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
夏有凉风,冬有雪
Paddle
提交
272a272b
P
Paddle
项目概览
夏有凉风,冬有雪
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
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
1
Issue
1
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 >>
提交
272a272b
编写于
11月 07, 2017
作者:
Y
Yang Yu
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of github.com:baidu/Paddle into feature/shrink_memory_op
上级
01425309
c365c61a
变更
44
隐藏空白更改
内联
并排
Showing
44 changed file
with
984 addition
and
243 deletion
+984
-243
benchmark/paddle/image/resnet.py
benchmark/paddle/image/resnet.py
+213
-0
benchmark/paddle/image/run_mkldnn.sh
benchmark/paddle/image/run_mkldnn.sh
+14
-16
doc/design/float16.md
doc/design/float16.md
+1
-1
paddle/framework/ddim.cc
paddle/framework/ddim.cc
+1
-1
paddle/framework/ddim.h
paddle/framework/ddim.h
+1
-1
paddle/framework/lod_rank_table.cc
paddle/framework/lod_rank_table.cc
+1
-0
paddle/framework/lod_tensor.cc
paddle/framework/lod_tensor.cc
+31
-19
paddle/framework/lod_tensor.h
paddle/framework/lod_tensor.h
+5
-4
paddle/framework/lod_tensor_test.cc
paddle/framework/lod_tensor_test.cc
+20
-19
paddle/framework/op_registry.h
paddle/framework/op_registry.h
+1
-2
paddle/framework/operator.cc
paddle/framework/operator.cc
+34
-3
paddle/framework/operator.h
paddle/framework/operator.h
+27
-52
paddle/framework/operator_test.cc
paddle/framework/operator_test.cc
+2
-2
paddle/framework/var_desc.cc
paddle/framework/var_desc.cc
+4
-2
paddle/gserver/layers/MKLDNNFcLayer.cpp
paddle/gserver/layers/MKLDNNFcLayer.cpp
+2
-4
paddle/gserver/layers/MKLDNNLayer.cpp
paddle/gserver/layers/MKLDNNLayer.cpp
+5
-9
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+11
-2
paddle/operators/accuracy_op.cc
paddle/operators/accuracy_op.cc
+4
-3
paddle/operators/array_to_lod_tensor_op.cc
paddle/operators/array_to_lod_tensor_op.cc
+152
-0
paddle/operators/auc_op.cc
paddle/operators/auc_op.cc
+4
-3
paddle/operators/batch_norm_op.cc
paddle/operators/batch_norm_op.cc
+4
-2
paddle/operators/crf_decoding_op.cc
paddle/operators/crf_decoding_op.cc
+4
-2
paddle/operators/cross_entropy_op.cc
paddle/operators/cross_entropy_op.cc
+8
-4
paddle/operators/fill_constant_batch_size_like_op.cc
paddle/operators/fill_constant_batch_size_like_op.cc
+4
-2
paddle/operators/fill_constant_op.cc
paddle/operators/fill_constant_op.cc
+3
-2
paddle/operators/gather_op.cc
paddle/operators/gather_op.cc
+8
-4
paddle/operators/gaussian_random_op.cc
paddle/operators/gaussian_random_op.cc
+4
-2
paddle/operators/linear_chain_crf_op.cc
paddle/operators/linear_chain_crf_op.cc
+10
-5
paddle/operators/lod_rank_table_op.cc
paddle/operators/lod_rank_table_op.cc
+1
-0
paddle/operators/lod_tensor_to_array_op.cc
paddle/operators/lod_tensor_to_array_op.cc
+143
-0
paddle/operators/lookup_table_op.cc
paddle/operators/lookup_table_op.cc
+8
-4
paddle/operators/lstm_op.cc
paddle/operators/lstm_op.cc
+8
-6
paddle/operators/multiplex_op.cc
paddle/operators/multiplex_op.cc
+8
-4
paddle/operators/positive_negative_pair_op.cc
paddle/operators/positive_negative_pair_op.cc
+4
-2
paddle/operators/precision_recall_op.cc
paddle/operators/precision_recall_op.cc
+4
-2
paddle/operators/scatter_op.cc
paddle/operators/scatter_op.cc
+8
-4
paddle/operators/sequence_pool_op.cc
paddle/operators/sequence_pool_op.cc
+4
-2
paddle/operators/softmax_with_cross_entropy_op.cc
paddle/operators/softmax_with_cross_entropy_op.cc
+9
-5
paddle/operators/sum_op.cc
paddle/operators/sum_op.cc
+10
-6
paddle/operators/uniform_random_op.cc
paddle/operators/uniform_random_op.cc
+4
-2
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+44
-39
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+24
-0
python/paddle/v2/framework/tests/test_lod_rank_table.py
python/paddle/v2/framework/tests/test_lod_rank_table.py
+0
-1
python/paddle/v2/framework/tests/test_lod_tensor_array_ops.py
...on/paddle/v2/framework/tests/test_lod_tensor_array_ops.py
+127
-0
未找到文件。
benchmark/paddle/image/resnet.py
0 → 100644
浏览文件 @
272a272b
#!/usr/bin/env python
from
paddle.trainer_config_helpers
import
*
height
=
224
width
=
224
num_class
=
1000
batch_size
=
get_config_arg
(
'batch_size'
,
int
,
64
)
layer_num
=
get_config_arg
(
"layer_num"
,
int
,
50
)
is_test
=
get_config_arg
(
"is_test"
,
bool
,
False
)
args
=
{
'height'
:
height
,
'width'
:
width
,
'color'
:
True
,
'num_class'
:
num_class
}
define_py_data_sources2
(
"train.list"
,
None
,
module
=
"provider"
,
obj
=
"process"
,
args
=
args
)
settings
(
batch_size
=
batch_size
,
learning_rate
=
0.01
/
batch_size
,
learning_method
=
MomentumOptimizer
(
0.9
),
regularization
=
L2Regularization
(
0.0005
*
batch_size
))
#######################Network Configuration #############
def
conv_bn_layer
(
name
,
input
,
filter_size
,
num_filters
,
stride
,
padding
,
channels
=
None
,
active_type
=
ReluActivation
()):
"""
A wrapper for conv layer with batch normalization layers.
Note:
conv layer has no activation.
"""
tmp
=
img_conv_layer
(
name
=
name
+
"_conv"
,
input
=
input
,
filter_size
=
filter_size
,
num_channels
=
channels
,
num_filters
=
num_filters
,
stride
=
stride
,
padding
=
padding
,
act
=
LinearActivation
(),
bias_attr
=
False
)
return
batch_norm_layer
(
name
=
name
+
"_bn"
,
input
=
tmp
,
act
=
active_type
,
use_global_stats
=
is_test
)
def
bottleneck_block
(
name
,
input
,
num_filters1
,
num_filters2
):
"""
A wrapper for bottlenect building block in ResNet.
Last conv_bn_layer has no activation.
Addto layer has activation of relu.
"""
last_name
=
conv_bn_layer
(
name
=
name
+
'_branch2a'
,
input
=
input
,
filter_size
=
1
,
num_filters
=
num_filters1
,
stride
=
1
,
padding
=
0
)
last_name
=
conv_bn_layer
(
name
=
name
+
'_branch2b'
,
input
=
last_name
,
filter_size
=
3
,
num_filters
=
num_filters1
,
stride
=
1
,
padding
=
1
)
last_name
=
conv_bn_layer
(
name
=
name
+
'_branch2c'
,
input
=
last_name
,
filter_size
=
1
,
num_filters
=
num_filters2
,
stride
=
1
,
padding
=
0
,
active_type
=
LinearActivation
())
return
addto_layer
(
name
=
name
+
"_addto"
,
input
=
[
input
,
last_name
],
act
=
ReluActivation
())
def
mid_projection
(
name
,
input
,
num_filters1
,
num_filters2
,
stride
=
2
):
"""
A wrapper for middile projection in ResNet.
projection shortcuts are used for increasing dimensions,
and other shortcuts are identity
branch1: projection shortcuts are used for increasing
dimensions, has no activation.
branch2x: bottleneck building block, shortcuts are identity.
"""
# stride = 2
branch1
=
conv_bn_layer
(
name
=
name
+
'_branch1'
,
input
=
input
,
filter_size
=
1
,
num_filters
=
num_filters2
,
stride
=
stride
,
padding
=
0
,
active_type
=
LinearActivation
())
last_name
=
conv_bn_layer
(
name
=
name
+
'_branch2a'
,
input
=
input
,
filter_size
=
1
,
num_filters
=
num_filters1
,
stride
=
stride
,
padding
=
0
)
last_name
=
conv_bn_layer
(
name
=
name
+
'_branch2b'
,
input
=
last_name
,
filter_size
=
3
,
num_filters
=
num_filters1
,
stride
=
1
,
padding
=
1
)
last_name
=
conv_bn_layer
(
name
=
name
+
'_branch2c'
,
input
=
last_name
,
filter_size
=
1
,
num_filters
=
num_filters2
,
stride
=
1
,
padding
=
0
,
active_type
=
LinearActivation
())
return
addto_layer
(
name
=
name
+
"_addto"
,
input
=
[
branch1
,
last_name
],
act
=
ReluActivation
())
img
=
data_layer
(
name
=
'image'
,
size
=
height
*
width
*
3
)
def
deep_res_net
(
res2_num
=
3
,
res3_num
=
4
,
res4_num
=
6
,
res5_num
=
3
):
"""
A wrapper for 50,101,152 layers of ResNet.
res2_num: number of blocks stacked in conv2_x
res3_num: number of blocks stacked in conv3_x
res4_num: number of blocks stacked in conv4_x
res5_num: number of blocks stacked in conv5_x
"""
# For ImageNet
# conv1: 112x112
tmp
=
conv_bn_layer
(
"conv1"
,
input
=
img
,
filter_size
=
7
,
channels
=
3
,
num_filters
=
64
,
stride
=
2
,
padding
=
3
)
tmp
=
img_pool_layer
(
name
=
"pool1"
,
input
=
tmp
,
pool_size
=
3
,
stride
=
2
)
# conv2_x: 56x56
tmp
=
mid_projection
(
name
=
"res2_1"
,
input
=
tmp
,
num_filters1
=
64
,
num_filters2
=
256
,
stride
=
1
)
for
i
in
xrange
(
2
,
res2_num
+
1
,
1
):
tmp
=
bottleneck_block
(
name
=
"res2_"
+
str
(
i
),
input
=
tmp
,
num_filters1
=
64
,
num_filters2
=
256
)
# conv3_x: 28x28
tmp
=
mid_projection
(
name
=
"res3_1"
,
input
=
tmp
,
num_filters1
=
128
,
num_filters2
=
512
)
for
i
in
xrange
(
2
,
res3_num
+
1
,
1
):
tmp
=
bottleneck_block
(
name
=
"res3_"
+
str
(
i
),
input
=
tmp
,
num_filters1
=
128
,
num_filters2
=
512
)
# conv4_x: 14x14
tmp
=
mid_projection
(
name
=
"res4_1"
,
input
=
tmp
,
num_filters1
=
256
,
num_filters2
=
1024
)
for
i
in
xrange
(
2
,
res4_num
+
1
,
1
):
tmp
=
bottleneck_block
(
name
=
"res4_"
+
str
(
i
),
input
=
tmp
,
num_filters1
=
256
,
num_filters2
=
1024
)
# conv5_x: 7x7
tmp
=
mid_projection
(
name
=
"res5_1"
,
input
=
tmp
,
num_filters1
=
512
,
num_filters2
=
2048
)
for
i
in
xrange
(
2
,
res5_num
+
1
,
1
):
tmp
=
bottleneck_block
(
name
=
"res5_"
+
str
(
i
),
input
=
tmp
,
num_filters1
=
512
,
num_filters2
=
2048
)
tmp
=
img_pool_layer
(
name
=
'avgpool'
,
input
=
tmp
,
pool_size
=
7
,
stride
=
1
,
pool_type
=
AvgPooling
())
return
fc_layer
(
input
=
tmp
,
size
=
num_class
,
act
=
SoftmaxActivation
())
if
layer_num
==
50
:
resnet
=
deep_res_net
(
3
,
4
,
6
,
3
)
elif
layer_num
==
101
:
resnet
=
deep_res_net
(
3
,
4
,
23
,
3
)
elif
layer_num
==
152
:
resnet
=
deep_res_net
(
3
,
8
,
36
,
3
)
else
:
print
(
"Wrong layer number."
)
lbl
=
data_layer
(
name
=
"label"
,
size
=
num_class
)
loss
=
cross_entropy
(
name
=
'loss'
,
input
=
resnet
,
label
=
lbl
)
inputs
(
img
,
lbl
)
outputs
(
loss
)
benchmark/paddle/image/run_mkldnn.sh
浏览文件 @
272a272b
...
...
@@ -5,22 +5,23 @@ function train() {
export
OMP_DYNAMIC
=
"FALSE"
export
KMP_AFFINITY
=
"granularity=fine,compact,0,0"
topology
=
$1
bs
=
$2
use_mkldnn
=
$3
if
[
$3
==
"True"
]
;
then
layer_num
=
$2
bs
=
$3
use_mkldnn
=
$4
if
[
$4
==
"True"
]
;
then
thread
=
1
log
=
"logs/
${
topology
}
-mkldnn-
${
bs
}
.log"
elif
[
$
3
==
"False"
]
;
then
log
=
"logs/
${
topology
}
-
${
layer_num
}
-
mkldnn-
${
bs
}
.log"
elif
[
$
4
==
"False"
]
;
then
thread
=
`
nproc
`
# each trainer_count use only 1 core to avoid conflict
export
OMP_NUM_THREADS
=
1
export
MKL_NUM_THREADS
=
1
log
=
"logs/
${
topology
}
-
${
thread
}
mklml-
${
bs
}
.log"
log
=
"logs/
${
topology
}
-
${
layer_num
}
-
${
thread
}
mklml-
${
bs
}
.log"
else
echo
"Wrong input
$3
, use True or False."
exit
0
fi
args
=
"batch_size=
${
bs
}
"
args
=
"batch_size=
${
bs
}
,layer_num=
${
layer_num
}
"
config
=
"
${
topology
}
.py"
paddle train
--job
=
time
\
--config
=
$config
\
...
...
@@ -40,12 +41,9 @@ if [ ! -d "logs" ]; then
mkdir
logs
fi
#========== mkldnn ==========#
train vgg 64 True
train vgg 128 True
train vgg 256 True
#========== mklml ===========#
train vgg 64 False
train vgg 128 False
train vgg 256 False
for
use_mkldnn
in
True False
;
do
for
batchsize
in
64 128 256
;
do
train vgg 19
$batchsize
$use_mkldnn
train resnet 50
$batchsize
$use_mkldnn
done
done
doc/design/float16.md
浏览文件 @
272a272b
...
...
@@ -55,6 +55,6 @@ After float16 class is available, some of the future items are below:
-
Update pybind/tensor_py.h to bind c++ float16 with numpy float16.
-
Modify
`
IndicateData
Type()`
method in
`framework/operator.h`
to make it compatible with float16.
-
Modify
`
GetKernel
Type()`
method in
`framework/operator.h`
to make it compatible with float16.
-
Create a type-casting operator that can convert the data type in tensor between float16 and other types.
paddle/framework/ddim.cc
浏览文件 @
272a272b
...
...
@@ -117,7 +117,7 @@ int64_t DDim::operator[](int idx) const {
return
boost
::
apply_visitor
(
DynamicConstIndexer
(
idx
),
var
);
}
int
64_t
DDim
::
size
()
const
{
return
arity
(
*
this
);
}
int
DDim
::
size
()
const
{
return
arity
(
*
this
);
}
bool
DDim
::
operator
==
(
DDim
d
)
const
{
if
(
var
.
which
()
!=
d
.
getVar
().
which
())
{
...
...
paddle/framework/ddim.h
浏览文件 @
272a272b
...
...
@@ -71,7 +71,7 @@ struct DDim {
DDim
operator
*
(
DDim
d
)
const
;
int
64_t
size
()
const
;
int
size
()
const
;
};
/**
...
...
paddle/framework/lod_rank_table.cc
浏览文件 @
272a272b
...
...
@@ -31,6 +31,7 @@ void LoDRankTable::Reset(const LoD& lod, size_t level) {
TableItem
item
;
item
.
index
=
i
;
item
.
length
=
vec
[
i
+
1
]
-
vec
[
i
];
VLOG
(
10
)
<<
"Add item to rank table "
<<
item
.
index
<<
" "
<<
item
.
length
;
items_
.
emplace_back
(
item
);
}
// NOTE(yuyang18):
...
...
paddle/framework/lod_tensor.cc
浏览文件 @
272a272b
...
...
@@ -27,6 +27,20 @@
namespace
paddle
{
namespace
framework
{
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
LoD
&
lod
)
{
os
<<
"{"
;
for
(
auto
&
v
:
lod
)
{
os
<<
"{"
;
for
(
auto
&
i
:
v
)
{
os
<<
i
<<
","
;
}
os
<<
"}"
;
}
os
<<
"}"
;
return
os
;
}
LoD
SliceLevels
(
const
LoD
&
in
,
size_t
level_begin
,
size_t
level_end
)
{
LoD
new_lod
;
new_lod
.
reserve
(
level_end
-
level_begin
);
...
...
@@ -136,37 +150,35 @@ void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
ShareDataWith
(
Slice
(
begin
,
end
));
}
void
GetFineGrainedLoDLength
(
const
LoD
&
lod
,
size_t
start_idx
,
size_t
end_idx
,
std
::
vector
<
std
::
vector
<
size_t
>>*
lod_length
,
size_t
*
start_offset
)
{
lod_length
->
clear
();
PADDLE_ENFORCE
(
start_idx
<
lod
.
size
()
-
1
,
"start_idx should be >= 0 and < lod.size() - 1."
);
PADDLE_ENFORCE
(
end_idx
<
lod
.
size
(),
"end_idx should be >= 0 and < lod.size()."
);
PADDLE_ENFORCE_LE
(
start_idx
,
end_idx
,
"start_idx should be less than end_idx."
);
for
(
size_t
level_idx
=
0
;
level_idx
<
lod
.
size
();
++
level_idx
)
{
using
LoDAndOffset
=
std
::
pair
<
LoD
,
std
::
pair
<
size_t
,
size_t
>>
;
LoDAndOffset
GetSubLoDAndAbsoluteOffset
(
const
LoD
&
lod
,
size_t
start_idx
,
size_t
end_idx
,
size_t
start_level
)
{
LoD
sub_lod
;
for
(
size_t
level_idx
=
start_level
;
level_idx
<
lod
.
size
();
++
level_idx
)
{
PADDLE_ENFORCE_LE
(
start_idx
,
end_idx
);
PADDLE_ENFORCE_LT
(
end_idx
,
lod
[
level_idx
].
size
());
std
::
vector
<
size_t
>
level_lens
;
for
(
size_t
i
=
start_idx
;
i
<
end_idx
;
++
i
)
{
level_lens
.
push_back
(
lod
[
level_idx
][
i
+
1
]
-
lod
[
level_idx
][
i
]);
}
lod_length
->
emplace_back
(
level_lens
);
sub_lod
.
emplace_back
(
level_lens
);
start_idx
=
lod
[
level_idx
][
start_idx
];
end_idx
=
lod
[
level_idx
][
end_idx
];
}
*
start_offset
=
start_idx
;
return
LoDAndOffset
{
sub_lod
,
{
start_idx
,
end_idx
}};
}
void
AppendLoD
(
LoD
*
lod
,
const
std
::
vector
<
std
::
vector
<
size_t
>>
&
lod_length
)
{
PADDLE_ENFORCE
_EQ
(
lod
->
size
(),
lod_length
.
size
(),
void
AppendLoD
(
LoD
*
lod
,
const
LoD
&
lod_length
)
{
PADDLE_ENFORCE
(
lod
->
empty
()
||
lod
->
size
()
==
lod_length
.
size
(),
"The lod_length should has the same size with the appended lod."
);
if
(
lod
->
empty
())
{
*
lod
=
LoD
(
lod_length
.
size
(),
std
::
vector
<
size_t
>
({
0
}));
}
for
(
size_t
i
=
0
;
i
<
lod
->
size
();
++
i
)
{
auto
&
level
=
(
*
lod
)[
i
];
if
(
level
.
empty
())
{
level
.
push_back
(
0
);
}
for
(
size_t
len
:
lod_length
[
i
])
{
level
.
push_back
(
level
.
back
()
+
len
);
}
...
...
paddle/framework/lod_tensor.h
浏览文件 @
272a272b
...
...
@@ -56,6 +56,8 @@ using Vector = thrust::host_vector<
*/
using
LoD
=
std
::
vector
<
Vector
<
size_t
>>
;
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
LoD
&
lod
);
/*
* Slice levels from a LoD.
* NOTE the lowest level should always be the absolute offsets of the underlying
...
...
@@ -181,11 +183,10 @@ LoDTensor LodExpand(const LoDTensor& source, const LoD& lod, size_t level,
return
tensor
;
}
void
GetFineGrainedLoDLength
(
const
LoD
&
lod
,
size_t
start_idx
,
size_t
end_idx
,
std
::
vector
<
std
::
vector
<
size_t
>>*
lod_length
,
size_t
*
start_offset
);
std
::
pair
<
LoD
,
std
::
pair
<
size_t
,
size_t
>>
GetSubLoDAndAbsoluteOffset
(
const
LoD
&
lod
,
size_t
start_idx
,
size_t
end_idx
,
size_t
start_level
);
void
AppendLoD
(
LoD
*
lod
,
const
std
::
vector
<
std
::
vector
<
size_t
>>
&
lod_length
);
void
AppendLoD
(
LoD
*
lod
,
const
LoD
&
lod_length
);
}
// namespace framework
}
// namespace paddle
paddle/framework/lod_tensor_test.cc
浏览文件 @
272a272b
...
...
@@ -146,43 +146,44 @@ TEST(LodExpand, test) {
TEST
(
LoD
,
GetFineGrainedLoDLength
)
{
LoD
lod
;
lod
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
,
4
,
5
}
);
lod
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
1
,
6
,
8
,
10
,
11
}
);
lod
.
push_back
(
std
::
vector
<
size_t
>
({
0
,
2
,
4
,
5
})
);
lod
.
push_back
(
std
::
vector
<
size_t
>
({
0
,
1
,
6
,
8
,
10
,
11
})
);
lod
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
,
5
,
7
,
10
,
12
,
15
,
17
,
20
,
24
,
26
,
29
}
);
std
::
vector
<
size_t
>
({
0
,
2
,
5
,
7
,
10
,
12
,
15
,
17
,
20
,
24
,
26
,
29
})
);
std
::
vector
<
std
::
vector
<
size_t
>>
lod_length
;
size_t
start_offset
;
paddle
::
framework
::
GetFineGrainedLoDLength
(
lod
,
1
,
2
,
&
lod_length
,
&
start_offset
);
auto
lod_and_offset
=
paddle
::
framework
::
GetSubLoDAndAbsoluteOffset
(
lod
,
1
,
2
,
0
);
LoD
lod_length
=
lod_and_offset
.
first
;
size_t
start_offset
=
lod_and_offset
.
second
.
first
;
size_t
end_offset
=
lod_and_offset
.
second
.
second
;
std
::
vector
<
std
::
vector
<
size_t
>>
expected
;
LoD
expected
;
expected
.
push_back
(
std
::
vector
<
size_t
>
{
2
});
expected
.
push_back
(
std
::
vector
<
size_t
>
{
2
,
2
});
expected
.
push_back
(
std
::
vector
<
size_t
>
{
2
,
3
,
4
,
2
});
EXPECT_EQ
(
lod_length
,
expected
);
EXPECT_EQ
(
start_offset
,
15UL
);
EXPECT_EQ
(
end_offset
,
26UL
);
}
TEST
(
LoD
,
AppendLoD
)
{
std
::
vector
<
std
::
vector
<
size_t
>>
lod_lens
;
lod_lens
.
push_back
(
std
::
vector
<
size_t
>
{
2
}
);
lod_lens
.
push_back
(
std
::
vector
<
size_t
>
{
2
,
2
}
);
lod_lens
.
push_back
(
std
::
vector
<
size_t
>
{
2
,
3
,
4
,
2
}
);
LoD
lod_lens
;
lod_lens
.
push_back
(
std
::
vector
<
size_t
>
({
2
})
);
lod_lens
.
push_back
(
std
::
vector
<
size_t
>
({
2
,
2
})
);
lod_lens
.
push_back
(
std
::
vector
<
size_t
>
({
2
,
3
,
4
,
2
})
);
LoD
origin
;
origin
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
}
);
origin
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
1
,
6
}
);
origin
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
,
5
,
7
,
10
,
12
,
15
}
);
origin
.
push_back
(
std
::
vector
<
size_t
>
({
0
,
2
})
);
origin
.
push_back
(
std
::
vector
<
size_t
>
({
0
,
1
,
6
})
);
origin
.
push_back
(
std
::
vector
<
size_t
>
({
0
,
2
,
5
,
7
,
10
,
12
,
15
})
);
paddle
::
framework
::
AppendLoD
(
&
origin
,
lod_lens
);
LoD
expected
;
expected
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
,
4
}
);
expected
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
1
,
6
,
8
,
10
}
);
expected
.
push_back
(
std
::
vector
<
size_t
>
({
0
,
2
,
4
})
);
expected
.
push_back
(
std
::
vector
<
size_t
>
({
0
,
1
,
6
,
8
,
10
})
);
expected
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
,
5
,
7
,
10
,
12
,
15
,
17
,
20
,
24
,
26
});
std
::
vector
<
size_t
>
({
0
,
2
,
5
,
7
,
10
,
12
,
15
,
17
,
20
,
24
,
26
}));
EXPECT_EQ
(
origin
,
expected
);
}
...
...
paddle/framework/op_registry.h
浏览文件 @
272a272b
...
...
@@ -92,8 +92,7 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
void
operator
()(
const
char
*
op_type
)
const
{
using
T
=
typename
KERNEL_TYPE
::
ELEMENT_TYPE
;
OperatorWithKernel
::
OpKernelKey
key
(
ToDataType
(
std
::
type_index
(
typeid
(
T
))),
PlaceType
());
OpKernelType
key
(
ToDataType
(
std
::
type_index
(
typeid
(
T
))),
PlaceType
());
OperatorWithKernel
::
AllOpKernels
()[
op_type
][
key
].
reset
(
new
KERNEL_TYPE
);
constexpr
auto
size
=
std
::
tuple_size
<
std
::
tuple
<
KernelTypes
...
>>::
value
;
...
...
paddle/framework/operator.cc
浏览文件 @
272a272b
...
...
@@ -254,8 +254,7 @@ std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
return
res
;
}
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
OperatorWithKernel
::
OpKernelKey
&
kernel_key
)
{
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
OpKernelType
&
kernel_key
)
{
os
<<
"place["
<<
kernel_key
.
place_
<<
"]:data_type["
<<
kernel_key
.
data_type_
<<
"]"
;
return
os
;
...
...
@@ -432,7 +431,7 @@ void OperatorWithKernel::Run(const Scope& scope,
// check if op[type] have kernel for kernel_key
OpKernelMap
&
kernels
=
kernels_iter
->
second
;
auto
kernel_key
=
OpKernelKey
(
IndicateDataType
(
ctx
),
dev_
ctx
);
auto
kernel_key
=
GetKernelType
(
ctx
);
auto
kernel_iter
=
kernels
.
find
(
kernel_key
);
if
(
kernel_iter
==
kernels
.
end
())
{
...
...
@@ -444,6 +443,38 @@ void OperatorWithKernel::Run(const Scope& scope,
// throws errors if have.
dev_ctx
.
Finish
();
}
OpKernelType
OperatorWithKernel
::
GetKernelType
(
const
ExecutionContext
&
ctx
)
const
{
return
OpKernelType
(
IndicateDataType
(
ctx
),
ctx
.
device_context
());
}
DataType
OperatorWithKernel
::
IndicateDataType
(
const
ExecutionContext
&
ctx
)
const
{
auto
&
scope
=
ctx
.
scope
();
int
data_type
=
-
1
;
for
(
auto
&
input
:
this
->
inputs_
)
{
for
(
auto
&
ipt_name
:
input
.
second
)
{
auto
*
var
=
scope
.
FindVar
(
ipt_name
);
if
(
var
!=
nullptr
)
{
const
Tensor
*
t
=
nullptr
;
if
(
var
->
IsType
<
Tensor
>
())
{
t
=
&
var
->
Get
<
Tensor
>
();
}
else
if
(
var
->
IsType
<
LoDTensor
>
())
{
t
=
&
var
->
Get
<
LoDTensor
>
();
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
t
=
&
(
var
->
Get
<
SelectedRows
>
().
value
());
}
if
(
t
!=
nullptr
)
{
int
tmp
=
static_cast
<
int
>
(
ToDataType
(
t
->
type
()));
PADDLE_ENFORCE
(
tmp
==
data_type
||
data_type
==
-
1
,
"DataType of Paddle Op %s must be the same."
,
Type
());
data_type
=
tmp
;
}
}
}
}
PADDLE_ENFORCE
(
data_type
!=
-
1
,
"DataType should be indicated by input"
);
return
static_cast
<
DataType
>
(
data_type
);
}
}
// namespace framework
}
// namespace paddle
paddle/framework/operator.h
浏览文件 @
272a272b
...
...
@@ -345,27 +345,10 @@ class OpKernel : public OpKernelBase {
using
ELEMENT_TYPE
=
T
;
};
class
OperatorWithKernel
:
public
OperatorBase
{
public:
struct
OpKernelKey
{
platform
::
Place
place_
;
DataType
data_type_
;
OpKernelKey
(
DataType
data_type
,
platform
::
Place
place
)
:
place_
(
place
),
data_type_
(
data_type
)
{}
OpKernelKey
(
DataType
data_type
,
const
platform
::
DeviceContext
&
dev_ctx
)
:
place_
(
dev_ctx
.
GetPlace
()),
data_type_
(
data_type
)
{}
bool
operator
==
(
const
OpKernelKey
&
o
)
const
{
return
platform
::
places_are_same_class
(
place_
,
o
.
place_
)
&&
data_type_
==
o
.
data_type_
;
}
};
struct
OpKernelHash
{
struct
OpKernelType
{
struct
Hash
{
std
::
hash
<
int
>
hash_
;
size_t
operator
()(
const
OpKernel
Key
&
key
)
const
{
size_t
operator
()(
const
OpKernel
Type
&
key
)
const
{
int
place
=
key
.
place_
.
which
();
int
data_type
=
static_cast
<
int
>
(
key
.
data_type_
);
int
pre_hash
=
data_type
<<
NUM_PLACE_TYPE_LIMIT_IN_BIT
|
...
...
@@ -374,9 +357,26 @@ class OperatorWithKernel : public OperatorBase {
}
};
platform
::
Place
place_
;
DataType
data_type_
;
OpKernelType
(
DataType
data_type
,
platform
::
Place
place
)
:
place_
(
place
),
data_type_
(
data_type
)
{}
OpKernelType
(
DataType
data_type
,
const
platform
::
DeviceContext
&
dev_ctx
)
:
place_
(
dev_ctx
.
GetPlace
()),
data_type_
(
data_type
)
{}
bool
operator
==
(
const
OpKernelType
&
o
)
const
{
return
platform
::
places_are_same_class
(
place_
,
o
.
place_
)
&&
data_type_
==
o
.
data_type_
;
}
};
class
OperatorWithKernel
:
public
OperatorBase
{
public:
using
OpKernelMap
=
std
::
unordered_map
<
OpKernel
Key
,
std
::
unique_ptr
<
OpKernelBase
>
,
OpKernelHash
>
;
std
::
unordered_map
<
OpKernel
Type
,
std
::
unique_ptr
<
OpKernelBase
>
,
OpKernel
Type
::
Hash
>
;
OperatorWithKernel
(
const
std
::
string
&
type
,
const
VariableNameMap
&
inputs
,
const
VariableNameMap
&
outputs
,
const
AttributeMap
&
attrs
)
...
...
@@ -404,40 +404,15 @@ class OperatorWithKernel : public OperatorBase {
}
protected:
virtual
OpKernelType
GetKernelType
(
const
ExecutionContext
&
ctx
)
const
;
private:
// indicate kernel DataType by input data. Defaultly all input data must be
// same.
virtual
DataType
IndicateDataType
(
const
ExecutionContext
&
ctx
)
const
{
auto
&
scope
=
ctx
.
scope
();
int
data_type
=
-
1
;
for
(
auto
&
input
:
this
->
inputs_
)
{
for
(
auto
&
ipt_name
:
input
.
second
)
{
auto
*
var
=
scope
.
FindVar
(
ipt_name
);
if
(
var
!=
nullptr
)
{
const
Tensor
*
t
=
nullptr
;
if
(
var
->
IsType
<
Tensor
>
())
{
t
=
&
var
->
Get
<
Tensor
>
();
}
else
if
(
var
->
IsType
<
LoDTensor
>
())
{
t
=
&
var
->
Get
<
LoDTensor
>
();
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
t
=
&
(
var
->
Get
<
SelectedRows
>
().
value
());
}
if
(
t
!=
nullptr
)
{
int
tmp
=
static_cast
<
int
>
(
ToDataType
(
t
->
type
()));
PADDLE_ENFORCE
(
tmp
==
data_type
||
data_type
==
-
1
,
"DataType of Paddle Op %s must be the same."
,
Type
());
data_type
=
tmp
;
}
}
}
}
PADDLE_ENFORCE
(
data_type
!=
-
1
,
"DataType should be indicated by input"
);
return
static_cast
<
DataType
>
(
data_type
);
}
DataType
IndicateDataType
(
const
ExecutionContext
&
ctx
)
const
;
};
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
OperatorWithKernel
::
OpKernelKey
&
kernel_key
);
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
OpKernelType
&
kernel_key
);
extern
bool
OpSupportGPU
(
const
std
::
string
&
op_type
);
...
...
paddle/framework/operator_test.cc
浏览文件 @
272a272b
...
...
@@ -114,8 +114,8 @@ class OpWithKernelTest : public OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{}
DataType
IndicateData
Type
(
const
ExecutionContext
&
ctx
)
const
override
{
return
DataType
::
FP32
;
OpKernelType
GetKernel
Type
(
const
ExecutionContext
&
ctx
)
const
override
{
return
OpKernelType
(
DataType
::
FP32
,
ctx
.
device_context
())
;
}
};
...
...
paddle/framework/var_desc.cc
浏览文件 @
272a272b
...
...
@@ -45,7 +45,8 @@ void VarDescBind::SetLoDLevel(int32_t lod_level) {
desc_
.
mutable_tensor_array
()
->
set_lod_level
(
lod_level
);
break
;
default:
PADDLE_THROW
(
"Tensor type=%d does not support LoDLevel"
,
desc_
.
type
());
PADDLE_THROW
(
"Tensor type=%d does not support LoDLevel"
,
desc_
.
tensor_array
().
lod_level
());
}
}
...
...
@@ -56,7 +57,8 @@ int32_t VarDescBind::GetLodLevel() const {
case
VarDesc
::
LOD_TENSOR_ARRAY
:
return
desc_
.
tensor_array
().
lod_level
();
default:
PADDLE_THROW
(
"Tensor type=%d does not support LoDLevel"
,
desc_
.
type
());
PADDLE_THROW
(
"Tensor type=%d does not support LoDLevel"
,
desc_
.
tensor_array
().
lod_level
());
}
}
...
...
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @
272a272b
...
...
@@ -60,18 +60,16 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
}
CHECK
(
wgtVal_
)
<<
"should have been initialized"
;
bool
hasNoSpatial_
=
ih_
==
1
&&
iw_
==
1
;
auto
targetDim
=
wgtVal_
->
getDims
();
auto
srcFmt
=
hasNoSpatial_
?
format
::
io
:
format
::
ihwo
;
auto
srcFmt
=
targetDim
.
size
()
==
2
?
format
::
io
:
format
::
ihwo
;
wgtVal_
->
reorderDataFrom
(
wgtVal_
,
srcFmt
,
targetDim
);
hasInitedWgt_
=
true
;
}
void
MKLDNNFcLayer
::
convertWeightsToPaddle
()
{
CHECK
(
wgtVal_
)
<<
"should have been initialized"
;
bool
hasNoSpatial_
=
ih_
==
1
&&
iw_
==
1
;
auto
targetDim
=
wgtVal_
->
getDims
();
auto
dstFmt
=
hasNoSpatial_
?
format
::
io
:
format
::
ihwo
;
auto
dstFmt
=
targetDim
.
size
()
==
2
?
format
::
io
:
format
::
ihwo
;
wgtVal_
->
reorderDataTo
(
wgtVal_
,
dstFmt
,
targetDim
);
}
...
...
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @
272a272b
...
...
@@ -181,21 +181,17 @@ void MKLDNNLayer::resetInValue(
auto
extPD
=
MKLDNNMatrix
::
createPrimitiveDesc
(
{
bs_
,
ic_
,
ih_
,
iw_
},
format
::
nchw
,
engine_
);
const
MatrixPtr
&
inMat
=
inputLayers_
[
inputIdx
]
->
getOutputValue
();
in
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
inMat
);
CHECK_EQ
(
inputIsOnlyMKLDNN
(),
in
!=
nullptr
);
if
(
in
==
nullptr
||
in
->
getFormat
()
==
format
::
nc
)
{
in
=
MKLDNNMatrix
::
create
(
extPD
,
inMat
);
}
extInVal_
=
isPaddleFormat
(
in
->
getFormat
())
?
in
:
nullptr
;
if
(
in
->
getFormat
()
==
format
::
nc
)
{
CHECK
(
ih_
==
1
&&
iw_
==
1
);
extInVal_
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
inMat
);
CHECK_EQ
(
inputIsOnlyMKLDNN
(),
extInVal_
!=
nullptr
);
if
(
extInVal_
==
nullptr
||
extInVal_
->
getFormat
()
==
format
::
nc
)
{
extInVal_
=
MKLDNNMatrix
::
create
(
extPD
,
inMat
);
}
in
=
extInVal_
;
if
(
nullptr
==
intPD
||
in
->
getPrimitiveDesc
()
==
*
intPD
)
{
return
;
}
// need create reorder
in
=
MKLDNNMatrix
::
create
(
*
intPD
);
extInVal_
=
extInVal_
?
extInVal_
:
MKLDNNMatrix
::
create
(
extPD
,
inMat
);
cvtInVal_
=
MKLDNNMatrix
::
createReorder
(
extInVal_
,
in
);
CHECK
(
cvtInVal_
)
<<
"should not be emptry"
;
}
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
272a272b
...
...
@@ -170,6 +170,8 @@ set(DEPS_OPS
sequence_conv_op
sequence_pool_op
lod_rank_table_op
lod_tensor_to_array_op
array_to_lod_tensor_op
lstm_op
tensor_array_read_write_op
gru_op
)
...
...
@@ -182,6 +184,8 @@ op_library(sum_op DEPS net_op selected_rows_functor)
op_library
(
pool_op DEPS pooling
)
op_library
(
pool_with_index_op DEPS pooling
)
op_library
(
lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table
)
op_library
(
lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op
)
op_library
(
array_to_lod_tensor_op SRCS array_to_lod_tensor_op.cc DEPS lod_rank_table_op
)
op_library
(
tensor_array_read_write_op SRCS tensor_array_read_write_op.cc
)
if
(
WITH_GPU
)
op_library
(
nccl_op DEPS nccl_common
)
...
...
@@ -191,8 +195,13 @@ op_library(sequence_pool_op DEPS sequence_pooling)
op_library
(
lstm_op DEPS sequence2batch lstm_compute
)
op_library
(
conv_transpose_op DEPS vol2col
)
op_library
(
gru_op DEPS sequence2batch gru_compute
)
op_library
(
dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS net_op tensor_array
)
if
(
WITH_TESTING
)
op_library
(
dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS net_op tensor_array gtest
)
else
()
op_library
(
dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS net_op tensor_array
)
endif
()
op_library
(
recurrent_op SRCS recurrent_op.cc DEPS executor
)
list
(
REMOVE_ITEM GENERAL_OPS
${
DEPS_OPS
}
)
...
...
paddle/operators/accuracy_op.cc
浏览文件 @
272a272b
...
...
@@ -47,10 +47,11 @@ class AccuracyOp : public framework::OperatorWithKernel {
}
protected:
// IndicateDataType
framework
::
DataType
IndicateDataType
(
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Out"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Out"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/array_to_lod_tensor_op.cc
0 → 100644
浏览文件 @
272a272b
/* Copyright (c) 2016 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 <numeric>
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
#include "paddle/memory/memcpy.h"
namespace
paddle
{
namespace
operators
{
using
LoD
=
framework
::
LoD
;
class
ArrayToLoDTensorOp
:
public
framework
::
OperatorBase
{
public:
ArrayToLoDTensorOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
&
x
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensorArray
>
();
auto
&
rank_table
=
scope
.
FindVar
(
Input
(
"RankTable"
))
->
Get
<
framework
::
LoDRankTable
>
();
auto
*
out
=
scope
.
FindVar
(
Output
(
"Out"
))
->
GetMutable
<
framework
::
LoDTensor
>
();
// Check dims, place and data type of input's elements and infer output's
// dim
PADDLE_ENFORCE
(
!
x
.
empty
(),
"There's no element in the input array."
);
int
rank
=
x
[
0
].
dims
().
size
();
platform
::
Place
place
=
x
[
0
].
place
();
std
::
type_index
data_type
=
x
[
0
].
type
();
framework
::
DDim
ins_dims
=
framework
::
slice_ddim
(
x
[
0
].
dims
(),
1
,
rank
);
int64_t
batch_size
=
x
[
0
].
dims
()[
0
];
for
(
size_t
i
=
1
;
i
<
x
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x
[
i
].
dims
(),
1
,
rank
),
ins_dims
,
"The dimension of the %zu'th element in LoDTensorArray "
"differs from previous ones."
,
i
);
PADDLE_ENFORCE
(
platform
::
places_are_same_class
(
x
[
i
].
place
(),
place
),
"The place class of the %zu'th element in LoDTensorArray "
"differs from previous ones."
,
i
);
PADDLE_ENFORCE
(
x
[
i
].
type
()
==
data_type
,
"The date type of the %zu'th element in LoDTensorArray "
"differs from previous ones."
,
i
);
batch_size
+=
x
[
i
].
dims
()[
0
];
}
auto
ins_dim_vec
=
framework
::
vectorize
(
ins_dims
);
ins_dim_vec
.
insert
(
ins_dim_vec
.
begin
(),
batch_size
);
framework
::
DDim
out_dims
=
framework
::
make_ddim
(
ins_dim_vec
);
out
->
Resize
(
out_dims
);
out
->
mutable_data
(
place
,
data_type
);
auto
&
table_items
=
rank_table
.
items
();
std
::
vector
<
size_t
>
table_item_idx
(
table_items
.
size
());
// table_item_idx = range(table_items_idx.size())
std
::
iota
(
table_item_idx
.
begin
(),
table_item_idx
.
end
(),
0
);
std
::
sort
(
table_item_idx
.
begin
(),
table_item_idx
.
end
(),
[
&
](
size_t
a
,
size_t
b
)
{
return
table_items
[
a
].
index
<
table_items
[
b
].
index
;
});
// Build LoDTensor `out`
framework
::
LoD
*
out_lod
=
out
->
mutable_lod
();
out_lod
->
clear
();
size_t
out_offset
=
0
;
auto
prefix_lod
=
rank_table
.
coarse_lod
();
prefix_lod
.
emplace_back
();
auto
&
cur_level_lod
=
prefix_lod
.
back
();
cur_level_lod
.
push_back
(
0
);
for
(
size_t
idx
:
table_item_idx
)
{
cur_level_lod
.
push_back
(
cur_level_lod
.
back
()
+
table_items
[
idx
].
length
);
for
(
size_t
x_idx
=
0
;
x_idx
<
table_items
[
idx
].
length
;
++
x_idx
)
{
auto
lod_and_offset
=
framework
::
GetSubLoDAndAbsoluteOffset
(
x
[
x_idx
].
lod
(),
idx
,
idx
+
1
,
0
);
auto
&
lod_length
=
lod_and_offset
.
first
;
framework
::
AppendLoD
(
out_lod
,
lod_length
);
size_t
start_offset
=
lod_and_offset
.
second
.
first
;
size_t
end_offset
=
lod_and_offset
.
second
.
second
;
VLOG
(
10
)
<<
"idx="
<<
idx
<<
" x_idx="
<<
x_idx
<<
" ["
<<
", "
<<
end_offset
<<
"]"
;
// Copy data
PADDLE_ENFORCE_GE
(
end_offset
,
start_offset
);
size_t
len
=
end_offset
-
start_offset
;
if
(
len
==
0
)
{
continue
;
}
out
->
Slice
(
out_offset
,
out_offset
+
len
)
.
CopyFrom
(
x
[
x_idx
].
Slice
(
start_offset
,
end_offset
),
place
,
dev_ctx
);
out_offset
+=
len
;
}
}
out_lod
->
insert
(
out_lod
->
begin
(),
prefix_lod
.
begin
(),
prefix_lod
.
end
());
}
};
class
ArrayToLoDTensorOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ArrayToLoDTensorOpProtoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(std::vector<LodTensor>) A vector of tensors that is going to "
"be casted to a big LoDTensor."
);
AddInput
(
"RankTable"
,
"(LoDRankTable) RankTable provides the coarse lod infomation to "
"build the output LoDTensor. See "
"'paddle/framework/lod_rank_table.h' for more details."
);
AddOutput
(
"Out"
,
"(LoDTensor) The LoDTensor formed by input tensor array."
);
AddComment
(
R"DOC(This Op build a big LoDTensor from a std::vector<LoDTensor>
and a LoDRankTable. It is supposed to be used in getting dynamic RNN's
outputs back to a normal LoDTensor. The std::vector<LoDTensor>
would be the output of RNN Op and the LoDRankTable would be build
with RNN's input.)DOC"
);
}
};
class
ArrayToLoDTensorInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
),
"ArrayToLoDTensorOp must has input X."
);
PADDLE_ENFORCE
(
context
->
HasInput
(
"RankTable"
),
"ArrayToLoDTensorOp must has input RankTable."
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
array_to_lod_tensor
,
ops
::
ArrayToLoDTensorOp
,
ops
::
ArrayToLoDTensorOpProtoMaker
,
ops
::
ArrayToLoDTensorInferShape
);
paddle/operators/auc_op.cc
浏览文件 @
272a272b
...
...
@@ -39,10 +39,11 @@ class AucOp : public framework::OperatorWithKernel {
}
protected:
// IndicateDataType
framework
::
DataType
IndicateDataType
(
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Out"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Out"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/batch_norm_op.cc
浏览文件 @
272a272b
...
...
@@ -303,7 +303,8 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Bias"
),
{
C
});
}
framework
::
DataType
IndicateDataType
(
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
var
=
ctx
.
InputVar
(
framework
::
GradVarName
(
"Y"
));
if
(
var
==
nullptr
)
{
...
...
@@ -318,7 +319,8 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
if
(
t
==
nullptr
)
{
PADDLE_THROW
(
"can't find Y@GRAD"
);
}
return
framework
::
ToDataType
(
t
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
t
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/crf_decoding_op.cc
浏览文件 @
272a272b
...
...
@@ -120,9 +120,11 @@ class CRFDecodingOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"Emission"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"Emission"
)
->
type
()),
ctx
.
device_context
());
}
};
}
// namespace operators
...
...
paddle/operators/cross_entropy_op.cc
浏览文件 @
272a272b
...
...
@@ -51,9 +51,11 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
protected:
// Explicitly set that the data type of computation kernel of cross_entropy
// is determined by its input "X".
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -98,9 +100,11 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
protected:
// Explicitly set that the data type of computation kernel of cross_entropy
// is determined by its input "X".
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/fill_constant_batch_size_like_op.cc
浏览文件 @
272a272b
...
...
@@ -49,9 +49,11 @@ class FillConstantBatchSizeLikeOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
static_cast
<
framework
::
DataType
>
(
ctx
.
Attr
<
int
>
(
"data_type"
));
return
framework
::
OpKernelType
(
static_cast
<
framework
::
DataType
>
(
ctx
.
Attr
<
int
>
(
"data_type"
)),
ctx
.
device_context
());
}
};
...
...
paddle/operators/fill_constant_op.cc
浏览文件 @
272a272b
...
...
@@ -33,11 +33,12 @@ class FillConstantOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
int
data_type
=
ctx
.
Attr
<
int
>
(
"data_type"
);
VLOG
(
10
)
<<
" FillConstant data_type = "
<<
data_type
;
return
static_cast
<
framework
::
DataType
>
(
data_type
);
return
framework
::
OpKernelType
(
static_cast
<
framework
::
DataType
>
(
data_type
),
ctx
.
device_context
());
}
};
...
...
paddle/operators/gather_op.cc
浏览文件 @
272a272b
...
...
@@ -40,9 +40,11 @@ class GatherOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -55,9 +57,11 @@ class GatherGradOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/gaussian_random_op.cc
浏览文件 @
272a272b
...
...
@@ -57,9 +57,11 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
static_cast
<
framework
::
DataType
>
(
ctx
.
Attr
<
int
>
(
"data_type"
));
return
framework
::
OpKernelType
(
static_cast
<
framework
::
DataType
>
(
ctx
.
Attr
<
int
>
(
"data_type"
)),
ctx
.
device_context
());
}
};
...
...
paddle/operators/linear_chain_crf_op.cc
浏览文件 @
272a272b
...
...
@@ -183,9 +183,11 @@ class LinearChainCRFOp : public framework::OperatorWithKernel {
protected:
// Explicitly set that the data type of computation kernel of linear_chain_crf
// is determined by its input "Emission".
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"Emission"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"Emission"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -240,10 +242,13 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
protected:
// Explicitly set that the data type of output of the linear_chain_crf_grad
// operator is determined by its input: gradients of LogLikelihood.
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"LogLikelihood"
))
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"LogLikelihood"
))
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/lod_rank_table_op.cc
浏览文件 @
272a272b
...
...
@@ -28,6 +28,7 @@ class LoDRankTableOp : public framework::OperatorBase {
auto
x
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensor
>
();
auto
*
out
=
scope
.
FindVar
(
Output
(
"Out"
))
->
GetMutable
<
framework
::
LoDRankTable
>
();
VLOG
(
10
)
<<
"Level = "
<<
static_cast
<
size_t
>
(
Attr
<
int
>
(
"level"
));
out
->
Reset
(
x
.
lod
(),
static_cast
<
size_t
>
(
Attr
<
int
>
(
"level"
)));
}
};
...
...
paddle/operators/lod_tensor_to_array_op.cc
0 → 100644
浏览文件 @
272a272b
/* Copyright (c) 2016 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/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
struct
CopyRange
{
size_t
begin
;
size_t
end
;
};
class
LoDTensorToArrayOp
:
public
framework
::
OperatorBase
{
public:
LoDTensorToArrayOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
Run
(
const
framework
::
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
override
{
auto
&
x
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensor
>
();
auto
&
rank_table
=
scope
.
FindVar
(
Input
(
"RankTable"
))
->
Get
<
framework
::
LoDRankTable
>
();
auto
&
out
=
*
scope
.
FindVar
(
Output
(
"Out"
))
->
GetMutable
<
framework
::
LoDTensorArray
>
();
auto
&
items
=
rank_table
.
items
();
auto
max_seq_len
=
items
[
0
].
length
;
auto
rank_level
=
rank_table
.
level
();
out
.
resize
(
max_seq_len
);
std
::
vector
<
std
::
vector
<
CopyRange
>>
copy_ranges
(
max_seq_len
);
// set out[i] lod
for
(
size_t
t
=
0
;
t
<
max_seq_len
;
t
++
)
{
auto
&
lod
=
*
out
[
t
].
mutable_lod
();
lod
.
clear
();
for
(
auto
&
item
:
items
)
{
if
(
t
>=
item
.
length
)
{
break
;
}
size_t
start_idx
=
x
.
lod
()[
rank_level
][
item
.
index
]
+
t
;
auto
lod_and_offset
=
framework
::
GetSubLoDAndAbsoluteOffset
(
x
.
lod
(),
start_idx
,
start_idx
+
1
,
rank_level
+
1
);
auto
&
lod_length
=
lod_and_offset
.
first
;
framework
::
AppendLoD
(
&
lod
,
lod_length
);
size_t
start_offset
=
lod_and_offset
.
second
.
first
;
size_t
end_offset
=
lod_and_offset
.
second
.
second
;
copy_ranges
[
t
].
emplace_back
(
CopyRange
{
start_offset
,
end_offset
});
}
}
for
(
size_t
i
=
0
;
i
<
max_seq_len
;
++
i
)
{
auto
&
ranges
=
copy_ranges
[
i
];
size_t
height
=
std
::
accumulate
(
ranges
.
begin
(),
ranges
.
end
(),
0UL
,
[](
size_t
a
,
const
CopyRange
&
b
)
{
return
a
+
b
.
end
-
b
.
begin
;
});
auto
x_dim
=
x
.
dims
();
x_dim
[
0
]
=
static_cast
<
int64_t
>
(
height
);
out
[
i
].
Resize
(
x_dim
);
out
[
i
].
mutable_data
(
x
.
place
(),
x
.
type
());
size_t
offset
=
0
;
for
(
auto
&
each_range
:
ranges
)
{
size_t
len
=
each_range
.
end
-
each_range
.
begin
;
if
(
len
==
0
)
{
continue
;
}
// out[i][offset: offset+len] = x[each_range.begin: each_range.end]
out
[
i
]
.
Slice
(
static_cast
<
int
>
(
offset
),
static_cast
<
int
>
(
offset
+
len
))
.
CopyFrom
(
x
.
Slice
(
static_cast
<
int
>
(
each_range
.
begin
),
static_cast
<
int
>
(
each_range
.
end
)),
x
.
place
(),
dev_ctx
);
offset
+=
len
;
}
}
}
};
class
LoDTensorToArrayOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
LoDTensorToArrayOpProtoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
""
);
AddInput
(
"RankTable"
,
""
);
AddOutput
(
"Out"
,
""
);
AddComment
(
""
);
}
};
class
LoDTensorToArrayInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
),
"Input(X) of LoDTensorToArrayOp should not be null."
);
PADDLE_ENFORCE
(
context
->
HasInput
(
"RankTable"
),
"Input(RankTable) of LoDTensorToArrayOp should not be null."
);
PADDLE_ENFORCE
(
context
->
HasOutput
(
"Out"
),
"Output(Out) of LoDTensorToArrayOp should not be null."
);
auto
x_dim
=
context
->
GetInputDim
(
"X"
);
// The first dim of each LoDTensor in Output can only be set at run-time.;
// We still have to Resize each LoDTensor in Output.
context
->
SetOutputDim
(
"Out"
,
x_dim
);
}
};
class
LoDTensorToArrayInferVarType
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDescBind
&
op_desc
,
framework
::
BlockDescBind
*
block
)
const
override
{
for
(
auto
&
out_var
:
op_desc
.
Output
(
"Out"
))
{
block
->
Var
(
out_var
)
->
SetType
(
framework
::
VarDesc
::
LOD_TENSOR_ARRAY
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
lod_tensor_to_array
,
ops
::
LoDTensorToArrayOp
,
ops
::
LoDTensorToArrayOpProtoMaker
,
ops
::
LoDTensorToArrayInferShape
,
ops
::
LoDTensorToArrayInferVarType
);
paddle/operators/lookup_table_op.cc
浏览文件 @
272a272b
...
...
@@ -41,9 +41,11 @@ class LookupTableOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"W"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"W"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -97,9 +99,11 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"W"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
LoDTensor
>
(
"W"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/lstm_op.cc
浏览文件 @
272a272b
...
...
@@ -84,10 +84,11 @@ class LSTMOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Input"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Input"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -245,10 +246,11 @@ class LSTMGradOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Input"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Input"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/multiplex_op.cc
浏览文件 @
272a272b
...
...
@@ -51,9 +51,11 @@ class MultiplexOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
MultiInput
<
Tensor
>
(
"X"
)[
0
]
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
MultiInput
<
Tensor
>
(
"X"
)[
0
]
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -107,9 +109,11 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
MultiInput
<
Tensor
>
(
"X"
)[
0
]
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
MultiInput
<
Tensor
>
(
"X"
)[
0
]
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/positive_negative_pair_op.cc
浏览文件 @
272a272b
...
...
@@ -85,9 +85,11 @@ class PositiveNegativePairOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Score"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Score"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/precision_recall_op.cc
浏览文件 @
272a272b
...
...
@@ -80,9 +80,11 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"MaxProbs"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"MaxProbs"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/scatter_op.cc
浏览文件 @
272a272b
...
...
@@ -49,9 +49,11 @@ class ScatterOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Ref"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Ref"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -66,9 +68,11 @@ class ScatterGradOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Ref"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Ref"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/sequence_pool_op.cc
浏览文件 @
272a272b
...
...
@@ -107,9 +107,11 @@ class SequencePoolGradOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/softmax_with_cross_entropy_op.cc
浏览文件 @
272a272b
...
...
@@ -121,9 +121,11 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Logits"
)
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Logits"
)
->
type
()),
ctx
.
device_context
());
}
};
...
...
@@ -160,10 +162,12 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Loss"
))
->
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Loss"
))
->
type
()),
ctx
.
device_context
());
}
};
...
...
paddle/operators/sum_op.cc
浏览文件 @
272a272b
...
...
@@ -47,20 +47,24 @@ class SumOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
x_vars
=
ctx
.
MultiInputVar
(
"X"
);
if
(
x_vars
[
0
]
->
IsType
<
framework
::
LoDTensor
>
())
{
return
framework
::
ToDataType
(
x_vars
[
0
]
->
Get
<
framework
::
LoDTensor
>
().
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
x_vars
[
0
]
->
Get
<
framework
::
LoDTensor
>
().
type
()),
ctx
.
device_context
());
}
else
if
(
x_vars
[
0
]
->
IsType
<
framework
::
SelectedRows
>
())
{
return
framework
::
ToDataType
(
x_vars
[
0
]
->
Get
<
framework
::
SelectedRows
>
().
value
().
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
x_vars
[
0
]
->
Get
<
framework
::
SelectedRows
>
().
value
().
type
()),
ctx
.
device_context
());
}
else
if
(
x_vars
[
0
]
->
IsType
<
framework
::
LoDTensorArray
>
())
{
auto
&
array
=
x_vars
[
0
]
->
Get
<
framework
::
LoDTensorArray
>
();
for
(
auto
&
each
:
array
)
{
if
(
each
.
numel
()
!=
0
)
{
return
framework
::
ToDataType
(
each
.
type
());
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
each
.
type
()),
ctx
.
device_context
());
}
}
}
...
...
paddle/operators/uniform_random_op.cc
浏览文件 @
272a272b
...
...
@@ -63,9 +63,11 @@ class UniformRandomOp : public framework::OperatorWithKernel {
}
protected:
framework
::
DataType
IndicateData
Type
(
framework
::
OpKernelType
GetKernel
Type
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
static_cast
<
framework
::
DataType
>
(
ctx
.
Attr
<
int
>
(
"data_type"
));
return
framework
::
OpKernelType
(
static_cast
<
framework
::
DataType
>
(
ctx
.
Attr
<
int
>
(
"data_type"
)),
ctx
.
device_context
());
}
};
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
272a272b
...
...
@@ -6548,26 +6548,27 @@ def switch_order_layer(input,
@
layer_support
()
def
crop_layer
(
input
,
offset
,
axis
=
2
,
shape
=
None
,
name
=
None
,
layer_attr
=
None
):
"""
This layer crops images by offset and shape. User can set crop shape by
args 'shape' explicitly or by reference input layer.
This layer crops images according to the offset and shape. Users can set
the crop shape through the argument 'shape' explicitly or by specifying a
reference input layer.
The example usage is:
.. code-block:: python
crop = crop_layer(input=[image_input, reference_input], axis=2, offset=[2, 3])
:param input: The input of this layer. If two inputs are given, the second
input
will be regarded as
reference input
.
:param input: The input of this layer. If two inputs are given, the second
one
will be regarded as
the reference
.
:type input: LayerOutput | Sequence
:param offset: The crop offset.
:type offset: Sequence
:param axis:
start axis to be cropped. To
image input layer:
:param axis:
The start axis to be cropped. For
image input layer:
- 0: batch size
- 1: channels
- 2: height
- 3: width
:type
partial_sum
: int
:param shape: The shape to be cropped. Default is None.
:type
axis
: int
:param shape: The shape to be cropped
to
. Default is None.
:type shape: Sequence | None
:param name: The name of this layer. It is optional.
:type name: basestring
...
...
@@ -6702,9 +6703,9 @@ def seq_slice_layer(input, starts, ends, name=None):
:type name: basestring
:param input: The input of this layer, which should be a sequence.
:type input: LayerOutput
:param starts: start indices to slice the input sequence.
:param starts:
The
start indices to slice the input sequence.
:type starts: LayerOutput | None
:param ends: end indices to slice the input sequence.
:param ends:
The
end indices to slice the input sequence.
:type ends: LayerOutput | None
:return: LayerOutput object.
:rtype: LayerOutput
...
...
@@ -6744,7 +6745,7 @@ def seq_slice_layer(input, starts, ends, name=None):
@
layer_support
()
def
kmax_seq_score_layer
(
input
,
name
=
None
,
beam_size
=
1
):
"""
This layer accepts one input which
are
scores over a sequence or a nested
This layer accepts one input which
is
scores over a sequence or a nested
sequence, and returns indices of beam_size sequences with highest scores.
.. code-block:: python
...
...
@@ -6754,11 +6755,11 @@ def kmax_seq_score_layer(input, name=None, beam_size=1):
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer. It stores scores over a sequence or
a nested
sequence and its size must be 1.
:param input: The input of this layer. It stores scores over a sequence or
a nested
sequence and its size must be 1.
:type input: LayerOutput
:param beam_size:
sequence indi
ces with top beam_size scores are returned.
:type beam_size:
double
:param beam_size:
The indices of the sequen
ces with top beam_size scores are returned.
:type beam_size:
int
:return: LayerOutput object.
:rtype: LayerOutput
"""
...
...
@@ -6814,38 +6815,42 @@ def img_conv3d_layer(input,
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param filter_size: The x dimension of a filter kernel. Or input a list.
:param filter_size: The dimensions of the filter kernel along three axises. If the parameter
is set to one integer, the three dimensions will be same.
:type filter_size: int | tuple | list
:param num_filters: Each filter group's number of filter
:param num_filters: The number of filters in each group.
:type num_filters: int
:param act: Activation type. ReluActivation is the default.
:type act: BaseActivation
:param groups:
Group size of filter
s.
:param groups:
The number of the filter group
s.
:type groups: int
:param stride: The
x dimension of the stride. Or input a tuple for two image
dimension
.
:param stride: The
strides of the convolution along three axises. If the parameter
is set to one integer, the three strides will be same
.
:type stride: int | tuple | list
:param padding: The
x dimension of the padding. Or input a tuple for tw
o
image dimension
:param padding: The
numbers of padding along three axises. If the parameter is set t
o
one integer, they will be same.
:type padding: int | tuple | list
:param bias_attr: Convolution bias attribute. None means default bias.
False means no bias.
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param num_channels: number of input channels. If None will be set
automatically from previous output.
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input .
:type num_channels: int
:param param_attr:
Convolution param attribute. None means default attribute
:param param_attr:
The parameter attribute of the convolution.
:type param_attr: ParameterAttribute
:param shared_biases:
Is
biases will be shared between filters or not.
:param shared_biases:
Whether
biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr:
Layer Extra Attribute
.
:param layer_attr:
Extra layer attributes
.
:type layer_attr: ExtraLayerAttribute
:param trans:
true if it is a convTransLayer, f
alse if it is a convLayer
:param trans:
True if it is a convTransLayer, F
alse if it is a convLayer
:type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True,
layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or
"cudnn_conv"
:type layer_type: String
:param layer_type: Specify the layer_type. If the parameter is set, it must be "deconv3d"
when trans=True. If not set, it will be automatically set to "deconv3d"
when trans=True and "conv3d" when trans=False.
:type layer_type: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
...
...
@@ -6927,7 +6932,7 @@ def img_conv3d_layer(input,
def
scale_shift_layer
(
input
,
name
=
None
,
param_attr
=
None
,
bias_attr
=
None
):
"""
A layer applies a linear transformation to each element in each row of
the input matrix. For each element, the layer first re-scale it and then
the input matrix. For each element, the layer first re-scale
s
it and then
adds a bias to it.
This layer is very like the SlopeInterceptLayer, except the scale and
...
...
@@ -7001,12 +7006,12 @@ def sub_seq_layer(input, offsets, sizes, act=None, bias_attr=None, name=None):
:type name: basestring
:param input: The input of this layer, which should be sequence.
:type input: LayerOutput
:param offsets:
offset indices to slice the input sequence, which should be
sequence type.
:param offsets:
The offset indices to slice the input sequence, which should
be
sequence type.
:type offsets: LayerOutput
:param sizes: sizes of the sub-sequences, which should be sequence type.
:param sizes:
The
sizes of the sub-sequences, which should be sequence type.
:type sizes: LayerOutput
:param act:
Layer activation, default is LinearActivation
:param act:
Activation type, LinearActivation is the default.
:type act: BaseActivation.
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
272a272b
...
...
@@ -775,6 +775,30 @@ def lod_rank_table(x, level=0, main_program=None):
return
table
def
lod_tensor_to_array
(
x
,
table
,
main_program
=
None
):
helper
=
LayerHelper
(
"lod_tensor_to_array"
,
**
locals
())
array
=
helper
.
create_variable
(
name
=
unique_name
(
"lod_tensor_to_array"
),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
)
helper
.
append_op
(
type
=
'lod_tensor_to_array'
,
inputs
=
{
'X'
:
x
,
'RankTable'
:
table
},
outputs
=
{
'Out'
:
array
})
return
array
def
array_to_lod_tensor
(
x
,
table
,
main_program
=
None
):
helper
=
LayerHelper
(
"array_to_lod_tensor"
,
**
locals
())
tmp
=
helper
.
create_tmp_variable
(
dtype
=
x
.
data_type
)
helper
.
append_op
(
type
=
"array_to_lod_tensor"
,
inputs
=
{
'X'
:
x
,
'RankTable'
:
table
},
outputs
=
{
'Out'
:
tmp
})
return
tmp
def
fill_constant
(
shape
,
dtype
,
value
,
main_program
=
None
):
helper
=
LayerHelper
(
"ones"
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
dtype
)
...
...
python/paddle/v2/framework/tests/test_lod_rank_table.py
浏览文件 @
272a272b
...
...
@@ -18,7 +18,6 @@ class TestLoDRankTable(unittest.TestCase):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
random
.
random
(
size
=
(
17
,
100
)),
cpu
)
tensor
.
set_lod
([[
0
,
1
,
3
],
[
0
,
5
,
6
,
7
],
[
0
,
3
,
4
,
9
,
10
,
13
,
16
,
17
]])
exe
.
run
(
g_main_program
,
scope
=
scope
,
feed
=
{
'x'
:
tensor
})
var
=
scope
.
find_var
(
rank_table
.
name
)
table
=
var
.
get_lod_rank_table
()
...
...
python/paddle/v2/framework/tests/test_lod_tensor_array_ops.py
0 → 100644
浏览文件 @
272a272b
import
unittest
import
paddle.v2.framework.core
as
core
import
numpy
import
paddle.v2.framework.layers
as
layers
from
paddle.v2.framework.framework
import
Program
from
paddle.v2.framework.executor
import
Executor
class
TestCPULoDTensorArrayOps
(
unittest
.
TestCase
):
def
place
(
self
):
return
core
.
CPUPlace
()
def
test_lod_tensor_to_array_level_0
(
self
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
arange
(
10
).
reshape
(
10
,
1
).
astype
(
'int32'
),
self
.
place
())
tensor
.
set_lod
([[
0
,
3
,
9
,
10
]])
expect
=
map
(
lambda
x
:
numpy
.
array
(
x
).
astype
(
'int32'
),
[[
3
,
0
,
9
],
[
4
,
1
],
[
5
,
2
],
[
6
],
[
7
],
[
8
]])
self
.
main
(
tensor
=
tensor
,
expect_array
=
expect
,
expect_lod
=
[]
*
6
)
def
test_lod_tensor_to_array_level_0_empty_seq
(
self
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
arange
(
10
).
reshape
(
10
,
1
).
astype
(
'int32'
),
self
.
place
())
tensor
.
set_lod
([[
0
,
3
,
9
,
9
,
10
]])
expect
=
map
(
lambda
x
:
numpy
.
array
(
x
).
astype
(
'int32'
),
[[
3
,
0
,
9
],
[
4
,
1
],
[
5
,
2
],
[
6
],
[
7
],
[
8
]])
self
.
main
(
tensor
=
tensor
,
expect_array
=
expect
,
expect_lod
=
[]
*
6
)
def
test_lod_tensor_to_array_level_1
(
self
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
arange
(
20
).
reshape
(
20
,
1
).
astype
(
'int32'
),
self
.
place
())
tensor
.
set_lod
([[
0
,
2
,
5
],
[
0
,
3
,
9
,
11
,
17
,
20
]])
expect
=
[
numpy
.
array
(
[
9
,
10
,
0
,
1
,
2
],
dtype
=
'int32'
),
numpy
.
array
(
[
11
,
12
,
13
,
14
,
15
,
16
,
3
,
4
,
5
,
6
,
7
,
8
],
dtype
=
'int32'
),
numpy
.
array
(
[
17
,
18
,
19
],
dtype
=
'int32'
)
]
lod
=
[[[
0
,
2
,
5
]],
[[
0
,
6
,
12
]],
[[
0
,
3
]]]
self
.
main
(
tensor
=
tensor
,
expect_array
=
expect
,
expect_lod
=
lod
)
def
test_lod_tensor_to_array_level_1_empty_seq
(
self
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
arange
(
31
).
reshape
(
31
,
1
).
astype
(
'int32'
),
self
.
place
())
tensor
.
set_lod
([[
0
,
3
,
5
,
9
,
11
],
[
0
,
3
,
7
,
11
,
11
,
12
,
17
,
19
,
21
,
23
,
30
,
31
]])
expect
=
[
numpy
.
array
(
item
,
dtype
=
'int32'
)
for
item
in
[[
12
,
13
,
14
,
15
,
16
,
0
,
1
,
2
,
23
,
24
,
25
,
26
,
27
,
28
,
29
],
[
17
,
18
,
3
,
4
,
5
,
6
,
11
,
30
],
[
19
,
20
,
7
,
8
,
9
,
10
],
[
21
,
22
]]
]
lod
=
[[[
0
,
5
,
8
,
8
,
15
]],
[[
0
,
2
,
6
,
7
,
8
]],
[[
0
,
2
,
6
]],
[[
0
,
2
]]]
self
.
main
(
tensor
=
tensor
,
expect_array
=
expect
,
expect_lod
=
lod
)
def
test_lod_tensor_to_array_level_2
(
self
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
arange
(
50
).
reshape
(
50
,
1
).
astype
(
'int32'
),
self
.
place
())
tensor
.
set_lod
([[
0
,
2
,
5
,
6
],
[
0
,
2
,
5
,
6
,
10
,
12
,
13
],
[
0
,
3
,
7
,
11
,
17
,
21
,
22
,
23
,
27
,
31
,
39
,
45
,
46
,
50
]])
expect
=
[
numpy
.
array
(
item
,
dtype
=
'int32'
)
for
item
in
[[
21
,
0
,
1
,
2
,
3
,
4
,
5
,
6
,
46
,
47
,
48
,
49
],
range
(
22
,
39
)
+
range
(
7
,
21
),
range
(
39
,
46
)]
]
lod
=
[[[
0
,
1
,
3
,
4
],
[
0
,
1
,
4
,
8
,
12
]],
[[
0
,
4
,
7
],
[
0
,
1
,
5
,
9
,
17
,
21
,
27
,
31
]],
[[
0
,
2
],
[
0
,
6
,
7
]]]
self
.
main
(
tensor
=
tensor
,
expect_array
=
expect
,
expect_lod
=
lod
)
def
test_lod_tensor_to_array_level_2_skip_level
(
self
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
numpy
.
arange
(
50
).
reshape
(
50
,
1
).
astype
(
'int32'
),
self
.
place
())
tensor
.
set_lod
([[
0
,
2
,
5
,
6
],
[
0
,
2
,
5
,
6
,
10
,
12
,
13
],
[
0
,
3
,
7
,
11
,
17
,
21
,
22
,
23
,
27
,
31
,
39
,
45
,
46
,
50
]])
self
.
main
(
tensor
=
tensor
,
expect_array
=
None
,
expect_lod
=
None
,
level
=
1
)
def
main
(
self
,
tensor
,
expect_array
,
expect_lod
,
level
=
0
):
place
=
self
.
place
()
program
=
Program
()
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
10
],
main_program
=
program
)
x
.
persistable
=
True
table
=
layers
.
lod_rank_table
(
x
,
level
=
level
,
main_program
=
program
)
array
=
layers
.
lod_tensor_to_array
(
x
,
table
,
main_program
=
program
)
array
.
persistable
=
True
result
=
layers
.
array_to_lod_tensor
(
array
,
table
,
main_program
=
program
)
result
.
persistable
=
True
exe
=
Executor
(
place
)
scope
=
core
.
Scope
()
exe
.
run
(
program
,
feed
=
{
'x'
:
tensor
},
scope
=
scope
)
var
=
scope
.
find_var
(
array
.
name
)
array
=
var
.
get_lod_tensor_array
()
if
expect_array
is
not
None
and
expect_lod
is
not
None
:
self
.
check_array_same
(
array
,
expect_array
,
expect_lod
)
self
.
check_tensor_same
(
scope
.
find_var
(
result
.
name
).
get_tensor
(),
tensor
)
def
check_array_same
(
self
,
array
,
expect_tensor
,
expect_lod
):
self
.
assertEqual
(
len
(
expect_tensor
),
len
(
array
))
for
i
,
exp
in
enumerate
(
zip
(
expect_tensor
,
expect_lod
)):
exp_tensor
,
exp_lod
=
exp
exp_tensor
=
numpy
.
expand_dims
(
exp_tensor
,
axis
=
1
)
self
.
assertTrue
(
numpy
.
allclose
(
exp_tensor
,
numpy
.
array
(
array
[
i
])))
self
.
assertEqual
(
exp_lod
,
array
[
i
].
lod
())
def
check_tensor_same
(
self
,
actual
,
expect
):
self
.
assertTrue
(
numpy
.
allclose
(
numpy
.
array
(
actual
),
numpy
.
array
(
expect
)))
self
.
assertEqual
(
actual
.
lod
(),
expect
.
lod
())
if
__name__
==
'__main__'
:
unittest
.
main
()
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