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f5fd8b20
编写于
4月 07, 2020
作者:
Y
Yin Zhenhua
浏览文件
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电子邮件补丁
差异文件
add dropout op; add sigmoid and elementwise_mul to subgraph_bridge
上级
a3a27beb
变更
4
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4 changed file
with
248 addition
and
0 deletion
+248
-0
lite/kernels/mlu/bridges/CMakeLists.txt
lite/kernels/mlu/bridges/CMakeLists.txt
+3
-0
lite/kernels/mlu/bridges/dropout_op.cc
lite/kernels/mlu/bridges/dropout_op.cc
+84
-0
lite/kernels/mlu/bridges/dropout_op_test.cc
lite/kernels/mlu/bridges/dropout_op_test.cc
+158
-0
lite/kernels/mlu/bridges/paddle_use_bridges.h
lite/kernels/mlu/bridges/paddle_use_bridges.h
+3
-0
未找到文件。
lite/kernels/mlu/bridges/CMakeLists.txt
浏览文件 @
f5fd8b20
...
...
@@ -19,6 +19,7 @@ lite_cc_library(subgraph_bridge_scale_op_mlu SRCS scale_op.cc DEPS ${subgraph_br
lite_cc_library
(
subgraph_bridge_interp_op_mlu SRCS interpolate_op.cc DEPS
${
subgraph_bridge_deps_mlu
}
)
lite_cc_library
(
subgraph_bridge_concat_op_mlu SRCS concat_op.cc DEPS
${
subgraph_bridge_deps_mlu
}
)
lite_cc_library
(
subgraph_bridge_transpose_op_mlu SRCS transpose_op.cc DEPS
${
subgraph_bridge_deps_mlu
}
)
lite_cc_library
(
subgraph_bridge_dropout_op_mlu SRCS dropout_op.cc DEPS
${
subgraph_bridge_deps_mlu
}
)
set
(
mlu_subgraph_bridges
subgraph_bridge_registry
subgraph_bridge_utility_mlu
...
...
@@ -34,6 +35,7 @@ set(mlu_subgraph_bridges
subgraph_bridge_scale_op_mlu
subgraph_bridge_interp_op_mlu
subgraph_bridge_concat_op_mlu
subgraph_bridge_dropout_op_mlu
CACHE INTERNAL
"mlu_subgraph_bridges"
)
lite_cc_library
(
subgraph_test_helper_mlu SRCS test_helper.cc DEPS
${
mlu_subgraph_bridges
}
)
...
...
@@ -48,4 +50,5 @@ lite_cc_test(test_scale_converter_mlu SRCS scale_op_test.cc DEPS scope optimizer
lite_cc_test
(
test_interp_converter_mlu SRCS interpolate_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program
${
mlu_subgraph_bridges
}
subgraph_compute_mlu subgraph_test_helper_mlu
)
lite_cc_test
(
test_concat_converter_mlu SRCS concat_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program
${
mlu_subgraph_bridges
}
subgraph_compute_mlu subgraph_test_helper_mlu
)
lite_cc_test
(
test_transpose_converter_mlu SRCS transpose_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program
${
mlu_subgraph_bridges
}
subgraph_compute_mlu subgraph_test_helper_mlu
)
lite_cc_test
(
test_dropout_converter_mlu SRCS dropout_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program
${
mlu_subgraph_bridges
}
subgraph_compute_mlu subgraph_test_helper_mlu
)
message
(
STATUS
"+++++ mlu_subgraph_bridges:
${
mlu_subgraph_bridges
}
"
)
lite/kernels/mlu/bridges/dropout_op.cc
0 → 100644
浏览文件 @
f5fd8b20
// 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 "lite/kernels/mlu/bridges/graph.h"
#include "lite/kernels/mlu/bridges/utility.h"
#include "lite/kernels/npu/bridges/registry.h"
namespace
paddle
{
namespace
lite
{
namespace
subgraph
{
namespace
mlu
{
int
DropoutConverter
(
void
*
ctx
,
OpLite
*
op
,
KernelBase
*
kernel
)
{
CHECK
(
ctx
!=
nullptr
);
CHECK
(
op
!=
nullptr
);
auto
graph
=
static_cast
<
Graph
*>
(
ctx
);
auto
op_info
=
op
->
op_info
();
auto
op_type
=
op_info
->
Type
();
auto
scope
=
op
->
scope
();
VLOG
(
3
)
<<
"[MLU] Converting "
+
op_type
+
"..."
;
// Create act node and set params from op
auto
x_var_name
=
op_info
->
Input
(
"X"
).
front
();
auto
out_var_name
=
op_info
->
Output
(
"Out"
).
front
();
auto
output
=
scope
->
FindVar
(
out_var_name
)
->
GetMutable
<
Tensor
>
();
auto
output_dims
=
output
->
dims
().
Vectorize
();
auto
output_tensor
=
graph
->
AddNode
(
out_var_name
,
output_dims
,
CNML_TENSOR
,
CNML_NCHW
,
graph
->
FPType
());
// if(op_info->HasAttr("is_test")){
// auto is_test = op_info->GetAttr<bool>("is_test");
// CHECK(is_test != true); // The dropout op has no training
// implementation, only inference implementation
// }
auto
dropout_implementation
=
op_info
->
GetAttr
<
std
::
string
>
(
"dropout_implementation"
);
auto
dropout_prob
=
op_info
->
GetAttr
<
float
>
(
"dropout_prob"
);
float
alpha
=
1.0
f
-
dropout_prob
;
if
(
dropout_implementation
==
"upscale_in_train"
)
{
alpha
=
1.
;
}
float
beta
=
0.
;
std
::
vector
<
int64_t
>
shape
=
{
1
,
1
,
1
,
1
};
std
::
string
prefix
=
string_format
(
"_%p"
,
op
);
auto
alpha_tensor
=
graph
->
AddNode
(
"Alpha"
+
prefix
,
shape
,
CNML_CONST
,
CNML_NHWC
,
graph
->
FPType
());
auto
beta_tensor
=
graph
->
AddNode
(
"Beta"
+
prefix
,
shape
,
CNML_CONST
,
CNML_NHWC
,
graph
->
FPType
());
graph
->
BindConstRawData
(
"Alpha"
+
prefix
,
&
alpha
,
1
);
graph
->
BindConstRawData
(
"Beta"
+
prefix
,
&
beta
,
1
);
auto
input_tensor
=
graph
->
GetNode
(
x_var_name
);
cnmlBaseOp_t
scale_op
;
CNML_CALL
(
cnmlCreateScaleOp
(
&
scale_op
,
input_tensor
->
mlu_tensor
(),
output_tensor
->
mlu_tensor
(),
alpha_tensor
->
mlu_tensor
(),
beta_tensor
->
mlu_tensor
()));
graph
->
FuseOp
(
scale_op
);
return
SUCCESS
;
}
}
// namespace mlu
}
// namespace subgraph
}
// namespace lite
}
// namespace paddle
REGISTER_SUBGRAPH_BRIDGE
(
dropout
,
kMLU
,
paddle
::
lite
::
subgraph
::
mlu
::
DropoutConverter
);
lite/kernels/mlu/bridges/dropout_op_test.cc
0 → 100644
浏览文件 @
f5fd8b20
// 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 "lite/operators/dropout_op.h"
#include <gtest/gtest.h>
#include <random>
#include "lite/core/op_registry.h"
#include "lite/kernels/mlu/bridges/test_helper.h"
#include "lite/kernels/npu/bridges/registry.h"
namespace
paddle
{
namespace
lite
{
namespace
subgraph
{
namespace
mlu
{
void
dropout_ref
(
const
std
::
shared_ptr
<
operators
::
DropoutOp
>
op
)
{
Scope
*
scope
=
op
->
scope
();
const
OpInfo
*
op_info
=
op
->
op_info
();
auto
x
=
scope
->
FindVar
(
op_info
->
Input
(
"X"
).
front
())
->
GetMutable
<
Tensor
>
();
auto
out
=
scope
->
FindVar
(
op_info
->
Output
(
"Out"
).
front
())
->
GetMutable
<
Tensor
>
();
auto
dropout_implementation
=
op_info
->
GetAttr
<
std
::
string
>
(
"dropout_implementation"
);
auto
dropout_prob
=
op_info
->
GetAttr
<
float
>
(
"dropout_prob"
);
float
alpha
=
1.0
f
-
dropout_prob
;
if
(
dropout_implementation
==
"upscale_in_train"
)
{
alpha
=
1.
;
}
float
beta
=
0.
;
auto
x_data
=
x
->
data
<
float
>
();
auto
out_data
=
out
->
mutable_data
<
float
>
();
DDim
x_dims
=
x
->
dims
();
DDim
out_dims
=
out
->
dims
();
CHECK_EQ
(
x_dims
.
production
(),
out_dims
.
production
());
for
(
int
i
=
0
;
i
<
out_dims
.
production
();
i
++
)
{
out_data
[
i
]
=
x_data
[
i
]
*
alpha
+
beta
;
}
}
void
test_dropout
(
int
bs
,
int
ic
,
int
ih
,
int
iw
,
std
::
string
dropout_implementation
,
float
dropout_prob
,
float
bias
)
{
// prepare input&output variables
Scope
scope
;
std
::
string
x_var_name
(
"x"
);
std
::
string
out_var_name
(
"out"
);
std
::
string
mask_var_name
(
"mask"
);
std
::
string
out_ref_var_name
(
"out_ref"
);
auto
*
x
=
scope
.
Var
(
x_var_name
)
->
GetMutable
<
Tensor
>
();
auto
*
out
=
scope
.
Var
(
out_var_name
)
->
GetMutable
<
Tensor
>
();
auto
*
mask
=
scope
.
Var
(
mask_var_name
)
->
GetMutable
<
Tensor
>
();
auto
*
out_ref
=
scope
.
Var
(
out_ref_var_name
)
->
GetMutable
<
Tensor
>
();
x
->
Resize
({
bs
,
ic
,
ih
,
iw
});
// initialize input&output data
FillTensor
<
float
,
int
>
(
x
);
// initialize op desc
bool
is_test
=
true
;
bool
fix_seed
=
false
;
int
seed
=
0
;
cpp
::
OpDesc
opdesc
;
opdesc
.
SetType
(
"dropout"
);
opdesc
.
SetInput
(
"X"
,
{
x_var_name
});
opdesc
.
SetOutput
(
"Out"
,
{
out_var_name
});
opdesc
.
SetOutput
(
"Mask"
,
{
mask_var_name
});
opdesc
.
SetAttr
(
"is_test"
,
is_test
);
opdesc
.
SetAttr
(
"fix_seed"
,
fix_seed
);
opdesc
.
SetAttr
(
"seed"
,
seed
);
opdesc
.
SetAttr
(
"dropout_implementation"
,
dropout_implementation
);
opdesc
.
SetAttr
(
"dropout_prob"
,
dropout_prob
);
VLOG
(
6
)
<<
"mask: "
<<
mask
->
dims
()[
0
]
<<
std
::
endl
;
// create and convert op to MLU model, then run it on MLU
auto
op
=
CreateOp
<
operators
::
DropoutOp
>
(
opdesc
,
&
scope
);
dropout_ref
(
op
);
out_ref
->
CopyDataFrom
(
*
out
);
Tensor
input_trans
;
input_trans
.
Resize
({
bs
,
ic
,
ih
,
iw
});
transpose
(
x
->
mutable_data
<
float
>
(),
input_trans
.
mutable_data
<
float
>
(),
{
bs
,
ic
,
ih
,
iw
},
{
0
,
2
,
3
,
1
});
auto
os
=
out
->
dims
();
out
->
Resize
({
static_cast
<
int
>
(
os
[
0
]),
static_cast
<
int
>
(
os
[
2
]),
static_cast
<
int
>
(
os
[
3
]),
static_cast
<
int
>
(
os
[
1
])});
x
->
CopyDataFrom
(
input_trans
);
x
->
Resize
({
bs
,
ih
,
iw
,
ic
});
LaunchOp
(
op
,
{
x_var_name
},
{
out_var_name
});
// execute reference implementation and save to output tensor('out')
// compare results
auto
*
out_data
=
out
->
mutable_data
<
float
>
();
auto
*
out_ref_data
=
out_ref
->
mutable_data
<
float
>
();
Tensor
output_trans
;
output_trans
.
Resize
(
os
);
transpose
(
out_data
,
output_trans
.
mutable_data
<
float
>
(),
{
static_cast
<
int
>
(
os
[
0
]),
static_cast
<
int
>
(
os
[
2
]),
static_cast
<
int
>
(
os
[
3
]),
static_cast
<
int
>
(
os
[
1
])},
{
0
,
3
,
1
,
2
});
out_data
=
output_trans
.
mutable_data
<
float
>
();
for
(
int
i
=
0
;
i
<
out
->
dims
().
production
();
i
++
)
{
VLOG
(
5
)
<<
i
;
EXPECT_NEAR
(
out_data
[
i
],
out_ref_data
[
i
],
1e-5
);
}
}
TEST
(
MLUBridges
,
dropout
)
{
for
(
auto
bs
:
{
1
,
3
})
{
for
(
auto
ic
:
{
1
,
3
})
{
for
(
auto
ih
:
{
3
,
4
})
{
for
(
auto
iw
:
{
4
,
3
})
{
for
(
auto
dropout_implementation
:
{
"downgrade_in_infer"
,
"upscale_in_train"
})
{
for
(
auto
dropout_prob
:
{
0.
f
,
1.0
f
})
{
VLOG
(
3
)
<<
"bs: "
<<
bs
<<
" ic: "
<<
ic
<<
" ih: "
<<
ih
<<
" iw: "
<<
iw
<<
" dropout_implementation: "
<<
dropout_implementation
<<
" dropout_prob: "
<<
dropout_prob
;
test_dropout
(
bs
,
ic
,
ih
,
iw
,
dropout_implementation
,
dropout_prob
,
0.
);
}
}
}
}
}
}
}
}
// namespace mlu
}
// namespace subgraph
}
// namespace lite
}
// namespace paddle
USE_SUBGRAPH_BRIDGE
(
dropout
,
kMLU
);
lite/kernels/mlu/bridges/paddle_use_bridges.h
浏览文件 @
f5fd8b20
...
...
@@ -28,3 +28,6 @@ USE_SUBGRAPH_BRIDGE(transpose, kMLU);
USE_SUBGRAPH_BRIDGE
(
transpose2
,
kMLU
);
USE_SUBGRAPH_BRIDGE
(
concat
,
kMLU
);
USE_SUBGRAPH_BRIDGE
(
scale
,
kMLU
);
USE_SUBGRAPH_BRIDGE
(
sigmoid
,
kMLU
);
USE_SUBGRAPH_BRIDGE
(
elementwise_mul
,
kMLU
);
USE_SUBGRAPH_BRIDGE
(
dropout
,
kMLU
);
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