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PaddleDetection
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PaddleDetection
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28ff4bdd
编写于
6月 20, 2018
作者:
S
sneaxiy
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into python_data_feeding
上级
882a9327
1d7e60fd
变更
48
展开全部
隐藏空白更改
内联
并排
Showing
48 changed file
with
2358 addition
and
459 deletion
+2358
-459
benchmark/fluid/fluid_benchmark.py
benchmark/fluid/fluid_benchmark.py
+1
-1
benchmark/fluid/kube_gen_job.py
benchmark/fluid/kube_gen_job.py
+11
-5
cmake/external/mkldnn.cmake
cmake/external/mkldnn.cmake
+2
-1
doc/fluid/howto/cluster/fluid_cluster_train_cn.md
doc/fluid/howto/cluster/fluid_cluster_train_cn.md
+2
-2
doc/fluid/howto/cluster/fluid_recordio.md
doc/fluid/howto/cluster/fluid_recordio.md
+2
-2
paddle/fluid/operators/activation_op.cc
paddle/fluid/operators/activation_op.cc
+2
-2
paddle/fluid/operators/detection_map_op.cc
paddle/fluid/operators/detection_map_op.cc
+6
-6
paddle/fluid/operators/gaussian_random_mkldnn_op.cc
paddle/fluid/operators/gaussian_random_mkldnn_op.cc
+55
-0
paddle/fluid/operators/gaussian_random_op.cc
paddle/fluid/operators/gaussian_random_op.cc
+19
-2
paddle/fluid/operators/math/concat.cu
paddle/fluid/operators/math/concat.cu
+12
-31
paddle/fluid/operators/parallel_do_op.cc
paddle/fluid/operators/parallel_do_op.cc
+1
-1
paddle/fluid/operators/recurrent_op.cc
paddle/fluid/operators/recurrent_op.cc
+2
-1
paddle/fluid/operators/sum_mkldnn_op.cc
paddle/fluid/operators/sum_mkldnn_op.cc
+240
-0
paddle/fluid/operators/sum_op.cc
paddle/fluid/operators/sum_op.cc
+26
-6
paddle/fluid/operators/while_op.cc
paddle/fluid/operators/while_op.cc
+2
-2
paddle/fluid/platform/mkldnn_helper.h
paddle/fluid/platform/mkldnn_helper.h
+6
-0
python/paddle/fluid/average.py
python/paddle/fluid/average.py
+19
-0
python/paddle/fluid/backward.py
python/paddle/fluid/backward.py
+65
-17
python/paddle/fluid/clip.py
python/paddle/fluid/clip.py
+123
-11
python/paddle/fluid/data_feeder.py
python/paddle/fluid/data_feeder.py
+10
-1
python/paddle/fluid/inferencer.py
python/paddle/fluid/inferencer.py
+37
-9
python/paddle/fluid/initializer.py
python/paddle/fluid/initializer.py
+141
-91
python/paddle/fluid/io.py
python/paddle/fluid/io.py
+568
-100
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+30
-3
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+43
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+196
-77
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+17
-13
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+295
-19
python/paddle/fluid/profiler.py
python/paddle/fluid/profiler.py
+112
-5
python/paddle/fluid/regularizer.py
python/paddle/fluid/regularizer.py
+43
-3
python/paddle/fluid/tests/book/notest_understand_sentiment.py
...on/paddle/fluid/tests/book/notest_understand_sentiment.py
+5
-5
python/paddle/fluid/tests/book/test_fit_a_line.py
python/paddle/fluid/tests/book/test_fit_a_line.py
+5
-5
python/paddle/fluid/tests/book/test_image_classification.py
python/paddle/fluid/tests/book/test_image_classification.py
+5
-5
python/paddle/fluid/tests/book/test_label_semantic_roles.py
python/paddle/fluid/tests/book/test_label_semantic_roles.py
+5
-5
python/paddle/fluid/tests/book/test_machine_translation.py
python/paddle/fluid/tests/book/test_machine_translation.py
+5
-5
python/paddle/fluid/tests/book/test_recognize_digits.py
python/paddle/fluid/tests/book/test_recognize_digits.py
+5
-5
python/paddle/fluid/tests/book/test_recommender_system.py
python/paddle/fluid/tests/book/test_recommender_system.py
+5
-5
python/paddle/fluid/tests/book/test_word2vec.py
python/paddle/fluid/tests/book/test_word2vec.py
+5
-5
python/paddle/fluid/tests/unittests/test_concat_op.py
python/paddle/fluid/tests/unittests/test_concat_op.py
+12
-1
python/paddle/fluid/tests/unittests/test_gaussian_random_mkldnn_op.py
...e/fluid/tests/unittests/test_gaussian_random_mkldnn_op.py
+26
-0
python/paddle/fluid/tests/unittests/test_gaussian_random_op.py
...n/paddle/fluid/tests/unittests/test_gaussian_random_op.py
+12
-1
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+9
-0
python/paddle/fluid/tests/unittests/test_optimizer.py
python/paddle/fluid/tests/unittests/test_optimizer.py
+66
-0
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py
+26
-0
python/paddle/fluid/tests/unittests/test_sum_op.py
python/paddle/fluid/tests/unittests/test_sum_op.py
+6
-0
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+4
-2
python/paddle/reader/decorator.py
python/paddle/reader/decorator.py
+2
-2
tools/print_signatures.py
tools/print_signatures.py
+67
-0
未找到文件。
benchmark/fluid/fluid_benchmark.py
浏览文件 @
28ff4bdd
...
...
@@ -97,7 +97,7 @@ def dist_transpile(trainer_id, args):
return
train_program
,
fluid
.
default_startup_program
()
else
:
raise
ValueError
(
'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
'
PADDLE_
TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
...
...
benchmark/fluid/kube_gen_job.py
浏览文件 @
28ff4bdd
...
...
@@ -108,10 +108,10 @@ def gen_job():
tn_container
[
"ports"
][
0
][
"containerPort"
]
=
spreadport
envs
.
append
({
"name"
:
"PADDLE_JOB_NAME"
,
"value"
:
args
.
jobname
})
envs
.
append
({
"name"
:
"TRAINERS"
,
"value"
:
str
(
args
.
trainers
)})
envs
.
append
({
"name"
:
"
PADDLE_
TRAINERS"
,
"value"
:
str
(
args
.
trainers
)})
envs
.
append
({
"name"
:
"PSERVERS"
,
"value"
:
str
(
args
.
pservers
)})
envs
.
append
({
"name"
:
"ENTRY"
,
"value"
:
args
.
entry
})
envs
.
append
({
"name"
:
"PADDLE_
INIT
_PORT"
,
"value"
:
str
(
args
.
port
)})
envs
.
append
({
"name"
:
"PADDLE_
PSERVER
_PORT"
,
"value"
:
str
(
args
.
port
)})
envs
.
append
({
"name"
:
"PADDLE_PSERVER_PORT"
,
"value"
:
str
(
args
.
port
)})
# NOTE: these directories below are cluster specific, please modify
# this settings before you run on your own cluster.
...
...
@@ -167,16 +167,22 @@ def gen_job():
tn_container
[
"volumeMounts"
]
=
volumeMounts
ps_container
[
"env"
]
=
envs
ps_container
[
"env"
].
append
({
"name"
:
"TRAINING_ROLE"
,
"value"
:
"PSERVER"
})
ps_container
[
"env"
].
append
({
"name"
:
"PADDLE_TRAINING_ROLE"
,
"value"
:
"PSERVER"
})
tn_container
[
"env"
]
=
envs
if
args
.
disttype
==
"pserver"
:
tn_container
[
"env"
].
append
({
"name"
:
"TRAINING_ROLE"
,
"name"
:
"
PADDLE_
TRAINING_ROLE"
,
"value"
:
"TRAINER"
})
elif
args
.
disttype
==
"nccl2"
or
args
.
disttype
==
"local"
:
# NCCL2 have no training role, set to plain WORKER
tn_container
[
"env"
].
append
({
"name"
:
"TRAINING_ROLE"
,
"value"
:
"WORKER"
})
tn_container
[
"env"
].
append
({
"name"
:
"PADDLE_TRAINING_ROLE"
,
"value"
:
"WORKER"
})
os
.
mkdir
(
args
.
jobname
)
if
args
.
disttype
==
"pserver"
:
...
...
cmake/external/mkldnn.cmake
浏览文件 @
28ff4bdd
...
...
@@ -45,7 +45,8 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
ELSE
()
MESSAGE
(
FATAL_ERROR
"Should enable MKLML when build MKLDNN"
)
ENDIF
()
SET
(
MKLDNN_FLAG
"-Wno-error=strict-overflow -Wno-error=unused-result -Wno-unused-result"
)
SET
(
MKLDNN_FLAG
"-Wno-error=strict-overflow -Wno-error=unused-result"
)
SET
(
MKLDNN_FLAG
"
${
MKLDNN_FLAG
}
-Wno-unused-result -Wno-unused-value"
)
SET
(
MKLDNN_CFLAG
"
${
CMAKE_C_FLAGS
}
${
MKLDNN_FLAG
}
"
)
SET
(
MKLDNN_CXXFLAG
"
${
CMAKE_CXX_FLAGS
}
${
MKLDNN_FLAG
}
"
)
ExternalProject_Add
(
...
...
doc/fluid/howto/cluster/fluid_cluster_train_cn.md
浏览文件 @
28ff4bdd
...
...
@@ -168,13 +168,13 @@ cd /paddle/python/paddle/fluid/tests/book
第二步,启动Parameter Server:
```
bash
PADDLE_
INIT_PORT
=
6174
PADDLE_INIT_PSERVERS
=
192.168.1.2
TRAINERS
=
2
POD_IP
=
192.168.1.2
PADDLE_INIT_TRAINER_ID
=
1
TRAINING_ROLE
=
PSERVER python test_fit_a_line.py
PADDLE_
PSERVER_PORT
=
6174
PADDLE_PSERVER_IPS
=
192.168.1.2
PADDLE_TRAINERS
=
2
PADDLE_CURRENT_IP
=
192.168.1.2
PADDLE_TRAINER_ID
=
1
PADDLE_
TRAINING_ROLE
=
PSERVER python test_fit_a_line.py
```
执行命令后请等待出现提示:
```Server listening on 192.168.1.2:6174 ```
, 表示Paramter Server已经正常启动。
第三步,启动Trainer:
```
bash
PADDLE_
INIT_PORT
=
6174
PADDLE_INIT_PSERVERS
=
192.168.1.3
TRAINERS
=
2
POD_IP
=
192.168.1.3
PADDLE_INIT_TRAINER_ID
=
1
TRAINING_ROLE
=
TRAINER python test_fit_a_line.py
PADDLE_
PSERVER_PORT
=
6174
PADDLE_PSERVER_IPS
=
192.168.1.3
PADDLE_TRAINERS
=
2
PADDLE_CURRENT_IPP
=
192.168.1.3
PADDLE_TRAINER_ID
=
1
PADDLE_
TRAINING_ROLE
=
TRAINER python test_fit_a_line.py
```
由于我们定义的Trainer的数量是2个,因此需要在另外一个计算节点上再启动一个Trainer。
...
...
doc/fluid/howto/cluster/fluid_recordio.md
浏览文件 @
28ff4bdd
...
...
@@ -114,8 +114,8 @@ def gen_train_list(file_pattern, trainers, trainer_id):
ret_list
.
append
(
f
)
return
ret_list
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
data_file
=
fluid
.
layers
.
io
.
open_files
(
filenames
=
gen_train_list
(
"./mnist-[0-9]*.recordio"
,
2
,
0
),
thread_num
=
1
,
...
...
paddle/fluid/operators/activation_op.cc
浏览文件 @
28ff4bdd
...
...
@@ -143,7 +143,7 @@ $$out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
__attribute__
((
unused
))
constexpr
char
TanhShrinkDoc
[]
=
R"DOC(
TanhShrink Activation Operator.
$$out = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$out = x - \
\
frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC"
;
...
...
@@ -385,7 +385,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
STanh Activation Operator.
$$out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
$$out = b * \
\
frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
)DOC"
);
}
...
...
paddle/fluid/operators/detection_map_op.cc
浏览文件 @
28ff4bdd
...
...
@@ -175,12 +175,12 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
Detection mAP evaluate operator.
The general steps are as follows. First, calculate the true positive and
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
)DOC"
);
}
...
...
paddle/fluid/operators/gaussian_random_mkldnn_op.cc
0 → 100644
浏览文件 @
28ff4bdd
/* 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 <string>
#include "paddle/fluid/operators/mean_op.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
DataLayout
;
template
<
typename
T
>
class
GaussianMKLDNNKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
float
mean
=
context
.
Attr
<
float
>
(
"mean"
);
float
std
=
context
.
Attr
<
float
>
(
"std"
);
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
std
::
minstd_rand
engine
;
if
(
seed
==
0
)
{
seed
=
std
::
random_device
()();
}
engine
.
seed
(
seed
);
std
::
normal_distribution
<
T
>
dist
(
mean
,
std
);
int64_t
size
=
tensor
->
numel
();
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
{
data
[
i
]
=
dist
(
engine
);
}
// The format of output is set as the mkldnn's format
// TODO(@mozga-intel) The format of matrix sets inside the another layers.
tensor
->
set_layout
(
DataLayout
::
kMKLDNN
);
tensor
->
set_format
(
mkldnn
::
memory
::
format
::
oihw
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_KERNEL
(
gaussian_random
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
GaussianMKLDNNKernel
<
float
>
);
paddle/fluid/operators/gaussian_random_op.cc
浏览文件 @
28ff4bdd
...
...
@@ -15,6 +15,10 @@ limitations under the License. */
#include <random>
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace
paddle
{
namespace
operators
{
...
...
@@ -62,9 +66,20 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
LibraryType
library
{
framework
::
LibraryType
::
kPlain
};
framework
::
DataLayout
layout
{
framework
::
DataLayout
::
kAnyLayout
};
#ifdef PADDLE_WITH_MKLDNN
if
(
library
==
framework
::
LibraryType
::
kPlain
&&
platform
::
CanMKLDNNBeUsed
(
ctx
))
{
library
=
framework
::
LibraryType
::
kMKLDNN
;
layout
=
framework
::
DataLayout
::
kMKLDNN
;
}
#endif
return
framework
::
OpKernelType
(
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
ctx
.
Attr
<
int
>
(
"dtype"
)),
ctx
.
device_context
());
ctx
.
device_context
()
,
layout
,
library
);
}
};
...
...
@@ -95,7 +110,9 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
"(int, default 5(FP32)) "
"Output data type."
)
.
SetDefault
(
framework
::
proto
::
VarType
::
FP32
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
GaussianRandom Operator.
...
...
paddle/fluid/operators/math/concat.cu
浏览文件 @
28ff4bdd
...
...
@@ -22,43 +22,24 @@ namespace paddle {
namespace
operators
{
namespace
math
{
template
<
typename
T
>
__device__
T
upper_bound
(
const
T
*
first
,
T
count
,
T
val
)
{
const
T
*
orig
=
first
;
const
T
*
it
=
nullptr
;
T
step
=
0
;
while
(
count
>
0
)
{
it
=
first
;
step
=
count
/
2
;
it
+=
step
;
if
(
!
(
val
<
*
it
))
{
first
=
++
it
;
count
-=
step
+
1
;
}
else
{
count
=
step
;
}
}
return
first
-
orig
;
}
template
<
typename
T
>
__global__
void
KernelConcat
(
T
**
inputs
,
const
int
*
input_cols
,
int
col_size
,
const
int
output_rows
,
const
int
output_cols
,
T
*
output
)
{
int
tid_x
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
segment
=
upper_bound
<
int
>
(
input_cols
,
col_size
,
tid_x
)
-
1
;
int
curr_offset
=
input_cols
[
segment
];
int
curr_segment
=
segment
;
int
curr_segment
=
0
;
int
curr_offset
=
input_cols
[
0
];
for
(;
tid_x
<
output_cols
;
tid_x
+=
blockDim
.
x
*
gridDim
.
x
)
{
T
curr_col_offset
;
while
(
(
curr_col_offset
=
input_cols
[
curr_segment
+
1
])
<=
tid_x
)
{
int
curr_col_offset
=
input_cols
[
curr_segment
+
1
]
;
while
(
curr_col_offset
<=
tid_x
)
{
curr_offset
=
curr_col_offset
;
++
curr_segment
;
curr_col_offset
=
input_cols
[
curr_segment
+
1
];
}
int
local_col
=
tid_x
-
curr_offset
;
int
segment_width
=
curr_col_offset
-
curr_offset
;
T
*
input_ptr
=
inputs
[
curr_segment
];
int
tid_y
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
for
(;
tid_y
<
output_rows
;
tid_y
+=
blockDim
.
y
*
gridDim
.
y
)
...
...
@@ -89,14 +70,14 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
const
int
in_col
,
const
int
*
out_cols
,
int
out_cols_size
,
T
**
outputs_data
)
{
int
tid_x
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
segment
=
upper_bound
<
int
>
(
out_cols
,
out_cols_size
,
tid_x
)
-
1
;
int
curr_offset
=
out_cols
[
segment
];
int
curr_segment
=
segment
;
int
curr_segment
=
0
;
int
curr_offset
=
out_cols
[
0
];
for
(;
tid_x
<
in_col
;
tid_x
+=
blockDim
.
x
*
gridDim
.
x
)
{
T
curr_col_offset
;
while
(
(
curr_col_offset
=
out_cols
[
curr_segment
+
1
])
<=
tid_x
)
{
int
curr_col_offset
=
out_cols
[
curr_segment
+
1
]
;
while
(
curr_col_offset
<=
tid_x
)
{
curr_offset
=
curr_col_offset
;
++
curr_segment
;
curr_col_offset
=
out_cols
[
curr_segment
+
1
];
}
int
local_col
=
tid_x
-
curr_offset
;
...
...
@@ -228,7 +209,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
outputs_cols
[
0
]
=
0
;
for
(
int
i
=
0
;
i
<
o_num
;
++
i
)
{
int
t_col
=
outputs
->
at
(
i
)
->
numel
()
/
out_row
;
int
t_col
=
ref_inputs
.
at
(
i
)
->
numel
()
/
out_row
;
if
(
sameShape
)
{
if
(
t_col
!=
out0_col
)
sameShape
=
false
;
}
...
...
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @
28ff4bdd
...
...
@@ -295,7 +295,7 @@ class ParallelDoGradOp : public framework::OperatorBase {
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
s
,
tmp_name
}}},
{{
"Out"
,
{
s
}}},
framework
::
AttributeMap
{});
framework
::
AttributeMap
{
{
"use_mkldnn"
,
{
false
}}
});
VLOG
(
10
)
<<
sum_op
->
DebugStringEx
(
sub_scopes
[
0
]);
sum_op
->
Run
(
*
sub_scopes
[
0
],
places
[
0
]);
WaitOnPlace
(
places
[
0
]);
...
...
paddle/fluid/operators/recurrent_op.cc
浏览文件 @
28ff4bdd
...
...
@@ -429,7 +429,8 @@ class RecurrentGradOp : public RecurrentBase {
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
pg_names
[
param_id
],
new_inside_name
}}},
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{});
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{{
"use_mkldnn"
,
{
false
}}});
sum_op
->
Run
(
cur_scope
,
place
);
cur_scope
.
Rename
(
new_inside_name
,
inside_grad_name
);
...
...
paddle/fluid/operators/sum_mkldnn_op.cc
0 → 100644
浏览文件 @
28ff4bdd
// 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.
/*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 "mkldnn.hpp"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/operators/sum_op.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace
paddle
{
namespace
operators
{
using
paddle
::
framework
::
Tensor
;
using
paddle
::
platform
::
MKLDNNDeviceContext
;
using
paddle
::
platform
::
CPUDeviceContext
;
using
framework
::
DataLayout
;
using
mkldnn
::
memory
;
using
mkldnn
::
primitive
;
using
mkldnn
::
stream
;
using
mkldnn
::
sum
;
using
mkldnn
::
reorder
;
using
platform
::
to_void_cast
;
template
<
typename
T
>
class
SumMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
auto
in_vars
=
ctx
.
MultiInputVar
(
"X"
);
const
int
N
=
in_vars
.
size
();
auto
out_var
=
ctx
.
OutputVar
(
"Out"
);
bool
in_place
=
out_var
==
in_vars
[
0
];
if
(
out_var
->
IsType
<
framework
::
LoDTensor
>
())
{
LoDTensor
*
output
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
std
::
vector
<
int
>
dst_tz
=
framework
::
vectorize2int
(
output
->
dims
());
auto
src_tz
=
dst_tz
;
memory
::
format
output_format
{
memory
::
format
::
format_undef
};
std
::
vector
<
float
>
scales
;
std
::
vector
<
memory
::
primitive_desc
>
srcs_mpd
;
std
::
vector
<
mkldnn
::
memory
>
srcs_mem
;
PADDLE_ENFORCE
(
in_vars
[
0
]
->
IsType
<
LoDTensor
>
(),
"Input[0] must be LoDTensors"
);
auto
&
input0
=
in_vars
[
0
]
->
Get
<
LoDTensor
>
();
PADDLE_ENFORCE
(
input0
.
layout
()
==
DataLayout
::
kMKLDNN
&&
input0
.
format
()
!=
memory
::
format
::
format_undef
,
"Wrong layout/format for inputs[0]"
);
memory
::
format
input_format
=
input0
.
format
();
if
(
src_tz
.
size
()
==
1
&&
(
input_format
==
memory
::
format
::
nchw
||
input_format
==
memory
::
format
::
nhwc
))
{
input_format
=
memory
::
format
::
x
;
}
if
(
src_tz
.
size
()
==
2
&&
(
input_format
==
memory
::
format
::
nchw
||
input_format
==
memory
::
format
::
nhwc
))
{
input_format
=
memory
::
format
::
nc
;
}
for
(
int
i
=
in_place
?
1
:
0
;
i
<
N
;
i
++
)
{
PADDLE_ENFORCE
(
in_vars
[
i
]
->
IsType
<
LoDTensor
>
(),
"all inputs must be all LoDTensors"
);
auto
&
input
=
in_vars
[
i
]
->
Get
<
LoDTensor
>
();
PADDLE_ENFORCE
(
input
.
layout
()
==
DataLayout
::
kMKLDNN
&&
input
.
format
()
!=
memory
::
format
::
format_undef
,
"Wrong layout/format for inputs"
);
if
(
input
.
numel
()
==
0
)
{
continue
;
}
const
T
*
input_data
=
input
.
data
<
T
>
();
auto
src_md
=
memory
::
desc
(
src_tz
,
memory
::
data_type
::
f32
,
input_format
);
auto
src_mpd
=
memory
::
primitive_desc
(
src_md
,
mkldnn_engine
);
auto
src_mem
=
memory
(
src_mpd
,
to_void_cast
(
input_data
));
srcs_mpd
.
push_back
(
src_mpd
);
srcs_mem
.
push_back
(
src_mem
);
scales
.
push_back
(
1.0
);
}
auto
dst_md
=
memory
::
desc
(
dst_tz
,
memory
::
data_type
::
f32
,
memory
::
format
::
any
);
auto
sum_pd
=
sum
::
primitive_desc
(
dst_md
,
scales
,
srcs_mpd
);
std
::
shared_ptr
<
memory
>
dst_mem
;
if
(
in_place
)
{
dst_mem
.
reset
(
new
memory
(
sum_pd
.
dst_primitive_desc
()));
}
else
{
dst_mem
.
reset
(
new
memory
(
sum_pd
.
dst_primitive_desc
(),
output_data
));
}
std
::
vector
<
mkldnn
::
primitive
::
at
>
inputs
;
for
(
size_t
i
=
0
;
i
<
srcs_mem
.
size
();
++
i
)
{
inputs
.
push_back
(
srcs_mem
[
i
]);
}
auto
sum_prim
=
mkldnn
::
sum
(
sum_pd
,
inputs
,
*
dst_mem
);
output_format
=
(
memory
::
format
)
platform
::
GetMKLDNNFormat
(
sum_pd
);
primitive
reorder_prim
;
std
::
shared_ptr
<
memory
>
target_mem
;
if
(
in_place
)
{
output_format
=
input_format
;
target_mem
.
reset
(
new
memory
(
{{{
src_tz
},
memory
::
data_type
::
f32
,
output_format
},
mkldnn_engine
},
output_data
));
reorder_prim
=
reorder
(
*
dst_mem
,
*
target_mem
);
}
std
::
vector
<
primitive
>
pipeline
;
pipeline
.
push_back
(
sum_prim
);
if
(
in_place
)
pipeline
.
push_back
(
reorder_prim
);
stream
(
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
output
->
set_layout
(
DataLayout
::
kMKLDNN
);
output
->
set_format
(
output_format
);
}
else
if
(
out_var
->
IsType
<
framework
::
SelectedRows
>
())
{
// TODO(@mozga-intel) Add MKLDNN SelectedRows support
std
::
unique_ptr
<
framework
::
SelectedRows
>
in0
;
if
(
in_place
)
{
// If is in_place, we store the input[0] to in0
auto
&
in_sel0
=
in_vars
[
0
]
->
Get
<
SelectedRows
>
();
auto
&
rows
=
in_sel0
.
rows
();
in0
.
reset
(
new
framework
::
SelectedRows
(
rows
,
in_sel0
.
height
()));
in0
->
mutable_value
()
->
ShareDataWith
(
in_sel0
.
value
());
}
auto
get_selected_row
=
[
&
](
size_t
i
)
->
const
SelectedRows
&
{
if
(
i
==
0
&&
in0
)
{
return
*
in0
.
get
();
}
else
{
return
in_vars
[
i
]
->
Get
<
SelectedRows
>
();
}
};
auto
*
out
=
ctx
.
Output
<
SelectedRows
>
(
"Out"
);
out
->
mutable_rows
()
->
clear
();
auto
*
out_value
=
out
->
mutable_value
();
// Runtime InferShape
size_t
first_dim
=
0
;
for
(
int
i
=
0
;
i
<
N
;
i
++
)
{
auto
&
sel_row
=
get_selected_row
(
i
);
first_dim
+=
sel_row
.
rows
().
size
();
}
auto
in_dim
=
framework
::
vectorize
(
get_selected_row
(
N
-
1
).
value
().
dims
());
in_dim
[
0
]
=
static_cast
<
int64_t
>
(
first_dim
);
out_value
->
Resize
(
framework
::
make_ddim
(
in_dim
));
// if all the input sparse vars are empty, no need to
// merge these vars.
if
(
first_dim
==
0UL
)
{
return
;
}
out_value
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
math
::
SelectedRowsAddTo
<
CPUDeviceContext
,
T
>
functor
;
int64_t
offset
=
0
;
for
(
int
i
=
0
;
i
<
N
;
i
++
)
{
auto
&
sel_row
=
get_selected_row
(
i
);
if
(
sel_row
.
rows
().
size
()
==
0
)
{
continue
;
}
PADDLE_ENFORCE_EQ
(
out
->
height
(),
sel_row
.
height
());
functor
(
ctx
.
template
device_context
<
CPUDeviceContext
>(),
sel_row
,
offset
,
out
);
offset
+=
sel_row
.
value
().
numel
();
}
}
else
if
(
out_var
->
IsType
<
framework
::
LoDTensorArray
>
())
{
// TODO(@mozga-intel) Add MKLDNN LoDTensorArray support
auto
&
out_array
=
*
out_var
->
GetMutable
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
in_place
?
1
:
0
;
i
<
in_vars
.
size
();
++
i
)
{
PADDLE_ENFORCE
(
in_vars
[
i
]
->
IsType
<
framework
::
LoDTensorArray
>
(),
"Only support all inputs are TensorArray"
);
auto
&
in_array
=
in_vars
[
i
]
->
Get
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
0
;
i
<
in_array
.
size
();
++
i
)
{
if
(
in_array
[
i
].
numel
()
!=
0
)
{
if
(
i
>=
out_array
.
size
())
{
out_array
.
resize
(
i
+
1
);
}
if
(
out_array
[
i
].
numel
()
==
0
)
{
framework
::
TensorCopy
(
in_array
[
i
],
in_array
[
i
].
place
(),
ctx
.
device_context
(),
&
out_array
[
i
]);
out_array
[
i
].
set_lod
(
in_array
[
i
].
lod
());
}
else
{
PADDLE_ENFORCE
(
out_array
[
i
].
lod
()
==
in_array
[
i
].
lod
());
auto
in
=
EigenVector
<
T
>::
Flatten
(
in_array
[
i
]);
auto
result
=
EigenVector
<
T
>::
Flatten
(
out_array
[
i
]);
result
.
device
(
*
ctx
.
template
device_context
<
MKLDNNDeviceContext
>()
.
eigen_device
())
=
result
+
in
;
}
}
}
}
}
else
{
PADDLE_THROW
(
"Unexpected branch, output variable type is %s"
,
out_var
->
Type
().
name
());
}
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OP_KERNEL
(
sum
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
paddle
::
operators
::
SumMKLDNNOpKernel
<
float
>
);
paddle/fluid/operators/sum_op.cc
浏览文件 @
28ff4bdd
...
...
@@ -18,6 +18,10 @@ limitations under the License. */
#include "paddle/fluid/framework/var_type_inference.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
...
...
@@ -63,6 +67,18 @@ class SumOp : public framework::OperatorWithKernel {
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
x_vars
=
ctx
.
MultiInputVar
(
"X"
);
framework
::
LibraryType
library
{
framework
::
LibraryType
::
kPlain
};
framework
::
DataLayout
layout
{
framework
::
DataLayout
::
kAnyLayout
};
#ifdef PADDLE_WITH_MKLDNN
if
(
library
==
framework
::
LibraryType
::
kPlain
&&
platform
::
CanMKLDNNBeUsed
(
ctx
))
{
library
=
framework
::
LibraryType
::
kMKLDNN
;
layout
=
framework
::
DataLayout
::
kMKLDNN
;
}
#endif
if
(
x_vars
[
0
]
->
IsType
<
framework
::
LoDTensor
>
())
{
int
dtype
=
-
1
;
for
(
auto
&
x_var
:
x_vars
)
{
...
...
@@ -80,26 +96,27 @@ class SumOp : public framework::OperatorWithKernel {
"Sum operator should have at least one tensor"
);
return
framework
::
OpKernelType
(
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
dtype
),
ctx
.
device_context
()
);
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
dtype
),
ctx
.
GetPlace
(),
layout
,
library
);
}
else
if
(
x_vars
[
0
]
->
IsType
<
framework
::
SelectedRows
>
())
{
for
(
auto
&
var
:
x_vars
)
{
auto
&
value
=
var
->
Get
<
framework
::
SelectedRows
>
().
value
();
if
(
value
.
IsInitialized
())
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
value
.
type
()),
ctx
.
device_context
());
ctx
.
device_context
()
,
layout
,
library
);
}
}
// if input sparse vars are not initialized, use an default kernel type.
return
framework
::
OpKernelType
(
framework
::
proto
::
VarType
::
FP32
,
ctx
.
device_context
());
ctx
.
device_context
()
,
layout
,
library
);
}
else
if
(
x_vars
[
0
]
->
IsType
<
framework
::
LoDTensorArray
>
())
{
for
(
auto
&
x_var
:
x_vars
)
{
auto
&
array
=
x_var
->
Get
<
framework
::
LoDTensorArray
>
();
for
(
auto
&
each
:
array
)
{
if
(
each
.
numel
()
!=
0
)
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
each
.
type
()),
ctx
.
device_context
());
ctx
.
device_context
(),
layout
,
library
);
}
}
}
...
...
@@ -116,6 +133,9 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"X"
,
"(vector<Tensor>) The input tensors of sum operator."
)
.
AsDuplicable
();
AddOutput
(
"Out"
,
"(Tensor) The output tensor of sum operator."
).
Reuse
(
"X"
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Sum operator.
...
...
@@ -132,7 +152,6 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
framework
::
BlockDesc
*
block
)
const
override
{
auto
&
inputs
=
op_desc
.
Input
(
"X"
);
auto
var_type
=
framework
::
proto
::
VarType
::
SELECTED_ROWS
;
for
(
auto
&
name
:
op_desc
.
Input
(
"X"
))
{
VLOG
(
10
)
<<
name
<<
" "
<<
block
->
FindRecursiveOrCreateVar
(
name
).
GetType
();
...
...
@@ -206,6 +225,7 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR
(
sum
,
ops
::
SumOp
,
ops
::
SumOpMaker
,
ops
::
SumGradMaker
,
ops
::
SumOpVarTypeInference
);
REGISTER_OP_CPU_KERNEL
(
sum
,
ops
::
SumKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SumKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
...
...
paddle/fluid/operators/while_op.cc
浏览文件 @
28ff4bdd
...
...
@@ -203,11 +203,11 @@ class WhileGradOp : public framework::OperatorBase {
->
set_lod
(
inside_tensor
.
lod
());
}
}
auto
new_inside_name
=
cur_scope
.
Rename
(
inside_grad_name
);
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
pg_names
[
param_id
],
new_inside_name
}}},
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{});
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{{
"use_mkldnn"
,
{
false
}}});
sum_op
->
Run
(
cur_scope
,
dev_place
);
cur_scope
.
Rename
(
new_inside_name
,
inside_grad_name
);
}
...
...
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @
28ff4bdd
...
...
@@ -99,5 +99,11 @@ inline mkldnn::memory::format GetMKLDNNFormat(const mkldnn::memory memory) {
memory
.
get_primitive_desc
().
desc
().
data
.
format
);
}
inline
mkldnn
::
memory
::
format
GetMKLDNNFormat
(
const
mkldnn
::
sum
::
primitive_desc
&
memory
)
{
return
static_cast
<
mkldnn
::
memory
::
format
>
(
memory
.
dst_primitive_desc
().
desc
().
data
.
format
);
}
}
// namespace platform
}
// namespace paddle
python/paddle/fluid/average.py
浏览文件 @
28ff4bdd
...
...
@@ -36,6 +36,25 @@ def _is_number_or_matrix_(var):
class
WeightedAverage
(
object
):
"""
Calculate weighted average.
The average calculating is accomplished via Python totally.
They do not change Paddle's Program, nor do anything to
modify NN model's configuration. They are completely
wrappers of Python functions.
Examples:
.. code-block:: python
avg = fluid.average.WeightedAverage()
avg.add(value=2.0, weight=1)
avg.add(value=4.0, weight=2)
avg.eval()
# The result is 3.333333333.
# For (2.0 * 1 + 4.0 * 2) / (1 + 2) = 3.333333333
"""
def
__init__
(
self
):
warnings
.
warn
(
"The %s is deprecated, please use fluid.metrics.Accuracy instead."
%
...
...
python/paddle/fluid/backward.py
浏览文件 @
28ff4bdd
...
...
@@ -132,9 +132,9 @@ def _addup_repetitive_outputs_(op_descs):
for
idx
,
op_desc
in
enumerate
(
op_descs
):
for
var_name
in
op_desc
.
input_arg_names
():
if
len
(
renamed_vars
[
var_name
])
>
1
:
pending_sum_ops
.
append
(
(
_create_op_desc_
(
"sum"
,
{
"X"
:
renamed_vars
[
var_name
]},
{
"Out"
:
[
var_name
]},
{
}),
idx
))
pending_sum_ops
.
append
(
(
_create_op_desc_
(
"sum"
,
{
"X"
:
renamed_vars
[
var_name
]},
{
"Out"
:
[
var_name
]},
{
"use_mkldnn"
:
False
}),
idx
))
renamed_vars
[
var_name
]
=
[
var_name
]
for
var_name
in
op_desc
.
output_arg_names
():
if
var_name
==
core
.
empty_var_name
(
...
...
@@ -147,7 +147,7 @@ def _addup_repetitive_outputs_(op_descs):
else
:
if
len
(
renamed_vars
[
var_name
])
==
1
:
new_name
=
var_name
+
"@RENAME@"
+
\
str
(
var_rename_count
[
var_name
])
str
(
var_rename_count
[
var_name
])
var_rename_count
[
var_name
]
+=
1
# rename original var_name
renamed_vars
[
var_name
][
0
]
=
new_name
...
...
@@ -155,14 +155,15 @@ def _addup_repetitive_outputs_(op_descs):
_rename_arg_
(
pending_sum_ops
,
var_name
,
new_name
)
new_name
=
var_name
+
"@RENAME@"
+
\
str
(
var_rename_count
[
var_name
])
str
(
var_rename_count
[
var_name
])
var_rename_count
[
var_name
]
+=
1
op_desc
.
rename_output
(
var_name
,
new_name
)
renamed_vars
[
var_name
].
append
(
new_name
)
for
var_name
,
inputs
in
renamed_vars
.
iteritems
():
if
len
(
inputs
)
>
1
:
pending_sum_ops
.
append
((
_create_op_desc_
(
"sum"
,
{
"X"
:
inputs
},
{
"Out"
:
[
var_name
]},
{}),
len
(
op_descs
)))
pending_sum_ops
.
append
(
(
_create_op_desc_
(
"sum"
,
{
"X"
:
inputs
},
{
"Out"
:
[
var_name
]},
{
"use_mkldnn"
:
False
}),
len
(
op_descs
)))
# sum_op descs are sorted according to their insert position
for
p
in
reversed
(
pending_sum_ops
):
op_descs
.
insert
(
p
[
1
],
p
[
0
])
...
...
@@ -434,18 +435,65 @@ def _get_stop_gradients_(program):
def
append_backward
(
loss
,
parameter_list
=
None
,
no_grad_set
=
None
,
callbacks
=
None
):
"""
Append backward part to main_program
Append backward part to main_program
.
Args:
loss(Variable): The variable generated by cost function.
parameter_list(list[string]): Parameters that need to be updated by
optimizer. If None, it means all parameters need to be updated.
no_grad_set(set): Variables that have no gradients in Block 0.
All variables with `step_gradient=True` from all blocks will be
automatically added.
A complete neural network training is made up of forward and backward
propagation. However, when we configure a network, we only need to
specify its forwrd part. The backward part is generated automatically
according to the forward part by this function.
Return:
(list[(Variable,Variable)]): list of (parameter, gradient) pair.
In most cases, users do not need to invoke this function manually. It
will be automatically invoked by the optimizer's `minimize` function.
Args:
loss(Variable): The loss variable of the network.
parameter_list(list[string]|None): Names of parameters that need
to be updated by optimizers.
If it is None, all parameters
will be updated.
Default: None
no_grad_set(set|None): Variables in the Block 0 whose gradients
should be ignored. All variables with
`step_gradient=True` from all blocks will
be automatically added into this set.
Default: None
callbacks(list[callable object]|None): The callbacks are used for
doing some custom jobs during
backward part building. All
callable objects in it will
be invoked once each time a
new gradient operator is added
into the program. The callable
object must has two input
parameters: 'block' and 'context'.
The 'block' is the block which
the new gradient operator will
be added to. The 'context' is a
map, whose keys are gradient
variable names and values are
corresponding original variables.
In addition to this, the 'context'
has another special key-value pair:
the key is string '__current_op_desc__'
and the value is the op_desc of the
gradient operator who has just
triggered the callable object.
Returns:
list[(Variable,Variable)]: Pairs of parameter and its
corresponding gradients. The key is the parameter and the
value is gradient variable.
Raises:
AssertionError: If `loss` is not an instance of Variable.
Examples:
.. code-block:: python
# network configuration code
# ...
avg_loss = fluid.layers.mean(loss)
param_grad_list = fluid.backward.append_backward(loss=avg_loss)
"""
assert
isinstance
(
loss
,
framework
.
Variable
)
...
...
python/paddle/fluid/clip.py
浏览文件 @
28ff4bdd
...
...
@@ -24,8 +24,6 @@ __all__ = [
'GradientClipByValue'
,
'GradientClipByNorm'
,
'GradientClipByGlobalNorm'
,
'append_gradient_clip_ops'
,
'error_clip_callback'
,
]
...
...
@@ -38,6 +36,25 @@ class BaseErrorClipAttr(object):
class
ErrorClipByValue
(
BaseErrorClipAttr
):
"""
Clips tensor values to the range [min, max].
Given a tensor t, this operation clips its value to min and max inplace.
- Any values less than min are set to min.
- Any values greater than max are set to max.
Args:
max (float): The maximum value to clip by.
min (float, optional): The minimum value to clip by. if not set by user,
\
will be set to -max by framework.
Examples:
.. code-block:: python
var = fluid.framework.Variable(..., error_clip=ErrorClipByValue(max=5.0), ...)
"""
def
__init__
(
self
,
max
,
min
=
None
):
max
=
float
(
max
)
if
min
is
None
:
...
...
@@ -99,6 +116,31 @@ class NullGradientClipAttr(BaseGradientClipAttr):
class
GradientClipByValue
(
BaseGradientClipAttr
):
"""
Clips gradient values to the range [min, max].
Given a tensor t, this operation clips its value to min and max inplace.
- Any values less than min are set to min.
- Any values greater than max are set to max.
Args:
max (float): The maximum value to clip by.
min (float, optional): The minimum value to clip by. if not set by user,
\
will be set to -max by framework.
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
trainable=True,
clip=GradientClipByValue(-1.0, 1.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
def
__init__
(
self
,
max
,
min
=
None
):
max
=
float
(
max
)
if
min
is
None
:
...
...
@@ -120,6 +162,37 @@ class GradientClipByValue(BaseGradientClipAttr):
class
GradientClipByNorm
(
BaseGradientClipAttr
):
"""
Clips tensor values to a maximum L2-norm.
This operator limits the L2 norm of the input :math:`X` within :math:`max\_norm`.
If the L2 norm of :math:`X` is less than or equal to :math:`max\_norm`, :math:`Out`
will be the same as :math:`X`. If the L2 norm of :math:`X` is greater than
:math:`max\_norm`, :math:`X` will be linearly scaled to make the L2 norm of
:math:`Out` equal to :math:`max\_norm`, as shown in the following formula:
.. math::
Out =
\\
frac{max\_norm * X}{norm(X)},
where :math:`norm(X)` represents the L2 norm of :math:`X`.
Args:
clip_norm (float): The maximum norm value
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
trainable=True,
clip=GradientClipByNorm(clip_norm=2.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
def
__init__
(
self
,
clip_norm
):
self
.
clip_norm
=
clip_norm
...
...
@@ -135,6 +208,44 @@ class GradientClipByNorm(BaseGradientClipAttr):
class
GradientClipByGlobalNorm
(
BaseGradientClipAttr
):
"""
Clips values of multiple tensors by the ratio of the sum of their norms.
Given a list of tensors t_list, and a clipping ratio clip_norm, this
operation returns a list of clipped tensors list_clipped and the global
norm (global_norm) of all tensors in t_list.
To perform the clipping, the values :math:`t\_list[i]` are set to:
.. math::
t\_list[i] = t\_list[i] *
\\
frac{clip\_norm}{\max(global\_norm, clip\_norm)}
where:
.. math::
global\_norm = \sqrt{\sum_{i=0}^{N-1}(l2norm(t\_list[i]))^2}
If :math:`clip\_norm > global\_norm` then the entries in t_list remain as they are,
otherwise they're all shrunk by the global ratio.
Args:
clip_norm (float): The maximum norm value
group_name (str, optional): The group name for this clip.
Examples:
.. code-block:: python
p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
with fluid.program_guard(main_program=prog_clip):
fluid.clip.set_gradient_clip(
fluid.clip.GradientClipByGlobalNorm(clip_norm=2.0))
p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
"""
def
__init__
(
self
,
clip_norm
,
group_name
=
"default_group"
):
if
not
isinstance
(
group_name
,
basestring
):
raise
TypeError
(
"'group_name' must be a basestring."
)
...
...
@@ -183,15 +294,16 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
def
set_gradient_clip
(
clip
,
param_list
=
None
,
program
=
None
):
"""
To specify parameters that require gradient clip.
Args:
clip(BaseGradientClipAttr): An instance of some derived class of BaseGradientClipAttr,
which describes the type and detailed attributes of required gradient clip.
param_list(list, None by default): Parameters that require gradient clip.
It can be a list of parameter or a list of parameter's name.
When it's None, all parameters in the program will be included.
program(Program, None by default): The program where parameters are.
Will be the default main program when assigned with None.
To specify parameters that require gradient clip.
Args:
clip(BaseGradientClipAttr): An instance of some derived class of BaseGradientClipAttr,
which describes the type and detailed attributes of required gradient clip.
param_list(list(Variable)): Parameters that require gradient clip.
It can be a list of parameter or a list of parameter's name.
When it's None, all parameters in the program will be included.
program(Program): The program where parameters are.
Will be the default main program when assigned with None.
"""
if
not
isinstance
(
clip
,
BaseGradientClipAttr
):
raise
TypeError
(
...
...
python/paddle/fluid/data_feeder.py
浏览文件 @
28ff4bdd
...
...
@@ -29,6 +29,13 @@ class DataToLoDTensorConverter(object):
self
.
place
=
place
self
.
lod_level
=
lod_level
self
.
shape
=
shape
negtive_count
=
0
for
s
in
self
.
shape
:
if
s
<
0
:
negtive_count
+=
1
if
negtive_count
>
1
:
self
.
shape
=
None
break
if
dtype
==
core
.
VarDesc
.
VarType
.
FP32
:
self
.
dtype
=
'float32'
elif
dtype
==
core
.
VarDesc
.
VarType
.
INT64
:
...
...
@@ -61,7 +68,9 @@ class DataToLoDTensorConverter(object):
self
.
_feed_impl_
(
each_data
,
lod
[
1
:],
lod_level
-
1
)
def
done
(
self
):
arr
=
numpy
.
array
(
self
.
data
,
dtype
=
self
.
dtype
).
reshape
(
self
.
shape
)
arr
=
numpy
.
array
(
self
.
data
,
dtype
=
self
.
dtype
)
if
self
.
shape
:
arr
=
arr
.
reshape
(
self
.
shape
)
t
=
core
.
LoDTensor
()
t
.
set
(
arr
,
self
.
place
)
if
self
.
lod_level
>
0
:
...
...
python/paddle/fluid/inferencer.py
浏览文件 @
28ff4bdd
...
...
@@ -27,13 +27,30 @@ __all__ = ['Inferencer', ]
class
Inferencer
(
object
):
"""
Inferencer High Level API.
Args:
infer_func (Python func): Infer function that will return predict Variable
param_path (str): The path where the inference model is saved by fluid.io.save_params
place (Place): place to do the inference
parallel (bool): use parallel_executor to run the inference, it will use multi CPU/GPU.
Examples:
.. code-block:: python
def inference_program():
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
return y_predict
place = fluid.CPUPlace()
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path="/tmp/model", place=place)
"""
def
__init__
(
self
,
infer_func
,
param_path
,
place
=
None
,
parallel
=
False
):
"""
:param infer_func: a function that will return predict Variable
:param param_path: the path where the inference model is saved by fluid.io.save_params
:param place: place to do the inference
:param parallel: use parallel_executor to run the inference, it will use multi CPU/GPU.
"""
self
.
param_path
=
param_path
self
.
scope
=
core
.
Scope
()
self
.
parallel
=
parallel
...
...
@@ -60,9 +77,20 @@ class Inferencer(object):
def
infer
(
self
,
inputs
,
return_numpy
=
True
):
"""
:param inputs: a map of {"input_name": input_var} that will be feed into the inference program
to get the predict value
:return: the predict value of the inference model
Do Inference for Inputs
Args:
inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
return_numpy (bool): transform return value into numpy or not
Returns:
Tensor or Numpy: the predict value of the inference model for the inputs
Examples:
.. code-block:: python
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
"""
if
not
isinstance
(
inputs
,
dict
):
raise
ValueError
(
...
...
python/paddle/fluid/initializer.py
浏览文件 @
28ff4bdd
...
...
@@ -19,26 +19,39 @@ from framework import convert_np_dtype_to_dtype_
from
core
import
VarDesc
__all__
=
[
'Constant'
,
'Uniform'
,
'Normal'
,
'Xavier'
,
'Bilinear'
,
'force_init_on_cpu'
,
'init_on_cpu'
,
'ConstantInitializer'
,
'UniformInitializer'
,
'NormalInitializer'
,
'XavierInitializer'
,
'BilinearInitializer'
'Constant'
,
'Uniform'
,
'Normal'
,
'Xavier'
,
'Bilinear'
,
'MSRA'
,
'force_init_on_cpu'
,
'init_on_cpu'
,
'ConstantInitializer'
,
'UniformInitializer'
,
'NormalInitializer'
,
'XavierInitializer'
,
'BilinearInitializer'
,
'MSRAInitializer'
]
_force_init_on_cpu_
=
False
def
force_init_on_cpu
():
"""
The flag of whether force to init variables on CPU.
Examples:
.. code-block:: python
if force_init_on_cpu():
pass
"""
return
_force_init_on_cpu_
@
contextlib
.
contextmanager
def
init_on_cpu
():
"""
Switch program with `with` statement
Force the variable to be inited on CPU.
Examples:
>>> with init_on_cpu():
>>> step = layers.create_global_var()
.. code-block:: python
with init_on_cpu():
step = layers.create_global_var()
"""
global
_force_init_on_cpu_
...
...
@@ -104,14 +117,18 @@ class Initializer(object):
class
ConstantInitializer
(
Initializer
):
"""Implements the constant initializer
Args:
value (float): constant value to initialize the variable
Examples:
.. code-block:: python
fc = fluid.layers.fc(input=x, size=10,
param_attr=fluid.initializer.Constant(value=2.0))
"""
def
__init__
(
self
,
value
=
0.0
,
force_cpu
=
False
):
"""Constructor for ConstantInitializer
Args:
value: constant value to initialize the variable
"""
assert
value
is
not
None
super
(
ConstantInitializer
,
self
).
__init__
()
self
.
_value
=
value
...
...
@@ -146,16 +163,20 @@ class ConstantInitializer(Initializer):
class
UniformInitializer
(
Initializer
):
"""Implements the random uniform distribution initializer
Args:
low (float): lower boundary of the uniform distribution
high (float): upper boundary of the uniform distribution
seed (int): random seed
Examples:
.. code-block:: python
fc = fluid.layers.fc(input=x, size=10,
param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5))
"""
def
__init__
(
self
,
low
=-
1.0
,
high
=
1.0
,
seed
=
0
):
"""Constructor for UniformInitializer
Args:
low: lower boundary of the uniform distribution
high: upper boundary of the uniform distribution
seed: random seed
"""
assert
low
is
not
None
assert
high
is
not
None
assert
high
>=
low
...
...
@@ -196,17 +217,21 @@ class UniformInitializer(Initializer):
class
NormalInitializer
(
Initializer
):
"""Implements the random Normal(Gaussian) distribution initializer
"""Implements the Random Normal(Gaussian) distribution initializer
Args:
loc (float): mean of the normal distribution
scale (float): standard deviation of the normal distribution
seed (int): random seed
Examples:
.. code-block:: python
fc = fluid.layers.fc(input=x, size=10,
param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0))
"""
def
__init__
(
self
,
loc
=
0.0
,
scale
=
1.0
,
seed
=
0
):
"""Constructor for NormalInitializer
Args:
loc: mean of the normal distribution
scale: standard deviation of the normal distribution
seed: random seed
"""
assert
loc
is
not
None
assert
scale
is
not
None
assert
seed
is
not
None
...
...
@@ -246,39 +271,49 @@ class NormalInitializer(Initializer):
class
XavierInitializer
(
Initializer
):
"""Implements the Xavier initializer
"""
This class implements the Xavier weight initializer from the paper
Understanding the difficulty of training deep feedforward neural
networks[1] by Xavier Glorot and Yoshua Bengio.
`Understanding the difficulty of training deep feedforward neural
networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
by Xavier Glorot and Yoshua Bengio.
This initializer is designed to keep the scale of the gradients
approximately same in all the layers. In case of Uniform distribution,
the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)).
the range is [-x, x], where
.. math::
x = \sqrt{
\\
frac{6.0}{fan\_in + fan\_out}}
In case of Normal distribution, the mean is 0 and the standard deviation
is sqrt(2/ (fan_in + fan_out)).
is
.. math::
\sqrt{
\\
frac{2.0}{fan\_in + fan\_out}}
Args:
uniform (bool): whether to use uniform or normal distribution
fan_in (float): fan_in for Xavier initialization. If None, it is
inferred from the variable.
fan_out (float): fan_out for Xavier initialization. If None, it is
inferred from the variable.
seed (int): random seed
Note:
It is recommended to set fan_in and fan_out to None for most cases.
Examples:
.. code-block:: python
fc = fluid.layers.fc(
input=queries, size=10,
param_attr=fluid.initializer.Xavier(uniform=False))
References:
[1] Understanding the difficulty of training deep feedforward neural
networks. International conference on artificial intelligence and
statistics.
(http://proceedings.mlr.press/v9/glorot10a.html)
"""
def
__init__
(
self
,
uniform
=
True
,
fan_in
=
None
,
fan_out
=
None
,
seed
=
0
):
"""Constructor for XavierInitializer
Args:
uniform: whether to use uniform or normal distribution
fan_in: fan_in for Xavier initialization. If None, it is
inferred from the variable.
fan_out: fan_out for Xavier initialization. If None, it is
inferred from the variable.
seed: random seed
Note: It is recommended to set fan_in and fan_out to None for
most cases.
"""
assert
uniform
is
not
None
assert
seed
is
not
None
super
(
XavierInitializer
,
self
).
__init__
()
...
...
@@ -342,30 +377,42 @@ class MSRAInitializer(Initializer):
"""Implements the MSRA initializer a.k.a. Kaiming Initializer
This class implements the weight initialization from the paper
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren
and Jian Sun. This is a robust initialization method that particularly
considers the rectifier nonlinearities. In case of Uniform distribution,
the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal
distribution, the mean is 0 and the standard deviation
is sqrt(2/ fan_in).
References:
[1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
on ImageNet Classification
(https://arxiv.org/abs/1502.01852)
`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification <https://arxiv.org/abs/1502.01852>`_
by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
robust initialization method that particularly considers the rectifier
nonlinearities. In case of Uniform distribution, the range is [-x, x], where
.. math::
x = \sqrt{
\\
frac{6.0}{fan\_in}}
In case of Normal distribution, the mean is 0 and the standard deviation
is
.. math::
\sqrt{
\\
frac{2.0}{fan\_in}}
Args:
uniform (bool): whether to use uniform or normal distribution
fan_in (float): fan_in for MSRAInitializer. If None, it is
\
inferred from the variable.
seed (int): random seed
Note:
It is recommended to set fan_in to None for most cases.
Examples:
.. code-block:: python
fc = fluid.layers.fc(
input=queries, size=10,
param_attr=fluid.initializer.MSRA(uniform=False))
"""
def
__init__
(
self
,
uniform
=
True
,
fan_in
=
None
,
seed
=
0
):
"""Constructor for MSRAInitializer
Args:
uniform: whether to use uniform or normal distribution
fan_in: fan_in for MSRAInitializer. If None, it is
inferred from the variable.
seed: random seed
Note: It is recommended to set fan_in to None for most cases.
"""
assert
uniform
is
not
None
assert
seed
is
not
None
...
...
@@ -425,34 +472,37 @@ class MSRAInitializer(Initializer):
class
BilinearInitializer
(
Initializer
):
"""Implements the bilinear initializer.
"""
This initializer can be used in transposed convolution operator to
act as upsampling. Users can upsample a feature map with shape of
(B, C, H, W) by any integer factor. The usage is:
>>> factor = 2
>>> w_attr = ParamAttr(learning_rate=0., regularizer=L2Decay(0.),
>>> initializer=Bilinear())
>>> conv_up = fluid.layers.conv2d_transpose(
>>> input,
>>> num_filters=C,
>>> output_size=None,
>>> filter_size=2 * factor - factor % 2,
>>> padding=ceil((factor - 1) / 2.),
>>> stride=factor,
>>> groups=C,
>>> param_attr=w_attr,
>>> bias_attr=False)
Where, `num_filters=C` and `groups=C` means this is channel-wise tranposed
Examples:
.. code-block:: python
factor = 2
w_attr = ParamAttr(learning_rate=0., regularizer=L2Decay(0.),
initializer=Bilinear())
conv_up = fluid.layers.conv2d_transpose(
input,
num_filters=C,
output_size=None,
filter_size=2 * factor - factor % 2,
padding=ceil((factor - 1) / 2.),
stride=factor,
groups=C,
param_attr=w_attr,
bias_attr=False)
Where, `num_filters=C` and `groups=C` means this is channel-wise transposed
convolution. The filter shape will be (C, 1, K, K) where K is `filer_size`,
This initializer will set a (K, K) interpolation kernel for every channel
of the filter identically. The resulting shape of the output feature map
will be (B, C, factor * H, factor * W). Note that the learning rate and the
weight decay are set to 0 in order to keep coefficient values of bilinear
interpolation unchanged during training.
interpolation unchanged during training.
"""
def
__init__
(
self
):
...
...
@@ -469,7 +519,7 @@ class BilinearInitializer(Initializer):
be added.
Returns:
the initialization op
Operator:
the initialization op
Raises:
ValueError: If type of `var` and `block` is not right.
...
...
python/paddle/fluid/io.py
浏览文件 @
28ff4bdd
此差异已折叠。
点击以展开。
python/paddle/fluid/layers/control_flow.py
浏览文件 @
28ff4bdd
...
...
@@ -185,12 +185,14 @@ def Print(input,
Returns:
Variable: Output tensor, same data with input tensor.
Examples:
.. code-block:: python
value = some_layer(...)
Print(value, summarize=10,
message="The content of some_layer: ")
value = some_layer(...)
Print(value, summarize=10,
message="The content of some_layer: ")
'''
helper
=
LayerHelper
(
'print'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
...
...
@@ -1201,6 +1203,31 @@ class ConditionalBlockGuard(BlockGuard):
class
ConditionalBlock
(
object
):
'''
**ConditionalBlock**
ConditionalBlock is an operator that bind a block to a specific condition,
if the condition matches, the corresponding block will be executed.
Args:
inputs (Variable): bool conditions.
is_scalar_condition (bool): whether the branch is controled by a scalar.
name(str): name of this ConditionalBlock.
Examples:
.. code-block:: python
cond = layers.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
true_cond = layers.ConditionalBlock([true_image])
with true_cond.block():
...
with false_cond.block():
...
'''
def
__init__
(
self
,
inputs
,
is_scalar_condition
=
False
,
name
=
None
):
for
each_input
in
inputs
:
if
not
isinstance
(
each_input
,
Variable
):
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
28ff4bdd
...
...
@@ -16,7 +16,7 @@ All layers just related to the detection neural network.
"""
from
layer_function_generator
import
generate_layer_fn
from
layer_function_generator
import
autodoc
from
layer_function_generator
import
autodoc
,
templatedoc
from
..layer_helper
import
LayerHelper
import
tensor
import
nn
...
...
@@ -155,7 +155,7 @@ def detection_output(loc,
return
nmsed_outs
@
auto
doc
()
@
template
doc
()
def
detection_map
(
detect_res
,
label
,
class_num
,
...
...
@@ -166,6 +166,47 @@ def detection_map(detect_res,
input_states
=
None
,
out_states
=
None
,
ap_version
=
'integral'
):
"""
${comment}
Args:
detect_res: ${detect_res_comment}
label: ${label_comment}
class_num: ${class_num_comment}
background_label: ${background_label_comment}
overlap_threshold: ${overlap_threshold_comment}
evaluate_difficult: ${evaluate_difficult_comment}
has_state: ${has_state_comment}
input_states: If not None, It contains 3 elements:
1. pos_count ${pos_count_comment}.
2. true_pos ${true_pos_comment}.
3. false_pos ${false_pos_comment}.
out_states: If not None, it contains 3 elements.
1. accum_pos_count ${accum_pos_count_comment}.
2. accum_true_pos ${accum_true_pos_comment}.
3. accum_false_pos ${accum_false_pos_comment}.
ap_version: ${ap_type_comment}
Returns:
${map_comment}
Examples:
.. code-block:: python
detect_res = fluid.layers.data(
name='detect_res',
shape=[10, 6],
append_batch_size=False,
dtype='float32')
label = fluid.layers.data(
name='label',
shape=[10, 6],
append_batch_size=False,
dtype='float32')
map_out = fluid.layers.detection_map(detect_res, label, 21)
"""
helper
=
LayerHelper
(
"detection_map"
,
**
locals
())
def
__create_var
(
type
):
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
28ff4bdd
此差异已折叠。
点击以展开。
python/paddle/fluid/layers/tensor.py
浏览文件 @
28ff4bdd
...
...
@@ -230,7 +230,11 @@ def sums(input, out=None):
helper
=
LayerHelper
(
'sum'
,
**
locals
())
if
out
is
None
:
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'sum'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
})
helper
.
append_op
(
type
=
'sum'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'use_mkldnn'
:
False
})
return
out
...
...
@@ -380,7 +384,7 @@ def argmin(x, axis=0):
"""
**argmin**
This function computes the indices of the min elements
This function computes the indices of the min elements
of the input tensor's element along the provided axis.
Args:
...
...
@@ -395,7 +399,7 @@ def argmin(x, axis=0):
.. code-block:: python
out = fluid.layers.argmin(x=in, axis=0)
out = fluid.layers.argmin(x=in, axis=-1)
out = fluid.layers.argmin(x=in, axis=-1)
"""
helper
=
LayerHelper
(
"arg_min"
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
VarDesc
.
VarType
.
INT64
)
...
...
@@ -411,7 +415,7 @@ def argmax(x, axis=0):
"""
**argmax**
This function computes the indices of the max elements
This function computes the indices of the max elements
of the input tensor's element along the provided axis.
Args:
...
...
@@ -426,7 +430,7 @@ def argmax(x, axis=0):
.. code-block:: python
out = fluid.layers.argmax(x=in, axis=0)
out = fluid.layers.argmax(x=in, axis=-1)
out = fluid.layers.argmax(x=in, axis=-1)
"""
helper
=
LayerHelper
(
"arg_max"
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
VarDesc
.
VarType
.
INT64
)
...
...
@@ -495,9 +499,9 @@ def reverse(x, axis):
Args:
x(Vairbale): the input to be reversed.
axis(int|tuple|list): Axis that along which order of elements
is reversed. If it is a tuple or a list, reversing
will be apply on each axis in the tuple or list.
axis(int|tuple|list): Axis that along which order of elements
is reversed. If it is a tuple or a list, reversing
will be apply on each axis in the tuple or list.
Returns:
Variable: The reversed tensor.
...
...
@@ -528,9 +532,9 @@ def save(x, file_path, overwrite=True):
Args:
x(variable): The Tensor/LoDTensor to be saved.
file_path(str): The file path where the variable will be saved.
overwrite(bool): Whether or not cover the given file when it has already
existed. If it's set 'False' and the file is existed, a runtime
error will be thrown.
overwrite(bool): Whether or not cover the given file when it has already
existed. If it's set 'False' and the file is existed, a runtime
error will be thrown.
"""
helper
=
LayerHelper
(
"save"
,
**
locals
())
helper
.
append_op
(
...
...
@@ -550,8 +554,8 @@ def save_combine(x, file_path, overwrite=True):
a single file.
file_path(str): The file path where variables will be saved.
overwrite(bool): Whether or not cover the given file when it has already
existed. If it's set 'False' and the file is existed, a runtime
error will be thrown.
existed. If it's set 'False' and the file is existed, a runtime
error will be thrown.
Returns:
There is no return value.
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
28ff4bdd
...
...
@@ -26,10 +26,10 @@ from clip import append_gradient_clip_ops, error_clip_callback
from
contextlib
import
contextmanager
__all__
=
[
'SGD'
,
'Momentum'
,
'Adagrad'
,
'Adam'
,
'Adamax'
,
'DecayedAdagrad'
,
'SGD'
,
'Momentum'
,
'Adagrad'
,
'Adam'
,
'Adamax'
,
'DecayedAdagrad'
,
'Ftrl'
,
'SGDOptimizer'
,
'MomentumOptimizer'
,
'AdagradOptimizer'
,
'AdamOptimizer'
,
'AdamaxOptimizer'
,
'DecayedAdagradOptimizer'
,
'RMSPropOptimizer'
,
'
Adadelta'
,
'ModelAverage'
,
'
Optimizer'
'
FtrlOptimizer'
,
'Adadelta'
,
'ModelAverage'
,
'Optimizer'
,
'RMSProp
Optimizer'
]
...
...
@@ -192,15 +192,15 @@ class Optimizer(object):
"""Add optimization operators to update gradients to variables.
Args:
loss: the target that this optimization is for.
parameters_and_grads: a list of (variable, gradient) pair to update.
loss(Variable): the target that this optimization is for.
parameters_and_grads(list(tuple(Variable, Variable))):
a list of (variable, gradient) pair to update.
Returns:
return_op_list: a list of operators that will complete one step of
optimization. This will include parameter update ops, global step
update ops and any other custom ops required by subclasses to manage
their internal state.
:param startup_program:
"""
# This is a default implementation of create_optimization_pass that
# can be shared by most optimizers. This implementation assumes that
...
...
@@ -268,7 +268,22 @@ class Optimizer(object):
class
SGDOptimizer
(
Optimizer
):
""" Simple SGD optimizer without any state.
"""
Optimizer of the stochastic gradient descent algorithm.
.. math::
param\_out = param - learning\_rate * grad
Args:
learning_rate (float|Variable): the learning rate used to update parameters.
\
Can be a float value or a Variable with one float value as data element.
Examples:
.. code-block:: python
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.2)
sgd_optimizer.minimize(cost)
"""
def
__init__
(
self
,
learning_rate
,
**
kwargs
):
...
...
@@ -294,7 +309,37 @@ class SGDOptimizer(Optimizer):
class
MomentumOptimizer
(
Optimizer
):
"""Simple Momentum optimizer with velocity state
"""
Simple Momentum optimizer with velocity state
This optimizer has a flag for Nestrov Momentum.
The update equations are as follows:
.. math::
& velocity = mu * velocity + gradient
& if (use\_nesterov):
&\quad param = param - gradient * learning\_rate + mu * velocity * learning\_rate
& else:
&\quad param = param - learning\_rate * velocity
Args:
learning_rate (float|Variable): the learning rate used to update parameters.
\
Can be a float value or a Variable with one float value as data element.
momentum (float): momentum factor
use_nesterov (bool): enables Nesterov momentum
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
optimizer.minimize(cost)
"""
_velocity_acc_str
=
"velocity"
...
...
@@ -338,7 +383,32 @@ class MomentumOptimizer(Optimizer):
class
AdagradOptimizer
(
Optimizer
):
"""Simple Adagrad optimizer with moment state
"""
**Adaptive Gradient Algorithm (Adagrad)**
The update is done as follows:
.. math::
moment\_out &= moment + grad * grad
param\_out &= param -
\\
frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
does not have the epsilon attribute. It is added here in our implementation
as also proposed here: http://cs231n.github.io/neural-networks-3/#ada
for numerical stability to avoid the division by zero error.
Args:
learning_rate (float|Variable): the learning rate used to update parameters.
\
Can be a float value or a Variable with one float value as data element.
epsilon (float): a small float value for numerical stability.
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Adagrad(learning_rate=0.2)
optimizer.minimize(cost)
"""
_moment_acc_str
=
"moment"
...
...
@@ -379,7 +449,40 @@ class AdagradOptimizer(Optimizer):
class
AdamOptimizer
(
Optimizer
):
"""Implements the Adam Optimizer
"""
This implements the Adam optimizer from Section 2 of the Adam
paper : https://arxiv.org/abs/1412.6980.
Adam is a first-order gradient-based optimization method based on
adaptive estimates of lower-order moments.
Adam updates:
.. math::
t & = t + 1
moment\_1\_out & = {
\\
beta}_1 * moment\_1 + (1 - {
\\
beta}_1) * grad
moment\_2\_out & = {
\\
beta}_2 * moment\_2 + (1 - {
\\
beta}_2) * grad * grad
learning\_rate & = learning\_rate *
\\
\\
frac{\sqrt{1 - {
\\
beta}_2^t}}{1 - {
\\
beta}_1^t}
param\_out & = param - learning\_rate *
\\
frac{moment\_1}{\sqrt{moment\_2} + \epsilon}
Args:
learning_rate (float|Variable): the learning rate used to update parameters.
\
Can be a float value or a Variable with one float value as data element.
beta1 (float): The exponential decay rate for the 1st moment estimates.
beta2 (float): The exponential decay rate for the 2nd moment estimates.
epsilon (float): a small float value for numerical stability.
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Adam(learning_rate=0.2)
optimizer.minimize(cost)
"""
_moment1_acc_str
=
"moment1"
_moment2_acc_str
=
"moment2"
...
...
@@ -484,7 +587,42 @@ class AdamOptimizer(Optimizer):
class
AdamaxOptimizer
(
Optimizer
):
"""Implements the Adamax Optimizer
"""
We implement the Adamax optimizer from Section 7 of the Adam
paper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the
Adam algorithm based on the infinity norm.
Adamax updates:
.. math::
t & = t + 1
moment\_out & = {
\\
beta}_1 * moment + (1 - {
\\
beta}_1) * grad
inf\_norm\_out & = max({
\\
beta}_2 * inf\_norm + \epsilon, |grad|)
learning\_rate & =
\\
frac{learning\_rate}{1 - {
\\
beta}_1^t}
param\_out & = param - learning\_rate *
\\
frac{moment\_out}{inf\_norm\_out}
The original paper does not have an epsilon attribute.
However, it is added here for numerical stability to prevent the
division by 0 error.
Args:
learning_rate (float|Variable): the learning rate used to update parameters.
\
Can be a float value or a Variable with one float value as data element.
beta1 (float): The exponential decay rate for the 1st moment estimates.
beta2 (float): The exponential decay rate for the 2nd moment estimates.
epsilon (float): a small float value for numerical stability.
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Adamax(learning_rate=0.2)
optimizer.minimize(cost)
"""
_moment_acc_str
=
"moment"
_inf_norm_acc_str
=
"inf_norm"
...
...
@@ -568,7 +706,34 @@ class AdamaxOptimizer(Optimizer):
class
DecayedAdagradOptimizer
(
Optimizer
):
"""Simple Decayed Adagrad optimizer with moment state
"""
**Decayed Adagrad Optimizer**
The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
The update is done as follows:
.. math::
moment\_out & = decay * moment + (1 - decay) * grad * grad
param\_out & = param -
\\
frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
does not have an epsilon attribute. It is added here for numerical
stability to avoid the division by zero error.
Args:
learning_rate (float|Variable): the learning rate used to update parameters.
\
Can be a float value or a Variable with one float value as data element.
decay (float): decay rate.
epsilon (float): a small float value for numerical stability.
Examples:
.. code-block:: python
optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2)
optimizer.minimize(cost)
"""
_moment_acc_str
=
"moment"
...
...
@@ -614,6 +779,7 @@ class DecayedAdagradOptimizer(Optimizer):
class
AdadeltaOptimizer
(
Optimizer
):
"""
**Adadelta Optimizer**
Simple Adadelta optimizer with average squared grad state and
average squared update state.
The details of adadelta please refer to this
...
...
@@ -628,7 +794,7 @@ class AdadeltaOptimizer(Optimizer):
E(dx_t^2) &=
\\
rho * E(dx_{t-1}^2) + (1-
\\
rho) * (-g*learning
\\
_rate)^2
Args:
learning_rate(float): global le
ra
ning rate
learning_rate(float): global le
ar
ning rate
rho(float): rho in equation
epsilon(float): epsilon in equation
...
...
@@ -703,37 +869,37 @@ class RMSPropOptimizer(Optimizer):
.. math::
r(w, t) & =
\\
rho r(w, t-1) + (1 -
\\
rho)(
\\
nabla Q_{i}(w))^2
\\\\
r(w, t) & =
\\
rho r(w, t-1) + (1 -
\\
rho)(
\\
nabla Q_{i}(w))^2
w & = w -
\\
frac{
\\
eta} {
\\
sqrt{r(w,t) +
\\
epsilon}}
\\
nabla Q_{i}(w)
The first equation calculates moving average of the squared gradient for
each weight. Then dividing the gradient by :math:
`sqrt{v(w,t)}`.
each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`.
In some cases, adding a momentum term :math: `
\\
beta` is beneficial.
In our implementation, Nesterov momentum is used:
.. math::
r(w, t) & =
\\
rho r(w, t-1) + (1 -
\\
rho)(
\\
nabla Q_{i}(w))^2
\\\\
r(w, t) & =
\\
rho r(w, t-1) + (1 -
\\
rho)(
\\
nabla Q_{i}(w))^2
v(w, t) & =
\\
beta v(w, t-1) +
\\
frac{
\\
eta} {
\\
sqrt{v(w,t) +
\\
epsilon}}
\\
nabla Q_{i}(w)
w & = w - v(w, t)
where, :math:
`
\\
rho` is a hyperparameter and typical values are 0.9, 0.95
where, :math:`
\\
rho` is a hyperparameter and typical values are 0.9, 0.95
and so on. :math: `beta` is the momentum term. :math: `
\\
epsilon` is a
smoothing term to avoid division by zero, usually set somewhere in range
from 1e-4 to 1e-8.
Args:
learning_rate(float): global le
ra
ning rate.
learning_rate(float): global le
ar
ning rate.
rho(float): rho is :math: `
\\
rho` in equation, set 0.95 by default.
epsilon(float): :math: `
\\
epsilon` in equation is smoothing term to
avoid division by zero, set 1e-6 by default.
momentum(float): :math:
`
\\
beta` in equation is the momentum term,
momentum(float): :math:`
\\
beta` in equation is the momentum term,
set 0.0 by default.
Raises:
...
...
@@ -810,6 +976,113 @@ class RMSPropOptimizer(Optimizer):
return
rmsprop_op
class
FtrlOptimizer
(
Optimizer
):
"""
FTRL (Follow The Regularized Leader) Optimizer.
The paper that proposed Follow The Regularized Leader (FTRL):
(https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf)
.. math::
&new\_accum = squared\_accum + grad^2
&if (lr\_power == -0.5):
&\quad linear\_accum += grad -
\\
frac{
\\
sqrt{new\_accum} -
\\
sqrt{squared\_accum}}{learning\_rate * param}
&else:
&\quad linear\_accum += grad -
\\
frac{new\_accum^{-lr\_power} - accum^{-lr\_power}}{learning\_rate * param}
&x = l1 * sign(linear\_accum) - linear\_accum
&if (lr\_power == -0.5):
&\quad y =
\\
frac{
\\
sqrt{new\_accum}}{learning\_rate} + (2 * l2)
&\quad pre\_shrink =
\\
frac{x}{y}
&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
&else:
&\quad y =
\\
frac{new\_accum^{-lr\_power}}{learning\_rate} + (2 * l2)
&\quad pre\_shrink =
\\
frac{x}{y}
&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
&squared\_accum += grad^2
Args:
learning_rate (float|Variable): global learning rate.
l1 (float):
l2 (float):
lr_power (float):
Raises:
ValueError: If learning_rate, rho, epsilon, momentum are None.
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Ftrl(0.0001)
_, params_grads = optimizer.minimize(cost)
"""
_squared_acc_str
=
"squared"
_linear_acc_str
=
"linear"
def
__init__
(
self
,
learning_rate
,
l1
=
0.0
,
l2
=
0.0
,
lr_power
=-
0.5
,
**
kwargs
):
super
(
FtrlOptimizer
,
self
).
__init__
(
learning_rate
=
learning_rate
,
**
kwargs
)
if
learning_rate
is
None
:
raise
ValueError
(
"learning_rate is not set."
)
self
.
type
=
"ftrl"
self
.
_l1
=
l1
self
.
_l2
=
l2
self
.
_lr_power
=
lr_power
def
_create_accumulators
(
self
,
block
,
parameters
):
if
not
isinstance
(
block
,
framework
.
Block
):
raise
TypeError
(
"block is not instance of framework.Block."
)
for
p
in
parameters
:
self
.
_add_accumulator
(
self
.
_squared_acc_str
,
p
)
self
.
_add_accumulator
(
self
.
_linear_acc_str
,
p
)
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
if
not
isinstance
(
block
,
framework
.
Block
):
raise
TypeError
(
"block is not instance of framework.Block."
)
squared_acc
=
self
.
_get_accumulator
(
self
.
_squared_acc_str
,
param_and_grad
[
0
])
linear_acc
=
self
.
_get_accumulator
(
self
.
_linear_acc_str
,
param_and_grad
[
0
])
ftrl_op
=
block
.
append_op
(
type
=
self
.
type
,
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"SquaredAccumulator"
:
squared_acc
,
"LinearAccumulator"
:
linear_acc
,
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"SquaredAccumOut"
:
squared_acc
,
"LinearAccumOut"
:
linear_acc
},
attrs
=
{
"l1"
:
self
.
_l1
,
"l2"
:
self
.
_l1
,
"lr_power"
:
self
.
_lr_power
})
return
ftrl_op
# We short the class name, since users will use the optimizer with the package
# name. The sample code:
#
...
...
@@ -826,6 +1099,7 @@ Adamax = AdamaxOptimizer
DecayedAdagrad
=
DecayedAdagradOptimizer
Adadelta
=
AdadeltaOptimizer
RMSProp
=
RMSPropOptimizer
Ftrl
=
FtrlOptimizer
class
ModelAverage
(
Optimizer
):
...
...
@@ -844,7 +1118,9 @@ class ModelAverage(Optimizer):
max_average_window: The maximum size of average window.
Examples:
...
.. code-block:: python
optimizer = fluid.optimizer.Momentum()
_, params_grads = optimizer.minimize(cost)
model_average = fluid.optimizer.ModelAverage(params_grads, 0.15,
...
...
python/paddle/fluid/profiler.py
浏览文件 @
28ff4bdd
...
...
@@ -42,6 +42,9 @@ def cuda_profiler(output_file, output_mode=None, config=None):
counters/options for profiling by `config` argument. The default config
is ['gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'].
Then users can use NVIDIA Visual Profiler
(https://developer.nvidia.com/nvidia-visual-profiler) tools to load this
this output file to visualize results.
Args:
output_file (string) : The output file name, the result will be
...
...
@@ -50,6 +53,33 @@ def cuda_profiler(output_file, output_mode=None, config=None):
Comma separated values format. It should be 'kvp' or 'csv'.
config (list of string) : The profiler options and counters can refer
to "Compute Command Line Profiler User Guide".
Raises:
ValueError: If `output_mode` is not in ['kvp', 'csv'].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
epoc = 8
dshape = [4, 3, 28, 28]
data = fluid.layers.data(name='data', shape=[3, 28, 28], dtype='float32')
conv = fluid.layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
output_file = 'cuda_profiler.txt'
with profiler.cuda_profiler(output_file, 'csv') as nvprof:
for i in range(epoc):
input = np.random.random(dshape).astype('float32')
exe.run(fluid.default_main_program(), feed={'data': input})
# then use NVIDIA Visual Profiler (nvvp) to load this output file
# to visualize results.
"""
if
output_mode
is
None
:
output_mode
=
'csv'
...
...
@@ -69,19 +99,52 @@ def cuda_profiler(output_file, output_mode=None, config=None):
def
reset_profiler
():
"""The profiler clear interface.
reset_profiler will clear the previous time record.
"""
Clear the previous time record. This interface does not work for
`fluid.profiler.cuda_profiler`, it only works for
`fluid.profiler.start_profiler`, `fluid.profiler.stop_profiler`,
and `fluid.profiler.profiler`.
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
with profiler.profiler(state, 'total', '/tmp/profile'):
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# ...
"""
core
.
reset_profiler
()
def
start_profiler
(
state
):
"""Enable the profiler.
"""
Enable the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to insert the code, except the usage of
`fluid.profiler.profiler` interface.
Args:
state (string) : The profiling state, which should be 'CPU', 'GPU'
or 'All'. 'CPU' means only profile CPU. 'GPU' means profiling
GPU as well. 'All' also generates timeline.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All'].
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU')
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# except each iteration
profiler.stop_profiler('total', '/tmp/profile')
"""
if
core
.
is_profiler_enabled
():
return
...
...
@@ -97,7 +160,10 @@ def start_profiler(state):
def
stop_profiler
(
sorted_key
=
None
,
profile_path
=
'/tmp/profile'
):
"""Stop the profiler.
"""
Stop the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to insert the code, except the usage of
`fluid.profiler.profiler` interface.
Args:
sorted_key (string) : If None, the profiling results will be printed
...
...
@@ -111,6 +177,23 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
The `ave` means sorting by the average execution time.
profile_path (string) : If state == 'All', it will write a profile
proto output file.
Raises:
ValueError: If `sorted_key` is not in
['calls', 'total', 'max', 'min', 'ave'].
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU')
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# except each iteration
profiler.stop_profiler('total', '/tmp/profile')
"""
if
not
core
.
is_profiler_enabled
():
return
...
...
@@ -137,7 +220,12 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
Different from cuda_profiler, this profiler can be used to profile both CPU
and GPU program. By defalut, it records the CPU and GPU operator kernels,
if you want to profile other program, you can refer the profiling tutorial
to add more records.
to add more records in C++ code.
If the state == 'All', a profile proto file will be written to
`profile_path`. This file records timeline information during the execution.
Then users can visualize this file to see the timeline, please refer
https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/optimization/timeline.md
Args:
state (string) : The profiling state, which should be 'CPU' or 'GPU',
...
...
@@ -156,6 +244,25 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
The `ave` means sorting by the average execution time.
profile_path (string) : If state == 'All', it will write a profile
proto output file.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All']. If `sorted_key` is
not in ['calls', 'total', 'max', 'min', 'ave'].
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
with profiler.profiler('All', 'total', '/tmp/profile') as prof:
for pass_id in range(pass_num):
for batch_id, data in enumerate(train_reader()):
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[],
use_program_cache=True)
# ...
"""
start_profiler
(
state
)
yield
...
...
python/paddle/fluid/regularizer.py
浏览文件 @
28ff4bdd
...
...
@@ -16,8 +16,8 @@ import framework
from
.
import
core
__all__
=
[
'append_regularization_ops'
,
'
WeightDecayRegularizer'
,
'L1Decay'
,
'L2Decay
'
,
'L
1DecayRegularizer'
,
'L
2DecayRegularizer'
'append_regularization_ops'
,
'
L1Decay'
,
'L2Decay'
,
'L1DecayRegularizer
'
,
'L2DecayRegularizer'
]
...
...
@@ -36,7 +36,8 @@ def append_regularization_ops(parameters_and_grads, regularization=None):
set. It will be applied with regularizer.
Returns:
list of (parameters, gradients) pair with the regularized gradient
list[(Variable, Variable)]: list of (parameters, gradients)
\
pair with the regularized gradient
Raises:
Exception: Unknown regularization type
...
...
@@ -100,6 +101,24 @@ class WeightDecayRegularizer(object):
class
L2DecayRegularizer
(
WeightDecayRegularizer
):
"""Implements the L2 Weight Decay Regularization
Small values of L2 can help prevent over fitting the training data.
.. math::
L2WeightDecay = reg\_coeff * parameter
Args:
regularization_coeff(float): regularization coeff
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Adagrad(
learning_rate=1e-4,
regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.1))
optimizer.minimize(avg_cost)
"""
def
__init__
(
self
,
regularization_coeff
=
0.0
):
...
...
@@ -154,6 +173,27 @@ class L2DecayRegularizer(WeightDecayRegularizer):
class
L1DecayRegularizer
(
WeightDecayRegularizer
):
"""Implements the L1 Weight Decay Regularization
L1 regularization encourages sparsity.
.. math::
L1WeightDecay = reg\_coeff * sign(parameter)
Args:
regularization_coeff(float): regularization coeff
Examples:
.. code-block:: python
program = fluid.framework.Program()
block = program.global_block()
mul_x = block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="mul.x",
regularizer=fluid.regularizer.L1DecayRegularizer(0.5))
"""
def
__init__
(
self
,
regularization_coeff
=
0.0
):
...
...
python/paddle/fluid/tests/book/notest_understand_sentiment.py
浏览文件 @
28ff4bdd
...
...
@@ -194,16 +194,16 @@ def train(word_dict,
if
is_local
:
train_loop
(
fluid
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @
28ff4bdd
...
...
@@ -69,16 +69,16 @@ def train(use_cuda, save_dirname, is_local):
if
is_local
:
train_loop
(
fluid
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @
28ff4bdd
...
...
@@ -178,16 +178,16 @@ def train(net_type, use_cuda, save_dirname, is_local):
if
is_local
:
train_loop
(
fluid
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @
28ff4bdd
...
...
@@ -209,16 +209,16 @@ def train(use_cuda, save_dirname=None, is_local=True):
if
is_local
:
train_loop
(
fluid
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/book/test_machine_translation.py
浏览文件 @
28ff4bdd
...
...
@@ -200,16 +200,16 @@ def train_main(use_cuda, is_sparse, is_local=True):
if
is_local
:
train_loop
(
framework
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @
28ff4bdd
...
...
@@ -151,16 +151,16 @@ def train(nn_type,
if
is_local
:
train_loop
(
fluid
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @
28ff4bdd
...
...
@@ -220,16 +220,16 @@ def train(use_cuda, save_dirname, is_local=True):
if
is_local
:
train_loop
(
fluid
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @
28ff4bdd
...
...
@@ -125,16 +125,16 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
if
is_local
:
train_loop
(
fluid
.
default_main_program
())
else
:
port
=
os
.
getenv
(
"PADDLE_
INIT
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
INIT_PSERVER
S"
)
# ip,ip...
port
=
os
.
getenv
(
"PADDLE_
PSERVER
_PORT"
,
"6174"
)
pserver_ips
=
os
.
getenv
(
"PADDLE_
PSERVER_IP
S"
)
# ip,ip...
eplist
=
[]
for
ip
in
pserver_ips
.
split
(
","
):
eplist
.
append
(
':'
.
join
([
ip
,
port
]))
pserver_endpoints
=
","
.
join
(
eplist
)
# ip:port,ip:port...
trainers
=
int
(
os
.
getenv
(
"TRAINERS"
))
trainers
=
int
(
os
.
getenv
(
"
PADDLE_
TRAINERS"
))
current_endpoint
=
os
.
getenv
(
"POD_IP"
)
+
":"
+
port
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_
INIT_
TRAINER_ID"
))
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
training_role
=
os
.
getenv
(
"
PADDLE_
TRAINING_ROLE"
,
"TRAINER"
)
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
if
training_role
==
"PSERVER"
:
...
...
python/paddle/fluid/tests/unittests/test_concat_op.py
浏览文件 @
28ff4bdd
...
...
@@ -43,7 +43,7 @@ class TestConcatOp(OpTest):
self
.
axis
=
1
class
TestConcatOp2
(
OpTest
):
class
TestConcatOp2
(
TestConcatOp
):
def
init_test_data
(
self
):
self
.
x0
=
np
.
random
.
random
((
2
,
3
,
4
,
5
)).
astype
(
'float32'
)
self
.
x1
=
np
.
random
.
random
((
2
,
3
,
4
,
5
)).
astype
(
'float32'
)
...
...
@@ -51,5 +51,16 @@ class TestConcatOp2(OpTest):
self
.
axis
=
1
class
TestConcatOp3
(
TestConcatOp
):
def
init_test_data
(
self
):
self
.
x0
=
np
.
random
.
random
((
1
,
256
,
170
,
256
)).
astype
(
'float32'
)
self
.
x1
=
np
.
random
.
random
((
1
,
128
,
170
,
256
)).
astype
(
'float32'
)
self
.
x2
=
np
.
random
.
random
((
1
,
128
,
170
,
256
)).
astype
(
'float32'
)
self
.
axis
=
1
def
test_check_grad
(
self
):
pass
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_gaussian_random_mkldnn_op.py
0 → 100644
浏览文件 @
28ff4bdd
# 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
from
test_gaussian_random_op
import
TestGaussianRandomOp
class
TestMKLDNN
(
TestGaussianRandomOp
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_gaussian_random_op.py
浏览文件 @
28ff4bdd
...
...
@@ -25,7 +25,15 @@ class TestGaussianRandomOp(unittest.TestCase):
def
setUp
(
self
):
self
.
op_type
=
"gaussian_random"
self
.
inputs
=
{}
self
.
attrs
=
{
"shape"
:
[
1000
,
784
],
"mean"
:
.
0
,
"std"
:
1.
,
"seed"
:
10
}
self
.
use_mkldnn
=
False
self
.
init_kernel_type
()
self
.
attrs
=
{
"shape"
:
[
1000
,
784
],
"mean"
:
.
0
,
"std"
:
1.
,
"seed"
:
10
,
"use_mkldnn"
:
self
.
use_mkldnn
}
self
.
outputs
=
[
"Out"
]
...
...
@@ -58,6 +66,9 @@ class TestGaussianRandomOp(unittest.TestCase):
self
.
assertAlmostEqual
(
numpy
.
mean
(
tensor
),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
numpy
.
std
(
tensor
),
1.
,
delta
=
0.1
)
def
init_kernel_type
(
self
):
pass
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
28ff4bdd
...
...
@@ -401,6 +401,15 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
output
)
print
(
str
(
program
))
def
test_maxout
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
3
,
5
],
dtype
=
"float32"
)
y
=
layers
.
data
(
name
=
'y'
,
shape
=
[
2
,
3
],
dtype
=
"float32"
)
output
=
layers
.
crop
(
x
,
shape
=
y
)
self
.
assertIsNotNone
(
output
)
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @
28ff4bdd
...
...
@@ -434,5 +434,71 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
self
.
assertAlmostEqual
(
init_ops
[
1
].
attr
(
'value'
),
0.0
)
class
TestFtrlOptimizer
(
unittest
.
TestCase
):
class
MockFtrl
(
optimizer
.
FtrlOptimizer
):
def
get_accumulators
(
self
):
return
self
.
_accumulators
def
get_squared_str
(
self
):
return
self
.
_squared_acc_str
def
get_linear_str
(
self
):
return
self
.
_linear_acc_str
def
test_ftrl_optimizer
(
self
):
init_program
=
framework
.
Program
()
program
=
framework
.
Program
()
block
=
program
.
global_block
()
mul_x
=
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
5
,
10
],
lod_level
=
0
,
name
=
"mul.x"
,
optimize_attr
=
{
'learning_rate'
:
1.1
})
mul_y
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
10
,
8
],
lod_level
=
0
,
name
=
"mul.y"
)
mul_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
5
,
8
],
lod_level
=
0
,
name
=
"mul.out"
)
block
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
mul_x
,
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
learning_rate
=
0.01
ftrl_optimizer
=
self
.
MockFtrl
(
learning_rate
=
learning_rate
,
l1
=
0.0
,
l2
=
0.0
,
lr_power
=-
0.5
)
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
ftrl_optimizer
.
get_accumulators
()),
0
)
opts
=
ftrl_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
3
)
self
.
assertEqual
([
op
.
type
for
op
in
opts
],
[
"fill_constant"
,
"elementwise_mul"
,
"ftrl"
])
# Check accumulators
accumulators
=
ftrl_optimizer
.
get_accumulators
()
self
.
assertEqual
(
len
(
accumulators
),
2
)
self
.
assertTrue
(
ftrl_optimizer
.
get_squared_str
()
in
accumulators
)
self
.
assertTrue
(
ftrl_optimizer
.
get_linear_str
()
in
accumulators
)
squared_acc
=
accumulators
[
ftrl_optimizer
.
get_squared_str
()]
linear_acc
=
accumulators
[
ftrl_optimizer
.
get_linear_str
()]
self
.
assertEqual
(
len
(
squared_acc
),
1
)
self
.
assertEqual
(
len
(
linear_acc
),
1
)
self
.
assertTrue
(
mul_x
.
name
in
squared_acc
)
self
.
assertTrue
(
mul_x
.
name
in
linear_acc
)
# Check init_program
init_ops
=
init_program
.
global_block
().
ops
self
.
assertEqual
(
len
(
init_ops
),
3
)
self
.
assertEqual
(
init_ops
[
0
].
type
,
"fill_constant"
)
self
.
assertAlmostEqual
(
init_ops
[
0
].
attr
(
'value'
),
learning_rate
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py
0 → 100644
浏览文件 @
28ff4bdd
# 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
from
test_sum_op
import
TestSumOp
class
TestMKLDNN
(
TestSumOp
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_sum_op.py
浏览文件 @
28ff4bdd
...
...
@@ -20,12 +20,15 @@ from op_test import OpTest
class
TestSumOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sum"
self
.
use_mkldnn
=
False
self
.
init_kernel_type
()
x0
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
x1
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
x2
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
self
.
inputs
=
{
"X"
:
[(
"x0"
,
x0
),
(
"x1"
,
x1
),
(
"x2"
,
x2
)]}
y
=
x0
+
x1
+
x2
self
.
outputs
=
{
'Out'
:
y
}
self
.
attrs
=
{
'use_mkldnn'
:
self
.
use_mkldnn
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -33,6 +36,9 @@ class TestSumOp(OpTest):
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
def
init_kernel_type
(
self
):
pass
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
28ff4bdd
...
...
@@ -824,7 +824,8 @@ class DistributeTranspiler:
table_opt_block
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
pserver_side_table_grad_list
},
outputs
=
{
"Out"
:
[
grad_var
]})
outputs
=
{
"Out"
:
[
grad_var
]},
attrs
=
{
"use_mkldnn"
:
False
})
else
:
# in async_mode, for table gradient, it also need to be splited to each parameter server
origin_grad_name
=
grad_var
.
name
...
...
@@ -1056,7 +1057,8 @@ class DistributeTranspiler:
optimize_block
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
vars2merge
},
outputs
=
{
"Out"
:
merged_var
})
outputs
=
{
"Out"
:
merged_var
},
attrs
=
{
"use_mkldnn"
:
False
})
# TODO(panyx0718): What if it's SELECTED_ROWS.
if
not
merged_var
.
type
==
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
:
optimize_block
.
append_op
(
...
...
python/paddle/reader/decorator.py
浏览文件 @
28ff4bdd
...
...
@@ -336,7 +336,7 @@ def _buf2lines(buf, line_break="\n"):
class
PipeReader
:
"""
PipeReader read data by stream from a command, take it's
PipeReader read data by stream from a command, take it's
stdout into a pipe buffer and redirect it to the parser to
parse, then yield data as your desired format.
...
...
@@ -352,7 +352,7 @@ class PipeReader:
An example:
.. code-block:: python
def example_reader():
for f in myfiles:
pr = PipeReader("cat %s"%f)
...
...
tools/print_signatures.py
0 → 100644
浏览文件 @
28ff4bdd
# 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.
"""
Print all signature of a python module in alphabet order.
Usage:
./print_signature "paddle.fluid" > signature.txt
"""
import
importlib
import
inspect
import
collections
import
sys
import
pydoc
member_dict
=
collections
.
OrderedDict
()
def
visit_member
(
parent_name
,
member
):
cur_name
=
"."
.
join
([
parent_name
,
member
.
__name__
])
if
inspect
.
isclass
(
member
):
for
name
,
value
in
inspect
.
getmembers
(
member
):
if
hasattr
(
value
,
'__name__'
)
and
(
not
name
.
startswith
(
"_"
)
or
name
==
"__init__"
):
visit_member
(
cur_name
,
value
)
elif
callable
(
member
):
try
:
member_dict
[
cur_name
]
=
inspect
.
getargspec
(
member
)
except
TypeError
:
# special for PyBind method
member_dict
[
cur_name
]
=
" "
.
join
([
line
.
strip
()
for
line
in
pydoc
.
render_doc
(
member
).
split
(
'
\n
'
)
if
"->"
in
line
])
else
:
raise
RuntimeError
(
"Unsupported generate signature of member, type {0}"
.
format
(
str
(
type
(
member
))))
def
visit_all_module
(
mod
):
for
member_name
in
(
name
for
name
in
(
mod
.
__all__
if
hasattr
(
mod
,
"__all__"
)
else
dir
(
mod
))
if
not
name
.
startswith
(
"_"
)):
instance
=
getattr
(
mod
,
member_name
,
None
)
if
instance
is
None
:
continue
if
inspect
.
ismodule
(
instance
):
visit_all_module
(
instance
)
else
:
visit_member
(
mod
.
__name__
,
instance
)
visit_all_module
(
importlib
.
import_module
(
sys
.
argv
[
1
]))
for
name
in
member_dict
:
print
name
,
member_dict
[
name
]
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