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体验新版 GitCode,发现更多精彩内容 >>
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58ed412f
编写于
9月 28, 2018
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refactor(memory): rewrite memory allocation and make it extentable
Use OO style to rewrite memory allocation.
上级
643b6faa
变更
38
显示空白变更内容
内联
并排
Showing
38 changed file
with
1552 addition
and
676 deletion
+1552
-676
paddle/fluid/framework/details/exception_holder.h
paddle/fluid/framework/details/exception_holder.h
+2
-0
paddle/fluid/framework/executor.cc
paddle/fluid/framework/executor.cc
+0
-12
paddle/fluid/framework/lod_tensor.h
paddle/fluid/framework/lod_tensor.h
+0
-3
paddle/fluid/framework/mixed_vector.h
paddle/fluid/framework/mixed_vector.h
+22
-67
paddle/fluid/framework/tensor.cc
paddle/fluid/framework/tensor.cc
+4
-23
paddle/fluid/framework/tensor.h
paddle/fluid/framework/tensor.h
+4
-55
paddle/fluid/framework/tensor_impl.h
paddle/fluid/framework/tensor_impl.h
+6
-6
paddle/fluid/memory/CMakeLists.txt
paddle/fluid/memory/CMakeLists.txt
+2
-5
paddle/fluid/memory/allocation/CMakeLists.txt
paddle/fluid/memory/allocation/CMakeLists.txt
+43
-0
paddle/fluid/memory/allocation/aligned_allocator.cc
paddle/fluid/memory/allocation/aligned_allocator.cc
+26
-0
paddle/fluid/memory/allocation/aligned_allocator.h
paddle/fluid/memory/allocation/aligned_allocator.h
+68
-0
paddle/fluid/memory/allocation/allocator.cc
paddle/fluid/memory/allocation/allocator.cc
+29
-0
paddle/fluid/memory/allocation/allocator.h
paddle/fluid/memory/allocation/allocator.h
+93
-0
paddle/fluid/memory/allocation/allocator_facade.cc
paddle/fluid/memory/allocation/allocator_facade.cc
+102
-0
paddle/fluid/memory/allocation/allocator_facade.h
paddle/fluid/memory/allocation/allocator_facade.h
+47
-0
paddle/fluid/memory/allocation/best_fit_allocator.cc
paddle/fluid/memory/allocation/best_fit_allocator.cc
+169
-0
paddle/fluid/memory/allocation/best_fit_allocator.h
paddle/fluid/memory/allocation/best_fit_allocator.h
+132
-0
paddle/fluid/memory/allocation/best_fit_allocator_test.cc
paddle/fluid/memory/allocation/best_fit_allocator_test.cc
+144
-0
paddle/fluid/memory/allocation/best_fit_allocator_test.cu
paddle/fluid/memory/allocation/best_fit_allocator_test.cu
+88
-0
paddle/fluid/memory/allocation/cpu_allocator.cc
paddle/fluid/memory/allocation/cpu_allocator.cc
+40
-0
paddle/fluid/memory/allocation/cpu_allocator.h
paddle/fluid/memory/allocation/cpu_allocator.h
+38
-0
paddle/fluid/memory/allocation/cuda_allocator.cc
paddle/fluid/memory/allocation/cuda_allocator.cc
+69
-0
paddle/fluid/memory/allocation/cuda_allocator.h
paddle/fluid/memory/allocation/cuda_allocator.h
+45
-0
paddle/fluid/memory/allocation/locked_allocator.cc
paddle/fluid/memory/allocation/locked_allocator.cc
+49
-0
paddle/fluid/memory/allocation/locked_allocator.h
paddle/fluid/memory/allocation/locked_allocator.h
+38
-0
paddle/fluid/memory/allocation/naive_managed_allocator.cc
paddle/fluid/memory/allocation/naive_managed_allocator.cc
+69
-0
paddle/fluid/memory/allocation/naive_managed_allocator.h
paddle/fluid/memory/allocation/naive_managed_allocator.h
+71
-0
paddle/fluid/memory/allocation/naive_managed_allocator_test.cc
...e/fluid/memory/allocation/naive_managed_allocator_test.cc
+80
-0
paddle/fluid/memory/malloc.cc
paddle/fluid/memory/malloc.cc
+8
-170
paddle/fluid/memory/malloc.h
paddle/fluid/memory/malloc.h
+10
-80
paddle/fluid/memory/malloc_test.cc
paddle/fluid/memory/malloc_test.cc
+0
-198
paddle/fluid/operators/detection/generate_proposals_op.cu
paddle/fluid/operators/detection/generate_proposals_op.cu
+11
-13
paddle/fluid/operators/strided_memcpy_test.cc
paddle/fluid/operators/strided_memcpy_test.cc
+9
-11
paddle/fluid/platform/device_context.cc
paddle/fluid/platform/device_context.cc
+24
-16
paddle/fluid/platform/transform_test.cu
paddle/fluid/platform/transform_test.cu
+4
-5
paddle/fluid/platform/variant.h
paddle/fluid/platform/variant.h
+1
-0
paddle/testing/paddle_gtest_main.cc
paddle/testing/paddle_gtest_main.cc
+1
-8
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+4
-4
未找到文件。
paddle/fluid/framework/details/exception_holder.h
浏览文件 @
58ed412f
...
...
@@ -30,6 +30,8 @@ class ExceptionHolder {
Catch
(
exp
);
}
catch
(
platform
::
EnforceNotMet
exp
)
{
Catch
(
exp
);
}
catch
(
std
::
exception
&
ex
)
{
LOG
(
FATAL
)
<<
"std::exception caught, "
<<
ex
.
what
();
}
catch
(...)
{
LOG
(
FATAL
)
<<
"Unknown exception caught"
;
}
...
...
paddle/fluid/framework/executor.cc
浏览文件 @
58ed412f
...
...
@@ -395,11 +395,6 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
if
(
!
erase_tensors
.
empty
())
gc
->
Add
(
erase_tensors
);
}
}
if
(
FLAGS_benchmark
)
{
VLOG
(
2
)
<<
"Memory used after operator "
+
op
->
Type
()
+
" running: "
<<
memory
::
memory_usage
(
place_
);
}
}
if
(
gc
!=
nullptr
)
{
...
...
@@ -421,13 +416,6 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
scope
->
DropKids
();
}
}
if
(
FLAGS_benchmark
)
{
VLOG
(
2
)
<<
"-------------------------------------------------------"
;
VLOG
(
2
)
<<
"Memory used after deleting local scope: "
<<
memory
::
memory_usage
(
place_
);
VLOG
(
2
)
<<
"-------------------------------------------------------"
;
}
}
void
Executor
::
RunPreparedContext
(
...
...
paddle/fluid/framework/lod_tensor.h
浏览文件 @
58ed412f
...
...
@@ -111,9 +111,6 @@ class LoDTensor : public Tensor {
public:
LoDTensor
()
:
Tensor
()
{}
/* Constructor with place should only be used in pybind */
explicit
LoDTensor
(
const
platform
::
Place
&
place
)
:
Tensor
(
place
)
{}
explicit
LoDTensor
(
const
LoD
&
lod
)
:
lod_
(
lod
)
{}
void
set_lod
(
const
LoD
&
lod
)
{
lod_
=
lod
;
}
...
...
paddle/fluid/framework/mixed_vector.h
浏览文件 @
58ed412f
...
...
@@ -23,6 +23,7 @@
#include "paddle/fluid/framework/details/cow_ptr.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/memory/memcpy.h"
#include "glog/logging.h"
...
...
@@ -31,46 +32,6 @@ namespace paddle {
namespace
framework
{
#if defined(PADDLE_WITH_CUDA)
namespace
details
{
struct
CUDABuffer
{
void
*
data_
{
nullptr
};
size_t
size_
{
0
};
platform
::
CUDAPlace
place_
;
CUDABuffer
()
{}
CUDABuffer
(
platform
::
Place
place
,
size_t
size
)
:
size_
(
size
),
place_
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place
))
{
data_
=
memory
::
Alloc
(
place_
,
size
);
}
~
CUDABuffer
()
{
ClearMemory
();
}
CUDABuffer
(
const
CUDABuffer
&
o
)
=
delete
;
CUDABuffer
&
operator
=
(
const
CUDABuffer
&
o
)
=
delete
;
void
Resize
(
platform
::
Place
place
,
size_t
size
)
{
ClearMemory
();
place_
=
boost
::
get
<
platform
::
CUDAPlace
>
(
place
);
data_
=
memory
::
Alloc
(
place_
,
size
);
PADDLE_ENFORCE_NOT_NULL
(
data_
);
size_
=
size
;
}
void
Swap
(
CUDABuffer
&
o
)
{
std
::
swap
(
data_
,
o
.
data_
);
std
::
swap
(
place_
,
o
.
place_
);
std
::
swap
(
size_
,
o
.
size_
);
}
private:
void
ClearMemory
()
const
{
if
(
data_
!=
nullptr
)
{
memory
::
Free
(
place_
,
data_
);
}
}
};
}
// namespace details
// Vector<T> implements the std::vector interface, and can get Data or
// MutableData from any place. The data will be synced implicitly inside.
template
<
typename
T
>
...
...
@@ -103,8 +64,6 @@ class Vector {
o
.
ImmutableCPU
();
cpu_
=
o
.
cpu_
;
flag_
=
kDataInCPU
;
details
::
CUDABuffer
null
;
gpu_
.
Swap
(
null
);
return
*
this
;
}
...
...
@@ -199,7 +158,7 @@ class Vector {
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
place
),
"CUDA Data must on CUDA place"
);
ImmutableCUDA
(
place
);
return
reinterpret_cast
<
T
*>
(
gpu_
.
data_
);
return
reinterpret_cast
<
T
*>
(
gpu_
->
ptr
()
);
}
// get cuda ptr. mutable
...
...
@@ -234,13 +193,11 @@ class Vector {
std
::
mutex
&
Mutex
()
const
{
return
mtx_
;
}
std
::
unique_ptr
<
platform
::
CUDAPlace
>
CUDAPlace
()
const
{
if
(
gpu_
.
data_
==
nullptr
)
{
return
nullptr
;
}
else
{
return
std
::
unique_ptr
<
platform
::
CUDAPlace
>
(
new
platform
::
CUDAPlace
(
gpu_
.
place_
));
}
boost
::
optional
<
platform
::
CUDAPlace
>
CUDAPlace
()
const
{
return
gpu_
==
nullptr
?
boost
::
none
:
boost
::
optional
<
platform
::
CUDAPlace
>
(
boost
::
get
<
platform
::
CUDAPlace
>
(
gpu_
->
place
()));
}
private:
...
...
@@ -254,13 +211,12 @@ class Vector {
void
CopyToCPU
()
const
{
// COPY GPU Data To CPU
auto
*
dev_ctx
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
platform
::
Place
(
gpu_
.
place_
)));
platform
::
DeviceContextPool
::
Instance
().
Get
(
gpu_
->
place
()));
auto
stream
=
dev_ctx
->
stream
();
void
*
src
=
gpu_
.
data_
;
void
*
src
=
gpu_
->
ptr
()
;
void
*
dst
=
cpu_
.
data
();
memory
::
Copy
(
platform
::
CPUPlace
(),
dst
,
gpu_
.
place_
,
src
,
gpu_
.
size_
,
stream
);
memory
::
Copy
(
platform
::
CPUPlace
(),
dst
,
CUDAPlace
().
get
(),
src
,
gpu_
->
size
(),
stream
);
dev_ctx
->
Wait
();
}
...
...
@@ -277,8 +233,7 @@ class Vector {
CopyCPUDataToCUDA
(
place
);
UnsetFlag
(
kDirty
);
SetFlag
(
kDataInCUDA
);
}
else
if
(
IsInCUDA
()
&&
!
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place
)
==
gpu_
.
place_
))
{
}
else
if
(
IsInCUDA
()
&&
!
(
place
==
gpu_
->
place
()))
{
PADDLE_THROW
(
"This situation should not happen"
);
// Still dirty
}
else
{
...
...
@@ -290,7 +245,7 @@ class Vector {
// Even data is not dirty. However, data is not in CUDA. Copy data.
CopyCPUDataToCUDA
(
place
);
SetFlag
(
kDataInCUDA
);
}
else
if
(
!
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place
)
==
gpu_
.
place_
))
{
}
else
if
(
!
(
place
==
gpu_
->
place
()
))
{
PADDLE_THROW
(
"This situation should not happen."
);
}
else
{
// Not Dirty && DataInCUDA && Device is same
...
...
@@ -301,13 +256,13 @@ class Vector {
void
CopyCPUDataToCUDA
(
const
platform
::
Place
&
place
)
const
{
void
*
src
=
cpu_
.
data
();
gpu_
.
Resize
(
place
,
cpu_
.
size
()
*
sizeof
(
T
));
void
*
dst
=
gpu_
.
data_
;
gpu_
=
memory
::
Alloc
(
place
,
cpu_
.
size
()
*
sizeof
(
T
));
void
*
dst
=
gpu_
->
ptr
()
;
auto
*
dev_ctx
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
auto
stream
=
dev_ctx
->
stream
();
memory
::
Copy
(
gpu_
.
place_
,
dst
,
platform
::
CPUPlace
(),
src
,
gpu_
.
size_
,
stream
);
memory
::
Copy
(
CUDAPlace
().
get
(),
dst
,
platform
::
CPUPlace
(),
src
,
gpu_
->
size
(),
stream
);
}
void
ImmutableCPU
()
const
{
...
...
@@ -329,7 +284,7 @@ class Vector {
bool
IsInCPU
()
const
{
return
flag_
&
kDataInCPU
;
}
mutable
std
::
vector
<
T
>
cpu_
;
mutable
details
::
CUDABuffer
gpu_
;
mutable
std
::
unique_ptr
<
memory
::
Allocation
>
gpu_
;
mutable
int
flag_
;
mutable
std
::
mutex
mtx_
;
...
...
@@ -428,8 +383,8 @@ class Vector {
auto
&
mtx
=
m_
.
Data
().
Mutex
();
std
::
lock_guard
<
std
::
mutex
>
guard
(
mtx
);
auto
cuda_place
=
m_
.
Data
().
CUDAPlace
();
if
(
cuda_place
==
nullptr
||
*
cuda_place
==
boost
::
get
<
platform
::
CUDAPlace
>
(
place
))
{
if
(
cuda_place
==
boost
::
none
||
cuda_place
==
boost
::
get
<
platform
::
CUDAPlace
>
(
place
))
{
return
m_
.
Data
().
CUDAData
(
place
);
}
}
...
...
@@ -444,8 +399,8 @@ class Vector {
auto
&
mtx
=
m_
.
Data
().
Mutex
();
std
::
lock_guard
<
std
::
mutex
>
guard
(
mtx
);
auto
cuda_place
=
m_
.
Data
().
CUDAPlace
();
if
(
cuda_place
==
nullptr
||
*
cuda_place
==
boost
::
get
<
platform
::
CUDAPlace
>
(
place
))
{
if
(
cuda_place
==
boost
::
none
||
cuda_place
==
boost
::
get
<
platform
::
CUDAPlace
>
(
place
))
{
return
m_
.
MutableData
()
->
CUDAMutableData
(
place
);
}
}
...
...
paddle/fluid/framework/tensor.cc
浏览文件 @
58ed412f
...
...
@@ -33,9 +33,7 @@ size_t Tensor::memory_size() const {
void
*
Tensor
::
mutable_data
(
platform
::
Place
place
,
std
::
type_index
type
,
size_t
requested_size
)
{
if
(
holder_
!=
nullptr
)
{
holder_
->
set_type
(
type
);
}
type_
=
type
;
PADDLE_ENFORCE_GE
(
numel
(),
0
,
"When calling this method, the Tensor's numel must be "
"equal or larger than zero. "
...
...
@@ -48,25 +46,7 @@ void* Tensor::mutable_data(platform::Place place, std::type_index type,
/* some versions of boost::variant don't have operator!= */
if
(
holder_
==
nullptr
||
!
(
holder_
->
place
()
==
place
)
||
holder_
->
size
()
<
size
+
offset_
)
{
if
(
platform
::
is_cpu_place
(
place
))
{
holder_
.
reset
(
new
PlaceholderImpl
<
platform
::
CPUPlace
>
(
boost
::
get
<
platform
::
CPUPlace
>
(
place
),
size
,
type
));
}
else
if
(
platform
::
is_gpu_place
(
place
)
||
platform
::
is_cuda_pinned_place
(
place
))
{
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW
(
"CUDAPlace or CUDAPinnedPlace is not supported in CPU-only mode."
);
}
#else
if
(
platform
::
is_gpu_place
(
place
))
{
holder_
.
reset
(
new
PlaceholderImpl
<
platform
::
CUDAPlace
>
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place
),
size
,
type
));
}
else
if
(
platform
::
is_cuda_pinned_place
(
place
))
{
holder_
.
reset
(
new
PlaceholderImpl
<
platform
::
CUDAPinnedPlace
>
(
boost
::
get
<
platform
::
CUDAPinnedPlace
>
(
place
),
size
,
type
));
}
}
#endif
holder_
=
memory
::
AllocShared
(
place
,
size
);
offset_
=
0
;
}
return
reinterpret_cast
<
void
*>
(
reinterpret_cast
<
uintptr_t
>
(
holder_
->
ptr
())
+
...
...
@@ -76,7 +56,7 @@ void* Tensor::mutable_data(platform::Place place, std::type_index type,
void
*
Tensor
::
mutable_data
(
platform
::
Place
place
,
size_t
requested_size
)
{
PADDLE_ENFORCE
(
this
->
holder_
!=
nullptr
,
"Cannot invoke mutable data if current hold nothing."
);
return
mutable_data
(
place
,
holder_
->
type
()
,
requested_size
);
return
mutable_data
(
place
,
type_
,
requested_size
);
}
Tensor
&
Tensor
::
ShareDataWith
(
const
Tensor
&
src
)
{
...
...
@@ -101,6 +81,7 @@ Tensor Tensor::Slice(int begin_idx, int end_idx) const {
Tensor
dst
;
dst
.
holder_
=
holder_
;
dst
.
set_layout
(
layout_
);
dst
.
type_
=
type_
;
DDim
dst_dims
=
dims_
;
dst_dims
[
0
]
=
end_idx
-
begin_idx
;
dst
.
Resize
(
dst_dims
);
...
...
paddle/fluid/framework/tensor.h
浏览文件 @
58ed412f
...
...
@@ -67,12 +67,7 @@ class Tensor {
friend
struct
EigenVector
;
public:
Tensor
()
:
offset_
(
0
)
{}
/*! Constructor with place should only be used in pybind. */
explicit
Tensor
(
const
platform
::
Place
&
place
)
:
offset_
(
0
)
{
holder_
->
set_place
(
place
);
}
Tensor
()
:
type_
(
typeid
(
float
)),
offset_
(
0
)
{}
/*! Return a pointer to mutable memory block. */
template
<
typename
T
>
...
...
@@ -139,7 +134,7 @@ class Tensor {
std
::
type_index
type
()
const
{
PADDLE_ENFORCE_NOT_NULL
(
holder_
,
"Tensor not initialized yet when Tensor::type() is called."
);
return
holder_
->
type
()
;
return
type_
;
}
// memory size returns the holding memory size in byte.
...
...
@@ -154,55 +149,9 @@ class Tensor {
void
clear
()
{
holder_
=
nullptr
;
}
private:
/**
* @note Placeholder hides type T, so it doesn't appear as a template
* parameter of Variable.
*/
struct
Placeholder
{
virtual
~
Placeholder
()
=
default
;
virtual
void
*
ptr
()
const
=
0
;
virtual
size_t
size
()
const
=
0
;
virtual
std
::
type_index
type
()
const
=
0
;
virtual
platform
::
Place
place
()
const
=
0
;
virtual
void
set_type
(
std
::
type_index
type
)
=
0
;
virtual
void
set_place
(
platform
::
Place
place
)
=
0
;
};
template
<
typename
Place
>
struct
PlaceholderImpl
:
public
Placeholder
{
PlaceholderImpl
(
Place
place
,
size_t
size
,
std
::
type_index
type
)
:
ptr_
(
static_cast
<
uint8_t
*>
(
memory
::
Alloc
(
place
,
size
)),
memory
::
PODDeleter
<
uint8_t
,
Place
>
(
place
)),
place_
(
place
),
size_
(
size
),
type_
(
type
)
{
PADDLE_ENFORCE_NOT_NULL
(
ptr_
,
"Insufficient %s memory to allocation."
,
(
is_cpu_place
(
place_
)
?
"CPU"
:
"GPU"
));
}
virtual
size_t
size
()
const
{
return
size_
;
}
virtual
platform
::
Place
place
()
const
{
return
place_
;
}
virtual
void
*
ptr
()
const
{
return
static_cast
<
void
*>
(
ptr_
.
get
());
}
virtual
std
::
type_index
type
()
const
{
return
type_
;
}
virtual
void
set_type
(
std
::
type_index
type
)
{
type_
=
type
;
}
virtual
void
set_place
(
platform
::
Place
place
)
{
place_
=
place
;
}
/*! the pointer of memory block. */
std
::
unique_ptr
<
uint8_t
,
memory
::
PODDeleter
<
uint8_t
,
Place
>>
ptr_
;
/*! the place of memory block. */
platform
::
Place
place_
;
/*! the size of memory block. */
size_t
size_
;
/* the current type of memory */
std
::
type_index
type_
;
};
/*! holds the memory block if allocated. */
std
::
shared_ptr
<
Placeholder
>
holder_
;
std
::
shared_ptr
<
memory
::
Allocation
>
holder_
;
std
::
type_index
type_
;
/**
* @brief points to elements dimensions.
*
...
...
paddle/fluid/framework/tensor_impl.h
浏览文件 @
58ed412f
...
...
@@ -23,10 +23,10 @@ namespace framework {
template
<
typename
T
>
inline
const
T
*
Tensor
::
data
()
const
{
check_memory_size
();
bool
valid
=
std
::
is_same
<
T
,
void
>::
value
||
holder_
->
type
()
==
std
::
type_index
(
typeid
(
T
));
bool
valid
=
std
::
is_same
<
T
,
void
>::
value
||
type_
==
std
::
type_index
(
typeid
(
T
));
PADDLE_ENFORCE
(
valid
,
"Tensor holds the wrong type, it holds %s"
,
t
his
->
holder_
->
type
()
.
name
());
t
ype_
.
name
());
return
reinterpret_cast
<
const
T
*>
(
reinterpret_cast
<
uintptr_t
>
(
holder_
->
ptr
())
+
offset_
);
...
...
@@ -37,10 +37,10 @@ inline bool Tensor::IsInitialized() const { return holder_ != nullptr; }
template
<
typename
T
>
inline
T
*
Tensor
::
data
()
{
check_memory_size
();
bool
valid
=
std
::
is_same
<
T
,
void
>::
value
||
holder_
->
type
()
==
std
::
type_index
(
typeid
(
T
));
bool
valid
=
std
::
is_same
<
T
,
void
>::
value
||
type_
==
std
::
type_index
(
typeid
(
T
));
PADDLE_ENFORCE
(
valid
,
"Tensor holds the wrong type, it holds %s"
,
t
his
->
holder_
->
type
()
.
name
());
t
ype_
.
name
());
return
reinterpret_cast
<
T
*>
(
reinterpret_cast
<
uintptr_t
>
(
holder_
->
ptr
())
+
offset_
);
}
...
...
paddle/fluid/memory/CMakeLists.txt
浏览文件 @
58ed412f
add_subdirectory
(
detail
)
cc_library
(
malloc SRCS malloc.cc DEPS
buddy_allocator place enforc
e
)
add_subdirectory
(
allocation
)
cc_library
(
malloc SRCS malloc.cc DEPS
allocator_facad
e
)
cc_library
(
memcpy SRCS memcpy.cc DEPS place
)
cc_library
(
memory
DEPS
malloc
memcpy
)
cc_test
(
malloc_test SRCS malloc_test.cc DEPS malloc
)
#if (WITH_GPU)
# nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place memory)
#endif()
paddle/fluid/memory/allocation/CMakeLists.txt
0 → 100644
浏览文件 @
58ed412f
cc_library
(
allocator SRCS allocator.cc DEPS place
)
cc_library
(
cpu_allocator SRCS cpu_allocator.cc DEPS allocator
)
cc_library
(
best_fit_allocator SRCS best_fit_allocator.cc DEPS allocator
)
cc_library
(
locked_allocator SRCS locked_allocator.cc DEPS allocator
)
nv_library
(
cuda_allocator SRCS cuda_allocator.cc DEPS allocator gpu_info
)
if
(
WITH_GPU
)
nv_test
(
best_fit_allocator_test
SRCS best_fit_allocator_test.cc
best_fit_allocator_test.cu
DEPS best_fit_allocator
locked_allocator
cpu_allocator
cuda_allocator
device_context
memcpy
)
else
()
cc_test
(
best_fit_allocator_test
SRCS best_fit_allocator_test.cc
DEPS best_fit_allocator
locked_allocator
cpu_allocator
)
endif
()
cc_library
(
naive_managed_allocator SRCS naive_managed_allocator.cc DEPS allocator
)
cc_test
(
naive_managed_allocator_test SRCS naive_managed_allocator_test.cc DEPS naive_managed_allocator
)
if
(
WITH_GPU
)
set
(
AllocatorFacadeDeps gpu_info cuda_allocator
)
else
()
set
(
AllocatorFacadeDeps
)
endif
()
cc_library
(
aligned_allocator SRCS aligned_allocator.cc DEPS allocator
)
cc_library
(
allocator_facade SRCS allocator_facade.cc DEPS
${
AllocatorFacadeDeps
}
cpu_allocator
locked_allocator
best_fit_allocator
naive_managed_allocator
aligned_allocator
)
paddle/fluid/memory/allocation/aligned_allocator.cc
0 → 100644
浏览文件 @
58ed412f
// 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/memory/allocation/aligned_allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
ThinAlignedAllocator
::
ThinAlignedAllocator
(
std
::
shared_ptr
<
ManagedAllocator
>
underlyning_allocator
)
:
underlying_allocator_
(
std
::
move
(
underlyning_allocator
))
{}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/aligned_allocator.h
0 → 100644
浏览文件 @
58ed412f
// 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.
#pragma once
#include <memory>
#include "paddle/fluid/memory/allocation/allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
template
<
size_t
kAlignment
>
class
AlignedAllocation
:
public
Allocation
{
public:
AlignedAllocation
(
std
::
unique_ptr
<
Allocation
>&&
underlying_allocation
,
size_t
size
)
:
Allocation
(
AlignedPtr
(
underlying_allocation
->
ptr
()),
size
,
underlying_allocation
->
place
()),
underlying_allocation_
(
std
::
move
(
underlying_allocation
))
{}
private:
static
void
*
AlignedPtr
(
void
*
ptr
)
{
auto
ptr_addr
=
reinterpret_cast
<
uintptr_t
>
(
ptr
);
ptr_addr
=
(
ptr_addr
&
~
(
kAlignment
-
1
))
+
kAlignment
;
return
reinterpret_cast
<
void
*>
(
ptr_addr
);
}
std
::
unique_ptr
<
Allocation
>
underlying_allocation_
;
};
class
ThinAlignedAllocator
:
public
ManagedAllocator
{
public:
explicit
ThinAlignedAllocator
(
std
::
shared_ptr
<
ManagedAllocator
>
underlyning_allocator
);
protected:
std
::
shared_ptr
<
ManagedAllocator
>
underlying_allocator_
;
};
template
<
size_t
kAlignment
>
class
AlignedAllocator
:
public
ThinAlignedAllocator
{
public:
using
ThinAlignedAllocator
::
ThinAlignedAllocator
;
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Attr
attr
)
override
{
auto
raw_allocation
=
underlying_allocator_
->
Allocate
(
size
+
kAlignment
,
attr
);
return
std
::
unique_ptr
<
Allocation
>
(
new
AlignedAllocation
<
kAlignment
>
(
std
::
move
(
raw_allocation
),
size
));
}
std
::
shared_ptr
<
Allocation
>
AllocateShared
(
size_t
size
,
Attr
attr
)
override
{
return
std
::
shared_ptr
<
Allocation
>
(
Allocate
(
size
,
attr
).
release
());
}
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/allocator.cc
0 → 100644
浏览文件 @
58ed412f
// 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/memory/allocation/allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
Allocation
::~
Allocation
()
{}
Allocator
::~
Allocator
()
{}
bool
Allocator
::
IsAllocThreadSafe
()
const
{
return
false
;
}
const
char
*
BadAlloc
::
what
()
const
noexcept
{
return
msg_
.
c_str
();
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/allocator.h
0 → 100644
浏览文件 @
58ed412f
// 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.
#pragma once
#include <memory>
#include <string>
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
BadAlloc
:
public
std
::
exception
{
public:
explicit
BadAlloc
(
const
std
::
string
&
msg
)
:
msg_
(
msg
)
{}
const
char
*
what
()
const
noexcept
override
;
private:
std
::
string
msg_
;
};
class
Allocation
{
public:
Allocation
(
void
*
ptr
,
size_t
size
,
platform
::
Place
place
)
:
ptr_
(
ptr
),
size_
(
size
),
place_
(
place
)
{}
Allocation
(
const
Allocation
&
o
)
=
delete
;
Allocation
&
operator
=
(
const
Allocation
&
o
)
=
delete
;
void
*
ptr
()
const
{
return
ptr_
;
}
size_t
size
()
const
{
return
size_
;
}
const
platform
::
Place
&
place
()
const
{
return
place_
;
}
virtual
~
Allocation
();
private:
void
*
ptr_
;
size_t
size_
;
platform
::
Place
place_
;
};
class
Allocator
{
public:
enum
Attr
{
kDefault
=
0
,
kTiny
=
1
,
kFixedHuge
=
2
,
kFluxHuge
=
3
,
kTmp
=
4
,
NumOfAttrs
=
5
};
virtual
~
Allocator
();
virtual
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Allocator
::
Attr
attr
=
kDefault
)
=
0
;
virtual
bool
IsAllocThreadSafe
()
const
;
};
// User need to invoke `Free` or `FreeUniquePtr` manually if allocated by
// a manally managed allocator.
class
UnmanagedAllocator
:
public
Allocator
{
public:
virtual
void
Free
(
Allocation
*
allocation
)
=
0
;
void
FreeUniquePtr
(
std
::
unique_ptr
<
Allocation
>
allocation
)
{
Free
(
allocation
.
get
());
}
};
// The allocation will be managed by smart pointers
class
ManagedAllocator
:
public
Allocator
{
public:
virtual
std
::
shared_ptr
<
Allocation
>
AllocateShared
(
size_t
size
,
Allocator
::
Attr
attr
=
kDefault
)
=
0
;
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/allocator_facade.cc
0 → 100644
浏览文件 @
58ed412f
// 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/memory/allocation/allocator.h"
#include <map>
#include <vector>
#include "paddle/fluid/memory/allocation/aligned_allocator.h"
#include "paddle/fluid/memory/allocation/allocator_facade.h"
#include "paddle/fluid/memory/allocation/best_fit_allocator.h"
#include "paddle/fluid/memory/allocation/cpu_allocator.h"
#include "paddle/fluid/memory/allocation/locked_allocator.h"
#include "paddle/fluid/memory/allocation/naive_managed_allocator.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/place.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/memory/allocation/cuda_allocator.h"
#endif
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
AllocatorFacadePrivate
{
public:
std
::
map
<
platform
::
Place
,
std
::
shared_ptr
<
ManagedAllocator
>>
allocators_
;
std
::
vector
<
std
::
unique_ptr
<
Allocation
>>
pre_allocations_
;
std
::
vector
<
std
::
shared_ptr
<
Allocator
>>
holding_allocators_
;
~
AllocatorFacadePrivate
()
{
// Specify destruct order.
pre_allocations_
.
clear
();
allocators_
.
clear
();
holding_allocators_
.
clear
();
}
AllocatorFacadePrivate
()
{
InitCPUAllocator
();
InitCUDAAllocator
();
}
private:
void
InitCPUAllocator
()
{
auto
all
=
NaiveManagedAllocator
::
Create
(
std
::
unique_ptr
<
Allocator
>
(
new
CPUAllocator
()));
allocators_
[
platform
::
CPUPlace
()]
=
all
;
}
void
InitCUDAAllocator
()
{
#ifdef PADDLE_WITH_CUDA
for
(
int
dev_id
=
0
;
dev_id
<
platform
::
GetCUDADeviceCount
();
++
dev_id
)
{
auto
cuda_allocator
=
NaiveManagedAllocator
::
Create
(
std
::
unique_ptr
<
Allocator
>
(
new
CUDAAllocator
(
platform
::
CUDAPlace
(
dev_id
))));
auto
allocation
=
cuda_allocator
->
Allocate
(
platform
::
GpuMaxChunkSize
());
auto
allocator
=
NaiveManagedAllocator
::
Create
(
std
::
unique_ptr
<
Allocator
>
(
new
LockedAllocator
(
std
::
unique_ptr
<
Allocator
>
(
new
BestFitAllocator
(
allocation
.
get
())))));
pre_allocations_
.
emplace_back
(
std
::
move
(
allocation
));
holding_allocators_
.
emplace_back
(
cuda_allocator
);
allocators_
[
platform
::
CUDAPlace
(
dev_id
)]
=
std
::
make_shared
<
AlignedAllocator
<
64
>>
(
std
::
move
(
allocator
));
}
#endif
}
};
AllocatorFacade
::
AllocatorFacade
()
:
m_
(
new
AllocatorFacadePrivate
())
{}
AllocatorFacade
::~
AllocatorFacade
()
{
delete
m_
;
}
AllocatorFacade
&
AllocatorFacade
::
Instance
()
{
static
AllocatorFacade
instance
;
return
instance
;
}
std
::
shared_ptr
<
Allocation
>
AllocatorFacade
::
AllocShared
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
)
{
return
m_
->
allocators_
[
place
]
->
AllocateShared
(
size
,
attr
);
}
std
::
unique_ptr
<
Allocation
>
AllocatorFacade
::
Alloc
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
)
{
return
m_
->
allocators_
[
place
]
->
Allocate
(
size
,
attr
);
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/allocator_facade.h
0 → 100644
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58ed412f
// 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.
#pragma once
#include <memory>
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
AllocatorFacadePrivate
;
class
AllocatorFacade
{
public:
~
AllocatorFacade
();
AllocatorFacade
(
const
AllocatorFacade
&
o
)
=
delete
;
const
AllocatorFacade
&
operator
=
(
const
AllocatorFacade
&
o
)
=
delete
;
static
AllocatorFacade
&
Instance
();
std
::
shared_ptr
<
Allocation
>
AllocShared
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
=
Allocator
::
kDefault
);
std
::
unique_ptr
<
Allocation
>
Alloc
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
=
Allocator
::
kDefault
);
private:
AllocatorFacade
();
AllocatorFacadePrivate
*
m_
;
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/best_fit_allocator.cc
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58ed412f
// 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/memory/allocation/best_fit_allocator.h"
#include <bits/stdc++.h>
#include <list>
#include <map>
#include <string>
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
static
int
HighestBitPos
(
size_t
N
)
{
if
(
UNLIKELY
(
N
==
0
))
{
return
0
;
}
else
{
// NOTE: here we can use __builtin_clz in GCC.
// However, let's use std::log2 for better readability
// and trust std::log2's performance.
return
static_cast
<
int
>
(
std
::
log2
(
N
)
+
1
);
}
}
BestFitAllocator
::
BestFitAllocator
(
Allocation
*
allocation
)
:
allocation_
(
allocation
)
{
details
::
Chunk
chunk
;
chunk
.
size_
=
allocation_
->
size
();
chunk
.
offset_
=
0
;
chunk
.
is_free
=
true
;
chunks_
.
emplace_back
(
chunk
);
free_chunks_
[
HighestBitPos
(
chunk
.
size_
)].
insert
(
{
chunk
.
size_
,
chunks_
.
begin
()});
}
std
::
unique_ptr
<
Allocation
>
BestFitAllocator
::
Allocate
(
size_t
size
,
Attr
attr
)
{
auto
highest_set_bit
=
static_cast
<
size_t
>
(
HighestBitPos
(
size
));
MapIt
map_it
;
for
(;
highest_set_bit
<
free_chunks_
.
size
();
++
highest_set_bit
)
{
map_it
=
free_chunks_
[
highest_set_bit
].
lower_bound
(
size
);
if
(
map_it
!=
free_chunks_
[
highest_set_bit
].
end
())
{
break
;
}
}
if
(
UNLIKELY
(
highest_set_bit
==
free_chunks_
.
size
()))
{
throw
BadAlloc
(
string
::
Sprintf
(
"Cannot allocate %d, All fragments size is %d"
,
size
,
FreeSize
()));
}
auto
chunk_it
=
SplitChunk
(
size
,
highest_set_bit
,
map_it
);
return
std
::
unique_ptr
<
Allocation
>
(
new
BestFitAllocation
(
this
,
chunk_it
));
}
size_t
BestFitAllocator
::
FreeSize
()
const
{
size_t
acc
=
0
;
for
(
auto
&
array_item
:
free_chunks_
)
{
for
(
auto
&
pair
:
array_item
)
{
acc
+=
pair
.
second
->
size_
;
}
}
return
acc
;
}
BestFitAllocator
::
ListIt
BestFitAllocator
::
SplitChunk
(
size_t
request_size
,
size_t
free_chunk_offset
,
MapIt
bin_iterator
)
{
auto
to_split_it
=
bin_iterator
->
second
;
free_chunks_
[
free_chunk_offset
].
erase
(
bin_iterator
);
PADDLE_ENFORCE
(
to_split_it
->
is_free
);
PADDLE_ENFORCE_GE
(
to_split_it
->
size_
,
request_size
);
auto
remaining_size
=
to_split_it
->
size_
-
request_size
;
details
::
Chunk
to_use
;
details
::
Chunk
remaining
;
to_use
.
size_
=
request_size
;
to_use
.
is_free
=
false
;
remaining
.
size_
=
remaining_size
;
remaining
.
is_free
=
true
;
// calc offsets
to_use
.
offset_
=
to_split_it
->
offset_
;
remaining
.
offset_
=
to_use
.
offset_
+
to_use
.
size_
;
// insert to chunk list
auto
to_use_it
=
chunks_
.
insert
(
to_split_it
,
to_use
);
if
(
remaining
.
size_
!=
0
)
{
auto
bit_size
=
static_cast
<
size_t
>
(
HighestBitPos
(
remaining
.
size_
));
free_chunks_
[
bit_size
].
insert
(
{
remaining
.
size_
,
chunks_
.
insert
(
to_split_it
,
remaining
)});
}
chunks_
.
erase
(
to_split_it
);
return
to_use_it
;
}
void
BestFitAllocator
::
Free
(
Allocation
*
allocation
)
{
auto
*
bf_allocation
=
dynamic_cast
<
BestFitAllocation
*>
(
allocation
);
auto
chunk_it
=
bf_allocation
->
ChunkIterator
();
PADDLE_ENFORCE
(
!
chunk_it
->
is_free
);
chunk_it
->
is_free
=
true
;
if
(
chunk_it
!=
chunks_
.
begin
())
{
auto
prev_it
=
chunk_it
;
--
prev_it
;
if
(
prev_it
->
is_free
)
{
// Merge Left.
EraseFreeNode
(
prev_it
);
prev_it
->
size_
+=
chunk_it
->
size_
;
chunks_
.
erase
(
chunk_it
);
chunk_it
=
prev_it
;
}
}
auto
next_it
=
chunk_it
;
++
next_it
;
if
(
next_it
!=
chunks_
.
end
()
&&
next_it
->
is_free
)
{
EraseFreeNode
(
next_it
);
chunk_it
->
size_
+=
next_it
->
size_
;
chunks_
.
erase
(
next_it
);
}
InsertFreeNode
(
chunk_it
);
}
void
BestFitAllocator
::
InsertFreeNode
(
const
ListIt
&
it
)
{
auto
pos
=
static_cast
<
size_t
>
(
HighestBitPos
(
it
->
size_
));
auto
&
free_map
=
free_chunks_
[
pos
];
free_map
.
insert
({
it
->
size_
,
it
});
}
void
BestFitAllocator
::
EraseFreeNode
(
const
ListIt
&
it
)
{
size_t
pos
=
static_cast
<
size_t
>
(
HighestBitPos
(
it
->
size_
));
auto
&
free_map
=
free_chunks_
[
pos
];
auto
map_it
=
free_map
.
find
(
it
->
size_
);
while
(
map_it
->
second
!=
it
&&
map_it
!=
free_map
.
end
())
{
++
map_it
;
}
PADDLE_ENFORCE
(
map_it
!=
free_map
.
end
());
free_map
.
erase
(
map_it
);
}
size_t
BestFitAllocator
::
NumFreeChunks
()
const
{
size_t
num
=
0
;
for
(
auto
&
array_item
:
free_chunks_
)
{
num
+=
array_item
.
size
();
}
return
num
;
}
BestFitAllocation
::
BestFitAllocation
(
paddle
::
memory
::
allocation
::
BestFitAllocator
*
allocator
,
typename
details
::
ChunkList
::
iterator
chunk_it
)
:
Allocation
(
reinterpret_cast
<
void
*>
(
reinterpret_cast
<
uintptr_t
>
(
allocator
->
BasePtr
())
+
chunk_it
->
offset_
),
chunk_it
->
size_
,
allocator
->
Place
()),
allocator_
(
allocator
),
chunk_it_
(
chunk_it
)
{}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/best_fit_allocator.h
0 → 100644
浏览文件 @
58ed412f
// 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.
#pragma once
#include <array>
#include <list>
#include <map>
#include "paddle/fluid/memory/allocation/allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
namespace
details
{
struct
Chunk
{
bool
is_free
{
true
};
// Offset to the base allocation.
uintptr_t
offset_
;
size_t
size_
;
};
// Here we use std::list to maintain chunk list.
// NOTE(yy): The traditional implementation of ChunkList is add `prev`/`next`
// pointers in `Chunk`, and split the allocation as `ChunkHeader` and
// `Payload`. Such as
// *-------*---------------*---------------*--------------*
// | Chunk | prev_ pointer | next_ pointer | payload .... |
// *-------*---------------*---------------*--------------*
// This implementation can just return a raw pointer, and we can get the list
// structure by it. However, we cannot use the same code on GPU since CPU
// cannot access GPU memory directly.
//
// So we choose to use `std::list` and return an allocation instance, which
// contains the list node iterator, then we can unify CPU/GPU code.
//
// To return an allocation is not a bad idea, since Tensor/Vector should holds
// an allocation instead of raw pointer directly.
using
ChunkList
=
std
::
list
<
Chunk
>
;
// Here we use a multi-level map of free chunks.
// the map is
// MSB offset --> size --> [ChunkList::iterator]
//
// The time complexities:
// find a free chunk:
// O(logN),
// where N is the number of free nodes with the same MSB offset.
// find the position of a chunk iterator:
// O(logN + K),
// where N is the number of free nodes with the same MSB offset.
// where K is the number of free nodes with the same size.
// insert a free chunk:
// O(logN),
// where N is the number of free nodes with the same MSB offset.
// erase a free chunk:
// O(1)
using
FreeChunkBin
=
std
::
array
<
std
::
multimap
<
size_t
,
ChunkList
::
iterator
>
,
sizeof
(
size_t
)
*
8
>
;
}
// namespace details
class
BestFitAllocator
;
// The BestFitAllocation maintain the List Node iterator.
class
BestFitAllocation
:
public
Allocation
{
private:
using
ListIt
=
typename
details
::
ChunkList
::
iterator
;
public:
BestFitAllocation
(
BestFitAllocator
*
allocator
,
ListIt
chunk_it
);
const
ListIt
&
ChunkIterator
()
const
{
return
chunk_it_
;
}
private:
BestFitAllocator
*
allocator_
;
typename
details
::
ChunkList
::
iterator
chunk_it_
;
};
// TODO(yy): Current BestFitAllocator is not thread-safe. To make it thread
// safe, we must wrap a locked_allocator. However, we can implement a thread
// safe allocator by locking each bin and chunks list independently. It will
// make BestFitAllocator faster in multi-thread situation.
//
// This allocator implements a best-fit allocator with merging the free nodes.
//
// To allocate a buffer, it will find the best-fit chunk. If the best-fit chunk
// is larger than request size, the original block will be split into two
// chunks. The first block will be used and the second block will be put into
// free chunks.
//
// To free an allocation, it will set the chunk of allocation to free and merge
// the prev-chunk and the next-chunk when possible.
class
BestFitAllocator
:
public
UnmanagedAllocator
{
public:
explicit
BestFitAllocator
(
Allocation
*
allocation
);
void
*
BasePtr
()
const
{
return
allocation_
->
ptr
();
}
const
platform
::
Place
&
Place
()
const
{
return
allocation_
->
place
();
}
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Attr
attr
=
kDefault
)
override
;
void
Free
(
Allocation
*
allocation
)
override
;
size_t
NumFreeChunks
()
const
;
private:
size_t
FreeSize
()
const
;
using
MapIt
=
typename
details
::
FreeChunkBin
::
value_type
::
iterator
;
using
ListIt
=
typename
details
::
ChunkList
::
iterator
;
ListIt
SplitChunk
(
size_t
request_size
,
size_t
free_chunk_offset
,
MapIt
bin_iterator
);
void
EraseFreeNode
(
const
ListIt
&
it
);
void
InsertFreeNode
(
const
ListIt
&
it
);
Allocation
*
allocation_
;
// not owned
details
::
ChunkList
chunks_
;
details
::
FreeChunkBin
free_chunks_
;
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/best_fit_allocator_test.cc
0 → 100644
浏览文件 @
58ed412f
// 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/memory/allocation/best_fit_allocator.h"
#include <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/memory/allocation/cpu_allocator.h"
#include "paddle/fluid/memory/allocation/locked_allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
StubAllocation
:
public
Allocation
{
public:
explicit
StubAllocation
(
size_t
size
)
:
Allocation
(
0
,
size
,
platform
::
CPUPlace
())
{}
};
TEST
(
BestFitAllocator
,
test_allocation
)
{
StubAllocation
stub
(
4UL
*
1024
*
1024
*
1024
);
BestFitAllocator
allocator
(
&
stub
);
{
auto
allocation
=
allocator
.
Allocate
(
64
);
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation
));
}
{
auto
allocation
=
allocator
.
Allocate
(
80
);
{
auto
best_fit_allocation
=
dynamic_cast
<
BestFitAllocation
*>
(
allocation
.
get
());
ASSERT_NE
(
best_fit_allocation
,
nullptr
);
ASSERT_FALSE
(
best_fit_allocation
->
ChunkIterator
()
->
is_free
);
ASSERT_EQ
(
best_fit_allocation
->
ChunkIterator
()
->
offset_
,
0
);
ASSERT_EQ
(
allocation
->
size
(),
80
);
ASSERT_EQ
(
allocation
->
ptr
(),
nullptr
);
}
auto
allocation2
=
allocator
.
Allocate
(
60
);
auto
allocation3
=
allocator
.
Allocate
(
90
);
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation2
));
allocation2
=
allocator
.
Allocate
(
30
);
{
auto
best_fit_allocation
=
dynamic_cast
<
BestFitAllocation
*>
(
allocation2
.
get
());
ASSERT_EQ
(
best_fit_allocation
->
ChunkIterator
()
->
offset_
,
80
);
}
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation2
));
allocation2
=
allocator
.
Allocate
(
60
);
{
auto
best_fit_allocation
=
dynamic_cast
<
BestFitAllocation
*>
(
allocation2
.
get
());
ASSERT_EQ
(
best_fit_allocation
->
ChunkIterator
()
->
offset_
,
80
);
}
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation
));
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation2
));
allocation
=
allocator
.
Allocate
(
80
+
60
);
{
auto
best_fit_allocation
=
dynamic_cast
<
BestFitAllocation
*>
(
allocation
.
get
());
ASSERT_EQ
(
best_fit_allocation
->
ChunkIterator
()
->
offset_
,
0
);
}
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation
));
allocation
=
allocator
.
Allocate
(
80
);
allocation2
=
allocator
.
Allocate
(
60
);
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation
));
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation3
));
allocator
.
FreeUniquePtr
(
std
::
move
(
allocation2
));
ASSERT_EQ
(
allocator
.
NumFreeChunks
(),
1U
);
}
}
TEST
(
BestFitAllocator
,
test_concurrent_cpu_allocation
)
{
CPUAllocator
allocator
;
auto
global_allocation
=
allocator
.
Allocate
(
256UL
*
1024
*
1024
);
std
::
unique_ptr
<
Allocator
>
best_fit_allocator
(
new
BestFitAllocator
(
global_allocation
.
get
()));
LockedAllocator
locked_allocator
(
std
::
move
(
best_fit_allocator
));
auto
th_main
=
[
&
]
{
std
::
random_device
dev
;
std
::
default_random_engine
engine
(
dev
());
std
::
uniform_int_distribution
<
size_t
>
dist
(
1U
,
1024U
);
for
(
size_t
i
=
0
;
i
<
128
;
++
i
)
{
size_t
allocate_size
=
dist
(
engine
);
auto
allocation
=
locked_allocator
.
Allocate
(
sizeof
(
size_t
)
*
allocate_size
);
size_t
*
data
=
reinterpret_cast
<
size_t
*>
(
allocation
->
ptr
());
for
(
size_t
j
=
0
;
j
<
allocate_size
;
++
j
)
{
data
[
j
]
=
j
;
}
std
::
this_thread
::
yield
();
for
(
size_t
j
=
0
;
j
<
allocate_size
;
++
j
)
{
ASSERT_EQ
(
data
[
j
],
j
);
}
locked_allocator
.
FreeUniquePtr
(
std
::
move
(
allocation
));
}
};
{
std
::
vector
<
std
::
thread
>
threads
;
for
(
size_t
i
=
0
;
i
<
1024
;
++
i
)
{
threads
.
emplace_back
(
th_main
);
}
for
(
auto
&
th
:
threads
)
{
th
.
join
();
}
}
allocator
.
FreeUniquePtr
(
std
::
move
(
global_allocation
));
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/best_fit_allocator_test.cu
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// 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 <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/memory/allocation/best_fit_allocator.h"
#include "paddle/fluid/memory/allocation/cuda_allocator.h"
#include "paddle/fluid/memory/allocation/locked_allocator.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/for_range.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
struct
ForEachFill
{
size_t
*
ptr_
;
explicit
ForEachFill
(
size_t
*
ptr
)
:
ptr_
(
ptr
)
{}
__device__
void
operator
()(
size_t
i
)
{
ptr_
[
i
]
=
i
;
}
};
TEST
(
BestFitAllocator
,
concurrent_cuda
)
{
CUDAAllocator
allocator
(
platform
::
CUDAPlace
(
0
));
// 256 MB
auto
cuda_allocation
=
allocator
.
Allocate
(
256U
*
1024
*
1024
);
LockedAllocator
concurrent_allocator
(
std
::
unique_ptr
<
Allocator
>
(
new
BestFitAllocator
(
cuda_allocation
.
get
())));
auto
th_main
=
[
&
]
{
std
::
random_device
dev
;
std
::
default_random_engine
engine
(
dev
());
std
::
uniform_int_distribution
<
size_t
>
dist
(
1U
,
1024U
);
platform
::
CUDAPlace
gpu
(
0
);
platform
::
CUDADeviceContext
dev_ctx
(
gpu
);
std
::
array
<
size_t
,
1024
>
buf
;
for
(
size_t
i
=
0
;
i
<
128
;
++
i
)
{
size_t
allocate_size
=
dist
(
engine
);
auto
allocation
=
concurrent_allocator
.
Allocate
(
sizeof
(
size_t
)
*
allocate_size
);
size_t
*
data
=
reinterpret_cast
<
size_t
*>
(
allocation
->
ptr
());
ForEachFill
fill
(
data
);
platform
::
ForRange
<
platform
::
CUDADeviceContext
>
for_range
(
dev_ctx
,
allocate_size
);
for_range
(
fill
);
memory
::
Copy
(
platform
::
CPUPlace
(),
buf
.
data
(),
gpu
,
data
,
sizeof
(
size_t
)
*
allocate_size
,
dev_ctx
.
stream
());
dev_ctx
.
Wait
();
for
(
size_t
j
=
0
;
j
<
allocate_size
;
++
j
)
{
ASSERT_EQ
(
buf
[
j
],
j
);
}
concurrent_allocator
.
FreeUniquePtr
(
std
::
move
(
allocation
));
}
};
{
std
::
vector
<
std
::
thread
>
threads
;
for
(
size_t
i
=
0
;
i
<
1024
;
++
i
)
{
threads
.
emplace_back
(
th_main
);
}
for
(
auto
&
th
:
threads
)
{
th
.
join
();
}
}
allocator
.
FreeUniquePtr
(
std
::
move
(
cuda_allocation
));
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/cpu_allocator.cc
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// 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/memory/allocation/cpu_allocator.h"
#include <stdlib.h>
#include <string>
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
std
::
unique_ptr
<
Allocation
>
CPUAllocator
::
Allocate
(
size_t
size
,
Attr
attr
)
{
void
*
ptr
;
auto
status
=
posix_memalign
(
&
ptr
,
kAlignment
,
size
);
if
(
UNLIKELY
(
status
)
!=
0
)
{
throw
BadAlloc
(
string
::
Sprintf
(
"Cannot allocate cpu memory %d. Errno is %d"
,
size
,
status
));
}
return
std
::
unique_ptr
<
Allocation
>
(
new
CPUAllocation
(
ptr
,
size
));
}
void
CPUAllocator
::
Free
(
Allocation
*
allocation
)
{
PADDLE_ENFORCE_NOT_NULL
(
dynamic_cast
<
CPUAllocation
*>
(
allocation
));
free
(
allocation
->
ptr
());
}
bool
CPUAllocator
::
IsAllocThreadSafe
()
const
{
return
true
;
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/cpu_allocator.h
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// 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.
#pragma once
#include "paddle/fluid/memory/allocation/allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
CPUAllocation
:
public
Allocation
{
public:
CPUAllocation
(
void
*
ptr
,
size_t
size
)
:
Allocation
(
ptr
,
size
,
platform
::
CPUPlace
())
{}
};
class
CPUAllocator
:
public
UnmanagedAllocator
{
public:
constexpr
static
size_t
kAlignment
=
64u
;
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Attr
attr
=
kDefault
)
override
;
void
Free
(
Allocation
*
allocation
)
override
;
bool
IsAllocThreadSafe
()
const
override
;
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/cuda_allocator.cc
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// 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/memory/allocation/cuda_allocator.h"
#include <cuda.h>
#include <cuda_runtime.h>
#include <string>
#include "paddle/fluid/platform/gpu_info.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
CUDADeviceGuard
{
public:
explicit
CUDADeviceGuard
(
int
dev_id
)
{
int
prev_id
=
platform
::
GetCurrentDeviceId
();
if
(
prev_id
!=
dev_id
)
{
prev_id_
=
prev_id
;
platform
::
SetDeviceId
(
dev_id
);
}
}
~
CUDADeviceGuard
()
{
if
(
prev_id_
!=
-
1
)
{
platform
::
SetDeviceId
(
prev_id_
);
}
}
private:
int
prev_id_
{
-
1
};
};
std
::
unique_ptr
<
Allocation
>
CUDAAllocator
::
Allocate
(
size_t
size
,
Attr
attr
)
{
CUDADeviceGuard
guard
(
place_
.
device
);
void
*
ptr
;
auto
status
=
cudaMalloc
(
&
ptr
,
size
);
if
(
UNLIKELY
(
status
!=
cudaSuccess
))
{
throw
BadAlloc
(
string
::
Sprintf
(
"Cannot allocate %d on GPU %d, cuda status %d, %s"
,
size
,
place_
.
device
,
status
,
cudaGetErrorString
(
status
)));
}
return
std
::
unique_ptr
<
Allocation
>
(
new
CUDAAllocation
(
ptr
,
size
,
platform
::
Place
(
place_
)));
}
void
CUDAAllocator
::
Free
(
Allocation
*
allocation
)
{
auto
*
cuda_allocation
=
dynamic_cast
<
CUDAAllocation
*>
(
allocation
);
PADDLE_ENFORCE_NOT_NULL
(
cuda_allocation
);
PADDLE_ENFORCE_EQ
(
boost
::
get
<
platform
::
CUDAPlace
>
(
cuda_allocation
->
place
()),
place_
);
PADDLE_ENFORCE
(
cudaFree
(
allocation
->
ptr
()));
}
bool
CUDAAllocator
::
IsAllocThreadSafe
()
const
{
return
true
;
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/cuda_allocator.h
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// 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.
#pragma once
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
// Just a flag type.
class
CUDAAllocation
:
public
Allocation
{
public:
using
Allocation
::
Allocation
;
};
class
CUDAAllocator
:
public
UnmanagedAllocator
{
public:
explicit
CUDAAllocator
(
const
platform
::
CUDAPlace
&
place
)
:
place_
(
place
)
{}
explicit
CUDAAllocator
(
const
platform
::
Place
&
place
)
:
place_
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place
))
{}
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Attr
attr
=
kDefault
)
override
;
void
Free
(
Allocation
*
allocation
)
override
;
bool
IsAllocThreadSafe
()
const
override
;
private:
platform
::
CUDAPlace
place_
;
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/locked_allocator.cc
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// 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/memory/allocation/locked_allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
std
::
unique_ptr
<
Allocation
>
LockedAllocator
::
Allocate
(
size_t
size
,
Attr
attr
)
{
if
(
underlying_allocator_
->
IsAllocThreadSafe
())
{
return
underlying_allocator_
->
Allocate
(
size
,
attr
);
}
else
{
std
::
lock_guard
<
std
::
mutex
>
guard
(
mtx_
);
return
underlying_allocator_
->
Allocate
(
size
,
attr
);
}
}
void
LockedAllocator
::
Free
(
Allocation
*
allocation
)
{
if
(
underlying_allocator_
->
IsAllocThreadSafe
())
{
return
underlying_allocator_
->
Free
(
allocation
);
}
else
{
std
::
lock_guard
<
std
::
mutex
>
guard
(
mtx_
);
return
underlying_allocator_
->
Free
(
allocation
);
}
}
bool
LockedAllocator
::
IsAllocThreadSafe
()
const
{
return
true
;
}
LockedAllocator
::
LockedAllocator
(
std
::
unique_ptr
<
Allocator
>
&&
underlying_allocator
)
{
auto
*
allocator
=
dynamic_cast
<
UnmanagedAllocator
*>
(
underlying_allocator
.
get
());
PADDLE_ENFORCE_NOT_NULL
(
allocator
);
underlying_allocator
.
release
();
underlying_allocator_
.
reset
(
allocator
);
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/locked_allocator.h
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// 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.
#pragma once
#include <memory>
#include <thread> // NOLINT
#include "paddle/fluid/memory/allocation/allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
LockedAllocator
:
public
UnmanagedAllocator
{
public:
explicit
LockedAllocator
(
std
::
unique_ptr
<
Allocator
>&&
underlying_allocator
);
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Attr
attr
=
kDefault
)
override
;
void
Free
(
Allocation
*
allocation
)
override
;
bool
IsAllocThreadSafe
()
const
override
;
private:
std
::
unique_ptr
<
UnmanagedAllocator
>
underlying_allocator_
;
std
::
mutex
mtx_
;
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/naive_managed_allocator.cc
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// 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/memory/allocation/naive_managed_allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
NaiveManagedAllocator
::
NaiveManagedAllocator
(
std
::
unique_ptr
<
Allocator
>
&&
allocator
)
{
auto
*
underlying_allocator
=
dynamic_cast
<
UnmanagedAllocator
*>
(
allocator
.
get
());
PADDLE_ENFORCE_NOT_NULL
(
underlying_allocator
);
allocator
.
release
();
Init
(
std
::
unique_ptr
<
UnmanagedAllocator
>
(
underlying_allocator
));
}
NaiveManagedAllocator
::
NaiveManagedAllocator
(
std
::
unique_ptr
<
UnmanagedAllocator
>
&&
allocator
)
{
Init
(
std
::
move
(
allocator
));
}
void
NaiveManagedAllocator
::
Init
(
std
::
unique_ptr
<
UnmanagedAllocator
>
&&
allocator
)
{
underlying_allocator_
=
std
::
move
(
allocator
);
}
bool
NaiveManagedAllocator
::
IsAllocThreadSafe
()
const
{
return
underlying_allocator_
->
IsAllocThreadSafe
();
}
std
::
unique_ptr
<
Allocation
>
NaiveManagedAllocator
::
Allocate
(
size_t
size
,
Attr
attr
)
{
std
::
unique_ptr
<
Allocation
>
allocation
=
underlying_allocator_
->
Allocate
(
size
,
attr
);
return
std
::
unique_ptr
<
Allocation
>
(
new
NaiveManagedAllocation
(
std
::
move
(
allocation
),
shared_from_this
()));
}
std
::
shared_ptr
<
Allocation
>
NaiveManagedAllocator
::
AllocateShared
(
size_t
size
,
Attr
attr
)
{
std
::
unique_ptr
<
Allocation
>
allocation
=
underlying_allocator_
->
Allocate
(
size
,
attr
);
return
std
::
shared_ptr
<
Allocation
>
(
new
NaiveManagedAllocation
(
std
::
move
(
allocation
),
shared_from_this
()));
}
NaiveManagedAllocation
::~
NaiveManagedAllocation
()
{
auto
allocator
=
allocator_
.
lock
();
if
(
UNLIKELY
(
allocator
==
nullptr
))
{
// the allocator is destructed before allocations.
// do nothing.
return
;
}
// invoke Free
allocator
->
UnderlyingAllocator
().
FreeUniquePtr
(
std
::
move
(
underlying_allocation_
));
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/naive_managed_allocator.h
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58ed412f
// 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.
#pragma once
#include <memory>
#include "paddle/fluid/memory/allocation/allocator.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
NaiveManagedAllocator
;
class
NaiveManagedAllocation
:
public
Allocation
{
public:
NaiveManagedAllocation
(
std
::
unique_ptr
<
Allocation
>&&
underlying_allocation
,
std
::
shared_ptr
<
NaiveManagedAllocator
>
allocator
)
:
Allocation
(
underlying_allocation
->
ptr
(),
underlying_allocation
->
size
(),
underlying_allocation
->
place
()),
underlying_allocation_
(
std
::
move
(
underlying_allocation
)),
allocator_
(
allocator
)
{}
~
NaiveManagedAllocation
()
final
;
private:
std
::
unique_ptr
<
Allocation
>
underlying_allocation_
;
std
::
weak_ptr
<
NaiveManagedAllocator
>
allocator_
;
};
class
NaiveManagedAllocator
:
public
ManagedAllocator
,
public
std
::
enable_shared_from_this
<
NaiveManagedAllocator
>
{
public:
template
<
typename
...
ARGS
>
static
std
::
shared_ptr
<
ManagedAllocator
>
Create
(
ARGS
...
args
)
{
return
std
::
static_pointer_cast
<
ManagedAllocator
>
(
std
::
shared_ptr
<
NaiveManagedAllocator
>
(
new
NaiveManagedAllocator
(
std
::
move
(
args
)...)));
}
inline
UnmanagedAllocator
&
UnderlyingAllocator
()
{
return
*
underlying_allocator_
;
}
bool
IsAllocThreadSafe
()
const
override
;
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Attr
attr
=
kDefault
)
override
;
std
::
shared_ptr
<
Allocation
>
AllocateShared
(
size_t
size
,
Attr
attr
=
kDefault
)
override
;
private:
explicit
NaiveManagedAllocator
(
std
::
unique_ptr
<
Allocator
>&&
allocator
);
explicit
NaiveManagedAllocator
(
std
::
unique_ptr
<
UnmanagedAllocator
>&&
allocator
);
void
Init
(
std
::
unique_ptr
<
UnmanagedAllocator
>&&
allocator
);
std
::
unique_ptr
<
UnmanagedAllocator
>
underlying_allocator_
;
};
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/allocation/naive_managed_allocator_test.cc
0 → 100644
浏览文件 @
58ed412f
// 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/memory/allocation/naive_managed_allocator.h"
#include <atomic> // NOLINT
#include <random>
#include <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
namespace
paddle
{
namespace
memory
{
namespace
allocation
{
class
StubAllocator
:
public
UnmanagedAllocator
{
public:
std
::
unique_ptr
<
Allocation
>
Allocate
(
size_t
size
,
Attr
attr
=
kDefault
)
override
{
counter_
.
fetch_add
(
1
);
return
std
::
unique_ptr
<
Allocation
>
(
new
Allocation
(
nullptr
,
size
,
platform
::
CPUPlace
()));
}
void
Free
(
Allocation
*
allocation
)
override
{
counter_
.
fetch_sub
(
1
);
}
bool
IsAllocThreadSafe
()
const
override
{
return
true
;
}
std
::
atomic
<
int
>
counter_
{
0
};
};
TEST
(
NaiveManagedAllocator
,
main
)
{
auto
allocator
=
NaiveManagedAllocator
::
Create
(
std
::
unique_ptr
<
Allocator
>
(
new
StubAllocator
()));
auto
th_main
=
[
=
]
{
std
::
random_device
dev
;
std
::
default_random_engine
engine
(
dev
());
std
::
uniform_int_distribution
<
int
>
dist
(
0
,
1
);
std
::
vector
<
std
::
shared_ptr
<
Allocation
>>
allocations
;
for
(
int
j
=
0
;
j
<
1024
;
++
j
)
{
bool
to_insert
=
static_cast
<
bool
>
(
dist
(
engine
));
if
(
to_insert
)
{
allocations
.
emplace_back
(
allocator
->
AllocateShared
(
10
));
}
else
{
if
(
!
allocations
.
empty
())
{
allocations
.
pop_back
();
}
}
}
};
{
std
::
vector
<
std
::
thread
>
threads
;
for
(
size_t
i
=
0
;
i
<
1024
;
++
i
)
{
threads
.
emplace_back
(
th_main
);
}
for
(
auto
&
th
:
threads
)
{
th
.
join
();
}
}
ASSERT_EQ
(
reinterpret_cast
<
StubAllocator
&>
(
std
::
dynamic_pointer_cast
<
NaiveManagedAllocator
>
(
allocator
)
->
UnderlyingAllocator
())
.
counter_
,
0
);
}
}
// namespace allocation
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/malloc.cc
浏览文件 @
58ed412f
...
...
@@ -14,13 +14,9 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/memory/malloc.h"
#include "glog/logging.h"
#include "paddle/fluid/memory/detail/buddy_allocator.h"
#include "paddle/fluid/memory/detail/system_allocator.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/memory/allocation/allocator_facade.h"
#include "paddle/fluid/memory/malloc.h"
DEFINE_bool
(
init_allocated_mem
,
false
,
"It is a mistake that the values of the memory allocated by "
...
...
@@ -33,172 +29,14 @@ DECLARE_double(fraction_of_gpu_memory_to_use);
namespace
paddle
{
namespace
memory
{
using
BuddyAllocator
=
detail
::
BuddyAllocator
;
BuddyAllocator
*
GetCPUBuddyAllocator
()
{
static
std
::
once_flag
init_flag
;
static
detail
::
BuddyAllocator
*
a
=
nullptr
;
std
::
call_once
(
init_flag
,
[]()
{
a
=
new
detail
::
BuddyAllocator
(
std
::
unique_ptr
<
detail
::
SystemAllocator
>
(
new
detail
::
CPUAllocator
),
platform
::
CpuMinChunkSize
(),
platform
::
CpuMaxChunkSize
());
});
return
a
;
}
template
<
>
void
*
Alloc
<
platform
::
CPUPlace
>
(
platform
::
CPUPlace
place
,
size_t
size
)
{
VLOG
(
10
)
<<
"Allocate "
<<
size
<<
" bytes on "
<<
platform
::
Place
(
place
);
void
*
p
=
GetCPUBuddyAllocator
()
->
Alloc
(
size
);
if
(
FLAGS_init_allocated_mem
)
{
memset
(
p
,
0xEF
,
size
);
}
VLOG
(
10
)
<<
" pointer="
<<
p
;
return
p
;
}
template
<
>
void
Free
<
platform
::
CPUPlace
>
(
platform
::
CPUPlace
place
,
void
*
p
)
{
VLOG
(
10
)
<<
"Free pointer="
<<
p
<<
" on "
<<
platform
::
Place
(
place
);
GetCPUBuddyAllocator
()
->
Free
(
p
);
}
template
<
>
size_t
Used
<
platform
::
CPUPlace
>
(
platform
::
CPUPlace
place
)
{
return
GetCPUBuddyAllocator
()
->
Used
();
}
#ifdef PADDLE_WITH_CUDA
BuddyAllocator
*
GetGPUBuddyAllocator
(
int
gpu_id
)
{
static
std
::
once_flag
init_flag
;
static
detail
::
BuddyAllocator
**
a_arr
=
nullptr
;
std
::
call_once
(
init_flag
,
[
gpu_id
]()
{
int
gpu_num
=
platform
::
GetCUDADeviceCount
();
PADDLE_ENFORCE
(
gpu_id
<
gpu_num
,
"gpu_id:%d should < gpu_num:%d"
,
gpu_id
,
gpu_num
);
a_arr
=
new
BuddyAllocator
*
[
gpu_num
];
for
(
int
i
=
0
;
i
<
gpu_num
;
i
++
)
{
a_arr
[
i
]
=
nullptr
;
platform
::
SetDeviceId
(
i
);
a_arr
[
i
]
=
new
BuddyAllocator
(
std
::
unique_ptr
<
detail
::
SystemAllocator
>
(
new
detail
::
GPUAllocator
(
i
)),
platform
::
GpuMinChunkSize
(),
platform
::
GpuMaxChunkSize
());
VLOG
(
10
)
<<
"
\n\n
NOTE: each GPU device use "
<<
FLAGS_fraction_of_gpu_memory_to_use
*
100
<<
"% of GPU memory.
\n
"
<<
"You can set GFlags environment variable '"
<<
"FLAGS_fraction_of_gpu_memory_to_use"
<<
"' to change the fraction of GPU usage.
\n\n
"
;
}
});
platform
::
SetDeviceId
(
gpu_id
);
return
a_arr
[
gpu_id
];
}
template
<
>
size_t
Used
<
platform
::
CUDAPlace
>
(
platform
::
CUDAPlace
place
)
{
return
GetGPUBuddyAllocator
(
place
.
device
)
->
Used
();
std
::
shared_ptr
<
Allocation
>
AllocShared
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
)
{
return
allocation
::
AllocatorFacade
::
Instance
().
AllocShared
(
place
,
size
,
attr
);
}
template
<
>
void
*
Alloc
<
platform
::
CUDAPlace
>
(
platform
::
CUDAPlace
place
,
size_t
size
)
{
auto
*
buddy_allocator
=
GetGPUBuddyAllocator
(
place
.
device
);
auto
*
ptr
=
buddy_allocator
->
Alloc
(
size
);
if
(
ptr
==
nullptr
)
{
int
cur_dev
=
platform
::
GetCurrentDeviceId
();
platform
::
SetDeviceId
(
place
.
device
);
size_t
avail
,
total
;
platform
::
GpuMemoryUsage
(
&
avail
,
&
total
);
LOG
(
WARNING
)
<<
"Cannot allocate "
<<
size
<<
" bytes in GPU "
<<
place
.
device
<<
", available "
<<
avail
<<
" bytes"
;
LOG
(
WARNING
)
<<
"total "
<<
total
;
LOG
(
WARNING
)
<<
"GpuMinChunkSize "
<<
buddy_allocator
->
GetMinChunkSize
();
LOG
(
WARNING
)
<<
"GpuMaxChunkSize "
<<
buddy_allocator
->
GetMaxChunkSize
();
LOG
(
WARNING
)
<<
"GPU memory used: "
<<
Used
<
platform
::
CUDAPlace
>
(
place
);
platform
::
SetDeviceId
(
cur_dev
);
}
if
(
FLAGS_init_allocated_mem
)
{
cudaMemset
(
ptr
,
0xEF
,
size
);
}
return
ptr
;
std
::
unique_ptr
<
Allocation
>
Alloc
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
)
{
return
allocation
::
AllocatorFacade
::
Instance
().
Alloc
(
place
,
size
,
attr
);
}
template
<
>
void
Free
<
platform
::
CUDAPlace
>
(
platform
::
CUDAPlace
place
,
void
*
p
)
{
GetGPUBuddyAllocator
(
place
.
device
)
->
Free
(
p
);
}
BuddyAllocator
*
GetCUDAPinnedBuddyAllocator
()
{
static
std
::
once_flag
init_flag
;
static
BuddyAllocator
*
ba
=
nullptr
;
std
::
call_once
(
init_flag
,
[]()
{
ba
=
new
BuddyAllocator
(
std
::
unique_ptr
<
detail
::
SystemAllocator
>
(
new
detail
::
CUDAPinnedAllocator
),
platform
::
CUDAPinnedMinChunkSize
(),
platform
::
CUDAPinnedMaxChunkSize
());
});
return
ba
;
}
template
<
>
size_t
Used
<
platform
::
CUDAPinnedPlace
>
(
platform
::
CUDAPinnedPlace
place
)
{
return
GetCUDAPinnedBuddyAllocator
()
->
Used
();
}
template
<
>
void
*
Alloc
<
platform
::
CUDAPinnedPlace
>
(
platform
::
CUDAPinnedPlace
place
,
size_t
size
)
{
auto
*
buddy_allocator
=
GetCUDAPinnedBuddyAllocator
();
void
*
ptr
=
buddy_allocator
->
Alloc
(
size
);
if
(
ptr
==
nullptr
)
{
LOG
(
WARNING
)
<<
"cudaMallocHost Cannot allocate "
<<
size
<<
" bytes in CUDAPinnedPlace"
;
}
if
(
FLAGS_init_allocated_mem
)
{
memset
(
ptr
,
0xEF
,
size
);
}
return
ptr
;
}
template
<
>
void
Free
<
platform
::
CUDAPinnedPlace
>
(
platform
::
CUDAPinnedPlace
place
,
void
*
p
)
{
GetCUDAPinnedBuddyAllocator
()
->
Free
(
p
);
}
#endif
size_t
Usage
::
operator
()(
const
platform
::
CPUPlace
&
cpu
)
const
{
return
Used
(
cpu
);
}
size_t
Usage
::
operator
()(
const
platform
::
CUDAPlace
&
gpu
)
const
{
#ifdef PADDLE_WITH_CUDA
return
Used
(
gpu
);
#else
PADDLE_THROW
(
"'CUDAPlace' is not supported in CPU only device."
);
#endif
}
size_t
Usage
::
operator
()(
const
platform
::
CUDAPinnedPlace
&
cuda_pinned
)
const
{
#ifdef PADDLE_WITH_CUDA
return
Used
(
cuda_pinned
);
#else
PADDLE_THROW
(
"'CUDAPinnedPlace' is not supported in CPU only device."
);
#endif
}
size_t
memory_usage
(
const
platform
::
Place
&
p
)
{
return
boost
::
apply_visitor
(
Usage
(),
p
);
}
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/malloc.h
浏览文件 @
58ed412f
...
...
@@ -14,91 +14,21 @@ limitations under the License. */
#pragma once
#include <memory>
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
memory
{
using
allocation
::
Allocation
;
using
allocation
::
Allocator
;
/**
* \brief Allocate memory block in one place.
*
* \param[in] place Allocation place (CPU or GPU).
* \param[in] size Allocation size.
*
* \return Allocated memory block address.
*
* \note If return nullptr, it indicates memory allocation failed
* because insufficient memory in current system. When Alloc
* function is invoked, you must check the returned memory
* address is valid or not.
*/
template
<
typename
Place
>
void
*
Alloc
(
Place
place
,
size_t
size
);
/**
* \brief Free memory block in one place.
*
* \param[in] place Allocation place (CPU or GPU).
* \param[in] ptr Memory block address to free.
*
*/
template
<
typename
Place
>
void
Free
(
Place
place
,
void
*
ptr
);
/**
* \brief Total size of used memory in one place.
*
* \param[in] place Allocation place (CPU or GPU).
*
*/
template
<
typename
Place
>
size_t
Used
(
Place
place
);
struct
Usage
:
public
boost
::
static_visitor
<
size_t
>
{
size_t
operator
()(
const
platform
::
CPUPlace
&
cpu
)
const
;
size_t
operator
()(
const
platform
::
CUDAPlace
&
gpu
)
const
;
size_t
operator
()(
const
platform
::
CUDAPinnedPlace
&
cuda_pinned
)
const
;
};
size_t
memory_usage
(
const
platform
::
Place
&
p
);
/**
* \brief Free memory block in one place.
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
*
*/
template
<
typename
T
,
typename
Place
>
class
PODDeleter
{
static_assert
(
std
::
is_pod
<
T
>::
value
,
"T must be POD"
);
public:
explicit
PODDeleter
(
Place
place
)
:
place_
(
place
)
{}
void
operator
()(
T
*
ptr
)
{
Free
(
place_
,
static_cast
<
void
*>
(
ptr
));
}
private:
Place
place_
;
};
/**
* \brief Free memory block in one place does not meet POD
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
*
*/
template
<
typename
T
,
typename
Place
>
class
PlainDeleter
{
public:
explicit
PlainDeleter
(
Place
place
)
:
place_
(
place
)
{}
void
operator
()(
T
*
ptr
)
{
Free
(
place_
,
reinterpret_cast
<
void
*>
(
ptr
));
}
extern
std
::
shared_ptr
<
Allocation
>
AllocShared
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
=
Allocator
::
kDefault
);
private:
Place
place_
;
}
;
extern
std
::
unique_ptr
<
Allocation
>
Alloc
(
const
platform
::
Place
&
place
,
size_t
size
,
Allocator
::
Attr
attr
=
Allocator
::
kDefault
)
;
}
// namespace memory
}
// namespace paddle
paddle/fluid/memory/malloc_test.cc
已删除
100644 → 0
浏览文件 @
643b6faa
/* Copyright (c) 2016 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/memory/malloc.h"
#include <unordered_map>
#include "gtest/gtest.h"
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/place.h"
inline
bool
is_aligned
(
void
const
*
p
)
{
return
0
==
(
reinterpret_cast
<
uintptr_t
>
(
p
)
&
0x3
);
}
size_t
align
(
size_t
size
,
paddle
::
platform
::
CPUPlace
place
)
{
size
+=
sizeof
(
paddle
::
memory
::
detail
::
MemoryBlock
::
Desc
);
size_t
alignment
=
paddle
::
platform
::
CpuMinChunkSize
();
size_t
remaining
=
size
%
alignment
;
return
remaining
==
0
?
size
:
size
+
(
alignment
-
remaining
);
}
TEST
(
BuddyAllocator
,
CPUAllocation
)
{
void
*
p
=
nullptr
;
EXPECT_EQ
(
p
,
nullptr
);
paddle
::
platform
::
CPUPlace
cpu
;
p
=
paddle
::
memory
::
Alloc
(
cpu
,
4096
);
EXPECT_NE
(
p
,
nullptr
);
paddle
::
platform
::
Place
place
=
cpu
;
EXPECT_EQ
(
paddle
::
memory
::
Used
(
cpu
),
paddle
::
memory
::
memory_usage
(
place
));
paddle
::
memory
::
Free
(
cpu
,
p
);
}
TEST
(
BuddyAllocator
,
CPUMultAlloc
)
{
paddle
::
platform
::
CPUPlace
cpu
;
std
::
unordered_map
<
void
*
,
size_t
>
ps
;
size_t
total_size
=
paddle
::
memory
::
Used
(
cpu
);
EXPECT_EQ
(
total_size
,
0UL
);
for
(
auto
size
:
{
0
,
128
,
256
,
1024
,
4096
,
16384
,
65536
,
262144
,
1048576
,
4194304
})
{
ps
[
paddle
::
memory
::
Alloc
(
cpu
,
size
)]
=
size
;
// Buddy Allocator doesn't manage too large memory chunk
if
(
paddle
::
memory
::
Used
(
cpu
)
==
total_size
)
continue
;
size_t
aligned_size
=
align
(
size
,
cpu
);
total_size
+=
aligned_size
;
EXPECT_EQ
(
total_size
,
paddle
::
memory
::
Used
(
cpu
));
}
for
(
auto
p
:
ps
)
{
EXPECT_EQ
(
is_aligned
(
p
.
first
),
true
);
paddle
::
memory
::
Free
(
cpu
,
p
.
first
);
// Buddy Allocator doesn't manage too large memory chunk
if
(
paddle
::
memory
::
Used
(
cpu
)
==
total_size
)
continue
;
size_t
aligned_size
=
align
(
p
.
second
,
cpu
);
total_size
-=
aligned_size
;
EXPECT_EQ
(
total_size
,
paddle
::
memory
::
Used
(
cpu
));
}
}
#ifdef PADDLE_WITH_CUDA
size_t
align
(
size_t
size
,
paddle
::
platform
::
CUDAPlace
place
)
{
size
+=
sizeof
(
paddle
::
memory
::
detail
::
MemoryBlock
::
Desc
);
size_t
alignment
=
paddle
::
platform
::
GpuMinChunkSize
();
size_t
remaining
=
size
%
alignment
;
return
remaining
==
0
?
size
:
size
+
(
alignment
-
remaining
);
}
TEST
(
BuddyAllocator
,
GPUAllocation
)
{
void
*
p
=
nullptr
;
EXPECT_EQ
(
p
,
nullptr
);
paddle
::
platform
::
CUDAPlace
gpu
(
0
);
p
=
paddle
::
memory
::
Alloc
(
gpu
,
4096
);
EXPECT_NE
(
p
,
nullptr
);
paddle
::
platform
::
Place
place
=
gpu
;
EXPECT_EQ
(
paddle
::
memory
::
Used
(
gpu
),
paddle
::
memory
::
memory_usage
(
place
));
paddle
::
memory
::
Free
(
gpu
,
p
);
}
TEST
(
BuddyAllocator
,
GPUMultAlloc
)
{
paddle
::
platform
::
CUDAPlace
gpu
;
std
::
unordered_map
<
void
*
,
size_t
>
ps
;
size_t
total_size
=
paddle
::
memory
::
Used
(
gpu
);
EXPECT_EQ
(
total_size
,
0UL
);
for
(
auto
size
:
{
0
,
128
,
256
,
1024
,
4096
,
16384
,
65536
,
262144
,
1048576
,
4194304
})
{
ps
[
paddle
::
memory
::
Alloc
(
gpu
,
size
)]
=
size
;
// Buddy Allocator doesn't manage too large memory chunk
if
(
paddle
::
memory
::
Used
(
gpu
)
==
total_size
)
continue
;
size_t
aligned_size
=
align
(
size
,
gpu
);
total_size
+=
aligned_size
;
EXPECT_EQ
(
total_size
,
paddle
::
memory
::
Used
(
gpu
));
}
for
(
auto
p
:
ps
)
{
EXPECT_EQ
(
is_aligned
(
p
.
first
),
true
);
paddle
::
memory
::
Free
(
gpu
,
p
.
first
);
// Buddy Allocator doesn't manage too large memory chunk
if
(
paddle
::
memory
::
Used
(
gpu
)
==
total_size
)
continue
;
size_t
aligned_size
=
align
(
p
.
second
,
gpu
);
total_size
-=
aligned_size
;
EXPECT_EQ
(
total_size
,
paddle
::
memory
::
Used
(
gpu
));
}
}
size_t
align
(
size_t
size
,
paddle
::
platform
::
CUDAPinnedPlace
place
)
{
size
+=
sizeof
(
paddle
::
memory
::
detail
::
MemoryBlock
::
Desc
);
size_t
alignment
=
paddle
::
platform
::
CUDAPinnedMinChunkSize
();
size_t
remaining
=
size
%
alignment
;
return
remaining
==
0
?
size
:
size
+
(
alignment
-
remaining
);
}
TEST
(
BuddyAllocator
,
CUDAPinnedAllocator
)
{
void
*
p
=
nullptr
;
EXPECT_EQ
(
p
,
nullptr
);
paddle
::
platform
::
CUDAPinnedPlace
cpu
;
p
=
paddle
::
memory
::
Alloc
(
cpu
,
4096
);
EXPECT_NE
(
p
,
nullptr
);
paddle
::
platform
::
Place
place
=
cpu
;
EXPECT_EQ
(
paddle
::
memory
::
Used
(
cpu
),
paddle
::
memory
::
memory_usage
(
place
));
paddle
::
memory
::
Free
(
cpu
,
p
);
}
TEST
(
BuddyAllocator
,
CUDAPinnedMultAllocator
)
{
paddle
::
platform
::
CUDAPinnedPlace
cpu
;
std
::
unordered_map
<
void
*
,
size_t
>
ps
;
size_t
total_size
=
paddle
::
memory
::
Used
(
cpu
);
EXPECT_EQ
(
total_size
,
0UL
);
for
(
auto
size
:
{
0
,
128
,
256
,
1024
,
4096
,
16384
,
65536
,
262144
,
1048576
,
4194304
})
{
ps
[
paddle
::
memory
::
Alloc
(
cpu
,
size
)]
=
size
;
// Buddy Allocator doesn't manage too large memory chunk
if
(
paddle
::
memory
::
Used
(
cpu
)
==
total_size
)
continue
;
size_t
aligned_size
=
align
(
size
,
cpu
);
total_size
+=
aligned_size
;
EXPECT_EQ
(
total_size
,
paddle
::
memory
::
Used
(
cpu
));
}
for
(
auto
p
:
ps
)
{
EXPECT_EQ
(
is_aligned
(
p
.
first
),
true
);
paddle
::
memory
::
Free
(
cpu
,
p
.
first
);
// Buddy Allocator doesn't manage too large memory chunk
if
(
paddle
::
memory
::
Used
(
cpu
)
==
total_size
)
continue
;
size_t
aligned_size
=
align
(
p
.
second
,
cpu
);
total_size
-=
aligned_size
;
EXPECT_EQ
(
total_size
,
paddle
::
memory
::
Used
(
cpu
));
}
}
#endif
paddle/fluid/operators/detection/generate_proposals_op.cu
浏览文件 @
58ed412f
...
...
@@ -16,6 +16,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "cub/cub.cuh"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/gather.cu.h"
...
...
@@ -57,22 +58,18 @@ void SortDescending(const platform::CUDADeviceContext &ctx, const Tensor &value,
T
*
keys_out
=
value_out
->
mutable_data
<
T
>
({
num
},
ctx
.
GetPlace
());
// Determine temporary device storage requirements
void
*
d_temp_storage
=
NULL
;
size_t
temp_storage_bytes
=
0
;
cub
::
DeviceRadixSort
::
SortPairsDescending
<
T
,
int
>
(
d_temp_storage
,
temp_storage_bytes
,
keys_in
,
keys_out
,
idx_in
,
idx_out
,
num
);
nullptr
,
temp_storage_bytes
,
keys_in
,
keys_out
,
idx_in
,
idx_out
,
num
);
// Allocate temporary storage
auto
place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
ctx
.
GetPlace
());
d_temp_storage
=
memory
::
Alloc
(
place
,
temp_storage_bytes
);
auto
d_temp_storage
=
memory
::
Alloc
(
place
,
temp_storage_bytes
,
memory
::
Allocator
::
kTmp
);
// Run sorting operation
cub
::
DeviceRadixSort
::
SortPairsDescending
<
T
,
int
>
(
d_temp_storage
,
temp_storage_bytes
,
keys_in
,
keys_out
,
idx_in
,
idx_out
,
num
);
memory
::
Free
(
place
,
d_temp_storage
);
d_temp_storage
->
ptr
(),
temp_storage_bytes
,
keys_in
,
keys_out
,
idx_in
,
idx_out
,
num
);
}
template
<
typename
T
>
...
...
@@ -248,11 +245,12 @@ void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
const
T
*
boxes
=
proposals
.
data
<
T
>
();
auto
place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
ctx
.
GetPlace
());
int
size_bytes
=
boxes_num
*
col_blocks
*
sizeof
(
uint64_t
);
uint64_t
*
d_mask
=
reinterpret_cast
<
uint64_t
*>
(
memory
::
Alloc
(
place
,
size_bytes
));
auto
d_mask_allocation
=
memory
::
Alloc
(
place
,
size_bytes
);
uint64_t
*
d_mask
=
reinterpret_cast
<
uint64_t
*>
(
d_mask_allocation
->
ptr
(
));
NMSKernel
<<<
blocks
,
threads
>>>
(
boxes_num
,
nms_threshold
,
boxes
,
d_mask
);
uint64_t
*
h_mask
=
reinterpret_cast
<
uint64_t
*>
(
memory
::
Alloc
(
platform
::
CPUPlace
(),
size_bytes
));
auto
h_mask_allocation
=
memory
::
Alloc
(
platform
::
CPUPlace
(),
size_bytes
);
uint64_t
*
h_mask
=
reinterpret_cast
<
uint64_t
*>
(
h_mask_allocation
->
ptr
());
memory
::
Copy
(
platform
::
CPUPlace
(),
h_mask
,
place
,
d_mask
,
size_bytes
,
0
);
std
::
vector
<
uint64_t
>
remv
(
col_blocks
);
...
...
paddle/fluid/operators/strided_memcpy_test.cc
浏览文件 @
58ed412f
...
...
@@ -87,13 +87,16 @@ TEST(StridedMemcpy, GPUCrop) {
platform
::
CUDADeviceContext
ctx
(
gpu0
);
int
*
gpu_src
=
reinterpret_cast
<
int
*>
(
memory
::
Alloc
(
gpu0
,
sizeof
(
src
)));
auto
src_allocation
=
memory
::
Alloc
(
gpu0
,
sizeof
(
src
));
int
*
gpu_src
=
reinterpret_cast
<
int
*>
(
src_allocation
->
ptr
());
memory
::
Copy
(
gpu0
,
gpu_src
,
cpu
,
src
,
sizeof
(
src
),
ctx
.
stream
());
framework
::
DDim
src_stride
({
5
,
1
});
int
dst
[
4
];
int
*
gpu_dst
=
reinterpret_cast
<
int
*>
(
memory
::
Alloc
(
gpu0
,
sizeof
(
dst
)));
auto
dst_allocation
=
memory
::
Alloc
(
gpu0
,
sizeof
(
dst
));
int
*
gpu_dst
=
reinterpret_cast
<
int
*>
(
dst_allocation
->
ptr
());
framework
::
DDim
dst_dim
({
2
,
2
});
framework
::
DDim
dst_stride
({
2
,
1
});
...
...
@@ -108,9 +111,6 @@ TEST(StridedMemcpy, GPUCrop) {
ASSERT_EQ
(
2
,
dst
[
1
]);
ASSERT_EQ
(
3
,
dst
[
2
]);
ASSERT_EQ
(
4
,
dst
[
3
]);
memory
::
Free
(
gpu0
,
gpu_dst
);
memory
::
Free
(
gpu0
,
gpu_src
);
}
TEST
(
StridedMemcpy
,
GPUConcat
)
{
...
...
@@ -124,12 +124,13 @@ TEST(StridedMemcpy, GPUConcat) {
platform
::
CUDAPlace
gpu0
(
0
);
platform
::
CPUPlace
cpu
;
platform
::
CUDADeviceContext
ctx
(
gpu0
);
int
*
gpu_src
=
reinterpret_cast
<
int
*>
(
memory
::
Alloc
(
gpu0
,
sizeof
(
src
)
));
auto
gpu_src_allocation
=
memory
::
Alloc
(
gpu0
,
sizeof
(
src
));
int
*
gpu_src
=
reinterpret_cast
<
int
*>
(
gpu_src_allocation
->
ptr
(
));
memory
::
Copy
(
gpu0
,
gpu_src
,
cpu
,
src
,
sizeof
(
src
),
ctx
.
stream
());
int
dst
[
8
];
int
*
gpu_dst
=
reinterpret_cast
<
int
*>
(
memory
::
Alloc
(
gpu0
,
sizeof
(
dst
)));
auto
gpu_dst_allocation
=
memory
::
Alloc
(
gpu0
,
sizeof
(
dst
));
int
*
gpu_dst
=
reinterpret_cast
<
int
*>
(
gpu_dst_allocation
->
ptr
());
framework
::
DDim
src_stride
({
2
,
1
});
framework
::
DDim
dst_dim
({
2
,
2
});
...
...
@@ -151,9 +152,6 @@ TEST(StridedMemcpy, GPUConcat) {
for
(
size_t
i
=
0
;
i
<
sizeof
(
expect_dst
)
/
sizeof
(
int
);
++
i
)
{
ASSERT_EQ
(
expect_dst
[
i
],
dst
[
i
]);
}
memory
::
Free
(
gpu0
,
gpu_dst
);
memory
::
Free
(
gpu0
,
gpu_src
);
}
#endif
...
...
paddle/fluid/platform/device_context.cc
浏览文件 @
58ed412f
...
...
@@ -112,11 +112,15 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface {
}
void
*
allocate
(
size_t
num_bytes
)
const
override
{
return
paddle
::
memory
::
Alloc
(
place_
,
num_bytes
);
auto
buf
=
paddle
::
memory
::
Alloc
(
place_
,
num_bytes
,
memory
::
Allocator
::
kTiny
);
void
*
retv
=
buf
->
ptr
();
allocations_
[
buf
->
ptr
()]
=
std
::
move
(
buf
);
return
retv
;
}
void
deallocate
(
void
*
buffer
)
const
override
{
paddle
::
memory
::
Free
(
place_
,
buffer
);
allocations_
.
erase
(
allocations_
.
find
(
buffer
)
);
}
void
*
scratchpad
()
const
override
{
...
...
@@ -143,12 +147,14 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface {
const
cudaDeviceProp
*
device_prop_
;
// not owned;
mutable
void
*
scratch_
;
mutable
unsigned
int
*
semaphore_
;
mutable
std
::
unordered_map
<
void
*
,
std
::
unique_ptr
<
memory
::
Allocation
>>
allocations_
;
};
class
CudnnHolder
{
public:
CudnnHolder
(
const
cudaStream_t
*
stream
,
const
CUDAPlace
&
place
)
:
workspace_
(
nullptr
),
workspace_len_
(
0
),
stream_
(
stream
),
place_
(
place
)
{
:
workspace_
(
nullptr
),
stream_
(
stream
),
place_
(
place
)
{
PADDLE_ENFORCE
(
dynload
::
cudnnCreate
(
&
cudnn_handle_
));
PADDLE_ENFORCE
(
dynload
::
cudnnSetStream
(
cudnn_handle_
,
*
stream_
));
}
...
...
@@ -158,36 +164,38 @@ class CudnnHolder {
void
RunFunc
(
const
std
::
function
<
void
(
void
*
)
>&
cudnn_func
,
size_t
required_workspace_len
)
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
mtx_
);
if
(
required_workspace_len
>
workspace_len_
)
{
if
(
required_workspace_len
>
WorkspaceSize
()
)
{
ReallocateWorkspace
(
required_workspace_len
);
}
cudnn_func
(
workspace_
);
cudnn_func
(
workspace_
->
ptr
()
);
}
~
CudnnHolder
()
{
PADDLE_ENFORCE
(
dynload
::
cudnnDestroy
(
cudnn_handle_
));
if
(
workspace_
!=
nullptr
)
{
paddle
::
memory
::
Free
(
place_
,
workspace_
);
~
CudnnHolder
()
{
PADDLE_ENFORCE
(
dynload
::
cudnnDestroy
(
cudnn_handle_
));
}
private:
size_t
WorkspaceSize
()
const
{
if
(
workspace_
==
nullptr
)
{
return
0
;
}
else
{
return
workspace_
->
size
();
}
}
private:
void
ReallocateWorkspace
(
size_t
required_workspace_len
)
{
if
(
required_workspace_len
<=
workspace_len_
)
{
if
(
required_workspace_len
<=
WorkspaceSize
()
)
{
return
;
}
if
(
workspace_
!=
nullptr
)
{
// Maybe someone is using the current workspace
PADDLE_ENFORCE
(
cudaStreamSynchronize
(
*
stream_
));
paddle
::
memory
::
Free
(
place_
,
workspace_
);
workspace_
.
reset
(
);
}
workspace_
=
paddle
::
memory
::
Alloc
(
place_
,
required_workspace_len
);
workspace_len_
=
required_workspace_len
;
workspace_
=
paddle
::
memory
::
Alloc
(
place_
,
required_workspace_len
,
memory
::
Allocator
::
kFluxHuge
)
;
}
cudnnHandle_t
cudnn_handle_
;
void
*
workspace_
;
size_t
workspace_len_
;
std
::
unique_ptr
<
memory
::
Allocation
>
workspace_
;
const
cudaStream_t
*
stream_
;
// not owned;
const
CUDAPlace
place_
;
...
...
paddle/fluid/platform/transform_test.cu
浏览文件 @
58ed412f
...
...
@@ -39,7 +39,6 @@ class Multiply {
}
// namespace
using
paddle
::
memory
::
Alloc
;
using
paddle
::
memory
::
Free
;
using
paddle
::
memory
::
Copy
;
using
paddle
::
platform
::
CPUPlace
;
...
...
@@ -63,13 +62,13 @@ TEST(Transform, GPUUnary) {
CUDAPlace
gpu0
(
0
);
CUDADeviceContext
ctx
(
gpu0
);
float
cpu_buf
[
4
]
=
{
0.1
,
0.2
,
0.3
,
0.4
};
float
*
gpu_buf
=
static_cast
<
float
*>
(
Alloc
(
gpu0
,
sizeof
(
float
)
*
4
));
auto
gpu_allocation
=
Alloc
(
gpu0
,
sizeof
(
float
)
*
4
);
float
*
gpu_buf
=
static_cast
<
float
*>
(
gpu_allocation
->
ptr
());
Copy
(
gpu0
,
gpu_buf
,
CPUPlace
(),
cpu_buf
,
sizeof
(
cpu_buf
),
ctx
.
stream
());
Transform
<
CUDADeviceContext
>
trans
;
trans
(
ctx
,
gpu_buf
,
gpu_buf
+
4
,
gpu_buf
,
Scale
<
float
>
(
10
));
ctx
.
Wait
();
Copy
(
CPUPlace
(),
cpu_buf
,
gpu0
,
gpu_buf
,
sizeof
(
cpu_buf
),
ctx
.
stream
());
Free
(
gpu0
,
gpu_buf
);
for
(
int
i
=
0
;
i
<
4
;
++
i
)
{
ASSERT_NEAR
(
cpu_buf
[
i
],
static_cast
<
float
>
(
i
+
1
),
1e-5
);
}
...
...
@@ -89,13 +88,13 @@ TEST(Transform, GPUBinary) {
int
buf
[
4
]
=
{
1
,
2
,
3
,
4
};
CUDAPlace
gpu0
(
0
);
CUDADeviceContext
ctx
(
gpu0
);
int
*
gpu_buf
=
static_cast
<
int
*>
(
Alloc
(
gpu0
,
sizeof
(
buf
)));
auto
gpu_allocation
=
Alloc
(
gpu0
,
sizeof
(
buf
));
int
*
gpu_buf
=
static_cast
<
int
*>
(
gpu_allocation
->
ptr
());
Copy
(
gpu0
,
gpu_buf
,
CPUPlace
(),
buf
,
sizeof
(
buf
),
ctx
.
stream
());
Transform
<
CUDADeviceContext
>
trans
;
trans
(
ctx
,
gpu_buf
,
gpu_buf
+
4
,
gpu_buf
,
gpu_buf
,
Multiply
<
int
>
());
ctx
.
Wait
();
Copy
(
CPUPlace
(),
buf
,
gpu0
,
gpu_buf
,
sizeof
(
buf
),
ctx
.
stream
());
Free
(
gpu0
,
gpu_buf
);
for
(
int
i
=
0
;
i
<
4
;
++
i
)
{
ASSERT_EQ
((
i
+
1
)
*
(
i
+
1
),
buf
[
i
]);
}
...
...
paddle/fluid/platform/variant.h
浏览文件 @
58ed412f
...
...
@@ -41,4 +41,5 @@ limitations under the License. */
#include <boost/any.hpp>
#include <boost/mpl/comparison.hpp>
#include <boost/mpl/less_equal.hpp>
#include <boost/optional.hpp>
#include <boost/variant.hpp>
paddle/testing/paddle_gtest_main.cc
浏览文件 @
58ed412f
...
...
@@ -27,8 +27,7 @@ int main(int argc, char** argv) {
new_argv
.
push_back
(
argv
[
i
]);
}
#ifdef PADDLE_WITH_CUDA
new_argv
.
push_back
(
strdup
(
"--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"
));
new_argv
.
push_back
(
strdup
(
"--tryfromenv=fraction_of_gpu_memory_to_use"
));
#else
new_argv
.
push_back
(
strdup
(
"--tryfromenv=use_pinned_memory,use_mkldnn,initial_cpu_memory_in_mb"
));
...
...
@@ -37,12 +36,6 @@ int main(int argc, char** argv) {
int
new_argc
=
static_cast
<
int
>
(
new_argv
.
size
());
char
**
new_argv_address
=
new_argv
.
data
();
google
::
ParseCommandLineFlags
(
&
new_argc
,
&
new_argv_address
,
false
);
paddle
::
memory
::
Used
(
paddle
::
platform
::
CPUPlace
());
#ifdef PADDLE_WITH_CUDA
paddle
::
memory
::
Used
(
paddle
::
platform
::
CUDAPlace
(
0
));
#endif
paddle
::
framework
::
InitDevices
(
true
);
return
RUN_ALL_TESTS
();
}
python/paddle/fluid/__init__.py
浏览文件 @
58ed412f
...
...
@@ -110,10 +110,10 @@ def __bootstrap__():
os
.
environ
[
'OMP_NUM_THREADS'
]
=
str
(
num_threads
)
read_env_flags
=
[
'
use_pinned_memory'
,
'check_nan_inf'
,
'benchmark'
,
'warpctc_dir
'
,
'
eager_delete_scope'
,
'use_mkldnn'
,
'initial_cpu_memory_in_mb
'
,
'
init_allocated_mem'
,
'free_idle_memory'
,
'paddle_num_threads
'
,
"dist_threadpool_size"
,
'cpu_deterministic'
,
'eager_delete_tensor_gb'
'
check_nan_inf'
,
'benchmark'
,
'warpctc_dir'
,
'eager_delete_scope
'
,
'
use_mkldnn'
,
'initial_cpu_memory_in_mb'
,
'init_allocated_mem
'
,
'
paddle_num_threads'
,
"dist_threadpool_size"
,
'cpu_deterministic
'
,
'eager_delete_tensor_gb'
]
if
core
.
is_compiled_with_dist
():
read_env_flags
.
append
(
'rpc_deadline'
)
...
...
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