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89df8f01
编写于
11月 27, 2019
作者:
myq406450149
提交者:
GitHub
11月 27, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fill_constant op support param shape can be tensor or tensorlist, test=develop (#2459)
* fill_constant can support shape is tensor or tensorlist
上级
826f6605
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
277 addition
and
0 deletion
+277
-0
lite/kernels/arm/fill_constant_compute.cc
lite/kernels/arm/fill_constant_compute.cc
+37
-0
lite/kernels/x86/fill_constant_compute.cc
lite/kernels/x86/fill_constant_compute.cc
+36
-0
lite/operators/fill_constant_op.cc
lite/operators/fill_constant_op.cc
+23
-0
lite/operators/op_params.h
lite/operators/op_params.h
+3
-0
lite/tests/kernels/fill_constant_compute_test.cc
lite/tests/kernels/fill_constant_compute_test.cc
+178
-0
未找到文件。
lite/kernels/arm/fill_constant_compute.cc
浏览文件 @
89df8f01
...
...
@@ -25,6 +25,38 @@ class FillConstantCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
using
param_t
=
operators
::
FillConstantParam
;
inline
DDimLite
GetShape
(
const
param_t
&
param
)
{
// 1. shape is a Tensor
if
(
param
.
shape_tensor
!=
nullptr
)
{
auto
*
shape_tensor
=
param
.
shape_tensor
;
auto
*
shape_data
=
shape_tensor
->
data
<
int
>
();
auto
vec_shape
=
std
::
vector
<
int64_t
>
(
shape_data
,
shape_data
+
shape_tensor
->
numel
());
return
DDimLite
(
vec_shape
);
}
// 2. shape is a list/tuple containing Tensor
auto
shape_tensor_list
=
param
.
shape_tensor_list
;
if
(
shape_tensor_list
.
size
()
>
0
)
{
std
::
vector
<
int64_t
>
vec_shape
;
for
(
size_t
i
=
0
;
i
<
shape_tensor_list
.
size
();
++
i
)
{
auto
tensor
=
shape_tensor_list
[
i
];
vec_shape
.
push_back
(
*
tensor
->
data
<
int
>
());
}
return
DDimLite
(
vec_shape
);
}
// 3. shape is a list/tuple without containing Tensor
auto
vec_shape
=
param
.
shape
;
return
DDimLite
(
vec_shape
);
}
void
PrepareForRun
()
override
{
auto
&
param
=
*
param_
.
get_mutable
<
param_t
>
();
auto
outdims
=
GetShape
(
param
);
param
.
Out
->
Resize
(
outdims
);
}
void
Run
()
override
{
auto
&
param
=
*
param_
.
get_mutable
<
param_t
>
();
auto
&
context
=
ctx_
->
As
<
ARMContext
>
();
...
...
@@ -107,6 +139,11 @@ REGISTER_LITE_KERNEL(fill_constant,
kNCHW
,
paddle
::
lite
::
kernels
::
arm
::
FillConstantCompute
<
float
>
,
def
)
.
BindInput
(
"X"
,
{
LiteType
::
GetTensorTy
(
TARGET
(
kARM
))})
.
BindInput
(
"ShapeTensor"
,
{
LiteType
::
GetTensorTy
(
TARGET
(
kARM
),
PRECISION
(
kInt32
))})
.
BindInput
(
"ShapeTensorList"
,
{
LiteType
::
GetTensorTy
(
TARGET
(
kARM
),
PRECISION
(
kInt32
))})
.
BindOutput
(
"Out"
,
{
LiteType
::
GetTensorTy
(
TARGET
(
kARM
))})
.
Finalize
();
REGISTER_LITE_KERNEL
(
...
...
lite/kernels/x86/fill_constant_compute.cc
浏览文件 @
89df8f01
...
...
@@ -29,6 +29,38 @@ class FillConstantCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
public:
using
param_t
=
operators
::
FillConstantParam
;
inline
DDimLite
GetShape
(
const
param_t
&
param
)
{
// 1. shape is a Tensor
if
(
param
.
shape_tensor
!=
nullptr
)
{
auto
*
shape_tensor
=
param
.
shape_tensor
;
auto
*
shape_data
=
shape_tensor
->
data
<
int
>
();
auto
vec_shape
=
std
::
vector
<
int64_t
>
(
shape_data
,
shape_data
+
shape_tensor
->
numel
());
return
DDimLite
(
vec_shape
);
}
// 2. shape is a list/tuple containing Tensor
auto
shape_tensor_list
=
param
.
shape_tensor_list
;
if
(
shape_tensor_list
.
size
()
>
0
)
{
std
::
vector
<
int64_t
>
vec_shape
;
for
(
size_t
i
=
0
;
i
<
shape_tensor_list
.
size
();
++
i
)
{
auto
tensor
=
shape_tensor_list
[
i
];
vec_shape
.
push_back
(
*
tensor
->
data
<
int
>
());
}
return
DDimLite
(
vec_shape
);
}
// 3. shape is a list/tuple without containing Tensor
auto
vec_shape
=
param
.
shape
;
return
DDimLite
(
vec_shape
);
}
void
PrepareForRun
()
override
{
auto
&
param
=
*
param_
.
get_mutable
<
param_t
>
();
auto
outdims
=
GetShape
(
param
);
param
.
Out
->
Resize
(
outdims
);
}
void
Run
()
override
{
auto
&
param
=
*
param_
.
get_mutable
<
param_t
>
();
auto
&
context
=
ctx_
->
As
<
X86Context
>
();
...
...
@@ -55,5 +87,9 @@ REGISTER_LITE_KERNEL(fill_constant,
kNCHW
,
paddle
::
lite
::
kernels
::
x86
::
FillConstantCompute
<
float
>
,
def
)
.
BindInput
(
"ShapeTensor"
,
{
LiteType
::
GetTensorTy
(
TARGET
(
kARM
),
PRECISION
(
kInt32
))})
.
BindInput
(
"ShapeTensorList"
,
{
LiteType
::
GetTensorTy
(
TARGET
(
kARM
),
PRECISION
(
kInt32
))})
.
BindOutput
(
"Out"
,
{
LiteType
::
GetTensorTy
(
TARGET
(
kX86
))})
.
Finalize
();
lite/operators/fill_constant_op.cc
浏览文件 @
89df8f01
...
...
@@ -29,6 +29,12 @@ class FillConstantOp : public OpLite {
}
bool
InferShape
()
const
override
{
lite
::
Tensor
*
shape_tensor_
=
param_
.
shape_tensor
;
if
(
param_
.
shape
.
empty
()
&&
shape_tensor_
!=
nullptr
)
{
param_
.
Out
->
Resize
(
shape_tensor_
->
dims
());
return
true
;
}
param_
.
Out
->
Resize
(
param_
.
shape
);
return
true
;
}
...
...
@@ -41,6 +47,23 @@ class FillConstantOp : public OpLite {
param_
.
shape
=
opdesc
.
GetAttr
<
std
::
vector
<
int64_t
>>
(
"shape"
);
param_
.
value
=
opdesc
.
GetAttr
<
float
>
(
"value"
);
param_
.
force_cpu
=
opdesc
.
GetAttr
<
bool
>
(
"force_cpu"
);
param_
.
shape_tensor
=
nullptr
;
param_
.
shape_tensor_list
=
{};
std
::
vector
<
std
::
string
>
input_arg_names
=
opdesc
.
InputArgumentNames
();
if
(
std
::
find
(
input_arg_names
.
begin
(),
input_arg_names
.
end
(),
"ShapeTensor"
)
!=
input_arg_names
.
end
())
{
auto
args
=
opdesc
.
Input
(
"ShapeTensor"
);
auto
*
var
=
scope
->
FindVar
(
args
.
front
());
param_
.
shape_tensor
=
var
->
GetMutable
<
lite
::
Tensor
>
();
}
if
(
opdesc
.
HasAttr
(
"ShapeTensorList"
))
{
auto
args
=
opdesc
.
Input
(
"ShapeTensorList"
);
auto
*
var
=
scope
->
FindVar
(
args
.
front
());
param_
.
shape_tensor_list
=
*
(
var
->
GetMutable
<
std
::
vector
<
lite
::
Tensor
*>>
());
}
return
true
;
}
...
...
lite/operators/op_params.h
浏览文件 @
89df8f01
...
...
@@ -408,6 +408,9 @@ struct MeanGradParam {
struct
FillConstantParam
{
int
dtype
{
static_cast
<
int
>
(
VarDescAPI
::
VarDataType
::
FP32
)};
std
::
vector
<
int64_t
>
shape
{};
lite
::
Tensor
*
shape_tensor
;
std
::
vector
<
lite
::
Tensor
*>
shape_tensor_list
{};
float
value
{
0.0
f
};
// useless for x86, keep it for compatibility
bool
force_cpu
{
false
};
...
...
lite/tests/kernels/fill_constant_compute_test.cc
0 → 100644
浏览文件 @
89df8f01
// 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 <gtest/gtest.h>
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/core/arena/framework.h"
namespace
paddle
{
namespace
lite
{
class
FillConstantComputeTester
:
public
arena
::
TestCase
{
protected:
// common attributes for this op.
std
::
string
out_
=
"out"
;
int
dtype_
{
static_cast
<
int
>
(
VarDescAPI
::
VarDataType
::
FP32
)};
std
::
vector
<
int64_t
>
shape_
{};
std
::
string
shape_tensor_
=
"ShapeTensor"
;
std
::
vector
<
std
::
string
>
shape_tensor_list_
;
bool
is_use_shape_tensor_
{
false
};
bool
is_use_shape_tensor_list_
{
false
};
float
value_
{
0.0
f
};
// useless for x86, keep it for compatibility
bool
force_cpu_
{
false
};
// DDim shape_tensor_data{{5, 3}};
std
::
vector
<
int32_t
>
shape_tensor_data
;
DDim
shape_test
{{
1
,
2
}};
public:
FillConstantComputeTester
(
const
Place
&
place
,
const
std
::
string
&
alias
,
std
::
vector
<
int64_t
>
shape
,
const
bool
is_use_shape_tensor
,
const
bool
is_use_shape_tensor_list
,
float
value
,
bool
force_cpu
)
:
TestCase
(
place
,
alias
)
{
shape_
=
shape
;
value_
=
value
;
force_cpu_
=
force_cpu
;
is_use_shape_tensor_
=
is_use_shape_tensor
;
is_use_shape_tensor_list_
=
is_use_shape_tensor_list
;
for
(
int
i
=
0
;
i
<
shape_test
.
size
();
i
++
)
{
shape_tensor_data
.
push_back
(
i
+
1
);
}
}
void
RunBaseline
(
Scope
*
scope
)
override
{
auto
*
out
=
scope
->
NewTensor
(
out_
);
DDim
output_dims
{
shape_
};
if
(
is_use_shape_tensor_
)
{
auto
*
temp_shape
=
scope
->
FindTensor
(
shape_tensor_
);
auto
*
shape_data
=
temp_shape
->
data
<
int
>
();
auto
vec_shape
=
std
::
vector
<
int64_t
>
(
shape_data
,
shape_data
+
temp_shape
->
numel
());
output_dims
.
ConstructFrom
(
vec_shape
);
}
if
(
is_use_shape_tensor_list_
)
{
std
::
vector
<
int64_t
>
vec_shape
;
for
(
int
i
=
0
;
i
<
shape_tensor_list_
.
size
();
i
++
)
{
auto
*
temp_shape
=
scope
->
FindTensor
(
shape_tensor_list_
[
i
]);
vec_shape
.
push_back
(
*
temp_shape
->
data
<
int
>
());
}
output_dims
.
ConstructFrom
(
vec_shape
);
}
out
->
Resize
(
output_dims
);
auto
*
output_data
=
out
->
mutable_data
<
float
>
();
for
(
int
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
output_data
[
i
]
=
value_
;
}
}
void
PrepareOpDesc
(
cpp
::
OpDesc
*
op_desc
)
{
LOG
(
INFO
)
<<
"PrepareOpDesc"
;
op_desc
->
SetType
(
"fill_constant"
);
op_desc
->
SetAttr
(
"dtype"
,
dtype_
);
op_desc
->
SetAttr
(
"shape"
,
shape_
);
op_desc
->
SetAttr
(
"value"
,
value_
);
op_desc
->
SetAttr
(
"force_cpu"
,
force_cpu_
);
if
(
is_use_shape_tensor_
)
{
op_desc
->
SetInput
(
"ShapeTensor"
,
{
shape_tensor_
});
}
if
(
is_use_shape_tensor_list_
)
{
// std::vector<std::string> shape_tensor_list_;
for
(
int
i
=
0
;
i
<
shape_test
.
size
();
++
i
)
{
shape_tensor_list_
.
push_back
(
"shape_tensor_list_"
+
std
::
to_string
(
i
));
}
op_desc
->
SetInput
(
"ShapeTensorList"
,
{
shape_tensor_list_
});
}
op_desc
->
SetOutput
(
"Out"
,
{
out_
});
}
void
PrepareData
()
override
{
if
(
is_use_shape_tensor_
)
{
// std::vector<int64_t> temp = x_dims_.data();
// int64_t* data = temp.data();
SetCommonTensor
(
shape_tensor_
,
shape_test
,
shape_tensor_data
.
data
());
}
if
(
is_use_shape_tensor_list_
)
{
Scope
&
scope_
=
this
->
scope
();
for
(
int
i
=
0
;
i
<
shape_test
.
size
();
++
i
)
{
auto
*
tensor
=
scope_
.
NewTensor
(
"shape_tensor_list_"
+
std
::
to_string
(
i
));
tensor
->
Resize
(
DDim
({
1
}));
auto
*
d
=
tensor
->
mutable_data
<
int
>
();
d
[
0
]
=
shape_tensor_data
[
i
];
}
}
}
};
TEST
(
fill_constant
,
precision
)
{
LOG
(
INFO
)
<<
"test fill_constant op, kARM"
;
#ifdef LITE_WITH_ARM
Place
place
(
TARGET
(
kARM
));
std
::
vector
<
int64_t
>
shape
{
1
,
2
};
for
(
int
dtype
:
{
static_cast
<
int
>
(
VarDescAPI
::
VarDataType
::
INT32
)})
{
for
(
float
value
:
{
1
,
2
})
{
for
(
bool
is_use_shape_tensor_list
:
{
false
,
true
})
{
for
(
bool
is_use_shape_tensor
:
{
false
,
true
})
{
if
(
is_use_shape_tensor
&&
is_use_shape_tensor_list
)
break
;
LOG
(
INFO
)
<<
"value:"
<<
value
<<
", is_use_shape_tensor:"
<<
is_use_shape_tensor
<<
", is_use_shape_tensor_list:"
<<
is_use_shape_tensor_list
;
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
FillConstantComputeTester
(
place
,
"def"
,
shape
,
is_use_shape_tensor
,
is_use_shape_tensor_list
,
value
,
false
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
2e-5
);
arena
.
TestPrecision
();
}
}
}
}
#endif
#ifdef LITE_WITH_X86
Place
place
(
TARGET
(
kX86
));
LOG
(
INFO
)
<<
"test concate op, x86"
;
for
(
int
axis
:
{
1
,
2
})
{
for
(
bool
is_use_axis_tensor
:
{
false
,
true
})
{
LOG
(
INFO
)
<<
"axis:"
<<
axis
<<
", is_use_axis_tensor:"
<<
is_use_axis_tensor
;
std
::
unique_ptr
<
arena
::
TestCase
>
tester
(
new
ConcateComputeTester
(
place
,
"def"
,
axis
,
is_use_axis_tensor
));
arena
::
Arena
arena
(
std
::
move
(
tester
),
place
,
2e-5
);
arena
.
TestPrecision
();
}
}
#endif
}
}
// namespace lite
}
// namespace paddle
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