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体验新版 GitCode,发现更多精彩内容 >>
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bfbbe19f
编写于
4月 24, 2018
作者:
C
chengduo
提交者:
GitHub
4月 24, 2018
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差异文件
Merge pull request #10150 from chengduoZH/fix_elementwise_gradient
Fix elementwise_gradient bug
上级
5ce57555
0f5d5b1f
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
105 addition
and
1 deletion
+105
-1
paddle/fluid/operators/elementwise_op_function.h
paddle/fluid/operators/elementwise_op_function.h
+2
-1
python/paddle/fluid/tests/unittests/test_elementwise_gradient_op.py
...dle/fluid/tests/unittests/test_elementwise_gradient_op.py
+103
-0
未找到文件。
paddle/fluid/operators/elementwise_op_function.h
浏览文件 @
bfbbe19f
...
...
@@ -356,8 +356,8 @@ __device__ T reduceSum(T val, int tid, int len) {
// I use Warp-Level Parallelism and assume the Warp size
// is 32 which may be different for different GPU,
// but most card's warp size is 32.
__shared__
T
shm
[
32
];
const
int
warpSize
=
32
;
__shared__
T
shm
[
warpSize
];
unsigned
mask
=
0u
;
CREATE_SHFL_MASK
(
mask
,
tid
<
len
);
...
...
@@ -371,6 +371,7 @@ __device__ T reduceSum(T val, int tid, int len) {
if
(
tid
%
warpSize
==
0
)
{
shm
[
tid
/
warpSize
]
=
val
;
}
__syncthreads
();
CREATE_SHFL_MASK
(
mask
,
tid
<
warpSize
);
...
...
python/paddle/fluid/tests/unittests/test_elementwise_gradient_op.py
0 → 100644
浏览文件 @
bfbbe19f
# 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
numpy
as
np
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
class
TestElementWiseAddOp
(
unittest
.
TestCase
):
def
__assert_close
(
self
,
tensor
,
np_array
,
msg
,
atol
=
1e-4
):
self
.
assertTrue
(
np
.
allclose
(
np
.
array
(
tensor
),
np_array
,
atol
=
atol
),
msg
)
def
check_forward_backward
(
self
):
def
test_with_place
(
place
):
out_grad
=
np
.
random
.
random_sample
(
self
.
x
.
shape
).
astype
(
np
.
float32
)
x_grad
=
out_grad
sum_axis
=
range
(
0
,
len
(
self
.
x
.
shape
))
del
sum_axis
[
self
.
axis
]
y_grad
=
np
.
sum
(
out_grad
,
axis
=
tuple
(
sum_axis
))
var_dict
=
locals
()
var_dict
[
'y'
]
=
self
.
y
var_dict
[
'x'
]
=
self
.
x
var_dict
[
'out'
]
=
self
.
out
var_dict
[
'y@GRAD'
]
=
y_grad
var_dict
[
'x@GRAD'
]
=
x_grad
var_dict
[
'out@GRAD'
]
=
out_grad
var_names
=
[
'x'
,
'y'
,
'out'
,
'y@GRAD'
,
'x@GRAD'
,
'out@GRAD'
]
ground_truth
=
{
name
:
var_dict
[
name
]
for
name
in
var_names
}
program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
program
):
block
=
program
.
global_block
()
for
name
in
ground_truth
:
block
.
create_var
(
name
=
name
,
dtype
=
'float32'
,
shape
=
ground_truth
[
name
].
shape
)
elementwise_add_op
=
block
.
append_op
(
type
=
"elementwise_add"
,
inputs
=
{
"X"
:
block
.
var
(
'x'
),
"Y"
:
block
.
var
(
'y'
),
},
outputs
=
{
"Out"
:
block
.
var
(
'out'
),
},
attrs
=
{
"axis"
:
self
.
axis
,
})
# generate backward op_desc
grad_op_desc_list
,
op_grad_to_var
=
core
.
get_grad_op_desc
(
elementwise_add_op
.
desc
,
set
(),
[])
grad_op_desc
=
grad_op_desc_list
[
0
]
new_op_desc
=
block
.
desc
.
append_op
()
new_op_desc
.
copy_from
(
grad_op_desc
)
for
var_name
in
grad_op_desc
.
output_arg_names
():
block
.
desc
.
var
(
var_name
.
encode
(
"ascii"
))
grad_op_desc
.
infer_var_type
(
block
.
desc
)
grad_op_desc
.
infer_shape
(
block
.
desc
)
for
arg
in
grad_op_desc
.
output_arg_names
():
grad_var
=
block
.
desc
.
find_var
(
arg
.
encode
(
"ascii"
))
grad_var
.
set_dtype
(
core
.
VarDesc
.
VarType
.
FP32
)
exe
=
fluid
.
Executor
(
place
)
out
=
exe
.
run
(
program
,
feed
=
{
name
:
var_dict
[
name
]
for
name
in
[
'x'
,
'y'
,
'out@GRAD'
]
},
fetch_list
=
[
'x@GRAD'
,
'y@GRAD'
])
self
.
__assert_close
(
x_grad
,
out
[
0
],
"x@GRAD"
)
self
.
__assert_close
(
y_grad
,
out
[
1
],
"y@GRAD"
,
atol
=
1.4
)
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
()
and
core
.
op_support_gpu
(
"elementwise_add"
):
places
.
append
(
core
.
CUDAPlace
(
0
))
for
place
in
places
:
test_with_place
(
place
)
def
test_check_forward_backward_with_scale_and_bias
(
self
):
np
.
random
.
seed
(
123
)
self
.
x
=
np
.
random
.
random
((
4
,
32
,
220
,
220
)).
astype
(
np
.
float32
)
self
.
y
=
np
.
random
.
random
((
32
)).
astype
(
np
.
float32
)
self
.
out
=
self
.
x
+
self
.
y
.
reshape
(
1
,
32
,
1
,
1
)
self
.
axis
=
1
self
.
check_forward_backward
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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