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9553fab2
编写于
5月 12, 2020
作者:
D
dingminghui
提交者:
jackzhang235
5月 21, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
feat(mlu): support NCHW node
上级
8d9cc823
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
117 addition
and
110 deletion
+117
-110
lite/core/mir/mlu_postprocess_pass.cc
lite/core/mir/mlu_postprocess_pass.cc
+2
-1
lite/kernels/mlu/bridges/graph.cc
lite/kernels/mlu/bridges/graph.cc
+2
-1
lite/kernels/mlu/bridges/graph.h
lite/kernels/mlu/bridges/graph.h
+2
-1
lite/kernels/mlu/bridges/tensor.cc
lite/kernels/mlu/bridges/tensor.cc
+28
-9
lite/kernels/mlu/bridges/tensor.h
lite/kernels/mlu/bridges/tensor.h
+5
-3
lite/kernels/mlu/bridges/test_helper.cc
lite/kernels/mlu/bridges/test_helper.cc
+35
-61
lite/kernels/mlu/bridges/utility.h
lite/kernels/mlu/bridges/utility.h
+22
-5
lite/kernels/mlu/layout_compute.h
lite/kernels/mlu/layout_compute.h
+7
-22
lite/kernels/mlu/subgraph_compute.h
lite/kernels/mlu/subgraph_compute.h
+14
-7
未找到文件。
lite/core/mir/mlu_postprocess_pass.cc
浏览文件 @
9553fab2
...
...
@@ -14,6 +14,7 @@
#include "lite/core/mir/mlu_postprocess_pass.h"
#include <list>
#include <map>
#include <memory>
#include <string>
#include <utility>
...
...
@@ -676,7 +677,7 @@ std::string CheckOutputAndInsert(cpp::BlockDesc* block_desc,
void
MLUPostprocessPass
::
AdjustSubgraph
(
Node
*
subgraph_node
,
const
Type
*
op_type
)
{
auto
subgraph_op
=
subgraph_node
->
AsStmt
().
op
();
CHECK
(
subgraph_op
->
Type
()
==
"subgraph"
);
CHECK
_EQ
(
subgraph_op
->
Type
(),
"subgraph"
);
auto
op
=
dynamic_cast
<
operators
::
SubgraphOp
*>
(
subgraph_op
.
get
());
CHECK
(
op
);
auto
block_desc
=
op
->
GetSubBlock
();
...
...
lite/kernels/mlu/bridges/graph.cc
浏览文件 @
9553fab2
...
...
@@ -27,10 +27,11 @@ std::shared_ptr<MLUTensor> Graph::AddNode(const std::string& name,
cnmlTensorType_t
tensor_type
,
cnmlDataOrder_t
shape_order
,
cnmlDataType_t
mlu_dtype
,
cnmlDataOrder_t
data_order
,
void
*
raw_ptr
)
{
CHECK
(
!
HasNode
(
name
));
auto
node
=
std
::
shared_ptr
<
MLUTensor
>
(
new
MLUTensor
(
shape
,
tensor_type
,
shape_order
,
mlu_dtype
));
new
MLUTensor
(
shape
,
tensor_type
,
shape_order
,
mlu_dtype
,
data_order
));
node
->
set_mlu_ptr
(
raw_ptr
);
nodes_
.
insert
(
std
::
make_pair
(
name
,
node
));
return
node
;
...
...
lite/kernels/mlu/bridges/graph.h
浏览文件 @
9553fab2
...
...
@@ -66,8 +66,9 @@ class Graph {
const
std
::
string
&
name
,
std
::
vector
<
int64_t
>
shape
,
cnmlTensorType_t
tensor_type
=
CNML_TENSOR
,
cnmlDataOrder_t
data
_order
=
CNML_NCHW
,
cnmlDataOrder_t
shape
_order
=
CNML_NCHW
,
cnmlDataType_t
mlu_dtype
=
CNML_DATA_FLOAT32
,
cnmlDataOrder_t
data_order
=
CNML_NHWC
,
void
*
raw_ptr
=
nullptr
);
std
::
shared_ptr
<
MLUTensor
>
GetNode
(
const
std
::
string
&
name
)
{
...
...
lite/kernels/mlu/bridges/tensor.cc
浏览文件 @
9553fab2
...
...
@@ -16,6 +16,8 @@
#include <glog/logging.h>
#include <algorithm>
#include <climits>
#include <fstream>
#include <sstream>
#include <string>
#include <vector>
...
...
@@ -26,8 +28,9 @@ namespace mlu {
MLUTensor
::
MLUTensor
(
const
std
::
vector
<
int64_t
>&
shape
,
cnmlTensorType_t
tensor_type
,
cnmlDataOrder_t
data_order
,
cnmlDataType_t
mlu_dtype
)
cnmlDataOrder_t
shape_order
,
cnmlDataType_t
mlu_dtype
,
cnmlDataOrder_t
data_order
)
:
mlu_tensor_
(
nullptr
),
tensor_type_
(
tensor_type
),
mlu_ptr_
(
nullptr
)
{
std
::
vector
<
int
>
int_shape
;
for
(
auto
i
:
shape
)
{
...
...
@@ -37,15 +40,17 @@ MLUTensor::MLUTensor(const std::vector<int64_t>& shape,
LOG
(
FATAL
)
<<
"Shape size is beyond the limitation of MLUTensor!"
;
}
}
remember
(
int_shape
,
tensor_type
,
mlu_dtype
,
data_order
);
remember
(
int_shape
,
tensor_type
,
mlu_dtype
,
shape_order
,
data_order
);
}
void
MLUTensor
::
remember
(
const
std
::
vector
<
int
>&
shape
,
cnmlTensorType_t
tensor_type
,
cnmlDataType_t
mlu_dtype
,
cnmlDataOrder_t
shape_order
)
{
cnmlDataOrder_t
shape_order
,
cnmlDataOrder_t
data_order
)
{
tensor_type_
=
tensor_type
;
mlu_dtype_
=
mlu_dtype
;
data_order_
=
data_order
;
origin_shape_
.
assign
(
shape
.
begin
(),
shape
.
end
());
int
size
=
4
;
...
...
@@ -248,6 +253,12 @@ void MLUTensor::Create() {
if
(
mlu_tensor_
==
nullptr
)
{
CNML_CALL
(
cnmlCreateTensor_V2
(
&
mlu_tensor_
,
tensor_type_
));
std
::
vector
<
int
>
dim_shape
(
shape_
);
if
(
data_order_
==
CNML_NCHW
)
{
std
::
transform
(
origin_shape_
.
cbegin
(),
origin_shape_
.
cend
(),
dim_shape
.
begin
(),
[](
DDim
::
value_type
in
)
{
return
static_cast
<
int
>
(
in
);
});
}
int
*
dim_strides
=
nullptr
;
CNML_CALL
(
cnmlSetTensorShape_V2
(
mlu_tensor_
,
dim_
,
dim_shape
.
data
(),
dim_strides
));
...
...
@@ -297,15 +308,23 @@ void MLUTensor::ToFile(std::string file_name) {
// trans to nchw
std
::
vector
<
float
>
cpu_data_trans
(
count
);
transpose
(
cpu_data_fp32
.
data
(),
cpu_data_trans
.
data
(),
shape_
,
{
0
,
3
,
1
,
2
});
if
(
data_order_
!=
CNML_NCHW
)
{
transpose
(
cpu_data_fp32
.
data
(),
cpu_data_trans
.
data
(),
shape_
,
{
0
,
3
,
1
,
2
});
}
// to file
std
::
ofstream
of
;
of
.
open
(
file_name
,
std
::
ios
::
out
);
std
::
ostringstream
outs
;
for
(
size_t
i
=
0
;
i
<
count
;
i
++
)
{
of
<<
cpu_data_trans
[
i
]
<<
std
::
endl
;
if
(
data_order_
==
CNML_NCHW
)
{
outs
<<
cpu_data_fp32
[
i
]
<<
std
::
endl
;
}
else
{
outs
<<
cpu_data_trans
[
i
]
<<
std
::
endl
;
}
}
std
::
ofstream
of
;
of
.
open
(
file_name
,
std
::
ios
::
out
);
of
<<
outs
.
str
();
of
.
close
();
}
else
{
LOG
(
FATAL
)
<<
"mlu ptr is null ,can not dump mlu content to : "
<<
file_name
...
...
lite/kernels/mlu/bridges/tensor.h
浏览文件 @
9553fab2
...
...
@@ -35,13 +35,15 @@ class MLUTensor {
MLUTensor
(
const
std
::
vector
<
int64_t
>&
shape
,
cnmlTensorType_t
tensor_type
=
CNML_TENSOR
,
cnmlDataOrder_t
data_order
=
CNML_NCHW
,
cnmlDataType_t
mlu_dtype
=
CNML_DATA_FLOAT32
);
cnmlDataOrder_t
shape_order
=
CNML_NCHW
,
cnmlDataType_t
mlu_dtype
=
CNML_DATA_FLOAT32
,
cnmlDataOrder_t
data_order
=
CNML_NHWC
);
void
remember
(
const
std
::
vector
<
int
>&
shape
,
cnmlTensorType_t
tensor_type
,
cnmlDataType_t
mlu_dtype
,
cnmlDataOrder_t
shape_order
);
cnmlDataOrder_t
shape_order
,
cnmlDataOrder_t
data_order
);
void
Create
();
cnmlTensor_t
mlu_tensor
();
void
*
mlu_data
()
{
...
...
lite/kernels/mlu/bridges/test_helper.cc
浏览文件 @
9553fab2
...
...
@@ -24,11 +24,35 @@ namespace lite {
namespace
subgraph
{
namespace
mlu
{
template
<
lite_api
::
PrecisionType
Dtype
>
void
PrepareInput
(
Graph
*
graph
,
const
std
::
string
&
input_name
,
Tensor
*
input_tensor
)
{
thread_local
Tensor
temp_input
;
temp_input
.
Resize
(
input_tensor
->
dims
().
Vectorize
());
temp_input
.
CopyDataFrom
(
*
input_tensor
);
using
data_type
=
typename
MLUTypeTraits
<
Dtype
>::
type
;
auto
input_node
=
graph
->
AddNode
(
input_name
,
input_tensor
->
dims
().
Vectorize
(),
CNML_TENSOR
,
CNML_NCHW
,
MLUTypeTraits
<
Dtype
>::
cnml_type
,
CNML_NHWC
,
reinterpret_cast
<
void
*>
(
input_tensor
->
template
mutable_data
<
data_type
>(
TARGET
(
kMLU
))));
CHECK
(
input_node
);
CNRT_CHECK
(
cnrtMemcpy
(
input_tensor
->
template
mutable_data
<
data_type
>(),
temp_input
.
mutable_data
<
data_type
>
(),
sizeof
(
data_type
)
*
input_tensor
->
dims
().
production
(),
CNRT_MEM_TRANS_DIR_HOST2DEV
));
}
void
LaunchOp
(
const
std
::
shared_ptr
<
lite
::
OpLite
>
op
,
const
std
::
vector
<
std
::
string
>&
input_var_names
,
const
std
::
vector
<
std
::
string
>&
output_var_names
)
{
CNRT_CALL
(
cnrtInit
(
0
));
::
paddle
::
lite
::
SetMluDevice
(
0
);
lite
::
SetMluDevice
(
0
);
cnrtQueue_t
queue_
;
cnrtInvokeFuncParam_t
forward_param
;
u32_t
affinity
=
1
;
...
...
@@ -51,70 +75,20 @@ void LaunchOp(const std::shared_ptr<lite::OpLite> op,
for
(
auto
&
input_name
:
input_var_names
)
{
auto
input_tensor
=
scope
->
FindMutableTensor
(
input_name
);
auto
data_type
=
input_tensor
->
precision
();
cnmlDataType_t
fp_type
;
switch
(
data_type
)
{
case
paddle
::
lite_api
::
PrecisionType
::
kFP16
:
fp_type
=
CNML_DATA_FLOAT16
;
break
;
case
paddle
::
lite_api
::
PrecisionType
::
kFloat
:
fp_type
=
CNML_DATA_FLOAT32
;
break
;
case
paddle
::
lite_api
::
PrecisionType
::
kInt32
:
fp_type
=
CNML_DATA_INT32
;
break
;
#define PREPARE_INPUT(type__) \
case PRECISION(type__): \
PrepareInput<PRECISION(type__)>(&graph, input_name, input_tensor); \
break;
PREPARE_INPUT
(
kFP16
)
PREPARE_INPUT
(
kFloat
)
PREPARE_INPUT
(
kInt8
)
PREPARE_INPUT
(
kInt32
)
#undef PREPARE_INPUT
default:
CHECK
(
0
);
}
CHECK
(
input_tensor
);
Tensor
temp_input
;
temp_input
.
Resize
(
input_tensor
->
dims
().
Vectorize
());
temp_input
.
CopyDataFrom
(
*
input_tensor
);
if
(
fp_type
==
CNML_DATA_INT32
)
{
auto
input_node
=
graph
.
AddNode
(
input_name
,
input_tensor
->
dims
().
Vectorize
(),
CNML_TENSOR
,
CNML_NCHW
,
fp_type
,
reinterpret_cast
<
void
*>
(
input_tensor
->
mutable_data
<
int
>
(
TARGET
(
kMLU
))));
CHECK
(
input_node
);
CNRT_CHECK
(
cnrtMemcpy
(
input_tensor
->
mutable_data
<
int
>
(),
temp_input
.
mutable_data
<
int
>
(),
sizeof
(
int
)
*
input_tensor
->
dims
().
production
(),
CNRT_MEM_TRANS_DIR_HOST2DEV
));
}
else
if
(
fp_type
==
CNML_DATA_FLOAT16
)
{
auto
input_node
=
graph
.
AddNode
(
input_name
,
input_tensor
->
dims
().
Vectorize
(),
CNML_TENSOR
,
CNML_NCHW
,
fp_type
,
reinterpret_cast
<
void
*>
(
input_tensor
->
mutable_data
<
paddle
::
lite
::
fluid
::
float16
>
(
TARGET
(
kMLU
))));
CHECK
(
input_node
);
CNRT_CHECK
(
cnrtMemcpy
(
input_tensor
->
mutable_data
<
paddle
::
lite
::
fluid
::
float16
>
(),
temp_input
.
mutable_data
<
paddle
::
lite
::
fluid
::
float16
>
(),
sizeof
(
paddle
::
lite
::
fluid
::
float16
)
*
input_tensor
->
dims
().
production
(),
CNRT_MEM_TRANS_DIR_HOST2DEV
));
}
else
{
auto
input_node
=
graph
.
AddNode
(
input_name
,
input_tensor
->
dims
().
Vectorize
(),
CNML_TENSOR
,
CNML_NCHW
,
fp_type
,
reinterpret_cast
<
void
*>
(
input_tensor
->
mutable_data
<
float
>
(
TARGET
(
kMLU
))));
CHECK
(
input_node
);
CNRT_CHECK
(
cnrtMemcpy
(
input_tensor
->
mutable_data
<
float
>
(),
temp_input
.
mutable_data
<
float
>
(),
sizeof
(
float
)
*
input_tensor
->
dims
().
production
(),
CNRT_MEM_TRANS_DIR_HOST2DEV
));
}
}
op
->
CheckShape
();
op
->
InferShape
();
...
...
lite/kernels/mlu/bridges/utility.h
浏览文件 @
9553fab2
...
...
@@ -144,16 +144,33 @@ inline const std::vector<DDimLite::value_type> DimNCHW2NHWC(
}
template
<
paddle
::
lite_api
::
PrecisionType
>
struct
FPTypeTraits
{};
struct
MLUTypeTraits
{
/* using type = void; */
/* static constexpr cnmlDataType_t cnml_type = CNML_DATA_INVALID; */
};
template
<
>
struct
MLUTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kFloat
>
{
using
type
=
float
;
static
constexpr
cnmlDataType_t
cnml_type
=
CNML_DATA_FLOAT32
;
};
template
<
>
struct
MLUTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kFP16
>
{
using
type
=
paddle
::
lite
::
fluid
::
float16
;
static
constexpr
cnmlDataType_t
cnml_type
=
CNML_DATA_FLOAT16
;
};
template
<
>
struct
FPTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kFloat
>
{
typedef
float
T
;
struct
MLUTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kInt8
>
{
using
type
=
int8_t
;
static
constexpr
cnmlDataType_t
cnml_type
=
CNML_DATA_INT8
;
};
template
<
>
struct
FPTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kFP16
>
{
typedef
paddle
::
lite
::
fluid
::
float16
T
;
struct
MLUTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kInt32
>
{
using
type
=
int32_t
;
static
constexpr
cnmlDataType_t
cnml_type
=
CNML_DATA_INT32
;
};
}
// namespace mlu
...
...
lite/kernels/mlu/layout_compute.h
浏览文件 @
9553fab2
...
...
@@ -22,6 +22,7 @@
#include "lite/core/op_lite.h"
#include "lite/core/op_registry.h"
#include "lite/core/type_system.h"
#include "lite/kernels/mlu/bridges/utility.h"
#include "lite/operators/layout_op.h"
namespace
paddle
{
...
...
@@ -29,24 +30,6 @@ namespace lite {
namespace
kernels
{
namespace
mlu
{
template
<
paddle
::
lite_api
::
PrecisionType
>
struct
FPTypeTraits
{};
template
<
>
struct
FPTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kFloat
>
{
using
type
=
float
;
};
template
<
>
struct
FPTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kFP16
>
{
using
type
=
paddle
::
lite
::
fluid
::
float16
;
};
template
<
>
struct
FPTypeTraits
<
paddle
::
lite_api
::
PrecisionType
::
kInt8
>
{
using
type
=
int8_t
;
};
template
<
lite
::
TargetType
Target
,
typename
T
>
inline
void
LayoutTransCompute
(
const
int
dim
,
const
lite
::
Context
<
Target
>&
context
,
...
...
@@ -81,7 +64,8 @@ class LayoutNchwToNhwcCompute
auto
&
param
=
this
->
template
Param
<
param_t
>();
auto
*
x
=
param
.
x
;
auto
*
out
=
param
.
y
;
out
->
template
mutable_data
<
typename
FPTypeTraits
<
Precision
>
::
type
>
();
out
->
template
mutable_data
<
typename
subgraph
::
mlu
::
MLUTypeTraits
<
Precision
>
::
type
>
();
auto
x_ndims
=
param
.
x
->
dims
().
size
();
auto
&
context
=
this
->
ctx_
->
template
As
<
X86Context
>();
...
...
@@ -107,7 +91,7 @@ class LayoutNchwToNhwcCompute
}
LayoutTransCompute
<
lite
::
TargetType
::
kX86
,
typename
FP
TypeTraits
<
Precision
>::
type
>
(
typename
subgraph
::
mlu
::
MLU
TypeTraits
<
Precision
>::
type
>
(
x_ndims
,
context
,
*
x
,
out
,
axis
);
if
(
x_ndims
>
2
)
{
...
...
@@ -130,7 +114,8 @@ class LayoutNhwcToNchwCompute
auto
&
param
=
this
->
template
Param
<
param_t
>();
auto
*
x
=
param
.
x
;
auto
*
out
=
param
.
y
;
out
->
template
mutable_data
<
typename
FPTypeTraits
<
Precision
>
::
type
>
();
out
->
template
mutable_data
<
typename
subgraph
::
mlu
::
MLUTypeTraits
<
Precision
>
::
type
>
();
auto
&
context
=
this
->
ctx_
->
template
As
<
X86Context
>();
TensorLite
tmp_t
;
...
...
@@ -157,7 +142,7 @@ class LayoutNhwcToNchwCompute
}
LayoutTransCompute
<
lite
::
TargetType
::
kX86
,
typename
FP
TypeTraits
<
Precision
>::
type
>
(
typename
subgraph
::
mlu
::
MLU
TypeTraits
<
Precision
>::
type
>
(
x_ndims
,
context
,
tmp_t
,
out
,
axis
);
}
...
...
lite/kernels/mlu/subgraph_compute.h
浏览文件 @
9553fab2
...
...
@@ -147,6 +147,9 @@ class SubgraphEngine : public subgraph::Engine {
origin_itensors_
.
clear
();
origin_otensors_
.
clear
();
auto
data_order
=
block_desc_
->
GetOp
<
cpp
::
OpDesc
>
(
0
)
->
Type
()
==
"cast"
?
CNML_NCHW
:
CNML_NHWC
;
// Convert all of input data vars and added into the MLU IR graph
status
|=
subgraph
::
REBUILD_WHEN_SHAPE_CHANGED
;
for
(
auto
&
input_name
:
input_names_
)
{
...
...
@@ -167,7 +170,8 @@ class SubgraphEngine : public subgraph::Engine {
input_tensor
->
dims
().
Vectorize
(),
CNML_TENSOR
,
CNML_NCHW
,
fp_type
);
fp_type
,
data_order
);
CHECK
(
input_node
);
// MLU doesn't support dynamic dimensions/shapes, so need to rebuild
// the program when the shape of any input tensor is changed.
...
...
@@ -367,8 +371,9 @@ class SubgraphEngine : public subgraph::Engine {
// origin_otensors_[i]->Resize(new_output_size.at(i));
void
*
p_data
=
static_cast
<
void
*>
(
origin_otensors_
[
i
]
->
mutable_data
<
typename
paddle
::
lite
::
subgraph
::
mlu
::
FPTypeTraits
<
Precision
>::
T
>
(
TARGET
(
kMLU
)));
->
template
mutable_data
<
typename
subgraph
::
mlu
::
MLUTypeTraits
<
Precision
>
::
type
>
(
TARGET
(
kMLU
)));
graph_out
[
i
]
->
set_mlu_ptr
(
p_data
);
}
}
else
{
...
...
@@ -377,8 +382,9 @@ class SubgraphEngine : public subgraph::Engine {
// origin_otensors_[i]->Resize(new_output_size.at(i));
void
*
p_data
=
static_cast
<
void
*>
(
origin_otensors_
[
i
]
->
mutable_data
<
typename
paddle
::
lite
::
subgraph
::
mlu
::
FPTypeTraits
<
Precision
>::
T
>
(
TARGET
(
kMLU
)));
->
template
mutable_data
<
typename
subgraph
::
mlu
::
MLUTypeTraits
<
Precision
>
::
type
>
(
TARGET
(
kMLU
)));
paddle
::
lite
::
subgraph
::
mlu
::
MLUTensor
tmp
(
origin_otensors_
[
i
]
->
dims
().
Vectorize
());
tmp
.
set_mlu_dtype
(
graph_output
->
at
(
i
)
->
dtype
());
...
...
@@ -398,8 +404,9 @@ class SubgraphEngine : public subgraph::Engine {
origin_otensors_
[
i
]
->
Resize
(
graph_output
->
at
(
i
)
->
get_origin_shape
());
void
*
p_data
=
static_cast
<
void
*>
(
origin_otensors_
[
i
]
->
mutable_data
<
typename
paddle
::
lite
::
subgraph
::
mlu
::
FPTypeTraits
<
Precision
>::
T
>
(
TARGET
(
kMLU
)));
->
template
mutable_data
<
typename
subgraph
::
mlu
::
MLUTypeTraits
<
Precision
>
::
type
>
(
TARGET
(
kMLU
)));
graph_output
->
at
(
i
)
->
set_mlu_ptr
(
p_data
);
}
graph
->
Compute
(
forward_param
,
exec_queue
);
...
...
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