
),
0
);
PADDLE_ENFORCE_LE
(
ctx
->
Attrs
().
Get
<
float
>
(
"dropout_prob"
),
1
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
out_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
PADDLE_ENFORCE_EQ
(
x_dims
,
out_dims
,
...
...
paddle/operators/dropout_op.cu
浏览文件 @
e2be6dd2
...
...
@@ -30,16 +30,15 @@ struct MaskGenerator {
__host__
__device__
MaskGenerator
(
AttrType
dropout_prob
,
int
seed
)
:
dropout_prob
(
dropout_prob
),
seed
(
seed
)
{}
__host__
__device__
T
operator
()(
const
unsigned
int
n
)
const
{
inline
__host__
__device__
T
operator
()(
const
unsigned
int
n
)
const
{
thrust
::
minstd_rand
rng
;
rng
.
seed
(
seed
);
thrust
::
uniform_real_distribution
<
AttrType
>
dist
(
0
,
1
);
rng
.
discard
(
n
);
if
(
dist
(
rng
)
<
dropout_prob
)
{
return
static_cast
<
T
>
(
0
);
}
else
{
return
static_cast
<
T
>
(
1
);
}
return
static_cast
<
T
>
(
1
);
}
};
...
...
paddle/operators/math/math_function.cc
浏览文件 @
e2be6dd2
...
...
@@ -302,8 +302,29 @@ void set_constant(const platform::DeviceContext& context,
#endif
}
template
<
typename
T
>
struct
RowwiseAdd
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
vector
,
framework
::
Tensor
*
output
)
{
auto
in_dims
=
input
.
dims
();
auto
size
=
input
.
numel
()
/
in_dims
[
0
];
PADDLE_ENFORCE_EQ
(
vector
.
numel
(),
size
);
PADDLE_ENFORCE_EQ
(
output
->
dims
(),
in_dims
);
auto
in
=
framework
::
EigenMatrix
<
T
>::
From
(
input
);
auto
vec
=
framework
::
EigenVector
<
T
>::
Flatten
(
vector
);
auto
out
=
framework
::
EigenMatrix
<
T
>::
From
(
*
output
);
for
(
int64_t
i
=
0
;
i
<
in_dims
[
0
];
++
i
)
{
out
.
chip
(
i
,
0
)
=
in
.
chip
(
i
,
0
)
+
vec
;
}
}
};
template
struct
RowwiseAdd
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
RowwiseAdd
<
platform
::
CPUDeviceContext
,
double
>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
ColwiseSum
<
platform
::
CPUDeviceContext
,
double
>;
...
...
paddle/operators/math/math_function.cu
浏览文件 @
e2be6dd2
...
...
@@ -273,6 +273,35 @@ void set_constant_with_place<platform::CUDAPlace>(
TensorSetConstantGPU
(
context
,
tensor
,
value
));
}
template
<
typename
T
>
__global__
void
RowwiseAddKernel
(
const
T
*
a
,
const
T
*
b
,
T
*
c
,
int
width
,
int
num
)
{
T
tmp
=
1.0
/
width
;
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
num
;
i
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
h
=
i
*
tmp
;
int
w
=
i
-
h
*
width
;
c
[
i
]
=
a
[
i
]
+
b
[
w
];
}
}
template
<
typename
T
>
struct
RowwiseAdd
<
platform
::
CUDADeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
vector
,
framework
::
Tensor
*
output
)
{
auto
in_dims
=
input
.
dims
();
auto
size
=
input
.
numel
()
/
in_dims
[
0
];
PADDLE_ENFORCE_EQ
(
vector
.
numel
(),
size
);
PADDLE_ENFORCE_EQ
(
output
->
dims
(),
in_dims
);
int
blocks
=
512
;
int
grids
=
(
input
.
numel
()
+
blocks
-
1
)
/
blocks
;
RowwiseAddKernel
<
T
><<<
grids
,
blocks
,
0
,
context
.
stream
()
>>>
(
input
.
data
<
T
>
(),
vector
.
data
<
T
>
(),
output
->
data
<
T
>
(),
static_cast
<
int
>
(
in_dims
[
1
]),
static_cast
<
int
>
(
input
.
numel
()));
}
};
template
struct
RowwiseAdd
<
platform
::
CUDADeviceContext
,
float
>;
template
struct
RowwiseAdd
<
platform
::
CUDADeviceContext
,
double
>;
template
struct
ColwiseSum
<
platform
::
CUDADeviceContext
,
float
>;
...
...
paddle/operators/math/math_function_impl.h
浏览文件 @
e2be6dd2
...
...
@@ -45,25 +45,6 @@ void Transpose<DeviceContext, T, Rank>::operator()(
eigen_out
.
device
(
*
dev
)
=
eigen_in
.
shuffle
(
permute
);
}
template
<
typename
DeviceContext
,
typename
T
>
void
RowwiseAdd
<
DeviceContext
,
T
>::
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
vector
,
framework
::
Tensor
*
output
)
{
auto
in_dims
=
input
.
dims
();
auto
size
=
input
.
numel
()
/
in_dims
[
0
];
PADDLE_ENFORCE_EQ
(
vector
.
numel
(),
size
);
PADDLE_ENFORCE_EQ
(
output
->
dims
(),
in_dims
);
auto
in
=
framework
::
EigenMatrix
<
T
>::
From
(
input
);
auto
vec
=
framework
::
EigenMatrix
<
T
>::
From
(
vector
);
auto
out
=
framework
::
EigenMatrix
<
T
>::
From
(
*
output
);
Eigen
::
array
<
int
,
2
>
shape
({{
1
,
static_cast
<
int
>
(
size
)}});
Eigen
::
array
<
int
,
2
>
bcast
({{
static_cast
<
int
>
(
in_dims
[
0
]),
1
}});
out
.
device
(
*
context
.
eigen_device
())
=
in
+
vec
.
reshape
(
shape
).
broadcast
(
bcast
);
}
template
<
typename
DeviceContext
,
typename
T
>
void
ColwiseSum
<
DeviceContext
,
T
>::
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
...
...
paddle/operators/send_op.cc
浏览文件 @
e2be6dd2
...
...
@@ -79,7 +79,7 @@ class SendOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
Recv operator
This operator will
recv tensor from send_op
This operator will
send tensor to recv_op.
)DOC"
);
AddAttr
<
std
::
vector
<
std
::
string
>>
(
"endpoints"
,
"(string vector, default 127.0.0.1:6164)"
...
...
paddle/pybind/protobuf.cc
浏览文件 @
e2be6dd2
...
...
@@ -171,12 +171,23 @@ void BindBlockDesc(py::module &m) {
std
::
string
name
=
byte_name
;
return
self
.
HasVar
(
name
);
})
.
def
(
"has_var_recursive"
,
[](
BlockDesc
&
self
,
py
::
bytes
byte_name
)
{
std
::
string
name
=
byte_name
;
return
self
.
HasVarRecursive
(
name
);
})
.
def
(
"find_var"
,
[](
BlockDesc
&
self
,
py
::
bytes
byte_name
)
{
std
::
string
name
=
byte_name
;
return
self
.
FindVar
(
name
);
},
py
::
return_value_policy
::
reference
)
.
def
(
"find_var_recursive"
,
[](
BlockDesc
&
self
,
py
::
bytes
byte_name
)
{
std
::
string
name
=
byte_name
;
return
self
.
FindVarRecursive
(
name
);
},
py
::
return_value_policy
::
reference
)
.
def
(
"all_vars"
,
&
BlockDesc
::
AllVars
,
py
::
return_value_policy
::
reference
)
.
def
(
"op_size"
,
&
BlockDesc
::
OpSize
)
.
def
(
"op"
,
&
BlockDesc
::
Op
,
py
::
return_value_policy
::
reference
)
...
...
@@ -204,7 +215,7 @@ void BindVarDsec(py::module &m) {
.
def
(
"set_shape"
,
&
VarDesc
::
SetShape
)
.
def
(
"set_dtype"
,
&
VarDesc
::
SetDataType
)
.
def
(
"shape"
,
&
VarDesc
::
Shape
,
py
::
return_value_policy
::
reference
)
.
def
(
"dtype"
,
&
VarDesc
::
GetDataType
)
.
def
(
"dtype"
,
&
VarDesc
::
GetDataType
,
py
::
return_value_policy
::
reference
)
.
def
(
"lod_level"
,
&
VarDesc
::
GetLodLevel
)
.
def
(
"set_lod_level"
,
&
VarDesc
::
SetLoDLevel
)
.
def
(
"type"
,
&
VarDesc
::
GetType
)
...
...
@@ -236,14 +247,22 @@ void BindOpDesc(py::module &m) {
.
value
(
"BLOCK"
,
proto
::
AttrType
::
BLOCK
);
py
::
class_
<
OpDesc
>
op_desc
(
m
,
"OpDesc"
,
""
);
op_desc
.
def
(
"type"
,
&
OpDesc
::
Type
)
op_desc
.
def
(
"__init__"
,
[](
OpDesc
&
self
)
{
new
(
&
self
)
OpDesc
();
},
py
::
return_value_policy
::
reference
)
.
def
(
"copy_from"
,
&
OpDesc
::
CopyFrom
)
.
def
(
"type"
,
&
OpDesc
::
Type
)
.
def
(
"set_type"
,
&
OpDesc
::
SetType
)
.
def
(
"input"
,
&
OpDesc
::
Input
)
.
def
(
"input_names"
,
&
OpDesc
::
InputNames
)
.
def
(
"set_input"
,
&
OpDesc
::
SetInput
)
.
def
(
"output"
,
&
OpDesc
::
Output
)
.
def
(
"output_names"
,
&
OpDesc
::
OutputNames
)
.
def
(
"set_input"
,
&
OpDesc
::
SetInput
)
.
def
(
"set_output"
,
&
OpDesc
::
SetOutput
)
.
def
(
"input_arg_names"
,
&
OpDesc
::
InputArgumentNames
)
.
def
(
"output_arg_names"
,
&
OpDesc
::
OutputArgumentNames
)
.
def
(
"rename_input"
,
&
OpDesc
::
RenameInput
)
.
def
(
"rename_output"
,
&
OpDesc
::
RenameOutput
)
.
def
(
"has_attr"
,
&
OpDesc
::
HasAttr
)
.
def
(
"attr_type"
,
&
OpDesc
::
GetAttrType
)
.
def
(
"attr_names"
,
&
OpDesc
::
AttrNames
)
...
...
paddle/pybind/pybind.cc
浏览文件 @
e2be6dd2
...
...
@@ -269,23 +269,22 @@ All parameter, weight, gradient are variables in Paddle.
}
return
ret_values
;
});
m
.
def
(
"get_grad_op_descs"
,
[](
const
OpDesc
&
op_desc
,
const
std
::
unordered_set
<
std
::
string
>
&
no_grad_set
,
std
::
unordered_map
<
std
::
string
,
std
::
string
>
&
grad_to_var
,
const
std
::
vector
<
BlockDesc
*>
&
grad_sub_block
)
{
std
::
vector
<
std
::
unique_ptr
<
OpDesc
>>
grad_op_descs
=
framework
::
OpInfoMap
::
Instance
()
.
Get
(
op_desc
.
Type
())
.
GradOpMaker
()(
op_desc
,
no_grad_set
,
&
grad_to_var
,
grad_sub_block
);
std
::
vector
<
OpDesc
*>
grad_op_desc_ptrs
(
grad_op_descs
.
size
());
std
::
transform
(
grad_op_descs
.
begin
(),
grad_op_descs
.
end
(),
grad_op_desc_ptrs
.
begin
(),
[](
std
::
unique_ptr
<
OpDesc
>
&
p
)
{
return
p
.
release
();
});
return
grad_op_desc_ptrs
;
});
m
.
def
(
"get_grad_op_desc"
,
[](
const
OpDesc
&
op_desc
,
const
std
::
unordered_set
<
std
::
string
>
&
no_grad_set
,
const
std
::
vector
<
BlockDesc
*>
&
grad_sub_block
)
{
std
::
unordered_map
<
std
::
string
,
std
::
string
>
grad_to_var
;
std
::
vector
<
std
::
unique_ptr
<
OpDesc
>>
grad_op_descs
=
framework
::
OpInfoMap
::
Instance
()
.
Get
(
op_desc
.
Type
())
.
GradOpMaker
()(
op_desc
,
no_grad_set
,
&
grad_to_var
,
grad_sub_block
);
std
::
vector
<
OpDesc
*>
grad_op_desc_ptrs
(
grad_op_descs
.
size
());
std
::
transform
(
grad_op_descs
.
begin
(),
grad_op_descs
.
end
(),
grad_op_desc_ptrs
.
begin
(),
[](
std
::
unique_ptr
<
OpDesc
>
&
p
)
{
return
p
.
release
();
});
return
std
::
make_pair
(
grad_op_desc_ptrs
,
grad_to_var
);
});
m
.
def
(
"prune"
,
[](
const
ProgramDesc
&
origin
,
const
std
::
vector
<
std
::
array
<
size_t
,
2
>>
&
targets
)
{
ProgramDesc
prog_with_targets
(
origin
);
...
...
@@ -301,6 +300,8 @@ All parameter, weight, gradient are variables in Paddle.
InferenceOptimize
(
*
(
origin
.
Proto
()),
&
pruned_desc
);
return
new
ProgramDesc
(
pruned_desc
);
});
m
.
def
(
"empty_var_name"
,
[]()
{
return
framework
::
kEmptyVarName
;
});
m
.
def
(
"grad_var_suffix"
,
[]()
{
return
framework
::
kGradVarSuffix
;
});
m
.
def_submodule
(
"var_names"
,
"The module will return special predefined variable name in Paddle"
)
...
...
python/paddle/v2/fluid/backward.py
浏览文件 @
e2be6dd2
from
paddle.v2.fluid
import
framework
as
framework
from
.
import
core
import
collections
__all__
=
[
'append_backward
_ops
'
]
__all__
=
[
'append_backward'
]
def
append_backward_ops
(
loss
,
parameter_list
=
None
,
no_grad_set
=
None
):
def
_rename_arg_
(
op_desc_list
,
old_name
,
new_name
,
begin_idx
=
None
,
end_idx
=
None
):
if
begin_idx
is
None
:
begin_idx
=
0
if
end_idx
is
None
:
end_idx
=
len
(
op_desc_list
)
for
i
in
range
(
begin_idx
,
end_idx
):
op_desc
=
op_desc_list
[
i
]
if
isinstance
(
op_desc
,
tuple
):
op_desc
=
op_desc
[
0
]
op_desc
.
rename_input
(
old_name
,
new_name
)
op_desc
.
rename_output
(
old_name
,
new_name
)
def
_create_op_desc_
(
op_type
,
inputs
,
outputs
,
attrs
):
op_desc
=
core
.
OpDesc
()
op_desc
.
set_type
(
op_type
)
for
para
,
args
in
inputs
.
iteritems
():
op_desc
.
set_input
(
para
,
args
)
for
para
,
args
in
outputs
.
iteritems
():
op_desc
.
set_output
(
para
,
args
)
for
name
,
val
in
attrs
.
iteritems
():
if
isinstance
(
val
,
framework
.
Block
):
op_desc
.
set_block_attr
(
name
,
val
.
desc
)
else
:
op_desc
.
set_attr
(
name
,
val
)
return
op_desc
def
_infer_var_data_type_
(
var_name
,
block
):
grad_var
=
block
.
desc
.
find_var
(
var_name
.
encode
(
"ascii"
))
fwd_name
=
_strip_grad_suffix_
(
var_name
.
encode
(
"ascii"
))
if
block
.
desc
.
has_var_recursive
(
fwd_name
):
fwd_var
=
block
.
desc
.
find_var_recursive
(
fwd_name
.
encode
(
"ascii"
))
grad_var
.
set_dtype
(
fwd_var
.
dtype
())
else
:
grad_var
.
set_dtype
(
core
.
DataType
.
FP32
)
def
_all_in_set_
(
cands
,
s
):
for
c
in
cands
:
if
not
c
in
s
:
return
False
return
True
def
_strip_grad_suffix_
(
name
):
pos
=
name
.
find
(
core
.
grad_var_suffix
())
return
name
[:
pos
]
if
pos
!=
-
1
else
name
def
_append_grad_suffix_
(
name
):
return
name
+
core
.
grad_var_suffix
()
def
_addup_repetitive_outputs_
(
op_descs
):
# In backward part, an variable my be the output of more than one ops.
# In this case, the variable should be the accumulation of all the outputs.
# We adopt adding `sum_op`s to implement the accumulate.
pending_sum_ops
=
[]
var_rename_count
=
collections
.
defaultdict
(
int
)
renamed_vars
=
collections
.
defaultdict
(
list
)
for
idx
,
op_desc
in
enumerate
(
op_descs
):
for
var_name
in
op_desc
.
input_arg_names
():
if
len
(
renamed_vars
[
var_name
])
>
1
:
pending_sum_ops
.
append
(
(
_create_op_desc_
(
"sum"
,
{
"X"
:
renamed_vars
[
var_name
]},
{
"Out"
:
[
var_name
]},
{}),
idx
))
renamed_vars
[
var_name
]
=
[
var_name
]
for
var_name
in
op_desc
.
output_arg_names
():
if
var_name
==
core
.
empty_var_name
(
)
or
var_name
in
op_desc
.
input_arg_names
():
# empty variable or inplace op
continue
if
len
(
renamed_vars
[
var_name
])
==
0
:
# it's the first time we get the variable
renamed_vars
[
var_name
]
=
[
var_name
]
else
:
if
len
(
renamed_vars
[
var_name
])
==
1
:
new_name
=
var_name
+
"@RENAME@"
+
\
str
(
var_rename_count
[
var_name
])
var_rename_count
[
var_name
]
+=
1
# rename original var_name
renamed_vars
[
var_name
][
0
]
=
new_name
_rename_arg_
(
op_descs
,
var_name
,
new_name
,
0
,
idx
)
_rename_arg_
(
pending_sum_ops
,
var_name
,
new_name
)
new_name
=
var_name
+
"@RENAME@"
+
\
str
(
var_rename_count
[
var_name
])
var_rename_count
[
var_name
]
+=
1
op_desc
.
rename_output
(
var_name
,
new_name
)
renamed_vars
[
var_name
].
append
(
new_name
)
for
var_name
,
inputs
in
renamed_vars
.
iteritems
():
if
len
(
inputs
)
>
1
:
pending_sum_ops
.
append
((
_create_op_desc_
(
"sum"
,
{
"X"
:
inputs
},
{
"Out"
:
[
var_name
]},
{}),
len
(
op_descs
)))
# sum_op descs are sorted according to their insert position
for
p
in
reversed
(
pending_sum_ops
):
op_descs
.
insert
(
p
[
1
],
p
[
0
])
return
op_descs
def
_remove_no_grad_branch_
(
op_descs
,
no_grad_set
):
# Remove ops whose outputs are all in no_grad_dict
op_descs
=
filter
(
lambda
op_desc
:
not
_all_in_set_
(
op_desc
.
output_arg_names
(),
no_grad_set
),
op_descs
)
# Insert fill_zeros_like_op
to_insert
=
[]
for
idx
,
op_desc
in
enumerate
(
op_descs
):
for
arg
in
op_desc
.
input_arg_names
():
if
core
.
grad_var_suffix
()
in
arg
and
arg
in
no_grad_set
:
to_insert
.
append
((
_create_op_desc_
(
"fill_zeros_like"
,
{
"X"
:
[
_strip_grad_suffix_
(
arg
)]
},
{
"Y"
:
[
arg
]},
{}),
idx
))
map
(
lambda
p
:
op_descs
.
insert
(
p
[
1
],
p
[
0
]),
reversed
(
to_insert
))
return
op_descs
def
_append_backward_ops_
(
target
,
block
,
target_block
,
no_grad_dict
,
grad_to_var
,
callback
=
None
):
grad_op_descs
=
[]
program
=
block
.
program
for
op
in
reversed
(
block
.
ops
):
grad_sub_block_list
=
[]
# If the op has its own sub-block, deal with the sub-block first
if
op
.
has_attr
(
"sub_block"
):
sub_block
=
program
.
block
(
op
.
block_attr
(
"sub_block"
))
grad_sub_block
=
program
.
create_block
(
parent_idx
=
sub_block
.
idx
)
_append_backward_ops_
(
target
,
sub_block
,
grad_sub_block
,
no_grad_dict
,
grad_to_var
,
callback
)
grad_sub_block_list
.
append
(
grad_sub_block
.
desc
)
grad_op_desc
,
op_grad_to_var
=
core
.
get_grad_op_desc
(
op
.
desc
,
no_grad_dict
[
block
.
idx
],
grad_sub_block_list
)
grad_op_descs
.
extend
(
grad_op_desc
)
grad_to_var
.
update
(
op_grad_to_var
)
grad_op_descs
=
_addup_repetitive_outputs_
(
grad_op_descs
)
grad_op_descs
=
_remove_no_grad_branch_
(
grad_op_descs
,
no_grad_dict
[
block
.
idx
])
if
target_block
.
idx
==
0
:
grad_op_descs
.
insert
(
0
,
_create_op_desc_
(
"fill_constant"
,
{},
{
"Out"
:
[
_append_grad_suffix_
(
target
.
name
)]
},
{
"shape"
:
[
1
],
"value"
:
1.0
,
"dtype"
:
target
.
dtype
}))
# append op_desc in grad_op_descs to target_block
for
op_desc
in
grad_op_descs
:
new_op_desc
=
target_block
.
desc
.
append_op
()
new_op_desc
.
copy_from
(
op_desc
)
def
_append_backward_vars_
(
block
,
start_op_idx
,
grad_to_var
,
grad_info_map
):
for
op_idx
in
range
(
start_op_idx
,
block
.
desc
.
op_size
()):
op_desc
=
block
.
desc
.
op
(
op_idx
)
if
op_desc
.
has_attr
(
"sub_block"
):
sub_block
=
block
.
program
.
block
(
op_desc
.
block_attr
(
"sub_block"
))
_append_backward_vars_
(
sub_block
,
0
,
grad_to_var
,
grad_info_map
)
new_vars
=
set
()
# create new gradient variables
for
grad_var_name
in
op_desc
.
output_arg_names
():
grad_var_name
=
grad_var_name
.
encode
(
"ascii"
)
if
block
.
desc
.
has_var_recursive
(
grad_var_name
)
or
grad_var_name
==
core
.
empty_var_name
():
continue
block
.
desc
.
var
(
grad_var_name
)
new_vars
.
add
(
grad_var_name
)
if
not
grad_to_var
.
has_key
(
grad_var_name
):
continue
grad_info_map
[
grad_to_var
[
grad_var_name
]]
=
(
grad_var_name
,
block
)
# infer_shape and infer_type
op_desc
.
infer_var_type
(
block
.
desc
)
op_desc
.
infer_shape
(
block
.
desc
)
for
arg
in
op_desc
.
output_arg_names
():
if
arg
in
new_vars
:
_infer_var_data_type_
(
arg
,
block
)
def
append_backward
(
loss
,
parameter_list
=
None
,
no_grad_set
=
None
):
"""
Create and add gradient Operators in BlockDesc to compute
gradients of `loss` for parameters in parameter_list
:param loss: an variable generated by cost function.
:type loss: Variable
:param no_grad_
se
t: variable that should not create gradient
:type no_grad_
se
t: set
:param no_grad_
dic
t: variable that should not create gradient
:type no_grad_
dic
t: set
:param parameter_list: parameters that need to compute gradient and
update to optimize the lost.
:type: list
...
...
@@ -20,35 +212,53 @@ def append_backward_ops(loss, parameter_list=None, no_grad_set=None):
"""
assert
isinstance
(
loss
,
framework
.
Variable
)
program
=
loss
.
block
.
program
no_grad_dict
=
dict
()
if
no_grad_set
is
None
:
program
=
loss
.
block
.
program
assert
isinstance
(
program
,
framework
.
Program
)
no_grad_set
=
list
()
for
block
in
program
.
blocks
:
assert
isinstance
(
block
,
framework
.
Block
)
block_no_grad_set
=
set
()
for
var
in
block
.
vars
.
itervalues
():
assert
isinstance
(
var
,
framework
.
Variable
)
if
var
.
stop_gradient
:
no_grad_set
.
append
(
var
.
name
)
no_grad_set
=
set
(
no_grad_set
)
block_no_grad_set
.
add
(
_append_grad_suffix_
(
var
.
name
))
no_grad_dict
[
block
.
idx
]
=
block_no_grad_set
elif
isinstance
(
no_grad_set
,
set
):
no_grad_dict
=
{
0
:
no_grad_set
}
else
:
raise
ValueError
(
"'no_grad_set' should be a set or None."
)
grad_info_map
=
dict
()
root_block
=
program
.
block
(
0
)
fwd_op_num
=
root_block
.
desc
.
op_size
()
current_block_idx
=
program
.
current_block_idx
grad_to_var
=
dict
()
_append_backward_ops_
(
loss
,
root_block
,
root_block
,
no_grad_dict
,
grad_to_var
)
_append_backward_vars_
(
root_block
,
fwd_op_num
,
grad_to_var
,
grad_info_map
)
program
.
current_block_idx
=
current_block_idx
program
.
sync_with_cpp
()
param_grad_map
=
loss
.
block
.
program
.
append_backward
(
loss
,
no_grad_set
)
if
parameter_list
is
not
None
:
parameters
=
parameter_list
else
:
params
=
loss
.
block
.
program
.
global_block
().
all_parameters
()
params
=
program
.
global_block
().
all_parameters
()
parameters
=
[
param
.
name
for
param
in
params
]
params_and_grads
=
[]
for
param
in
parameters
:
if
param
not
in
param_grad
_map
:
if
param
not
in
grad_info
_map
:
raise
ValueError
(
"param %s is not in map"
%
param
)
grad_info
=
param_grad
_map
[
param
]
grad_block
=
loss
.
block
.
program
.
block
(
grad_info
[
1
])
grad_info
=
grad_info
_map
[
param
]
grad_block
=
grad_info
[
1
]
if
not
grad_block
.
has_var
(
grad_info
[
0
]):
raise
ValueError
(
"grad block[{0}] did not have grad var {1}"
.
format
(
grad_info
[
1
],
grad_info
[
0
]))
# Get the param var from the global block
param_var
=
loss
.
block
.
program
.
global_block
().
var
(
param
)
param_var
=
program
.
global_block
().
var
(
param
)
grad_var
=
grad_block
.
var
(
grad_info
[
0
])
if
loss
.
block
.
has_var
(
grad_info
[
0
]):
params_and_grads
.
append
((
param_var
,
grad_var
))
...
...
python/paddle/v2/fluid/distribute_transpiler.py
浏览文件 @
e2be6dd2
...
...
@@ -95,7 +95,9 @@ class DistributeTranspiler:
"""
if
program
is
None
:
program
=
default_main_program
()
self
.
program
=
program
self
.
trainers
=
trainers
self
.
optimize_ops
=
optimize_ops
self
.
_optimize_distributed
(
optimize_ops
,
program
,
...
...
@@ -156,9 +158,10 @@ class DistributeTranspiler:
attrs
=
{
"endpoints"
:
pserver_endpoints
,
"epmap"
:
epmap
})
def
get_trainer_program
(
optimize_ops
,
program
):
def
get_trainer_program
(
self
):
# remove optimize ops and add a send op to main_program
program
.
global_block
().
delete_ops
(
optimize_ops
)
self
.
program
.
global_block
().
delete_ops
(
self
.
optimize_ops
)
return
self
.
program
def
_create_var_for_trainers
(
self
,
block
,
var
,
trainers
):
var_list
=
[]
...
...
@@ -210,7 +213,6 @@ class DistributeTranspiler:
if
opt_op
.
inputs
.
has_key
(
"Grad"
):
if
opt_op
.
inputs
[
"Grad"
].
name
in
grad_var_names
:
print
"appending "
,
opt_op
.
type
,
opt_op
.
inputs
optimize_sub_program
.
global_block
().
append_op
(
type
=
opt_op
.
type
,
inputs
=
opt_op
.
inputs
,
...
...
python/paddle/v2/fluid/framework.py
浏览文件 @
e2be6dd2
...
...
@@ -663,7 +663,7 @@ class Block(object):
end
=
list
(
self
.
ops
).
index
(
ops
[
-
1
])
except
Exception
,
e
:
raise
e
self
.
desc
.
remove_op
(
start
,
end
)
self
.
desc
.
remove_op
(
start
,
end
+
1
)
def
prepend_op
(
self
,
*
args
,
**
kwargs
):
op_desc
=
self
.
desc
.
prepend_op
()
...
...
@@ -846,9 +846,11 @@ class Program(object):
self
.
sync_with_cpp
()
return
param_to_grad_info
def
create_block
(
self
):
def
create_block
(
self
,
parent_idx
=
None
):
new_block_idx
=
len
(
self
.
blocks
)
self
.
desc
.
append_block
(
self
.
current_block
().
desc
)
parent
=
self
.
current_block
()
if
parent_idx
is
None
else
self
.
block
(
parent_idx
)
self
.
desc
.
append_block
(
parent
.
desc
)
self
.
current_block_idx
=
new_block_idx
self
.
blocks
.
append
(
Block
(
self
,
self
.
current_block_idx
))
return
self
.
current_block
()
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
e2be6dd2
...
...
@@ -270,6 +270,7 @@ def gru_unit(input,
attr
=
helper
.
param_attr
,
shape
=
[
size
,
3
*
size
],
dtype
=
dtype
)
# create bias
if
bias
is
None
:
bias_size
=
[
1
,
3
*
size
]
bias
=
helper
.
create_parameter
(
...
...
@@ -358,7 +359,59 @@ def cos_sim(X, Y, **kwargs):
def
cross_entropy
(
input
,
label
,
**
kwargs
):
"""
This function computes cross_entropy using the input and label.
**Cross Entropy Layer**
This layer computes the cross entropy between `input` and `label`. It supports
both standard cross-entropy and soft-label cross-entropy loss computation.
1) One-hot cross-entropy:
`soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
.. math::
Y[i] = -\log(X[i, Label[i]])
2) Soft-label cross-entropy:
`soft_label = True`, `Label[i, j]` indicates the soft label of class j
for sample i:
.. math::
Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
Please make sure that in this case the summation of each row of `label`
equals one.
3) One-hot cross-entropy with vecterized `label`:
As a special case of 2), when each row of 'label' has only one
non-zero element which is equal to 1, soft-label cross-entropy degenerates
to a one-hot cross-entropy with one-hot label representation.
Args:
input (Variable|list): a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of classes. This input is a probability
computed by the previous operator, which is almost always the result
of a softmax operator.
label (Variable|list): the ground truth which is a 2-D tensor. When
`soft_label` is set to `False`, `label` is a tensor<int64> with shape
[N x 1]. When `soft_label` is set to `True`, `label` is a
tensor<float/double> with shape [N x D].
soft_label (bool, via `**kwargs`): a flag indicating whether to interpretate
the given labels as soft labels, default `False`.
Returns:
A 2-D tensor with shape [N x 1], the cross entropy loss.
Raises:
`ValueError`: 1) the 1st dimension of `input` and `label` are not equal; 2) when \
`soft_label == True`, and the 2nd dimension of `input` and `label` are not
\
equal; 3) when `soft_label == False`, and the 2nd dimension of `label` is not 1.
Examples:
.. code-block:: python
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
"""
helper
=
LayerHelper
(
'cross_entropy'
,
**
kwargs
)
out
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
...
...
@@ -514,14 +567,83 @@ def conv2d(input,
groups
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
act
=
None
,
name
=
None
):
act
=
None
):
"""
This function creates the op for a 2-dimensional Convolution.
This is performed using the parameters of filters(size, dimensionality etc)
, stride and other configurations for a Convolution operation.
This funciton can also append an activation on top of the
conv-2d output, if mentioned in the input parameters.
**Convlution2D Layer**
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output)
are in NCHW format. Where N is batch size, C is the number of channels, H is the height
of the feature, and W is the width of the feature.
The details of convolution layer, please refer UFLDL's `convolution,
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_ .
If bias attribution and activation type are provided, bias is added to the output of the convolution,
and the corresponding activation function is applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \sigma (W
\\
ast X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`
\\
ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`
\\
sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
.. math::
H_{out}&=
\\
frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1
\\\\
W_{out}&=
\\
frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args:
input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
groups(int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
act(str): Activation type. Default: None
Returns:
Variable: The tensor variable storing the convolution and
\
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
"""
if
stride
is
None
:
...
...
python/paddle/v2/fluid/optimizer.py
浏览文件 @
e2be6dd2
from
collections
import
defaultdict
import
framework
from
backward
import
append_backward
_ops
from
backward
import
append_backward
from
framework
import
unique_name
,
program_guard
from
initializer
import
Constant
from
layer_helper
import
LayerHelper
...
...
@@ -194,10 +194,10 @@ class Optimizer(object):
no_grad_set
=
None
):
"""Add operations to minimize `loss` by updating `parameter_list`.
This method combines interface `append_backward
_ops
()` and
This method combines interface `append_backward()` and
`create_optimization_pass()` into one.
"""
params_grads
=
append_backward
_ops
(
loss
,
parameter_list
,
no_grad_set
)
params_grads
=
append_backward
(
loss
,
parameter_list
,
no_grad_set
)
params_grads
=
append_gradient_clip_ops
(
params_grads
)
...
...
python/paddle/v2/fluid/tests/book/notest_recognize_digits_conv_dist.py
→
python/paddle/v2/fluid/tests/book
_distribute
/notest_recognize_digits_conv_dist.py
浏览文件 @
e2be6dd2
...
...
@@ -38,35 +38,43 @@ train_reader = paddle.batch(
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
t
=
fluid
.
DistributeTranspiler
()
# all parameter server endpoints list for spliting parameters
pserver_endpoints
=
os
.
getenv
(
"PSERVERS"
)
# server endpoint for current node
current_endpoint
=
os
.
getenv
(
"SERVER_ENDPOINT"
)
# run as trainer or parameter server
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
# get the training role: trainer/pserver
t
.
transpile
(
optimize_ops
,
params_grads
,
pservers
=
pserver_endpoints
,
trainers
=
1
)
t
.
transpile
(
optimize_ops
,
params_grads
,
pservers
=
pserver_endpoints
,
trainers
=
2
)
if
training_role
==
"PSERVER"
:
pserver_prog
=
t
.
get_pserver_program
(
pserver_endpoints
,
optimize_ops
)
if
not
current_endpoint
:
print
(
"need env SERVER_ENDPOINT"
)
exit
(
1
)
pserver_prog
=
t
.
get_pserver_program
(
current_endpoint
,
optimize_ops
)
exe
.
run
(
fluid
.
default_startup_program
())
exe
.
run
(
pserver_prog
)
elif
training_role
==
"TRAINER"
:
trainer_prog
=
t
.
get_trainer_program
()
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
images
,
label
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
range
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
batch_id
=
0
for
data
in
train_reader
():
loss
,
acc
=
exe
.
run
(
fluid
.
default_main_program
()
,
loss
,
acc
=
exe
.
run
(
trainer_prog
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
pass_acc
=
accuracy
.
eval
(
exe
)
# print loss, acc
if
loss
<
10.0
and
pass_acc
>
0.9
:
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
exit
(
0
)
if
batch_id
%
100
==
0
:
print
(
"batch_id %d, loss: %f, acc: %f"
%
(
batch_id
,
loss
,
pass_acc
))
batch_id
+=
1
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"pass_id="
+
str
(
pass_id
)
+
" pass_acc="
+
str
(
pass_acc
))
else
:
print
(
"environment var TRAINER_ROLE should be TRAINER os PSERVER"
)
exit
(
1
)
python/paddle/v2/fluid/tests/op_test.py
浏览文件 @
e2be6dd2
...
...
@@ -4,7 +4,7 @@ import random
import
itertools
import
paddle.v2.fluid.core
as
core
import
collections
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
from
paddle.v2.fluid.op
import
Operator
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.framework
import
Program
,
OpProtoHolder
...
...
@@ -491,7 +491,7 @@ class OpTest(unittest.TestCase):
op_loss
.
desc
.
infer_var_type
(
block
.
desc
)
op_loss
.
desc
.
infer_shape
(
block
.
desc
)
param_grad_list
=
append_backward
_ops
(
param_grad_list
=
append_backward
(
loss
=
loss
,
parameter_list
=
input_to_check
,
no_grad_set
=
no_grad_set
)
feed_dict
=
{
...
...
python/paddle/v2/fluid/tests/test_array_read_write_op.py
浏览文件 @
e2be6dd2
...
...
@@ -2,7 +2,7 @@ import unittest
import
paddle.v2.fluid.core
as
core
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
from
paddle.v2.fluid.framework
import
default_main_program
import
numpy
...
...
@@ -64,7 +64,7 @@ class TestArrayReadWrite(unittest.TestCase):
total_sum
=
layers
.
sums
(
input
=
[
a_sum
,
x_sum
])
total_sum_scaled
=
layers
.
scale
(
x
=
total_sum
,
scale
=
1
/
6.0
)
append_backward
_ops
(
total_sum_scaled
)
append_backward
(
total_sum_scaled
)
g_vars
=
map
(
default_main_program
().
global_block
().
var
,
[
each_x
.
name
+
"@GRAD"
for
each_x
in
x
])
...
...
python/paddle/v2/fluid/tests/test_conditional_block.py
浏览文件 @
e2be6dd2
...
...
@@ -3,7 +3,7 @@ import paddle.v2.fluid.layers as layers
import
paddle.v2.fluid.core
as
core
from
paddle.v2.fluid.framework
import
default_startup_program
,
default_main_program
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
import
numpy
...
...
@@ -26,7 +26,7 @@ class ConditionalBlock(unittest.TestCase):
outs
=
exe
.
run
(
feed
=
{
'X'
:
x
},
fetch_list
=
[
out
])[
0
]
print
outs
loss
=
layers
.
mean
(
x
=
out
)
append_backward
_ops
(
loss
=
loss
)
append_backward
(
loss
=
loss
)
outs
=
exe
.
run
(
feed
=
{
'X'
:
x
},
fetch_list
=
[
...
...
python/paddle/v2/fluid/tests/test_lod_tensor_array_ops.py
浏览文件 @
e2be6dd2
...
...
@@ -4,7 +4,7 @@ import numpy
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.framework
import
Program
,
program_guard
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
class
TestCPULoDTensorArrayOps
(
unittest
.
TestCase
):
...
...
@@ -170,7 +170,7 @@ class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
mean
=
layers
.
mean
(
x
=
result
)
append_backward
_ops
(
mean
)
append_backward
(
mean
)
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
arange
(
10
).
reshape
(
10
,
1
).
astype
(
'float32'
),
place
)
...
...
python/paddle/v2/fluid/tests/test_optimizer.py
浏览文件 @
e2be6dd2
...
...
@@ -2,7 +2,7 @@ import unittest
import
paddle.v2.fluid.framework
as
framework
import
paddle.v2.fluid.optimizer
as
optimizer
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
class
TestOptimizer
(
unittest
.
TestCase
):
...
...
@@ -102,7 +102,7 @@ class TestMomentumOptimizer(unittest.TestCase):
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
momentum_optimizer
.
get_accumulators
()),
0
)
opts
=
momentum_optimizer
.
create_optimization_pass
(
...
...
@@ -151,7 +151,7 @@ class TestMomentumOptimizer(unittest.TestCase):
learning_rate
=
0.01
momentum_optimizer
=
self
.
MockMomentum
(
learning_rate
=
learning_rate
,
momentum
=
0.2
,
use_nesterov
=
True
)
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
momentum_optimizer
.
get_accumulators
()),
0
)
opts
=
momentum_optimizer
.
create_optimization_pass
(
...
...
@@ -209,7 +209,7 @@ class TestAdagradOptimizer(unittest.TestCase):
learning_rate
=
0.01
adagrad_optimizer
=
self
.
MockAdagrad
(
learning_rate
=
learning_rate
,
epsilon
=
1.0e-6
)
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adagrad_optimizer
.
get_accumulators
()),
0
)
opts
=
adagrad_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
...
...
@@ -269,7 +269,7 @@ class TestAdamOptimizer(unittest.TestCase):
learning_rate
=
0.01
adam_optimizer
=
self
.
MockAdam
(
learning_rate
=
learning_rate
,
beta1
=
0.9
,
beta2
=
0.999
)
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adam_optimizer
.
get_accumulators
()),
0
)
opts
=
adam_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
...
...
@@ -331,7 +331,7 @@ class TestAdamaxOptimizer(unittest.TestCase):
learning_rate
=
0.01
adamax_optimizer
=
self
.
MockAdamax
(
learning_rate
=
learning_rate
,
beta1
=
0.9
,
beta2
=
0.999
)
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adamax_optimizer
.
get_accumulators
()),
0
)
opts
=
adamax_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
...
...
@@ -390,7 +390,7 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
learning_rate
=
0.01
decayed_adagrad_optimizer
=
self
.
MockDecayedAdagrad
(
learning_rate
=
learning_rate
,
decay
=
0.95
,
epsilon
=
1.0e-6
)
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
decayed_adagrad_optimizer
.
get_accumulators
()),
0
)
opts
=
decayed_adagrad_optimizer
.
create_optimization_pass
(
...
...
python/paddle/v2/fluid/tests/test_recurrent_op.py
浏览文件 @
e2be6dd2
...
...
@@ -3,7 +3,7 @@ import unittest
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.framework
import
Program
,
grad_var_name
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
import
numpy
as
np
import
paddle.v2.fluid.core
as
core
...
...
@@ -177,7 +177,7 @@ class RecurrentOpTest1(unittest.TestCase):
def
test_backward
(
self
):
self
.
check_forward
()
append_backward
_ops
(
self
.
output
)
append_backward
(
self
.
output
)
ana_grad
=
[
np
.
array
(
x
)
for
x
in
self
.
backward
()]
...
...
python/paddle/v2/fluid/tests/test_regularizer.py
浏览文件 @
e2be6dd2
...
...
@@ -3,7 +3,7 @@ import unittest
import
paddle.v2.fluid.framework
as
framework
import
paddle.v2.fluid.optimizer
as
optimizer
import
paddle.v2.fluid.regularizer
as
regularizer
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
class
TestL2DecayRegularizer
(
unittest
.
TestCase
):
...
...
@@ -33,7 +33,7 @@ class TestL2DecayRegularizer(unittest.TestCase):
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
count_ops
=
len
(
block
.
ops
)
params_grads
=
optimizer
.
append_regularization_ops
(
params_grads
)
...
...
@@ -70,7 +70,7 @@ class TestL1DecayRegularizer(unittest.TestCase):
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
params_grads
=
append_backward
_ops
(
mean_out
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
count_ops
=
len
(
block
.
ops
)
params_grads
=
optimizer
.
append_regularization_ops
(
params_grads
)
...
...
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
浏览文件 @
e2be6dd2
...
...
@@ -12,7 +12,7 @@ class TestReorderLoDTensor(unittest.TestCase):
new_dat
=
fluid
.
layers
.
reorder_lod_tensor_by_rank
(
x
=
dat
,
rank_table
=
table
)
loss
=
fluid
.
layers
.
mean
(
x
=
new_dat
)
fluid
.
backward
.
append_backward
_ops
(
loss
=
loss
)
fluid
.
backward
.
append_backward
(
loss
=
loss
)
cpu
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
cpu
)
...
...
python/paddle/v2/fluid/tests/test_rnn_memory_helper_op.py
浏览文件 @
e2be6dd2
...
...
@@ -2,7 +2,7 @@ import unittest
from
paddle.v2.fluid.framework
import
Program
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
import
numpy
as
np
import
paddle.v2.fluid.core
as
core
...
...
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
浏览文件 @
e2be6dd2
...
...
@@ -2,7 +2,7 @@ import unittest
import
paddle.v2.fluid.core
as
core
from
paddle.v2.fluid.executor
import
Executor
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
from
paddle.v2.fluid.framework
import
default_main_program
import
numpy
...
...
@@ -35,7 +35,7 @@ class TestShrinkRNNMemory(unittest.TestCase):
self
.
assertTrue
(
numpy
.
allclose
(
tensor_np
[
0
:
1
],
outs
[
2
]))
mem3_mean
=
layers
.
mean
(
x
=
mem3
)
append_backward
_ops
(
loss
=
mem3_mean
)
append_backward
(
loss
=
mem3_mean
)
x_grad
=
exe
.
run
(
feed
=
{
'x'
:
tensor
},
fetch_list
=
[
main_program
.
global_block
().
var
(
'x@GRAD'
)])[
0
]
...
...
python/paddle/v2/fluid/tests/test_split_and_merge_lod_tensor_op.py
浏览文件 @
e2be6dd2
...
...
@@ -4,7 +4,7 @@ import numpy as np
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.framework
import
Program
,
program_guard
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
class
TestCPULoDTensorArrayOps
(
unittest
.
TestCase
):
...
...
@@ -133,7 +133,7 @@ class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
in_true
=
out_true
,
in_false
=
out_false
,
mask
=
y
,
x
=
x
,
level
=
level
)
mean
=
layers
.
mean
(
x
=
out
)
append_backward
_ops
(
mean
)
append_backward
(
mean
)
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
np
.
arange
(
10
).
reshape
(
10
,
1
).
astype
(
'float32'
),
place
)
...
...
python/paddle/v2/fluid/tests/test_while_op.py
浏览文件 @
e2be6dd2
...
...
@@ -2,7 +2,7 @@ import unittest
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.executor
import
Executor
import
paddle.v2.fluid.core
as
core
from
paddle.v2.fluid.backward
import
append_backward
_ops
from
paddle.v2.fluid.backward
import
append_backward
import
numpy
...
...
@@ -46,7 +46,7 @@ class TestWhileOp(unittest.TestCase):
sum_result
=
layers
.
array_read
(
array
=
mem_array
,
i
=
i
)
loss
=
layers
.
mean
(
x
=
sum_result
)
append_backward
_ops
(
loss
)
append_backward
(
loss
)
cpu
=
core
.
CPUPlace
()
exe
=
Executor
(
cpu
)
...
...
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