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3db7c829
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3db7c829
编写于
12月 27, 2017
作者:
Y
Yibing Liu
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of upstream into fix_cross_entropy_doc
上级
e2c2652f
b775b6cb
变更
32
隐藏空白更改
内联
并排
Showing
32 changed file
with
506 addition
and
112 deletion
+506
-112
doc/design/optimizer.md
doc/design/optimizer.md
+1
-1
paddle/framework/CMakeLists.txt
paddle/framework/CMakeLists.txt
+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_test.cc
paddle/framework/op_kernel_type_test.cc
+1
-1
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/var_desc.cc
paddle/framework/var_desc.cc
+1
-1
paddle/operators/conv_cudnn_op.cu.cc
paddle/operators/conv_cudnn_op.cu.cc
+4
-0
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/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/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/design/optimizer.md
浏览文件 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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/library_type.h
浏览文件 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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_test.cc
浏览文件 @
3db7c829
...
...
@@ -48,4 +48,4 @@ 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 @
3db7c829
...
...
@@ -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 @
3db7c829
/* 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/var_desc.cc
浏览文件 @
3db7c829
...
...
@@ -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/conv_cudnn_op.cu.cc
浏览文件 @
3db7c829
...
...
@@ -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/math/math_function.cc
浏览文件 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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/pybind/protobuf.cc
浏览文件 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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/optimizer.py
浏览文件 @
3db7c829
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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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 @
3db7c829
...
...
@@ -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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