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6535a7b0
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6535a7b0
编写于
8月 08, 2017
作者:
D
dongzhihong
浏览文件
操作
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差异文件
Merge remote-tracking branch 'origin/develop' into random_op
上级
23ac8459
e31a469e
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
214 addition
and
76 deletion
+214
-76
paddle/framework/op_registry.h
paddle/framework/op_registry.h
+0
-6
paddle/framework/operator.h
paddle/framework/operator.h
+9
-1
paddle/framework/pybind.cc
paddle/framework/pybind.cc
+20
-2
paddle/operators/cross_entropy_op.cc
paddle/operators/cross_entropy_op.cc
+2
-1
paddle/operators/net_op.h
paddle/operators/net_op.h
+9
-0
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+1
-0
python/paddle/v2/framework/tests/gradient_checker.py
python/paddle/v2/framework/tests/gradient_checker.py
+149
-3
python/paddle/v2/framework/tests/op_test_util.py
python/paddle/v2/framework/tests/op_test_util.py
+4
-3
python/paddle/v2/framework/tests/test_cross_entropy_op.py
python/paddle/v2/framework/tests/test_cross_entropy_op.py
+14
-2
python/paddle/v2/framework/tests/test_softmax_op.py
python/paddle/v2/framework/tests/test_softmax_op.py
+6
-58
未找到文件。
paddle/framework/op_registry.h
浏览文件 @
6535a7b0
...
...
@@ -260,12 +260,6 @@ class OpRegistry {
return
CreateOp
(
op_desc
.
type
(),
inputs
,
outputs
,
attrs
);
}
static
bool
SupportGPU
(
const
std
::
string
&
op_type
)
{
OperatorWithKernel
::
OpKernelKey
key
;
key
.
place_
=
platform
::
GPUPlace
();
return
OperatorWithKernel
::
AllOpKernels
().
at
(
op_type
).
count
(
key
)
!=
0
;
}
static
std
::
shared_ptr
<
OperatorBase
>
CreateGradOp
(
const
OperatorBase
&
op
)
{
PADDLE_ENFORCE
(
!
op
.
IsNetOp
(),
"Use framework::Backward to get backward ops"
);
...
...
paddle/framework/operator.h
浏览文件 @
6535a7b0
...
...
@@ -88,6 +88,8 @@ class OperatorBase {
virtual
bool
IsNetOp
()
const
{
return
false
;
}
virtual
bool
SupportGPU
()
const
{
return
false
;
}
/// rename inputs outputs name
void
Rename
(
const
std
::
string
&
old_name
,
const
std
::
string
&
new_name
);
...
...
@@ -308,7 +310,7 @@ class OperatorWithKernel : public OperatorBase {
using
OpKernelMap
=
std
::
unordered_map
<
OpKernelKey
,
std
::
unique_ptr
<
OpKernel
>
,
OpKernelHash
>
;
void
InferShape
(
const
Scope
&
scope
)
const
{
void
InferShape
(
const
Scope
&
scope
)
const
override
{
InferShape
(
InferShapeContext
(
this
,
scope
));
}
...
...
@@ -324,6 +326,12 @@ class OperatorWithKernel : public OperatorBase {
return
g_all_op_kernels
;
}
bool
SupportGPU
()
const
override
{
OperatorWithKernel
::
OpKernelKey
key
;
key
.
place_
=
platform
::
GPUPlace
();
return
OperatorWithKernel
::
AllOpKernels
().
at
(
type_
).
count
(
key
)
!=
0
;
}
protected:
virtual
void
InferShape
(
const
InferShapeContext
&
ctx
)
const
=
0
;
};
...
...
paddle/framework/pybind.cc
浏览文件 @
6535a7b0
...
...
@@ -59,6 +59,26 @@ void ExposeOperator(ClassType &m) {
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
vector
<
std
::
string
>
{
return
op
.
outputs_
;
})
.
def
(
"inputs"
,
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
vector
<
std
::
string
>
{
return
op
.
inputs_
;
})
.
def
(
"support_gpu"
,
&
ClassType
::
type
::
SupportGPU
)
.
def
(
"temp_outputs"
,
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
vector
<
std
::
string
>
{
auto
iter
=
op
.
attrs_
.
find
(
"temporary_index"
);
std
::
vector
<
std
::
string
>
ret
;
if
(
iter
==
op
.
attrs_
.
end
())
{
return
ret
;
}
else
{
auto
tmp_idx
=
boost
::
get
<
std
::
vector
<
int
>>
(
iter
->
second
);
ret
.
reserve
(
tmp_idx
.
size
());
for
(
auto
&
index
:
tmp_idx
)
{
ret
.
push_back
(
op
.
outputs_
.
at
(
index
));
}
return
ret
;
}
})
.
def
(
"__str__"
,
&
ClassType
::
type
::
DebugString
);
}
...
...
@@ -204,8 +224,6 @@ All parameter, weight, gradient are variables in Paddle.
return
OpRegistry
::
CreateOp
(
desc
);
});
operator_base
.
def_static
(
"support_gpu"
,
&
OpRegistry
::
SupportGPU
);
operator_base
.
def
(
"backward"
,
[](
const
OperatorBase
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>
&
no_grad_vars
)
{
...
...
paddle/operators/cross_entropy_op.cc
浏览文件 @
6535a7b0
...
...
@@ -70,7 +70,8 @@ REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
ops
::
OnehotCrossEntropyOpMaker
);
REGISTER_OP_CPU_KERNEL
(
onehot_cross_entropy
,
ops
::
OnehotCrossEntropyOpKernel
<
ops
::
CPUPlace
,
float
>
);
REGISTER_GRADIENT_OP
(
onehot_cross_entropy
,
onehot_cross_entropy_grad
,
ops
::
OnehotCrossEntropyGradientOp
);
REGISTER_OP_CPU_KERNEL
(
onehot_cross_entropy_grad
,
ops
::
OnehotCrossEntropyGradientOpKernel
<
ops
::
CPUPlace
,
float
>
);
paddle/operators/net_op.h
浏览文件 @
6535a7b0
...
...
@@ -65,6 +65,15 @@ class NetOp : public framework::OperatorBase {
}
}
bool
SupportGPU
()
const
override
{
for
(
auto
&
op
:
ops_
)
{
if
(
!
op
->
SupportGPU
())
{
return
false
;
}
}
return
true
;
}
/**
* @brief Add an operator by ptr
*/
...
...
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
6535a7b0
...
...
@@ -13,6 +13,7 @@ py_test(test_protobuf SRCS test_protobuf.py)
py_test
(
test_add_two_op SRCS test_add_two_op.py
)
py_test
(
test_sigmoid_op SRCS test_sigmoid_op.py
)
py_test
(
test_softmax_op SRCS test_softmax_op.py
)
py_test
(
test_cross_entropy_op SRCS test_cross_entropy_op.py
)
py_test
(
test_fill_zeros_like_op SRCS test_fill_zeros_like_op.py
)
py_test
(
gradient_checker SRCS gradient_checker.py
)
...
...
python/paddle/v2/framework/tests/gradient_checker.py
浏览文件 @
6535a7b0
import
unittest
import
numpy
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
import
numpy
import
unittest
__all__
=
[
'get_numeric_gradient'
]
def
create_op
(
op_type
):
kwargs
=
dict
()
for
in_name
in
Operator
.
get_op_input_names
(
op_type
):
kwargs
[
in_name
]
=
in_name
for
out_name
in
Operator
.
get_op_output_names
(
op_type
):
kwargs
[
out_name
]
=
out_name
return
Operator
(
op_type
,
**
kwargs
)
def
grad_var_name
(
var_name
):
return
var_name
+
"@GRAD"
def
get_numeric_gradient
(
op
,
input_values
,
output_name
,
input_to_check
,
delta
=
1e-2
,
delta
=
0.005
,
local_scope
=
None
):
"""
Get Numeric Gradient for an operator's input.
...
...
@@ -76,6 +91,113 @@ def get_numeric_gradient(op,
return
gradient_flat
.
reshape
(
tensor_to_check
.
get_dims
())
class
GradientChecker
(
unittest
.
TestCase
):
def
__is_close
(
self
,
numeric_grads
,
scope
,
max_relative_error
):
for
name
in
numeric_grads
:
op_grad
=
numpy
.
array
(
scope
.
find_var
(
grad_var_name
(
name
)).
get_tensor
())
is_close
=
numpy
.
allclose
(
numeric_grads
[
name
],
op_grad
,
rtol
=
max_relative_error
,
atol
=
100
)
if
not
is_close
:
return
False
return
True
def
check_grad
(
self
,
forward_op
,
input_vars
,
inputs_to_check
,
output_name
,
no_grad_set
=
None
,
only_cpu
=
False
,
max_relative_error
=
0.005
):
"""
:param forward_op: used to create backward_op
:param input_vars: numpy value of input variable. The following
computation will use these variables.
:param inputs_to_check: inputs var names that should check gradient.
:param output_name: output name that used to
:param max_relative_error: The relative tolerance parameter.
:param no_grad_set: used when create backward ops
:param only_cpu: only compute and check gradient on cpu kernel.
:return:
"""
if
no_grad_set
is
None
:
no_grad_set
=
set
()
tmp_outs
=
forward_op
.
temp_outputs
()
no_tmp_out
=
filter
(
lambda
name
:
name
not
in
tmp_outs
,
forward_op
.
outputs
())
if
len
(
no_tmp_out
)
!=
1
:
raise
ValueError
(
"non temp out_names should be 1"
)
in_names
=
forward_op
.
inputs
()
for
no_grad
in
no_grad_set
:
if
no_grad
not
in
in_names
:
raise
ValueError
(
"no_grad should be in in_names"
)
backward_op
=
core
.
Operator
.
backward
(
forward_op
,
no_grad_set
)
places
=
[
core
.
CPUPlace
()]
if
not
only_cpu
and
core
.
is_compile_gpu
()
and
backward_op
.
support_gpu
():
places
.
append
(
core
.
GPUPlace
(
0
))
numeric_grad
=
dict
()
# get numeric gradient
for
check_name
in
inputs_to_check
:
numeric_grad
[
check_name
]
=
\
get_numeric_gradient
(
forward_op
,
input_vars
,
output_name
,
check_name
)
# get operator gradient according to different device
for
place
in
places
:
scope
=
core
.
Scope
()
ctx
=
core
.
DeviceContext
.
create
(
place
)
# create input var and set value
for
name
,
value
in
input_vars
.
iteritems
():
if
name
not
in
in_names
:
raise
ValueError
(
name
+
" not in op.inputs_"
)
var
=
scope
.
new_var
(
name
).
get_tensor
()
var
.
set_dims
(
value
.
shape
)
var
.
set
(
value
,
place
)
# create output var
for
out_name
in
forward_op
.
outputs
():
scope
.
new_var
(
out_name
).
get_tensor
()
# infer the shape of output var and compute/set value of output var
forward_op
.
infer_shape
(
scope
)
forward_op
.
run
(
scope
,
ctx
)
# create output grad var
# set shape as the output var
# set value of this grad to ones
for
name
in
forward_op
.
outputs
():
out_tensor
=
scope
.
find_var
(
name
).
get_tensor
()
grad_tensor
=
scope
.
new_var
(
grad_var_name
(
name
)).
get_tensor
()
grad_tensor
.
set_dims
(
out_tensor
.
shape
())
data
=
1.0
*
numpy
.
ones
(
out_tensor
.
shape
())
grad_tensor
.
set
(
data
,
place
)
# create input grad var
for
name
in
backward_op
.
outputs
():
scope
.
new_var
(
name
).
get_tensor
()
# infer the shape of input gradient var and compute/set it's value
# with backward op
backward_op
.
infer_shape
(
scope
)
backward_op
.
run
(
scope
,
ctx
)
if
isinstance
(
place
,
core
.
CPUPlace
):
msg
=
"CPU kernel gradient is not close to numeric gradient"
else
:
if
isinstance
(
place
,
core
.
GPUPlace
):
msg
=
"GPU kernel gradient is not close to numeric gradient"
else
:
raise
ValueError
(
"unknown place "
+
type
(
place
))
self
.
assertTrue
(
self
.
__is_close
(
numeric_grad
,
scope
,
max_relative_error
),
msg
)
if
__name__
==
'__main__'
:
class
GetNumericGradientTest
(
unittest
.
TestCase
):
...
...
@@ -87,4 +209,28 @@ if __name__ == '__main__':
arr
=
get_numeric_gradient
(
add_op
,
{
'X'
:
x
,
"Y"
:
y
},
'Z'
,
'X'
)
self
.
assertAlmostEqual
(
arr
.
mean
(),
1.0
,
delta
=
1e-2
)
def
test_softmax_op
(
self
):
def
stable_softmax
(
x
):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx
=
x
-
numpy
.
max
(
x
)
exps
=
numpy
.
exp
(
shiftx
)
return
exps
/
numpy
.
sum
(
exps
)
def
label_softmax_grad
(
Y
,
dY
):
dX
=
Y
*
0.0
for
i
in
range
(
Y
.
shape
[
0
]):
d
=
numpy
.
dot
(
Y
[
i
,
:],
dY
[
i
,
:])
dX
[
i
,
:]
=
Y
[
i
,
:]
*
(
dY
[
i
,
:]
-
d
)
return
dX
softmax_op
=
Operator
(
"softmax"
,
X
=
"X"
,
Y
=
"Y"
)
X
=
numpy
.
random
.
random
((
2
,
2
)).
astype
(
"float32"
)
Y
=
numpy
.
apply_along_axis
(
stable_softmax
,
1
,
X
)
dY
=
numpy
.
ones
(
Y
.
shape
)
dX
=
label_softmax_grad
(
Y
,
dY
)
arr
=
get_numeric_gradient
(
softmax_op
,
{
"X"
:
X
},
'Y'
,
'X'
)
numpy
.
testing
.
assert_almost_equal
(
arr
,
dX
,
decimal
=
1e-2
)
unittest
.
main
()
python/paddle/v2/framework/tests/op_test_util.py
浏览文件 @
6535a7b0
import
paddle.v2.framework.core
as
core
import
unittest
import
numpy
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
...
...
@@ -24,7 +23,7 @@ class OpTestMeta(type):
scope
=
core
.
Scope
()
kwargs
=
dict
()
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compile_gpu
()
and
core
.
Operator
.
support_gpu
(
self
.
type
)
:
if
core
.
is_compile_gpu
():
places
.
append
(
core
.
GPUPlace
(
0
))
for
place
in
places
:
...
...
@@ -53,6 +52,8 @@ class OpTestMeta(type):
kwargs
[
attr_name
]
=
self
.
attrs
[
attr_name
]
op
=
Operator
(
self
.
type
,
**
kwargs
)
if
isinstance
(
place
,
core
.
GPUPlace
)
and
not
op
.
support_gpu
():
return
op
.
infer_shape
(
scope
)
...
...
python/paddle/v2/framework/tests/test_cross_entropy_op.py
浏览文件 @
6535a7b0
import
unittest
import
numpy
from
op_test_util
import
OpTestMeta
from
gradient_checker
import
GradientChecker
,
create_op
class
Test
SGD
(
unittest
.
TestCase
):
class
Test
CrossEntropy
(
unittest
.
TestCase
):
__metaclass__
=
OpTestMeta
def
setUp
(
self
):
...
...
@@ -20,7 +21,18 @@ class TestSGD(unittest.TestCase):
self
.
outputs
=
{
'Y'
:
numpy
.
array
(
Y
).
astype
(
"float32"
)}
# TODO(superjom) add gradient check
class
CrossEntropyGradOpTest
(
GradientChecker
):
def
test_softmax_grad
(
self
):
op
=
create_op
(
"onehot_cross_entropy"
)
batch_size
=
100
class_num
=
10
inputs
=
{
"X"
:
numpy
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
),
"label"
:
(
class_num
/
2
)
*
numpy
.
ones
(
batch_size
).
astype
(
"int32"
)
}
self
.
check_grad
(
op
,
inputs
,
set
(
"X"
),
"Y"
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/v2/framework/tests/test_softmax_op.py
浏览文件 @
6535a7b0
import
unittest
import
numpy
as
np
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
from
gradient_checker
import
GradientChecker
,
create_op
from
op_test_util
import
OpTestMeta
...
...
@@ -25,62 +24,11 @@ class TestSoftmaxOp(unittest.TestCase):
}
class
TestSoftmaxGradOp
(
unittest
.
TestCase
):
def
test_softmax_grad
(
self
):
op
=
Operator
(
'softmax'
,
X
=
"X"
,
Y
=
"Y"
)
backward_op
=
core
.
Operator
.
backward
(
op
,
set
())
self
.
assertEqual
(
backward_op
.
type
(),
"softmax_grad"
)
expected
=
'''Op(softmax_grad), inputs:(X, Y, Y@GRAD), outputs:(X@GRAD).'''
self
.
assertEqual
(
expected
,
str
(
backward_op
))
batch_size
=
3
class_num
=
5
# Initialize X and add 1e-2 for numerical stability
Y
=
np
.
random
.
rand
(
batch_size
,
class_num
).
astype
(
np
.
float32
)
Y
=
Y
+
1e-2
dY
=
np
.
random
.
rand
(
batch_size
,
class_num
).
astype
(
np
.
float32
)
# Reference implementation of cross entropy with soft labels
def
label_softmax_grad
(
Y
,
dY
):
dX
=
Y
*
0.0
for
i
in
range
(
batch_size
):
d
=
np
.
dot
(
Y
[
i
,
:],
dY
[
i
,
:])
dX
[
i
,
:]
=
Y
[
i
,
:]
*
(
dY
[
i
,
:]
-
d
)
return
dX
expected
=
label_softmax_grad
(
Y
,
dY
)
scope
=
core
.
Scope
()
places
=
[]
places
.
append
(
core
.
CPUPlace
())
if
core
.
is_compile_gpu
():
places
.
append
(
core
.
GPUPlace
(
0
))
for
place
in
places
:
y
=
scope
.
new_var
(
"Y"
)
y_tensor
=
y
.
get_tensor
()
y_tensor
.
set_dims
([
batch_size
,
class_num
])
y_tensor
.
alloc_float
(
place
)
y_tensor
.
set
(
Y
,
place
)
dy
=
scope
.
new_var
(
"Y@GRAD"
)
dy_tensor
=
dy
.
get_tensor
()
dy_tensor
.
set_dims
([
batch_size
,
class_num
])
dy_tensor
.
alloc_float
(
place
)
dy_tensor
.
set
(
dY
,
place
)
x
=
scope
.
new_var
(
"X"
)
dx
=
scope
.
new_var
(
"X@GRAD"
)
tensor
=
scope
.
find_var
(
"X@GRAD"
).
get_tensor
()
backward_op
.
infer_shape
(
scope
)
self
.
assertEqual
([
batch_size
,
class_num
],
tensor
.
shape
())
ctx
=
core
.
DeviceContext
.
create
(
place
)
backward_op
.
run
(
scope
,
ctx
)
actual
=
np
.
array
(
tensor
)
np
.
testing
.
assert_almost_equal
(
actual
,
expected
,
decimal
=
3
)
class
SoftmaxGradOpTest
(
GradientChecker
):
def
test_softmax
(
self
):
op
=
create_op
(
"softmax"
)
inputs
=
{
"X"
:
np
.
random
.
uniform
(
0.1
,
1
,
[
10
,
10
]).
astype
(
"float32"
)}
self
.
check_grad
(
op
,
inputs
,
set
(
"X"
),
"Y"
)
if
__name__
==
'__main__'
:
...
...
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