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47b13014
编写于
8月 07, 2017
作者:
D
dongzhihong
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/* 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 <Python.h>
#include <fstream>
#include <vector>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/pybind/tensor_bind.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
namespace
py
=
pybind11
;
namespace
pd
=
paddle
::
framework
;
USE_OP
(
add_two
);
USE_OP
(
onehot_cross_entropy
);
USE_OP_WITHOUT_KERNEL
(
fc
);
USE_OP
(
sgd
);
USE_OP
(
mul
);
USE_OP
(
mean
);
USE_OP
(
sigmoid
);
USE_OP
(
softmax
);
USE_OP
(
rowwise_add
);
USE_OP_WITHOUT_KERNEL
(
recurrent_op
);
template
<
typename
ClassType
>
void
ExposeOperator
(
ClassType
&
m
)
{
m
.
def
(
"infer_shape"
,
&
ClassType
::
type
::
InferShape
)
.
def
(
"run"
,
&
ClassType
::
type
::
Run
)
.
def
(
"outputs"
,
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
vector
<
std
::
string
>
{
return
op
.
outputs_
;
})
.
def
(
"__str__"
,
&
ClassType
::
type
::
DebugString
);
}
static
size_t
UniqueIntegerGenerator
()
{
static
std
::
atomic
<
size_t
>
generator
;
return
generator
.
fetch_add
(
1
);
}
bool
IsCompileGPU
()
{
#ifdef PADDLE_ONLY_CPU
return
false
;
#else
return
true
;
#endif
}
PYBIND11_PLUGIN
(
core
)
{
py
::
module
m
(
"core"
,
"C++ core of PaddlePaddle"
);
py
::
class_
<
pd
::
Tensor
>
(
m
,
"Tensor"
,
py
::
buffer_protocol
())
.
def_buffer
([](
pd
::
Tensor
&
self
)
->
py
::
buffer_info
{
return
paddle
::
pybind
::
CastToPyBuffer
(
self
);
})
.
def
(
"get_dims"
,
[](
const
pd
::
Tensor
&
self
)
{
return
pd
::
vectorize
(
self
.
dims
());
})
.
def
(
"set_dims"
,
[](
pd
::
Tensor
&
self
,
const
std
::
vector
<
int
>&
dim
)
{
self
.
Resize
(
pd
::
make_ddim
(
dim
));
})
.
def
(
"alloc_float"
,
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
GPUPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
})
.
def
(
"alloc_float"
,
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
})
.
def
(
"alloc_int"
,
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
})
.
def
(
"alloc_int"
,
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
GPUPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
})
.
def
(
"set"
,
paddle
::
pybind
::
PyCPUTensorSetFromArray
<
float
>
)
.
def
(
"set"
,
paddle
::
pybind
::
PyCPUTensorSetFromArray
<
int
>
)
#ifndef PADDLE_ONLY_CPU
.
def
(
"set"
,
paddle
::
pybind
::
PyCUDATensorSetFromArray
<
float
>
)
.
def
(
"set"
,
paddle
::
pybind
::
PyCUDATensorSetFromArray
<
int
>
)
#endif
.
def
(
"shape"
,
[](
pd
::
Tensor
&
self
)
{
return
pd
::
vectorize
(
self
.
dims
());
});
py
::
class_
<
pd
::
Variable
>
(
m
,
"Variable"
,
R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
)DOC"
)
.
def
(
"is_int"
,
[](
const
pd
::
Variable
&
var
)
{
return
var
.
IsType
<
int
>
();
})
.
def
(
"set_int"
,
[](
pd
::
Variable
&
var
,
int
val
)
->
void
{
*
var
.
GetMutable
<
int
>
()
=
val
;
})
.
def
(
"get_int"
,
[](
const
pd
::
Variable
&
var
)
->
int
{
return
var
.
Get
<
int
>
();
})
.
def
(
"get_tensor"
,
[](
pd
::
Variable
&
self
)
->
pd
::
Tensor
*
{
return
self
.
GetMutable
<
pd
::
Tensor
>
();
},
py
::
return_value_policy
::
reference
)
.
def
(
"get_net"
,
[](
pd
::
Variable
&
self
)
->
pd
::
NetOp
*
{
return
self
.
GetMutable
<
pd
::
NetOp
>
();
},
py
::
return_value_policy
::
reference
);
py
::
class_
<
pd
::
Scope
>
(
m
,
"Scope"
,
""
)
.
def
(
"new_var"
,
[](
pd
::
Scope
&
self
,
const
std
::
string
&
name
)
->
pd
::
Variable
*
{
return
self
.
NewVar
(
name
);
},
py
::
return_value_policy
::
reference
)
.
def
(
"find_var"
,
&
pd
::
Scope
::
FindVar
,
py
::
return_value_policy
::
reference
)
.
def
(
py
::
init
<>
())
.
def
(
"new_scope"
,
[](
pd
::
Scope
&
self
)
->
pd
::
Scope
*
{
return
&
self
.
NewScope
();
},
py
::
return_value_policy
::
reference
)
.
def
(
"drop_kids"
,
&
pd
::
Scope
::
DropKids
);
//! @note: Be careful! PyBind will return std::string as an unicode, not
//! Python str. If you want a str object, you should cast them in Python.
m
.
def
(
"get_all_op_protos"
,
[]()
->
std
::
vector
<
py
::
bytes
>
{
auto
&
protos
=
pd
::
OpRegistry
::
protos
();
std
::
vector
<
py
::
bytes
>
ret_values
;
for
(
auto
it
=
protos
.
begin
();
it
!=
protos
.
end
();
++
it
)
{
PADDLE_ENFORCE
(
it
->
second
.
IsInitialized
(),
"OpProto must all be initialized"
);
std
::
string
str
;
PADDLE_ENFORCE
(
it
->
second
.
SerializeToString
(
&
str
),
"Serialize OpProto Error. This could be a bug of Paddle."
);
ret_values
.
push_back
(
py
::
bytes
(
str
));
}
return
ret_values
;
});
m
.
def_submodule
(
"var_names"
,
"The module will return special predefined variable name in Paddle"
)
.
def
(
"empty"
,
pd
::
OperatorBase
::
EMPTY_VAR_NAME
)
.
def
(
"temp"
,
pd
::
OperatorBase
::
TMP_VAR_NAME
);
// clang-format off
py
::
class_
<
paddle
::
platform
::
DeviceContext
>
(
m
,
"DeviceContext"
)
.
def_static
(
"create"
,
[](
paddle
::
platform
::
CPUPlace
&
place
)
->
paddle
::
platform
::
DeviceContext
*
{
return
new
paddle
::
platform
::
CPUDeviceContext
();
})
.
def_static
(
"create"
,
[](
paddle
::
platform
::
GPUPlace
&
place
)
->
paddle
::
platform
::
DeviceContext
*
{
#ifdef PADDLE_ONLY_CPU
PADDLE_THROW
(
"GPUPlace is not supported in CPU device."
);
#else
return
new
paddle
::
platform
::
CUDADeviceContext
(
place
);
#endif
});
// clang-format on
py
::
class_
<
paddle
::
platform
::
GPUPlace
>
(
m
,
"GPUPlace"
).
def
(
py
::
init
<
int
>
());
py
::
class_
<
paddle
::
platform
::
CPUPlace
>
(
m
,
"CPUPlace"
).
def
(
py
::
init
<>
());
py
::
class_
<
pd
::
OperatorBase
,
std
::
shared_ptr
<
pd
::
OperatorBase
>>
operator_base
(
m
,
"Operator"
);
operator_base
.
def_static
(
"create"
,
[](
py
::
bytes
protobin
)
{
pd
::
OpDesc
desc
;
PADDLE_ENFORCE
(
desc
.
ParsePartialFromString
(
protobin
),
"Cannot parse user input to OpDesc"
);
PADDLE_ENFORCE
(
desc
.
IsInitialized
(),
"User OpDesc is not initialized, reason %s"
,
desc
.
InitializationErrorString
());
return
pd
::
OpRegistry
::
CreateOp
(
desc
);
});
ExposeOperator
(
operator_base
);
py
::
class_
<
pd
::
NetOp
,
std
::
shared_ptr
<
pd
::
NetOp
>>
net
(
m
,
"Net"
);
net
.
def_static
(
"create"
,
[]()
->
std
::
shared_ptr
<
pd
::
NetOp
>
{
auto
retv
=
std
::
make_shared
<
pd
::
NetOp
>
();
retv
->
type_
=
"plain_net"
;
return
retv
;
})
.
def
(
"add_op"
,
&
pd
::
NetOp
::
AddOp
)
.
def
(
"add_op"
,
[](
pd
::
NetOp
&
self
,
const
std
::
shared_ptr
<
pd
::
NetOp
>&
net
)
->
void
{
self
.
AddOp
(
std
::
static_pointer_cast
<
pd
::
OperatorBase
>
(
net
));
})
.
def
(
"complete_add_op"
,
&
pd
::
NetOp
::
CompleteAddOp
)
.
def
(
"complete_add_op"
,
[](
std
::
shared_ptr
<
pd
::
NetOp
>&
self
)
{
self
->
CompleteAddOp
();
});
ExposeOperator
(
net
);
m
.
def
(
"unique_integer"
,
UniqueIntegerGenerator
);
m
.
def
(
"is_compile_gpu"
,
IsCompileGPU
);
return
m
.
ptr
();
}
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