Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
恒星小白
Paddle
提交
4152d399
P
Paddle
项目概览
恒星小白
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
4152d399
编写于
7月 24, 2020
作者:
X
xujiaqi01
提交者:
GitHub
7月 24, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add fleet metric (#25463)
* add fleet distributed metrics * test=develop
上级
98899b73
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
485 addition
and
0 deletion
+485
-0
python/paddle/fleet/metrics/metric.py
python/paddle/fleet/metrics/metric.py
+372
-0
python/paddle/fluid/tests/unittests/test_fleet_metric.py
python/paddle/fluid/tests/unittests/test_fleet_metric.py
+113
-0
未找到文件。
python/paddle/fleet/metrics/metric.py
浏览文件 @
4152d399
...
...
@@ -11,3 +11,375 @@
# 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.
"""Fleet Metrics"""
import
paddle.fluid
as
fluid
import
math
import
numpy
as
np
from
paddle.fluid.framework
import
Variable
from
paddle.fluid.incubate.fleet.parameter_server.pslib
import
fleet
as
fleet
def
sum
(
input
,
scope
=
None
):
"""
distributed sum in fleet
Args:
input(numpy.array|Variable|string): output of a layer
scope(Scope): specific scope
Returns:
global_metric(numpy.array): sum array
Example:
.. code-block:: python
# in model.py
input = fluid.layers.cast(some_input, dtype='float32')
cnt = fluid.layers.reduce_sum(input)
global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
tmp = fluid.layers.elementwise_add(cnt, global_cnt)
fluid.layers.assign(tmp, global_cnt)
# in train.py, after train or infer
res = np.array(scope.find_var(global_cnt.name).get_tensor())
print("sum array: ", paddle.fleet.sum(res))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
input
,
Variable
):
input
=
np
.
array
(
scope
.
find_var
(
input
.
name
).
get_tensor
())
elif
isinstance
(
input
,
str
):
input
=
np
.
array
(
scope
.
find_var
(
input
).
get_tensor
())
old_shape
=
np
.
array
(
input
.
shape
)
output
=
np
.
copy
(
input
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
input
,
output
,
mode
=
"sum"
)
output
=
output
.
reshape
(
old_shape
)
return
output
def
max
(
input
,
scope
=
None
):
"""
distributed max in fleet
Args:
input(numpy.array|Variable|string): output of a layer
scope(Scope): specific scope
Returns:
global_metric(numpy.array): max array
Example:
.. code-block:: python
# in model.py
input = fluid.layers.cast(some_input, dtype='float32')
cnt = fluid.layers.reduce_sum(input)
global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
tmp = fluid.layers.elementwise_max(cnt, global_cnt)
fluid.layers.assign(tmp, global_cnt)
# in train.py, after train or infer
res = np.array(scope.find_var(global_cnt.name).get_tensor())
print("max array: ", paddle.fleet.max(res))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
input
,
Variable
):
input
=
np
.
array
(
scope
.
find_var
(
input
.
name
).
get_tensor
())
elif
isinstance
(
input
,
str
):
input
=
np
.
array
(
scope
.
find_var
(
input
).
get_tensor
())
old_shape
=
np
.
array
(
input
.
shape
)
output
=
np
.
copy
(
input
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
input
,
output
,
mode
=
"max"
)
output
=
output
.
reshape
(
old_shape
)
return
output
def
min
(
input
,
scope
=
None
):
"""
distributed min in fleet
Args:
input(numpy.array|Variable|string): output of a layer
scope(Scope): specific scope
Returns:
global_metric(numpy.array): min array
Example:
.. code-block:: python
# in model.py
input = fluid.layers.cast(some_input, dtype='float32')
cnt = fluid.layers.reduce_sum(input)
global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
tmp = fluid.layers.elementwise_min(cnt, global_cnt)
fluid.layers.assign(tmp, global_cnt)
# in train.py, after train or infer
res = np.array(scope.find_var(global_cnt.name).get_tensor())
print("min array: ", paddle.fleet.min(res))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
input
,
Variable
):
input
=
np
.
array
(
scope
.
find_var
(
input
.
name
).
get_tensor
())
elif
isinstance
(
input
,
str
):
input
=
np
.
array
(
scope
.
find_var
(
input
).
get_tensor
())
old_shape
=
np
.
array
(
input
.
shape
)
output
=
np
.
copy
(
input
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
input
,
output
,
mode
=
"min"
)
output
=
output
.
reshape
(
old_shape
)
return
output
def
auc
(
stat_pos
,
stat_neg
,
scope
=
None
):
"""
distributed auc in fleet
Args:
stat_pos(numpy.array|Variable|string): stat_pos in output of fluid.layers.auc
stat_neg(numpy.array|Variable|string): stat_neg in output of fluid.layers.auc
scope(Scope): specific scope
Returns:
auc_value(float): auc value
Example:
.. code-block:: python
# in model.py
similarity_norm = fluid.layers.sigmoid(fluid.layers.clip(output, min=-15.0, max=15.0))
binary_predict = fluid.layers.concat(
input=[fluid.layers.elementwise_sub(fluid.layers.ceil(similarity_norm), similarity_norm), similarity_norm], axis=1)
self.auc, batch_auc, [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg] =
fluid.layers.auc(input=binary_predict, label=label, curve='ROC', num_thresholds=4096)
# in train.py, after train or infer
pos = np.array(scope.find_var(stat_pos.name).get_tensor())
neg = np.array(scope.find_var(stat_neg.name).get_tensor())
print("auc: ", paddle.fleet.auc(pos, neg))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
stat_pos
,
Variable
):
stat_pos
=
np
.
array
(
scope
.
find_var
(
stat_pos
.
name
).
get_tensor
())
elif
isinstance
(
stat_pos
,
str
):
stat_pos
=
np
.
array
(
scope
.
find_var
(
stat_pos
).
get_tensor
())
if
isinstance
(
stat_neg
,
Variable
):
stat_neg
=
np
.
array
(
scope
.
find_var
(
stat_neg
.
name
).
get_tensor
())
elif
isinstance
(
stat_neg
,
str
):
stat_neg
=
np
.
array
(
scope
.
find_var
(
stat_neg
).
get_tensor
())
# auc pos bucket shape
old_pos_shape
=
np
.
array
(
stat_pos
.
shape
)
# reshape to one dim
stat_pos
=
stat_pos
.
reshape
(
-
1
)
global_pos
=
np
.
copy
(
stat_pos
)
*
0
# mpi allreduce
fleet
.
_role_maker
.
_all_reduce
(
stat_pos
,
global_pos
)
# reshape to its original shape
global_pos
=
global_pos
.
reshape
(
old_pos_shape
)
# auc neg bucket
old_neg_shape
=
np
.
array
(
stat_neg
.
shape
)
stat_neg
=
stat_neg
.
reshape
(
-
1
)
global_neg
=
np
.
copy
(
stat_neg
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
stat_neg
,
global_neg
)
global_neg
=
global_neg
.
reshape
(
old_neg_shape
)
# calculate auc
num_bucket
=
len
(
global_pos
[
0
])
area
=
0.0
pos
=
0.0
neg
=
0.0
new_pos
=
0.0
new_neg
=
0.0
total_ins_num
=
0
for
i
in
range
(
num_bucket
):
index
=
num_bucket
-
1
-
i
new_pos
=
pos
+
global_pos
[
0
][
index
]
total_ins_num
+=
global_pos
[
0
][
index
]
new_neg
=
neg
+
global_neg
[
0
][
index
]
total_ins_num
+=
global_neg
[
0
][
index
]
area
+=
(
new_neg
-
neg
)
*
(
pos
+
new_pos
)
/
2
pos
=
new_pos
neg
=
new_neg
auc_value
=
None
if
pos
*
neg
==
0
or
total_ins_num
==
0
:
auc_value
=
0.5
else
:
auc_value
=
area
/
(
pos
*
neg
)
fleet
.
_role_maker
.
_barrier_worker
()
return
auc_value
def
mae
(
abserr
,
total_ins_num
,
scope
=
None
):
"""
distributed mae in fleet
Args:
abserr(numpy.array|Variable|string): abserr in output of fluid.contrib.layers.ctr_metric_bundle
total_ins_num(int|float): total train/infer instance count
scope(Scope): specific scope
Returns:
mae(float): mae value
Example:
.. code-block:: python
# in model.py
sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
# in train.py, after train or infer
res = np.array(scope.find_var(abserr.name).get_tensor())
print("mae: ", paddle.fleet.mae(res, total_ins_num))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
abserr
,
Variable
):
abserr
=
np
.
array
(
scope
.
find_var
(
abserr
.
name
).
get_tensor
())
elif
isinstance
(
abserr
,
str
):
abserr
=
np
.
array
(
scope
.
find_var
(
abserr
).
get_tensor
())
old_metric_shape
=
np
.
array
(
abserr
.
shape
)
abserr
=
abserr
.
reshape
(
-
1
)
global_metric
=
np
.
copy
(
abserr
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
abserr
,
global_metric
)
global_metric
=
global_metric
.
reshape
(
old_metric_shape
)
mae_value
=
global_metric
[
0
]
/
total_ins_num
return
mae_value
def
rmse
(
sqrerr
,
total_ins_num
,
scope
=
None
):
"""
distributed rmse in fleet
Args:
sqrerr(numpy.array|Variable|string): sqrerr in output of fluid.contrib.layers.ctr_metric_bundle
total_ins_num(int|float): total train/infer instance count
scope(Scope): specific scope
Returns:
rmse(float): rmse value
Example:
.. code-block:: python
# in model.py
sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
# in train.py, after train or infer
res = np.array(scope.find_var(sqrerr.name).get_tensor())
print("rmse: ", paddle.fleet.rmse(res, total_ins_num))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
sqrerr
,
Variable
):
sqrerr
=
np
.
array
(
scope
.
find_var
(
sqrerr
.
name
).
get_tensor
())
elif
isinstance
(
sqrerr
,
str
):
sqrerr
=
np
.
array
(
scope
.
find_var
(
sqrerr
).
get_tensor
())
old_metric_shape
=
np
.
array
(
sqrerr
.
shape
)
sqrerr
=
sqrerr
.
reshape
(
-
1
)
global_metric
=
np
.
copy
(
sqrerr
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
sqrerr
,
global_metric
)
global_metric
=
global_metric
.
reshape
(
old_metric_shape
)
rmse_value
=
math
.
sqrt
(
global_metric
[
0
]
/
total_ins_num
)
return
rmse_value
def
mse
(
sqrerr
,
total_ins_num
,
scope
=
None
):
"""
distributed mse in fleet
Args:
sqrerr(numpy.array|Variable|string): sqrerr in output of fluid.contrib.layers.ctr_metric_bundle
total_ins_num(int|float): total train/infer instance count
scope(Scope): specific scope
Returns:
mse(float): mse value
Example:
.. code-block:: python
# in model.py
sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
# in train.py, after train or infer
metric = np.array(scope.find_var(sqrerr.name).get_tensor())
print("mse: ", paddle.fleet.mse(metric, total_ins_num))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
sqrerr
,
Variable
):
sqrerr
=
np
.
array
(
scope
.
find_var
(
sqrerr
.
name
).
get_tensor
())
elif
isinstance
(
sqrerr
,
str
):
sqrerr
=
np
.
array
(
scope
.
find_var
(
sqrerr
).
get_tensor
())
old_metric_shape
=
np
.
array
(
sqrerr
.
shape
)
sqrerr
=
sqrerr
.
reshape
(
-
1
)
global_metric
=
np
.
copy
(
sqrerr
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
sqrerr
,
global_metric
)
global_metric
=
global_metric
.
reshape
(
old_metric_shape
)
mse_value
=
global_metric
[
0
]
/
total_ins_num
return
mse_value
def
acc
(
correct
,
total
,
scope
=
None
):
"""
distributed accuracy in fleet
Args:
correct(numpy.array|Variable|string): correct Variable
total(numpy.array|Variable): total Variable
scope(Scope): specific scope
Returns:
acc(float): accuracy value
Example:
.. code-block:: python
# in model.py
correct = fluid.layers.create_global_var(dtype='float32', shape=[1], value=0)
total = fluid.layers.create_global_var(dtype='float32', shape=[1], value=0)
acc = fluid.layers.acc(predict, label, k=1, correct=correct, total=total)
global_correct = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
tmp1 = fluid.layers.elementwise_min(correct, global_correct)
fluid.layers.assign(tmp1, global_correct)
global_total = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
tmp2 = fluid.layers.elementwise_min(total, global_total)
fluid.layers.assign(tmp2, global_total)
# in train.py, after train or infer
correct_num = np.array(scope.find_var(correct.name).get_tensor())
total_num = np.array(scope.find_var(total.name).get_tensor())
print("accuracy: ", paddle.fleet.acc(correct_num, total_num))
"""
fleet
.
_role_maker
.
_barrier_worker
()
if
scope
is
None
:
scope
=
fluid
.
global_scope
()
if
isinstance
(
correct
,
Variable
):
correct
=
np
.
array
(
scope
.
find_var
(
correct
.
name
).
get_tensor
())
elif
isinstance
(
correct
,
str
):
correct
=
np
.
array
(
scope
.
find_var
(
correct
).
get_tensor
())
if
isinstance
(
total
,
Variable
):
total
=
np
.
array
(
scope
.
find_var
(
total
.
name
).
get_tensor
())
elif
isinstance
(
total
,
str
):
total
=
np
.
array
(
scope
.
find_var
(
total
).
get_tensor
())
global_correct_num
=
np
.
copy
(
correct
)
*
0
global_total_num
=
np
.
copy
(
total
)
*
0
fleet
.
_role_maker
.
_all_reduce
(
correct
,
global_correct_num
)
fleet
.
_role_maker
.
_all_reduce
(
total
,
global_total_num
)
return
float
(
global_correct_num
[
0
])
/
float
(
global_total_num
[
0
])
python/paddle/fluid/tests/unittests/test_fleet_metric.py
0 → 100644
浏览文件 @
4152d399
# Copyright (c) 2020 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.
"""Test fleet metric."""
from
__future__
import
print_function
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
os
import
unittest
import
paddle.fleet.metrics.metric
as
metric
from
paddle.fluid.incubate.fleet.base.role_maker
import
GeneralRoleMaker
from
paddle.fluid.incubate.fleet.parameter_server.pslib
import
fleet
as
fleet
class
TestFleetMetric
(
unittest
.
TestCase
):
"""Test cases for fleet metric."""
def
setUp
(
self
):
"""Set up, set envs."""
class
FakeFleet
:
"""Fake fleet only for test."""
def
__init__
(
self
):
"""Init."""
self
.
gloo
=
fluid
.
core
.
Gloo
()
self
.
gloo
.
set_rank
(
0
)
self
.
gloo
.
set_size
(
1
)
self
.
gloo
.
set_prefix
(
"123"
)
self
.
gloo
.
set_iface
(
"lo"
)
self
.
gloo
.
set_hdfs_store
(
"./tmp_test_metric"
,
""
,
""
)
self
.
gloo
.
init
()
def
_all_reduce
(
self
,
input
,
output
,
mode
=
"sum"
):
"""All reduce using gloo."""
input_list
=
[
i
for
i
in
input
]
ans
=
self
.
gloo
.
all_reduce
(
input_list
,
mode
)
for
i
in
range
(
len
(
ans
)):
output
[
i
]
=
1
def
_barrier_worker
(
self
):
"""Fake barrier worker, do nothing."""
pass
self
.
fleet
=
FakeFleet
()
fleet
.
_role_maker
=
self
.
fleet
def
test_metric_1
(
self
):
"""Test cases for metrics."""
train
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train
,
startup
):
t
=
fluid
.
layers
.
create_global_var
(
shape
=
[
1
,
1
],
value
=
1
,
dtype
=
'int64'
,
persistable
=
True
,
force_cpu
=
True
)
t1
=
fluid
.
layers
.
create_global_var
(
shape
=
[
1
,
1
],
value
=
1
,
dtype
=
'int64'
,
persistable
=
True
,
force_cpu
=
True
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
scope
=
fluid
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
exe
.
run
(
startup
)
metric
.
sum
(
t
,
scope
)
metric
.
max
(
t
,
scope
)
metric
.
min
(
t
,
scope
)
metric
.
auc
(
t
,
t1
,
scope
)
metric
.
mae
(
t1
,
3
,
scope
)
metric
.
rmse
(
t1
,
3
,
scope
)
metric
.
mse
(
t1
,
3
,
scope
)
metric
.
acc
(
t
,
t1
,
scope
)
metric
.
sum
(
str
(
t
.
name
),
scope
)
metric
.
max
(
str
(
t
.
name
),
scope
)
metric
.
min
(
str
(
t
.
name
),
scope
)
metric
.
auc
(
str
(
t1
.
name
),
str
(
t
.
name
),
scope
)
metric
.
mae
(
str
(
t1
.
name
),
3
,
scope
)
metric
.
rmse
(
str
(
t1
.
name
),
3
,
scope
)
metric
.
mse
(
str
(
t1
.
name
),
3
,
scope
)
metric
.
acc
(
str
(
t
.
name
),
str
(
t1
.
name
),
scope
)
arr
=
np
.
array
([
1
,
2
,
3
,
4
])
metric
.
sum
(
arr
)
metric
.
max
(
arr
)
metric
.
min
(
arr
)
arr1
=
np
.
array
([[
1
,
2
,
3
,
4
]])
arr2
=
np
.
array
([[
1
,
2
,
3
,
4
]])
arr3
=
np
.
array
([
1
,
2
,
3
,
4
])
metric
.
auc
(
arr1
,
arr2
)
metric
.
mae
(
arr
,
3
)
metric
.
rmse
(
arr
,
3
)
metric
.
mse
(
arr
,
3
)
metric
.
acc
(
arr
,
arr3
)
if
__name__
==
"__main__"
:
unittest
.
main
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录