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29697c2e
编写于
12月 20, 2018
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add stop_gradient to VarBase to support loss function
test=develop
上级
fba3712a
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
99 addition
and
29 deletion
+99
-29
paddle/fluid/framework/framework.proto
paddle/fluid/framework/framework.proto
+1
-1
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+33
-6
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+5
-2
paddle/fluid/operators/cross_entropy_op.h
paddle/fluid/operators/cross_entropy_op.h
+2
-0
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+9
-2
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+10
-2
python/paddle/fluid/imperative/layers.py
python/paddle/fluid/imperative/layers.py
+10
-0
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
+29
-16
未找到文件。
paddle/fluid/framework/framework.proto
浏览文件 @
29697c2e
...
...
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
syntax
=
"proto2"
;
option
optimize_for
=
LITE_RUNTIME
;
/* option optimize_for = LITE_RUNTIME; */
package
paddle
.
framework.proto
;
// Any incompatible changes to ProgramDesc and its dependencies should
...
...
paddle/fluid/imperative/layer.cc
浏览文件 @
29697c2e
...
...
@@ -115,6 +115,7 @@ framework::Variable* CreateVariable(const std::string& name,
varname
=
string
::
Sprintf
(
"%s@%d"
,
varname
,
id
);
}
LOG
(
ERROR
)
<<
"creating var "
<<
varname
;
VLOG
(
3
)
<<
"creating var "
<<
varname
;
framework
::
Variable
*
var
=
scope
->
Var
(
varname
);
framework
::
LoDTensor
*
tensor
=
var
->
GetMutable
<
framework
::
LoDTensor
>
();
...
...
@@ -130,13 +131,22 @@ framework::LoDTensor& VarBase::Grad() {
}
void
VarBase
::
ApplyGrad
(
framework
::
Scope
*
scope
,
Variable
*
grad
)
{
PADDLE_ENFORCE
(
grad
->
IsInitialized
(),
"grad %s must be initialized"
,
var_desc_
->
Name
());
PADDLE_ENFORCE
(
grad
->
Get
<
framework
::
LoDTensor
>
().
IsInitialized
(),
"variable %s has NO gradient, please set stop_gradient to it"
,
var_desc_
->
Name
());
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
];
...
...
@@ -153,8 +163,9 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
// 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
);
VLOG
(
3
)
<<
"op grad input var "
<<
grad_invar
;
framework
::
VarDesc
&
grad_invar_desc
=
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_
)
{
...
...
@@ -165,21 +176,33 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
break
;
}
}
grad_invar_desc
.
SetShape
(
framework
::
vectorize
(
var
->
Get
<
framework
::
LoDTensor
>
().
dims
()));
VLOG
(
3
)
<<
"set op grad var desc's shape size "
<<
framework
::
vectorize
(
var
->
Get
<
framework
::
LoDTensor
>
().
dims
()).
size
();
}
LOG
(
ERROR
)
<<
"grad_op_desc_"
<<
grad_op_desc_
->
Proto
()
->
DebugString
();
for
(
const
std
::
string
&
outvar
:
grad_op_desc_
->
OutputArgumentNames
())
{
VLOG
(
3
)
<<
"
grad out
var "
<<
outvar
;
VLOG
(
3
)
<<
"
op grad output
var "
<<
outvar
;
block_
->
FindRecursiveOrCreateVar
(
outvar
);
framework
::
Variable
*
var
=
scope
->
Var
(
outvar
);
if
(
!
var
->
IsInitialized
())
{
VLOG
(
3
)
<<
"init op grad output var "
<<
outvar
;
framework
::
VarDesc
*
var_desc
=
block_
->
FindVar
(
outvar
);
if
(
var_desc
->
GetType
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
var
->
GetMutable
<
framework
::
LoDTensor
>
();
// framework::Tensor* tensor = var->GetMutable<framework::LoDTensor>();
// tensor->mutable_data(platform::CPUPlace());
}
else
{
LOG
(
ERROR
)
<<
"tracer doesn't support yet"
;
}
}
VLOG
(
3
)
<<
"op grad output var "
<<
outvar
<<
" is inited"
;
}
grad_op_desc_
->
InferShape
(
*
block_
);
grad_op_desc_
->
InferVarType
(
block_
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
...
...
@@ -194,11 +217,15 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
VarBase
*
origin_var
=
(
*
input_vars_
)[
i
];
for
(
const
std
::
string
&
outvar
:
grad_op_desc_
->
OutputArgumentNames
())
{
Variable
*
var
=
scope
->
FindVar
(
outvar
);
std
::
string
orig_var
=
grad_to_var_
->
at
(
outvar
);
if
(
origin_var
->
var_desc_
->
Name
()
!=
orig_var
)
{
if
(
var
->
IsInitialized
())
{
VLOG
(
3
)
<<
"get grad op output var "
<<
outvar
;
}
std
::
string
orig_var_name
=
grad_to_var_
->
at
(
outvar
);
if
(
origin_var
->
var_desc_
->
Name
()
!=
orig_var_name
||
origin_var
->
stop_gradient_
)
{
continue
;
}
VLOG
(
3
)
<<
"apply grad "
<<
outvar
<<
" with origin "
<<
orig_var
;
VLOG
(
3
)
<<
"apply grad "
<<
outvar
<<
" with origin "
<<
orig_var
_name
;
origin_var
->
ApplyGrad
(
scope
,
var
);
found
=
true
;
ret
.
push_back
(
var
);
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
29697c2e
...
...
@@ -29,12 +29,13 @@ class OpBase;
class
VarBase
{
public:
VarBase
(
)
explicit
VarBase
(
bool
stop_gradient
=
false
)
:
pre_op_
(
nullptr
),
pre_op_out_idx_
(
-
1
),
var_desc_
(
nullptr
),
var_
(
nullptr
),
grads_
(
nullptr
)
{}
grads_
(
nullptr
),
stop_gradient_
(
stop_gradient
)
{}
virtual
~
VarBase
()
{}
...
...
@@ -50,6 +51,8 @@ class VarBase {
framework
::
VarDesc
*
var_desc_
;
framework
::
Variable
*
var_
;
framework
::
Variable
*
grads_
;
bool
stop_gradient_
;
};
class
OpBase
{
...
...
paddle/fluid/operators/cross_entropy_op.h
浏览文件 @
29697c2e
...
...
@@ -110,6 +110,8 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
auto
*
dy
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
LOG
(
ERROR
)
<<
"CROSS ENTROPY GRAD DX: "
<<
ctx
.
op
().
Output
(
framework
::
GradVarName
(
"X"
));
T
*
dx_data
=
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// Following computation only depends on the last dimension size. So it's
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
29697c2e
...
...
@@ -111,7 +111,8 @@ PYBIND11_MODULE(core, m) {
BindException
(
&
m
);
py
::
class_
<
imperative
::
VarBase
,
PyVarBase
>
(
m
,
"VarBase"
,
R"DOC()DOC"
)
.
def
(
py
::
init
<>
())
// .def(py::init<>())
.
def
(
py
::
init
<
bool
>
(),
py
::
arg
(
"stop_gradient"
)
=
false
)
.
def
(
"_run_backward"
,
[](
imperative
::
VarBase
&
self
,
framework
::
Scope
*
scope
)
{
self
.
RunBackward
(
scope
);
...
...
@@ -129,7 +130,13 @@ PYBIND11_MODULE(core, m) {
[](
imperative
::
VarBase
&
self
,
framework
::
VarDesc
*
var_desc
)
{
self
.
var_desc_
=
var_desc
;
},
py
::
return_value_policy
::
reference
);
py
::
return_value_policy
::
reference
)
.
def_property
(
"stop_gradient"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
stop_gradient_
;
},
[](
imperative
::
VarBase
&
self
,
bool
stop_gradient
)
{
self
.
stop_gradient_
=
stop_gradient
;
});
py
::
class_
<
imperative
::
OpBase
,
PyOpBase
>
(
m
,
"OpBase"
,
R"DOC()DOC"
)
.
def
(
py
::
init
<>
())
...
...
python/paddle/fluid/framework.py
浏览文件 @
29697c2e
...
...
@@ -354,11 +354,11 @@ class Variable(object):
self
.
block
.
vars
[
name
]
=
self
self
.
op
=
None
self
.
stop_gradient
=
stop_gradient
self
.
is_data
=
is_data
if
_in_imperative_mode
():
self
.
_ivar
=
core
.
VarBase
()
self
.
_ivar
.
desc
=
self
.
desc
self
.
_ivar
.
stop_gradient
=
stop_gradient
def
_numpy
(
self
):
scope
=
_imperative_tracer
().
get_scope
()
...
...
@@ -366,7 +366,7 @@ class Variable(object):
return
np
.
array
(
tensor
)
def
_backward
(
self
):
scope
=
_imperative_tracer
().
get_scope
(
self
.
block
.
desc
)
scope
=
_imperative_tracer
().
get_scope
()
self
.
_ivar
.
_run_backward
(
scope
)
def
_gradient
(
self
):
...
...
@@ -415,6 +415,14 @@ class Variable(object):
"""
self
.
desc
=
input
@
property
def
_stop_gradient
(
self
):
return
self
.
_ivar
.
stop_gradient
@
_stop_gradient
.
setter
def
_stop_gradient
(
self
,
s
):
self
.
_ivar
.
stop_gradient
=
s
@
property
def
persistable
(
self
):
return
self
.
desc
.
persistable
()
...
...
python/paddle/fluid/imperative/layers.py
浏览文件 @
29697c2e
...
...
@@ -25,12 +25,22 @@ __all__ = ['PyLayer']
class
PyLayer
(
core
.
Layer
):
def
__init__
(
self
,
*
args
,
**
kwargs
):
self
.
_once_built
=
True
from
..layer_helper
import
LayerHelper
self
.
_helper
=
LayerHelper
(
type
(
self
).
__name__
,
**
kwargs
)
self
.
_dtype
=
kwargs
.
get
(
"dtype"
,
core
.
VarDesc
.
VarType
.
FP32
)
def
_build_once
(
self
,
inputs
):
pass
def
__call__
(
self
,
*
inputs
):
if
self
.
_once_built
:
self
.
_build_once
(
*
inputs
)
self
.
_once_built
=
False
outputs
=
self
.
forward
(
*
inputs
)
return
outputs
def
forward
(
self
,
*
inputs
):
...
...
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
浏览文件 @
29697c2e
...
...
@@ -18,14 +18,15 @@ import numpy as np
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.imperative.nn
import
Conv2D
,
Pool2D
from
paddle.fluid.imperative.nn
import
Conv2D
,
Pool2D
,
FC
from
paddle.fluid.imperative.base
import
to_variable
class
SimpleImgConvPool
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
filter_size
,
num_filters
,
pool_size
,
pool_stride
,
pool_padding
=
0
,
...
...
@@ -81,24 +82,24 @@ class MNIST(fluid.imperative.PyLayer):
super
(
MNIST
,
self
).
__init__
(
param_attr
=
param_attr
,
bias_attr
=
bias_attr
)
self
.
_simple_img_conv_pool_1
=
SimpleImgConvPool
(
num_channels
=
3
,
filter_size
=
5
,
num_filters
=
20
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
1
,
5
,
20
,
2
,
2
,
act
=
"relu"
)
self
.
_simple_img_conv_pool_2
=
SimpleImgConvPool
(
num_channels
=
3
,
filter_size
=
5
,
num_filters
=
50
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
20
,
5
,
50
,
2
,
2
,
act
=
"relu"
)
pool_2_shape
=
50
*
8
*
8
SIZE
=
10
scale
=
(
2.0
/
(
pool_2_shape
**
2
*
SIZE
))
**
0.5
self
.
_fc
=
FC
(
-
1
,
10
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NormalInitializer
(
loc
=
0.0
,
scale
=
scale
)))
def
forward
(
self
,
inputs
):
x
=
self
.
_simple_img_conv_pool_1
(
inputs
)
x
=
self
.
_simple_img_conv_pool_2
(
x
)
x
=
self
.
_fc
(
x
)
return
x
...
...
@@ -107,8 +108,20 @@ class TestImperativeMnist(unittest.TestCase):
with
fluid
.
imperative
.
guard
():
mnist
=
MNIST
()
data
=
np
.
random
.
rand
(
2
,
3
,
5
,
5
).
astype
(
'float32'
)
mnist
(
data
)
x_data
=
np
.
random
.
rand
(
128
,
1
,
28
,
28
).
astype
(
'float32'
)
img
=
to_variable
(
x_data
)
y_data
=
np
.
random
.
rand
(
128
,
1
).
astype
(
'int64'
)
label
=
to_variable
(
y_data
)
label
.
_stop_gradient
=
True
predict
=
mnist
(
img
)
print
(
predict
.
shape
,
predict
.
dtype
,
label
.
shape
,
label
.
dtype
)
out
=
fluid
.
layers
.
cross_entropy
(
predict
,
label
)
print
(
out
.
shape
,
out
.
dtype
)
out
.
_backward
()
filter_grad
=
mnist
.
_simple_img_conv_pool_1
.
_conv2d
.
_filter_param
.
_gradient
(
)
print
(
filter_grad
)
# np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
# with fluid.imperative.guard():
# mlp = MLP()
...
...
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