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096673f6
编写于
11月 29, 2018
作者:
S
sneaxiy
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refactor eager deletion
test=develop
上级
400cf19f
变更
22
隐藏空白更改
内联
并排
Showing
22 changed file
with
631 addition
and
441 deletion
+631
-441
paddle/fluid/framework/details/CMakeLists.txt
paddle/fluid/framework/details/CMakeLists.txt
+4
-8
paddle/fluid/framework/details/computation_op_handle.cc
paddle/fluid/framework/details/computation_op_handle.cc
+4
-2
paddle/fluid/framework/details/computation_op_handle.h
paddle/fluid/framework/details/computation_op_handle.h
+5
-1
paddle/fluid/framework/details/eager_deletion_op_handle.cc
paddle/fluid/framework/details/eager_deletion_op_handle.cc
+117
-0
paddle/fluid/framework/details/eager_deletion_op_handle.h
paddle/fluid/framework/details/eager_deletion_op_handle.h
+64
-0
paddle/fluid/framework/details/eager_deletion_pass.cc
paddle/fluid/framework/details/eager_deletion_pass.cc
+96
-0
paddle/fluid/framework/details/eager_deletion_pass.h
paddle/fluid/framework/details/eager_deletion_pass.h
+32
-0
paddle/fluid/framework/details/multi_devices_graph_pass.cc
paddle/fluid/framework/details/multi_devices_graph_pass.cc
+3
-3
paddle/fluid/framework/details/reference_count_op_handle.h
paddle/fluid/framework/details/reference_count_op_handle.h
+0
-138
paddle/fluid/framework/details/reference_count_pass.cc
paddle/fluid/framework/details/reference_count_pass.cc
+62
-151
paddle/fluid/framework/details/reference_count_pass.h
paddle/fluid/framework/details/reference_count_pass.h
+0
-5
paddle/fluid/framework/details/reference_count_pass_helper.h
paddle/fluid/framework/details/reference_count_pass_helper.h
+49
-0
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc
...id/framework/details/scope_buffered_ssa_graph_executor.cc
+10
-20
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h
...uid/framework/details/scope_buffered_ssa_graph_executor.h
+4
-0
paddle/fluid/framework/garbage_collector.h
paddle/fluid/framework/garbage_collector.h
+7
-5
paddle/fluid/framework/ir/graph.h
paddle/fluid/framework/ir/graph.h
+9
-2
paddle/fluid/framework/ir/pass.h
paddle/fluid/framework/ir/pass.h
+9
-2
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+70
-36
paddle/fluid/framework/parallel_executor.h
paddle/fluid/framework/parallel_executor.h
+1
-23
paddle/fluid/platform/CMakeLists.txt
paddle/fluid/platform/CMakeLists.txt
+8
-1
paddle/fluid/platform/stream_callback_manager.cc
paddle/fluid/platform/stream_callback_manager.cc
+70
-0
paddle/fluid/platform/stream_callback_manager.h
paddle/fluid/platform/stream_callback_manager.h
+7
-44
未找到文件。
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @
096673f6
...
@@ -33,10 +33,9 @@ cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base s
...
@@ -33,10 +33,9 @@ cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base s
cc_library
(
modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper
)
cc_library
(
modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper
)
if
(
WITH_GPU
)
cc_library
(
eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows op_handle_base
)
cc_library
(
reference_count_pass SRCS reference_count_pass.cc DEPS computation_op_handle scale_loss_grad_op_handle rpc_op_handle
cc_library
(
eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass
)
all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass
)
cc_library
(
reference_count_pass SRCS reference_count_pass.cc DEPS computation_op_handle graph graph_helper pass
)
endif
()
cc_library
(
sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass
)
cc_library
(
sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass
)
cc_library
(
all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_helper pass
)
cc_library
(
all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_helper pass
)
...
@@ -44,10 +43,7 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
...
@@ -44,10 +43,7 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
cc_library
(
multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
cc_library
(
multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle
)
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle
)
set
(
SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass
)
set
(
SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass
)
if
(
WITH_GPU
)
list
(
APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass
)
endif
()
cc_library
(
ssa_graph_executor SRCS ssa_graph_executor.cc DEPS
${
SSA_GRAPH_EXECUTOR_DEPS
}
)
cc_library
(
ssa_graph_executor SRCS ssa_graph_executor.cc DEPS
${
SSA_GRAPH_EXECUTOR_DEPS
}
)
...
...
paddle/fluid/framework/details/computation_op_handle.cc
浏览文件 @
096673f6
...
@@ -20,11 +20,13 @@ namespace paddle {
...
@@ -20,11 +20,13 @@ namespace paddle {
namespace
framework
{
namespace
framework
{
namespace
details
{
namespace
details
{
ComputationOpHandle
::
ComputationOpHandle
(
ir
::
Node
*
node
,
Scope
*
scope
,
ComputationOpHandle
::
ComputationOpHandle
(
ir
::
Node
*
node
,
Scope
*
scope
,
platform
::
Place
place
)
platform
::
Place
place
,
size_t
scope_idx
)
:
OpHandleBase
(
node
),
:
OpHandleBase
(
node
),
op_
(
framework
::
OpRegistry
::
CreateOp
(
*
node
->
Op
())),
op_
(
framework
::
OpRegistry
::
CreateOp
(
*
node
->
Op
())),
scope_
(
scope
),
scope_
(
scope
),
place_
(
place
)
{}
place_
(
place
),
scope_idx_
(
scope_idx
)
{}
void
ComputationOpHandle
::
RunImpl
()
{
void
ComputationOpHandle
::
RunImpl
()
{
WaitInputVarGenerated
(
place_
);
WaitInputVarGenerated
(
place_
);
...
...
paddle/fluid/framework/details/computation_op_handle.h
浏览文件 @
096673f6
...
@@ -28,7 +28,8 @@ namespace framework {
...
@@ -28,7 +28,8 @@ namespace framework {
namespace
details
{
namespace
details
{
struct
ComputationOpHandle
:
public
OpHandleBase
{
struct
ComputationOpHandle
:
public
OpHandleBase
{
public:
public:
ComputationOpHandle
(
ir
::
Node
*
node
,
Scope
*
scope
,
platform
::
Place
place
);
ComputationOpHandle
(
ir
::
Node
*
node
,
Scope
*
scope
,
platform
::
Place
place
,
size_t
scope_idx
);
std
::
string
Name
()
const
override
;
std
::
string
Name
()
const
override
;
...
@@ -38,6 +39,8 @@ struct ComputationOpHandle : public OpHandleBase {
...
@@ -38,6 +39,8 @@ struct ComputationOpHandle : public OpHandleBase {
void
SetLockAndRecordEventFree
(
bool
b
)
{
is_lock_and_record_event_free_
=
b
;
}
void
SetLockAndRecordEventFree
(
bool
b
)
{
is_lock_and_record_event_free_
=
b
;
}
size_t
GetScopeIdx
()
const
{
return
scope_idx_
;
}
protected:
protected:
void
RunImpl
()
override
;
void
RunImpl
()
override
;
...
@@ -47,6 +50,7 @@ struct ComputationOpHandle : public OpHandleBase {
...
@@ -47,6 +50,7 @@ struct ComputationOpHandle : public OpHandleBase {
std
::
unique_ptr
<
OperatorBase
>
op_
;
std
::
unique_ptr
<
OperatorBase
>
op_
;
Scope
*
scope_
;
Scope
*
scope_
;
platform
::
Place
place_
;
platform
::
Place
place_
;
size_t
scope_idx_
;
bool
is_lock_and_record_event_free_
{
false
};
bool
is_lock_and_record_event_free_
{
false
};
};
};
}
// namespace details
}
// namespace details
...
...
paddle/fluid/framework/details/eager_deletion_op_handle.cc
0 → 100644
浏览文件 @
096673f6
// 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/eager_deletion_op_handle.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
EagerDeletionOpHandle
::
EagerDeletionOpHandle
(
ir
::
Node
*
node
,
const
Scope
*
scope
,
const
platform
::
Place
&
place
,
const
std
::
vector
<
std
::
string
>
&
var_names
,
GarbageCollector
<
Tensor
>
*
gc
,
AtomicReferenceCountMap
*
ref_cnts
)
:
OpHandleBase
(
node
),
scope_
(
scope
),
gc_
(
gc
),
ref_cnts_
(
ref_cnts
)
{
#ifdef PADDLE_WITH_CUDA
if
(
platform
::
is_gpu_place
(
place
))
{
dev_ctx_
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
if
(
dynamic_cast
<
StreamGarbageCollector
<
Tensor
>
*>
(
gc_
))
{
platform
::
SetDeviceId
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place
).
device
);
PADDLE_ENFORCE
(
cudaEventCreateWithFlags
(
&
event_
,
cudaEventDisableTiming
));
}
}
#endif
for
(
auto
&
name
:
var_names
)
AddVar
(
name
);
}
EagerDeletionOpHandle
::~
EagerDeletionOpHandle
()
{
#ifdef PADDLE_WITH_CUDA
if
(
event_
)
{
auto
gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dev_ctx_
->
GetPlace
());
platform
::
SetDeviceId
(
gpu_place
.
device
);
PADDLE_ENFORCE
(
cudaEventDestroy
(
event_
));
}
#endif
}
std
::
string
EagerDeletionOpHandle
::
Name
()
const
{
return
"eager_deletion"
;
}
void
EagerDeletionOpHandle
::
AddVar
(
const
std
::
string
&
name
)
{
var_names_
.
insert
(
name
);
}
void
EagerDeletionOpHandle
::
RunImpl
()
{
auto
*
exec_scope
=
scope_
->
FindVar
(
kLocalExecScopeName
)
->
Get
<
Scope
*>
();
std
::
vector
<
Tensor
*>
tensors
;
for
(
auto
&
name
:
var_names_
)
{
auto
it
=
ref_cnts_
->
find
(
name
);
if
(
it
==
ref_cnts_
->
end
())
{
continue
;
}
auto
*
var
=
exec_scope
->
FindVar
(
name
);
if
(
var
==
nullptr
)
{
continue
;
}
if
(
var
->
IsType
<
LoDTensor
>
())
{
if
(
it
->
second
.
fetch_sub
(
1
)
==
1
)
{
tensors
.
emplace_back
(
var
->
GetMutable
<
LoDTensor
>
());
}
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
if
(
it
->
second
.
fetch_sub
(
1
)
==
1
)
{
tensors
.
emplace_back
(
var
->
GetMutable
<
SelectedRows
>
()
->
mutable_value
());
}
}
else
if
(
var
->
IsType
<
LoDTensorArray
>
())
{
if
(
it
->
second
.
fetch_sub
(
1
)
==
1
)
{
auto
*
tensor_arr
=
var
->
GetMutable
<
LoDTensorArray
>
();
for
(
auto
&
t
:
*
tensor_arr
)
{
tensors
.
emplace_back
(
&
t
);
}
}
}
}
if
(
!
tensors
.
empty
())
{
ClearTensors
(
tensors
);
}
}
void
EagerDeletionOpHandle
::
ClearTensors
(
const
std
::
vector
<
Tensor
*>
&
tensors
)
{
#ifdef PADDLE_WITH_CUDA
if
(
event_
)
{
auto
compute_stream
=
dev_ctx_
->
stream
();
auto
callback_stream
=
static_cast
<
StreamGarbageCollector
<
Tensor
>
*>
(
gc_
)
->
stream
();
auto
callback_func
=
[
=
]()
{
PADDLE_ENFORCE
(
cudaEventRecord
(
event_
,
compute_stream
));
PADDLE_ENFORCE
(
cudaStreamWaitEvent
(
callback_stream
,
event_
,
0
));
};
gc_
->
Add
(
tensors
,
callback_func
);
}
else
{
#endif
gc_
->
Add
(
tensors
);
#ifdef PADDLE_WITH_CUDA
}
#endif
}
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/eager_deletion_op_handle.h
0 → 100644
浏览文件 @
096673f6
// 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 "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
namespace
paddle
{
namespace
framework
{
class
Scope
;
namespace
details
{
class
EagerDeletionPass
;
class
EagerDeletionOpHandle
:
public
OpHandleBase
{
public:
EagerDeletionOpHandle
(
ir
::
Node
*
node
,
const
Scope
*
scope
,
const
platform
::
Place
&
place
,
const
std
::
vector
<
std
::
string
>
&
var_names
,
GarbageCollector
<
Tensor
>
*
gc
,
AtomicReferenceCountMap
*
ref_cnts
);
~
EagerDeletionOpHandle
();
std
::
string
Name
()
const
override
;
protected:
void
RunImpl
()
override
;
private:
void
ClearTensors
(
const
std
::
vector
<
Tensor
*>
&
tensors
);
void
AddVar
(
const
std
::
string
&
name
);
const
Scope
*
scope_
;
std
::
unordered_set
<
std
::
string
>
var_names_
;
GarbageCollector
<
Tensor
>
*
gc_
;
// not own
AtomicReferenceCountMap
*
ref_cnts_
;
// not own
#ifdef PADDLE_WITH_CUDA
platform
::
CUDADeviceContext
*
dev_ctx_
{
nullptr
};
cudaEvent_t
event_
{
nullptr
};
#endif
friend
class
EagerDeletionPass
;
};
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/eager_deletion_pass.cc
0 → 100644
浏览文件 @
096673f6
// 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 <queue>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/eager_deletion_op_handle.h"
#include "paddle/fluid/framework/details/eager_deletion_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
static
void
AddDependencyBetween
(
OpHandleBase
*
in
,
OpHandleBase
*
out
,
ir
::
Graph
*
graph
)
{
auto
it
=
std
::
find_if
(
in
->
Outputs
().
begin
(),
in
->
Outputs
().
end
(),
[](
VarHandleBase
*
var
)
{
return
dynamic_cast
<
DummyVarHandle
*>
(
var
)
!=
nullptr
;
});
if
(
it
!=
in
->
Outputs
().
end
())
{
out
->
AddInput
(
*
it
);
}
else
{
auto
*
dep_var
=
new
DummyVarHandle
(
graph
->
CreateControlDepVar
());
graph
->
Get
<
GraphDepVars
>
(
kGraphDepVars
).
emplace
(
dep_var
);
in
->
AddOutput
(
dep_var
);
out
->
AddInput
(
dep_var
);
}
// Add leaf node to eager_deletion_node
if
(
out
->
Outputs
().
empty
())
{
auto
*
dummy_leaf
=
new
DummyVarHandle
(
graph
->
CreateControlDepVar
());
graph
->
Get
<
GraphDepVars
>
(
kGraphDepVars
).
emplace
(
dummy_leaf
);
out
->
AddOutput
(
dummy_leaf
);
}
}
std
::
unique_ptr
<
ir
::
Graph
>
EagerDeletionPass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
auto
&
vars
=
graph
->
Get
<
GraphVars
>
(
kGraphVars
);
auto
&
ref_cnts
=
Get
<
std
::
vector
<
AtomicReferenceCountMap
>>
(
kCurReferenceCount
);
auto
&
last_live_ops
=
Get
<
std
::
vector
<
LastLiveOpsOfVars
>>
(
kLastLiveOpsOfVars
);
auto
&
gcs
=
Get
<
GarbageCollectorList
>
(
kGarbageCollector
);
ref_cnts
=
std
::
vector
<
AtomicReferenceCountMap
>
(
vars
.
size
());
std
::
unordered_map
<
ComputationOpHandle
*
,
EagerDeletionOpHandle
*>
op_map
;
for
(
auto
&
var_ops_map
:
last_live_ops
)
{
for
(
auto
&
var_ops_pair
:
var_ops_map
)
{
const
std
::
string
&
var_name
=
var_ops_pair
.
first
;
for
(
ComputationOpHandle
*
op
:
var_ops_pair
.
second
)
{
auto
it
=
op_map
.
find
(
op
);
if
(
it
!=
op_map
.
end
())
{
it
->
second
->
AddVar
(
var_name
);
}
else
{
auto
*
eager_deletion_node
=
graph
->
CreateEmptyNode
(
"eager_deletion"
,
ir
::
Node
::
Type
::
kOperation
);
auto
*
eager_deletion_op
=
new
EagerDeletionOpHandle
(
eager_deletion_node
,
op
->
GetScope
(),
op
->
GetPlace
(),
{
var_name
},
gcs
[
op
->
GetScopeIdx
()].
get
(),
&
(
ref_cnts
[
op
->
GetScopeIdx
()]));
AddDependencyBetween
(
op
,
eager_deletion_op
,
graph
.
get
());
op_map
[
op
]
=
eager_deletion_op
;
}
}
}
}
VLOG
(
10
)
<<
"Create "
<<
op_map
.
size
()
<<
" EagerDeletionOpHandle(s)"
;
return
graph
;
}
}
// namespace details
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
eager_deletion_pass
,
paddle
::
framework
::
details
::
EagerDeletionPass
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kCurReferenceCount
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kLastLiveOpsOfVars
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kGarbageCollector
);
paddle/fluid/framework/details/eager_deletion_pass.h
0 → 100644
浏览文件 @
096673f6
// 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 "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
class
EagerDeletionPass
:
public
ir
::
Pass
{
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
override
;
};
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @
096673f6
...
@@ -562,7 +562,7 @@ void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
...
@@ -562,7 +562,7 @@ void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
int
dev_id
)
const
{
int
dev_id
)
const
{
result
->
Get
<
GraphOps
>
(
kGraphOps
).
emplace_back
(
result
->
Get
<
GraphOps
>
(
kGraphOps
).
emplace_back
(
new
ComputationOpHandle
(
result
->
CreateOpNode
(
node
->
Op
()),
new
ComputationOpHandle
(
result
->
CreateOpNode
(
node
->
Op
()),
local_scopes_
[
dev_id
],
places_
[
dev_id
]));
local_scopes_
[
dev_id
],
places_
[
dev_id
]
,
dev_id
));
CreateOpHandleIOs
(
result
,
node
,
dev_id
);
CreateOpHandleIOs
(
result
,
node
,
dev_id
);
}
}
...
@@ -685,8 +685,8 @@ void MultiDevSSAGraphBuilder::CreateComputationalOps(ir::Graph *result,
...
@@ -685,8 +685,8 @@ void MultiDevSSAGraphBuilder::CreateComputationalOps(ir::Graph *result,
for
(
size_t
scope_idx
=
0
;
scope_idx
<
num_places
;
++
scope_idx
)
{
for
(
size_t
scope_idx
=
0
;
scope_idx
<
num_places
;
++
scope_idx
)
{
auto
p
=
places_
[
scope_idx
];
auto
p
=
places_
[
scope_idx
];
auto
s
=
local_scopes_
[
scope_idx
];
auto
s
=
local_scopes_
[
scope_idx
];
result
->
Get
<
GraphOps
>
(
kGraphOps
).
emplace_back
(
result
->
Get
<
GraphOps
>
(
kGraphOps
).
emplace_back
(
new
ComputationOpHandle
(
new
ComputationOpHandle
(
result
->
CreateOpNode
(
node
->
Op
()),
s
,
p
));
result
->
CreateOpNode
(
node
->
Op
()),
s
,
p
,
scope_idx
));
CreateOpHandleIOs
(
result
,
node
,
scope_idx
);
CreateOpHandleIOs
(
result
,
node
,
scope_idx
);
}
}
}
}
...
...
paddle/fluid/framework/details/reference_count_op_handle.h
已删除
100644 → 0
浏览文件 @
400cf19f
// 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 <atomic>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
using
ReferenceCountMap
=
std
::
unordered_map
<
std
::
string
,
int
>
;
using
AtomicReferenceCountMap
=
std
::
unordered_map
<
std
::
string
,
std
::
atomic
<
int
>>
;
using
DeviceReferenceCountMap
=
std
::
unordered_map
<
int
,
std
::
unique_ptr
<
ReferenceCountMap
>>
;
using
AtomicDeviceReferenceCountMap
=
std
::
unordered_map
<
int
,
std
::
unique_ptr
<
AtomicReferenceCountMap
>>
;
using
DeviceGarbageCollectorMap
=
std
::
unordered_map
<
int
,
std
::
unique_ptr
<
GarbageCollector
<
framework
::
Tensor
>>>
;
class
ReferenceCountOpHandle
:
public
OpHandleBase
{
public:
ReferenceCountOpHandle
(
ir
::
Node
*
node
,
const
Scope
*
scope
,
const
platform
::
CUDAPlace
&
place
,
const
std
::
vector
<
std
::
string
>
&
var_names
,
GarbageCollector
<
Tensor
>
*
gc
,
AtomicReferenceCountMap
*
ref_cnts
)
:
OpHandleBase
(
node
),
scope_
(
scope
),
gc_
(
gc
),
ref_cnts_
(
ref_cnts
)
{
dev_ctx_
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
if
(
IsStreamGarabageCollector
())
{
platform
::
SetDeviceId
(
place
.
device
);
PADDLE_ENFORCE
(
cudaEventCreateWithFlags
(
&
event_
,
cudaEventDisableTiming
));
}
for
(
auto
&
name
:
var_names
)
AddVar
(
name
);
}
~
ReferenceCountOpHandle
()
{
if
(
IsStreamGarabageCollector
())
{
auto
gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dev_ctx_
->
GetPlace
());
platform
::
SetDeviceId
(
gpu_place
.
device
);
PADDLE_ENFORCE
(
cudaEventDestroy
(
event_
));
}
}
std
::
string
Name
()
const
override
{
return
"reference_count"
;
}
void
AddVar
(
const
std
::
string
&
name
)
{
auto
it
=
var_names_
.
find
(
name
);
if
(
it
!=
var_names_
.
end
())
++
(
it
->
second
);
else
var_names_
[
name
]
=
1
;
}
protected:
void
RunImpl
()
override
{
auto
*
exec_scope
=
scope_
->
FindVar
(
kLocalExecScopeName
)
->
Get
<
Scope
*>
();
std
::
vector
<
Tensor
*>
tensors
;
for
(
auto
&
pair
:
var_names_
)
{
auto
&
name
=
pair
.
first
;
auto
it
=
ref_cnts_
->
find
(
name
);
if
(
it
==
ref_cnts_
->
end
())
continue
;
auto
*
var
=
exec_scope
->
FindVar
(
name
);
if
(
var
==
nullptr
)
continue
;
if
(
var
->
IsType
<
LoDTensor
>
())
{
if
(
it
->
second
.
fetch_sub
(
pair
.
second
)
<=
pair
.
second
)
{
tensors
.
emplace_back
(
var
->
GetMutable
<
LoDTensor
>
());
}
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
if
(
it
->
second
.
fetch_sub
(
pair
.
second
)
<=
pair
.
second
)
{
tensors
.
emplace_back
(
var
->
GetMutable
<
SelectedRows
>
()
->
mutable_value
());
}
}
}
if
(
!
tensors
.
empty
())
{
ClearTensors
(
tensors
);
}
}
private:
void
ClearTensors
(
const
std
::
vector
<
Tensor
*>
&
tensors
)
{
auto
*
gc
=
dynamic_cast
<
StreamGarbageCollector
<
Tensor
>
*>
(
gc_
);
if
(
gc
!=
nullptr
)
{
auto
compute_stream
=
dev_ctx_
->
stream
();
auto
callback_stream
=
gc
->
stream
();
auto
callback_func
=
[
=
]()
{
PADDLE_ENFORCE
(
cudaEventRecord
(
event_
,
compute_stream
));
PADDLE_ENFORCE
(
cudaStreamWaitEvent
(
callback_stream
,
event_
,
0
));
};
gc_
->
Add
(
tensors
,
callback_func
);
}
else
{
gc_
->
Add
(
tensors
);
}
}
bool
IsStreamGarabageCollector
()
const
{
return
dynamic_cast
<
const
StreamGarbageCollector
<
Tensor
>
*>
(
gc_
)
!=
nullptr
;
}
const
Scope
*
scope_
;
platform
::
CUDADeviceContext
*
dev_ctx_
;
std
::
unordered_map
<
std
::
string
,
int
>
var_names_
;
GarbageCollector
<
Tensor
>
*
gc_
;
// not own
AtomicReferenceCountMap
*
ref_cnts_
;
// not own
cudaEvent_t
event_
;
};
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/reference_count_pass.cc
浏览文件 @
096673f6
...
@@ -17,184 +17,96 @@
...
@@ -17,184 +17,96 @@
#include <vector>
#include <vector>
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/eager_deletion_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/reference_count_pass.h"
#include "paddle/fluid/framework/details/reference_count_pass.h"
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace
paddle
{
namespace
paddle
{
namespace
framework
{
namespace
framework
{
namespace
details
{
namespace
details
{
static
ComputationOpHandle
*
FindNextComputationOpHandle
(
VarHandle
*
var_in
)
{
static
ComputationOpHandle
*
FindNextComputationOpHandleOrReturnItself
(
std
::
queue
<
VarHandleBase
*>
queue
;
OpHandleBase
*
op
,
size_t
scope_idx
)
{
queue
.
push
(
var_in
);
std
::
queue
<
OpHandleBase
*>
q
;
std
::
unordered_set
<
OpHandleBase
*>
visited
;
q
.
push
(
op
);
do
{
do
{
auto
*
var
=
queue
.
front
();
auto
*
op
=
q
.
front
();
queue
.
pop
();
q
.
pop
();
for
(
auto
*
op
:
var
->
PendingOps
())
{
auto
*
compute_op
=
dynamic_cast
<
ComputationOpHandle
*>
(
op
);
auto
*
compute_op
=
dynamic_cast
<
ComputationOpHandle
*>
(
op
);
if
(
compute_op
!=
nullptr
&&
compute_op
->
GetScopeIdx
()
==
scope_idx
)
{
if
(
compute_op
!=
nullptr
&&
compute_op
->
GetPlace
()
==
var_in
->
place_
)
{
return
compute_op
;
return
compute_op
;
}
}
for
(
auto
*
out_var
:
op
->
Outputs
())
{
for
(
auto
*
out_var
:
op
->
Outputs
())
{
for
(
auto
*
pending_op
:
out_var
->
PendingOps
())
{
queue
.
push
(
out_var
);
if
(
visited
.
count
(
pending_op
))
continue
;
visited
.
insert
(
pending_op
);
}
}
}
}
}
while
(
!
q
ueue
.
empty
());
}
while
(
!
q
.
empty
());
return
nullptr
;
return
nullptr
;
}
}
static
void
AddDependencyBetween
(
OpHandleBase
*
in
,
OpHandleBase
*
out
,
ir
::
Graph
*
graph
)
{
auto
it
=
std
::
find_if
(
in
->
Outputs
().
begin
(),
in
->
Outputs
().
end
(),
[](
VarHandleBase
*
var
)
{
return
dynamic_cast
<
DummyVarHandle
*>
(
var
)
!=
nullptr
;
});
if
(
it
!=
in
->
Outputs
().
end
())
{
out
->
AddInput
(
*
it
);
}
else
{
auto
*
dep_var
=
new
DummyVarHandle
(
graph
->
CreateControlDepVar
());
graph
->
Get
<
GraphDepVars
>
(
kGraphDepVars
).
emplace
(
dep_var
);
in
->
AddOutput
(
dep_var
);
out
->
AddInput
(
dep_var
);
}
}
std
::
unique_ptr
<
ir
::
Graph
>
ReferenceCountPass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
ReferenceCountPass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
auto
&
ref_cnts
=
Get
<
DeviceReferenceCountMap
>
(
kGlobalReferenceCount
);
auto
&
vars
=
graph
->
Get
<
GraphVars
>
(
kGraphVars
);
auto
&
cur_ref_cnts
=
Get
<
AtomicDeviceReferenceCountMap
>
(
kCurReferenceCount
);
auto
&
ref_cnts
=
Get
<
std
::
vector
<
ReferenceCountMap
>>
(
kGlobalReferenceCount
);
auto
&
gcs
=
Get
<
DeviceGarbageCollectorMap
>
(
kGarbageCollector
);
auto
&
last_live_ops_of_vars
=
Get
<
std
::
vector
<
LastLiveOpsOfVars
>>
(
kLastLiveOpsOfVars
);
// It is not easy to find the right reference counts of varaibles in graph
// Step 1: Find all variables in computation ops
last_live_ops_of_vars
=
std
::
vector
<
LastLiveOpsOfVars
>
(
vars
.
size
());
// Step 2: Find all variables in non-computation ops which refers to variables
ref_cnts
=
std
::
vector
<
ReferenceCountMap
>
(
vars
.
size
());
// in computation ops
std
::
unordered_set
<
std
::
string
>
names
;
for
(
size_t
i
=
0
;
i
<
vars
.
size
();
++
i
)
{
std
::
unordered_map
<
OpHandleBase
*
,
ReferenceCountOpHandle
*>
for
(
auto
&
name_var_pair
:
vars
[
i
])
{
compute_ref_cnt_map
;
if
(
name_var_pair
.
second
.
empty
())
continue
;
auto
*
last_ver_var
=
name_var_pair
.
second
.
back
();
auto
get_ref_cnts_from_compute_op
=
[
&
](
OpHandleBase
*
op
,
const
std
::
vector
<
VarHandleBase
*>
&
vars
)
{
VarDesc
*
var_desc
=
nullptr
;
std
::
vector
<
std
::
string
>
var_names_in_op
;
std
::
find_if
(
name_var_pair
.
second
.
rbegin
(),
name_var_pair
.
second
.
rend
(),
auto
*
compute_op
=
dynamic_cast
<
ComputationOpHandle
*>
(
op
);
[
&
](
VarHandle
*
var_handle
)
->
bool
{
if
(
compute_op
==
nullptr
||
var_desc
=
var_handle
->
Node
()
->
Var
();
!
platform
::
is_gpu_place
(
compute_op
->
GetPlace
()))
return
var_desc
!=
nullptr
;
return
var_names_in_op
;
});
auto
place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
compute_op
->
GetPlace
());
for
(
VarHandleBase
*
var_handle_base
:
vars
)
{
if
(
var_desc
==
nullptr
||
var_desc
->
Persistable
())
{
auto
*
var_handle
=
dynamic_cast
<
VarHandle
*>
(
var_handle_base
);
if
(
var_handle
==
nullptr
||
!
var_handle
->
Node
()
->
IsVar
())
continue
;
if
(
!
platform
::
is_gpu_place
(
var_handle
->
place_
)
||
boost
::
get
<
platform
::
CUDAPlace
>
(
var_handle
->
place_
)
!=
place
)
continue
;
continue
;
VarDesc
*
var_desc
=
var_handle
->
Node
()
->
Var
();
auto
var_name
=
var_handle
->
Node
()
->
Name
();
// This is weird but there is really some variables without var_desc
// in computation_op
if
(
var_desc
==
nullptr
)
{
var_desc
=
compute_op
->
Node
()
->
Op
()
->
Block
()
->
FindVar
(
var_name
);
if
(
var_desc
==
nullptr
)
continue
;
}
}
if
(
var_desc
->
Persistable
())
continue
;
auto
var_type
=
var_desc
->
Proto
()
->
type
().
type
();
auto
var_type
=
var_desc
->
Proto
()
->
type
().
type
();
if
(
var_type
!=
proto
::
VarType
::
LOD_TENSOR
&&
if
(
var_type
!=
proto
::
VarType
::
LOD_TENSOR
&&
var_type
!=
proto
::
VarType
::
SELECTED_ROWS
)
{
var_type
!=
proto
::
VarType
::
SELECTED_ROWS
&&
var_type
!=
proto
::
VarType
::
LOD_TENSOR_ARRAY
)
{
continue
;
continue
;
}
}
// compute op only runs in one device
std
::
unordered_set
<
ComputationOpHandle
*>
last_live_op
;
if
(
ref_cnts
[
place
.
device
]
->
count
(
var_name
))
auto
add_last_live_op
=
[
&
](
OpHandleBase
*
op
)
{
++
(
*
ref_cnts
[
place
.
device
])[
var_name
];
auto
*
compute_op
=
FindNextComputationOpHandleOrReturnItself
(
op
,
i
);
else
if
(
compute_op
)
{
(
*
ref_cnts
[
place
.
device
])[
var_name
]
=
1
;
last_live_op
.
insert
(
compute_op
);
}
names
.
insert
(
var_name
);
};
var_names_in_op
.
push_back
(
var_name
);
const
std
::
string
&
var_name
=
name_var_pair
.
first
;
}
auto
&
pending_ops
=
last_ver_var
->
PendingOps
();
return
var_names_in_op
;
if
(
pending_ops
.
empty
())
{
};
auto
*
generated_op
=
last_ver_var
->
GeneratedOp
();
if
(
generated_op
)
{
auto
update_ref_cnts_from_non_compute_op
=
[
&
](
ref_cnts
[
i
].
emplace
(
var_name
,
1
);
OpHandleBase
*
op
,
const
std
::
vector
<
VarHandleBase
*>
&
vars
)
{
add_last_live_op
(
generated_op
);
if
(
dynamic_cast
<
ComputationOpHandle
*>
(
op
)
!=
nullptr
)
return
;
}
for
(
VarHandleBase
*
var_handle_base
:
vars
)
{
}
else
{
auto
*
var_handle
=
dynamic_cast
<
VarHandle
*>
(
var_handle_base
);
ref_cnts
[
i
].
emplace
(
var_name
,
pending_ops
.
size
());
if
(
var_handle
==
nullptr
||
!
var_handle
->
Node
()
->
IsVar
())
continue
;
for
(
auto
*
pending_op
:
pending_ops
)
{
add_last_live_op
(
pending_op
);
auto
var_name
=
var_handle
->
Node
()
->
Name
();
auto
var_place
=
var_handle
->
place_
;
if
(
!
platform
::
is_gpu_place
(
var_place
))
continue
;
auto
place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
var_place
);
if
(
names
.
count
(
var_name
)
==
0
)
continue
;
if
(
ref_cnts
.
count
(
place
.
device
)
&&
ref_cnts
[
place
.
device
]
->
count
(
var_name
))
{
++
(
*
ref_cnts
[
place
.
device
])[
var_name
];
auto
*
next_compute_op
=
FindNextComputationOpHandle
(
var_handle
);
if
(
next_compute_op
!=
nullptr
)
{
if
(
compute_ref_cnt_map
.
count
(
next_compute_op
))
{
compute_ref_cnt_map
[
next_compute_op
]
->
AddVar
(
var_name
);
VLOG
(
5
)
<<
"Add reference count of "
<<
var_name
<<
" to Operator "
<<
next_compute_op
->
Name
();
}
else
{
// Create new reference_count_op_handle
ir
::
Node
*
ref_cnt_node
=
graph
->
CreateEmptyNode
(
"reference_count"
,
ir
::
Node
::
Type
::
kOperation
);
auto
*
ref_cnt_handle
=
new
ReferenceCountOpHandle
(
ref_cnt_node
,
next_compute_op
->
GetScope
(),
place
,
{
var_name
},
gcs
[
place
.
device
].
get
(),
cur_ref_cnts
[
place
.
device
].
get
());
AddDependencyBetween
(
next_compute_op
,
ref_cnt_handle
,
graph
.
get
());
compute_ref_cnt_map
[
next_compute_op
]
=
ref_cnt_handle
;
}
}
}
}
}
}
};
auto
all_ops
=
ir
::
FilterByNodeWrapper
<
OpHandleBase
>
(
*
graph
);
last_live_ops_of_vars
[
i
].
emplace
(
var_name
,
std
::
move
(
last_live_op
));
for
(
auto
&
op
:
all_ops
)
{
auto
in_var_names
=
get_ref_cnts_from_compute_op
(
op
,
op
->
Inputs
());
auto
out_var_names
=
get_ref_cnts_from_compute_op
(
op
,
op
->
Outputs
());
if
(
in_var_names
.
empty
()
&&
out_var_names
.
empty
())
continue
;
in_var_names
.
insert
(
in_var_names
.
end
(),
out_var_names
.
begin
(),
out_var_names
.
end
());
auto
*
compute_op
=
dynamic_cast
<
ComputationOpHandle
*>
(
op
);
auto
place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
compute_op
->
GetPlace
());
ir
::
Node
*
ref_cnt_node
=
graph
->
CreateEmptyNode
(
"reference_count"
,
ir
::
Node
::
Type
::
kOperation
);
auto
*
ref_cnt_handle
=
new
ReferenceCountOpHandle
(
ref_cnt_node
,
compute_op
->
GetScope
(),
place
,
in_var_names
,
gcs
[
place
.
device
].
get
(),
cur_ref_cnts
[
place
.
device
].
get
());
AddDependencyBetween
(
compute_op
,
ref_cnt_handle
,
graph
.
get
());
compute_ref_cnt_map
[
compute_op
]
=
ref_cnt_handle
;
}
for
(
auto
&
op
:
all_ops
)
{
update_ref_cnts_from_non_compute_op
(
op
,
op
->
Inputs
());
update_ref_cnts_from_non_compute_op
(
op
,
op
->
Outputs
());
}
std
::
vector
<
OpHandleBase
*>
new_all_ops
;
new_all_ops
.
reserve
(
compute_ref_cnt_map
.
size
()
+
all_ops
.
size
());
for
(
auto
&
op
:
all_ops
)
{
new_all_ops
.
emplace_back
(
std
::
move
(
op
));
auto
it
=
compute_ref_cnt_map
.
find
(
new_all_ops
.
back
());
if
(
it
!=
compute_ref_cnt_map
.
end
())
{
// Add LeafNode to ReferenceCountOpHandle
auto
*
dummy_leaf
=
new
DummyVarHandle
(
graph
->
CreateControlDepVar
());
graph
->
Get
<
GraphDepVars
>
(
kGraphDepVars
).
emplace
(
dummy_leaf
);
it
->
second
->
AddOutput
(
dummy_leaf
);
new_all_ops
.
emplace_back
(
std
::
move
(
it
->
second
));
}
}
}
}
all_ops
.
swap
(
new_all_ops
);
return
graph
;
return
graph
;
}
}
...
@@ -205,5 +117,4 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
...
@@ -205,5 +117,4 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
REGISTER_PASS
(
reference_count_pass
,
REGISTER_PASS
(
reference_count_pass
,
paddle
::
framework
::
details
::
ReferenceCountPass
)
paddle
::
framework
::
details
::
ReferenceCountPass
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kGlobalReferenceCount
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kGlobalReferenceCount
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kCurReferenceCount
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kLastLiveOpsOfVars
);
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kGarbageCollector
);
paddle/fluid/framework/details/reference_count_pass.h
浏览文件 @
096673f6
...
@@ -14,7 +14,6 @@
...
@@ -14,7 +14,6 @@
#pragma once
#pragma once
#include "paddle/fluid/framework/details/reference_count_op_handle.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/ir/pass.h"
...
@@ -22,10 +21,6 @@ namespace paddle {
...
@@ -22,10 +21,6 @@ namespace paddle {
namespace
framework
{
namespace
framework
{
namespace
details
{
namespace
details
{
constexpr
char
kGlobalReferenceCount
[]
=
"reference_count"
;
constexpr
char
kCurReferenceCount
[]
=
"current_reference_count"
;
constexpr
char
kGarbageCollector
[]
=
"garbage_collector"
;
class
ReferenceCountPass
:
public
ir
::
Pass
{
class
ReferenceCountPass
:
public
ir
::
Pass
{
protected:
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
...
...
paddle/fluid/framework/details/reference_count_pass_helper.h
0 → 100644
浏览文件 @
096673f6
// 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 <atomic>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/tensor.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
class
ComputationOpHandle
;
using
ReferenceCountMap
=
std
::
unordered_map
<
std
::
string
,
size_t
>
;
using
AtomicReferenceCountMap
=
std
::
unordered_map
<
std
::
string
,
std
::
atomic
<
size_t
>>
;
using
GarbageCollectorList
=
std
::
vector
<
std
::
unique_ptr
<
GarbageCollector
<
Tensor
>>>
;
const
char
kGlobalReferenceCount
[]
=
"reference_count"
;
const
char
kCurReferenceCount
[]
=
"current_reference_count"
;
const
char
kGarbageCollector
[]
=
"garbage_collector"
;
using
LastLiveOpsOfVars
=
std
::
unordered_map
<
std
::
string
,
std
::
unordered_set
<
ComputationOpHandle
*>>
;
const
char
kLastLiveOpsOfVars
[]
=
"last_live_ops_of_var"
;
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc
浏览文件 @
096673f6
...
@@ -18,9 +18,6 @@
...
@@ -18,9 +18,6 @@
#include <vector>
#include <vector>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/platform/profiler.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/framework/details/reference_count_op_handle.h"
#endif
namespace
paddle
{
namespace
paddle
{
namespace
framework
{
namespace
framework
{
...
@@ -33,7 +30,11 @@ ScopeBufferedSSAGraphExecutor::ScopeBufferedSSAGraphExecutor(
...
@@ -33,7 +30,11 @@ ScopeBufferedSSAGraphExecutor::ScopeBufferedSSAGraphExecutor(
underlying_executor_
(
std
::
move
(
underlying_executor
)),
underlying_executor_
(
std
::
move
(
underlying_executor
)),
local_scopes_
(
std
::
move
(
local_scopes
)),
local_scopes_
(
std
::
move
(
local_scopes
)),
var_infos_
(
std
::
move
(
var_infos
)),
var_infos_
(
std
::
move
(
var_infos
)),
places_
(
std
::
move
(
places
))
{}
places_
(
std
::
move
(
places
))
{
if
(
Graph
().
Has
(
details
::
kGarbageCollector
))
{
gc_
=
&
(
Graph
().
Get
<
GarbageCollectorList
>
(
details
::
kGarbageCollector
));
}
}
FeedFetchList
ScopeBufferedSSAGraphExecutor
::
Run
(
FeedFetchList
ScopeBufferedSSAGraphExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
)
{
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
)
{
...
@@ -69,27 +70,16 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
...
@@ -69,27 +70,16 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
platform
::
RecordEvent
e
(
"ScopeBufferedSSAGraphExecutorAfterRun"
,
nullptr
);
platform
::
RecordEvent
e
(
"ScopeBufferedSSAGraphExecutorAfterRun"
,
nullptr
);
drop_scope_counter_
+=
1
;
drop_scope_counter_
+=
1
;
#ifdef PADDLE_WITH_CUDA
const
std
::
string
gc_name
=
"garbage_collector"
;
DeviceGarbageCollectorMap
*
gc
=
Graph
().
Has
(
gc_name
)
?
&
(
Graph
().
Get
<
DeviceGarbageCollectorMap
>
(
gc_name
))
:
nullptr
;
#endif
if
(
!
fetch_tensors
.
empty
()
||
if
(
!
fetch_tensors
.
empty
()
||
drop_scope_counter_
==
strategy_
.
num_iteration_per_drop_scope_
)
{
drop_scope_counter_
==
strategy_
.
num_iteration_per_drop_scope_
)
{
drop_scope_counter_
=
0
;
drop_scope_counter_
=
0
;
// Wait All computational streams
// Wait All computational streams
for
(
auto
p
:
places_
)
{
for
(
size_t
i
=
0
;
i
<
places_
.
size
();
++
i
)
{
platform
::
DeviceContextPool
::
Instance
().
Get
(
p
)
->
Wait
();
platform
::
DeviceContextPool
::
Instance
().
Get
(
places_
[
i
])
->
Wait
();
#ifdef PADDLE_WITH_CUDA
if
(
gc_
)
{
if
(
gc
!=
nullptr
&&
platform
::
is_gpu_place
(
p
))
{
(
*
gc_
)[
i
]
->
Wait
();
auto
gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
p
);
(
*
gc_
)[
i
]
->
Reset
();
auto
&
gc_at_place
=
gc
->
at
(
gpu_place
.
device
);
gc_at_place
->
Wait
();
gc_at_place
->
Reset
();
}
}
#endif
}
}
for
(
auto
&
scope
:
local_scopes_
)
{
for
(
auto
&
scope
:
local_scopes_
)
{
auto
&
local_scope
=
auto
&
local_scope
=
...
...
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h
浏览文件 @
096673f6
...
@@ -21,9 +21,11 @@
...
@@ -21,9 +21,11 @@
#include "paddle/fluid/framework/details/var_handle.h"
#include "paddle/fluid/framework/details/var_handle.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
paddle
{
namespace
framework
{
namespace
framework
{
namespace
details
{
namespace
details
{
...
@@ -55,6 +57,8 @@ class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
...
@@ -55,6 +57,8 @@ class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
VariableInfo
>
var_infos_
;
std
::
vector
<
VariableInfo
>
var_infos_
;
std
::
vector
<
platform
::
Place
>
places_
;
std
::
vector
<
platform
::
Place
>
places_
;
GarbageCollectorList
*
gc_
{
nullptr
};
};
};
}
// namespace details
}
// namespace details
}
// namespace framework
}
// namespace framework
...
...
paddle/fluid/framework/garbage_collector.h
浏览文件 @
096673f6
...
@@ -65,7 +65,7 @@ class GarbageCollector {
...
@@ -65,7 +65,7 @@ class GarbageCollector {
if
(
clear_deque
!=
nullptr
)
{
if
(
clear_deque
!=
nullptr
)
{
callback
();
callback
();
ClearCallback
([
=
]()
{
ClearCallback
([
clear_deque
]()
{
for
(
auto
*
obj
:
*
clear_deque
)
obj
->
clear
();
for
(
auto
*
obj
:
*
clear_deque
)
obj
->
clear
();
});
});
}
}
...
@@ -109,7 +109,6 @@ class DefaultStreamGarbageCollector : public GarbageCollector<T> {
...
@@ -109,7 +109,6 @@ class DefaultStreamGarbageCollector : public GarbageCollector<T> {
}
}
void
Wait
()
const
override
{
void
Wait
()
const
override
{
this
->
dev_ctx_
->
Wait
();
static_cast
<
const
platform
::
CUDADeviceContext
*>
(
this
->
dev_ctx_
)
static_cast
<
const
platform
::
CUDADeviceContext
*>
(
this
->
dev_ctx_
)
->
WaitStreamCallback
();
->
WaitStreamCallback
();
}
}
...
@@ -127,14 +126,14 @@ class StreamGarbageCollector : public GarbageCollector<T> {
...
@@ -127,14 +126,14 @@ class StreamGarbageCollector : public GarbageCollector<T> {
StreamGarbageCollector
(
const
platform
::
CUDAPlace
&
place
,
StreamGarbageCollector
(
const
platform
::
CUDAPlace
&
place
,
size_t
max_memory_size
)
size_t
max_memory_size
)
:
GarbageCollector
<
T
>
(
place
,
max_memory_size
)
{
:
GarbageCollector
<
T
>
(
place
,
max_memory_size
)
{
PADDLE_ENFORCE
(
cudaSetDevice
(
place
.
device
)
);
platform
::
SetDeviceId
(
place
.
device
);
PADDLE_ENFORCE
(
cudaStreamCreate
(
&
stream_
));
PADDLE_ENFORCE
(
cudaStreamCreate
(
&
stream_
));
callback_manager_
.
reset
(
new
platform
::
StreamCallbackManager
(
stream_
));
callback_manager_
.
reset
(
new
platform
::
StreamCallbackManager
(
stream_
));
}
}
~
StreamGarbageCollector
()
{
~
StreamGarbageCollector
()
{
auto
place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
this
->
dev_ctx_
->
GetPlace
());
auto
place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
this
->
dev_ctx_
->
GetPlace
());
PADDLE_ENFORCE
(
cudaSetDevice
(
place
.
device
)
);
platform
::
SetDeviceId
(
place
.
device
);
PADDLE_ENFORCE
(
cudaStreamSynchronize
(
stream_
));
PADDLE_ENFORCE
(
cudaStreamSynchronize
(
stream_
));
PADDLE_ENFORCE
(
cudaStreamDestroy
(
stream_
));
PADDLE_ENFORCE
(
cudaStreamDestroy
(
stream_
));
}
}
...
@@ -148,8 +147,11 @@ class StreamGarbageCollector : public GarbageCollector<T> {
...
@@ -148,8 +147,11 @@ class StreamGarbageCollector : public GarbageCollector<T> {
cudaStream_t
stream
()
const
{
return
stream_
;
}
cudaStream_t
stream
()
const
{
return
stream_
;
}
protected:
protected:
// ClearCallback and Wait()/Reset() cannot be call in multiple threads
// But it is not important, because they would not be called in multiple
// threads
// either in Executor or ParallelExecutor
void
ClearCallback
(
const
std
::
function
<
void
()
>
&
callback
)
override
{
void
ClearCallback
(
const
std
::
function
<
void
()
>
&
callback
)
override
{
std
::
lock_guard
<
std
::
mutex
>
guard
(
this
->
mutex_
);
callback_manager_
->
AddCallback
(
callback
);
callback_manager_
->
AddCallback
(
callback
);
}
}
...
...
paddle/fluid/framework/ir/graph.h
浏览文件 @
096673f6
...
@@ -73,14 +73,21 @@ class Graph {
...
@@ -73,14 +73,21 @@ class Graph {
}
}
bool
Has
(
const
std
::
string
&
attr_name
)
const
{
bool
Has
(
const
std
::
string
&
attr_name
)
const
{
return
attrs_
.
find
(
attr_name
)
!=
attrs_
.
end
()
;
return
attrs_
.
count
(
attr_name
)
>
0
;
}
}
template
<
typename
AttrType
>
template
<
typename
AttrType
>
AttrType
&
Get
(
const
std
::
string
&
attr_name
)
const
{
AttrType
&
Get
(
const
std
::
string
&
attr_name
)
const
{
PADDLE_ENFORCE
(
Has
(
attr_name
),
"%s attr not registered for graph."
,
PADDLE_ENFORCE
(
Has
(
attr_name
),
"%s attr not registered for graph."
,
attr_name
);
attr_name
);
return
*
boost
::
any_cast
<
AttrType
*>
(
attrs_
.
at
(
attr_name
));
try
{
return
*
boost
::
any_cast
<
AttrType
*>
(
attrs_
.
at
(
attr_name
));
}
catch
(
boost
::
bad_any_cast
&
)
{
PADDLE_THROW
(
"Invalid attribute type of %s error, expected: %s, actual: %s"
,
attr_name
,
typeid
(
AttrType
*
).
name
(),
attrs_
.
at
(
attr_name
).
type
().
name
());
}
}
}
template
<
typename
AttrType
>
template
<
typename
AttrType
>
...
...
paddle/fluid/framework/ir/pass.h
浏览文件 @
096673f6
...
@@ -51,11 +51,18 @@ class Pass {
...
@@ -51,11 +51,18 @@ class Pass {
AttrType
&
Get
(
const
std
::
string
&
attr_name
)
const
{
AttrType
&
Get
(
const
std
::
string
&
attr_name
)
const
{
PADDLE_ENFORCE
(
attrs_
.
find
(
attr_name
)
!=
attrs_
.
end
(),
PADDLE_ENFORCE
(
attrs_
.
find
(
attr_name
)
!=
attrs_
.
end
(),
"%s attr not registered for pass."
,
attr_name
);
"%s attr not registered for pass."
,
attr_name
);
return
*
boost
::
any_cast
<
AttrType
*>
(
attrs_
.
at
(
attr_name
));
try
{
return
*
boost
::
any_cast
<
AttrType
*>
(
attrs_
.
at
(
attr_name
));
}
catch
(
boost
::
bad_any_cast
&
)
{
PADDLE_THROW
(
"Invalid attribute type of %s error, expected: %s, actual: %s"
,
attr_name
,
typeid
(
AttrType
*
).
name
(),
attrs_
.
at
(
attr_name
).
type
().
name
());
}
}
}
bool
Has
(
const
std
::
string
&
attr_name
)
const
{
bool
Has
(
const
std
::
string
&
attr_name
)
const
{
return
attrs_
.
find
(
attr_name
)
!=
attrs_
.
end
()
;
return
attrs_
.
count
(
attr_name
)
>
0
;
}
}
void
Erase
(
const
std
::
string
&
attr_name
)
{
void
Erase
(
const
std
::
string
&
attr_name
)
{
...
...
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
096673f6
...
@@ -26,6 +26,7 @@ limitations under the License. */
...
@@ -26,6 +26,7 @@ limitations under the License. */
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/platform/profiler.h"
...
@@ -49,6 +50,15 @@ class ParallelExecutorPrivate {
...
@@ -49,6 +50,15 @@ class ParallelExecutorPrivate {
}
}
}
}
}
}
void
ResetRuntimeReferenceCount
()
{
for
(
size_t
i
=
0
;
i
<
rt_ref_cnts_
.
size
();
++
i
)
{
for
(
auto
&
pair
:
rt_ref_cnts_
[
i
])
{
rt_cur_ref_cnts_
[
i
][
pair
.
first
]
=
pair
.
second
;
}
}
}
std
::
vector
<
platform
::
Place
>
places_
;
std
::
vector
<
platform
::
Place
>
places_
;
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
Scope
*>
local_scopes_
;
Scope
*
global_scope_
;
// not owned
Scope
*
global_scope_
;
// not owned
...
@@ -60,6 +70,13 @@ class ParallelExecutorPrivate {
...
@@ -60,6 +70,13 @@ class ParallelExecutorPrivate {
bool
own_local_scope_
;
bool
own_local_scope_
;
bool
use_cuda_
;
bool
use_cuda_
;
bool
use_all_reduce_
;
bool
use_all_reduce_
;
// rt_ref_cnts_ is only initialized when ParallelExecutor constructs, and then
// keeps unchanged
// Before each iteration, rt_cur_ref_cnts_ is reset to ref_cnts_
std
::
vector
<
details
::
ReferenceCountMap
>
rt_ref_cnts_
;
std
::
vector
<
details
::
AtomicReferenceCountMap
>
rt_cur_ref_cnts_
;
details
::
GarbageCollectorList
gcs_
;
};
};
std
::
vector
<
Scope
*>
&
ParallelExecutor
::
GetLocalScopes
()
{
std
::
vector
<
Scope
*>
&
ParallelExecutor
::
GetLocalScopes
()
{
...
@@ -128,35 +145,56 @@ ParallelExecutor::ParallelExecutor(
...
@@ -128,35 +145,56 @@ ParallelExecutor::ParallelExecutor(
std
::
unique_ptr
<
ir
::
Graph
>
graph
=
build_strategy
.
Apply
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
=
build_strategy
.
Apply
(
main_program
,
member_
->
places_
,
loss_var_name
,
params
,
main_program
,
member_
->
places_
,
loss_var_name
,
params
,
member_
->
local_scopes_
,
member_
->
use_cuda_
,
member_
->
nccl_ctxs_
.
get
());
member_
->
local_scopes_
,
member_
->
use_cuda_
,
member_
->
nccl_ctxs_
.
get
());
#else
std
::
unique_ptr
<
ir
::
Graph
>
graph
=
build_strategy
.
Apply
(
main_program
,
member_
->
places_
,
loss_var_name
,
params
,
member_
->
local_scopes_
,
member_
->
use_cuda_
);
#endif
auto
max_memory_size
=
GetEagerDeletionThreshold
();
auto
max_memory_size
=
GetEagerDeletionThreshold
();
if
(
max_memory_size
>=
0
)
{
if
(
max_memory_size
>=
0
)
{
for
(
auto
&
place
:
member_
->
places_
)
{
size_t
place_num
=
member_
->
places_
.
size
();
if
(
!
platform
::
is_gpu_place
(
place
))
continue
;
for
(
size_t
i
=
0
;
i
<
place_num
;
++
i
)
{
auto
gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
place
);
auto
&
place
=
member_
->
places_
[
i
];
if
(
gcs_
[
gpu_place
.
device
]
==
nullptr
)
{
#ifdef PADDLE_WITH_CUDA
ref_cnts_
[
gpu_place
.
device
].
reset
(
new
details
::
ReferenceCountMap
());
if
(
platform
::
is_gpu_place
(
place
))
{
cur_ref_cnts_
[
gpu_place
.
device
].
reset
(
member_
->
gcs_
.
emplace_back
(
new
StreamGarbageCollector
<
Tensor
>
(
new
details
::
AtomicReferenceCountMap
());
boost
::
get
<
platform
::
CUDAPlace
>
(
place
),
max_memory_size
));
gcs_
[
gpu_place
.
device
].
reset
(
VLOG
(
10
)
<<
"Created "
<<
i
<<
"-th GarbageCollector at "
<<
place
;
new
StreamGarbageCollector
<
Tensor
>
(
gpu_place
,
max_memory_size
));
}
else
if
(
platform
::
is_cpu_place
(
place
))
{
#endif
member_
->
gcs_
.
emplace_back
(
new
CPUGarbageCollector
<
Tensor
>
(
boost
::
get
<
platform
::
CPUPlace
>
(
place
),
max_memory_size
));
VLOG
(
10
)
<<
"Created "
<<
i
<<
"-th GarbageCollector at "
<<
place
;
#ifdef PADDLE_WITH_CUDA
}
}
}
#endif
if
(
!
gcs_
.
empty
())
{
auto
ref_cnt_pass
=
ir
::
PassRegistry
::
Instance
().
Get
(
"reference_count_pass"
);
ref_cnt_pass
->
SetNotOwned
(
details
::
kGlobalReferenceCount
,
&
ref_cnts_
);
ref_cnt_pass
->
SetNotOwned
(
details
::
kCurReferenceCount
,
&
cur_ref_cnts_
);
ref_cnt_pass
->
SetNotOwned
(
details
::
kGarbageCollector
,
&
gcs_
);
graph
=
ref_cnt_pass
->
Apply
(
std
::
move
(
graph
));
graph
->
SetNotOwned
(
"garbage_collector"
,
&
gcs_
);
}
}
}
}
#else
std
::
unique_ptr
<
ir
::
Graph
>
graph
=
if
(
!
member_
->
gcs_
.
empty
())
{
build_strategy
.
Apply
(
main_program
,
member_
->
places_
,
loss_var_name
,
std
::
vector
<
details
::
LastLiveOpsOfVars
>
last_live_ops_of_vars
;
params
,
member_
->
local_scopes_
,
member_
->
use_cuda_
);
#endif
auto
ref_cnt_pass
=
ir
::
PassRegistry
::
Instance
().
Get
(
"reference_count_pass"
);
ref_cnt_pass
->
SetNotOwned
(
details
::
kGlobalReferenceCount
,
&
(
member_
->
rt_ref_cnts_
));
ref_cnt_pass
->
SetNotOwned
(
details
::
kLastLiveOpsOfVars
,
&
last_live_ops_of_vars
);
VLOG
(
10
)
<<
"ReferenceCountPass Applied"
;
graph
=
ref_cnt_pass
->
Apply
(
std
::
move
(
graph
));
auto
eager_deletion_pass
=
ir
::
PassRegistry
::
Instance
().
Get
(
"eager_deletion_pass"
);
eager_deletion_pass
->
SetNotOwned
(
details
::
kCurReferenceCount
,
&
(
member_
->
rt_cur_ref_cnts_
));
eager_deletion_pass
->
SetNotOwned
(
details
::
kGarbageCollector
,
&
(
member_
->
gcs_
));
eager_deletion_pass
->
SetNotOwned
(
details
::
kLastLiveOpsOfVars
,
&
last_live_ops_of_vars
);
graph
=
eager_deletion_pass
->
Apply
(
std
::
move
(
graph
));
VLOG
(
10
)
<<
"EagerDeletionPass Applied"
;
}
// Step 3. Create vars in each scope. Passes may also create new vars.
// Step 3. Create vars in each scope. Passes may also create new vars.
// skip control vars and empty vars
// skip control vars and empty vars
...
@@ -271,18 +309,16 @@ void ParallelExecutor::BCastParamsToDevices(
...
@@ -271,18 +309,16 @@ void ParallelExecutor::BCastParamsToDevices(
void
ParallelExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
void
ParallelExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
const
std
::
string
&
fetched_var_name
)
{
const
std
::
string
&
fetched_var_name
)
{
platform
::
RecordBlock
b
(
0
);
platform
::
RecordBlock
b
(
0
);
#ifdef PADDLE_WITH_CUDA
if
(
!
member_
->
gcs_
.
empty
())
{
if
(
!
gcs_
.
empty
())
{
member_
->
ResetRuntimeReferenceCount
();
ResetReferenceCount
();
size_t
n
=
member_
->
rt_ref_cnts_
.
size
();
for
(
auto
&
pair
:
cur_ref_cnts_
)
{
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
auto
&
name_map
=
*
(
pair
.
second
);
for
(
auto
&
fetch_name
:
fetch_tensors
)
{
for
(
auto
&
fetch_name
:
fetch_tensors
)
{
name_map
.
erase
(
fetch_name
);
member_
->
rt_cur_ref_cnts_
[
i
]
.
erase
(
fetch_name
);
}
}
name_map
.
erase
(
fetched_var_name
);
member_
->
rt_cur_ref_cnts_
[
i
]
.
erase
(
fetched_var_name
);
}
}
}
}
#endif
auto
fetch_data
=
member_
->
executor_
->
Run
(
fetch_tensors
);
auto
fetch_data
=
member_
->
executor_
->
Run
(
fetch_tensors
);
*
member_
->
global_scope_
->
Var
(
fetched_var_name
)
->
GetMutable
<
FeedFetchList
>
()
=
*
member_
->
global_scope_
->
Var
(
fetched_var_name
)
->
GetMutable
<
FeedFetchList
>
()
=
fetch_data
;
fetch_data
;
...
@@ -326,13 +362,11 @@ ParallelExecutor::~ParallelExecutor() {
...
@@ -326,13 +362,11 @@ ParallelExecutor::~ParallelExecutor() {
for
(
auto
&
p
:
member_
->
places_
)
{
for
(
auto
&
p
:
member_
->
places_
)
{
platform
::
DeviceContextPool
::
Instance
().
Get
(
p
)
->
Wait
();
platform
::
DeviceContextPool
::
Instance
().
Get
(
p
)
->
Wait
();
}
}
// member_ must be destructed before gcs_ since the destructor of
delete
member_
;
// ReferenceCountOpHandle use raw pointers of gcs_ inside.
member_
.
reset
();
}
}
}
// namespace framework
}
// namespace framework
}
// namespace paddle
}
// namespace paddle
#ifdef PADDLE_WITH_CUDA
USE_PASS
(
reference_count_pass
);
USE_PASS
(
reference_count_pass
);
#endif
USE_PASS
(
eager_deletion_pass
);
paddle/fluid/framework/parallel_executor.h
浏览文件 @
096673f6
...
@@ -14,7 +14,6 @@ limitations under the License. */
...
@@ -14,7 +14,6 @@ limitations under the License. */
#pragma once
#pragma once
#include <atomic>
#include <string>
#include <string>
#include <unordered_map>
#include <unordered_map>
#include <unordered_set>
#include <unordered_set>
...
@@ -29,10 +28,6 @@ limitations under the License. */
...
@@ -29,10 +28,6 @@ limitations under the License. */
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/device_context.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/framework/details/reference_count_pass.h"
#endif
namespace
paddle
{
namespace
paddle
{
namespace
framework
{
namespace
framework
{
...
@@ -75,24 +70,7 @@ class ParallelExecutor {
...
@@ -75,24 +70,7 @@ class ParallelExecutor {
private:
private:
void
BCastParamsToDevices
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
;
void
BCastParamsToDevices
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
;
std
::
unique_ptr
<
ParallelExecutorPrivate
>
member_
;
ParallelExecutorPrivate
*
member_
;
#ifdef PADDLE_WITH_CUDA
// ref_cnts_ is only initialized when ParallelExecutor constructs, and then
// keeps unchanged
// Before each iteration, cur_ref_cnts_ is reset to ref_cnts_
details
::
DeviceReferenceCountMap
ref_cnts_
;
details
::
AtomicDeviceReferenceCountMap
cur_ref_cnts_
;
details
::
DeviceGarbageCollectorMap
gcs_
;
void
ResetReferenceCount
()
{
for
(
auto
&
pair1
:
ref_cnts_
)
{
for
(
auto
&
pair2
:
*
(
pair1
.
second
))
{
(
*
(
cur_ref_cnts_
[
pair1
.
first
]))[
pair2
.
first
]
=
pair2
.
second
;
}
}
}
#endif
};
};
}
// namespace framework
}
// namespace framework
...
...
paddle/fluid/platform/CMakeLists.txt
浏览文件 @
096673f6
...
@@ -56,9 +56,16 @@ ELSE()
...
@@ -56,9 +56,16 @@ ELSE()
set
(
MKLDNN_CTX_DEPS
)
set
(
MKLDNN_CTX_DEPS
)
ENDIF
()
ENDIF
()
nv_library
(
stream_callback_manager SRCS stream_callback_manager.cc DEPS simple_threadpool enforce
)
IF
(
WITH_GPU
)
set
(
STREAM_CALLBACK_DEPS stream_callback_manager
)
ELSE
()
set
(
STREAM_CALLBACK_DEPS
)
ENDIF
()
# memcpy depends on device_context, here add deps individually for
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
# avoiding cycle dependencies
cc_library
(
device_context SRCS device_context.cc init.cc DEPS simple_threadpool malloc
cc_library
(
device_context SRCS device_context.cc init.cc DEPS simple_threadpool malloc
${
STREAM_CALLBACK_DEPS
}
place eigen3 stringpiece cpu_helper cpu_info framework_proto
${
GPU_CTX_DEPS
}
${
MKLDNN_CTX_DEPS
}
)
place eigen3 stringpiece cpu_helper cpu_info framework_proto
${
GPU_CTX_DEPS
}
${
MKLDNN_CTX_DEPS
}
)
nv_test
(
device_context_test SRCS device_context_test.cu DEPS device_context gpu_info
)
nv_test
(
device_context_test SRCS device_context_test.cu DEPS device_context gpu_info
)
...
...
paddle/fluid/platform/stream_callback_manager.cc
0 → 100644
浏览文件 @
096673f6
// 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/platform/stream_callback_manager.h"
#include "paddle/fluid/platform/enforce.h"
namespace
paddle
{
namespace
platform
{
struct
StreamCallbackContext
{
inline
StreamCallbackContext
(
const
StreamCallbackManager
*
manager
,
std
::
function
<
void
()
>
callback
)
:
manager_
(
manager
),
callback_
(
std
::
move
(
callback
))
{}
const
StreamCallbackManager
*
manager_
;
// do not own
std
::
function
<
void
()
>
callback_
;
};
StreamCallbackManager
::
StreamCallbackManager
(
const
cudaStream_t
stream
)
:
stream_
(
stream
),
thread_pool_
(
new
::
ThreadPool
(
1
))
{}
void
StreamCallbackManager
::
AddCallback
(
std
::
function
<
void
()
>
callback
)
const
{
auto
*
stream_callback_context
=
new
StreamCallbackContext
(
this
,
std
::
move
(
callback
));
#if CUDA_VERSION >= 10000
PADDLE_ENFORCE
(
cudaLaunchHostFunc
(
stream_
,
StreamCallbackManager
::
StreamCallbackFunc
,
stream_callback_context
));
#else
PADDLE_ENFORCE
(
cudaStreamAddCallback
(
stream_
,
StreamCallbackManager
::
StreamCallbackFunc
,
stream_callback_context
,
0
));
#endif
}
void
StreamCallbackManager
::
Wait
()
const
{
thread_pool_
.
reset
(
new
::
ThreadPool
(
1
));
}
#if CUDA_VERSION >= 10000
void
CUDART_CB
StreamCallbackManager
::
StreamCallbackFunc
(
void
*
user_data
)
#else
void
CUDART_CB
StreamCallbackManager
::
StreamCallbackFunc
(
cudaStream_t
stream
,
cudaError_t
status
,
void
*
user_data
)
#endif
{
auto
*
callback_context_ptr
=
reinterpret_cast
<
StreamCallbackContext
*>
(
user_data
);
callback_context_ptr
->
manager_
->
thread_pool_
->
enqueue
(
[
callback_context_ptr
]()
{
std
::
unique_ptr
<
StreamCallbackContext
>
callback_context
(
callback_context_ptr
);
callback_context
->
callback_
();
});
}
}
// namespace platform
}
// namespace paddle
paddle/fluid/platform/stream_callback_manager.h
浏览文件 @
096673f6
...
@@ -19,66 +19,29 @@
...
@@ -19,66 +19,29 @@
#include <cuda_runtime.h>
#include <cuda_runtime.h>
#include <functional>
#include <functional>
#include <memory>
#include <memory>
#include "paddle/fluid/platform/enforce.h"
namespace
paddle
{
namespace
paddle
{
namespace
platform
{
namespace
platform
{
class
StreamCallbackManager
;
// NOTE(zjl): clean StreamCallback to make compilation faster
struct
StreamCallbackContext
{
template
<
typename
Callback
>
inline
StreamCallbackContext
(
const
StreamCallbackManager
*
manager
,
Callback
&&
callback
)
:
manager_
(
manager
),
callback_
(
callback
)
{}
const
StreamCallbackManager
*
manager_
;
// do not own
std
::
function
<
void
()
>
callback_
;
};
class
StreamCallbackManager
{
class
StreamCallbackManager
{
public:
public:
explicit
inline
StreamCallbackManager
(
cudaStream_t
stream
=
nullptr
)
explicit
StreamCallbackManager
(
const
cudaStream_t
stream
);
:
stream_
(
stream
),
thread_pool_
(
new
ThreadPool
(
1
))
{}
template
<
typename
Callback
>
void
AddCallback
(
std
::
function
<
void
()
>
callback
)
const
;
inline
void
AddCallback
(
Callback
&&
callback
)
const
{
auto
*
stream_callback_context
=
new
StreamCallbackContext
(
this
,
std
::
forward
<
Callback
>
(
callback
));
#if CUDA_VERSION >= 10000
PADDLE_ENFORCE
(
cudaLaunchHostFunc
(
stream_
,
StreamCallbackManager
::
StreamCallbackFunc
,
stream_callback_context
));
// NOLINT
#else
PADDLE_ENFORCE
(
cudaStreamAddCallback
(
stream_
,
StreamCallbackManager
::
StreamCallbackFunc
,
stream_callback_context
,
0
));
// NOLINT
#endif
}
void
Wait
()
const
{
thread_pool_
.
reset
(
new
ThreadPool
(
1
));
}
void
Wait
()
const
;
private:
private:
const
cudaStream_t
stream_
;
const
cudaStream_t
stream_
;
mutable
std
::
unique_ptr
<
ThreadPool
>
thread_pool_
;
mutable
std
::
unique_ptr
<
::
ThreadPool
>
thread_pool_
;
// cudaStreamCallback cannot call CUDA API inside, so we have to use
// thread_pool here
#if CUDA_VERSION >= 10000
#if CUDA_VERSION >= 10000
static
void
CUDART_CB
StreamCallbackFunc
(
void
*
user_data
)
static
void
CUDART_CB
StreamCallbackFunc
(
void
*
user_data
)
;
#else
#else
static
void
CUDART_CB
StreamCallbackFunc
(
cudaStream_t
stream
,
static
void
CUDART_CB
StreamCallbackFunc
(
cudaStream_t
stream
,
cudaError_t
status
,
void
*
user_data
)
cudaError_t
status
,
void
*
user_data
)
;
#endif
#endif
{
auto
*
callback_context_ptr
=
reinterpret_cast
<
StreamCallbackContext
*>
(
user_data
);
callback_context_ptr
->
manager_
->
thread_pool_
->
enqueue
([
=
]()
{
std
::
unique_ptr
<
StreamCallbackContext
>
callback_context
(
callback_context_ptr
);
callback_context
->
callback_
();
});
}
};
};
}
// namespace platform
}
// namespace platform
...
...
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