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208f9125
编写于
11月 30, 2018
作者:
M
Michal Gallus
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Implement MKL-DNN Concat
test=develop
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29d9fb53
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3
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paddle/fluid/operators/concat_mkldnn_op.cc
paddle/fluid/operators/concat_mkldnn_op.cc
+217
-0
paddle/fluid/operators/concat_op.cc
paddle/fluid/operators/concat_op.cc
+20
-0
python/paddle/fluid/tests/unittests/test_concat_mkldnn_op.py
python/paddle/fluid/tests/unittests/test_concat_mkldnn_op.py
+56
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未找到文件。
paddle/fluid/operators/concat_mkldnn_op.cc
0 → 100644
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208f9125
/* Copyright (c) 2018 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 "paddle/fluid/operators/concat_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
DataLayout
;
using
framework
::
Tensor
;
using
mkldnn
::
memory
;
using
mkldnn
::
primitive
;
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
;
const
bool
is_format_defined
=
input
->
format
()
!=
memory
::
format
::
format_undef
;
PADDLE_ENFORCE
(
is_layout_correct
&&
is_format_defined
,
"Wrong layout/format set for Input tensor"
);
}
}
static
memory
::
primitive_desc
CreateMemPrimDesc
(
const
framework
::
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
();
auto
description
=
memory
::
desc
(
dims
,
data_type
,
format
);
auto
mem_prim_desc
=
memory
::
primitive_desc
(
description
,
engine
);
return
mem_prim_desc
;
}
static
platform
::
CPUPlace
GetCpuPlace
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
{
auto
place
=
ctx
.
GetPlace
();
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
place
),
"It must use CPUPlace."
);
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
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
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"
));
EnforceLayouts
(
multi_input
);
std
::
vector
<
memory
::
primitive_desc
>
srcs_pd
;
std
::
vector
<
memory
>
srcs
;
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
>
())));
}
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};
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;
}
// 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
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_KERNEL
(
concat
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
ConcatMKLDNNOpKernel
<
float
>
)
paddle/fluid/operators/concat_op.cc
浏览文件 @
208f9125
...
...
@@ -16,6 +16,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include <paddle/fluid/platform/mkldnn_helper.h>
namespace
paddle
{
namespace
operators
{
...
...
@@ -59,6 +60,21 @@ class ConcatOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
input_data_type
=
framework
::
GetDataTypeOfVar
(
ctx
.
MultiInputVar
(
"X"
)[
0
]);
#ifdef PADDLE_WITH_MKLDNN
if
(
platform
::
CanMKLDNNBeUsed
(
ctx
))
{
return
framework
::
OpKernelType
(
input_data_type
,
ctx
.
GetPlace
(),
framework
::
DataLayout
::
kMKLDNN
,
framework
::
LibraryType
::
kMKLDNN
);
}
#endif
return
framework
::
OpKernelType
(
input_data_type
,
ctx
.
GetPlace
());
}
};
class
ConcatOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
...
@@ -66,6 +82,9 @@ class ConcatOpMaker : public framework::OpProtoAndCheckerMaker {
void
Make
()
override
{
AddInput
(
"X"
,
"Input tensors of concat operator."
).
AsDuplicable
();
AddOutput
(
"Out"
,
"Output tensor of concat operator."
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Indicates if MKL-DNN kernel will be used"
)
.
SetDefault
(
false
);
AddAttr
<
int
>
(
"axis"
,
"The axis along which the input tensors will be concatenated."
)
.
SetDefault
(
0
);
...
...
@@ -82,6 +101,7 @@ Examples:
[5,6]]
)DOC"
);
}
};
...
...
python/paddle/fluid/tests/unittests/test_concat_mkldnn_op.py
0 → 100644
浏览文件 @
208f9125
# Copyright (c) 2018 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.
from
__future__
import
print_function
import
unittest
from
test_concat_op
import
TestConcatOp
,
TestConcatOp2
,
TestConcatOp3
class
TestMKLDNNConcatOp
(
TestConcatOp
):
def
setUp
(
self
):
super
(
TestMKLDNNConcatOp
,
self
).
setUp
()
self
.
attrs
[
"use_mkldnn"
]
=
True
def
test_check_grad
(
self
):
pass
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
class
TestMKLDNNConcatOp2
(
TestConcatOp2
):
def
setUp
(
self
):
super
(
TestMKLDNNConcatOp2
,
self
).
setUp
()
self
.
attrs
[
"use_mkldnn"
]
=
True
def
test_check_grad
(
self
):
pass
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
class
TestMKLDNNConcatOp3
(
TestConcatOp3
):
def
setUp
(
self
):
super
(
TestMKLDNNConcatOp3
,
self
).
setUp
()
self
.
attrs
[
"use_mkldnn"
]
=
True
def
test_check_grad
(
self
):
pass
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
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