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体验新版 GitCode,发现更多精彩内容 >>
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20e23e1b
编写于
1月 25, 2022
作者:
Y
Yuang Liu
提交者:
GitHub
1月 25, 2022
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差异文件
[fleet_executor] Dist model run method Implementation (#39194)
上级
8bb509d5
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
391 addition
and
17 deletion
+391
-17
paddle/fluid/distributed/fleet_executor/dist_model.cc
paddle/fluid/distributed/fleet_executor/dist_model.cc
+282
-11
paddle/fluid/distributed/fleet_executor/dist_model.h
paddle/fluid/distributed/fleet_executor/dist_model.h
+13
-2
paddle/fluid/distributed/fleet_executor/dist_model_tensor_wrapper.h
...id/distributed/fleet_executor/dist_model_tensor_wrapper.h
+1
-1
paddle/fluid/pybind/bind_fleet_executor.cc
paddle/fluid/pybind/bind_fleet_executor.cc
+6
-1
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+2
-1
python/paddle/fluid/tests/unittests/test_fleet_exe_dist_model_run.py
...le/fluid/tests/unittests/test_fleet_exe_dist_model_run.py
+86
-0
python/paddle/fluid/tests/unittests/test_fleet_exe_dist_model_tensor.py
...fluid/tests/unittests/test_fleet_exe_dist_model_tensor.py
+1
-1
未找到文件。
paddle/fluid/distributed/fleet_executor/dist_model.cc
浏览文件 @
20e23e1b
...
...
@@ -13,11 +13,13 @@
// limitations under the License.
#include <glog/logging.h>
#include <chrono> // NOLINT
#include "paddle/fluid/distributed/fleet_executor/dist_model.h"
#include "paddle/fluid/distributed/fleet_executor/fleet_executor.h"
#include "paddle/fluid/distributed/fleet_executor/task_node.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/program_desc.h"
...
...
@@ -37,10 +39,110 @@ bool IsPersistable(const framework::VarDesc *var) {
}
return
false
;
}
bool
LoadDataFromDistModelTensor
(
const
DistModelTensor
&
input_data
,
framework
::
LoDTensor
*
input_tensor
,
const
platform
::
Place
&
place
)
{
VLOG
(
3
)
<<
"Loading data from DistModelTensor for "
<<
input_data
.
name
;
framework
::
DDim
dims
=
framework
::
make_ddim
(
input_data
.
shape
);
void
*
input_tensor_ptr
;
if
(
input_data
.
dtype
==
DistModelDataType
::
INT64
)
{
input_tensor_ptr
=
input_tensor
->
mutable_data
<
int64_t
>
(
dims
,
place
);
}
else
if
(
input_data
.
dtype
==
DistModelDataType
::
FLOAT32
)
{
input_tensor_ptr
=
input_tensor
->
mutable_data
<
float
>
(
dims
,
place
);
}
else
if
(
input_data
.
dtype
==
DistModelDataType
::
INT32
)
{
input_tensor_ptr
=
input_tensor
->
mutable_data
<
int32_t
>
(
dims
,
place
);
}
else
{
// Q(fleet exe dev): for input/output, should we support fp16
LOG
(
ERROR
)
<<
"unsupported feed type "
<<
input_data
.
dtype
;
return
false
;
}
PADDLE_ENFORCE_NOT_NULL
(
input_tensor_ptr
,
paddle
::
platform
::
errors
::
Fatal
(
"LoDTensor creation failed. DistModel loaded data failed."
));
PADDLE_ENFORCE_NOT_NULL
(
input_data
.
data
.
data
(),
paddle
::
platform
::
errors
::
InvalidArgument
(
"DistModelTensor contains no data."
));
if
(
platform
::
is_cpu_place
(
place
))
{
VLOG
(
3
)
<<
"Loading data for CPU."
;
std
::
memcpy
(
static_cast
<
void
*>
(
input_tensor_ptr
),
input_data
.
data
.
data
(),
input_data
.
data
.
length
());
}
else
if
(
platform
::
is_gpu_place
(
place
))
{
VLOG
(
3
)
<<
"Loading data for GPU."
;
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
*
dev_ctx
=
dynamic_cast
<
const
platform
::
CUDADeviceContext
*>
(
pool
.
Get
(
place
));
auto
gpu_place
=
place
;
memory
::
Copy
(
gpu_place
,
static_cast
<
void
*>
(
input_tensor_ptr
),
platform
::
CPUPlace
(),
input_data
.
data
.
data
(),
input_data
.
data
.
length
(),
dev_ctx
->
stream
());
#else
PADDLE_THROW
(
paddle
::
platform
::
errors
::
Fatal
(
"Paddle wasn't compiled with CUDA, but place is GPU."
));
#endif
}
else
{
PADDLE_THROW
(
paddle
::
platform
::
errors
::
InvalidArgument
(
"DistModel only supports CPU and GPU."
));
}
framework
::
LoD
dst_lod
;
for
(
auto
&
src_lod
:
input_data
.
lod
)
{
dst_lod
.
emplace_back
(
src_lod
);
}
input_tensor
->
set_lod
(
dst_lod
);
return
true
;
}
std
::
string
DistModelDTypeToString
(
DistModelDataType
dtype
)
{
switch
(
dtype
)
{
case
DistModelDataType
::
FLOAT32
:
return
"float32"
;
case
DistModelDataType
::
FLOAT16
:
return
"float16"
;
case
DistModelDataType
::
INT64
:
return
"int64"
;
case
DistModelDataType
::
INT32
:
return
"int32"
;
case
DistModelDataType
::
INT8
:
return
"int8"
;
}
return
"NOT SUPPORT DTYPE"
;
}
bool
IsPPFirstStage
(
const
DistModelConfig
&
config
)
{
return
config
.
local_rank
-
config
.
mp_degree
<
0
;
}
bool
IsPPLastStage
(
const
DistModelConfig
&
config
)
{
return
config
.
local_rank
+
config
.
mp_degree
>=
config
.
nranks
;
}
class
DistModelTimer
{
public:
void
tic
()
{
tic_time
=
std
::
chrono
::
high_resolution_clock
::
now
();
}
double
toc
()
{
std
::
chrono
::
high_resolution_clock
::
time_point
toc_time
=
std
::
chrono
::
high_resolution_clock
::
now
();
std
::
chrono
::
duration
<
double
>
time_elapse
=
std
::
chrono
::
duration_cast
<
std
::
chrono
::
duration
<
double
>>
(
toc_time
-
tic_time
);
double
time_elapse_in_ms
=
static_cast
<
double
>
(
time_elapse
.
count
())
*
1000.0
;
return
time_elapse_in_ms
;
}
private:
std
::
chrono
::
high_resolution_clock
::
time_point
tic_time
;
};
}
// namespace
bool
DistModel
::
Init
()
{
/* TODO(fleet exe dev): implement this funct */
carrier_id_
=
"inference"
;
bool
init_method
=
(
!
config_
.
model_dir
.
empty
()
||
config_
.
program_desc
);
PADDLE_ENFORCE_EQ
(
init_method
,
true
,
platform
::
errors
::
InvalidArgument
(
...
...
@@ -127,10 +229,9 @@ bool DistModel::CommInit() {
InsertCommOp
(
"mp_comm_id"
,
mp_group_nranks
,
mp_group_rank
,
peer_endpoints
,
comm_init_block
,
config_
.
mp_ring_id
);
}
if
(
config_
.
pp_degree
)
{
// NOTE: the last pp stage doesn't need init pp comm
if
(
config_
.
pp_degree
>
1
)
{
VLOG
(
3
)
<<
"Init comm group for pp."
;
if
(
config_
.
local_rank
-
config_
.
mp_degree
>=
0
)
{
if
(
!
IsPPFirstStage
(
config_
)
)
{
PADDLE_ENFORCE_EQ
(
config_
.
pp_upstream_ring_id
>=
0
,
true
,
platform
::
errors
::
InvalidArgument
(
"pp upstream ring id must be provided for "
...
...
@@ -143,7 +244,7 @@ bool DistModel::CommInit() {
comm_init_block
,
config_
.
pp_upstream_ring_id
);
}
if
(
config_
.
local_rank
+
config_
.
mp_degree
<
config_
.
nranks
)
{
if
(
!
IsPPLastStage
(
config_
)
)
{
PADDLE_ENFORCE_EQ
(
config_
.
pp_downstream_ring_id
>=
0
,
true
,
platform
::
errors
::
InvalidArgument
(
"pp downstream ring id must be provided for "
...
...
@@ -326,7 +427,7 @@ bool DistModel::PrepareFleetExe() {
id_to_rank
.
insert
({
i
,
i
});
}
fleet_exe
.
reset
(
new
FleetExecutor
(
executor_desc_
));
fleet_exe
->
Init
(
"inference"
,
*
(
program_
.
get
()),
scope_
.
get
(),
place_
,
1
,
fleet_exe
->
Init
(
carrier_id_
,
*
(
program_
.
get
()),
scope_
.
get
(),
place_
,
1
,
{
task_node_
.
get
()},
id_to_rank
);
return
true
;
}
...
...
@@ -340,8 +441,27 @@ bool DistModel::PrepareFeedAndFetch() {
feeds_
.
resize
(
idx
+
1
);
}
feeds_
[
idx
]
=
op
;
feed_names_
[
op
->
Output
(
"Out"
)[
0
]]
=
idx
;
idx_to_feeds_
[
idx
]
=
op
->
Output
(
"Out"
)[
0
];
std
::
string
var_name
=
op
->
Output
(
"Out"
)[
0
];
feed_names_
[
var_name
]
=
idx
;
idx_to_feeds_
[
idx
]
=
var_name
;
framework
::
VarDesc
*
real_var
=
program_
->
Block
(
0
).
FindVar
(
var_name
);
if
(
!
real_var
)
{
LOG
(
ERROR
)
<<
"The output of feed ops ["
<<
var_name
<<
"] cannot be found in the program. Check the inference program."
;
return
false
;
}
if
(
real_var
->
GetDataType
()
==
framework
::
proto
::
VarType
::
FP32
)
{
feeds_to_dtype_
.
insert
({
var_name
,
DistModelDataType
::
FLOAT32
});
}
else
if
(
real_var
->
GetDataType
()
==
framework
::
proto
::
VarType
::
INT32
)
{
feeds_to_dtype_
.
insert
({
var_name
,
DistModelDataType
::
INT32
});
}
else
if
(
real_var
->
GetDataType
()
==
framework
::
proto
::
VarType
::
INT64
)
{
feeds_to_dtype_
.
insert
({
var_name
,
DistModelDataType
::
INT64
});
}
else
{
LOG
(
ERROR
)
<<
"Don't support feed var dtype for: "
<<
real_var
->
GetDataType
();
return
false
;
}
}
else
if
(
op
->
Type
()
==
"fetch"
)
{
VLOG
(
3
)
<<
"fetch op with fetch var: "
<<
op
->
Input
(
"X"
)[
0
];
int
idx
=
BOOST_GET_CONST
(
int
,
op
->
GetAttr
(
"col"
));
...
...
@@ -349,15 +469,166 @@ bool DistModel::PrepareFeedAndFetch() {
fetches_
.
resize
(
idx
+
1
);
}
fetches_
[
idx
]
=
op
;
id_to_fetches_
[
idx
]
=
op
->
Input
(
"X"
)[
0
];
idx_to_fetches_
[
idx
]
=
op
->
Input
(
"X"
)[
0
];
}
}
if
(
config_
.
pp_degree
==
1
)
{
if
(
feeds_
.
size
()
==
0
)
{
LOG
(
ERROR
)
<<
"No feed ops in the inf program, please check the program."
;
return
false
;
}
if
(
fetches_
.
size
()
==
0
)
{
LOG
(
ERROR
)
<<
"No fetch op in the inf program, please check the program."
;
return
false
;
}
}
else
{
if
(
IsPPFirstStage
(
config_
))
{
if
(
feeds_
.
size
()
==
0
)
{
LOG
(
ERROR
)
<<
"Feed ops are needed for the first pp stage."
;
return
false
;
}
else
{
LOG
(
WARNING
)
<<
"No feed ops in non-first pp stage."
;
}
}
else
if
(
feeds_
.
size
()
>
0
)
{
LOG
(
WARNING
)
<<
"Feed op is found in the non-first stage of pp."
;
}
if
(
IsPPLastStage
(
config_
))
{
if
(
fetches_
.
size
()
==
0
)
{
LOG
(
ERROR
)
<<
"Fetch op is needed for the last pp stage."
;
return
false
;
}
else
{
LOG
(
WARNING
)
<<
"No fetch op in non-last pp stage."
;
}
}
else
if
(
fetches_
.
size
()
>
0
)
{
LOG
(
WARNING
)
<<
"Fetch op is found in the non-last stage of pp."
;
}
}
return
true
;
}
bool
DistModel
::
FeedData
(
const
std
::
vector
<
DistModelTensor
>
&
input_data
,
framework
::
Scope
*
scope
)
{
VLOG
(
3
)
<<
"DistModel is feeding data."
;
if
(
input_data
.
size
()
!=
feeds_
.
size
())
{
LOG
(
ERROR
)
<<
"Should provide "
<<
feeds_
.
size
()
<<
" feeds, but got "
<<
input_data
.
size
()
<<
" data."
;
return
false
;
}
feed_tensors_
.
resize
(
feeds_
.
size
());
for
(
size_t
i
=
0
;
i
<
input_data
.
size
();
++
i
)
{
// feed each data separately
framework
::
LoDTensor
*
input_tensor
=
&
(
feed_tensors_
[
i
]);
if
(
!
LoadDataFromDistModelTensor
(
input_data
[
i
],
input_tensor
,
place_
))
{
LOG
(
ERROR
)
<<
"Fail to load data from tensor "
<<
input_data
[
i
].
name
;
return
false
;
}
std
::
string
target_name
=
input_data
[
i
].
name
;
if
(
feed_names_
.
find
(
target_name
)
==
feed_names_
.
end
())
{
LOG
(
ERROR
)
<<
"The input name ["
<<
target_name
<<
"] cannot be found in the program."
<<
" DistModel loads data failed."
;
return
false
;
}
if
(
input_data
[
i
].
dtype
!=
feeds_to_dtype_
[
target_name
])
{
LOG
(
ERROR
)
<<
"Feed var ["
<<
target_name
<<
"] expected dtype is: "
<<
DistModelDTypeToString
(
feeds_to_dtype_
[
target_name
])
<<
". But received dtype is: "
<<
DistModelDTypeToString
(
input_data
[
i
].
dtype
)
<<
"."
;
return
false
;
}
int
feed_idx
=
feed_names_
[
target_name
];
framework
::
SetFeedVariable
(
scope
,
*
input_tensor
,
"feed"
,
feed_idx
);
}
return
true
;
}
bool
DistModel
::
FetchResults
(
std
::
vector
<
DistModelTensor
>
*
output_data
,
framework
::
Scope
*
scope
)
{
VLOG
(
3
)
<<
"DistModel is fetch results."
;
output_data
->
resize
(
fetches_
.
size
());
for
(
size_t
i
=
0
;
i
<
fetches_
.
size
();
++
i
)
{
int
idx
=
BOOST_GET_CONST
(
int
,
fetches_
[
i
]
->
GetAttr
(
"col"
));
VLOG
(
3
)
<<
"Fetching data for ["
<<
idx_to_fetches_
[
idx
]
<<
"]"
;
PADDLE_ENFORCE_EQ
(
static_cast
<
size_t
>
(
idx
),
i
,
platform
::
errors
::
InvalidArgument
(
"Fetch op's col attr(%d) should be equal to the index(%d)"
,
idx
,
i
));
framework
::
FetchType
&
fetch_var
=
framework
::
GetFetchVariable
(
*
scope
,
"fetch"
,
idx
);
auto
&
fetch
=
BOOST_GET
(
framework
::
LoDTensor
,
fetch_var
);
auto
type
=
fetch
.
type
();
auto
output
=
&
(
output_data
->
at
(
i
));
output
->
name
=
idx_to_fetches_
[
idx
];
bool
rst
=
false
;
if
(
type
==
framework
::
proto
::
VarType
::
FP32
)
{
rst
=
FetchResult
<
float
>
(
fetch
,
output
);
output
->
dtype
=
DistModelDataType
::
FLOAT32
;
}
else
if
(
type
==
framework
::
proto
::
VarType
::
INT64
)
{
rst
=
FetchResult
<
int64_t
>
(
fetch
,
output
);
output
->
dtype
=
DistModelDataType
::
INT64
;
}
else
if
(
type
==
framework
::
proto
::
VarType
::
INT32
)
{
rst
=
FetchResult
<
int32_t
>
(
fetch
,
output
);
output
->
dtype
=
DistModelDataType
::
INT32
;
}
else
{
LOG
(
ERROR
)
<<
"DistModel meets unknown fetch data type. DistModel only "
"supports float32, int64 and int32 fetch type for now."
;
}
if
(
!
rst
)
{
LOG
(
ERROR
)
<<
"DistModel fails to fetch result "
<<
idx_to_fetches_
[
idx
];
return
false
;
}
}
return
true
;
}
void
DistModel
::
Run
(
const
std
::
vector
<
DistModelTensor
>
&
input_data
,
template
<
typename
T
>
bool
DistModel
::
FetchResult
(
const
framework
::
LoDTensor
&
fetch
,
DistModelTensor
*
output_data
)
{
auto
shape
=
framework
::
vectorize
(
fetch
.
dims
());
output_data
->
shape
.
assign
(
shape
.
begin
(),
shape
.
end
());
const
T
*
data
=
fetch
.
data
<
T
>
();
int64_t
num_elems
=
fetch
.
numel
();
output_data
->
data
.
Resize
(
num_elems
*
sizeof
(
T
));
// The output of fetch op is always on the cpu, no need switch on place
memcpy
(
output_data
->
data
.
data
(),
data
,
num_elems
*
sizeof
(
T
));
output_data
->
lod
.
clear
();
for
(
auto
&
level
:
fetch
.
lod
())
{
output_data
->
lod
.
emplace_back
(
level
.
begin
(),
level
.
end
());
}
return
true
;
}
bool
DistModel
::
Run
(
const
std
::
vector
<
DistModelTensor
>
&
input_data
,
std
::
vector
<
DistModelTensor
>
*
output_data
)
{
/* TODO(fleet exe dev): implement this funct */
// TODO(fleet exe dev): support pipeline inf mode
VLOG
(
3
)
<<
"DistModel run for once."
;
DistModelTimer
timer
;
timer
.
tic
();
if
(
!
FeedData
(
input_data
,
scope_
.
get
()))
{
LOG
(
ERROR
)
<<
"DistModel failed at feeding data."
;
return
false
;
}
double
feed_elapse
=
timer
.
toc
();
VLOG
(
3
)
<<
"Finish loading data, cost "
<<
feed_elapse
<<
"ms."
;
fleet_exe
->
Run
(
carrier_id_
);
double
fleet_exe_elapse
=
timer
.
toc
();
VLOG
(
3
)
<<
"Finish FleetExe running, cost "
<<
fleet_exe_elapse
-
feed_elapse
<<
"ms."
;
if
(
!
FetchResults
(
output_data
,
scope_
.
get
()))
{
LOG
(
ERROR
)
<<
"DistModel failed at fetching result."
;
return
false
;
}
double
fetch_elapse
=
timer
.
toc
();
VLOG
(
3
)
<<
"Finish fetching data, cost "
<<
fetch_elapse
-
fleet_exe_elapse
<<
"ms."
;
VLOG
(
3
)
<<
"DistModel finish inf, cost "
<<
fetch_elapse
<<
"ms"
;
return
true
;
}
}
// namespace distributed
...
...
paddle/fluid/distributed/fleet_executor/dist_model.h
浏览文件 @
20e23e1b
...
...
@@ -19,6 +19,7 @@
#include "paddle/fluid/distributed/fleet_executor/dist_model_tensor_wrapper.h"
#include "paddle/fluid/distributed/fleet_executor/fleet_executor_desc.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/macros.h"
#include "paddle/fluid/platform/place.h"
...
...
@@ -57,7 +58,7 @@ class DistModel {
public:
explicit
DistModel
(
const
DistModelConfig
&
config
)
:
config_
(
config
)
{}
bool
Init
();
void
Run
(
const
std
::
vector
<
DistModelTensor
>&
input_data
,
bool
Run
(
const
std
::
vector
<
DistModelTensor
>&
input_data
,
std
::
vector
<
DistModelTensor
>*
output_data
);
~
DistModel
()
=
default
;
...
...
@@ -75,12 +76,22 @@ class DistModel {
void
InsertCommOp
(
std
::
string
tmp_var_name
,
int
nranks
,
int
rank
,
const
std
::
vector
<
std
::
string
>&
peer_endpoints
,
framework
::
BlockDesc
*
block
,
int
ring_id
);
bool
FeedData
(
const
std
::
vector
<
DistModelTensor
>&
input_data
,
framework
::
Scope
*
scope
);
bool
FetchResults
(
std
::
vector
<
DistModelTensor
>*
output_data
,
framework
::
Scope
*
scope
);
template
<
typename
T
>
bool
FetchResult
(
const
framework
::
LoDTensor
&
fetch
,
DistModelTensor
*
output_data
);
std
::
string
carrier_id_
;
std
::
vector
<
framework
::
LoDTensor
>
feed_tensors_
;
std
::
vector
<
framework
::
OpDesc
*>
feeds_
;
std
::
map
<
std
::
string
,
int64_t
>
feed_names_
;
std
::
map
<
int64_t
,
std
::
string
>
idx_to_feeds_
;
std
::
map
<
std
::
string
,
DistModelDataType
>
feeds_to_dtype_
;
std
::
vector
<
framework
::
OpDesc
*>
fetches_
;
std
::
map
<
int64_t
,
std
::
string
>
id_to_fetches_
;
std
::
map
<
int64_t
,
std
::
string
>
id
x
_to_fetches_
;
DistModelConfig
config_
;
FleetExecutorDesc
executor_desc_
;
std
::
shared_ptr
<
FleetExecutor
>
fleet_exe
;
...
...
paddle/fluid/distributed/fleet_executor/dist_model_tensor_wrapper.h
浏览文件 @
20e23e1b
...
...
@@ -62,7 +62,7 @@ class DistModelDataBuf {
void
Free
();
void
*
data_
{
nullptr
};
size_t
length_
{
0
};
bool
memory_owned_
{
fals
e
};
bool
memory_owned_
{
tru
e
};
};
struct
DistModelTensor
{
...
...
paddle/fluid/pybind/bind_fleet_executor.cc
浏览文件 @
20e23e1b
...
...
@@ -162,7 +162,12 @@ void BindFleetExecutor(py::module* m) {
py
::
class_
<
DistModel
>
(
*
m
,
"DistModel"
)
.
def
(
py
::
init
<
const
DistModelConfig
&>
())
.
def
(
"init"
,
&
DistModel
::
Init
)
.
def
(
"run"
,
&
DistModel
::
Run
,
py
::
call_guard
<
py
::
gil_scoped_release
>
());
.
def
(
"run"
,
[](
DistModel
&
self
,
const
std
::
vector
<
DistModelTensor
>&
inputs
)
{
std
::
vector
<
DistModelTensor
>
outputs
;
self
.
Run
(
inputs
,
&
outputs
);
return
outputs
;
});
py
::
class_
<
DistModelDataBuf
>
(
*
m
,
"DistModelDataBuf"
)
.
def
(
py
::
init
<
size_t
>
())
...
...
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
20e23e1b
...
...
@@ -156,7 +156,8 @@ if(((NOT WITH_ROCM) AND (NOT WITH_GPU)) OR WIN32)
LIST
(
REMOVE_ITEM TEST_OPS test_fleet_executor_origin_scheduler
)
LIST
(
REMOVE_ITEM TEST_OPS test_auto_parallel_mapper
)
LIST
(
REMOVE_ITEM TEST_OPS test_fleet_executor_task_node
)
LIST
(
REMOVE_ITEM TEST_OPS test_dist_model_tensor
)
LIST
(
REMOVE_ITEM TEST_OPS test_fleet_exe_dist_model_run
)
LIST
(
REMOVE_ITEM TEST_OPS test_fleet_exe_dist_model_tensor
)
endif
()
# Temporally disable test_deprecated_decorator
...
...
python/paddle/fluid/tests/unittests/test_fleet_exe_dist_model_run.py
0 → 100644
浏览文件 @
20e23e1b
# Copyright (c) 2021 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
import
paddle
import
numpy
as
np
import
os
from
paddle.fluid
import
core
paddle
.
enable_static
()
class
TestDistModelRun
(
unittest
.
TestCase
):
def
test_dist_model_run
(
self
):
# step 0: declare folder to save the model and params
folder
=
'./dist_model_run_test/'
file
=
'inf'
path_prefix
=
folder
+
file
# step 1: saving the inference model and params
x
=
paddle
.
static
.
data
(
name
=
'x'
,
shape
=
[
28
,
28
],
dtype
=
'float32'
)
y
=
paddle
.
static
.
data
(
name
=
'y'
,
shape
=
[
28
,
1
],
dtype
=
'int64'
)
predict
=
paddle
.
static
.
nn
.
fc
(
x
,
10
,
activation
=
'softmax'
)
loss
=
paddle
.
nn
.
functional
.
cross_entropy
(
predict
,
y
)
avg_loss
=
paddle
.
tensor
.
stat
.
mean
(
loss
)
exe
=
paddle
.
static
.
Executor
(
paddle
.
CUDAPlace
(
0
))
exe
.
run
(
paddle
.
static
.
default_startup_program
())
x_data
=
np
.
random
.
randn
(
28
,
28
).
astype
(
'float32'
)
y_data
=
np
.
random
.
randint
(
0
,
9
,
size
=
[
28
,
1
]).
astype
(
'int64'
)
exe
.
run
(
paddle
.
static
.
default_main_program
(),
feed
=
{
'x'
:
x_data
,
'y'
:
y_data
},
fetch_list
=
[
avg_loss
])
paddle
.
static
.
save_inference_model
(
path_prefix
,
[
x
,
y
],
[
avg_loss
],
exe
)
print
(
'save model to'
,
path_prefix
)
# step 2: prepare fake data for the inference
x_tensor
=
np
.
random
.
randn
(
28
,
28
).
astype
(
'float32'
)
y_tensor
=
np
.
random
.
randint
(
0
,
9
,
size
=
[
28
,
1
]).
astype
(
'int64'
)
# step 3: init the dist model to inference with fake data
config
=
core
.
DistModelConfig
()
config
.
model_dir
=
path_prefix
config
.
place
=
'GPU'
dist
=
core
.
DistModel
(
config
)
dist
.
init
()
dist_x
=
core
.
DistModelTensor
(
x_tensor
,
'x'
)
dist_y
=
core
.
DistModelTensor
(
y_tensor
,
'y'
)
input_data
=
[
dist_x
,
dist_y
]
output_rst
=
dist
.
run
(
input_data
)
dist_model_rst
=
output_rst
[
0
].
as_ndarray
().
ravel
().
tolist
()
print
(
"dist model rst:"
,
dist_model_rst
)
# step 4: use framework's api to inference with fake data
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
(
paddle
.
static
.
load_inference_model
(
path_prefix
,
exe
))
results
=
exe
.
run
(
inference_program
,
feed
=
{
'x'
:
x_tensor
,
'y'
:
y_tensor
},
fetch_list
=
fetch_targets
)
load_inference_model_rst
=
results
[
0
]
print
(
"load inference model api rst:"
,
load_inference_model_rst
)
# step 5: compare two results
self
.
assertTrue
(
np
.
allclose
(
dist_model_rst
,
load_inference_model_rst
))
# step 6: clean up the env, delete the saved model and params
os
.
remove
(
path_prefix
+
'.pdiparams'
)
os
.
remove
(
path_prefix
+
'.pdmodel'
)
os
.
rmdir
(
folder
)
print
(
'cleaned up the env'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_dist_model_tensor.py
→
python/paddle/fluid/tests/unittests/test_
fleet_exe_
dist_model_tensor.py
浏览文件 @
20e23e1b
# Copyright (c) 20
19
PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 20
21
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.
...
...
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