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体验新版 GitCode,发现更多精彩内容 >>
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4048cfa9
编写于
12月 27, 2018
作者:
Z
Zhaolong Xing
提交者:
GitHub
12月 27, 2018
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差异文件
Merge pull request #15048 from NHZlX/add_affine_channel_fuse
Add conv+ affine channel fuse pass
上级
efa630ea
02e17396
变更
13
隐藏空白更改
内联
并排
Showing
13 changed file
with
431 addition
and
16 deletion
+431
-16
paddle/fluid/framework/ir/CMakeLists.txt
paddle/fluid/framework/ir/CMakeLists.txt
+1
-0
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc
+222
-0
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h
+49
-0
paddle/fluid/framework/ir/graph_pattern_detector.cc
paddle/fluid/framework/ir/graph_pattern_detector.cc
+72
-0
paddle/fluid/framework/ir/graph_pattern_detector.h
paddle/fluid/framework/ir/graph_pattern_detector.h
+32
-0
paddle/fluid/inference/analysis/argument.h
paddle/fluid/inference/analysis/argument.h
+1
-0
paddle/fluid/inference/analysis/ir_pass_manager.cc
paddle/fluid/inference/analysis/ir_pass_manager.cc
+2
-0
paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc
...id/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc
+24
-4
paddle/fluid/inference/api/analysis_config.cc
paddle/fluid/inference/api/analysis_config.cc
+7
-3
paddle/fluid/inference/api/analysis_predictor.cc
paddle/fluid/inference/api/analysis_predictor.cc
+1
-0
paddle/fluid/inference/api/paddle_analysis_config.h
paddle/fluid/inference/api/paddle_analysis_config.h
+12
-1
paddle/fluid/inference/api/paddle_pass_builder.h
paddle/fluid/inference/api/paddle_pass_builder.h
+7
-5
paddle/fluid/operators/conv_fusion_op.cu.cc
paddle/fluid/operators/conv_fusion_op.cu.cc
+1
-3
未找到文件。
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @
4048cfa9
...
...
@@ -45,6 +45,7 @@ pass_library(is_test_pass base)
pass_library
(
conv_elementwise_add_act_fuse_pass inference
)
pass_library
(
conv_elementwise_add2_act_fuse_pass inference
)
pass_library
(
conv_elementwise_add_fuse_pass inference
)
pass_library
(
conv_affine_channel_fuse_pass inference
)
if
(
WITH_MKLDNN
)
pass_library
(
mkldnn_placement_pass base
)
pass_library
(
depthwise_conv_mkldnn_pass base
)
...
...
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc
0 → 100644
浏览文件 @
4048cfa9
// 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/ir/conv_affine_channel_fuse_pass.h"
#include <functional>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/platform/enforce.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
#define GET_CONV_BN_NODES(pattern_name) \
/* OPERATORS */
\
GET_IR_NODE_FROM_SUBGRAPH(conv, conv, pattern_name); \
GET_IR_NODE_FROM_SUBGRAPH(affine_channel, affine_channel, pattern_name); \
/* CONV inputs */
\
GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight, pattern_name); \
/* CONV outputs */
\
GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, pattern_name); \
/* Affine Channel inputs */
\
GET_IR_NODE_FROM_SUBGRAPH(ac_scale, ac_scale, pattern_name); \
GET_IR_NODE_FROM_SUBGRAPH(ac_bias, ac_bias, pattern_name); \
/* Affine channel outputs */
\
GET_IR_NODE_FROM_SUBGRAPH(ac_out, ac_out, pattern_name);
/* Out */
void
recompute_bias_and_weights
(
const
Scope
*
scope
,
ir
::
Node
*
conv_weight
,
const
ir
::
Node
&
ac_scale
,
const
LoDTensor
&
ac_bias_tensor
,
LoDTensor
*
eltwise_y_in_tensor
)
{
using
EigenVectorArrayMap
=
Eigen
::
Map
<
Eigen
::
Array
<
float
,
Eigen
::
Dynamic
,
1
>>
;
using
ConstEigenVectorArrayMap
=
Eigen
::
Map
<
const
Eigen
::
Array
<
float
,
Eigen
::
Dynamic
,
1
>>
;
using
EigenMatrixArrayMap
=
Eigen
::
Map
<
Eigen
::
Array
<
float
,
Eigen
::
Dynamic
,
Eigen
::
Dynamic
,
Eigen
::
RowMajor
>>
;
// Re-compute bias of conv2d from AffineChannel
PADDLE_ENFORCE_EQ
(
eltwise_y_in_tensor
->
dims
(),
ac_bias_tensor
.
dims
());
auto
*
scale_tensor
=
scope
->
FindVar
(
ac_scale
.
Name
())
->
GetMutable
<
LoDTensor
>
();
ConstEigenVectorArrayMap
scale_array
(
scale_tensor
->
data
<
float
>
(),
scale_tensor
->
numel
(),
1
);
ConstEigenVectorArrayMap
ac_bias_array
(
ac_bias_tensor
.
data
<
float
>
(),
ac_bias_tensor
.
numel
(),
1
);
EigenVectorArrayMap
eltwise_y_in_array
(
eltwise_y_in_tensor
->
mutable_data
<
float
>
(
platform
::
CPUPlace
()),
eltwise_y_in_tensor
->
numel
(),
1
);
eltwise_y_in_array
=
(
eltwise_y_in_array
*
scale_array
)
+
ac_bias_array
;
// Re-compute weight of conv2d from AffineChannel
auto
*
weights
=
scope
->
FindVar
(
conv_weight
->
Name
())
->
GetMutable
<
LoDTensor
>
();
auto
weights_shape
=
weights
->
dims
();
auto
weights_shape_2d
=
flatten_to_2d
(
weights_shape
,
1
);
EigenMatrixArrayMap
weights_array_2d
(
weights
->
mutable_data
<
float
>
(
platform
::
CPUPlace
()),
weights_shape_2d
[
0
],
weights_shape_2d
[
1
]);
weights_array_2d
.
colwise
()
*=
scale_array
;
}
std
::
unique_ptr
<
ir
::
Graph
>
ConvAffineChannelFusePass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
PADDLE_ENFORCE
(
graph
.
get
());
FusePassBase
::
Init
(
name_scope_
,
graph
.
get
());
auto
*
scope
=
param_scope
();
PADDLE_ENFORCE
(
scope
);
GraphPatternDetector
gpd
;
auto
*
conv_input
=
gpd
.
mutable_pattern
()
->
NewNode
(
patterns
::
PDNodeName
(
name_scope_
,
"conv_input"
))
->
AsInput
()
->
assert_is_op_input
(
"conv2d"
,
"Input"
);
patterns
::
ConvAffineChannel
conv_ac_pattern
(
gpd
.
mutable_pattern
(),
name_scope_
);
conv_ac_pattern
(
conv_input
,
false
/*with_eltwise_add*/
);
int
found_conv_ac_count
=
0
;
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
VLOG
(
4
)
<<
"handle ConvAffineChannel fuse"
;
GET_CONV_BN_NODES
(
conv_ac_pattern
);
// check if fuse can be done and if MKL-DNN should be used
FuseOptions
fuse_option
=
FindFuseOption
(
*
conv
,
*
affine_channel
);
if
(
fuse_option
==
DO_NOT_FUSE
)
{
VLOG
(
3
)
<<
"do not perform conv+affinechannel fuse"
;
return
;
}
// Create eltwise_y (conv bias) variable
VarDesc
eltwise_y_in_desc
(
patterns
::
PDNodeName
(
name_scope_
,
"eltwise_y_in"
));
eltwise_y_in_desc
.
SetPersistable
(
true
);
auto
*
eltwise_y_in_node
=
g
->
CreateVarNode
(
&
eltwise_y_in_desc
);
auto
*
eltwise_y_in_tensor
=
scope
->
Var
(
eltwise_y_in_node
->
Name
())
->
GetMutable
<
LoDTensor
>
();
// Get affine_channel bias
auto
*
ac_bias_tensor
=
scope
->
FindVar
(
ac_bias
->
Name
())
->
GetMutable
<
LoDTensor
>
();
// Initialize eltwise_y
eltwise_y_in_tensor
->
Resize
(
ac_bias_tensor
->
dims
());
std
::
fill_n
(
eltwise_y_in_tensor
->
mutable_data
<
float
>
(
platform
::
CPUPlace
()),
eltwise_y_in_tensor
->
numel
(),
0.0
f
);
// update weights and biases
recompute_bias_and_weights
(
scope
,
conv_weight
,
*
ac_scale
,
*
ac_bias_tensor
,
eltwise_y_in_tensor
);
// create an elementwise add node.
OpDesc
desc
;
desc
.
SetInput
(
"X"
,
std
::
vector
<
std
::
string
>
({
conv_out
->
Name
()}));
desc
.
SetInput
(
"Y"
,
std
::
vector
<
std
::
string
>
({
eltwise_y_in_node
->
Name
()}));
desc
.
SetOutput
(
"Out"
,
std
::
vector
<
std
::
string
>
({
ac_out
->
Name
()}));
desc
.
SetType
(
"elementwise_add"
);
desc
.
SetAttr
(
"axis"
,
1
);
auto
eltwise_op
=
g
->
CreateOpNode
(
&
desc
);
// OpDesc will be copied.
GraphSafeRemoveNodes
(
graph
.
get
(),
{
ac_scale
,
ac_bias
,
affine_channel
});
IR_NODE_LINK_TO
(
conv_out
,
eltwise_op
);
IR_NODE_LINK_TO
(
eltwise_y_in_node
,
eltwise_op
);
IR_NODE_LINK_TO
(
eltwise_op
,
ac_out
);
found_conv_ac_count
++
;
};
gpd
(
graph
.
get
(),
handler
);
AddStatis
(
found_conv_ac_count
);
return
graph
;
}
std
::
unique_ptr
<
ir
::
Graph
>
ConvEltwiseAddAffineChannelFusePass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
PADDLE_ENFORCE
(
graph
.
get
());
FusePassBase
::
Init
(
name_scope_
,
graph
.
get
());
auto
*
scope
=
param_scope
();
PADDLE_ENFORCE
(
scope
);
GraphPatternDetector
gpd
;
auto
*
conv_input
=
gpd
.
mutable_pattern
()
->
NewNode
(
patterns
::
PDNodeName
(
name_scope_
,
"conv_input"
))
->
AsInput
()
->
assert_is_op_input
(
"conv2d"
,
"Input"
);
patterns
::
ConvAffineChannel
conv_ac_pattern
(
gpd
.
mutable_pattern
(),
name_scope_
);
conv_ac_pattern
(
conv_input
,
true
/*with_eltwise_add*/
);
int
found_conv_ac_count
=
0
;
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
VLOG
(
4
)
<<
"handle ConvBN fuse"
;
GET_CONV_BN_NODES
(
conv_ac_pattern
);
// OPERATORS
GET_IR_NODE_FROM_SUBGRAPH
(
eltwise
,
eltwise
,
conv_ac_pattern
);
// BIAS inputs
GET_IR_NODE_FROM_SUBGRAPH
(
eltwise_y_in
,
eltwise_y_in
,
conv_ac_pattern
);
// BIAS outputs
GET_IR_NODE_FROM_SUBGRAPH
(
eltwise_out
,
eltwise_out
,
conv_ac_pattern
);
// Get eltwise_y (conv bias) variable
auto
*
eltwise_y_in_tensor
=
scope
->
FindVar
(
eltwise_y_in
->
Name
())
->
GetMutable
<
LoDTensor
>
();
// Get batch norm bias
auto
*
ac_bias_tensor
=
scope
->
FindVar
(
ac_bias
->
Name
())
->
GetMutable
<
LoDTensor
>
();
recompute_bias_and_weights
(
scope
,
conv_weight
,
*
ac_scale
,
*
ac_bias_tensor
,
eltwise_y_in_tensor
);
// Update the elementwise_add node
eltwise
->
Op
()
->
SetAttr
(
"axis"
,
1
);
eltwise
->
Op
()
->
SetOutput
(
"Out"
,
std
::
vector
<
std
::
string
>
({
ac_out
->
Name
()}));
GraphSafeRemoveNodes
(
graph
.
get
(),
{
ac_scale
,
ac_bias
,
affine_channel
,
eltwise_out
});
IR_NODE_LINK_TO
(
eltwise
,
ac_out
);
found_conv_ac_count
++
;
};
gpd
(
graph
.
get
(),
handler
);
AddStatis
(
found_conv_ac_count
);
return
graph
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
conv_affine_channel_fuse_pass
,
paddle
::
framework
::
ir
::
ConvAffineChannelFusePass
);
REGISTER_PASS
(
conv_eltwiseadd_affine_channel_fuse_pass
,
paddle
::
framework
::
ir
::
ConvEltwiseAddAffineChannelFusePass
);
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h
0 → 100644
浏览文件 @
4048cfa9
// 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 "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
/*
* Fuse the Conv and ConvAffineChannel.
*/
class
ConvAffineChannelFusePass
:
public
FusePassBase
{
public:
virtual
~
ConvAffineChannelFusePass
()
{}
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
;
const
std
::
string
name_scope_
{
"conv_affine_channel_fuse"
};
};
class
ConvEltwiseAddAffineChannelFusePass
:
public
FusePassBase
{
public:
virtual
~
ConvEltwiseAddAffineChannelFusePass
()
{}
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
;
const
std
::
string
name_scope_
{
"conv_eltwiseadd_affine_channel_fuse"
};
};
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @
4048cfa9
...
...
@@ -1234,6 +1234,78 @@ PDNode *patterns::ConvElementwiseadd::operator()(PDNode *conv_in) {
return
elementwise_add_out
;
}
PDNode
*
patterns
::
ConvAffineChannel
::
operator
()(
paddle
::
framework
::
ir
::
PDNode
*
conv_input
,
bool
with_eltwise_add
)
{
// Create Operators
conv_input
->
assert_is_op_input
(
"conv2d"
,
"Input"
);
auto
*
conv_op
=
pattern
->
NewNode
(
conv_repr
())
->
assert_is_op
(
"conv2d"
);
PDNode
*
eltwise_op
=
nullptr
;
if
(
with_eltwise_add
)
{
eltwise_op
=
pattern
->
NewNode
(
eltwise_repr
())
->
assert_is_op
(
"elementwise_add"
);
}
auto
*
affine_channel_op
=
pattern
->
NewNode
(
affine_channel_repr
())
->
assert_is_op
(
"affine_channel"
);
// Create variables
// Conv Filter
auto
*
conv_weight_var
=
pattern
->
NewNode
(
conv_weight_repr
())
->
AsInput
()
->
assert_is_persistable_var
()
->
assert_is_op_input
(
"conv2d"
,
"Filter"
);
auto
*
conv_out_var
=
pattern
->
NewNode
(
conv_out_repr
())
->
AsIntermediate
()
->
assert_is_only_output_of_op
(
"conv2d"
);
PDNode
*
eltwise_y_in_var
=
nullptr
;
PDNode
*
eltwise_out_var
=
nullptr
;
if
(
with_eltwise_add
)
{
// Conv output as Bias input
conv_out_var
->
assert_is_op_input
(
"elementwise_add"
,
"X"
);
// Bias
eltwise_y_in_var
=
pattern
->
NewNode
(
eltwise_y_in_repr
())
->
assert_is_op_input
(
"elementwise_add"
,
"Y"
)
->
AsInput
();
eltwise_out_var
=
pattern
->
NewNode
(
eltwise_out_repr
())
->
AsIntermediate
()
->
assert_is_only_output_of_op
(
"elementwise_add"
);
}
else
{
// Conv output as AffineChannel input
conv_out_var
->
assert_is_op_input
(
"affine_channel"
,
"X"
);
}
// AC Scale
auto
*
ac_scale_var
=
pattern
->
NewNode
(
ac_scale_repr
())
->
AsInput
()
->
assert_is_persistable_var
()
->
assert_is_op_input
(
"affine_channel"
,
"Scale"
);
// AC Bias
auto
*
ac_bias_var
=
pattern
->
NewNode
(
ac_bias_repr
())
->
AsInput
()
->
assert_is_persistable_var
()
->
assert_is_op_input
(
"affine_channel"
,
"Bias"
);
// AC output
auto
*
ac_out_var
=
pattern
->
NewNode
(
ac_out_repr
())
->
AsOutput
()
->
assert_is_op_output
(
"affine_channel"
);
conv_op
->
LinksFrom
({
conv_input
,
conv_weight_var
}).
LinksTo
({
conv_out_var
});
if
(
with_eltwise_add
)
{
eltwise_op
->
LinksFrom
({
conv_out_var
,
eltwise_y_in_var
})
.
LinksTo
({
eltwise_out_var
});
affine_channel_op
->
LinksFrom
({
eltwise_out_var
,
ac_scale_var
,
ac_bias_var
})
.
LinksTo
({
ac_out_var
});
}
else
{
affine_channel_op
->
LinksFrom
({
conv_out_var
,
ac_scale_var
,
ac_bias_var
})
.
LinksTo
({
ac_out_var
});
}
return
ac_out_var
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @
4048cfa9
...
...
@@ -734,6 +734,38 @@ struct ConvElementwiseadd : public PatternBase {
PATTERN_DECL_NODE
(
elementwise_add_out
);
};
// Conv with affine_channel
// op: conv + (elementwise_add +) affine_channel
// named nodes:
// conv_weight, conv_out, conv,
// ac_x, ac_scale, ac_bias
// affine_channel, ac_out
struct
ConvAffineChannel
:
public
PatternBase
{
ConvAffineChannel
(
PDPattern
*
pattern
,
const
std
::
string
&
name_scope
)
:
PatternBase
(
pattern
,
name_scope
,
"conv_affine_channel"
)
{}
PDNode
*
operator
()(
PDNode
*
conv_input
,
bool
with_eltwise_add
);
// declare operator node's name
PATTERN_DECL_NODE
(
conv
);
PATTERN_DECL_NODE
(
affine_channel
);
PATTERN_DECL_NODE
(
eltwise
);
// ELEMENTWISE_ADD
// CONV inputs
PATTERN_DECL_NODE
(
conv_weight
);
// Filter
// CONV outputs
PATTERN_DECL_NODE
(
conv_out
);
// tmp
// ELTWISE inputs
PATTERN_DECL_NODE
(
eltwise_y_in
);
// ELTWISE outputs
PATTERN_DECL_NODE
(
eltwise_out
);
// tmp
// AC(Affine_Channel) inputs
PATTERN_DECL_NODE
(
ac_scale
);
PATTERN_DECL_NODE
(
ac_bias
);
// AC outputs
PATTERN_DECL_NODE
(
ac_out
);
// Out
};
}
// namespace patterns
// Link two ir::Nodes from each other.
...
...
paddle/fluid/inference/analysis/argument.h
浏览文件 @
4048cfa9
...
...
@@ -127,6 +127,7 @@ struct Argument {
std
::
function
<
bool
(
const
framework
::
ir
::
Node
*
)
>
);
DECL_ARGUMENT_FIELD
(
tensorrt_max_batch_size
,
TensorRtMaxBatchSize
,
int
);
DECL_ARGUMENT_FIELD
(
tensorrt_workspace_size
,
TensorRtWorkspaceSize
,
int
);
DECL_ARGUMENT_FIELD
(
tensorrt_min_subgraph_size
,
TensorRtMinSubgraphSize
,
int
);
// The program transformed by IR analysis phase.
DECL_ARGUMENT_UNIQUE_FIELD
(
ir_analyzed_program
,
IrAnalyzedProgram
,
...
...
paddle/fluid/inference/analysis/ir_pass_manager.cc
浏览文件 @
4048cfa9
...
...
@@ -75,6 +75,8 @@ void IRPassManager::CreatePasses(Argument *argument,
argument
->
tensorrt_node_teller_ptr
());
pass
->
Set
(
"workspace_size"
,
new
int
(
argument
->
tensorrt_workspace_size
()));
pass
->
Set
(
"max_batch_size"
,
new
int
(
argument
->
tensorrt_max_batch_size
()));
pass
->
Set
(
"min_subgraph_size"
,
new
int
(
argument
->
tensorrt_min_subgraph_size
()));
}
// graph_ = pass->Apply(std::move(graph_));
...
...
paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc
浏览文件 @
4048cfa9
...
...
@@ -12,12 +12,14 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include
"paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.h"
#include
<algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h"
#include "paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.h"
namespace
paddle
{
namespace
inference
{
...
...
@@ -36,7 +38,8 @@ std::unique_ptr<framework::ir::Graph> analysis::TensorRtSubgraphPass::ApplyImpl(
auto
teller
=
Get
<
SubgraphDetector
::
NodeInsideSubgraphTeller
>
(
"tensorrt_node_teller"
);
SubGraphFuser
fuser
(
graph
.
get
(),
teller
,
2
/*min subgraph size*/
);
SubGraphFuser
fuser
(
graph
.
get
(),
teller
,
Get
<
int
>
(
"min_subgraph_size"
)
/*min subgraph size*/
);
fuser
();
for
(
auto
*
node
:
graph
->
Nodes
())
{
...
...
@@ -197,10 +200,26 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
std
::
vector
<
std
::
string
>
ExtractParameters
(
const
std
::
unordered_set
<
Node
*>
&
nodes
)
{
// We can judge whether a variable is a parameter by
// its presistable property, but sometimes the presistable
// of the feed op output is true, so we have to identify it.
std
::
vector
<
std
::
string
>
feed_outputs
;
for
(
const
auto
&
node
:
nodes
)
{
if
(
!
node
->
IsOp
())
continue
;
std
::
string
op_type
=
node
->
Op
()
->
Type
();
if
(
op_type
==
"feed"
)
{
std
::
vector
<
std
::
string
>
output_names
=
node
->
Op
()
->
OutputArgumentNames
();
std
::
copy
(
output_names
.
begin
(),
output_names
.
end
(),
std
::
back_inserter
(
feed_outputs
));
}
}
std
::
vector
<
std
::
string
>
parameters
;
for
(
const
auto
&
node
:
nodes
)
{
if
(
!
node
->
IsVar
())
continue
;
if
(
node
->
Var
()
->
Persistable
())
{
if
(
node
->
Var
()
->
Persistable
()
&&
std
::
find
(
feed_outputs
.
begin
(),
feed_outputs
.
end
(),
node
->
Name
())
==
feed_outputs
.
end
())
{
parameters
.
push_back
(
node
->
Name
());
}
}
...
...
@@ -215,4 +234,5 @@ REGISTER_PASS(tensorrt_subgraph_pass,
paddle
::
inference
::
analysis
::
TensorRtSubgraphPass
)
.
RequirePassAttr
(
"tensorrt_node_teller"
)
.
RequirePassAttr
(
"max_batch_size"
)
.
RequirePassAttr
(
"workspace_size"
);
.
RequirePassAttr
(
"workspace_size"
)
.
RequirePassAttr
(
"min_subgraph_size"
);
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @
4048cfa9
...
...
@@ -57,6 +57,7 @@ contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) {
use_tensorrt_
=
other
.
use_tensorrt_
;
tensorrt_max_batchsize_
=
other
.
tensorrt_max_batchsize_
;
tensorrt_workspace_size_
=
other
.
tensorrt_workspace_size_
;
tensorrt_min_subgraph_size_
=
other
.
tensorrt_min_subgraph_size_
;
model_from_memory_
=
other
.
model_from_memory_
;
if
(
use_gpu
)
{
...
...
@@ -89,6 +90,7 @@ contrib::AnalysisConfig::AnalysisConfig(contrib::AnalysisConfig &&other) {
use_tensorrt_
=
other
.
use_tensorrt_
;
tensorrt_max_batchsize_
=
other
.
tensorrt_max_batchsize_
;
tensorrt_workspace_size_
=
other
.
tensorrt_workspace_size_
;
tensorrt_min_subgraph_size_
=
other
.
tensorrt_min_subgraph_size_
;
model_from_memory_
=
other
.
model_from_memory_
;
pass_builder_
=
std
::
move
(
other
.
pass_builder_
);
...
...
@@ -105,12 +107,14 @@ void contrib::AnalysisConfig::EnableMKLDNN() {
}
void
contrib
::
AnalysisConfig
::
EnableTensorRtEngine
(
int
workspace_size
,
int
max_batch_size
)
{
int
max_batch_size
,
int
min_subgraph_size
)
{
use_tensorrt_
=
true
;
tensorrt_workspace_size_
=
workspace_size
;
tensorrt_max_batchsize_
=
max_batch_size
;
// Append after the infer_clean pass.
pass_builder
()
->
InsertPass
(
1
,
"tensorrt_subgraph_pass"
);
tensorrt_min_subgraph_size_
=
min_subgraph_size
;
// Append after the conv+affine_channel fuse pass.
pass_builder
()
->
InsertPass
(
3
,
"tensorrt_subgraph_pass"
);
}
void
contrib
::
AnalysisConfig
::
SetModelBuffer
(
const
char
*
prog_buffer
,
...
...
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @
4048cfa9
...
...
@@ -328,6 +328,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
argument_
.
SetUseTensorRT
(
true
);
argument_
.
SetTensorRtWorkspaceSize
(
config_
.
tensorrt_workspace_size_
);
argument_
.
SetTensorRtMaxBatchSize
(
config_
.
tensorrt_max_batchsize_
);
argument_
.
SetTensorRtMinSubgraphSize
(
config_
.
tensorrt_min_subgraph_size_
);
}
if
(
config_
.
use_mkldnn_
)
{
...
...
paddle/fluid/inference/api/paddle_analysis_config.h
浏览文件 @
4048cfa9
...
...
@@ -49,7 +49,7 @@ struct AnalysisConfig : public NativeConfig {
bool
use_feed_fetch_ops
{
true
};
void
EnableTensorRtEngine
(
int
workspace_size
=
1
<<
20
,
int
max_batch_size
=
1
);
int
max_batch_size
=
1
,
int
min_subgraph_size
=
3
);
bool
use_tensorrt
()
const
{
return
use_tensorrt_
;
}
void
EnableMKLDNN
();
...
...
@@ -69,8 +69,19 @@ struct AnalysisConfig : public NativeConfig {
bool
use_tensorrt_
{
false
};
bool
use_mkldnn_
{
false
};
std
::
unordered_set
<
std
::
string
>
mkldnn_enabled_op_types_
;
// For workspace_size, refer it from here:
// https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
int
tensorrt_workspace_size_
;
// While TensorRT allows an engine optimized for a given max batch size
// to run at any smaller size, the performance for those smaller
// sizes may not be as well-optimized. Therefore, Max batch is best
// equivalent to the runtime batch size.
int
tensorrt_max_batchsize_
;
// We transform the Ops that can be converted into TRT layer in the model,
// and aggregate these Ops into subgraphs for TRT execution.
// We set this variable to control the minimum number of nodes in the
// subgraph, 3 as default value.
int
tensorrt_min_subgraph_size_
{
3
};
std
::
unique_ptr
<
PassStrategy
>
pass_builder_
;
bool
model_from_memory_
{
false
};
};
...
...
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @
4048cfa9
...
...
@@ -118,11 +118,13 @@ class GpuPassStrategy : public PassStrategy {
public:
GpuPassStrategy
()
:
PassStrategy
({})
{
passes_
.
assign
({
"infer_clean_graph_pass"
,
//
"conv_bn_fuse_pass"
,
//
"conv_elementwise_add_act_fuse_pass"
,
//
"conv_elementwise_add2_act_fuse_pass"
,
//
"conv_elementwise_add_fuse_pass"
,
//
"infer_clean_graph_pass"
,
//
"conv_affine_channel_fuse_pass"
,
//
"conv_eltwiseadd_affine_channel_fuse_pass"
,
//
"conv_bn_fuse_pass"
,
//
"conv_elementwise_add_act_fuse_pass"
,
//
"conv_elementwise_add2_act_fuse_pass"
,
//
"conv_elementwise_add_fuse_pass"
,
//
});
}
...
...
paddle/fluid/operators/conv_fusion_op.cu.cc
浏览文件 @
4048cfa9
...
...
@@ -161,9 +161,7 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_LE
(
workspace_size_in_bytes
,
workspace_size_limit
,
"workspace_size to be allocated exceeds the limit"
);
if
((
activation
==
"identity"
)
&&
(
algo
!=
CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM
)
&&
(
!
residual
))
{
if
((
activation
==
"identity"
)
&&
(
!
residual
))
{
// Only the CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM algo is
// enabled with CUDNN_ACTIVATION_IDENTITY in cuDNN lib.
// But test in some case, the speed is slower, change to use
...
...
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