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体验新版 GitCode,发现更多精彩内容 >>
提交
5dd281f7
编写于
2月 25, 2019
作者:
X
Xin Pan
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
polish
test=develop
上级
8d83e38a
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
147 addition
and
104 deletion
+147
-104
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+12
-5
paddle/fluid/framework/operator.h
paddle/fluid/framework/operator.h
+3
-92
paddle/fluid/framework/operator_kernel_configs.h
paddle/fluid/framework/operator_kernel_configs.h
+118
-0
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+11
-3
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+3
-2
paddle/fluid/operators/conv_fusion_op.cu.cc
paddle/fluid/operators/conv_fusion_op.cu.cc
+0
-2
未找到文件。
paddle/fluid/framework/operator.cc
浏览文件 @
5dd281f7
...
...
@@ -904,6 +904,16 @@ void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
this
->
InferShape
(
&
infer_shape_ctx
);
}
std
::
vector
<
KernelConfig
>*
OperatorWithKernel
::
GetKernelConfig
(
const
OpKernelType
&
key
)
const
{
auto
config_iter
=
kernel_configs_map_
.
find
(
key
);
std
::
vector
<
KernelConfig
>*
kernel_configs
=
nullptr
;
if
(
config_iter
!=
kernel_configs_map_
.
end
())
{
kernel_configs
=
&
(
config_iter
->
second
);
}
return
kernel_configs
;
}
void
OperatorWithKernel
::
RunImpl
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{
RuntimeContext
ctx
(
Inputs
(),
Outputs
(),
scope
);
...
...
@@ -940,11 +950,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
KernelTypeToString
(
expected_kernel_key
));
}
auto
config_iter
=
kernel_configs_map_
.
find
(
expected_kernel_key
);
std
::
vector
<
KernelConfig
>*
kernel_configs
=
nullptr
;
if
(
config_iter
!=
kernel_configs_map_
.
end
())
{
kernel_configs
=
&
(
config_iter
->
second
);
}
std
::
vector
<
KernelConfig
>*
kernel_configs
=
GetKernelConfig
(
expected_kernel_key
);
// do data transformScope &transfer_scope;
std
::
vector
<
std
::
string
>
transfered_inplace_vars
;
...
...
paddle/fluid/framework/operator.h
浏览文件 @
5dd281f7
...
...
@@ -28,6 +28,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/operator_kernel_configs.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
...
...
@@ -184,98 +185,6 @@ class OperatorBase {
const
platform
::
Place
&
place
)
const
=
0
;
};
template
<
typename
TAlgorithm
>
class
AlgorithmsCache
{
public:
AlgorithmsCache
()
:
search_times_
(
0
)
{
hash_
.
clear
();
}
// Caches the best algorithm for a given
// combination of tensor dimensions & compute data type.
TAlgorithm
GetAlgorithm
(
const
std
::
vector
<
int64_t
>&
dims1
,
const
std
::
vector
<
int64_t
>&
dims2
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
dilations
,
int
algorithmFlags
,
// can set for different data type
std
::
function
<
TAlgorithm
()
>
gen_func
);
TAlgorithm
GetAlgorithm
(
int64_t
area
,
int
search_times
,
int
algorithmFlags
,
std
::
function
<
TAlgorithm
()
>
gen_func
);
private:
std
::
unordered_map
<
int64_t
,
TAlgorithm
>
hash_
;
int
search_times_
;
};
template
<
typename
TAlgorithm
>
TAlgorithm
framework
::
AlgorithmsCache
<
TAlgorithm
>::
GetAlgorithm
(
const
std
::
vector
<
int64_t
>&
dims1
,
const
std
::
vector
<
int64_t
>&
dims2
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
dilations
,
int
algorithmFlags
,
std
::
function
<
TAlgorithm
()
>
gen_func
)
{
int64_t
seed
=
0
;
// Hash all of the inputs, use to try and look up a previously
// discovered algorithm, or fall back to generating a new one.
std
::
hash
<
int64_t
>
hashFn
;
// do hash like boost
// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
for
(
const
auto
num
:
dims1
)
{
seed
^=
hashFn
(
num
)
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
);
}
for
(
const
auto
num
:
dims2
)
{
seed
^=
hashFn
(
num
)
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
1
;
}
for
(
const
auto
num
:
strides
)
{
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
num
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
2
;
}
for
(
const
auto
num
:
paddings
)
{
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
num
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
3
;
}
for
(
const
auto
num
:
dilations
)
{
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
num
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
4
;
}
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
algorithmFlags
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
5
;
if
(
seed
==
0
)
return
gen_func
();
if
(
hash_
.
find
(
seed
)
==
hash_
.
end
())
{
TAlgorithm
value
=
gen_func
();
hash_
[
seed
]
=
value
;
}
return
hash_
[
seed
];
}
template
<
typename
TAlgorithm
>
TAlgorithm
AlgorithmsCache
<
TAlgorithm
>::
GetAlgorithm
(
int64_t
area
,
int
search_times
,
int
algorithmFlags
,
std
::
function
<
TAlgorithm
()
>
gen_func
)
{
if
(
hash_
.
find
(
area
)
!=
hash_
.
end
())
{
return
hash_
[
area
];
}
if
(
search_times_
<
search_times
)
{
auto
algo
=
gen_func
();
hash_
[
area
]
=
algo
;
++
search_times_
;
return
algo
;
}
TAlgorithm
algo
;
int64_t
min
=
static_cast
<
uint64_t
>
(
INT_MAX
);
for
(
const
auto
&
m
:
hash_
)
{
if
(
m
.
first
<
min
)
{
min
=
m
.
first
;
algo
=
m
.
second
;
}
}
return
algo
;
}
#ifdef PADDLE_WITH_CUDA
using
KernelConfig
=
boost
::
variant
<
std
::
shared_ptr
<
AlgorithmsCache
<
cudnnConvolutionFwdAlgo_t
>>
,
...
...
@@ -602,6 +511,8 @@ class OperatorWithKernel : public OperatorBase {
virtual
OpKernelType
GetExpectedKernelType
(
const
ExecutionContext
&
ctx
)
const
;
std
::
vector
<
KernelConfig
>*
GetKernelConfig
(
const
OpKernelType
&
key
)
const
;
protected:
virtual
OpKernelType
GetKernelTypeForVar
(
const
std
::
string
&
var_name
,
const
Tensor
&
tensor
,
...
...
paddle/fluid/framework/operator_kernel_configs.h
0 → 100644
浏览文件 @
5dd281f7
/* Copyright (c) 2016 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 <algorithm>
#include <unordered_map>
#include <vector>
namespace
paddle
{
namespace
framework
{
// Not thread-safe. Should be owned per-kernel.
template
<
typename
TAlgorithm
>
class
AlgorithmsCache
{
public:
AlgorithmsCache
()
:
search_times_
(
0
)
{
hash_
.
clear
();
}
// Caches the best algorithm for a given
// combination of tensor dimensions & compute data type.
TAlgorithm
GetAlgorithm
(
const
std
::
vector
<
int64_t
>&
dims1
,
const
std
::
vector
<
int64_t
>&
dims2
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
dilations
,
int
algorithmFlags
,
// can set for different data type
std
::
function
<
TAlgorithm
()
>
gen_func
);
TAlgorithm
GetAlgorithm
(
int64_t
area
,
int
search_times
,
int
algorithmFlags
,
std
::
function
<
TAlgorithm
()
>
gen_func
);
private:
std
::
unordered_map
<
int64_t
,
TAlgorithm
>
hash_
;
int
search_times_
;
};
template
<
typename
TAlgorithm
>
TAlgorithm
framework
::
AlgorithmsCache
<
TAlgorithm
>::
GetAlgorithm
(
const
std
::
vector
<
int64_t
>&
dims1
,
const
std
::
vector
<
int64_t
>&
dims2
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
dilations
,
int
algorithmFlags
,
std
::
function
<
TAlgorithm
()
>
gen_func
)
{
int64_t
seed
=
0
;
// Hash all of the inputs, use to try and look up a previously
// discovered algorithm, or fall back to generating a new one.
std
::
hash
<
int64_t
>
hashFn
;
// do hash like boost
// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
for
(
const
auto
num
:
dims1
)
{
seed
^=
hashFn
(
num
)
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
);
}
for
(
const
auto
num
:
dims2
)
{
seed
^=
hashFn
(
num
)
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
1
;
}
for
(
const
auto
num
:
strides
)
{
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
num
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
2
;
}
for
(
const
auto
num
:
paddings
)
{
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
num
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
3
;
}
for
(
const
auto
num
:
dilations
)
{
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
num
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
4
;
}
seed
^=
hashFn
(
static_cast
<
int64_t
>
(
algorithmFlags
))
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
)
+
5
;
if
(
seed
==
0
)
return
gen_func
();
if
(
hash_
.
find
(
seed
)
==
hash_
.
end
())
{
TAlgorithm
value
=
gen_func
();
hash_
[
seed
]
=
value
;
}
return
hash_
[
seed
];
}
template
<
typename
TAlgorithm
>
TAlgorithm
AlgorithmsCache
<
TAlgorithm
>::
GetAlgorithm
(
int64_t
area
,
int
search_times
,
int
algorithmFlags
,
std
::
function
<
TAlgorithm
()
>
gen_func
)
{
if
(
hash_
.
find
(
area
)
!=
hash_
.
end
())
{
return
hash_
[
area
];
}
if
(
search_times_
<
search_times
)
{
auto
algo
=
gen_func
();
hash_
[
area
]
=
algo
;
++
search_times_
;
return
algo
;
}
TAlgorithm
algo
;
int64_t
min
=
static_cast
<
uint64_t
>
(
INT_MAX
);
for
(
const
auto
&
m
:
hash_
)
{
if
(
m
.
first
<
min
)
{
min
=
m
.
first
;
algo
=
m
.
second
;
}
}
return
algo
;
}
}
// namespace framework
}
// namespace paddle
paddle/fluid/imperative/layer.h
浏览文件 @
5dd281f7
...
...
@@ -44,8 +44,13 @@ class PreparedOp {
PreparedOp
(
const
framework
::
OperatorBase
&
op
,
const
framework
::
RuntimeContext
&
ctx
,
framework
::
OperatorWithKernel
::
OpKernelFunc
func
,
platform
::
DeviceContext
*
dev_ctx
)
:
op
(
op
),
ctx
(
ctx
),
func
(
func
),
dev_ctx
(
dev_ctx
)
{}
platform
::
DeviceContext
*
dev_ctx
,
std
::
vector
<
framework
::
KernelConfig
>*
kernel_configs
)
:
op
(
op
),
ctx
(
ctx
),
func
(
func
),
dev_ctx
(
dev_ctx
),
kernel_configs
(
kernel_configs
)
{}
static
PreparedOp
Prepare
(
const
framework
::
RuntimeContext
&
ctx
,
const
framework
::
OperatorWithKernel
&
op
,
...
...
@@ -84,7 +89,9 @@ class PreparedOp {
PADDLE_THROW
(
"op %s does not have kernel for %s"
,
op
.
Type
(),
KernelTypeToString
(
expected_kernel_key
));
}
return
PreparedOp
(
op
,
ctx
,
kernel_iter
->
second
,
dev_ctx
);
std
::
vector
<
framework
::
KernelConfig
>*
kernel_configs
=
op
.
GetKernelConfig
(
expected_kernel_key
);
return
PreparedOp
(
op
,
ctx
,
kernel_iter
->
second
,
dev_ctx
,
kernel_configs
);
}
inline
platform
::
DeviceContext
*
GetDeviceContext
()
const
{
return
dev_ctx
;
}
...
...
@@ -93,6 +100,7 @@ class PreparedOp {
const
framework
::
RuntimeContext
&
ctx
;
framework
::
OperatorWithKernel
::
OpKernelFunc
func
;
platform
::
DeviceContext
*
dev_ctx
;
std
::
vector
<
framework
::
KernelConfig
>*
kernel_configs
;
};
class
OpBase
;
...
...
paddle/fluid/imperative/tracer.cc
浏览文件 @
5dd281f7
...
...
@@ -138,8 +138,9 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
op
->
place_
=
GetExpectedPlace
(
expected_place
,
inputs
);
PreparedOp
prepared_op
=
PreparedOp
::
Prepare
(
ctx
,
*
op_kernel
,
op
->
place_
);
prepared_op
.
op
.
RuntimeInferShape
(
scope
,
op
->
place_
,
ctx
);
prepared_op
.
func
(
framework
::
ExecutionContext
(
prepared_op
.
op
,
scope
,
*
prepared_op
.
dev_ctx
,
prepared_op
.
ctx
,
nullptr
));
prepared_op
.
func
(
framework
::
ExecutionContext
(
prepared_op
.
op
,
scope
,
*
prepared_op
.
dev_ctx
,
prepared_op
.
ctx
,
prepared_op
.
kernel_configs
));
if
(
!
stop_gradient
)
{
std
::
unique_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
string
>>
grad_to_var
(
...
...
paddle/fluid/operators/conv_fusion_op.cu.cc
浏览文件 @
5dd281f7
...
...
@@ -154,8 +154,6 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
algo
=
algo_cache
.
GetAlgorithm
(
x_dims
[
2
]
*
x_dims
[
3
],
search_times
,
0
,
search_func
);
}
else
{
// Cache searched algo in Var(kCUDNNFwdAlgoCache).
// all conv ops use the same kCUDNNFwdAlgoCache variable.
algo
=
algo_cache
.
GetAlgorithm
(
x_dims
,
f_dims
,
strides
,
paddings
,
dilations
,
0
,
search_func
);
}
...
...
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