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体验新版 GitCode,发现更多精彩内容 >>
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ad4a1bd1
编写于
4月 08, 2019
作者:
T
Tao Luo
提交者:
GitHub
4月 08, 2019
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差异文件
Merge pull request #16339 from luotao1/core_opt_choose_kernel
Cache the chosen kernel of operators
上级
55e3c694
695f2db6
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
132 addition
and
36 deletion
+132
-36
paddle/fluid/framework/ir/CMakeLists.txt
paddle/fluid/framework/ir/CMakeLists.txt
+1
-0
paddle/fluid/framework/ir/expected_kernel_cache_pass.cc
paddle/fluid/framework/ir/expected_kernel_cache_pass.cc
+37
-0
paddle/fluid/framework/ir/expected_kernel_cache_pass.h
paddle/fluid/framework/ir/expected_kernel_cache_pass.h
+31
-0
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+49
-36
paddle/fluid/framework/operator.h
paddle/fluid/framework/operator.h
+11
-0
paddle/fluid/inference/api/analysis_config.cc
paddle/fluid/inference/api/analysis_config.cc
+1
-0
paddle/fluid/inference/api/paddle_pass_builder.cc
paddle/fluid/inference/api/paddle_pass_builder.cc
+2
-0
未找到文件。
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @
ad4a1bd1
...
...
@@ -68,6 +68,7 @@ pass_library(transpose_flatten_concat_fuse_pass inference)
pass_library
(
identity_scale_op_clean_pass base
)
pass_library
(
sync_batch_norm_pass base
)
pass_library
(
runtime_context_cache_pass base
)
pass_library
(
expected_kernel_cache_pass base
)
pass_library
(
quant_conv2d_dequant_fuse_pass inference
)
pass_library
(
fillconstant_elementwisemul_fuse inference
)
...
...
paddle/fluid/framework/ir/expected_kernel_cache_pass.cc
0 → 100644
浏览文件 @
ad4a1bd1
/* Copyright (c) 2019 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/ir/expected_kernel_cache_pass.h"
#include <memory>
#include "paddle/fluid/framework/operator.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
void
ExpectedKernelCachePass
::
ApplyImpl
(
ir
::
Graph
*
graph
)
const
{
VLOG
(
3
)
<<
"Applies Expected Kernel Cache strategy."
;
for
(
const
Node
*
n
:
graph
->
Nodes
())
{
if
(
n
->
IsOp
())
{
n
->
Op
()
->
SetAttr
(
kEnableCacheExpectedKernel
,
true
);
}
}
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
expected_kernel_cache_pass
,
paddle
::
framework
::
ir
::
ExpectedKernelCachePass
);
paddle/fluid/framework/ir/expected_kernel_cache_pass.h
0 → 100644
浏览文件 @
ad4a1bd1
/* Copyright (c) 2019 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 <memory>
#include "paddle/fluid/framework/ir/pass.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
class
ExpectedKernelCachePass
:
public
Pass
{
protected:
void
ApplyImpl
(
ir
::
Graph
*
graph
)
const
override
;
};
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @
ad4a1bd1
...
...
@@ -899,50 +899,23 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
*
dev_ctx
=
pool
.
Get
(
place
);
// check if op[type] has kernel registered.
auto
&
all_op_kernels
=
AllOpKernels
();
auto
kernels_iter
=
all_op_kernels
.
find
(
type_
);
if
(
kernels_iter
==
all_op_kernels
.
end
())
{
PADDLE_THROW
(
"There are no kernels which are registered in the %s operator."
,
type_
);
if
(
!
HasAttr
(
kEnableCacheExpectedKernel
)
||
!
kernel_type_
)
{
ChooseKernel
(
*
runtime_ctx
,
scope
,
place
);
}
OpKernelMap
&
kernels
=
kernels_iter
->
second
;
auto
expected_kernel_key
=
this
->
GetExpectedKernelType
(
ExecutionContext
(
*
this
,
scope
,
*
dev_ctx
,
*
runtime_ctx
,
nullptr
));
VLOG
(
3
)
<<
"expected_kernel_key:"
<<
expected_kernel_key
;
auto
kernel_iter
=
kernels
.
find
(
expected_kernel_key
);
#ifdef PADDLE_WITH_MKLDNN
// workaround for missing MKLDNN kernel when FLAGS_use_mkldnn env var is set
if
(
kernel_iter
==
kernels
.
end
()
&&
expected_kernel_key
.
library_type_
==
LibraryType
::
kMKLDNN
)
{
VLOG
(
3
)
<<
"missing MKLDNN kernel: fallbacking to PLAIN one"
;
expected_kernel_key
.
library_type_
=
LibraryType
::
kPlain
;
expected_kernel_key
.
data_layout_
=
DataLayout
::
kAnyLayout
;
kernel_iter
=
kernels
.
find
(
expected_kernel_key
);
}
#endif
if
(
kernel_iter
==
kernels
.
end
())
{
PADDLE_THROW
(
"op %s does not have kernel for %s"
,
type_
,
KernelTypeToString
(
expected_kernel_key
));
}
std
::
vector
<
KernelConfig
>*
kernel_configs
=
GetKernelConfig
(
expected_kernel_key
);
std
::
vector
<
KernelConfig
>*
kernel_configs
=
GetKernelConfig
(
*
kernel_type_
);
// do data transformScope &transfer_scope;
std
::
vector
<
std
::
string
>
transfered_inplace_vars
;
auto
*
transfer_scope
=
PrepareData
(
scope
,
expected_kernel_key
,
&
transfered_inplace_vars
,
runtime_ctx
);
auto
*
transfer_scope
=
PrepareData
(
scope
,
*
kernel_type_
,
&
transfered_inplace_vars
,
runtime_ctx
);
// exec scope is the scope that kernel actually executed on.
const
Scope
&
exec_scope
=
(
transfer_scope
==
nullptr
?
scope
:
*
transfer_scope
);
if
(
!
(
expected_kernel_key
.
place_
==
dev_ctx
->
GetPlace
()))
{
dev_ctx
=
pool
.
Get
(
expected_kernel_key
.
place_
);
if
(
!
(
kernel_type_
->
place_
==
dev_ctx
->
GetPlace
()))
{
dev_ctx
=
pool
.
Get
(
kernel_type_
->
place_
);
}
if
(
!
HasAttr
(
kAllKernelsMustComputeRuntimeShape
))
{
...
...
@@ -951,8 +924,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
}
// TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
// not Scope. Imperative mode only pass inputs and get outputs.
kernel_iter
->
second
(
ExecutionContext
(
*
this
,
exec_scope
,
*
dev
_ctx
,
*
runtime_ctx
,
kernel_configs
));
(
*
kernel_func_
)(
ExecutionContext
(
*
this
,
exec_scope
,
*
dev_ctx
,
*
runtime
_ctx
,
kernel_configs
));
if
(
!
transfered_inplace_vars
.
empty
())
{
// there is inplace variable has been transfered.
...
...
@@ -978,6 +951,46 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
}
}
void
OperatorWithKernel
::
ChooseKernel
(
const
RuntimeContext
&
ctx
,
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
*
dev_ctx
=
pool
.
Get
(
place
);
// check if op[type] has kernel registered.
auto
&
all_op_kernels
=
AllOpKernels
();
auto
kernels_iter
=
all_op_kernels
.
find
(
type_
);
if
(
kernels_iter
==
all_op_kernels
.
end
())
{
PADDLE_THROW
(
"There are no kernels which are registered in the %s operator."
,
type_
);
}
OpKernelMap
&
kernels
=
kernels_iter
->
second
;
auto
expected_kernel_key
=
this
->
GetExpectedKernelType
(
ExecutionContext
(
*
this
,
scope
,
*
dev_ctx
,
ctx
,
nullptr
));
VLOG
(
3
)
<<
"expected_kernel_key:"
<<
expected_kernel_key
;
auto
kernel_iter
=
kernels
.
find
(
expected_kernel_key
);
#ifdef PADDLE_WITH_MKLDNN
// workaround for missing MKLDNN kernel when FLAGS_use_mkldnn env var is set
if
(
kernel_iter
==
kernels
.
end
()
&&
expected_kernel_key
.
library_type_
==
LibraryType
::
kMKLDNN
)
{
VLOG
(
3
)
<<
"missing MKLDNN kernel: fallbacking to PLAIN one"
;
expected_kernel_key
.
library_type_
=
LibraryType
::
kPlain
;
expected_kernel_key
.
data_layout_
=
DataLayout
::
kAnyLayout
;
kernel_iter
=
kernels
.
find
(
expected_kernel_key
);
}
#endif
if
(
kernel_iter
==
kernels
.
end
())
{
PADDLE_THROW
(
"op %s does not have kernel for %s"
,
type_
,
KernelTypeToString
(
expected_kernel_key
));
}
kernel_type_
.
reset
(
new
OpKernelType
(
expected_kernel_key
));
kernel_func_
.
reset
(
new
OpKernelFunc
(
kernel_iter
->
second
));
}
void
OperatorWithKernel
::
TransferInplaceVarsBack
(
const
Scope
&
scope
,
const
std
::
vector
<
std
::
string
>&
inplace_vars
,
const
Scope
&
transfer_scope
)
const
{
...
...
paddle/fluid/framework/operator.h
浏览文件 @
ad4a1bd1
...
...
@@ -70,6 +70,12 @@ constexpr char kNewGradSuffix[] = "@NEWGRAD@";
/// this Op's execution to save the elapsed time.
constexpr
char
kEnableCacheRuntimeContext
[]
=
"@ENABLE_CACHE_RUNTIME_CONTEXT@"
;
/// If an Op has attribtue kEnableCacheExpectedKernel, it means that in a same
/// name scope and same place, since the expected kerenl of this Op does not
/// change in the execution, it could be recorded only at the first iteration of
/// this Op's execution to save the elapsed time.
constexpr
char
kEnableCacheExpectedKernel
[]
=
"@ENABLE_CACHE_EXPECTED_KERNEL@"
;
/// If an Op has this attribute, all its kernels should calculate output
/// variable's shape in the corresponding Compute() function. And
/// OperatorWithKernel::RunImpl() would skip call this Op's InferShape()
...
...
@@ -491,8 +497,13 @@ class OperatorWithKernel : public OperatorBase {
const
std
::
vector
<
std
::
string
>&
inplace_vars
,
const
Scope
&
exec_scope
)
const
;
void
ChooseKernel
(
const
RuntimeContext
&
ctx
,
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
;
protected:
mutable
OpKernelConfigsMap
kernel_configs_map_
;
mutable
std
::
unique_ptr
<
OpKernelType
>
kernel_type_
;
mutable
std
::
unique_ptr
<
OpKernelFunc
>
kernel_func_
;
mutable
std
::
unique_ptr
<
RuntimeContext
>
runtime_ctx_
;
mutable
const
Scope
*
pre_scope_
=
nullptr
;
};
...
...
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @
ad4a1bd1
...
...
@@ -231,6 +231,7 @@ void AnalysisConfig::Update() {
pass_builder
()
->
InsertPass
(
3
,
"tensorrt_subgraph_pass"
);
}
pass_builder
()
->
DeletePass
(
"runtime_context_cache_pass"
);
pass_builder
()
->
DeletePass
(
"expected_kernel_cache_pass"
);
}
if
(
use_mkldnn_
)
{
...
...
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @
ad4a1bd1
...
...
@@ -99,6 +99,7 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
"conv_elementwise_add_fuse_pass"
,
//
#endif //
"transpose_flatten_concat_fuse_pass"
,
"expected_kernel_cache_pass"
,
//
});
use_gpu_
=
true
;
...
...
@@ -136,6 +137,7 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
"conv_bn_fuse_pass"
,
//
"conv_eltwiseadd_bn_fuse_pass"
,
//
"is_test_pass"
,
//
"expected_kernel_cache_pass"
,
//
});
use_gpu_
=
false
;
...
...
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