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7cd585aa
编写于
5月 27, 2020
作者:
-
--get
提交者:
MaxwellDing
5月 28, 2020
浏览文件
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电子邮件补丁
差异文件
(feat): add norm mlu kernel and test
上级
e9e87ce7
变更
4
隐藏空白更改
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4 changed file
with
263 addition
and
0 deletion
+263
-0
lite/kernels/mlu/bridges/CMakeLists.txt
lite/kernels/mlu/bridges/CMakeLists.txt
+3
-0
lite/kernels/mlu/bridges/norm_op.cc
lite/kernels/mlu/bridges/norm_op.cc
+111
-0
lite/kernels/mlu/bridges/norm_op_test.cc
lite/kernels/mlu/bridges/norm_op_test.cc
+148
-0
lite/kernels/mlu/bridges/paddle_use_bridges.h
lite/kernels/mlu/bridges/paddle_use_bridges.h
+1
-0
未找到文件。
lite/kernels/mlu/bridges/CMakeLists.txt
浏览文件 @
7cd585aa
...
...
@@ -60,6 +60,8 @@ if (LITE_BUILD_EXTRA)
list
(
APPEND mlu_subgraph_bridges subgraph_bridge_lrn_op_mlu
)
lite_cc_library
(
subgraph_bridge_gather_op_mlu SRCS gather_op.cc DEPS
${
subgraph_bridge_deps_mlu
}
)
list
(
APPEND mlu_subgraph_bridges subgraph_bridge_gather_op_mlu
)
lite_cc_library
(
subgraph_bridge_norm_op_mlu SRCS norm_op.cc DEPS
${
subgraph_bridge_deps_mlu
}
)
list
(
APPEND mlu_subgraph_bridges subgraph_bridge_norm_op_mlu
)
endif
()
lite_cc_library
(
subgraph_test_helper_mlu SRCS test_helper.cc DEPS
${
mlu_subgraph_bridges
}
)
...
...
@@ -84,6 +86,7 @@ lite_cc_test(test_squeeze_converter_mlu SRCS squeeze_op_test.cc DEPS scope optim
lite_cc_test
(
test_reshape_converter_mlu SRCS reshape_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_flatten_converter_mlu SRCS flatten_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program
${
mlu_subgraph_bridges
}
subgraph_compute_mlu subgraph_test_helper_mlu
)
if
(
LITE_BUILD_EXTRA
)
lite_cc_test
(
test_norm_converter_mlu SRCS norm_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_lrn_converter_mlu SRCS lrn_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_gather_converter_mlu SRCS gather_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program
${
mlu_subgraph_bridges
}
subgraph_compute_mlu subgraph_test_helper_mlu
)
endif
()
...
...
lite/kernels/mlu/bridges/norm_op.cc
0 → 100644
浏览文件 @
7cd585aa
// 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
NormConverter
(
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
+
"..."
;
// Get input vars and op attributes
auto
x_var_name
=
op_info
->
Input
(
"X"
).
front
();
auto
x
=
scope
->
FindVar
(
x_var_name
)
->
GetMutable
<
Tensor
>
();
auto
x_dims
=
x
->
dims
().
Vectorize
();
auto
out_var_name
=
op_info
->
Output
(
"Out"
).
front
();
auto
output
=
scope
->
FindVar
(
out_var_name
)
->
GetMutable
<
Tensor
>
();
auto
output_dims
=
output
->
dims
().
Vectorize
();
int
axis
=
op_info
->
GetAttr
<
int
>
(
"axis"
);
int
epsilon
=
op_info
->
GetAttr
<
float
>
(
"epsilon"
);
if
(
axis
<
0
)
{
axis
=
axis
+
x_dims
.
size
();
}
std
::
vector
<
int
>
nchw2nhwc
=
{
0
,
3
,
1
,
2
};
int
nhwc_axis
=
nchw2nhwc
[
axis
];
CHECK
(
graph
->
HasNode
(
x_var_name
));
auto
input_tensor
=
graph
->
GetNode
(
x_var_name
);
auto
output_tensor
=
graph
->
AddNode
(
out_var_name
,
output_dims
,
CNML_TENSOR
,
CNML_NCHW
,
graph
->
FPType
());
// ======== DEBUG ===============
VLOG
(
6
)
<<
"x name="
<<
x_var_name
;
VLOG
(
6
)
<<
"out name="
<<
out_var_name
;
VLOG
(
6
)
<<
"x dims="
<<
x
->
dims
();
VLOG
(
6
)
<<
"out dims="
<<
output
->
dims
();
VLOG
(
6
)
<<
"axis ="
<<
axis
;
VLOG
(
6
)
<<
"nwhc axis="
<<
nhwc_axis
;
VLOG
(
6
)
<<
"epsilon ="
<<
epsilon
;
// cnmlPrintTensor(input_tensor->mlu_tensor(), CNML_TENSOR);
// cnmlPrintTensor(output_tensor->mlu_tensor(), CNML_TENSOR);
// ======== DEBUG END ============
cnmlBaseOp_t
norm_op
{
nullptr
};
cnmlNormalizeOpParam_t
param
;
int
mode
=
-
1
;
switch
(
axis
)
{
case
0
:
mode
=
3
;
// N
break
;
case
1
:
mode
=
0
;
// C
break
;
case
2
:
mode
=
4
;
// H
break
;
case
3
:
mode
=
5
;
// W
break
;
default:
CHECK
(
0
);
break
;
}
cnmlCreateNormalizeOpParamV2
(
&
param
,
0
,
// p
0
,
// use_scale
mode
,
1
,
// weight
epsilon
);
CNML_CALL
(
cnmlCreateNormalizeOp
(
&
norm_op
,
param
,
input_tensor
->
mlu_tensor
(),
output_tensor
->
mlu_tensor
(),
nullptr
,
false
/*is_fix8_mode*/
));
graph
->
FuseOp
(
norm_op
);
CNML_CALL
(
cnmlDestroyBaseOp
(
&
norm_op
));
return
SUCCESS
;
}
}
// namespace mlu
}
// namespace subgraph
}
// namespace lite
}
// namespace paddle
REGISTER_SUBGRAPH_BRIDGE
(
norm
,
kMLU
,
paddle
::
lite
::
subgraph
::
mlu
::
NormConverter
);
lite/kernels/mlu/bridges/norm_op_test.cc
0 → 100644
浏览文件 @
7cd585aa
// 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/norm_op.h"
#include <gtest/gtest.h>
#include <cmath>
#include <iostream>
#include "lite/core/op_registry.h"
#include "lite/kernels/mlu/bridges/test_helper.h"
#include "lite/kernels/mlu/bridges/utility.h"
#include "lite/kernels/npu/bridges/registry.h"
namespace
paddle
{
namespace
lite
{
namespace
subgraph
{
namespace
mlu
{
// void ToFile(std::string file_name, Tensor* tensor) {
// int count = tensor->dims().production();
// auto data = tensor->mutable_data<float>();
// std::ostringstream outs;
// for (size_t i = 0; i < count; i++) {
// outs << data[i] << std::endl;
// }
// std::ofstream of;
// of.open(file_name, std::ios::out);
// of << outs.str();
// of.close();
// }
void
norm_ref
(
const
std
::
shared_ptr
<
operators
::
NormOp
>
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
>
();
int
axis
=
op_info
->
GetAttr
<
int
>
(
"axis"
);
int
epsilon
=
op_info
->
GetAttr
<
float
>
(
"epsilon"
);
auto
x_dims
=
x
->
dims
();
if
(
axis
<
0
)
{
axis
+=
x_dims
.
size
();
}
out
->
Resize
(
x_dims
.
Vectorize
());
auto
*
out_data
=
out
->
mutable_data
<
float
>
();
const
auto
*
x_data
=
x
->
data
<
float
>
();
int
pre_n
=
x_dims
.
count
(
0
,
axis
);
int
n
=
x_dims
[
axis
];
int
post_n
=
x_dims
.
count
(
axis
+
1
,
x_dims
.
size
());
for
(
int
i
=
0
;
i
<
pre_n
;
i
++
)
{
for
(
int
k
=
0
;
k
<
post_n
;
k
++
)
{
float
sum
=
epsilon
;
const
float
*
in_tmp
=
x_data
+
i
*
n
*
post_n
+
k
;
for
(
int
j
=
0
;
j
<
n
;
j
++
)
{
sum
+=
in_tmp
[
j
*
post_n
]
*
in_tmp
[
j
*
post_n
];
}
sum
=
std
::
sqrt
(
sum
);
float
*
out_tmp
=
out_data
+
i
*
n
*
post_n
+
k
;
for
(
int
j
=
0
;
j
<
n
;
j
++
)
{
out_tmp
[
j
*
post_n
]
=
in_tmp
[
j
*
post_n
]
/
sum
;
}
}
}
}
void
test_norm
(
const
std
::
vector
<
int64_t
>&
input_shape
,
int
axis
)
{
// prepare input&output variables
Scope
scope
;
std
::
string
x_var_name
=
"x"
;
std
::
string
out_var_name
=
"out"
;
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
*
out_ref
=
scope
.
Var
(
out_ref_var_name
)
->
GetMutable
<
Tensor
>
();
x
->
Resize
(
input_shape
);
// initialize input&output data
FillTensor
<
float
,
float
>
(
x
,
-
9
,
9
);
// initialize op desc
cpp
::
OpDesc
opdesc
;
float
epsilon
=
1e-9
f
;
opdesc
.
SetType
(
"norm"
);
opdesc
.
SetInput
(
"X"
,
{
x_var_name
});
opdesc
.
SetOutput
(
"Out"
,
{
out_var_name
});
opdesc
.
SetAttr
(
"axis"
,
static_cast
<
int
>
(
axis
));
opdesc
.
SetAttr
(
"epsilon"
,
static_cast
<
float
>
(
epsilon
));
// create and convert op to MLU model, then run it on MLU
auto
op
=
CreateOp
<
operators
::
NormOp
>
(
opdesc
,
&
scope
);
norm_ref
(
op
);
out_ref
->
CopyDataFrom
(
*
out
);
Tensor
input_x
;
input_x
.
Resize
(
DDim
(
input_shape
));
// change input layout from NCHW to NHWC
transpose
<
float
>
(
x
->
mutable_data
<
float
>
(),
input_x
.
mutable_data
<
float
>
(),
{
static_cast
<
int
>
(
input_shape
[
0
]),
static_cast
<
int
>
(
input_shape
[
1
]),
static_cast
<
int
>
(
input_shape
[
2
]),
static_cast
<
int
>
(
input_shape
[
3
])},
{
0
,
2
,
3
,
1
});
x
->
CopyDataFrom
(
input_x
);
LaunchOp
(
op
,
{
x_var_name
},
{
out_var_name
});
auto
*
out_data
=
out
->
mutable_data
<
float
>
();
auto
*
out_ref_data
=
out_ref
->
mutable_data
<
float
>
();
std
::
vector
<
int64_t
>
out_shape
=
input_shape
;
Tensor
output_trans
;
output_trans
.
Resize
(
out_shape
);
// Change output layout from NHWC to NCHW
transpose
<
float
>
(
out_data
,
output_trans
.
mutable_data
<
float
>
(),
{
static_cast
<
int
>
(
out_shape
[
0
]),
static_cast
<
int
>
(
out_shape
[
2
]),
static_cast
<
int
>
(
out_shape
[
3
]),
static_cast
<
int
>
(
out_shape
[
1
])},
{
0
,
3
,
1
,
2
});
out_data
=
output_trans
.
mutable_data
<
float
>
();
for
(
int
i
=
0
;
i
<
out
->
dims
().
production
();
i
++
)
{
EXPECT_NEAR
(
out_data
[
i
],
out_ref_data
[
i
],
1e-2
);
}
}
TEST
(
MLUBridges
,
norm
)
{
test_norm
({
1
,
2
,
3
,
4
},
1
);
test_norm
({
1
,
2
,
3
,
4
},
2
);
test_norm
({
1
,
2
,
3
,
4
},
3
);
}
}
// namespace mlu
}
// namespace subgraph
}
// namespace lite
}
// namespace paddle
USE_SUBGRAPH_BRIDGE
(
norm
,
kMLU
);
lite/kernels/mlu/bridges/paddle_use_bridges.h
浏览文件 @
7cd585aa
...
...
@@ -42,4 +42,5 @@ USE_SUBGRAPH_BRIDGE(squeeze2, kMLU);
#ifdef LITE_BUILD_EXTRA
USE_SUBGRAPH_BRIDGE
(
gather
,
kMLU
);
USE_SUBGRAPH_BRIDGE
(
lrn
,
kMLU
)
USE_SUBGRAPH_BRIDGE
(
norm
,
kMLU
)
#endif
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