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体验新版 GitCode,发现更多精彩内容 >>
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d1220f23
编写于
1月 11, 2019
作者:
X
Xin Pan
提交者:
GitHub
1月 11, 2019
浏览文件
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差异文件
Merge pull request #15229 from panyx0718/imperative
support python codes in the imperative model
上级
576c740d
9597fd05
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
393 addition
and
60 deletion
+393
-60
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+98
-28
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+40
-5
paddle/fluid/imperative/tracer.h
paddle/fluid/imperative/tracer.h
+54
-2
paddle/fluid/pybind/imperative.cc
paddle/fluid/pybind/imperative.cc
+3
-1
paddle/fluid/pybind/imperative.h
paddle/fluid/pybind/imperative.h
+1
-5
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+35
-7
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+4
-1
python/paddle/fluid/imperative/layers.py
python/paddle/fluid/imperative/layers.py
+53
-3
python/paddle/fluid/imperative/nn.py
python/paddle/fluid/imperative/nn.py
+3
-3
python/paddle/fluid/tests/unittests/test_imperative.py
python/paddle/fluid/tests/unittests/test_imperative.py
+100
-3
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
...paddle/fluid/tests/unittests/test_imperative_optimizer.py
+2
-2
未找到文件。
paddle/fluid/imperative/layer.cc
浏览文件 @
d1220f23
...
@@ -27,6 +27,8 @@
...
@@ -27,6 +27,8 @@
namespace
paddle
{
namespace
paddle
{
namespace
imperative
{
namespace
imperative
{
std
::
map
<
int
,
py
::
object
>
py_funcs_
;
using
framework
::
Variable
;
using
framework
::
Variable
;
void
AddTo
(
Variable
*
src
,
Variable
*
dst
)
{
void
AddTo
(
Variable
*
src
,
Variable
*
dst
)
{
...
@@ -55,6 +57,7 @@ class Autograd {
...
@@ -55,6 +57,7 @@ class Autograd {
if
(
var
->
stop_gradient_
)
{
if
(
var
->
stop_gradient_
)
{
return
;
return
;
}
}
VLOG
(
3
)
<<
"start autograd"
;
std
::
deque
<
OpBase
*>
ready
;
std
::
deque
<
OpBase
*>
ready
;
ready
.
push_back
(
var
->
pre_op_
);
ready
.
push_back
(
var
->
pre_op_
);
...
@@ -120,51 +123,57 @@ framework::LoDTensor& VarBase::Grad() {
...
@@ -120,51 +123,57 @@ framework::LoDTensor& VarBase::Grad() {
}
}
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
OpBase
::
ApplyGrad
()
{
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
OpBase
::
ApplyGrad
()
{
if
(
!
grad_op_desc_
)
{
if
(
!
grad_op_desc_
&&
backward_id_
<=
0
)
{
LOG
(
WARNING
)
<<
"op with no grad: "
<<
op_desc_
->
Type
();
LOG
(
WARNING
)
<<
"op with no grad: "
<<
op_desc_
->
Type
();
return
{};
return
{};
}
}
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc_
->
Type
();
std
::
vector
<
std
::
unique_ptr
<
framework
::
Variable
>>
tmp_vars
;
std
::
map
<
std
::
string
,
std
::
vector
<
framework
::
Variable
*>>
grad_outputs
;
std
::
map
<
std
::
string
,
std
::
vector
<
framework
::
Variable
*>>
grad_outputs
;
for
(
auto
it
:
grad_output_vars_
)
{
if
(
backward_id_
>
0
)
{
auto
&
outputs
=
grad_outputs
[
it
.
first
];
VLOG
(
3
)
<<
"py_layer_grad"
;
for
(
size_t
i
=
0
;
i
<
it
.
second
.
size
();
++
i
)
{
grad_outputs
[
"Out@GRAD"
]
=
// Allocate a new variable
PyLayer
::
ApplyGrad
(
backward_id_
,
grad_input_vars_
[
"X@GRAD"
]);
Variable
*
tmp_var
=
new
framework
::
Variable
();
}
else
{
tmp_var
->
GetMutable
<
framework
::
LoDTensor
>
();
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc_
->
Type
();
for
(
auto
it
:
grad_output_vars_
)
{
tmp_vars
.
emplace_back
(
tmp_var
);
auto
&
outputs
=
grad_outputs
[
it
.
first
];
outputs
.
push_back
(
tmp_var
);
for
(
size_t
i
=
0
;
i
<
it
.
second
.
size
();
++
i
)
{
// Allocate a new variable
Variable
*
tmp_var
=
new
framework
::
Variable
();
tmp_var
->
GetMutable
<
framework
::
LoDTensor
>
();
outputs
.
push_back
(
tmp_var
);
}
}
}
}
framework
::
RuntimeContext
ctx
(
grad_input_vars_
,
grad_outputs
);
framework
::
RuntimeContext
ctx
(
grad_input_vars_
,
grad_outputs
);
// No need to do compile time infer shape here.
// No need to do compile time infer shape here.
// grad_op_desc_->InferShape(*block_);
// grad_op_desc_->InferShape(*block_);
grad_op_desc_
->
InferVarType
(
block_
);
grad_op_desc_
->
InferVarType
(
block_
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
framework
::
OpRegistry
::
CreateOp
(
*
grad_op_desc_
);
framework
::
OpRegistry
::
CreateOp
(
*
grad_op_desc_
);
framework
::
OperatorWithKernel
*
op_kernel
=
framework
::
OperatorWithKernel
*
op_kernel
=
dynamic_cast
<
framework
::
OperatorWithKernel
*>
(
opbase
.
get
());
dynamic_cast
<
framework
::
OperatorWithKernel
*>
(
opbase
.
get
());
PADDLE_ENFORCE_NOT_NULL
(
op_kernel
,
"only support op with kernel"
);
PADDLE_ENFORCE_NOT_NULL
(
op_kernel
,
"only support op with kernel"
);
framework
::
Scope
scope
;
framework
::
Scope
scope
;
platform
::
CPUPlace
place
;
platform
::
CPUPlace
place
;
PreparedOp
p
=
PreparedOp
::
Prepare
(
ctx
,
*
op_kernel
,
place
);
PreparedOp
p
=
PreparedOp
::
Prepare
(
ctx
,
*
op_kernel
,
place
);
p
.
op
.
RuntimeInferShape
(
scope
,
place
,
ctx
);
p
.
op
.
RuntimeInferShape
(
scope
,
place
,
ctx
);
p
.
func
(
framework
::
ExecutionContext
(
p
.
op
,
scope
,
*
p
.
dev_ctx
,
p
.
ctx
));
p
.
func
(
framework
::
ExecutionContext
(
p
.
op
,
scope
,
*
p
.
dev_ctx
,
p
.
ctx
));
}
for
(
auto
it
:
grad_output_vars_
)
{
for
(
auto
it
:
grad_output_vars_
)
{
auto
&
outputs
=
grad_outputs
[
it
.
first
];
auto
&
outputs
=
grad_outputs
[
it
.
first
];
auto
&
origin_outputs
=
it
.
second
;
auto
&
origin_outputs
=
it
.
second
;
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
origin_outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
framework
::
Variable
*
grad
=
outputs
[
i
];
framework
::
Variable
*
orig_grad
=
origin_outputs
[
i
];
framework
::
Variable
*
orig_grad
=
origin_outputs
[
i
];
AddTo
(
outputs
[
i
],
orig_grad
);
AddTo
(
grad
,
orig_grad
);
delete
grad
;
}
}
}
}
return
input_vars_
;
return
input_vars_
;
...
@@ -173,6 +182,7 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
...
@@ -173,6 +182,7 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
void
VarBase
::
RunBackward
()
{
void
VarBase
::
RunBackward
()
{
if
(
!
pre_op_
)
return
;
if
(
!
pre_op_
)
return
;
VLOG
(
3
)
<<
"start backward"
;
auto
grads_t
=
grads_
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
grads_t
=
grads_
->
GetMutable
<
framework
::
LoDTensor
>
();
float
*
data
=
grads_t
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
float
*
data
=
grads_t
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
std
::
fill
(
data
,
data
+
grads_t
->
numel
(),
1.0
);
std
::
fill
(
data
,
data
+
grads_t
->
numel
(),
1.0
);
...
@@ -183,5 +193,65 @@ void VarBase::RunBackward() {
...
@@ -183,5 +193,65 @@ void VarBase::RunBackward() {
Autograd
().
RunBackward
(
this
);
Autograd
().
RunBackward
(
this
);
}
}
void
PyLayer
::
RegisterFunc
(
int
func_id
,
const
py
::
object
&
py_func
)
{
py_funcs_
[
func_id
]
=
py_func
;
}
int
PyLayer
::
NumFuncs
()
{
return
py_funcs_
.
size
();
}
std
::
vector
<
VarBase
*>
PyLayer
::
Apply
(
int
func_id
,
const
std
::
vector
<
VarBase
*>&
inputs
)
{
std
::
vector
<
framework
::
Variable
*>
invars
;
for
(
const
VarBase
*
in
:
inputs
)
{
invars
.
push_back
(
in
->
var_
);
}
PADDLE_ENFORCE
(
py_funcs_
.
find
(
func_id
)
!=
py_funcs_
.
end
());
std
::
vector
<
Variable
*>
outvars
=
CallPythonFunc
(
py_funcs_
[
func_id
],
invars
);
std
::
vector
<
VarBase
*>
ret
;
for
(
Variable
*
v
:
outvars
)
{
ret
.
push_back
(
new
VarBase
(
v
,
new
Variable
()));
}
return
ret
;
}
std
::
vector
<
Variable
*>
PyLayer
::
ApplyGrad
(
int
func_id
,
const
std
::
vector
<
framework
::
Variable
*>&
inputs
)
{
PADDLE_ENFORCE
(
py_funcs_
.
find
(
func_id
)
!=
py_funcs_
.
end
());
return
CallPythonFunc
(
py_funcs_
[
func_id
],
inputs
);
}
std
::
vector
<
framework
::
Variable
*>
PyLayer
::
CallPythonFunc
(
const
py
::
object
&
callable
,
const
std
::
vector
<
framework
::
Variable
*>&
ins
)
{
py
::
gil_scoped_acquire
guard
;
py
::
tuple
in_args
(
ins
.
size
());
for
(
size_t
i
=
0
;
i
<
ins
.
size
();
++
i
)
{
const
framework
::
LoDTensor
&
t
=
ins
[
i
]
->
Get
<
framework
::
LoDTensor
>
();
in_args
[
i
]
=
t
.
IsInitialized
()
?
py
::
cast
(
t
)
:
py
::
cast
(
nullptr
);
}
VLOG
(
3
)
<<
"pyfunc in "
<<
py
::
len
(
in_args
);
// TODO(panyx0718): Who owns the returned LoDTensor.
auto
ret
=
callable
(
in_args
);
auto
ret_tuple
=
py
::
cast
<
py
::
tuple
>
(
ret
);
size_t
ret_num
=
py
::
len
(
ret_tuple
);
std
::
vector
<
framework
::
Variable
*>
outs
;
VLOG
(
3
)
<<
"pyfunc out "
<<
ret_num
;
for
(
size_t
i
=
0
;
i
<
ret_num
;
++
i
)
{
try
{
auto
*
py_out_tensor
=
py
::
cast
<
framework
::
LoDTensor
*>
(
ret_tuple
[
i
]);
PADDLE_ENFORCE_NOT_NULL
(
py_out_tensor
,
"Output tensor %d should not be nullptr"
,
i
);
auto
*
var
=
new
framework
::
Variable
();
auto
*
tensor
=
var
->
GetMutable
<
framework
::
LoDTensor
>
();
tensor
->
ShareDataWith
(
*
py_out_tensor
);
tensor
->
set_lod
(
py_out_tensor
->
lod
());
outs
.
push_back
(
var
);
}
catch
(
py
::
cast_error
&
)
{
PADDLE_THROW
(
"The %d-th output must be LoDTensor"
,
i
);
}
}
return
outs
;
}
}
// namespace imperative
}
// namespace imperative
}
// namespace paddle
}
// namespace paddle
paddle/fluid/imperative/layer.h
浏览文件 @
d1220f23
...
@@ -17,6 +17,9 @@
...
@@ -17,6 +17,9 @@
#include <map>
#include <map>
#include <string>
#include <string>
#include <vector>
#include <vector>
#include "pybind11/pybind11.h"
#include "Python.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/framework/var_desc.h"
...
@@ -25,6 +28,8 @@
...
@@ -25,6 +28,8 @@
namespace
paddle
{
namespace
paddle
{
namespace
imperative
{
namespace
imperative
{
namespace
py
=
::
pybind11
;
class
PreparedOp
{
class
PreparedOp
{
public:
public:
PreparedOp
(
const
framework
::
OperatorBase
&
op
,
PreparedOp
(
const
framework
::
OperatorBase
&
op
,
...
@@ -82,12 +87,15 @@ class OpBase;
...
@@ -82,12 +87,15 @@ class OpBase;
class
VarBase
{
class
VarBase
{
public:
public:
VarBase
()
VarBase
()
:
VarBase
(
new
framework
::
Variable
(),
new
framework
::
Variable
())
{}
// Owns `var` and `grad`
VarBase
(
framework
::
Variable
*
var
,
framework
::
Variable
*
grad
)
:
pre_op_
(
nullptr
),
:
pre_op_
(
nullptr
),
pre_op_out_idx_
(
-
1
),
pre_op_out_idx_
(
-
1
),
var_desc_
(
nullptr
),
var_desc_
(
nullptr
),
var_
(
new
framework
::
Variable
()
),
var_
(
var
),
grads_
(
new
framework
::
Variable
()
),
grads_
(
grad
),
stop_gradient_
(
false
)
{}
stop_gradient_
(
false
)
{}
explicit
VarBase
(
bool
stop_gradient
)
explicit
VarBase
(
bool
stop_gradient
)
...
@@ -124,7 +132,11 @@ class VarBase {
...
@@ -124,7 +132,11 @@ class VarBase {
class
OpBase
{
class
OpBase
{
public:
public:
OpBase
()
:
op_desc_
(
nullptr
),
grad_op_desc_
(
nullptr
)
{}
OpBase
()
:
op_desc_
(
nullptr
),
forward_id_
(
-
1
),
grad_op_desc_
(
nullptr
),
backward_id_
(
-
1
)
{}
virtual
~
OpBase
()
{
virtual
~
OpBase
()
{
if
(
grad_op_desc_
)
delete
grad_op_desc_
;
if
(
grad_op_desc_
)
delete
grad_op_desc_
;
...
@@ -132,8 +144,14 @@ class OpBase {
...
@@ -132,8 +144,14 @@ class OpBase {
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
ApplyGrad
();
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
ApplyGrad
();
// One of `op_desc_` or `forward_id_` is set, not both.
// For pure python PyLayer, use `forward_id_`, otherwise, use op_desc_.
framework
::
OpDesc
*
op_desc_
;
framework
::
OpDesc
*
op_desc_
;
int
forward_id_
;
// When has backward, one of `grad_op_desc_` or `backward_id_` is set,
// not both.
framework
::
OpDesc
*
grad_op_desc_
;
framework
::
OpDesc
*
grad_op_desc_
;
int
backward_id_
;
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
input_vars_
;
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
input_vars_
;
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
output_vars_
;
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
output_vars_
;
...
@@ -153,8 +171,25 @@ class Layer {
...
@@ -153,8 +171,25 @@ class Layer {
std
::
vector
<
VarBase
>
vars
;
std
::
vector
<
VarBase
>
vars
;
return
vars
;
return
vars
;
}
}
};
class
PyLayer
{
public:
virtual
~
PyLayer
()
{}
static
void
RegisterFunc
(
int
func_id
,
const
py
::
object
&
py_func
);
static
int
NumFuncs
();
static
std
::
vector
<
VarBase
*>
Apply
(
int
func_id
,
const
std
::
vector
<
VarBase
*>&
inputs
);
static
std
::
vector
<
framework
::
Variable
*>
ApplyGrad
(
int
func_id
,
const
std
::
vector
<
framework
::
Variable
*>&
inputs
);
virtual
void
Backward
()
{
LOG
(
ERROR
)
<<
"To support customize"
;
}
private:
static
std
::
vector
<
framework
::
Variable
*>
CallPythonFunc
(
const
py
::
object
&
callable
,
const
std
::
vector
<
framework
::
Variable
*>&
ins
);
};
};
}
// namespace imperative
}
// namespace imperative
...
...
paddle/fluid/imperative/tracer.h
浏览文件 @
d1220f23
...
@@ -131,8 +131,10 @@ class Tracer {
...
@@ -131,8 +131,10 @@ class Tracer {
if
(
!
stop_gradient
)
{
if
(
!
stop_gradient
)
{
framework
::
OpDesc
*
grad_op_desc
;
framework
::
OpDesc
*
grad_op_desc
;
auto
grad_to_var
=
new
std
::
unordered_map
<
std
::
string
,
std
::
string
>
();
// TODO(panyx): Is this leaked?
CreateGradOp
(
*
op_desc
,
{},
{
block
},
&
grad_op_desc
,
grad_to_var
);
std
::
unique_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
string
>>
grad_to_var
(
new
std
::
unordered_map
<
std
::
string
,
std
::
string
>
());
CreateGradOp
(
*
op_desc
,
{},
{
block
},
&
grad_op_desc
,
grad_to_var
.
get
());
op
->
grad_op_desc_
=
grad_op_desc
;
op
->
grad_op_desc_
=
grad_op_desc
;
for
(
auto
it
:
grad_op_desc
->
Inputs
())
{
for
(
auto
it
:
grad_op_desc
->
Inputs
())
{
...
@@ -143,12 +145,14 @@ class Tracer {
...
@@ -143,12 +145,14 @@ class Tracer {
if
(
var_it
==
grad_to_var
->
end
())
{
if
(
var_it
==
grad_to_var
->
end
())
{
auto
fwd_var_it
=
vars
.
find
(
grad_invar
);
auto
fwd_var_it
=
vars
.
find
(
grad_invar
);
PADDLE_ENFORCE
(
fwd_var_it
!=
vars
.
end
());
PADDLE_ENFORCE
(
fwd_var_it
!=
vars
.
end
());
// Forward inputs or outputs.
grad_in_vars
.
push_back
(
fwd_var_it
->
second
->
var_
);
grad_in_vars
.
push_back
(
fwd_var_it
->
second
->
var_
);
}
else
{
}
else
{
VarBase
*
var
=
vars
[
var_it
->
second
];
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
IsInitialized
())
{
if
(
!
var
->
grads_
->
IsInitialized
())
{
InitVar
(
var
->
var_
,
var
->
grads_
);
InitVar
(
var
->
var_
,
var
->
grads_
);
}
}
// Douts.
grad_in_vars
.
push_back
(
var
->
grads_
);
grad_in_vars
.
push_back
(
var
->
grads_
);
}
}
}
}
...
@@ -172,6 +176,54 @@ class Tracer {
...
@@ -172,6 +176,54 @@ class Tracer {
op
->
block_
=
block
;
op
->
block_
=
block
;
}
}
std
::
vector
<
VarBase
*>
PyTrace
(
OpBase
*
op
,
const
std
::
vector
<
VarBase
*>&
inputs
,
bool
stop_gradient
=
false
)
{
VLOG
(
3
)
<<
"py_trace"
;
op
->
input_vars_
[
"X"
]
=
inputs
;
op
->
output_vars_
[
"Out"
]
=
PyLayer
::
Apply
(
op
->
forward_id_
,
inputs
);
for
(
VarBase
*
inp
:
inputs
)
{
if
(
inp
->
pre_op_
)
{
op
->
pre_ops_
[
"X"
].
push_back
(
inp
->
pre_op_
);
op
->
pre_ops_out_idx_
[
"X"
].
push_back
(
inp
->
pre_op_out_idx_
);
}
else
{
op
->
pre_ops_
[
"X"
].
push_back
(
nullptr
);
}
}
auto
&
outputs
=
op
->
output_vars_
[
"Out"
];
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
VarBase
*
out
=
outputs
[
i
];
out
->
stop_gradient_
=
stop_gradient
;
out
->
pre_op_
=
op
;
out
->
pre_op_out_name_
=
"Out"
;
out
->
pre_op_out_idx_
=
i
;
}
if
(
!
stop_gradient
)
{
auto
&
grad_input_vars
=
op
->
grad_input_vars_
[
"X@GRAD"
];
auto
&
grad_output_vars
=
op
->
grad_output_vars_
[
"Out@GRAD"
];
for
(
const
VarBase
*
inp
:
inputs
)
{
grad_input_vars
.
push_back
(
inp
->
var_
);
}
for
(
VarBase
*
out
:
outputs
)
{
grad_input_vars
.
push_back
(
out
->
var_
);
}
for
(
VarBase
*
out
:
outputs
)
{
grad_input_vars
.
push_back
(
out
->
grads_
);
if
(
!
grad_input_vars
.
back
()
->
IsInitialized
())
{
InitVar
(
out
->
var_
,
grad_input_vars
.
back
());
}
}
for
(
const
VarBase
*
inp
:
inputs
)
{
grad_output_vars
.
push_back
(
inp
->
grads_
);
if
(
!
grad_output_vars
.
back
()
->
IsInitialized
())
{
InitVar
(
inp
->
var_
,
grad_output_vars
.
back
());
}
}
}
return
outputs
;
}
private:
private:
framework
::
BlockDesc
*
root_block_
;
framework
::
BlockDesc
*
root_block_
;
};
};
...
...
paddle/fluid/pybind/imperative.cc
浏览文件 @
d1220f23
...
@@ -26,7 +26,9 @@ void BindTracer(pybind11::module *m) {
...
@@ -26,7 +26,9 @@ void BindTracer(pybind11::module *m) {
[](
imperative
::
Tracer
&
self
,
framework
::
BlockDesc
*
root_block
)
{
[](
imperative
::
Tracer
&
self
,
framework
::
BlockDesc
*
root_block
)
{
new
(
&
self
)
imperative
::
Tracer
(
root_block
);
new
(
&
self
)
imperative
::
Tracer
(
root_block
);
})
})
.
def
(
"trace"
,
&
imperative
::
Tracer
::
Trace
);
.
def
(
"trace"
,
&
imperative
::
Tracer
::
Trace
)
.
def
(
"py_trace"
,
&
imperative
::
Tracer
::
PyTrace
,
pybind11
::
return_value_policy
::
take_ownership
);
}
}
}
// namespace pybind
}
// namespace pybind
...
...
paddle/fluid/pybind/imperative.h
浏览文件 @
d1220f23
...
@@ -22,7 +22,7 @@ limitations under the License. */
...
@@ -22,7 +22,7 @@ limitations under the License. */
namespace
paddle
{
namespace
paddle
{
namespace
pybind
{
namespace
pybind
{
class
Py
Layer
:
public
imperative
::
Layer
{
class
Layer
:
public
imperative
::
Layer
{
public:
public:
using
imperative
::
Layer
::
Layer
;
// Inherit constructors
using
imperative
::
Layer
::
Layer
;
// Inherit constructors
...
@@ -31,10 +31,6 @@ class PyLayer : public imperative::Layer {
...
@@ -31,10 +31,6 @@ class PyLayer : public imperative::Layer {
PYBIND11_OVERLOAD
(
std
::
vector
<
imperative
::
VarBase
>
,
Layer
,
Forward
,
PYBIND11_OVERLOAD
(
std
::
vector
<
imperative
::
VarBase
>
,
Layer
,
Forward
,
inputs
);
// NOLINT
inputs
);
// NOLINT
}
}
void
Backward
()
override
{
PYBIND11_OVERLOAD
(
void
,
Layer
,
Backward
,
);
// NOLINT
}
};
};
class
PyOpBase
:
public
imperative
::
OpBase
{
class
PyOpBase
:
public
imperative
::
OpBase
{
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
d1220f23
...
@@ -169,16 +169,44 @@ PYBIND11_MODULE(core, m) {
...
@@ -169,16 +169,44 @@ PYBIND11_MODULE(core, m) {
self
.
op_desc_
=
op_desc
;
self
.
op_desc_
=
op_desc
;
}
}
},
},
py
::
return_value_policy
::
reference
)
.
def_property
(
"forward_id"
,
[](
const
imperative
::
OpBase
&
self
)
{
return
self
.
forward_id_
;
},
[](
imperative
::
OpBase
&
self
,
int
forward_id
)
{
self
.
forward_id_
=
forward_id
;
},
py
::
return_value_policy
::
reference
)
.
def_property
(
"backward_id"
,
[](
const
imperative
::
OpBase
&
self
)
{
return
self
.
backward_id_
;
},
[](
imperative
::
OpBase
&
self
,
int
backward_id
)
{
self
.
backward_id_
=
backward_id
;
},
py
::
return_value_policy
::
reference
);
py
::
return_value_policy
::
reference
);
py
::
class_
<
imperative
::
Layer
,
Py
Layer
/* <--- trampoline*/
>
layer
(
m
,
"Layer"
);
py
::
class_
<
imperative
::
Layer
,
Layer
/* <--- trampoline*/
>
layer
(
m
,
"Layer"
);
layer
.
def
(
py
::
init
<>
())
layer
.
def
(
py
::
init
<>
())
.
def
(
"forward"
,
.
def
(
"forward"
,
[](
imperative
::
Layer
&
self
,
[](
imperative
::
Layer
&
self
,
const
std
::
vector
<
imperative
::
VarBase
>
&
inputs
)
{
const
std
::
vector
<
imperative
::
VarBase
>
&
inputs
)
{
return
self
.
Forward
(
inputs
);
return
self
.
Forward
(
inputs
);
});
})
.
def
(
"backward"
,
&
imperative
::
Layer
::
Backward
);
py
::
class_
<
imperative
::
PyLayer
>
(
m
,
"PyLayer"
)
.
def
(
py
::
init
<>
())
.
def_static
(
"apply"
,
[](
int
func_id
,
const
std
::
vector
<
imperative
::
VarBase
*>
&
inputs
)
->
std
::
vector
<
imperative
::
VarBase
*>
{
return
imperative
::
PyLayer
::
Apply
(
func_id
,
inputs
);
},
py
::
return_value_policy
::
take_ownership
)
.
def_static
(
"register_func"
,
[](
int
func_id
,
const
py
::
object
&
callable
)
{
imperative
::
PyLayer
::
RegisterFunc
(
func_id
,
callable
);
})
.
def_static
(
"num_funcs"
,
&
imperative
::
PyLayer
::
NumFuncs
);
BindTracer
(
&
m
);
BindTracer
(
&
m
);
py
::
class_
<
Tensor
>
(
m
,
"Tensor"
,
py
::
buffer_protocol
())
py
::
class_
<
Tensor
>
(
m
,
"Tensor"
,
py
::
buffer_protocol
())
...
...
python/paddle/fluid/framework.py
浏览文件 @
d1220f23
...
@@ -372,7 +372,10 @@ class Variable(object):
...
@@ -372,7 +372,10 @@ class Variable(object):
self
.
stop_gradient
=
stop_gradient
self
.
stop_gradient
=
stop_gradient
self
.
is_data
=
is_data
self
.
is_data
=
is_data
if
_in_imperative_mode
():
if
_in_imperative_mode
():
self
.
_ivar
=
core
.
VarBase
()
if
'ivar'
in
kwargs
:
self
.
_ivar
=
kwargs
[
'ivar'
]
else
:
self
.
_ivar
=
core
.
VarBase
()
self
.
_ivar
.
desc
=
self
.
desc
self
.
_ivar
.
desc
=
self
.
desc
self
.
_ivar
.
stop_gradient
=
stop_gradient
self
.
_ivar
.
stop_gradient
=
stop_gradient
...
...
python/paddle/fluid/imperative/layers.py
浏览文件 @
d1220f23
...
@@ -20,10 +20,12 @@ from paddle.fluid import core
...
@@ -20,10 +20,12 @@ from paddle.fluid import core
from
paddle.fluid
import
framework
from
paddle.fluid
import
framework
from
paddle.fluid.imperative
import
base
from
paddle.fluid.imperative
import
base
__all__
=
[
'PyLayer'
]
__all__
=
[
'
Layer'
,
'
PyLayer'
]
class
PyLayer
(
core
.
Layer
):
class
Layer
(
core
.
Layer
):
"""Layers composed of operators."""
def
__init__
(
self
,
dtype
=
core
.
VarDesc
.
VarType
.
FP32
,
name
=
None
):
def
__init__
(
self
,
dtype
=
core
.
VarDesc
.
VarType
.
FP32
,
name
=
None
):
self
.
_once_built
=
False
self
.
_once_built
=
False
self
.
_dtype
=
dtype
self
.
_dtype
=
dtype
...
@@ -37,8 +39,56 @@ class PyLayer(core.Layer):
...
@@ -37,8 +39,56 @@ class PyLayer(core.Layer):
self
.
_once_built
=
True
self
.
_once_built
=
True
outputs
=
self
.
forward
(
*
inputs
)
outputs
=
self
.
forward
(
*
inputs
)
return
outputs
return
outputs
def
forward
(
self
,
*
inputs
):
def
forward
(
self
,
*
inputs
):
raise
NotImplementedError
raise
NotImplementedError
def
backward
(
self
,
*
inputs
):
raise
ValueError
(
"Layer shouldn't implement backward"
)
class
PyLayer
(
core
.
PyLayer
):
"""Layers composed of user-defined python codes."""
def
__init__
(
self
):
super
(
PyLayer
,
self
).
__init__
()
@
staticmethod
def
forward
(
inputs
):
raise
NotImplementedError
@
staticmethod
def
backward
(
douts
):
raise
NotImplementedError
@
classmethod
def
__call__
(
cls
,
inputs
):
tracer
=
framework
.
_imperative_tracer
()
block
=
framework
.
default_main_program
().
current_block
()
inputs
=
[
x
.
_ivar
for
x
in
inputs
]
if
not
hasattr
(
cls
,
'forward_id'
):
cls
.
forward_id
=
core
.
PyLayer
.
num_funcs
()
+
1
PyLayer
.
register_func
(
cls
.
forward_id
,
cls
.
forward
)
cls
.
backward_id
=
core
.
PyLayer
.
num_funcs
()
+
1
PyLayer
.
register_func
(
cls
.
backward_id
,
cls
.
backward
)
iop
=
core
.
OpBase
()
iop
.
forward_id
=
cls
.
forward_id
iop
.
backward_id
=
cls
.
backward_id
block
.
ops
.
append
(
iop
)
ivars
=
tracer
.
py_trace
(
iop
,
inputs
,
False
)
# ivars = core.PyLayer.apply(cls.forward, inputs)
ret
=
[]
for
ivar
in
ivars
:
tensor
=
ivar
.
value
.
get_tensor
()
py_var
=
framework
.
Variable
(
block
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
name
=
None
,
shape
=
tensor
.
shape
(),
dtype
=
tensor
.
_dtype
(),
ivar
=
ivar
)
ret
.
append
(
py_var
)
return
ret
python/paddle/fluid/imperative/nn.py
浏览文件 @
d1220f23
...
@@ -30,7 +30,7 @@ __all__ = [
...
@@ -30,7 +30,7 @@ __all__ = [
]
]
class
Conv2D
(
layers
.
Py
Layer
):
class
Conv2D
(
layers
.
Layer
):
def
__init__
(
self
,
def
__init__
(
self
,
num_channels
,
num_channels
,
num_filters
,
num_filters
,
...
@@ -143,7 +143,7 @@ class Conv2D(layers.PyLayer):
...
@@ -143,7 +143,7 @@ class Conv2D(layers.PyLayer):
return
self
.
_helper
.
append_activation
(
pre_act
)
return
self
.
_helper
.
append_activation
(
pre_act
)
class
Pool2D
(
layers
.
Py
Layer
):
class
Pool2D
(
layers
.
Layer
):
def
__init__
(
self
,
def
__init__
(
self
,
pool_size
=-
1
,
pool_size
=-
1
,
pool_type
=
"max"
,
pool_type
=
"max"
,
...
@@ -205,7 +205,7 @@ class Pool2D(layers.PyLayer):
...
@@ -205,7 +205,7 @@ class Pool2D(layers.PyLayer):
return
pool_out
return
pool_out
class
FC
(
layers
.
Py
Layer
):
class
FC
(
layers
.
Layer
):
def
__init__
(
self
,
def
__init__
(
self
,
size
,
size
,
param_attr
=
None
,
param_attr
=
None
,
...
...
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @
d1220f23
...
@@ -15,6 +15,7 @@
...
@@ -15,6 +15,7 @@
import
contextlib
import
contextlib
import
unittest
import
unittest
import
numpy
as
np
import
numpy
as
np
import
sys
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid
import
core
...
@@ -22,7 +23,7 @@ from paddle.fluid.imperative.nn import FC
...
@@ -22,7 +23,7 @@ from paddle.fluid.imperative.nn import FC
from
test_imperative_base
import
new_program_scope
from
test_imperative_base
import
new_program_scope
class
MyLayer
(
fluid
.
imperative
.
Py
Layer
):
class
MyLayer
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
):
def
__init__
(
self
):
super
(
MyLayer
,
self
).
__init__
()
super
(
MyLayer
,
self
).
__init__
()
...
@@ -34,7 +35,35 @@ class MyLayer(fluid.imperative.PyLayer):
...
@@ -34,7 +35,35 @@ class MyLayer(fluid.imperative.PyLayer):
return
[
x
]
return
[
x
]
class
MLP
(
fluid
.
imperative
.
PyLayer
):
class
MyPyLayer
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
MyPyLayer
,
self
).
__init__
()
@
staticmethod
def
forward
(
inputs
):
sys
.
stderr
.
write
(
'before forward
\n
'
)
ret
=
np
.
tanh
(
inputs
[
0
])
sys
.
stderr
.
write
(
'after forward: %s
\n
'
%
ret
)
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
ret
,
core
.
CPUPlace
())
return
tuple
([
tensor
])
@
staticmethod
def
backward
(
inputs
):
sys
.
stderr
.
write
(
'calling into backward: %s
\n
'
%
str
(
inputs
))
inp
,
out
,
dout
=
inputs
inp
=
np
.
array
(
inp
)
out
=
np
.
array
(
out
)
dout
=
np
.
array
(
dout
)
sys
.
stderr
.
write
(
'calling into backward: %s, %s, %s
\n
'
%
(
inp
,
out
,
dout
))
ret
=
np
.
array
(
dout
)
*
(
1
-
np
.
square
(
np
.
array
(
out
)))
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
ret
,
core
.
CPUPlace
())
return
tuple
([
tensor
])
class
MLP
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
):
def
__init__
(
self
):
super
(
MLP
,
self
).
__init__
()
super
(
MLP
,
self
).
__init__
()
self
.
_fc1
=
FC
(
3
,
self
.
_fc1
=
FC
(
3
,
...
@@ -56,9 +85,77 @@ class TestImperative(unittest.TestCase):
...
@@ -56,9 +85,77 @@ class TestImperative(unittest.TestCase):
with
fluid
.
imperative
.
guard
():
with
fluid
.
imperative
.
guard
():
cl
=
core
.
Layer
()
cl
=
core
.
Layer
()
cl
.
forward
([])
cl
.
forward
([])
l
=
fluid
.
imperative
.
Py
Layer
()
l
=
fluid
.
imperative
.
Layer
()
self
.
assertRaises
(
NotImplementedError
,
l
.
forward
,
[])
self
.
assertRaises
(
NotImplementedError
,
l
.
forward
,
[])
def
test_pylayer_func_id
(
self
):
with
fluid
.
imperative
.
guard
():
class
PyLayer1
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
PyLayer1
,
self
).
__init__
()
@
staticmethod
def
forward
(
inputs
):
return
inputs
@
staticmethod
def
backward
(
inputs
):
return
inputs
class
PyLayer2
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
PyLayer2
,
self
).
__init__
()
@
staticmethod
def
forward
(
inputs
):
return
inputs
@
staticmethod
def
backward
(
inputs
):
return
inputs
py_layer_1
=
PyLayer1
()
py_layer_2
=
PyLayer2
()
py_layer_1
([
fluid
.
imperative
.
base
.
to_variable
(
np
.
ones
([
2
,
2
]))])
py_layer_2
([
fluid
.
imperative
.
base
.
to_variable
(
np
.
ones
([
2
,
2
]))])
id
=
py_layer_1
.
forward_id
self
.
assertGreater
(
id
,
0
)
self
.
assertEqual
(
py_layer_1
.
backward_id
,
id
+
1
)
self
.
assertEqual
(
py_layer_2
.
forward_id
,
id
+
2
)
self
.
assertEqual
(
py_layer_2
.
backward_id
,
id
+
3
)
py_layer_1
([
fluid
.
imperative
.
base
.
to_variable
(
np
.
ones
([
2
,
2
]))])
self
.
assertEqual
(
py_layer_1
.
forward_id
,
id
)
def
test_pylayer
(
self
):
np_inp
=
np
.
ones
([
2
,
2
],
np
.
float32
)
with
fluid
.
imperative
.
guard
():
my_py_layer
=
MyPyLayer
()
var_inp
=
fluid
.
imperative
.
base
.
to_variable
(
np_inp
)
outs
=
my_py_layer
([
var_inp
])
dy_out
=
np
.
sum
(
outs
[
0
].
_numpy
())
outs
[
0
].
_backward
()
dy_grad
=
var_inp
.
_gradient
()
with
new_program_scope
():
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
2
,
2
],
append_batch_size
=
False
)
# TODO(panyx0718): Paddle doesn't diff against data `inp`.
x1
=
inp
*
1
# TODO(panyx0718): If reduce_sum is skipped, the result is wrong.
x
=
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
tanh
(
x1
))
param_grads
=
fluid
.
backward
.
append_backward
(
x
,
parameter_list
=
[
x1
.
name
])[
0
]
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
static_out
,
static_grad
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
x
.
name
,
param_grads
[
1
].
name
])
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad
,
static_grad
))
def
test_layer_in_out
(
self
):
def
test_layer_in_out
(
self
):
np_inp
=
np
.
array
([
1.0
,
2.0
,
-
1.0
],
dtype
=
np
.
float32
)
np_inp
=
np
.
array
([
1.0
,
2.0
,
-
1.0
],
dtype
=
np
.
float32
)
with
fluid
.
imperative
.
guard
():
with
fluid
.
imperative
.
guard
():
...
...
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @
d1220f23
...
@@ -26,7 +26,7 @@ from paddle.fluid.imperative.base import to_variable
...
@@ -26,7 +26,7 @@ from paddle.fluid.imperative.base import to_variable
from
test_imperative_base
import
new_program_scope
from
test_imperative_base
import
new_program_scope
class
SimpleImgConvPool
(
fluid
.
imperative
.
Py
Layer
):
class
SimpleImgConvPool
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
def
__init__
(
self
,
num_channels
,
num_channels
,
num_filters
,
num_filters
,
...
@@ -72,7 +72,7 @@ class SimpleImgConvPool(fluid.imperative.PyLayer):
...
@@ -72,7 +72,7 @@ class SimpleImgConvPool(fluid.imperative.PyLayer):
return
x
return
x
class
MNIST
(
fluid
.
imperative
.
Py
Layer
):
class
MNIST
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
param_attr
=
None
,
bias_attr
=
None
):
def
__init__
(
self
,
param_attr
=
None
,
bias_attr
=
None
):
super
(
MNIST
,
self
).
__init__
()
super
(
MNIST
,
self
).
__init__
()
...
...
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