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254d7ff4
编写于
3月 16, 2018
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Refactor local_scopes
上级
b2c7a9b8
变更
1
显示空白变更内容
内联
并排
Showing
1 changed file
with
28 addition
and
48 deletion
+28
-48
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+28
-48
未找到文件。
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
254d7ff4
...
...
@@ -151,11 +151,10 @@ class ParallelExecutorPrivate {
explicit
ParallelExecutorPrivate
(
size_t
num_threads
=
12
)
:
pool_
(
num_threads
)
{}
std
::
unordered_map
<
platform
::
Place
,
Scope
*
,
platform
::
PlaceHash
>
local_scopes_
;
std
::
vector
<
platform
::
Place
>
places_
;
std
::
vector
<
Scope
*>
local_scopes_
;
#ifdef PADDLE_WITH_CUDA
struct
NCCLContext
{
std
::
unique_ptr
<
platform
::
CUDADeviceContext
>
ctx_
;
...
...
@@ -260,10 +259,11 @@ struct NCCLAllReduceOpHandle : public OpHandle {
platform
::
dynload
::
ncclGroupStart
();
for
(
auto
&
p
:
member_
->
places_
)
{
for
(
size_t
i
=
0
;
i
<
member_
->
local_scopes_
.
size
();
++
i
)
{
auto
&
p
=
member_
->
places_
[
i
];
auto
*
s
=
member_
->
local_scopes_
[
i
];
int
dev_id
=
boost
::
get
<
platform
::
CUDAPlace
>
(
p
).
device
;
Scope
*
s
=
member_
->
local_scopes_
[
p
];
auto
&
lod_tensor
=
s
->
FindVar
(
var_name
)
->
Get
<
framework
::
LoDTensor
>
();
void
*
buffer
=
const_cast
<
void
*>
(
lod_tensor
.
data
<
void
>
());
if
(
dtype
==
-
1
)
{
...
...
@@ -302,8 +302,8 @@ ParallelExecutor::ParallelExecutor(
Executor
exe
(
places
[
0
]);
exe
.
Run
(
startup_program
,
scope
,
0
);
// Create local scopes
for
(
auto
&
place
:
places
)
{
member_
->
local_scopes_
[
place
]
=
&
scope
->
NewScope
(
);
for
(
size_t
i
=
0
;
i
<
member_
->
places_
.
size
();
++
i
)
{
member_
->
local_scopes_
.
push_back
(
&
scope
->
NewScope
()
);
}
member_
->
main_place_
=
places
[
0
];
...
...
@@ -320,9 +320,7 @@ ParallelExecutor::ParallelExecutor(
ConstructDependencyGraph
(
params
,
main_program
,
loss_var_name
);
// Step 3. Create vars in each scope;
for
(
auto
&
pair
:
member_
->
local_scopes_
)
{
auto
*
scope
=
pair
.
second
;
for
(
auto
*
scope
:
member_
->
local_scopes_
)
{
for
(
auto
*
var
:
main_program
.
Block
(
0
).
AllVars
())
{
if
(
scope
->
FindVar
(
var
->
Name
())
!=
nullptr
)
{
continue
;
...
...
@@ -353,46 +351,44 @@ void ParallelExecutor::ConstructDependencyGraph(
}
}
for
(
auto
&
pair
:
member_
->
local_scopes_
)
{
member_
->
ops_
.
emplace_back
(
new
ComputationOpHandle
(
*
op
,
pair
.
second
,
pair
.
first
));
for
(
size_t
i
=
0
;
i
<
member_
->
places_
.
size
();
++
i
)
{
auto
&
p
=
member_
->
places_
[
i
];
auto
*
s
=
member_
->
local_scopes_
[
i
];
member_
->
ops_
.
emplace_back
(
new
ComputationOpHandle
(
*
op
,
s
,
p
));
auto
*
op_handle
=
member_
->
ops_
.
back
().
get
();
op_handle
->
dev_ctx_
[
p
air
.
first
]
=
const_cast
<
platform
::
DeviceContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
p
air
.
first
));
op_handle
->
dev_ctx_
[
p
]
=
const_cast
<
platform
::
DeviceContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
p
));
auto
var_names
=
op
->
InputArgumentNames
();
for
(
auto
&
each_var_name
:
var_names
)
{
auto
&
place
=
pair
.
first
;
VarHandle
*
var
=
GetVarHandle
(
each_var_name
,
place
);
VarHandle
*
var
=
GetVarHandle
(
each_var_name
,
p
);
op_handle
->
inputs_
.
emplace_back
(
var
);
var
->
pending_ops_
.
emplace_back
(
op_handle
);
}
var_names
=
op
->
OutputArgumentNames
();
for
(
auto
&
each_var_name
:
var_names
)
{
auto
&
place
=
pair
.
first
;
GenerateVar
(
op_handle
,
each_var_name
,
place
);
GenerateVar
(
op_handle
,
each_var_name
,
p
);
}
if
(
is_forwarding
)
{
if
(
var_names
.
size
()
==
1
&&
var_names
[
0
]
==
loss_var_name
)
{
// Insert ScaleCost OpHandle
member_
->
ops_
.
emplace_back
(
new
ScaleLossGradOpHandle
(
this
->
member_
->
local_scopes_
.
size
(),
pair
.
second
,
pair
.
first
));
this
->
member_
->
local_scopes_
.
size
(),
s
,
p
));
op_handle
=
member_
->
ops_
.
back
().
get
();
op_handle
->
dev_ctx_
[
pair
.
first
]
=
member_
->
CommunicationDevCtx
(
pair
.
first
);
op_handle
->
dev_ctx_
[
p
]
=
member_
->
CommunicationDevCtx
(
p
);
auto
&
place
=
pair
.
first
;
// FIXME: Currently ScaleLossGradOp only use device_count as scale
// factor. So it does not depend on any other operators.
// VarHandle *loss = GetVarHandle(loss_var_name, place);
// loss->pending_ops_.emplace_back(op_handle);
// op_handle->inputs_.emplace_back(loss);
GenerateVar
(
op_handle
,
loss_var_name
+
"@GRAD"
,
p
lace
);
GenerateVar
(
op_handle
,
loss_var_name
+
"@GRAD"
,
p
);
change_forward
=
true
;
LOG
(
INFO
)
<<
"Scale Loss "
<<
op_handle
->
DebugString
();
}
...
...
@@ -411,9 +407,9 @@ void ParallelExecutor::ConstructDependencyGraph(
member_
->
ops_
.
emplace_back
(
new
NCCLAllReduceOpHandle
(
member_
));
auto
*
op_handle
=
member_
->
ops_
.
back
().
get
();
for
(
auto
&
pair
:
member_
->
local_scopes_
)
{
auto
&
p
lace
=
pair
.
first
;
auto
&
vars
=
member_
->
vars_
[
p
lace
][
og
];
for
(
size_t
i
=
0
;
i
<
member_
->
places_
.
size
();
++
i
)
{
auto
&
p
=
member_
->
places_
[
i
]
;
auto
&
vars
=
member_
->
vars_
[
p
][
og
];
if
(
vars
.
empty
())
{
// This device has no data. continue.
continue
;
...
...
@@ -422,16 +418,13 @@ void ParallelExecutor::ConstructDependencyGraph(
op_handle
->
inputs_
.
emplace_back
(
prev_grad
);
prev_grad
->
pending_ops_
.
emplace_back
(
op_handle
);
auto
&
var
=
vars
[
vars
.
size
()];
var
.
place_
=
p
lace
;
var
.
place_
=
p
;
var
.
generated_op_
=
op_handle
;
var
.
name_
=
og
;
var
.
version_
=
vars
.
size
()
-
1
;
op_handle
->
outputs_
.
emplace_back
(
&
var
);
for
(
auto
&
pair
:
member_
->
local_scopes_
)
{
op_handle
->
dev_ctx_
[
pair
.
first
]
=
member_
->
CommunicationDevCtx
(
pair
.
first
);
}
op_handle
->
dev_ctx_
[
p
]
=
member_
->
CommunicationDevCtx
(
p
);
}
}
}
...
...
@@ -529,7 +522,7 @@ VarHandle *ParallelExecutor::GetVarHandle(const std::string &each_var_name,
void
ParallelExecutor
::
BCastParamsToGPUs
(
const
ProgramDesc
&
startup_program
)
const
{
#ifdef PADDLE_WITH_CUDA
auto
*
main_scope
=
member_
->
local_scopes_
[
member_
->
main_place_
];
auto
*
main_scope
=
member_
->
local_scopes_
[
0
];
for
(
auto
*
var_desc
:
startup_program
.
Block
(
0
).
AllVars
())
{
if
(
var_desc
->
GetType
()
==
proto
::
VarType
::
LOD_TENSOR
)
{
...
...
@@ -547,7 +540,7 @@ void ParallelExecutor::BCastParamsToGPUs(
if
(
i
==
0
)
{
buffer
=
const_cast
<
void
*>
(
main_tensor
.
data
<
void
>
());
}
else
{
auto
local_scope
=
member_
->
local_scopes_
[
place
];
auto
local_scope
=
member_
->
local_scopes_
[
i
];
auto
*
t
=
local_scope
->
Var
(
var_desc
->
Name
())
->
GetMutable
<
LoDTensor
>
();
t
->
Resize
(
dims
);
buffer
=
t
->
mutable_data
(
place
,
main_tensor
.
type
());
...
...
@@ -560,18 +553,6 @@ void ParallelExecutor::BCastParamsToGPUs(
platform
::
dynload
::
ncclGroupEnd
();
}
}
// Debug code, bias should be 1.0f.
for
(
auto
&
pair
:
member_
->
local_scopes_
)
{
member_
->
GetNCCLCtx
(
pair
.
first
).
ctx_
->
Wait
();
auto
&
b
=
pair
.
second
->
FindVar
(
"fc_0.b_0"
)
->
Get
<
framework
::
LoDTensor
>
();
framework
::
LoDTensor
cpu
;
framework
::
TensorCopy
(
b
,
platform
::
CPUPlace
(),
&
cpu
);
platform
::
DeviceContextPool
::
Instance
().
Get
(
b
.
place
())
->
Wait
();
LOG
(
INFO
)
<<
*
cpu
.
data
<
float
>
();
}
#else
PADDLE_THROW
(
"Not compiled with CUDA"
);
#endif
...
...
@@ -579,8 +560,7 @@ void ParallelExecutor::BCastParamsToGPUs(
void
ParallelExecutor
::
BuildNCCLCommunicator
()
const
{
#ifdef PADDLE_WITH_CUDA
for
(
auto
&
place_pair
:
member_
->
local_scopes_
)
{
auto
place
=
place_pair
.
first
;
for
(
auto
&
place
:
member_
->
places_
)
{
int
dev_id
=
boost
::
get
<
platform
::
CUDAPlace
>
(
place
).
device
;
member_
->
communication_streams_
.
emplace
(
...
...
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