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体验新版 GitCode,发现更多精彩内容 >>
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b2435a3a
编写于
8月 09, 2018
作者:
C
Chen Weihang
提交者:
GitHub
8月 09, 2018
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差异文件
Merge pull request #12374 from chenwhql/py_calc_memory
Add memory usage estimate API
上级
66be5326
8627ef33
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
175 addition
and
1 deletion
+175
-1
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
python/paddle/fluid/contrib/__init__.py
python/paddle/fluid/contrib/__init__.py
+3
-1
python/paddle/fluid/contrib/memory_usage_calc.py
python/paddle/fluid/contrib/memory_usage_calc.py
+102
-0
python/paddle/fluid/tests/unittests/test_memory_usage.py
python/paddle/fluid/tests/unittests/test_memory_usage.py
+69
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
b2435a3a
...
@@ -336,6 +336,7 @@ paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=Non
...
@@ -336,6 +336,7 @@ paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=Non
paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
...
...
python/paddle/fluid/contrib/__init__.py
浏览文件 @
b2435a3a
...
@@ -14,5 +14,7 @@
...
@@ -14,5 +14,7 @@
import
decoder
import
decoder
from
decoder
import
*
from
decoder
import
*
import
memory_usage_calc
from
memory_usage_calc
import
*
__all__
=
decoder
.
__all__
__all__
=
decoder
.
__all__
+
memory_usage_calc
.
__all__
python/paddle/fluid/contrib/memory_usage_calc.py
0 → 100644
浏览文件 @
b2435a3a
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
"""
This module privides a memory usage calculate function for user.
The purpose of this API is to allow users to estimate memory usage of
a program under a special batch size, then user can set appropriate
batch size to fully utilize a GPU.
This API is still under active development and may change drastically.
"""
from
..
import
core
from
..framework
import
Program
,
Variable
__all__
=
[
'memory_usage'
]
dtype_to_size
=
{
core
.
VarDesc
.
VarType
.
FP16
:
2
,
core
.
VarDesc
.
VarType
.
FP32
:
4
,
core
.
VarDesc
.
VarType
.
FP64
:
8
,
core
.
VarDesc
.
VarType
.
INT16
:
2
,
core
.
VarDesc
.
VarType
.
INT32
:
4
,
core
.
VarDesc
.
VarType
.
INT64
:
8
,
core
.
VarDesc
.
VarType
.
BOOL
:
1
,
core
.
VarDesc
.
VarType
.
UINT8
:
1
,
}
DEBUG
=
False
def
memory_usage
(
program
,
batch_size
):
"""
Get the estimate memory usage of program with input batch size.
Args:
program(Program): The current Program.
batch_size(int): The current input data batch_size.
Returns:
min_total_memory(float): the estimate memory usage lower bound.
max_total_memory(float): the estimate memory usage upper bound.
unit_str(string): the unit of estimate usage result.
Examples:
>>> import paddle.fluid as fluid
>>> lower_usage, upper_usage, unit = fluid.contrib.memory_usage(
fluid.default_main_program(), batch_size=10)
>>> print "memory usage is about %.3f - %.3f %s" %
\
(lower_usage, upper_usage, unit)
"""
# Parameters check
if
not
isinstance
(
program
,
Program
):
raise
TypeError
(
"Calculating Memory Usage requires Program as its Parameter."
"But you passed in %s"
%
(
type
(
prgram
)))
if
batch_size
<=
0
:
raise
ValueError
(
"The batch size need to be positive."
)
# Get the var_name list of first block and calculate
total_memory
=
0.0
for
var
in
program
.
global_block
().
vars
.
itervalues
():
data_count
=
1
for
x
in
var
.
shape
:
if
x
==
-
1
:
data_count
*=
batch_size
else
:
data_count
*=
x
var_memory
=
data_count
*
dtype_to_size
[
var
.
dtype
]
if
DEBUG
:
print
"%s memory usage: %d"
%
(
var
.
name
,
var_memory
)
total_memory
+=
var_memory
if
DEBUG
:
print
"total memory usage: %.2f"
%
(
total_memory
)
# Convert appropriate unit
unit_str
=
"B"
if
total_memory
>
1024
:
total_memory
/=
1024
unit_str
=
"KB"
if
total_memory
>
1024
:
total_memory
/=
1024
unit_str
=
"MB"
# Append extra memory consumption (5% - 10%)
min_total_memory
=
total_memory
*
1.05
max_total_memory
=
total_memory
*
1.1
return
min_total_memory
,
max_total_memory
,
unit_str
python/paddle/fluid/tests/unittests/test_memory_usage.py
0 → 100644
浏览文件 @
b2435a3a
# 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.
from
__future__
import
print_function
import
paddle
import
paddle.fluid
as
fluid
import
contextlib
import
unittest
def
train_simulator
(
test_batch_size
=
10
):
if
test_batch_size
<=
0
:
raise
ValueError
(
"batch_size should be a positive integeral value, "
"but got batch_size={}"
.
format
(
test_batch_size
))
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
13
],
dtype
=
'float32'
)
y_predict
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
1
,
act
=
None
)
y
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'float32'
)
cost
=
fluid
.
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
sgd_optimizer
.
minimize
(
avg_cost
)
# Calculate memory usage in current network config
lower_usage
,
upper_usage
,
unit
=
fluid
.
contrib
.
memory_usage
(
fluid
.
default_main_program
(),
batch_size
=
test_batch_size
)
print
(
"memory usage is about %.3f - %.3f %s"
%
(
lower_usage
,
upper_usage
,
unit
))
class
TestMemoryUsage
(
unittest
.
TestCase
):
def
test_with_unit_B
(
self
):
with
self
.
program_scope_guard
():
train_simulator
()
def
test_with_unit_KB
(
self
):
with
self
.
program_scope_guard
():
train_simulator
(
test_batch_size
=
1000
)
def
test_with_unit_MB
(
self
):
with
self
.
program_scope_guard
():
train_simulator
(
test_batch_size
=
100000
)
@
contextlib
.
contextmanager
def
program_scope_guard
(
self
):
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
yield
if
__name__
==
'__main__'
:
unittest
.
main
()
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