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738069e4
编写于
12月 03, 2018
作者:
M
Michal Gallus
浏览文件
操作
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电子邮件补丁
差异文件
Refactor MKL-DNN Concat
test=develop
上级
208f9125
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
72 addition
and
137 deletion
+72
-137
paddle/fluid/operators/concat_mkldnn_op.cc
paddle/fluid/operators/concat_mkldnn_op.cc
+72
-137
未找到文件。
paddle/fluid/operators/concat_mkldnn_op.cc
浏览文件 @
738069e4
...
...
@@ -12,6 +12,7 @@ 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 <memory>
#include "paddle/fluid/operators/concat_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
...
...
@@ -26,25 +27,6 @@ using mkldnn::concat;
using
mkldnn
::
stream
;
using
platform
::
to_void_cast
;
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Make hashing function more optimial
static
std
::
string
gethash
(
const
memory
::
dims
&
input_dims
,
const
std
::
string
&
pooling_type
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
string
&
suffix
)
{
auto
dims2str
=
[](
const
memory
::
dims
&
operand_dims
)
{
std
::
string
dstr
=
""
;
for
(
size_t
i
=
0
;
i
<
operand_dims
.
size
();
++
i
)
{
dstr
+=
std
::
to_string
(
operand_dims
[
i
])
+
"-"
;
}
return
dstr
;
};
return
dims2str
(
input_dims
)
+
dims2str
(
ksize
)
+
dims2str
(
strides
)
+
dims2str
(
paddings
)
+
pooling_type
+
suffix
;
}
static
void
EnforceLayouts
(
const
std
::
vector
<
const
Tensor
*>
inputs
)
{
for
(
auto
*
input
:
inputs
)
{
const
bool
is_layout_correct
=
input
->
layout
()
==
DataLayout
::
kMKLDNN
;
...
...
@@ -56,7 +38,7 @@ static void EnforceLayouts(const std::vector<const Tensor*> inputs) {
}
static
memory
::
primitive_desc
CreateMemPrimDesc
(
const
framework
::
Tensor
&
input
,
const
mkldnn
::
engine
&
engine
)
{
const
Tensor
&
input
,
const
mkldnn
::
engine
&
engine
)
{
constexpr
auto
data_type
=
mkldnn
::
memory
::
f32
;
const
auto
dims
=
paddle
::
framework
::
vectorize2int
(
input
.
dims
());
const
auto
format
=
input
.
format
();
...
...
@@ -65,6 +47,11 @@ static memory::primitive_desc CreateMemPrimDesc(
return
mem_prim_desc
;
}
static
mkldnn
::
memory
::
format
GetDstMemFormat
(
const
concat
::
primitive_desc
&
concat_pd
)
{
return
(
memory
::
format
)
concat_pd
.
dst_primitive_desc
().
desc
().
data
.
format
;
}
static
platform
::
CPUPlace
GetCpuPlace
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
{
auto
place
=
ctx
.
GetPlace
();
...
...
@@ -73,139 +60,87 @@ static platform::CPUPlace GetCpuPlace(
return
boost
::
get
<
platform
::
CPUPlace
>
(
place
);
}
template
<
typename
T
>
class
ConcatMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
place
=
GetCpuPlace
(
ctx
);
static
const
mkldnn
::
engine
&
GetMKLDNNEngine
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
{
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
return
dev_ctx
.
GetEngine
();
}
auto
multi_input
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
framework
::
Tensor
*
output
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
int64_t
concat_axis
=
static_cast
<
int64_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
template
<
typename
T
>
class
ConcatPrimitiveFactory
{
public:
concat
::
primitive_desc
CreateConcatPrimDescriptor
(
const
std
::
vector
<
const
Tensor
*>
multi_input
,
Tensor
*
output
,
int
concat_axis
,
const
mkldnn
::
engine
&
mkldnn_engine
)
{
CreateSourcesDescriptors
(
multi_input
,
mkldnn_engine
);
auto
dst_desc
=
CreateDstMemDescriptor
(
output
);
return
concat
::
primitive_desc
(
dst_desc
,
concat_axis
,
srcs_pd
);
}
EnforceLayouts
(
multi_input
);
concat
CreateConcatPrimitive
(
const
concat
::
primitive_desc
&
concat_pd
,
Tensor
*
output
,
platform
::
CPUPlace
place
)
{
CreateSourcePrimitiveAts
();
auto
dst_mem
=
CreateDstMemory
(
concat_pd
,
output
,
place
);
return
concat
(
concat_pd
,
inputs
,
dst_mem
);
}
private:
memory
::
desc
CreateDstMemDescriptor
(
Tensor
*
output
)
{
auto
dst_dims
=
paddle
::
framework
::
vectorize2int
(
output
->
dims
());
return
memory
::
desc
(
dst_dims
,
platform
::
MKLDNNGetDataType
<
T
>
(),
memory
::
format
::
any
);
}
mkldnn
::
memory
CreateDstMemory
(
const
concat
::
primitive_desc
&
concat_pd
,
Tensor
*
output
,
platform
::
CPUPlace
place
)
{
return
memory
(
concat_pd
.
dst_primitive_desc
(),
output
->
mutable_data
<
T
>
(
place
));
}
std
::
vector
<
memory
::
primitive_desc
>
srcs_pd
;
std
::
vector
<
memory
>
srcs
;
void
CreateSourcesDescriptors
(
const
std
::
vector
<
const
Tensor
*>
multi_input
,
const
mkldnn
::
engine
&
mkldnn_engine
)
{
for
(
size_t
i
=
0
;
i
<
multi_input
.
size
();
i
++
)
{
auto
mem_prim_desc
=
CreateMemPrimDesc
(
*
multi_input
[
i
],
mkldnn_engine
);
srcs_pd
.
push_back
(
mem_prim_desc
);
srcs
.
push_back
(
memory
(
mem_prim_desc
,
to_void_cast
(
multi_input
[
i
]
->
data
<
T
>
())));
srcs
.
push_back
(
memory
(
mem_prim_desc
,
to_void_cast
(
multi_input
[
i
]
->
data
<
T
>
())));
}
auto
dst_dims
=
paddle
::
framework
::
vectorize2int
(
output
->
dims
());
auto
dst_desc
=
memory
::
desc
(
dst_dims
,
mkldnn
::
memory
::
f32
,
memory
::
format
::
any
);
auto
concat_pd
=
concat
::
primitive_desc
(
dst_desc
,
static_cast
<
int
>
(
concat_axis
),
srcs_pd
);
auto
dst_mem
=
memory
(
concat_pd
.
dst_primitive_desc
(),
output
->
mutable_data
<
T
>
(
place
));
}
std
::
vector
<
primitive
::
at
>
inputs
;
//= {srcs};
void
CreateSourcePrimitiveAts
()
{
inputs
.
reserve
(
srcs
.
size
());
for
(
size_t
i
=
0
;
i
<
srcs
.
size
();
i
++
)
{
inputs
.
push_back
(
srcs
[
i
]);
}
auto
concat_prim
=
concat
(
concat_pd
,
inputs
,
dst_mem
);
std
::
vector
<
primitive
>
pipeline
;
pipeline
.
push_back
(
concat_prim
);
stream
(
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
// TODO(mgallus): When this is not workin' split into decl and def
/*
const T* input_data = input->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
auto input_format = input->format();
memory::format output_format{memory::format::format_undef};
const std::string key = gethash(src_tz, pooling_type, ksize, strides,
paddings, ctx.op().Output("Out"));
const std::string key_pool_p = key + "@pool_p";
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_src_mem_p = key + "@pool_src_mem_p";
const std::string key_pool_dst_mem_p = key + "@pool_dst_mem_p";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
auto pool_p =
std::static_pointer_cast<pooling_forward>(dev_ctx.GetBlob(key_pool_p));
if (pool_p == nullptr) {
const std::vector<int>& padding_left_top(paddings);
std::vector<int> padding_right_bottom(paddings);
bool ceil_mode = ctx.Attr<bool>("ceil_mode");
if (ceil_mode) {
CorrectOutputSize(src_tz, dst_tz, ksize, paddings, strides,
padding_right_bottom);
}
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), input_format);
auto dst_md = platform::MKLDNNMemDesc(dst_tz, mkldnn::memory::f32,
mkldnn::memory::format::any);
std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd =
CreatePrimitiveDesc(src_md, dst_md, strides, padding_left_top,
padding_right_bottom, ksize, pooling_type,
mkldnn_engine, ceil_mode, is_test);
// save pool_pd into global device context to be referred in backward path
if (!is_test) dev_ctx.SetBlob(key_pool_pd, pool_pd);
auto src_memory = std::make_shared<memory>(pool_pd->src_primitive_desc(),
to_void_cast<T>(input_data));
auto dst_memory =
std::make_shared<memory>(pool_pd->dst_primitive_desc(), output_data);
dev_ctx.SetBlob(key_pool_src_mem_p, src_memory);
dev_ctx.SetBlob(key_pool_dst_mem_p, dst_memory);
if (is_test) {
pool_p = std::make_shared<pooling_forward>(*pool_pd, *src_memory,
*dst_memory);
} else {
std::shared_ptr<mkldnn::memory> workspace_memory =
CreateWorkspaceMemory(pool_pd, pooling_type, mkldnn_engine);
// save pool_workspace_memory to be referred in backward path
dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
pool_p = std::make_shared<pooling_forward>(
*pool_pd, *src_memory, *dst_memory, *workspace_memory);
}
dev_ctx.SetBlob(key_pool_p, pool_p);
output_format =
(memory::format)dst_memory->get_primitive_desc().desc().data.format;
} else {
// Primitives already exist
auto pool_src_memory_p =
std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_src_mem_p));
PADDLE_ENFORCE(pool_src_memory_p != nullptr,
"Fail to find pooling src mem_p in device context");
auto pool_dst_memory_p =
std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_dst_mem_p));
PADDLE_ENFORCE(pool_dst_memory_p != nullptr,
"Fail to find pooling dst mem_p in device context");
pool_src_memory_p->set_data_handle(to_void_cast<T>(input_data));
pool_dst_memory_p->set_data_handle(output_data);
output_format = (memory::format)pool_dst_memory_p->get_primitive_desc()
.desc()
.data.format;
}
}
private:
std
::
vector
<
memory
::
primitive_desc
>
srcs_pd
;
std
::
vector
<
memory
>
srcs
;
std
::
vector
<
primitive
::
at
>
inputs
;
};
template
<
typename
T
>
class
ConcatMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
place
=
GetCpuPlace
(
ctx
);
const
auto
&
mkldnn_engine
=
GetMKLDNNEngine
(
ctx
);
auto
multi_input
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
EnforceLayouts
(
multi_input
);
Tensor
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
int64_t
concat_axis
=
static_cast
<
int64_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
ConcatPrimitiveFactory
<
T
>
prim_creator
;
auto
concat_pd
=
prim_creator
.
CreateConcatPrimDescriptor
(
multi_input
,
output
,
static_cast
<
int
>
(
concat_axis
),
mkldnn_engine
);
auto
concat
=
prim_creator
.
CreateConcatPrimitive
(
concat_pd
,
output
,
place
);
stream
(
stream
::
kind
::
eager
).
submit
({
concat
}).
wait
();
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{*(pool_p.get())};
stream(stream::kind::eager).submit(pipeline).wait();
*/
output
->
mutable_data
(
place
);
output
->
set_layout
(
DataLayout
::
kMKLDNN
);
output
->
set_format
((
memory
::
format
)
dst_mem
.
get_primitive_desc
().
desc
()
.
data
.
format
);
output
->
set_format
(
GetDstMemFormat
(
concat_pd
));
}
};
}
// namespace operators
...
...
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