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体验新版 GitCode,发现更多精彩内容 >>
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6217f42a
编写于
12月 06, 2018
作者:
X
Xin Pan
提交者:
GitHub
12月 06, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Revert "Imperative"
上级
f6a877bc
变更
25
隐藏空白更改
内联
并排
Showing
25 changed file
with
19 addition
and
960 deletion
+19
-960
paddle/fluid/CMakeLists.txt
paddle/fluid/CMakeLists.txt
+0
-1
paddle/fluid/framework/feed_fetch_method.cc
paddle/fluid/framework/feed_fetch_method.cc
+0
-9
paddle/fluid/framework/feed_fetch_method.h
paddle/fluid/framework/feed_fetch_method.h
+0
-2
paddle/fluid/framework/ir/graph.cc
paddle/fluid/framework/ir/graph.cc
+3
-2
paddle/fluid/imperative/CMakeLists.txt
paddle/fluid/imperative/CMakeLists.txt
+0
-3
paddle/fluid/imperative/engine.cc
paddle/fluid/imperative/engine.cc
+0
-53
paddle/fluid/imperative/engine.h
paddle/fluid/imperative/engine.h
+0
-39
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+0
-221
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+0
-102
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+0
-19
paddle/fluid/imperative/tracer.h
paddle/fluid/imperative/tracer.h
+0
-128
paddle/fluid/pybind/CMakeLists.txt
paddle/fluid/pybind/CMakeLists.txt
+2
-3
paddle/fluid/pybind/imperative.cc
paddle/fluid/pybind/imperative.cc
+0
-36
paddle/fluid/pybind/imperative.h
paddle/fluid/pybind/imperative.h
+0
-53
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+0
-39
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+0
-2
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+2
-52
python/paddle/fluid/imperative/__init__.py
python/paddle/fluid/imperative/__init__.py
+0
-25
python/paddle/fluid/imperative/base.py
python/paddle/fluid/imperative/base.py
+0
-56
python/paddle/fluid/imperative/layers.py
python/paddle/fluid/imperative/layers.py
+0
-44
python/paddle/fluid/layer_helper.py
python/paddle/fluid/layer_helper.py
+11
-12
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+1
-2
python/paddle/fluid/tests/unittests/test_imperative.py
python/paddle/fluid/tests/unittests/test_imperative.py
+0
-52
python/setup.py.in
python/setup.py.in
+0
-1
tools/print_signatures.py
tools/print_signatures.py
+0
-4
未找到文件。
paddle/fluid/CMakeLists.txt
浏览文件 @
6217f42a
add_subdirectory
(
memory
)
add_subdirectory
(
platform
)
add_subdirectory
(
framework
)
add_subdirectory
(
imperative
)
add_subdirectory
(
operators
)
add_subdirectory
(
string
)
add_subdirectory
(
recordio
)
...
...
paddle/fluid/framework/feed_fetch_method.cc
浏览文件 @
6217f42a
...
...
@@ -16,9 +16,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "glog/logging.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
framework
{
...
...
@@ -55,12 +53,5 @@ LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
return
tensor
;
}
LoDTensor
&
GetVariableTensor
(
const
Scope
&
scope
,
const
std
::
string
&
var_name
)
{
Variable
*
var
=
scope
.
FindVar
(
var_name
);
PADDLE_ENFORCE
(
var
,
"%s no in scope"
,
var_name
);
PADDLE_ENFORCE
(
var
->
IsType
<
LoDTensor
>
(),
"Only support lod tensor now."
);
return
*
var
->
GetMutable
<
LoDTensor
>
();
}
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/feed_fetch_method.h
浏览文件 @
6217f42a
...
...
@@ -27,7 +27,5 @@ void SetFeedVariable(Scope* scope, const LoDTensor& input,
LoDTensor
&
GetFetchVariable
(
const
Scope
&
scope
,
const
std
::
string
&
var_name
,
size_t
index
);
LoDTensor
&
GetVariableTensor
(
const
Scope
&
scope
,
const
std
::
string
&
var_name
);
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/graph.cc
浏览文件 @
6217f42a
...
...
@@ -38,8 +38,9 @@ void CheckProgram(const ProgramDesc &program) {
switch
(
role_id
)
{
case
_INT
(
OpRole
::
kForward
):
if
(
visit
.
find
(
_INT
(
OpRole
::
kBackward
))
!=
visit
.
end
())
{
LOG
(
ERROR
)
<<
"Cannot add backward operator before forward operator "
<<
op
->
Type
();
LOG
(
ERROR
)
<<
"Cannot add backward operator before forward operator %s."
<<
op
->
Type
();
}
break
;
case
_INT
(
OpRole
::
kBackward
):
...
...
paddle/fluid/imperative/CMakeLists.txt
已删除
100644 → 0
浏览文件 @
f6a877bc
cc_library
(
layer SRCS layer.cc DEPS proto_desc operator
)
cc_library
(
tracer SRCS tracer.cc DEPS proto_desc
)
cc_library
(
engine SRCS engine.cc
)
paddle/fluid/imperative/engine.cc
已删除
100644 → 0
浏览文件 @
f6a877bc
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/imperative/engine.h"
#include <mutex> // NOLINT
#include <vector>
#include "glog/logging.h"
namespace
paddle
{
namespace
imperative
{
static
std
::
once_flag
init_engine
;
static
Engine
*
engine
;
class
DummyEngine
:
public
Engine
{
public:
void
Enqueue
(
Runnable
*
runnable
)
override
{
queued_runnables_
.
push_back
(
runnable
);
}
size_t
Size
()
const
override
{
return
queued_runnables_
.
size
();
}
void
Sync
()
override
{
for
(
Runnable
*
l
:
queued_runnables_
)
{
LOG
(
INFO
)
<<
"running "
<<
reinterpret_cast
<
void
*>
(
l
);
}
queued_runnables_
.
clear
();
}
private:
std
::
vector
<
Runnable
*>
queued_runnables_
;
};
Engine
*
GetEngine
()
{
std
::
call_once
(
init_engine
,
[]()
{
engine
=
new
DummyEngine
();
});
return
engine
;
}
}
// namespace imperative
}
// namespace paddle
paddle/fluid/imperative/engine.h
已删除
100644 → 0
浏览文件 @
f6a877bc
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <cstddef>
#include <cstdint>
namespace
paddle
{
namespace
imperative
{
struct
Runnable
{};
class
Engine
{
public:
virtual
~
Engine
()
{}
virtual
void
Enqueue
(
Runnable
*
runnable
)
=
0
;
virtual
size_t
Size
()
const
=
0
;
virtual
void
Sync
()
=
0
;
};
Engine
*
GetEngine
();
}
// namespace imperative
}
// namespace paddle
paddle/fluid/imperative/layer.cc
已删除
100644 → 0
浏览文件 @
f6a877bc
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/imperative/layer.h"
#include <deque>
#include <limits>
#include <map>
#include <random>
#include <utility>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/string/printf.h"
namespace
paddle
{
namespace
imperative
{
using
framework
::
Variable
;
void
AddTo
(
Variable
*
src
,
Variable
*
dst
)
{
framework
::
LoDTensor
*
dst_tensor
=
dst
->
GetMutable
<
framework
::
LoDTensor
>
();
framework
::
LoDTensor
*
src_tensor
=
src
->
GetMutable
<
framework
::
LoDTensor
>
();
PADDLE_ENFORCE
(
dst_tensor
->
numel
()
==
src_tensor
->
numel
(),
"%lld vs %lld"
,
dst_tensor
->
numel
(),
src_tensor
->
numel
());
float
*
dst_data
=
dst_tensor
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
const
float
*
src_data
=
src_tensor
->
data
<
float
>
();
for
(
size_t
i
=
0
;
i
<
src_tensor
->
numel
();
++
i
)
{
dst_data
[
i
]
+=
src_data
[
i
];
}
}
class
Autograd
{
public:
explicit
Autograd
(
framework
::
Scope
*
scope
)
:
scope_
(
scope
)
{}
void
RunBackward
(
VarBase
*
var
)
{
PADDLE_ENFORCE
(
var
->
pre_op_
->
op_desc_
);
// TODO(panyx0718): Only create for vars that "require_grad"
(
*
var
->
pre_op_
->
output_vars_
)[
var
->
pre_op_out_idx_
]
->
grads_
=
var
->
grads_
;
std
::
deque
<
OpBase
*>
ready
;
ready
.
push_back
(
var
->
pre_op_
);
std
::
map
<
OpBase
*
,
int
>
dep_counts
=
ComputeDepCounts
(
var
->
pre_op_
);
while
(
!
ready
.
empty
())
{
OpBase
*
ready_op
=
ready
.
front
();
ready
.
pop_front
();
std
::
vector
<
Variable
*>
input_grads
=
ready_op
->
ApplyGrad
(
scope_
);
for
(
size_t
i
=
0
;
i
<
input_grads
.
size
();
++
i
)
{
if
(
!
input_grads
[
i
])
continue
;
OpBase
*
pre_op
=
ready_op
->
pre_ops_
->
at
(
i
);
if
(
!
pre_op
)
continue
;
dep_counts
[
pre_op
]
-=
1
;
PADDLE_ENFORCE
(
dep_counts
[
pre_op
]
>=
0
);
bool
pre_op_ready
=
dep_counts
[
pre_op
]
==
0
;
if
(
pre_op_ready
)
{
ready
.
push_back
(
pre_op
);
}
}
}
}
private:
std
::
map
<
OpBase
*
,
int
>
ComputeDepCounts
(
OpBase
*
op
)
{
std
::
map
<
OpBase
*
,
int
>
ret
;
std
::
deque
<
OpBase
*>
queue
;
queue
.
push_back
(
op
);
std
::
unordered_set
<
OpBase
*>
visited
;
visited
.
insert
(
op
);
while
(
!
queue
.
empty
())
{
OpBase
*
candidate
=
queue
.
front
();
queue
.
pop_front
();
for
(
OpBase
*
pre_op
:
*
(
candidate
->
pre_ops_
))
{
if
(
!
pre_op
)
continue
;
if
(
visited
.
find
(
pre_op
)
==
visited
.
end
())
{
visited
.
insert
(
pre_op
);
queue
.
push_back
(
pre_op
);
}
ret
[
pre_op
]
+=
1
;
}
}
return
ret
;
}
framework
::
Scope
*
scope_
;
};
framework
::
Variable
*
CreateVariable
(
const
std
::
string
&
name
,
const
framework
::
DDim
&
dim
,
float
val
,
framework
::
Scope
*
scope
,
bool
random_name
=
true
)
{
std
::
string
varname
=
name
;
if
(
random_name
)
{
std
::
mt19937
rng
;
rng
.
seed
(
std
::
random_device
()());
std
::
uniform_int_distribution
<
std
::
mt19937
::
result_type
>
dist6
(
1
,
std
::
numeric_limits
<
int
>::
max
());
int
id
=
dist6
(
rng
);
varname
=
string
::
Sprintf
(
"%s@%d"
,
varname
,
id
);
}
VLOG
(
3
)
<<
"creating var "
<<
varname
;
framework
::
Variable
*
var
=
scope
->
Var
(
varname
);
framework
::
LoDTensor
*
tensor
=
var
->
GetMutable
<
framework
::
LoDTensor
>
();
float
*
data
=
tensor
->
mutable_data
<
float
>
(
dim
,
platform
::
CPUPlace
());
std
::
fill
(
data
,
data
+
tensor
->
numel
(),
val
);
return
var
;
}
framework
::
LoDTensor
&
VarBase
::
Grad
()
{
VLOG
(
3
)
<<
"get var grad "
<<
var_desc_
->
Name
();
return
*
grads_
->
GetMutable
<
framework
::
LoDTensor
>
();
}
void
VarBase
::
ApplyGrad
(
framework
::
Scope
*
scope
,
Variable
*
grad
)
{
VLOG
(
3
)
<<
"apply var grad "
<<
var_desc_
->
Name
()
<<
" "
<<
grad
->
Get
<
framework
::
LoDTensor
>
().
data
<
float
>
()[
0
];
if
(
!
grads_
)
{
grads_
=
CreateVariable
(
string
::
Sprintf
(
"%s@IGrad"
,
var_desc_
->
Name
()),
var_
->
Get
<
framework
::
LoDTensor
>
().
dims
(),
0.0
,
scope
);
}
AddTo
(
grad
,
grads_
);
VLOG
(
3
)
<<
"grad_ after apply var grad "
<<
var_desc_
->
Name
()
<<
" "
<<
grads_
->
Get
<
framework
::
LoDTensor
>
().
data
<
float
>
()[
0
];
}
std
::
vector
<
Variable
*>
OpBase
::
ApplyGrad
(
framework
::
Scope
*
scope
)
{
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc_
->
Type
();
for
(
const
std
::
string
&
grad_invar
:
grad_op_desc_
->
InputArgumentNames
())
{
if
(
grad_to_var_
->
find
(
grad_invar
)
==
grad_to_var_
->
end
())
{
// grad op inputs can be forward inputs, so not in grad_to_var.
continue
;
}
VLOG
(
3
)
<<
"op grad in var "
<<
grad_invar
;
block_
->
FindRecursiveOrCreateVar
(
grad_invar
);
framework
::
Variable
*
var
=
scope
->
Var
(
grad_invar
);
const
std
::
string
&
invar
=
grad_to_var_
->
at
(
grad_invar
);
for
(
VarBase
*
varbase
:
*
output_vars_
)
{
// Use the accumulated grads_ by sharing the input with grads_.
if
(
varbase
->
var_desc_
->
Name
()
==
invar
)
{
var
->
GetMutable
<
framework
::
LoDTensor
>
()
->
ShareDataWith
(
varbase
->
grads_
->
Get
<
framework
::
LoDTensor
>
());
break
;
}
}
}
for
(
const
std
::
string
&
outvar
:
grad_op_desc_
->
OutputArgumentNames
())
{
VLOG
(
3
)
<<
"grad outvar "
<<
outvar
;
block_
->
FindRecursiveOrCreateVar
(
outvar
);
framework
::
Variable
*
var
=
scope
->
Var
(
outvar
);
if
(
!
var
->
IsInitialized
())
{
framework
::
VarDesc
*
var_desc
=
block_
->
FindVar
(
outvar
);
if
(
var_desc
->
GetType
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
var
->
GetMutable
<
framework
::
LoDTensor
>
();
}
else
{
LOG
(
ERROR
)
<<
"tracer doesn't support yet"
;
}
}
}
grad_op_desc_
->
InferShape
(
*
block_
);
grad_op_desc_
->
InferVarType
(
block_
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
framework
::
OpRegistry
::
CreateOp
(
*
grad_op_desc_
);
opbase
->
Run
(
*
scope
,
platform
::
CPUPlace
());
// `ret` matches exactly with `input_vars_` of forward op.
std
::
vector
<
Variable
*>
ret
;
for
(
size_t
i
=
0
;
i
<
input_vars_
->
size
();
++
i
)
{
bool
found
=
false
;
for
(
const
std
::
string
&
outvar
:
grad_op_desc_
->
OutputArgumentNames
())
{
Variable
*
var
=
scope
->
FindVar
(
outvar
);
VarBase
*
origin_var
=
(
*
input_vars_
)[
i
];
std
::
string
orig_var
=
grad_to_var_
->
at
(
outvar
);
PADDLE_ENFORCE
(
origin_var
->
var_desc_
->
Name
()
==
orig_var
);
VLOG
(
3
)
<<
"apply grad "
<<
outvar
<<
" with origin "
<<
orig_var
;
origin_var
->
ApplyGrad
(
scope
,
var
);
found
=
true
;
ret
.
push_back
(
var
);
// TODO(panyx0718): There might be another outvar with the same name.
// In that case, it doesn't matter the first one or the second one is
// used.
break
;
}
if
(
!
found
)
{
ret
.
push_back
(
nullptr
);
}
}
return
ret
;
}
void
VarBase
::
RunBackward
(
framework
::
Scope
*
scope
)
{
grads_
=
CreateVariable
(
framework
::
GradVarName
(
var_desc_
->
Name
()),
var_
->
Get
<
framework
::
LoDTensor
>
().
dims
(),
1.0
,
scope
,
false
);
if
(
!
pre_op_
)
return
;
Autograd
(
scope
).
RunBackward
(
this
);
}
}
// namespace imperative
}
// namespace paddle
paddle/fluid/imperative/layer.h
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/platform/enforce.h"
namespace
paddle
{
namespace
imperative
{
class
OpBase
;
class
VarBase
{
public:
VarBase
()
:
pre_op_
(
nullptr
),
pre_op_out_idx_
(
-
1
),
var_desc_
(
nullptr
),
var_
(
nullptr
),
grads_
(
nullptr
)
{}
virtual
~
VarBase
()
{}
void
ApplyGrad
(
framework
::
Scope
*
scope
,
framework
::
Variable
*
grad
);
void
RunBackward
(
framework
::
Scope
*
scope
);
framework
::
LoDTensor
&
Grad
();
OpBase
*
pre_op_
;
int
pre_op_out_idx_
;
framework
::
VarDesc
*
var_desc_
;
framework
::
Variable
*
var_
;
framework
::
Variable
*
grads_
;
};
class
OpBase
{
public:
OpBase
()
:
input_vars_
(
new
std
::
vector
<
VarBase
*>
()),
output_vars_
(
new
std
::
vector
<
VarBase
*>
()),
pre_ops_
(
new
std
::
vector
<
OpBase
*>
()),
pre_ops_out_idx_
(
new
std
::
vector
<
int
>
()),
op_desc_
(
nullptr
),
grad_op_desc_
(
nullptr
)
{}
virtual
~
OpBase
()
{
delete
input_vars_
;
delete
output_vars_
;
delete
pre_ops_
;
delete
pre_ops_out_idx_
;
if
(
grad_op_desc_
)
delete
grad_op_desc_
;
if
(
grad_to_var_
)
delete
grad_to_var_
;
}
std
::
vector
<
framework
::
Variable
*>
ApplyGrad
(
framework
::
Scope
*
scope
);
std
::
vector
<
VarBase
*>*
input_vars_
;
std
::
vector
<
VarBase
*>*
output_vars_
;
std
::
vector
<
OpBase
*>*
pre_ops_
;
std
::
vector
<
int
>*
pre_ops_out_idx_
;
framework
::
OpDesc
*
op_desc_
;
framework
::
OpDesc
*
grad_op_desc_
;
std
::
unordered_map
<
std
::
string
,
std
::
string
>*
grad_to_var_
;
framework
::
BlockDesc
*
block_
;
};
class
Layer
{
public:
virtual
~
Layer
()
{}
virtual
std
::
vector
<
VarBase
>
Forward
(
const
std
::
vector
<
VarBase
>&
inputs
)
{
std
::
vector
<
VarBase
>
vars
;
return
vars
;
}
virtual
void
Backward
()
{
LOG
(
ERROR
)
<<
"To support customize"
;
}
};
}
// namespace imperative
}
// namespace paddle
paddle/fluid/imperative/tracer.cc
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/imperative/tracer.h"
namespace
paddle
{
namespace
imperative
{}
// namespace imperative
}
// namespace paddle
paddle/fluid/imperative/tracer.h
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/imperative/engine.h"
#include "paddle/fluid/imperative/layer.h"
namespace
paddle
{
namespace
imperative
{
void
CreateGradOp
(
const
framework
::
OpDesc
&
op_desc
,
const
std
::
unordered_set
<
std
::
string
>&
no_grad_set
,
const
std
::
vector
<
framework
::
BlockDesc
*>&
grad_sub_block
,
framework
::
OpDesc
**
grad_op_desc
,
std
::
unordered_map
<
std
::
string
,
std
::
string
>*
grad_to_var
)
{
std
::
vector
<
std
::
unique_ptr
<
framework
::
OpDesc
>>
grad_op_descs
=
framework
::
OpInfoMap
::
Instance
()
.
Get
(
op_desc
.
Type
())
.
GradOpMaker
()(
op_desc
,
no_grad_set
,
grad_to_var
,
grad_sub_block
);
PADDLE_ENFORCE
(
grad_op_descs
.
size
()
==
1
,
"Only support 1 grad op now."
);
// TODO(panyx0718): Leak?
*
grad_op_desc
=
grad_op_descs
[
0
].
release
();
}
class
Tracer
{
public:
explicit
Tracer
(
framework
::
BlockDesc
*
root_block
)
:
root_block_
(
root_block
)
{
root_scope_
=
new
framework
::
Scope
();
scopes_
[
root_block_
]
=
root_scope_
;
}
virtual
~
Tracer
()
{
delete
root_scope_
;
}
void
Trace
(
OpBase
*
op
,
const
std
::
vector
<
VarBase
*>&
inputs
,
const
std
::
vector
<
VarBase
*>&
outputs
,
framework
::
BlockDesc
*
block
)
{
framework
::
Scope
*
scope
=
GetScope
(
block
);
framework
::
OpDesc
*
op_desc
=
op
->
op_desc_
;
VLOG
(
3
)
<<
"tracer tracing "
<<
op_desc
->
Type
();
op_desc
->
InferShape
(
*
block
);
op_desc
->
InferVarType
(
block
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
op_base
=
framework
::
OpRegistry
::
CreateOp
(
*
op_desc
);
*
op
->
input_vars_
=
inputs
;
for
(
VarBase
*
input
:
inputs
)
{
const
std
::
string
vname
=
input
->
var_desc_
->
Name
();
framework
::
Variable
*
var
=
scope
->
Var
(
vname
);
input
->
var_
=
var
;
if
(
!
var
->
IsInitialized
())
{
framework
::
VarDesc
*
var_desc
=
block
->
FindVar
(
vname
);
if
(
var_desc
->
GetType
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
var
->
GetMutable
<
framework
::
LoDTensor
>
();
}
else
{
LOG
(
ERROR
)
<<
"tracer doesn't support yet"
;
}
}
if
(
input
->
pre_op_
)
{
op
->
pre_ops_
->
push_back
(
input
->
pre_op_
);
op
->
pre_ops_out_idx_
->
push_back
(
input
->
pre_op_out_idx_
);
}
else
{
op
->
pre_ops_
->
push_back
(
nullptr
);
}
}
*
op
->
output_vars_
=
outputs
;
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
const
std
::
string
vname
=
outputs
[
i
]
->
var_desc_
->
Name
();
framework
::
Variable
*
var
=
scope
->
Var
(
vname
);
if
(
!
var
->
IsInitialized
())
{
framework
::
VarDesc
*
var_desc
=
block
->
FindVar
(
vname
);
if
(
var_desc
->
GetType
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
var
->
GetMutable
<
framework
::
LoDTensor
>
();
}
else
{
LOG
(
ERROR
)
<<
"tracer doesn't support yet"
;
}
}
outputs
[
i
]
->
var_
=
var
;
outputs
[
i
]
->
pre_op_
=
op
;
outputs
[
i
]
->
pre_op_out_idx_
=
i
;
}
op_base
->
Run
(
*
scope
,
platform
::
CPUPlace
());
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
);
op
->
grad_op_desc_
=
grad_op_desc
;
op
->
grad_to_var_
=
grad_to_var
;
op
->
block_
=
block
;
}
framework
::
Scope
*
GetScope
(
framework
::
BlockDesc
*
block
)
{
if
(
scopes_
.
find
(
block
)
!=
scopes_
.
end
())
{
return
scopes_
.
at
(
block
);
}
framework
::
BlockDesc
*
parent_block
=
block
->
ParentBlock
();
PADDLE_ENFORCE
(
scopes_
.
find
(
parent_block
)
!=
scopes_
.
end
());
framework
::
Scope
*
scope
=
&
scopes_
[
parent_block
]
->
NewScope
();
scopes_
[
block
]
=
scope
;
return
scope
;
}
private:
std
::
map
<
framework
::
BlockDesc
*
,
framework
::
Scope
*>
scopes_
;
framework
::
BlockDesc
*
root_block_
;
framework
::
Scope
*
root_scope_
;
};
}
// namespace imperative
}
// namespace paddle
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @
6217f42a
set
(
PYBIND_DEPS pybind python proto_desc memory executor async_executor prune feed_fetch_method pass_builder parallel_executor profiler layer
)
set
(
PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc async_executor_py.cc imperative.cc
)
set
(
PYBIND_DEPS pybind python proto_desc memory executor async_executor prune feed_fetch_method pass_builder parallel_executor profiler
)
set
(
PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc async_executor_py.cc
)
if
(
WITH_PYTHON
)
if
(
WITH_AMD_GPU
)
hip_library
(
paddle_pybind SHARED
...
...
paddle/fluid/pybind/imperative.cc
已删除
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浏览文件 @
f6a877bc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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/fluid/pybind/imperative.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/imperative/tracer.h"
namespace
paddle
{
namespace
pybind
{
// Bind Methods
void
BindTracer
(
pybind11
::
module
*
m
)
{
pybind11
::
class_
<
imperative
::
Tracer
>
(
*
m
,
"Tracer"
,
""
)
.
def
(
"__init__"
,
[](
imperative
::
Tracer
&
self
,
framework
::
BlockDesc
*
root_block
)
{
new
(
&
self
)
imperative
::
Tracer
(
root_block
);
})
.
def
(
"trace"
,
&
imperative
::
Tracer
::
Trace
)
.
def
(
"get_scope"
,
&
imperative
::
Tracer
::
GetScope
,
pybind11
::
return_value_policy
::
reference
);
}
}
// namespace pybind
}
// namespace paddle
paddle/fluid/pybind/imperative.h
已删除
100644 → 0
浏览文件 @
f6a877bc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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. */
#pragma once
#include <Python.h>
#include <vector>
#include "paddle/fluid/imperative/layer.h"
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
namespace
paddle
{
namespace
pybind
{
class
PyLayer
:
public
imperative
::
Layer
{
public:
using
imperative
::
Layer
::
Layer
;
// Inherit constructors
std
::
vector
<
imperative
::
VarBase
>
Forward
(
const
std
::
vector
<
imperative
::
VarBase
>&
inputs
)
override
{
PYBIND11_OVERLOAD
(
std
::
vector
<
imperative
::
VarBase
>
,
Layer
,
Forward
,
inputs
);
// NOLINT
}
void
Backward
()
override
{
PYBIND11_OVERLOAD
(
void
,
Layer
,
Backward
,
);
// NOLINT
}
};
class
PyOpBase
:
public
imperative
::
OpBase
{
public:
using
imperative
::
OpBase
::
OpBase
;
// Inherit constructors
};
class
PyVarBase
:
public
imperative
::
VarBase
{
public:
using
imperative
::
VarBase
::
VarBase
;
// Inherit constructors
};
void
BindTracer
(
pybind11
::
module
*
m
);
}
// namespace pybind
}
// namespace paddle
paddle/fluid/pybind/pybind.cc
浏览文件 @
6217f42a
...
...
@@ -34,7 +34,6 @@ limitations under the License. */
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/version.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/memory/allocation/allocator_strategy.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
...
...
@@ -46,7 +45,6 @@ limitations under the License. */
#include "paddle/fluid/pybind/async_executor_py.h"
#include "paddle/fluid/pybind/const_value.h"
#include "paddle/fluid/pybind/exception.h"
#include "paddle/fluid/pybind/imperative.h"
#include "paddle/fluid/pybind/protobuf.h"
#include "paddle/fluid/pybind/pybind.h" // NOLINT
#include "paddle/fluid/pybind/recordio.h"
...
...
@@ -102,42 +100,6 @@ PYBIND11_MODULE(core, m) {
BindException
(
&
m
);
py
::
class_
<
imperative
::
VarBase
,
PyVarBase
>
(
m
,
"VarBase"
,
R"DOC()DOC"
)
.
def
(
py
::
init
<>
())
.
def
(
"_run_backward"
,
[](
imperative
::
VarBase
&
self
,
framework
::
Scope
*
scope
)
{
self
.
RunBackward
(
scope
);
})
.
def
(
"_grad"
,
&
imperative
::
VarBase
::
Grad
)
.
def_property
(
"desc"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
var_desc_
;
},
[](
imperative
::
VarBase
&
self
,
framework
::
VarDesc
*
var_desc
)
{
self
.
var_desc_
=
var_desc
;
},
py
::
return_value_policy
::
reference
);
py
::
class_
<
imperative
::
OpBase
,
PyOpBase
>
(
m
,
"OpBase"
,
R"DOC()DOC"
)
.
def
(
py
::
init
<>
())
.
def_property
(
"desc"
,
[](
const
imperative
::
OpBase
&
self
)
{
return
self
.
op_desc_
;
},
[](
imperative
::
OpBase
&
self
,
framework
::
OpDesc
*
op_desc
)
{
if
(
op_desc
)
{
self
.
op_desc_
=
op_desc
;
}
},
py
::
return_value_policy
::
reference
);
py
::
class_
<
imperative
::
Layer
,
PyLayer
/* <--- 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
);
BindTracer
(
&
m
);
py
::
class_
<
Tensor
>
(
m
,
"Tensor"
,
py
::
buffer_protocol
())
.
def_buffer
(
[](
Tensor
&
self
)
->
py
::
buffer_info
{
return
CastToPyBuffer
(
self
);
})
...
...
@@ -639,7 +601,6 @@ All parameter, weight, gradient are variables in Paddle.
m
.
def
(
"set_feed_variable"
,
framework
::
SetFeedVariable
);
m
.
def
(
"get_fetch_variable"
,
framework
::
GetFetchVariable
);
m
.
def
(
"get_variable_tensor"
,
framework
::
GetVariableTensor
);
m
.
def
(
"_is_program_version_supported"
,
IsProgramVersionSupported
);
...
...
python/paddle/fluid/__init__.py
浏览文件 @
6217f42a
...
...
@@ -34,7 +34,6 @@ from . import io
from
.
import
evaluator
from
.
import
initializer
from
.
import
layers
from
.
import
imperative
from
.
import
contrib
from
.
import
nets
from
.
import
optimizer
...
...
@@ -68,7 +67,6 @@ __all__ = framework.__all__ + executor.__all__ + \
'initializer'
,
'layers'
,
'contrib'
,
'imperative'
,
'transpiler'
,
'nets'
,
'optimizer'
,
...
...
python/paddle/fluid/framework.py
浏览文件 @
6217f42a
...
...
@@ -18,7 +18,6 @@ import collections
import
contextlib
import
re
import
six
import
sys
import
numpy
as
np
...
...
@@ -50,16 +49,6 @@ GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX
=
core
.
kZeroVarSuffix
()
CONTROL_DEP_VAR_PREFIX
=
core
.
kControlDepVarName
()
_imperative_tracer_
=
None
def
_in_imperative_mode
():
return
_imperative_tracer_
is
not
None
def
_imperative_tracer
():
return
_imperative_tracer_
class
NameScope
(
object
):
def
__init__
(
self
,
name
=
""
,
parent
=
None
):
...
...
@@ -213,7 +202,7 @@ def _debug_string_(proto, throw_on_error=True):
return
proto
.
__str__
()
class
Variable
(
core
.
VarBase
):
class
Variable
(
object
):
"""
In Fluid, every input and output of an operator is a variable. In most
cases, variables are used for holding different kinds of data or training
...
...
@@ -277,7 +266,6 @@ class Variable(core.VarBase):
stop_gradient
=
False
,
is_data
=
False
,
**
kwargs
):
core
.
VarBase
.
__init__
(
self
)
self
.
block
=
block
self
.
error_clip
=
error_clip
...
...
@@ -358,18 +346,6 @@ class Variable(core.VarBase):
self
.
stop_gradient
=
stop_gradient
self
.
is_data
=
is_data
def
_numpy
(
self
):
scope
=
_imperative_tracer
().
get_scope
(
self
.
block
.
desc
)
tensor
=
core
.
get_variable_tensor
(
scope
,
self
.
desc
.
name
())
return
np
.
array
(
tensor
)
def
_backward
(
self
):
scope
=
_imperative_tracer
().
get_scope
(
self
.
block
.
desc
)
self
.
_run_backward
(
scope
)
def
_gradient
(
self
):
return
np
.
array
(
self
.
_grad
())
def
__str__
(
self
):
return
self
.
to_string
(
True
)
...
...
@@ -516,7 +492,7 @@ class OpProtoHolder(object):
}
class
Operator
(
core
.
OpBase
):
class
Operator
(
object
):
"""
In Fluid, all the operation are represented by Operator, and Operator
is regarded as a build in an instruction of a Block. Users can use the
...
...
@@ -572,7 +548,6 @@ class Operator(core.OpBase):
inputs
=
None
,
outputs
=
None
,
attrs
=
None
):
core
.
OpBase
.
__init__
(
self
)
self
.
block
=
block
self
.
desc
=
desc
# note: not add self.attrs here:
...
...
@@ -612,7 +587,6 @@ class Operator(core.OpBase):
return
True
return
False
self
.
inputs
=
[]
if
inputs
is
not
None
:
for
in_proto
in
proto
.
inputs
:
found
=
find_name
(
inputs
,
in_proto
.
name
)
...
...
@@ -639,13 +613,6 @@ class Operator(core.OpBase):
else
:
self
.
desc
.
set_input
(
in_proto
.
name
,
[])
for
inp
in
inputs
.
values
():
if
isinstance
(
inp
,
Variable
):
self
.
inputs
.
append
(
inp
)
elif
isinstance
(
inp
,
list
)
or
isinstance
(
inp
,
tuple
):
self
.
inputs
.
extend
(
inp
[:])
self
.
outputs
=
[]
if
outputs
is
not
None
:
given
=
set
()
need
=
set
()
...
...
@@ -674,12 +641,6 @@ class Operator(core.OpBase):
arg
.
op
=
self
self
.
desc
.
set_output
(
out_proto
.
name
,
out_arg_names
)
for
out
in
outputs
.
values
():
if
isinstance
(
out
,
Variable
):
self
.
outputs
.
append
(
out
)
elif
isinstance
(
out
,
list
)
or
isinstance
(
out
,
tuple
):
self
.
outputs
.
extend
(
out
[:])
if
op_attrs
is
not
None
:
if
not
isinstance
(
op_attrs
,
dict
):
raise
TypeError
(
"'attrs' should be a dict."
)
...
...
@@ -1245,8 +1206,6 @@ class Block(object):
"""
op_desc
=
self
.
desc
.
append_op
()
op
=
Operator
(
block
=
self
,
desc
=
op_desc
,
*
args
,
**
kwargs
)
if
_in_imperative_mode
():
_imperative_tracer
().
trace
(
op
,
op
.
inputs
,
op
.
outputs
,
self
.
desc
)
self
.
ops
.
append
(
op
)
return
op
...
...
@@ -2250,12 +2209,3 @@ def _get_var(name, program=None):
assert
isinstance
(
program
,
Program
)
return
program
.
global_block
().
var
(
name
)
@
contextlib
.
contextmanager
def
_imperative_guard
(
tracer
):
global
_imperative_tracer_
tmp_trace
=
_imperative_tracer_
_imperative_tracer_
=
tracer
yield
_imperative_tracer_
=
tmp_trace
python/paddle/fluid/imperative/__init__.py
已删除
100644 → 0
浏览文件 @
f6a877bc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from
__future__
import
print_function
from
.
import
base
from
.base
import
*
from
.
import
layers
from
.layers
import
*
__all__
=
[]
__all__
+=
layers
.
__all__
__all__
+=
base
.
__all__
python/paddle/fluid/imperative/base.py
已删除
100644 → 0
浏览文件 @
f6a877bc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
contextlib
import
numpy
as
np
from
paddle.fluid
import
core
from
paddle.fluid
import
framework
__all__
=
[
'enabled'
,
'guard'
,
'to_variable'
]
def
enabled
():
return
framework
.
_in_imperative_mode
()
@
contextlib
.
contextmanager
def
guard
():
train
=
framework
.
Program
()
startup
=
framework
.
Program
()
tracer
=
core
.
Tracer
(
train
.
current_block
().
desc
)
with
framework
.
program_guard
(
train
,
startup
):
with
framework
.
unique_name
.
guard
():
with
framework
.
_imperative_guard
(
tracer
):
yield
def
to_variable
(
value
,
block
=
None
):
if
isinstance
(
value
,
np
.
ndarray
):
if
not
block
:
block
=
framework
.
default_main_program
().
current_block
()
py_var
=
framework
.
Variable
(
block
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
name
=
None
,
shape
=
value
.
shape
,
dtype
=
value
.
dtype
)
scope
=
framework
.
_imperative_tracer
().
get_scope
(
block
.
desc
)
var
=
scope
.
var
(
py_var
.
name
)
tensor
=
var
.
get_tensor
()
tensor
.
set
(
value
,
core
.
CPUPlace
())
return
py_var
elif
isinstance
(
value
,
framework
.
Variable
):
return
value
else
:
raise
ValueError
(
"Unsupported type %s"
%
type
(
value
))
python/paddle/fluid/imperative/layers.py
已删除
100644 → 0
浏览文件 @
f6a877bc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
contextlib
import
sys
import
numpy
as
np
from
paddle.fluid
import
core
from
paddle.fluid
import
framework
from
paddle.fluid.imperative
import
base
__all__
=
[
'PyLayer'
]
class
PyLayer
(
core
.
Layer
):
def
__init__
(
self
):
pass
def
__call__
(
self
,
inputs
):
# TODO(panyx0718): Support declarative mode as well.
assert
base
.
enabled
()
if
not
isinstance
(
inputs
,
list
)
and
not
isinstance
(
inputs
,
tuple
):
inputs
=
[
inputs
]
var_inputs
=
[]
for
x
in
inputs
:
py_var
=
base
.
to_variable
(
x
)
var_inputs
.
append
(
py_var
)
outputs
=
self
.
forward
(
var_inputs
)
return
outputs
def
forward
(
self
,
inputs
):
return
[]
python/paddle/fluid/layer_helper.py
浏览文件 @
6217f42a
...
...
@@ -17,13 +17,10 @@ from __future__ import print_function
import
copy
import
itertools
import
six
import
sys
import
numpy
as
np
from
.framework
import
Variable
,
Parameter
,
default_main_program
,
default_startup_program
,
dtype_is_floating
from
.
import
unique_name
from
paddle.fluid.initializer
import
Constant
,
Xavier
from
paddle.fluid.imperative
import
base
from
.param_attr
import
ParamAttr
,
WeightNormParamAttr
from
.
import
core
from
six.moves
import
zip
...
...
@@ -49,21 +46,23 @@ class LayerHelper(object):
def
startup_program
(
self
):
return
default_startup_program
()
def
to_variable
(
self
,
x
):
return
base
.
to_variable
(
x
,
self
.
main_program
.
current_block
())
def
append_op
(
self
,
*
args
,
**
kwargs
):
return
self
.
main_program
.
current_block
().
append_op
(
*
args
,
**
kwargs
)
def
multiple_input
(
self
,
input_param_name
=
'input'
):
inputs
=
self
.
kwargs
.
get
(
input_param_name
,
[])
ret
=
[]
if
isinstance
(
inputs
,
list
)
or
isinstance
(
inputs
,
tuple
):
for
inp
in
inputs
:
ret
.
append
(
self
.
to_variable
(
inp
))
type_error
=
TypeError
(
"Input of {0} layer should be Variable or sequence of Variable"
.
format
(
self
.
layer_type
))
if
isinstance
(
inputs
,
Variable
):
inputs
=
[
inputs
]
elif
not
isinstance
(
inputs
,
list
)
and
not
isinstance
(
inputs
,
tuple
):
raise
type_error
else
:
ret
.
append
(
self
.
to_variable
(
inputs
))
return
ret
for
each
in
inputs
:
if
not
isinstance
(
each
,
Variable
):
raise
type_error
return
inputs
def
input
(
self
,
input_param_name
=
'input'
):
inputs
=
self
.
multiple_input
(
input_param_name
)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
6217f42a
...
...
@@ -6623,8 +6623,7 @@ def relu(x, name=None):
helper
=
LayerHelper
(
'relu'
,
**
locals
())
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
)
helper
.
append_op
(
type
=
"relu"
,
inputs
=
{
"X"
:
helper
.
input
(
'x'
)},
outputs
=
{
"Out"
:
out
})
helper
.
append_op
(
type
=
"relu"
,
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
out
})
return
out
...
...
python/paddle/fluid/tests/unittests/test_imperative.py
已删除
100644 → 0
浏览文件 @
f6a877bc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
unittest
import
sys
import
numpy
as
np
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
class
MyLayer
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
MyLayer
,
self
).
__init__
()
def
forward
(
self
,
inputs
):
x
=
fluid
.
layers
.
relu
(
inputs
[
0
])
self
.
_x_for_debug
=
x
return
[
fluid
.
layers
.
elementwise_mul
(
x
,
x
)]
class
TestImperative
(
unittest
.
TestCase
):
def
test_layer
(
self
):
with
fluid
.
imperative
.
guard
():
cl
=
core
.
Layer
()
cl
.
forward
([])
l
=
fluid
.
imperative
.
PyLayer
()
l
.
forward
([])
def
test_layer_in_out
(
self
):
with
fluid
.
imperative
.
guard
():
l
=
MyLayer
()
x
=
l
(
np
.
array
([
1.0
,
2.0
,
-
1.0
],
dtype
=
np
.
float32
))[
0
]
self
.
assertIsNotNone
(
x
)
sys
.
stderr
.
write
(
"%s output: %s
\n
"
%
(
x
,
x
.
_numpy
()))
x
.
_backward
()
sys
.
stderr
.
write
(
"grad %s
\n
"
%
l
.
_x_for_debug
.
_gradient
())
if
__name__
==
'__main__'
:
unittest
.
main
()
python/setup.py.in
浏览文件 @
6217f42a
...
...
@@ -101,7 +101,6 @@ packages=['paddle',
'paddle.dataset',
'paddle.reader',
'paddle.fluid',
'paddle.fluid.imperative',
'paddle.fluid.proto',
'paddle.fluid.proto.profiler',
'paddle.fluid.layers',
...
...
tools/print_signatures.py
浏览文件 @
6217f42a
...
...
@@ -27,8 +27,6 @@ import pydoc
member_dict
=
collections
.
OrderedDict
()
experimental_namespace
=
{
"paddle.fluid.imperative"
}
def
visit_member
(
parent_name
,
member
):
cur_name
=
"."
.
join
([
parent_name
,
member
.
__name__
])
...
...
@@ -53,8 +51,6 @@ def visit_member(parent_name, member):
def
visit_all_module
(
mod
):
if
(
mod
.
__name__
in
experimental_namespace
):
return
for
member_name
in
(
name
for
name
in
(
mod
.
__all__
if
hasattr
(
mod
,
"__all__"
)
else
dir
(
mod
))
...
...
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