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4e8bc024
编写于
3月 03, 2020
作者:
Z
Zhang Ting
提交者:
GitHub
3月 03, 2020
浏览文件
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电子邮件补丁
差异文件
add fluid.device_guard to specify the device type for Op (#22254)
* add fluid.device_guard to specify the device type for Op
上级
063c51c7
变更
10
显示空白变更内容
内联
并排
Showing
10 changed file
with
298 addition
and
12 deletion
+298
-12
paddle/fluid/framework/op_proto_maker.cc
paddle/fluid/framework/op_proto_maker.cc
+2
-1
paddle/fluid/framework/op_proto_maker.h
paddle/fluid/framework/op_proto_maker.h
+1
-0
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+16
-0
paddle/fluid/pybind/const_value.cc
paddle/fluid/pybind/const_value.cc
+2
-0
python/paddle/fluid/backward.py
python/paddle/fluid/backward.py
+6
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+81
-1
python/paddle/fluid/io.py
python/paddle/fluid/io.py
+3
-0
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+37
-9
python/paddle/fluid/tests/unittests/test_device_guard.py
python/paddle/fluid/tests/unittests/test_device_guard.py
+149
-0
python/paddle/fluid/tests/unittests/test_operator_desc.py
python/paddle/fluid/tests/unittests/test_operator_desc.py
+1
-1
未找到文件。
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @
4e8bc024
...
...
@@ -86,7 +86,8 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
AddAttr
<
std
::
vector
<
std
::
string
>>
(
OpCreationCallstackAttrName
(),
"Callstack for Op Creatation."
)
.
SetDefault
({});
AddAttr
<
std
::
string
>
(
OpDeviceAttrName
(),
"Device type of this operator."
)
.
SetDefault
(
""
);
Validate
();
}
...
...
paddle/fluid/framework/op_proto_maker.h
浏览文件 @
4e8bc024
...
...
@@ -48,6 +48,7 @@ class OpProtoAndCheckerMaker {
static
const
char
*
OpRoleVarAttrName
()
{
return
"op_role_var"
;
}
static
const
char
*
OpNamescopeAttrName
()
{
return
"op_namescope"
;
}
static
const
char
*
OpCreationCallstackAttrName
()
{
return
"op_callstack"
;
}
static
const
char
*
OpDeviceAttrName
()
{
return
"op_device"
;
}
void
operator
()(
proto
::
OpProto
*
proto
,
OpAttrChecker
*
attr_checker
);
...
...
paddle/fluid/framework/operator.cc
浏览文件 @
4e8bc024
...
...
@@ -1056,6 +1056,22 @@ void OperatorWithKernel::ChooseKernel(const RuntimeContext& ctx,
auto
expected_kernel_key
=
this
->
GetExpectedKernelType
(
ExecutionContext
(
*
this
,
scope
,
*
dev_ctx
,
ctx
,
nullptr
));
if
(
HasAttr
(
"op_device"
))
{
if
(
Attr
<
std
::
string
>
(
"op_device"
)
==
"cpu"
)
{
expected_kernel_key
.
place_
=
platform
::
CPUPlace
();
}
else
if
(
Attr
<
std
::
string
>
(
"op_device"
)
==
"gpu"
)
{
// when the Op that only has CPUKernel is assigned to GPU, the CPUKernel
// will be executed and a warning will be given at the same time.
if
(
SupportGPU
())
{
expected_kernel_key
.
place_
=
dev_ctx
->
GetPlace
();
}
else
{
expected_kernel_key
.
place_
=
platform
::
CPUPlace
();
LOG_FIRST_N
(
WARNING
,
1
)
<<
"Op("
<<
type_
<<
") has no CUDA implementation. It will be assigned to CPUPlace."
;
}
}
}
VLOG
(
3
)
<<
"expected_kernel_key:"
<<
expected_kernel_key
;
auto
kernel_iter
=
kernels
.
find
(
expected_kernel_key
);
...
...
paddle/fluid/pybind/const_value.cc
浏览文件 @
4e8bc024
...
...
@@ -57,6 +57,8 @@ void BindConstValue(pybind11::module* m) {
op_proto_and_checker_maker
.
def
(
"kOpCreationCallstackAttrName"
,
framework
::
OpProtoAndCheckerMaker
::
OpCreationCallstackAttrName
);
op_proto_and_checker_maker
.
def
(
"kOpDeviceAttrName"
,
framework
::
OpProtoAndCheckerMaker
::
OpDeviceAttrName
);
#if defined(PADDLE_WITH_DGC)
auto
dgc
=
m
->
def_submodule
(
"dgc"
);
dgc
.
def
(
"kDGCKName"
,
[]
{
return
framework
::
details
::
g_dgc_k
;
});
...
...
python/paddle/fluid/backward.py
浏览文件 @
4e8bc024
...
...
@@ -876,6 +876,12 @@ def _append_backward_ops_(block,
grad_op_desc
,
op_grad_to_var
=
core
.
get_grad_op_desc
(
op
.
desc
,
cpt
.
to_text
(
no_grad_dict
[
block
.
idx
]),
grad_sub_block_list
)
# Set device for grad_op according to forward Op
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
()
op_device
=
op
.
desc
.
attr
(
device_attr_name
)
for
op_desc
in
grad_op_desc
:
op_desc
.
_set_attr
(
device_attr_name
,
op_device
)
# If input_grad_names_set is not None, extend grad_op_descs only when
# any input grad in outputs of previous grad ops.
# But this strategy is not suited for while op for some control flow,
...
...
python/paddle/fluid/framework.py
浏览文件 @
4e8bc024
...
...
@@ -51,6 +51,7 @@ __all__ = [
'Variable'
,
'load_op_library'
,
'require_version'
,
'device_guard'
,
]
EMPTY_VAR_NAME
=
core
.
kEmptyVarName
()
...
...
@@ -61,6 +62,7 @@ CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
_dygraph_tracer_
=
None
_dygraph_current_expected_place_
=
None
_current_device
=
None
def
require_version
(
min_version
,
max_version
=
None
):
...
...
@@ -1696,7 +1698,8 @@ class OpProtoHolder(object):
core
.
op_proto_and_checker_maker
.
kOpRoleAttrName
(),
core
.
op_proto_and_checker_maker
.
kOpRoleVarAttrName
(),
core
.
op_proto_and_checker_maker
.
kOpNameScopeAttrName
(),
core
.
op_proto_and_checker_maker
.
kOpCreationCallstackAttrName
()
core
.
op_proto_and_checker_maker
.
kOpCreationCallstackAttrName
(),
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
()
}
...
...
@@ -1804,6 +1807,24 @@ class Operator(object):
namescope_var_name
=
op_maker
.
kOpNameScopeAttrName
()
op_attrs
[
namescope_var_name
]
=
_full_name_scope
()
# set device for op with kernels, give warning for op without kernels
# when force_cpu and device_guard are used at the same time, a warning will be given.
# TODO(zhangting2020): when force_cpu is removed, clear warning below.
if
_current_device
is
not
None
:
if
self
.
_has_kernel
(
type
):
op_device
=
op_maker
.
kOpDeviceAttrName
()
op_attrs
[
op_device
]
=
_current_device
else
:
warnings
.
warn
(
"The Op(%s) is not support to set device."
%
type
)
if
'force_cpu'
in
op_attrs
:
if
(
type
is
'less_than'
and
op_attrs
[
'force_cpu'
]
!=
None
)
or
op_attrs
[
'force_cpu'
]
!=
False
:
warnings
.
warn
(
"The Attr(force_cpu) of Op(%s) will be deprecated in the future, "
"please use 'device_guard' instead. 'device_guard' has higher priority when they are "
"used at the same time."
%
type
)
def
find_name
(
var_list
,
name
):
for
var_name
in
var_list
:
if
var_list
[
var_name
]
is
not
None
and
var_name
==
name
:
...
...
@@ -5056,3 +5077,62 @@ def load_op_library(lib_filename):
"""
core
.
load_op_library
(
lib_filename
)
OpProtoHolder
.
instance
().
update_op_proto
()
def
switch_device
(
device
):
global
_current_device
pre_device
=
_current_device
_current_device
=
device
return
pre_device
@
signature_safe_contextmanager
def
device_guard
(
device
=
None
):
"""
**Notes**:
**The API only supports static mode.**
A context manager that specifies the device on which the OP will be placed.
Args:
device(str|None): Specify the device to use in the context. It should be 'cpu' or 'gpu',
When it is set to 'cpu' or 'gpu', all OPs created in the context will be
placed on CPUPlace or CUDAPlace. When 'gpu' is set and the program runs on
single-card, the device index will be the same as the device on which the
executor runs. Default: None, OPs in this context will be automatically
assigned devices.
Examples:
.. code-block:: python
import paddle.fluid as fluid
support_gpu = fluid.is_compiled_with_cuda()
place = fluid.CPUPlace()
if support_gpu:
place = fluid.CUDAPlace(0)
# if GPU is supported, the three OPs below will be automatically assigned to CUDAPlace(0)
data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32')
data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32')
shape = fluid.layers.shape(data2)
with fluid.device_guard("cpu"):
# Ops created here will be placed on CPUPlace
shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4])
with fluid.device_guard('gpu'):
# if GPU is supported, OPs created here will be placed on CUDAPlace(0), otherwise on CPUPlace
out = fluid.layers.crop_tensor(data1, shape=shape)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
result = exe.run(fetch_list=[out])
"""
if
device
not
in
[
'cpu'
,
'gpu'
,
''
,
None
]:
raise
ValueError
(
"The Attr(device) should be 'cpu' or 'gpu', and it can also be empty string or None "
"when there is no need to specify device. But received %s"
%
device
)
pre_device
=
switch_device
(
device
)
yield
switch_device
(
pre_device
)
python/paddle/fluid/io.py
浏览文件 @
4e8bc024
...
...
@@ -1135,6 +1135,9 @@ def save_inference_model(dirname,
# remind user to set auc_states to zeros if the program contains auc op
all_ops
=
main_program
.
global_block
().
ops
for
op
in
all_ops
:
# clear device of Op
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
()
op
.
_set_attr
(
device_attr_name
,
""
)
if
op
.
type
==
'auc'
:
warnings
.
warn
(
"please ensure that you have set the auc states to zeros before saving inference model"
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
4e8bc024
...
...
@@ -18,7 +18,7 @@ import numpy as np
from
collections
import
defaultdict
from
paddle.fluid.distribute_lookup_table
import
find_distributed_lookup_table
from
paddle.fluid.framework
import
Program
,
Variable
,
name_scope
,
default_main_program
,
default_startup_program
from
paddle.fluid.framework
import
Program
,
Variable
,
name_scope
,
default_main_program
,
default_startup_program
,
device_guard
from
.
import
framework
from
.
import
layers
...
...
@@ -108,6 +108,7 @@ class Optimizer(object):
self
.
helper
=
None
self
.
_opti_name_list
=
[]
self
.
_accumulators_holder
=
{}
self
.
_param_device_map
=
dict
()
@
framework
.
dygraph_only
def
state_dict
(
self
):
...
...
@@ -405,7 +406,7 @@ class Optimizer(object):
fill_value
=
0.0
,
shape
=
None
,
type
=
None
,
force_cpu
=
Fals
e
):
device
=
Non
e
):
"""Utility function to add an accumulator for a parameter
Args:
...
...
@@ -438,10 +439,11 @@ class Optimizer(object):
type
=
param
.
type
if
type
is
None
else
type
,
shape
=
shape
,
belong_to_optimizer
=
True
)
if
device
is
None
:
device
=
self
.
_get_device_for_param
(
param
.
name
)
with
device_guard
(
device
):
self
.
helper
.
set_variable_initializer
(
var
,
initializer
=
Constant
(
value
=
float
(
fill_value
),
force_cpu
=
force_cpu
))
var
,
initializer
=
Constant
(
value
=
float
(
fill_value
)))
if
framework
.
in_dygraph_mode
():
if
len
(
self
.
_accumulators_holder
)
>
0
:
...
...
@@ -470,6 +472,27 @@ class Optimizer(object):
format
(
name
,
param
.
name
))
return
self
.
_accumulators
[
name
][
param
.
name
]
def
_update_param_device_map
(
self
,
parameters_and_grads
,
target_block
):
for
param_and_grad
in
parameters_and_grads
:
if
param_and_grad
[
0
].
trainable
is
True
:
param_name
=
param_and_grad
[
0
].
name
ops
=
target_block
.
ops
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
(
)
for
op
in
ops
:
input_arg_names
=
op
.
input_arg_names
if
param_name
in
input_arg_names
:
self
.
_param_device_map
[
param_name
]
=
op
.
attr
(
device_attr_name
)
else
:
self
.
_param_device_map
[
param_name
]
=
None
def
_get_device_for_param
(
self
,
param_name
):
device
=
None
if
param_name
in
self
.
_param_device_map
:
device
=
self
.
_param_device_map
[
param_name
]
return
device
def
_create_optimization_pass
(
self
,
parameters_and_grads
):
"""Add optimization operators to update gradients to variables.
...
...
@@ -505,6 +528,7 @@ class Optimizer(object):
start
=
len
(
target_block
.
ops
)
self
.
helper
=
LayerHelper
(
self
.
__class__
.
__name__
)
self
.
_update_param_device_map
(
parameters_and_grads
,
target_block
)
self
.
_create_accumulators
(
target_block
,
[
p
[
0
]
for
p
in
parameters_and_grads
if
p
[
0
].
trainable
])
...
...
@@ -523,7 +547,11 @@ class Optimizer(object):
with
param_and_grad
[
0
].
block
.
program
.
_optimized_guard
(
param_and_grad
),
name_scope
(
"optimizer"
):
if
param_and_grad
[
0
].
trainable
is
True
:
self
.
_append_optimize_op
(
target_block
,
param_and_grad
)
device
=
self
.
_get_device_for_param
(
param_and_grad
[
0
]
.
name
)
with
device_guard
(
device
):
optimize_op
=
self
.
_append_optimize_op
(
target_block
,
param_and_grad
)
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
...
...
@@ -1793,14 +1821,14 @@ class AdamOptimizer(Optimizer):
fill_value
=
0.9
if
isinstance
(
self
.
_beta1
,
Variable
)
\
else
self
.
_beta1
,
shape
=
[
1
],
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
force_cpu
=
True
)
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
device
=
'cpu'
)
self
.
_add_accumulator
(
name
=
self
.
_beta2_pow_acc_str
,
param
=
p
,
fill_value
=
0.999
if
isinstance
(
self
.
_beta2
,
Variable
)
\
else
self
.
_beta2
,
shape
=
[
1
],
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
force_cpu
=
True
)
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
device
=
'cpu'
)
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
python/paddle/fluid/tests/unittests/test_device_guard.py
0 → 100644
浏览文件 @
4e8bc024
# 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.
from
__future__
import
print_function
import
unittest
from
op_test
import
OpTest
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
warnings
def
execute
(
main_program
,
startup_program
):
if
core
.
is_compiled_with_cuda
():
place
=
core
.
CUDAPlace
(
0
)
else
:
place
=
core
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_program
)
exe
.
run
(
main_program
)
class
TestDeviceGuard
(
unittest
.
TestCase
):
def
test_device_guard
(
self
):
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
data1
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
,
3
,
8
,
8
],
value
=
0.5
,
dtype
=
'float32'
)
data2
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
,
3
,
5
,
5
],
value
=
0.5
,
dtype
=
'float32'
)
shape
=
fluid
.
layers
.
shape
(
data2
)
with
fluid
.
device_guard
(
"cpu"
):
shape
=
fluid
.
layers
.
slice
(
shape
,
axes
=
[
0
],
starts
=
[
0
],
ends
=
[
4
])
with
fluid
.
device_guard
(
"gpu"
):
out
=
fluid
.
layers
.
crop_tensor
(
data1
,
shape
=
shape
)
# check if the device attr is set correctly
all_ops
=
main_program
.
global_block
().
ops
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
()
for
op
in
all_ops
:
if
op
.
type
==
'slice'
:
self
.
assertEqual
(
op
.
desc
.
attr
(
device_attr_name
),
"cpu"
)
if
op
.
type
==
'crop_tensor'
:
self
.
assertEqual
(
op
.
desc
.
attr
(
device_attr_name
),
"gpu"
)
execute
(
main_program
,
startup_program
)
def
test_cpu_only_op
(
self
):
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
x
=
fluid
.
layers
.
fill_constant
(
shape
=
[
2
,
255
,
13
,
13
],
value
=
0.3
,
dtype
=
'float32'
)
gt_box
=
fluid
.
layers
.
fill_constant
(
shape
=
[
2
,
6
,
4
],
value
=
0.5
,
dtype
=
'float32'
)
gt_label
=
fluid
.
layers
.
fill_constant
(
shape
=
[
2
,
6
],
value
=
1.0
,
dtype
=
'int32'
)
gt_score
=
fluid
.
layers
.
fill_constant
(
shape
=
[
2
,
6
],
value
=
0.5
,
dtype
=
'float32'
)
anchors
=
[
10
,
13
,
16
,
30
,
33
,
23
,
30
,
61
,
62
,
45
,
59
,
119
,
116
,
90
,
156
,
198
,
373
,
326
]
anchor_mask
=
[
0
,
1
,
2
]
with
fluid
.
device_guard
(
"gpu"
):
# yolov3_loss only has cpu kernel, so its cpu kernel will be executed
loss
=
fluid
.
layers
.
yolov3_loss
(
x
=
x
,
gt_box
=
gt_box
,
gt_label
=
gt_label
,
gt_score
=
gt_score
,
anchors
=
anchors
,
anchor_mask
=
anchor_mask
,
class_num
=
80
,
ignore_thresh
=
0.7
,
downsample_ratio
=
32
)
execute
(
main_program
,
startup_program
)
def
test_without_kernel_op
(
self
):
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
i
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
0
)
loop_len
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
10
)
cond
=
fluid
.
layers
.
less_than
(
x
=
i
,
y
=
loop_len
)
with
warnings
.
catch_warnings
(
record
=
True
)
as
w
:
warnings
.
simplefilter
(
"always"
)
with
fluid
.
device_guard
(
"cpu"
):
while_op
=
fluid
.
layers
.
While
(
cond
=
cond
)
with
while_op
.
block
():
i
=
fluid
.
layers
.
increment
(
x
=
i
,
value
=
1
,
in_place
=
True
)
fluid
.
layers
.
less_than
(
x
=
i
,
y
=
loop_len
,
cond
=
cond
)
assert
len
(
w
)
==
1
all_ops
=
main_program
.
global_block
().
ops
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
()
for
op
in
all_ops
:
if
op
.
type
==
'while'
:
self
.
assertEqual
(
op
.
desc
.
attr
(
device_attr_name
),
""
)
execute
(
main_program
,
startup_program
)
def
test_error
(
self
):
def
device_attr
():
with
fluid
.
device_guard
(
"cpu1"
):
out
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
],
value
=
0.2
,
dtype
=
'float32'
)
self
.
assertRaises
(
ValueError
,
device_attr
)
def
test_warning
(
self
):
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
with
warnings
.
catch_warnings
(
record
=
True
)
as
w
:
warnings
.
simplefilter
(
"always"
)
with
fluid
.
device_guard
(
"gpu"
):
x
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
],
value
=
3.0
,
dtype
=
'float32'
,
force_cpu
=
True
)
y
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
],
value
=
4.0
,
dtype
=
'float32'
)
result
=
fluid
.
layers
.
less_than
(
x
=
x
,
y
=
y
,
force_cpu
=
False
)
assert
len
(
w
)
==
2
all_ops
=
main_program
.
global_block
().
ops
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
()
for
op
in
all_ops
:
self
.
assertEqual
(
op
.
desc
.
attr
(
device_attr_name
),
"gpu"
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_operator_desc.py
浏览文件 @
4e8bc024
...
...
@@ -70,7 +70,7 @@ class TestOperator(unittest.TestCase):
set
([
"x_num_col_dims"
,
"y_num_col_dims"
,
"op_role"
,
"op_role_var"
,
"use_mkldnn"
,
"scale_x"
,
"scale_y"
,
"scale_out"
,
"force_fp32_output"
,
"op_namescope"
,
"op_callstack"
"force_fp32_output"
,
"op_namescope"
,
"op_callstack"
,
"op_device"
]))
self
.
assertEqual
(
mul_op
.
has_attr
(
"x_num_col_dims"
),
True
)
self
.
assertEqual
(
mul_op
.
attr_type
(
"x_num_col_dims"
),
core
.
AttrType
.
INT
)
...
...
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