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体验新版 GitCode,发现更多精彩内容 >>
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6bfa6a0a
编写于
10月 30, 2018
作者:
Y
Yancey1989
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电子邮件补丁
差异文件
add fused broadcast op unit test, test=develop
上级
e74267ae
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
438 addition
and
221 deletion
+438
-221
paddle/fluid/framework/details/CMakeLists.txt
paddle/fluid/framework/details/CMakeLists.txt
+1
-0
paddle/fluid/framework/details/broadcast_op_handle_test.cc
paddle/fluid/framework/details/broadcast_op_handle_test.cc
+1
-221
paddle/fluid/framework/details/broadcast_op_handle_test.h
paddle/fluid/framework/details/broadcast_op_handle_test.h
+271
-0
paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc
...fluid/framework/details/fused_broadcast_op_handle_test.cc
+165
-0
未找到文件。
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @
6bfa6a0a
...
...
@@ -56,6 +56,7 @@ cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_execu
# device_context reduce_op_handle )
cc_library
(
fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context
)
cc_test
(
fused_broadcast_op_test SRCS fused_broadcast_op_handle_test.cc DEPS fused_broadcast_op_handle
)
cc_library
(
build_strategy SRCS build_strategy.cc DEPS
graph_viz_pass multi_devices_graph_pass
...
...
paddle/fluid/framework/details/broadcast_op_handle_test.cc
浏览文件 @
6bfa6a0a
...
...
@@ -12,232 +12,12 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "gtest/gtest.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/framework/details/broadcast_op_handle_test.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
namespace
f
=
paddle
::
framework
;
namespace
p
=
paddle
::
platform
;
// test data amount
const
f
::
DDim
kDims
=
{
20
,
20
};
struct
TestBroadcastOpHandle
{
std
::
vector
<
std
::
unique_ptr
<
p
::
DeviceContext
>>
ctxs_
;
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
Scope
*>
param_scopes_
;
Scope
g_scope_
;
std
::
unique_ptr
<
OpHandleBase
>
op_handle_
;
std
::
vector
<
std
::
unique_ptr
<
VarHandleBase
>>
vars_
;
std
::
vector
<
p
::
Place
>
gpu_list_
;
bool
use_gpu_
;
#ifdef PADDLE_WITH_CUDA
std
::
unique_ptr
<
platform
::
NCCLContextMap
>
nccl_ctxs_
;
#endif
void
WaitAll
()
{
for
(
size_t
j
=
0
;
j
<
ctxs_
.
size
();
++
j
)
{
ctxs_
[
j
]
->
Wait
();
}
#ifdef PADDLE_WITH_CUDA
if
(
nccl_ctxs_
)
{
nccl_ctxs_
->
WaitAll
();
}
#endif
}
void
InitCtxOnGpu
(
bool
use_gpu
)
{
use_gpu_
=
use_gpu
;
if
(
use_gpu_
)
{
#ifdef PADDLE_WITH_CUDA
int
count
=
p
::
GetCUDADeviceCount
();
if
(
count
<=
1
)
{
LOG
(
WARNING
)
<<
"Cannot test multi-gpu Broadcast, because the CUDA "
"device count is "
<<
count
;
exit
(
0
);
}
for
(
int
i
=
0
;
i
<
count
;
++
i
)
{
auto
p
=
p
::
CUDAPlace
(
i
);
gpu_list_
.
push_back
(
p
);
ctxs_
.
emplace_back
(
new
p
::
CUDADeviceContext
(
p
));
}
nccl_ctxs_
.
reset
(
new
platform
::
NCCLContextMap
(
gpu_list_
));
#else
PADDLE_THROW
(
"CUDA is not support."
);
#endif
}
else
{
int
count
=
8
;
for
(
int
i
=
0
;
i
<
count
;
++
i
)
{
auto
p
=
p
::
CPUPlace
();
gpu_list_
.
push_back
(
p
);
ctxs_
.
emplace_back
(
new
p
::
CPUDeviceContext
(
p
));
}
#ifdef PADDLE_WITH_CUDA
nccl_ctxs_
.
reset
(
nullptr
);
#endif
}
}
void
InitBroadcastOp
(
size_t
input_scope_idx
)
{
for
(
size_t
j
=
0
;
j
<
gpu_list_
.
size
();
++
j
)
{
local_scopes_
.
push_back
(
&
(
g_scope_
.
NewScope
()));
Scope
&
local_scope
=
local_scopes_
.
back
()
->
NewScope
();
*
local_scopes_
.
back
()
->
Var
(
details
::
kLocalExecScopeName
)
->
GetMutable
<
Scope
*>
()
=
&
local_scope
;
local_scope
.
Var
(
"out"
);
param_scopes_
.
emplace_back
(
&
local_scope
);
}
param_scopes_
[
input_scope_idx
]
->
Var
(
"input"
);
std
::
unique_ptr
<
ir
::
Node
>
n
=
ir
::
CreateNodeForTest
(
"node0"
,
ir
::
Node
::
Type
::
kOperation
);
if
(
use_gpu_
)
{
#ifdef PADDLE_WITH_CUDA
op_handle_
.
reset
(
new
BroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
gpu_list_
,
nccl_ctxs_
.
get
()));
#else
PADDLE_THROW
(
"CUDA is not support."
);
#endif
}
else
{
#ifdef PADDLE_WITH_CUDA
op_handle_
.
reset
(
new
BroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
gpu_list_
,
nccl_ctxs_
.
get
()));
#else
op_handle_
.
reset
(
new
BroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
gpu_list_
));
#endif
}
std
::
unique_ptr
<
ir
::
Node
>
v
=
ir
::
CreateNodeForTest
(
"node1"
,
ir
::
Node
::
Type
::
kVariable
);
auto
*
in_var_handle
=
new
VarHandle
(
v
.
get
(),
1
,
input_scope_idx
,
"input"
,
gpu_list_
[
input_scope_idx
]);
vars_
.
emplace_back
(
in_var_handle
);
op_handle_
->
AddInput
(
in_var_handle
);
// add dummy var
std
::
unique_ptr
<
ir
::
Node
>
v2
=
ir
::
CreateNodeForTest
(
"node2"
,
ir
::
Node
::
Type
::
kVariable
);
vars_
.
emplace_back
(
new
DummyVarHandle
(
v2
.
get
()));
DummyVarHandle
*
dummy_var_handle
=
static_cast
<
DummyVarHandle
*>
(
vars_
.
back
().
get
());
dummy_var_handle
->
ClearGeneratedOp
();
op_handle_
->
AddInput
(
dummy_var_handle
);
for
(
size_t
j
=
0
;
j
<
gpu_list_
.
size
();
++
j
)
{
if
(
!
use_gpu_
)
{
op_handle_
->
SetDeviceContext
(
gpu_list_
[
j
],
ctxs_
[
j
].
get
());
}
std
::
unique_ptr
<
ir
::
Node
>
v3
=
ir
::
CreateNodeForTest
(
"node3"
,
ir
::
Node
::
Type
::
kVariable
);
VarHandle
*
out_var_handle
=
new
VarHandle
(
v3
.
get
(),
2
,
j
,
"out"
,
gpu_list_
[
j
]);
vars_
.
emplace_back
(
out_var_handle
);
op_handle_
->
AddOutput
(
out_var_handle
);
}
// add dummy var
std
::
unique_ptr
<
ir
::
Node
>
v4
=
ir
::
CreateNodeForTest
(
"node4"
,
ir
::
Node
::
Type
::
kVariable
);
vars_
.
emplace_back
(
new
DummyVarHandle
(
v4
.
get
()));
DummyVarHandle
*
out_dummy_var_handle
=
static_cast
<
DummyVarHandle
*>
(
vars_
.
back
().
get
());
out_dummy_var_handle
->
ClearGeneratedOp
();
op_handle_
->
AddOutput
(
out_dummy_var_handle
);
}
void
TestBroadcastLodTensor
(
size_t
input_scope_idx
)
{
auto
in_var
=
param_scopes_
[
input_scope_idx
]
->
FindVar
(
"input"
);
PADDLE_ENFORCE_NOT_NULL
(
in_var
);
auto
in_lod_tensor
=
in_var
->
GetMutable
<
f
::
LoDTensor
>
();
in_lod_tensor
->
mutable_data
<
float
>
(
kDims
,
gpu_list_
[
input_scope_idx
]);
std
::
vector
<
float
>
send_vector
(
static_cast
<
size_t
>
(
f
::
product
(
kDims
)));
for
(
size_t
k
=
0
;
k
<
send_vector
.
size
();
++
k
)
{
send_vector
[
k
]
=
k
;
}
f
::
LoD
lod
{{
0
,
10
,
20
}};
paddle
::
framework
::
TensorFromVector
<
float
>
(
send_vector
,
*
(
ctxs_
[
input_scope_idx
]),
in_lod_tensor
);
in_lod_tensor
->
set_lod
(
lod
);
in_lod_tensor
->
Resize
(
kDims
);
op_handle_
->
Run
(
false
);
WaitAll
();
p
::
CPUPlace
cpu_place
;
for
(
size_t
j
=
0
;
j
<
gpu_list_
.
size
();
++
j
)
{
auto
out_var
=
param_scopes_
[
j
]
->
FindVar
(
"out"
);
PADDLE_ENFORCE_NOT_NULL
(
out_var
);
auto
out_tensor
=
out_var
->
Get
<
f
::
LoDTensor
>
();
PADDLE_ENFORCE_EQ
(
out_tensor
.
lod
(),
lod
,
"lod is not equal."
);
f
::
Tensor
result_tensor
;
f
::
TensorCopySync
(
out_tensor
,
cpu_place
,
&
result_tensor
);
float
*
ct
=
result_tensor
.
mutable_data
<
float
>
(
cpu_place
);
for
(
int64_t
i
=
0
;
i
<
f
::
product
(
kDims
);
++
i
)
{
ASSERT_NEAR
(
ct
[
i
],
send_vector
[
i
],
1e-5
);
}
}
}
void
TestBroadcastSelectedRows
(
size_t
input_scope_idx
)
{
auto
in_var
=
param_scopes_
[
input_scope_idx
]
->
FindVar
(
"input"
);
PADDLE_ENFORCE_NOT_NULL
(
in_var
);
auto
in_selected_rows
=
in_var
->
GetMutable
<
f
::
SelectedRows
>
();
auto
value
=
in_selected_rows
->
mutable_value
();
value
->
mutable_data
<
float
>
(
kDims
,
gpu_list_
[
input_scope_idx
]);
int
height
=
static_cast
<
int
>
(
kDims
[
0
])
*
2
;
std
::
vector
<
int64_t
>
rows
{
0
,
1
,
2
,
3
,
3
,
0
,
14
,
7
,
3
,
1
,
2
,
4
,
6
,
3
,
1
,
1
,
1
,
1
,
3
,
7
};
in_selected_rows
->
set_height
(
height
);
in_selected_rows
->
set_rows
(
rows
);
std
::
vector
<
float
>
send_vector
(
static_cast
<
size_t
>
(
f
::
product
(
kDims
)));
for
(
size_t
k
=
0
;
k
<
send_vector
.
size
();
++
k
)
{
send_vector
[
k
]
=
k
;
}
paddle
::
framework
::
TensorFromVector
<
float
>
(
send_vector
,
*
(
ctxs_
[
input_scope_idx
]),
value
);
op_handle_
->
Run
(
false
);
WaitAll
();
p
::
CPUPlace
cpu_place
;
for
(
size_t
j
=
0
;
j
<
gpu_list_
.
size
();
++
j
)
{
auto
out_var
=
param_scopes_
[
j
]
->
FindVar
(
"out"
);
PADDLE_ENFORCE_NOT_NULL
(
out_var
);
auto
&
out_select_rows
=
out_var
->
Get
<
f
::
SelectedRows
>
();
auto
rt
=
out_select_rows
.
value
();
PADDLE_ENFORCE_EQ
(
out_select_rows
.
height
(),
height
,
"height is not equal."
);
for
(
size_t
k
=
0
;
k
<
out_select_rows
.
rows
().
size
();
++
k
)
{
PADDLE_ENFORCE_EQ
(
out_select_rows
.
rows
()[
k
],
rows
[
k
]);
}
f
::
Tensor
result_tensor
;
f
::
TensorCopySync
(
rt
,
cpu_place
,
&
result_tensor
);
float
*
ct
=
result_tensor
.
data
<
float
>
();
for
(
int64_t
i
=
0
;
i
<
f
::
product
(
kDims
);
++
i
)
{
ASSERT_NEAR
(
ct
[
i
],
send_vector
[
i
],
1e-5
);
}
}
}
};
TEST
(
BroadcastTester
,
TestCPUBroadcastTestLodTensor
)
{
TestBroadcastOpHandle
test_op
;
size_t
input_scope_idx
=
0
;
...
...
paddle/fluid/framework/details/broadcast_op_handle_test.h
0 → 100644
浏览文件 @
6bfa6a0a
// 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.
#pragma once
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/platform/device_context.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
namespace
f
=
paddle
::
framework
;
namespace
p
=
paddle
::
platform
;
// test data amount
const
f
::
DDim
kDims
=
{
20
,
20
};
struct
TestBroadcastOpHandle
{
std
::
vector
<
std
::
unique_ptr
<
p
::
DeviceContext
>>
ctxs_
;
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
Scope
*>
param_scopes_
;
Scope
g_scope_
;
std
::
unique_ptr
<
OpHandleBase
>
op_handle_
;
std
::
vector
<
std
::
unique_ptr
<
VarHandleBase
>>
vars_
;
std
::
vector
<
p
::
Place
>
place_list_
;
bool
use_gpu_
;
#ifdef PADDLE_WITH_CUDA
std
::
unique_ptr
<
platform
::
NCCLContextMap
>
nccl_ctxs_
;
#endif
void
WaitAll
()
{
for
(
size_t
j
=
0
;
j
<
ctxs_
.
size
();
++
j
)
{
ctxs_
[
j
]
->
Wait
();
}
#ifdef PADDLE_WITH_CUDA
if
(
nccl_ctxs_
)
{
nccl_ctxs_
->
WaitAll
();
}
#endif
}
void
InitCtxOnGpu
(
bool
use_gpu
)
{
use_gpu_
=
use_gpu
;
if
(
use_gpu_
)
{
#ifdef PADDLE_WITH_CUDA
int
count
=
p
::
GetCUDADeviceCount
();
if
(
count
<=
1
)
{
LOG
(
WARNING
)
<<
"Cannot test multi-gpu Broadcast, because the CUDA "
"device count is "
<<
count
;
exit
(
0
);
}
for
(
int
i
=
0
;
i
<
count
;
++
i
)
{
auto
p
=
p
::
CUDAPlace
(
i
);
place_list_
.
push_back
(
p
);
ctxs_
.
emplace_back
(
new
p
::
CUDADeviceContext
(
p
));
}
nccl_ctxs_
.
reset
(
new
platform
::
NCCLContextMap
(
place_list_
));
#else
PADDLE_THROW
(
"CUDA is not support."
);
#endif
}
else
{
int
count
=
8
;
for
(
int
i
=
0
;
i
<
count
;
++
i
)
{
auto
p
=
p
::
CPUPlace
();
place_list_
.
push_back
(
p
);
ctxs_
.
emplace_back
(
new
p
::
CPUDeviceContext
(
p
));
}
#ifdef PADDLE_WITH_CUDA
nccl_ctxs_
.
reset
(
nullptr
);
#endif
}
}
void
InitBroadcastOp
(
size_t
input_scope_idx
)
{
for
(
size_t
j
=
0
;
j
<
place_list_
.
size
();
++
j
)
{
local_scopes_
.
push_back
(
&
(
g_scope_
.
NewScope
()));
Scope
&
local_scope
=
local_scopes_
.
back
()
->
NewScope
();
*
local_scopes_
.
back
()
->
Var
(
details
::
kLocalExecScopeName
)
->
GetMutable
<
Scope
*>
()
=
&
local_scope
;
local_scope
.
Var
(
"out"
);
param_scopes_
.
emplace_back
(
&
local_scope
);
}
param_scopes_
[
input_scope_idx
]
->
Var
(
"input"
);
std
::
unique_ptr
<
ir
::
Node
>
n
=
ir
::
CreateNodeForTest
(
"node0"
,
ir
::
Node
::
Type
::
kOperation
);
if
(
use_gpu_
)
{
#ifdef PADDLE_WITH_CUDA
op_handle_
.
reset
(
new
BroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
place_list_
,
nccl_ctxs_
.
get
()));
#else
PADDLE_THROW
(
"CUDA is not support."
);
#endif
}
else
{
#ifdef PADDLE_WITH_CUDA
op_handle_
.
reset
(
new
BroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
place_list_
,
nccl_ctxs_
.
get
()));
#else
op_handle_
.
reset
(
new
BroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
place_list_
));
#endif
}
std
::
unique_ptr
<
ir
::
Node
>
v
=
ir
::
CreateNodeForTest
(
"node1"
,
ir
::
Node
::
Type
::
kVariable
);
auto
*
in_var_handle
=
new
VarHandle
(
v
.
get
(),
1
,
input_scope_idx
,
"input"
,
place_list_
[
input_scope_idx
]);
vars_
.
emplace_back
(
in_var_handle
);
op_handle_
->
AddInput
(
in_var_handle
);
// add dummy var
std
::
unique_ptr
<
ir
::
Node
>
v2
=
ir
::
CreateNodeForTest
(
"node2"
,
ir
::
Node
::
Type
::
kVariable
);
vars_
.
emplace_back
(
new
DummyVarHandle
(
v2
.
get
()));
DummyVarHandle
*
dummy_var_handle
=
static_cast
<
DummyVarHandle
*>
(
vars_
.
back
().
get
());
dummy_var_handle
->
ClearGeneratedOp
();
op_handle_
->
AddInput
(
dummy_var_handle
);
for
(
size_t
j
=
0
;
j
<
place_list_
.
size
();
++
j
)
{
if
(
!
use_gpu_
)
{
op_handle_
->
SetDeviceContext
(
place_list_
[
j
],
ctxs_
[
j
].
get
());
}
std
::
unique_ptr
<
ir
::
Node
>
v3
=
ir
::
CreateNodeForTest
(
"node3"
,
ir
::
Node
::
Type
::
kVariable
);
VarHandle
*
out_var_handle
=
new
VarHandle
(
v3
.
get
(),
2
,
j
,
"out"
,
place_list_
[
j
]);
vars_
.
emplace_back
(
out_var_handle
);
op_handle_
->
AddOutput
(
out_var_handle
);
}
// add dummy var
std
::
unique_ptr
<
ir
::
Node
>
v4
=
ir
::
CreateNodeForTest
(
"node4"
,
ir
::
Node
::
Type
::
kVariable
);
vars_
.
emplace_back
(
new
DummyVarHandle
(
v4
.
get
()));
DummyVarHandle
*
out_dummy_var_handle
=
static_cast
<
DummyVarHandle
*>
(
vars_
.
back
().
get
());
out_dummy_var_handle
->
ClearGeneratedOp
();
op_handle_
->
AddOutput
(
out_dummy_var_handle
);
}
std
::
vector
<
float
>
InitLoDTensor
(
const
std
::
string
&
varname
,
size_t
input_scope_idx
,
const
f
::
LoD
&
lod
,
float
val_scalar
=
0.0
)
{
auto
var
=
param_scopes_
[
input_scope_idx
]
->
FindVar
(
varname
);
PADDLE_ENFORCE_NOT_NULL
(
var
);
auto
lod_tensor
=
var
->
GetMutable
<
f
::
LoDTensor
>
();
std
::
vector
<
float
>
send_vector
(
static_cast
<
size_t
>
(
f
::
product
(
kDims
)));
for
(
size_t
k
=
0
;
k
<
send_vector
.
size
();
++
k
)
{
send_vector
[
k
]
=
k
+
val_scalar
;
}
paddle
::
framework
::
TensorFromVector
<
float
>
(
send_vector
,
*
(
ctxs_
[
input_scope_idx
]),
lod_tensor
);
lod_tensor
->
set_lod
(
lod
);
lod_tensor
->
Resize
(
kDims
);
return
send_vector
;
}
std
::
vector
<
float
>
InitSelectedRows
(
const
std
::
string
&
varname
,
size_t
input_scope_idx
,
const
std
::
vector
<
int64_t
>&
rows
,
int
height
,
float
value_scalar
=
0.0
)
{
std
::
vector
<
float
>
send_vector
(
static_cast
<
size_t
>
(
f
::
product
(
kDims
)));
for
(
size_t
k
=
0
;
k
<
send_vector
.
size
();
++
k
)
{
send_vector
[
k
]
=
k
+
value_scalar
;
}
auto
var
=
param_scopes_
[
input_scope_idx
]
->
FindVar
(
varname
);
PADDLE_ENFORCE_NOT_NULL
(
var
);
auto
selected_rows
=
var
->
GetMutable
<
f
::
SelectedRows
>
();
auto
value
=
selected_rows
->
mutable_value
();
value
->
mutable_data
<
float
>
(
kDims
,
place_list_
[
input_scope_idx
]);
selected_rows
->
set_height
(
height
);
selected_rows
->
set_rows
(
rows
);
paddle
::
framework
::
TensorFromVector
<
float
>
(
send_vector
,
*
(
ctxs_
[
input_scope_idx
]),
value
);
return
send_vector
;
}
void
SelectedRowsEqual
(
const
std
::
string
&
varname
,
int
input_scope_idx
,
const
std
::
vector
<
float
>&
send_vector
,
const
std
::
vector
<
int64_t
>&
rows
,
int
height
)
{
auto
var
=
param_scopes_
[
input_scope_idx
]
->
FindVar
(
varname
);
PADDLE_ENFORCE_NOT_NULL
(
var
);
auto
&
selected_rows
=
var
->
Get
<
f
::
SelectedRows
>
();
auto
rt
=
selected_rows
.
value
();
PADDLE_ENFORCE_EQ
(
selected_rows
.
height
(),
height
,
"height is not equal."
);
for
(
size_t
k
=
0
;
k
<
selected_rows
.
rows
().
size
();
++
k
)
{
PADDLE_ENFORCE_EQ
(
selected_rows
.
rows
()[
k
],
rows
[
k
]);
}
p
::
CPUPlace
cpu_place
;
f
::
Tensor
result_tensor
;
f
::
TensorCopySync
(
rt
,
cpu_place
,
&
result_tensor
);
float
*
ct
=
result_tensor
.
data
<
float
>
();
for
(
int64_t
i
=
0
;
i
<
f
::
product
(
kDims
);
++
i
)
{
ASSERT_NEAR
(
ct
[
i
],
send_vector
[
i
],
1e-5
);
}
}
void
LoDTensorEqual
(
const
std
::
string
&
varname
,
const
std
::
vector
<
float
>&
send_vec
,
const
f
::
LoD
&
lod
,
framework
::
Scope
*
scope
)
{
p
::
CPUPlace
cpu_place
;
auto
var
=
scope
->
FindVar
(
varname
);
PADDLE_ENFORCE_NOT_NULL
(
var
);
auto
tensor
=
var
->
Get
<
f
::
LoDTensor
>
();
PADDLE_ENFORCE_EQ
(
tensor
.
lod
(),
lod
,
"lod is not equal."
);
f
::
Tensor
result_tensor
;
f
::
TensorCopySync
(
tensor
,
cpu_place
,
&
result_tensor
);
float
*
ct
=
result_tensor
.
mutable_data
<
float
>
(
cpu_place
);
for
(
int64_t
k
=
0
;
k
<
f
::
product
(
kDims
);
++
k
)
{
ASSERT_NEAR
(
ct
[
k
],
send_vec
[
k
],
1e-5
);
}
}
void
TestBroadcastLodTensor
(
size_t
input_scope_idx
)
{
f
::
LoD
lod
{{
0
,
10
,
20
}};
auto
send_vector
=
InitLoDTensor
(
"input"
,
input_scope_idx
,
lod
);
op_handle_
->
Run
(
false
);
WaitAll
();
for
(
size_t
j
=
0
;
j
<
place_list_
.
size
();
++
j
)
{
LoDTensorEqual
(
"out"
,
send_vector
,
lod
,
param_scopes_
[
j
]);
}
}
void
TestBroadcastSelectedRows
(
size_t
input_scope_idx
)
{
std
::
vector
<
int64_t
>
rows
{
0
,
1
,
2
,
3
,
3
,
0
,
14
,
7
,
3
,
1
,
2
,
4
,
6
,
3
,
1
,
1
,
1
,
1
,
3
,
7
};
int
height
=
static_cast
<
int
>
(
kDims
[
0
]
*
2
);
auto
send_vector
=
InitSelectedRows
(
"input"
,
input_scope_idx
,
rows
,
height
);
op_handle_
->
Run
(
false
);
WaitAll
();
for
(
size_t
j
=
0
;
j
<
place_list_
.
size
();
++
j
)
{
SelectedRowsEqual
(
"out"
,
input_scope_idx
,
send_vector
,
rows
,
height
);
}
}
};
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc
0 → 100644
浏览文件 @
6bfa6a0a
// 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.
#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/details/broadcast_op_handle_test.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
struct
TestFusedBroadcastOpHandle
:
TestBroadcastOpHandle
{
std
::
vector
<
std
::
string
>
out_varnames_
;
void
InitFusedBroadcastOp
(
std
::
vector
<
size_t
>
input_scope_idxes
)
{
// initialize scope and var
for
(
size_t
i
=
0
;
i
<
place_list_
.
size
();
++
i
)
{
local_scopes_
.
push_back
(
&
(
g_scope_
.
NewScope
()));
Scope
&
local_scope
=
local_scopes_
.
back
()
->
NewScope
();
*
local_scopes_
.
back
()
->
Var
(
details
::
kLocalExecScopeName
)
->
GetMutable
<
Scope
*>
()
=
&
local_scope
;
for
(
size_t
j
=
0
;
j
<
input_scope_idxes
.
size
();
++
j
)
{
local_scope
.
Var
(
"out_var"
+
j
);
if
(
i
==
j
)
local_scope
.
Var
(
"in_var"
+
j
);
}
param_scopes_
.
emplace_back
(
&
local_scope
);
}
// create op handle node
std
::
unique_ptr
<
ir
::
Node
>
n
=
ir
::
CreateNodeForTest
(
"fused_broadcast"
,
ir
::
Node
::
Type
::
kOperation
);
if
(
use_gpu_
)
{
#ifdef PADDLE_WITH_CUDA
op_handle_
.
reset
(
new
FusedBroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
place_list_
,
nccl_ctxs_
.
get
()));
#else
PADDLE_THROW
(
"CUDA is not supported."
);
#endif
}
else
{
#ifdef PADDLE_WITH_CUDA
op_handle_
.
reset
(
new
FusedBroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
place_list_
,
nccl_ctxs_
.
get
()));
#else
op_handle_
.
reset
(
new
FusedBroadcastOpHandle
(
n
.
get
(),
local_scopes_
,
place_list_
));
#endif
}
for
(
size_t
i
=
0
;
i
<
input_scope_idxes
.
size
();
++
i
)
{
// add input var handle
std
::
unique_ptr
<
ir
::
Node
>
in_node
=
ir
::
CreateNodeForTest
(
"in_node"
+
i
,
ir
::
Node
::
Type
::
kVariable
);
VarHandle
*
in_var_handle
=
new
VarHandle
(
in_node
.
get
(),
1
,
input_scope_idxes
[
i
],
"in_var"
+
i
,
place_list_
[
input_scope_idxes
[
i
]]);
vars_
.
emplace_back
(
in_var_handle
);
op_handle_
->
AddInput
(
in_var_handle
);
// add output var handle
for
(
size_t
j
=
0
;
j
<
place_list_
.
size
();
++
j
)
{
std
::
unique_ptr
<
ir
::
Node
>
out_node
=
ir
::
CreateNodeForTest
(
"out_node"
+
i
,
ir
::
Node
::
Type
::
kVariable
);
VarHandle
*
out_var_handle
=
new
VarHandle
(
out_node
.
get
(),
2
,
j
,
"out_var"
+
i
,
place_list_
[
j
]);
vars_
.
emplace_back
(
out_var_handle
);
op_handle_
->
AddOutput
(
out_var_handle
);
}
}
}
void
TestFusedBroadcastLoDTensor
(
std
::
vector
<
size_t
>
input_scope_idxes
)
{
std
::
vector
<
std
::
vector
<
float
>>
send_vec
;
f
::
LoD
lod
{{
0
,
10
,
20
}};
for
(
size_t
i
=
0
;
i
<
input_scope_idxes
.
size
();
++
i
)
{
const
std
::
string
varname
(
"in_var"
+
i
);
float
val_scalar
=
static_cast
<
float
>
(
i
);
send_vec
.
push_back
(
InitLoDTensor
(
varname
,
input_scope_idxes
[
i
],
lod
,
val_scalar
));
}
op_handle_
->
Run
(
false
);
WaitAll
();
for
(
size_t
i
=
0
;
i
<
input_scope_idxes
.
size
();
++
i
)
{
const
std
::
string
&
varname
(
"out_var"
+
i
);
for
(
size_t
j
=
0
;
j
<
place_list_
.
size
();
++
j
)
{
LoDTensorEqual
(
varname
,
send_vec
[
i
],
lod
,
param_scopes_
[
j
]);
}
}
}
void
TestFusedBroadcastSelectedRows
(
std
::
vector
<
size_t
>
input_scope_idxes
)
{
std
::
vector
<
std
::
vector
<
float
>>
send_vector
;
std
::
vector
<
int64_t
>
rows
{
0
,
1
,
2
,
3
,
3
,
0
,
14
,
7
,
3
,
1
,
2
,
4
,
6
,
3
,
1
,
1
,
1
,
1
,
3
,
7
};
int
height
=
static_cast
<
int
>
(
kDims
[
0
]
*
2
);
for
(
size_t
i
=
0
;
i
<
input_scope_idxes
.
size
();
++
i
)
{
const
std
::
string
varname
(
"in_var"
+
i
);
float
val_scalar
=
static_cast
<
float
>
(
i
);
send_vector
.
push_back
(
InitSelectedRows
(
varname
,
input_scope_idxes
[
i
],
rows
,
height
,
val_scalar
));
}
op_handle_
->
Run
(
false
);
WaitAll
();
for
(
size_t
i
=
0
;
i
<
input_scope_idxes
.
size
();
++
i
)
{
const
std
::
string
&
varname
(
"out_var"
+
i
);
for
(
size_t
j
=
0
;
j
<
place_list_
.
size
();
++
j
)
{
SelectedRowsEqual
(
varname
,
input_scope_idxes
[
i
],
send_vector
[
i
],
rows
,
height
);
}
}
}
};
TEST
(
FusedBroadcastTester
,
CPULodTensor
)
{
TestFusedBroadcastOpHandle
test_op
;
std
::
vector
<
size_t
>
input_scope_idxes
=
{
0
,
1
};
test_op
.
InitCtxOnGpu
(
false
);
test_op
.
InitFusedBroadcastOp
(
input_scope_idxes
);
test_op
.
TestFusedBroadcastLoDTensor
(
input_scope_idxes
);
}
TEST
(
FusedBroadcastTester
,
CPUSelectedRows
)
{
TestFusedBroadcastOpHandle
test_op
;
std
::
vector
<
size_t
>
input_scope_idxes
=
{
0
,
1
};
test_op
.
InitCtxOnGpu
(
false
);
test_op
.
InitFusedBroadcastOp
(
input_scope_idxes
);
test_op
.
TestFusedBroadcastSelectedRows
(
input_scope_idxes
);
}
#ifdef PADDLE_WITH_CUDA
TEST
(
FusedBroadcastTester
,
GPULodTensor
)
{
TestFusedBroadcastOpHandle
test_op
;
std
::
vector
<
size_t
>
input_scope_idxes
=
{
0
,
1
};
test_op
.
InitCtxOnGpu
(
true
);
test_op
.
InitFusedBroadcastOp
(
input_scope_idxes
);
test_op
.
TestFusedBroadcastLoDTensor
(
input_scope_idxes
);
}
TEST
(
FusedBroadcastTester
,
GPUSelectedRows
)
{
TestFusedBroadcastOpHandle
test_op
;
std
::
vector
<
size_t
>
input_scope_idxes
=
{
0
,
1
};
test_op
.
InitCtxOnGpu
(
true
);
test_op
.
InitFusedBroadcastOp
(
input_scope_idxes
);
test_op
.
TestFusedBroadcastSelectedRows
(
input_scope_idxes
);
}
#endif
}
// namespace details
}
// namespace framework
}
// namespace paddle
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