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体验新版 GitCode,发现更多精彩内容 >>
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971e4791
编写于
6月 08, 2022
作者:
Z
zhaoyingli
提交者:
GitHub
6月 08, 2022
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电子邮件补丁
差异文件
[AutoParallel] add fetch_list in engine api (#43312)
* add fetch_list * fix evaluate log * tiny fix
上级
07ede118
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
53 addition
and
28 deletion
+53
-28
python/paddle/distributed/auto_parallel/engine.py
python/paddle/distributed/auto_parallel/engine.py
+49
-25
python/paddle/fluid/tests/unittests/auto_parallel/engine_api.py
.../paddle/fluid/tests/unittests/auto_parallel/engine_api.py
+4
-3
未找到文件。
python/paddle/distributed/auto_parallel/engine.py
浏览文件 @
971e4791
...
...
@@ -28,7 +28,7 @@ from paddle.fluid import program_guard
from
paddle.fluid.layers.utils
import
flatten
from
paddle.fluid.executor
import
global_scope
from
paddle.fluid.backward
import
append_backward
from
paddle.fluid.framework
import
Operator
from
paddle.fluid.framework
import
Operator
,
Variable
from
paddle.fluid.framework
import
_current_expected_place
as
_get_device
from
paddle.fluid.dygraph.parallel
import
ParallelEnv
from
paddle.distributed
import
fleet
...
...
@@ -256,6 +256,7 @@ class Engine:
train_data
,
batch_size
=
1
,
epochs
=
1
,
fetch_list
=
None
,
steps_per_epoch
=
None
,
use_program_cache
=
False
,
return_numpy
=
True
):
...
...
@@ -266,13 +267,14 @@ class Engine:
"train model is not ready, please call `engine.prepare()` first."
train_dataloader
=
self
.
_create_dataloader
(
train_data
,
batch_size
,
epochs
,
steps_per_epoch
)
self
.
_usr_fetch_list
=
fetch_list
outputs
=
[]
for
epoch
in
range
(
epochs
):
for
step
,
data
in
enumerate
(
train_dataloader
):
logs
,
los
s
=
self
.
_train_step
(
data
,
use_program_cache
,
logs
,
out
s
=
self
.
_train_step
(
data
,
use_program_cache
,
return_numpy
)
outputs
.
append
(
los
s
)
outputs
.
append
(
out
s
)
train_logs
=
{
"train_"
+
name
:
val
for
name
,
val
in
logs
.
items
()
...
...
@@ -283,86 +285,97 @@ class Engine:
def
evaluate
(
self
,
eval_data
,
batch_size
=
1
,
fetch_list
=
None
,
use_program_cache
=
False
,
return_numpy
=
True
):
self
.
mode
=
'eval'
assert
self
.
mode
in
self
.
_dist_main_progs
,
\
"eval model is not ready, please call `engine.prepare()` first."
eval_dataloader
=
self
.
_create_dataloader
(
eval_data
,
batch_size
)
self
.
_usr_fetch_list
=
fetch_list
for
step
,
data
in
enumerate
(
eval_dataloader
):
eval_logs
=
dict
()
outs
=
self
.
_eval_step
(
data
,
use_program_cache
,
return_numpy
)
logs
,
outs
=
self
.
_eval_step
(
data
,
use_program_cache
,
return_numpy
)
eval_logs
[
"eval_loss"
]
=
outs
[
0
]
if
len
(
outs
)
>
0
else
[]
for
metric
in
self
.
_metrics
:
results
=
metric
.
accumulate
()
for
i
,
res
in
enumerate
(
to_list
(
results
)):
eval_logs
[
"eval_"
+
metric
.
name
()[
i
]]
=
res
for
name
,
val
in
logs
.
items
():
eval_logs
[
"eval_"
+
name
]
=
val
self
.
_logger
.
info
(
eval_logs
)
return
eval_logs
def
predict
(
self
,
test_data
,
batch_size
=
1
,
fetch_list
=
None
,
use_program_cache
=
False
,
return_numpy
=
True
):
self
.
mode
=
'predict'
assert
self
.
mode
in
self
.
_dist_main_progs
,
\
"predict model is not ready, please call `engine.prepare()` first."
test_dataloader
=
self
.
_create_dataloader
(
test_data
,
batch_size
)
self
.
_usr_fetch_list
=
fetch_list
outputs
=
[]
for
step
,
data
in
enumerate
(
test_dataloader
):
logs
,
outs
=
self
.
_predict_step
(
data
,
use_program_cache
,
return_numpy
)
outputs
.
append
(
outs
)
predict_logs
=
{
"predict_"
+
name
:
val
for
name
,
val
in
logs
.
items
()
}
predict_logs
=
{
"pred_"
+
name
:
val
for
name
,
val
in
logs
.
items
()}
self
.
_logger
.
info
(
predict_logs
)
return
outputs
def
_train_step
(
self
,
data
,
use_program_cache
=
False
,
return_numpy
=
True
):
logs
=
{}
fetch_vars
=
self
.
_fetch_vars
[
self
.
mode
][
"loss"
]
fetch_list
=
self
.
_fetch_list
(
fetch_vars
)
fetch_list
,
usr_fetch_list
=
self
.
_fetch_list
(
fetch_vars
)
fetch_list
+=
usr_fetch_list
los
s
=
self
.
_executor
.
run
(
self
.
main_program
,
out
s
=
self
.
_executor
.
run
(
self
.
main_program
,
fetch_list
=
fetch_list
,
use_program_cache
=
use_program_cache
,
return_numpy
=
return_numpy
)
logs
[
"loss"
]
=
loss
return
logs
,
loss
for
i
,
out
in
enumerate
(
outs
):
logs
[
fetch_list
[
i
]]
=
out
return
logs
,
outs
def
_eval_step
(
self
,
data
,
use_program_cache
=
False
,
return_numpy
=
True
):
logs
=
{}
metrics
=
self
.
_fetch_vars
[
self
.
mode
][
"metrics"
]
losses
=
self
.
_fetch_vars
[
self
.
mode
][
"loss"
]
fetch_loss
=
self
.
_fetch_list
(
losses
)
fetch_metrics
=
self
.
_fetch_list
(
metrics
)
fetch_loss
,
usr_fetch_list
=
self
.
_fetch_list
(
losses
)
fetch_metrics
,
usr_fetch_list
=
self
.
_fetch_list
(
metrics
)
fetch_list
=
fetch_loss
+
fetch_metrics
res
=
self
.
_executor
.
run
(
self
.
main_program
,
fetch_list
=
fetch_list
,
use_program_cache
=
use_program_cache
,
return_numpy
=
return_numpy
)
if
not
res
[
len
(
fetch_loss
):]:
return
res
[:
len
(
fetch_loss
)]
outs
=
self
.
_executor
.
run
(
self
.
main_program
,
fetch_list
=
fetch_list
+
usr_fetch_list
,
use_program_cache
=
use_program_cache
,
return_numpy
=
return_numpy
)
usr_out
=
outs
[
len
(
fetch_list
):]
for
i
,
out
in
enumerate
(
usr_out
):
logs
[
usr_fetch_list
[
i
]]
=
out
outs
=
outs
[:
len
(
fetch_list
)]
if
not
outs
[
len
(
fetch_loss
):]:
return
logs
,
outs
[:
len
(
fetch_loss
)]
for
metric
in
self
.
_metrics
:
metric
.
update
(
*
re
s
[
len
(
fetch_loss
):])
return
re
s
[:
len
(
fetch_loss
)]
metric
.
update
(
*
out
s
[
len
(
fetch_loss
):])
return
logs
,
out
s
[:
len
(
fetch_loss
)]
def
_predict_step
(
self
,
data
,
use_program_cache
=
False
,
return_numpy
=
True
):
logs
=
{}
fetch_vars
=
self
.
_fetch_vars
[
self
.
mode
][
"outputs"
]
fetch_list
=
self
.
_fetch_list
(
fetch_vars
)
fetch_list
,
usr_fetch_list
=
self
.
_fetch_list
(
fetch_vars
)
fetch_list
+=
usr_fetch_list
outs
=
self
.
_executor
.
run
(
self
.
main_program
,
fetch_list
=
fetch_list
,
use_program_cache
=
use_program_cache
,
return_numpy
=
return_numpy
)
logs
[
"pred"
]
=
outs
for
i
,
out
in
enumerate
(
outs
):
logs
[
fetch_list
[
i
]]
=
out
return
logs
,
outs
def
_fetch_list
(
self
,
fetch_vars
):
...
...
@@ -370,7 +383,18 @@ class Engine:
for
var
in
fetch_vars
:
if
var
.
name
in
self
.
main_program
.
global_block
().
vars
:
fetch_list
.
append
(
var
.
name
)
return
fetch_list
usr_fetch_list
=
[]
if
self
.
_usr_fetch_list
:
assert
isinstance
(
self
.
_usr_fetch_list
,
list
),
"'fetch_list' type should be list."
for
var
in
self
.
_usr_fetch_list
:
if
isinstance
(
var
,
str
):
if
var
in
self
.
main_program
.
global_block
().
vars
:
usr_fetch_list
.
append
(
var
)
elif
isinstance
(
var
,
Variable
):
if
var
.
name
in
self
.
main_program
.
global_block
().
vars
:
usr_fetch_list
.
append
(
var
.
name
)
return
fetch_list
,
usr_fetch_list
def
_create_dataloader
(
self
,
dataset
,
...
...
python/paddle/fluid/tests/unittests/auto_parallel/engine_api.py
浏览文件 @
971e4791
...
...
@@ -133,15 +133,16 @@ def train():
train_dataset
=
MyDataset
(
batch_num
*
batch_size
)
engine
.
fit
(
train_dataset
,
batch_size
=
batch_size
,
steps_per_epoch
=
batch_num
*
batch_size
)
steps_per_epoch
=
batch_num
*
batch_size
,
fetch_list
=
[
'label'
])
# eval
eval_dataset
=
MyDataset
(
batch_size
)
engine
.
evaluate
(
eval_dataset
,
batch_size
)
engine
.
evaluate
(
eval_dataset
,
batch_size
,
fetch_list
=
[
'label'
]
)
# predict
test_dataset
=
MyDataset
(
batch_size
)
engine
.
predict
(
test_dataset
,
batch_size
)
engine
.
predict
(
test_dataset
,
batch_size
,
fetch_list
=
[
'label'
]
)
# save
engine
.
save
(
'./mlp_inf'
,
training
=
False
,
mode
=
'predict'
)
...
...
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