Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
urhero
Paddle
提交
d1220f23
P
Paddle
项目概览
urhero
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
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
操作
浏览文件
下载
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 @@
namespace
paddle
{
namespace
imperative
{
std
::
map
<
int
,
py
::
object
>
py_funcs_
;
using
framework
::
Variable
;
void
AddTo
(
Variable
*
src
,
Variable
*
dst
)
{
...
...
@@ -55,6 +57,7 @@ class Autograd {
if
(
var
->
stop_gradient_
)
{
return
;
}
VLOG
(
3
)
<<
"start autograd"
;
std
::
deque
<
OpBase
*>
ready
;
ready
.
push_back
(
var
->
pre_op_
);
...
...
@@ -120,51 +123,57 @@ framework::LoDTensor& VarBase::Grad() {
}
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
();
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
;
for
(
auto
it
:
grad_output_vars_
)
{
auto
&
outputs
=
grad_outputs
[
it
.
first
];
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
>
();
tmp_vars
.
emplace_back
(
tmp_var
);
outputs
.
push_back
(
tmp_var
);
if
(
backward_id_
>
0
)
{
VLOG
(
3
)
<<
"py_layer_grad"
;
grad_outputs
[
"Out@GRAD"
]
=
PyLayer
::
ApplyGrad
(
backward_id_
,
grad_input_vars_
[
"X@GRAD"
]);
}
else
{
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc_
->
Type
();
for
(
auto
it
:
grad_output_vars_
)
{
auto
&
outputs
=
grad_outputs
[
it
.
first
];
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.
// grad_op_desc_->InferShape(*block_);
grad_op_desc_
->
InferVarType
(
block_
);
// No need to do compile time infer shape here.
// grad_op_desc_->InferShape(*block_);
grad_op_desc_
->
InferVarType
(
block_
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
framework
::
OpRegistry
::
CreateOp
(
*
grad_op_desc_
);
framework
::
OperatorWithKernel
*
op_kernel
=
dynamic_cast
<
framework
::
OperatorWithKernel
*>
(
opbase
.
get
());
PADDLE_ENFORCE_NOT_NULL
(
op_kernel
,
"only support op with kernel"
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
framework
::
OpRegistry
::
CreateOp
(
*
grad_op_desc_
);
framework
::
OperatorWithKernel
*
op_kernel
=
dynamic_cast
<
framework
::
OperatorWithKernel
*>
(
opbase
.
get
());
PADDLE_ENFORCE_NOT_NULL
(
op_kernel
,
"only support op with kernel"
);
framework
::
Scope
scope
;
platform
::
CPUPlace
place
;
PreparedOp
p
=
PreparedOp
::
Prepare
(
ctx
,
*
op_kernel
,
place
);
p
.
op
.
RuntimeInferShape
(
scope
,
place
,
ctx
);
p
.
func
(
framework
::
ExecutionContext
(
p
.
op
,
scope
,
*
p
.
dev_ctx
,
p
.
ctx
));
framework
::
Scope
scope
;
platform
::
CPUPlace
place
;
PreparedOp
p
=
PreparedOp
::
Prepare
(
ctx
,
*
op_kernel
,
place
);
p
.
op
.
RuntimeInferShape
(
scope
,
place
,
ctx
);
p
.
func
(
framework
::
ExecutionContext
(
p
.
op
,
scope
,
*
p
.
dev_ctx
,
p
.
ctx
));
}
for
(
auto
it
:
grad_output_vars_
)
{
auto
&
outputs
=
grad_outputs
[
it
.
first
];
auto
&
origin_outputs
=
it
.
second
;
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
origin_outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
framework
::
Variable
*
grad
=
outputs
[
i
];
framework
::
Variable
*
orig_grad
=
origin_outputs
[
i
];
AddTo
(
outputs
[
i
],
orig_grad
);
AddTo
(
grad
,
orig_grad
);
delete
grad
;
}
}
return
input_vars_
;
...
...
@@ -173,6 +182,7 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
void
VarBase
::
RunBackward
()
{
if
(
!
pre_op_
)
return
;
VLOG
(
3
)
<<
"start backward"
;
auto
grads_t
=
grads_
->
GetMutable
<
framework
::
LoDTensor
>
();
float
*
data
=
grads_t
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
std
::
fill
(
data
,
data
+
grads_t
->
numel
(),
1.0
);
...
...
@@ -183,5 +193,65 @@ void VarBase::RunBackward() {
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 paddle
paddle/fluid/imperative/layer.h
浏览文件 @
d1220f23
...
...
@@ -17,6 +17,9 @@
#include <map>
#include <string>
#include <vector>
#include "pybind11/pybind11.h"
#include "Python.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_desc.h"
...
...
@@ -25,6 +28,8 @@
namespace
paddle
{
namespace
imperative
{
namespace
py
=
::
pybind11
;
class
PreparedOp
{
public:
PreparedOp
(
const
framework
::
OperatorBase
&
op
,
...
...
@@ -82,12 +87,15 @@ class OpBase;
class
VarBase
{
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_out_idx_
(
-
1
),
var_desc_
(
nullptr
),
var_
(
new
framework
::
Variable
()
),
grads_
(
new
framework
::
Variable
()
),
var_
(
var
),
grads_
(
grad
),
stop_gradient_
(
false
)
{}
explicit
VarBase
(
bool
stop_gradient
)
...
...
@@ -124,7 +132,11 @@ class VarBase {
class
OpBase
{
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
()
{
if
(
grad_op_desc_
)
delete
grad_op_desc_
;
...
...
@@ -132,8 +144,14 @@ class OpBase {
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_
;
int
forward_id_
;
// When has backward, one of `grad_op_desc_` or `backward_id_` is set,
// not both.
framework
::
OpDesc
*
grad_op_desc_
;
int
backward_id_
;
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
input_vars_
;
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
output_vars_
;
...
...
@@ -153,8 +171,25 @@ class Layer {
std
::
vector
<
VarBase
>
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
...
...
paddle/fluid/imperative/tracer.h
浏览文件 @
d1220f23
...
...
@@ -131,8 +131,10 @@ class Tracer {
if
(
!
stop_gradient
)
{
framework
::
OpDesc
*
grad_op_desc
;
auto
grad_to_var
=
new
std
::
unordered_map
<
std
::
string
,
std
::
string
>
();
CreateGradOp
(
*
op_desc
,
{},
{
block
},
&
grad_op_desc
,
grad_to_var
);
// TODO(panyx): Is this leaked?
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
;
for
(
auto
it
:
grad_op_desc
->
Inputs
())
{
...
...
@@ -143,12 +145,14 @@ class Tracer {
if
(
var_it
==
grad_to_var
->
end
())
{
auto
fwd_var_it
=
vars
.
find
(
grad_invar
);
PADDLE_ENFORCE
(
fwd_var_it
!=
vars
.
end
());
// Forward inputs or outputs.
grad_in_vars
.
push_back
(
fwd_var_it
->
second
->
var_
);
}
else
{
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
IsInitialized
())
{
InitVar
(
var
->
var_
,
var
->
grads_
);
}
// Douts.
grad_in_vars
.
push_back
(
var
->
grads_
);
}
}
...
...
@@ -172,6 +176,54 @@ class Tracer {
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:
framework
::
BlockDesc
*
root_block_
;
};
...
...
paddle/fluid/pybind/imperative.cc
浏览文件 @
d1220f23
...
...
@@ -26,7 +26,9 @@ void BindTracer(pybind11::module *m) {
[](
imperative
::
Tracer
&
self
,
framework
::
BlockDesc
*
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
...
...
paddle/fluid/pybind/imperative.h
浏览文件 @
d1220f23
...
...
@@ -22,7 +22,7 @@ limitations under the License. */
namespace
paddle
{
namespace
pybind
{
class
Py
Layer
:
public
imperative
::
Layer
{
class
Layer
:
public
imperative
::
Layer
{
public:
using
imperative
::
Layer
::
Layer
;
// Inherit constructors
...
...
@@ -31,10 +31,6 @@ class PyLayer : public imperative::Layer {
PYBIND11_OVERLOAD
(
std
::
vector
<
imperative
::
VarBase
>
,
Layer
,
Forward
,
inputs
);
// NOLINT
}
void
Backward
()
override
{
PYBIND11_OVERLOAD
(
void
,
Layer
,
Backward
,
);
// NOLINT
}
};
class
PyOpBase
:
public
imperative
::
OpBase
{
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
d1220f23
...
...
@@ -169,16 +169,44 @@ PYBIND11_MODULE(core, m) {
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
::
class_
<
imperative
::
Layer
,
Py
Layer
/* <--- trampoline*/
>
layer
(
m
,
"Layer"
);
py
::
class_
<
imperative
::
Layer
,
Layer
/* <--- trampoline*/
>
layer
(
m
,
"Layer"
);
layer
.
def
(
py
::
init
<>
())
.
def
(
"forward"
,
[](
imperative
::
Layer
&
self
,
const
std
::
vector
<
imperative
::
VarBase
>
&
inputs
)
{
return
self
.
Forward
(
inputs
);
})
.
def
(
"backward"
,
&
imperative
::
Layer
::
Backward
);
.
def
(
"forward"
,
[](
imperative
::
Layer
&
self
,
const
std
::
vector
<
imperative
::
VarBase
>
&
inputs
)
{
return
self
.
Forward
(
inputs
);
});
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
);
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):
self
.
stop_gradient
=
stop_gradient
self
.
is_data
=
is_data
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
.
stop_gradient
=
stop_gradient
...
...
python/paddle/fluid/imperative/layers.py
浏览文件 @
d1220f23
...
...
@@ -20,10 +20,12 @@ from paddle.fluid import core
from
paddle.fluid
import
framework
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
):
self
.
_once_built
=
False
self
.
_dtype
=
dtype
...
...
@@ -37,8 +39,56 @@ class PyLayer(core.Layer):
self
.
_once_built
=
True
outputs
=
self
.
forward
(
*
inputs
)
return
outputs
def
forward
(
self
,
*
inputs
):
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__ = [
]
class
Conv2D
(
layers
.
Py
Layer
):
class
Conv2D
(
layers
.
Layer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
...
...
@@ -143,7 +143,7 @@ class Conv2D(layers.PyLayer):
return
self
.
_helper
.
append_activation
(
pre_act
)
class
Pool2D
(
layers
.
Py
Layer
):
class
Pool2D
(
layers
.
Layer
):
def
__init__
(
self
,
pool_size
=-
1
,
pool_type
=
"max"
,
...
...
@@ -205,7 +205,7 @@ class Pool2D(layers.PyLayer):
return
pool_out
class
FC
(
layers
.
Py
Layer
):
class
FC
(
layers
.
Layer
):
def
__init__
(
self
,
size
,
param_attr
=
None
,
...
...
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @
d1220f23
...
...
@@ -15,6 +15,7 @@
import
contextlib
import
unittest
import
numpy
as
np
import
sys
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
...
...
@@ -22,7 +23,7 @@ from paddle.fluid.imperative.nn import FC
from
test_imperative_base
import
new_program_scope
class
MyLayer
(
fluid
.
imperative
.
Py
Layer
):
class
MyLayer
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
):
super
(
MyLayer
,
self
).
__init__
()
...
...
@@ -34,7 +35,35 @@ class MyLayer(fluid.imperative.PyLayer):
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
):
super
(
MLP
,
self
).
__init__
()
self
.
_fc1
=
FC
(
3
,
...
...
@@ -56,9 +85,77 @@ class TestImperative(unittest.TestCase):
with
fluid
.
imperative
.
guard
():
cl
=
core
.
Layer
()
cl
.
forward
([])
l
=
fluid
.
imperative
.
Py
Layer
()
l
=
fluid
.
imperative
.
Layer
()
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
):
np_inp
=
np
.
array
([
1.0
,
2.0
,
-
1.0
],
dtype
=
np
.
float32
)
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
from
test_imperative_base
import
new_program_scope
class
SimpleImgConvPool
(
fluid
.
imperative
.
Py
Layer
):
class
SimpleImgConvPool
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
...
...
@@ -72,7 +72,7 @@ class SimpleImgConvPool(fluid.imperative.PyLayer):
return
x
class
MNIST
(
fluid
.
imperative
.
Py
Layer
):
class
MNIST
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
param_attr
=
None
,
bias_attr
=
None
):
super
(
MNIST
,
self
).
__init__
()
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录