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49710960
编写于
11月 08, 2018
作者:
M
minqiyang
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paddle/fluid/framework/tensor_util.cu
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/* 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 <algorithm>
#include <limits>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/tensor_util.h"
namespace
paddle
{
namespace
framework
{
void
TensorCopy
(
const
Tensor
&
src
,
const
platform
::
Place
&
dst_place
,
const
platform
::
DeviceContext
&
ctx
,
Tensor
*
dst
)
{
VLOG
(
30
)
<<
"TensorCopy "
<<
src
.
dims
()
<<
" from "
<<
src
.
place
()
<<
" to "
<<
dst_place
;
src
.
check_memory_size
();
dst
->
Resize
(
src
.
dims
());
dst
->
set_layout
(
src
.
layout
());
auto
src_place
=
src
.
place
();
auto
src_ptr
=
src
.
data
<
void
>
();
auto
dst_ptr
=
dst
->
mutable_data
(
dst_place
,
src
.
type
());
auto
size
=
src
.
numel
()
*
SizeOfType
(
src
.
type
());
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_cpu_place
(
dst_place
))
{
if
(
src_ptr
==
dst_ptr
)
{
VLOG
(
30
)
<<
"Skip copy the same data async from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
),
dst_ptr
,
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
),
src_ptr
,
size
);
}
#ifdef PADDLE_WITH_CUDA
else
if
(
platform
::
is_gpu_place
(
src_place
)
&&
// NOLINT
platform
::
is_cpu_place
(
dst_place
))
{
auto
src_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
src_place
);
auto
dst_cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
);
auto
ctx_place
=
ctx
.
GetPlace
();
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx_place
));
auto
ctx_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
ctx_place
);
PADDLE_ENFORCE_EQ
(
src_gpu_place
,
ctx_gpu_place
);
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
).
stream
();
memory
::
Copy
(
dst_cpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
stream
);
}
else
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
auto
src_cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
auto
ctx_place
=
ctx
.
GetPlace
();
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx_place
));
auto
ctx_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
ctx_place
);
PADDLE_ENFORCE_EQ
(
dst_gpu_place
,
ctx_gpu_place
);
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
).
stream
();
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_cpu_place
,
src_ptr
,
size
,
stream
);
}
else
if
(
platform
::
is_gpu_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
auto
src_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
auto
ctx_place
=
ctx
.
GetPlace
();
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx_place
));
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
).
stream
();
if
(
platform
::
is_same_place
(
src_place
,
dst_place
))
{
if
(
src_ptr
==
dst_ptr
)
{
VLOG
(
30
)
<<
"Skip copy the same data async from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
stream
);
}
else
{
if
(
platform
::
is_same_place
(
ctx_place
,
src_place
))
{
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
stream
);
platform
::
DeviceContextPool
::
Instance
().
Get
(
src
.
place
())
->
Wait
();
}
else
if
(
platform
::
is_same_place
(
ctx_place
,
dst_place
))
{
platform
::
DeviceContextPool
::
Instance
().
Get
(
src
.
place
())
->
Wait
();
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
stream
);
}
else
{
PADDLE_THROW
(
"ctx is not belong to dst_gpu_place or src_gpu_place."
);
}
}
}
#endif
}
void
TensorCopy
(
const
Tensor
&
src
,
const
platform
::
Place
&
dst_place
,
Tensor
*
dst
)
{
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
const
platform
::
DeviceContext
*
dev_ctx
;
if
(
platform
::
is_gpu_place
(
dst_place
))
{
dev_ctx
=
pool
.
Get
(
dst_place
);
}
else
{
dev_ctx
=
pool
.
Get
(
src
.
place
());
}
TensorCopy
(
src
,
dst_place
,
*
dev_ctx
,
dst
);
}
void
TensorCopySync
(
const
Tensor
&
src
,
const
platform
::
Place
&
dst_place
,
Tensor
*
dst
)
{
VLOG
(
30
)
<<
"TensorCopySync "
<<
src
.
dims
()
<<
" from "
<<
src
.
place
()
<<
" to "
<<
dst_place
;
src
.
check_memory_size
();
dst
->
Resize
(
src
.
dims
());
dst
->
set_layout
(
src
.
layout
());
auto
src_place
=
src
.
place
();
auto
src_ptr
=
src
.
data
<
void
>
();
auto
dst_ptr
=
dst
->
mutable_data
(
dst_place
,
src
.
type
());
auto
size
=
src
.
numel
()
*
SizeOfType
(
src
.
type
());
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_cpu_place
(
dst_place
))
{
if
(
src_ptr
==
dst_ptr
)
{
VLOG
(
30
)
<<
"Skip copy the same data from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
),
dst_ptr
,
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
),
src_ptr
,
size
);
}
#ifdef PADDLE_WITH_CUDA
else
if
(
platform
::
is_gpu_place
(
src_place
)
&&
// NOLINT
platform
::
is_cpu_place
(
dst_place
))
{
auto
src_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
src_place
);
auto
dst_cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
);
memory
::
Copy
(
dst_cpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
auto
src_cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_cpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_gpu_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
if
(
src_ptr
==
dst_ptr
&&
platform
::
is_same_place
(
src_place
,
dst_place
))
{
VLOG
(
30
)
<<
"Skip copy the same data from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
auto
src_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_cuda_pinned_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
auto
src_pinned_place
=
boost
::
get
<
platform
::
CUDAPinnedPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_pinned_place
,
src_ptr
,
size
,
nullptr
);
}
#endif
}
template
<
typename
Predicate
,
typename
DevCtx
>
struct
AnyDTypeVisitor
{
Predicate
predicate_
;
const
Tensor
&
tensor_
;
const
DevCtx
&
ctx_
;
Tensor
*
out_
;
AnyDTypeVisitor
(
Predicate
predicate
,
const
Tensor
&
tensor
,
const
DevCtx
&
ctx
,
Tensor
*
out
)
:
predicate_
(
predicate
),
tensor_
(
tensor
),
ctx_
(
ctx
),
out_
(
out
)
{}
template
<
typename
T
>
void
apply
()
const
{
auto
t
=
EigenVector
<
T
>::
Flatten
(
tensor_
);
auto
o
=
EigenScalar
<
bool
>::
From
(
*
out_
);
// return any of predicate_(t) is true.
o
.
device
(
*
ctx_
.
eigen_device
())
=
predicate_
(
t
).
any
();
}
};
template
<
typename
Predicate
,
typename
DevCtx
>
inline
void
AnyImpl
(
Predicate
predicate
,
const
framework
::
Tensor
&
tensor
,
const
DevCtx
&
ctx
,
framework
::
Tensor
*
out
)
{
VisitDataType
(
ToDataType
(
tensor
.
type
()),
AnyDTypeVisitor
<
Predicate
,
DevCtx
>
(
predicate
,
tensor
,
ctx
,
out
));
}
template
<
typename
Predicate
>
class
AnyVisitor
:
public
boost
::
static_visitor
<
bool
>
{
private:
const
framework
::
Tensor
&
tensor_
;
Predicate
predicate_
;
public:
AnyVisitor
(
const
framework
::
Tensor
&
tensor
,
Predicate
predicate
)
:
tensor_
(
tensor
),
predicate_
(
std
::
move
(
predicate
))
{}
template
<
typename
Place
>
bool
operator
()(
const
Place
&
place
)
const
{
framework
::
Tensor
out
;
out
.
Resize
({
1
});
out
.
mutable_data
<
bool
>
(
place
);
auto
*
ctx
=
platform
::
DeviceContextPool
::
Instance
().
GetByPlace
(
place
);
AnyImpl
(
predicate_
,
tensor_
,
*
ctx
,
&
out
);
return
this
->
GetResult
(
out
,
place
);
}
bool
GetResult
(
const
framework
::
Tensor
&
out
,
const
platform
::
CUDAPlace
&
gpu
)
const
{
platform
::
CPUPlace
cpu
;
framework
::
Tensor
tmp
;
tmp
.
Resize
({
1
});
tmp
.
mutable_data
<
bool
>
(
cpu
);
auto
gpuctx
=
platform
::
DeviceContextPool
::
Instance
().
Get
(
gpu
);
gpuctx
->
Wait
();
TensorCopy
(
out
,
cpu
,
*
gpuctx
,
&
tmp
);
gpuctx
->
Wait
();
return
GetResult
(
tmp
,
cpu
);
}
bool
GetResult
(
const
framework
::
Tensor
&
out
,
const
platform
::
CPUPlace
&
cpu
)
const
{
return
*
out
.
data
<
bool
>
();
}
bool
GetResult
(
const
framework
::
Tensor
&
out
,
const
platform
::
CUDAPinnedPlace
&
cpu
)
const
{
return
*
out
.
data
<
bool
>
();
}
};
template
<
typename
Predicate
>
class
AnyOutVisitor
:
public
boost
::
static_visitor
<>
{
private:
const
framework
::
Tensor
&
tensor_
;
mutable
framework
::
Tensor
*
out_
;
Predicate
predicate_
;
public:
AnyOutVisitor
(
const
framework
::
Tensor
&
tensor
,
Predicate
predicate
,
framework
::
Tensor
*
out
)
:
tensor_
(
tensor
),
out_
(
out
),
predicate_
(
std
::
move
(
predicate
))
{}
template
<
typename
Place
>
void
operator
()(
const
Place
&
place
)
const
{
auto
*
ctx
=
platform
::
DeviceContextPool
::
Instance
().
GetByPlace
(
place
);
out_
->
Resize
({
1
});
out_
->
mutable_data
<
bool
>
(
place
);
AnyImpl
(
predicate_
,
tensor_
,
*
ctx
,
out_
);
}
};
template
<
typename
Predicate
>
inline
bool
Any
(
const
framework
::
Tensor
&
tensor
,
Predicate
predicate
)
{
AnyVisitor
<
Predicate
>
visitor
(
tensor
,
predicate
);
auto
place
=
tensor
.
place
();
return
platform
::
VisitPlace
(
place
,
visitor
);
}
template
<
typename
Predicate
>
inline
void
Any
(
const
framework
::
Tensor
&
tensor
,
Predicate
predicate
,
framework
::
Tensor
*
out
)
{
AnyOutVisitor
<
Predicate
>
visitor
(
tensor
,
predicate
,
out
);
auto
place
=
tensor
.
place
();
platform
::
VisitPlace
(
place
,
visitor
);
}
struct
ContainsNANPredicate
{
template
<
typename
T
>
auto
operator
()(
const
T
&
eigen_vec
)
const
->
decltype
(
std
::
declval
<
T
>
().
isnan
())
{
// Cast eigen_vector to vector of bool. true if is inf.
return
eigen_vec
.
isnan
();
}
};
bool
TensorContainsNAN
(
const
framework
::
Tensor
&
tensor
)
{
ContainsNANPredicate
predicate
;
return
Any
(
tensor
,
predicate
);
}
void
TensorContainsNAN
(
const
framework
::
Tensor
&
tensor
,
framework
::
Tensor
*
out
)
{
ContainsNANPredicate
predicate
;
Any
(
tensor
,
predicate
,
out
);
}
struct
ContainsInfPredicate
{
template
<
typename
T
>
auto
operator
()(
const
T
&
eigen_vec
)
const
->
decltype
(
std
::
declval
<
T
>
().
isinf
())
{
// Cast eigen_vector to vector of bool. true if is inf.
return
eigen_vec
.
isinf
();
}
};
bool
TensorContainsInf
(
const
framework
::
Tensor
&
tensor
)
{
ContainsInfPredicate
predicate
;
return
Any
(
tensor
,
predicate
);
}
void
TensorContainsInf
(
const
framework
::
Tensor
&
tensor
,
framework
::
Tensor
*
out
)
{
ContainsInfPredicate
predicate
;
Any
(
tensor
,
predicate
,
out
);
}
// NOTE(dzhwinter):
// Isfinite need a AllVisitor to loop through all the elements.
// We choose two cuda call instead of one allvisitor. The AllVisitor
// should be implemented if the performance hurts.
bool
TensorIsfinite
(
const
framework
::
Tensor
&
tensor
)
{
ContainsInfPredicate
pred_inf
;
ContainsNANPredicate
pred_nan
;
return
!
Any
(
tensor
,
pred_inf
)
&&
!
Any
(
tensor
,
pred_nan
);
}
#ifdef PADDLE_WITH_CUDA
template
<
typename
T
>
static
inline
void
__global__
BothFalse
(
const
T
*
cmp
,
T
*
out
)
{
out
[
0
]
=
(
!
cmp
[
0
])
&&
(
!
out
[
0
]);
}
#endif
struct
BothFalseVisitor
:
public
boost
::
static_visitor
<>
{
const
framework
::
Tensor
&
in_
;
mutable
framework
::
Tensor
*
out_
;
BothFalseVisitor
(
const
framework
::
Tensor
&
in
,
framework
::
Tensor
*
out
)
:
in_
(
in
),
out_
(
out
)
{}
template
<
typename
Place
>
void
operator
()(
const
Place
&
place
)
const
{
VisitorImpl
(
place
);
}
void
VisitorImpl
(
const
platform
::
CUDAPlace
&
gpu
)
const
{
#ifdef PADDLE_WITH_CUDA
auto
*
ctx
=
platform
::
DeviceContextPool
::
Instance
().
GetByPlace
(
gpu
);
BothFalse
<
bool
><<<
1
,
1
,
0
,
ctx
->
stream
()
>>>
(
in_
.
data
<
bool
>
(),
out_
->
mutable_data
<
bool
>
(
gpu
));
#endif
}
void
VisitorImpl
(
const
platform
::
CPUPlace
&
cpu
)
const
{
bool
lhs
=
!
in_
.
data
<
bool
>
()[
0
];
bool
rhs
=
!
out_
->
mutable_data
<
bool
>
(
cpu
)[
0
];
out_
->
mutable_data
<
bool
>
(
cpu
)[
0
]
=
lhs
&&
rhs
;
}
void
VisitorImpl
(
const
platform
::
CUDAPinnedPlace
&
cpu
/* equals to cpu*/
)
const
{
bool
lhs
=
!
in_
.
data
<
bool
>
()[
0
];
bool
rhs
=
!
out_
->
mutable_data
<
bool
>
(
cpu
)[
0
];
out_
->
mutable_data
<
bool
>
(
cpu
)[
0
]
=
lhs
&&
rhs
;
}
};
void
TensorIsfinite
(
const
framework
::
Tensor
&
tensor
,
framework
::
Tensor
*
out
)
{
framework
::
Tensor
tmp
;
TensorContainsInf
(
tensor
,
&
tmp
);
TensorContainsNAN
(
tensor
,
out
);
BothFalseVisitor
visitor
(
tmp
,
out
);
auto
place
=
tensor
.
place
();
platform
::
VisitPlace
(
place
,
visitor
);
}
void
TensorToStream
(
std
::
ostream
&
os
,
const
Tensor
&
tensor
,
const
platform
::
DeviceContext
&
dev_ctx
)
{
{
// the 1st field, uint32_t version
constexpr
uint32_t
version
=
0
;
os
.
write
(
reinterpret_cast
<
const
char
*>
(
&
version
),
sizeof
(
version
));
}
{
// the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto
::
VarType
::
TensorDesc
desc
;
desc
.
set_data_type
(
framework
::
ToDataType
(
tensor
.
type
()));
auto
dims
=
framework
::
vectorize
(
tensor
.
dims
());
auto
*
pb_dims
=
desc
.
mutable_dims
();
pb_dims
->
Resize
(
static_cast
<
int
>
(
dims
.
size
()),
0
);
std
::
copy
(
dims
.
begin
(),
dims
.
end
(),
pb_dims
->
begin
());
int32_t
size
=
desc
.
ByteSize
();
os
.
write
(
reinterpret_cast
<
const
char
*>
(
&
size
),
sizeof
(
size
));
auto
out
=
desc
.
SerializeAsString
();
os
.
write
(
out
.
data
(),
size
);
}
{
// the 3rd field, tensor data
uint64_t
size
=
tensor
.
numel
()
*
framework
::
SizeOfType
(
tensor
.
type
());
auto
*
data_ptr
=
tensor
.
data
<
void
>
();
PADDLE_ENFORCE
(
size
<
std
::
numeric_limits
<
std
::
streamsize
>::
max
(),
"Index overflow when writing tensor"
);
if
(
platform
::
is_gpu_place
(
tensor
.
place
()))
{
#ifdef PADDLE_WITH_CUDA
constexpr
size_t
kBufSize
=
1024
*
1024
*
64
;
// 64MB
std
::
unique_ptr
<
char
[]
>
buf
(
new
char
[
kBufSize
]);
auto
&
gpu_dev_ctx
=
static_cast
<
const
platform
::
CUDADeviceContext
&>
(
dev_ctx
);
platform
::
CPUPlace
cpu
;
uintptr_t
data
=
reinterpret_cast
<
uintptr_t
>
(
data_ptr
);
while
(
size
!=
0
)
{
size_t
size_to_write
=
std
::
min
(
kBufSize
,
static_cast
<
size_t
>
(
size
));
memory
::
Copy
(
cpu
,
buf
.
get
(),
boost
::
get
<
platform
::
CUDAPlace
>
(
tensor
.
place
()),
reinterpret_cast
<
const
void
*>
(
data
),
size_to_write
,
gpu_dev_ctx
.
stream
());
gpu_dev_ctx
.
Wait
();
os
.
write
(
buf
.
get
(),
size_to_write
);
data
+=
size_to_write
;
size
-=
size_to_write
;
}
#else
PADDLE_THROW
(
"Unexpected branch"
);
#endif
}
else
{
os
.
write
(
static_cast
<
const
char
*>
(
data_ptr
),
static_cast
<
std
::
streamsize
>
(
size
));
}
}
}
struct
DeserializedDataFunctor
{
DeserializedDataFunctor
(
void
**
buf
,
Tensor
*
tensor
,
const
platform
::
Place
&
place
)
:
buf_
(
buf
),
tensor_
(
tensor
),
place_
(
place
)
{}
template
<
typename
T
>
void
apply
()
{
*
buf_
=
tensor_
->
mutable_data
<
T
>
(
place_
);
}
void
**
buf_
;
Tensor
*
tensor_
;
platform
::
Place
place_
;
};
void
TensorFromStream
(
std
::
istream
&
is
,
Tensor
*
tensor
,
const
platform
::
DeviceContext
&
dev_ctx
)
{
uint32_t
version
;
is
.
read
(
reinterpret_cast
<
char
*>
(
&
version
),
sizeof
(
version
));
PADDLE_ENFORCE_EQ
(
version
,
0U
,
"Only version 0 is supported"
);
proto
::
VarType
::
TensorDesc
desc
;
{
// int32_t size
// proto buffer
int32_t
size
;
is
.
read
(
reinterpret_cast
<
char
*>
(
&
size
),
sizeof
(
size
));
std
::
unique_ptr
<
char
[]
>
buf
(
new
char
[
size
]);
is
.
read
(
reinterpret_cast
<
char
*>
(
buf
.
get
()),
size
);
PADDLE_ENFORCE
(
desc
.
ParseFromArray
(
buf
.
get
(),
size
),
"Cannot parse tensor desc"
);
}
{
// read tensor
std
::
vector
<
int64_t
>
dims
;
dims
.
reserve
(
static_cast
<
size_t
>
(
desc
.
dims
().
size
()));
std
::
copy
(
desc
.
dims
().
begin
(),
desc
.
dims
().
end
(),
std
::
back_inserter
(
dims
));
tensor
->
Resize
(
framework
::
make_ddim
(
dims
));
void
*
buf
;
auto
ctx
=
platform
::
CPUDeviceContext
();
size_t
size
=
tensor
->
numel
()
*
framework
::
SizeOfType
(
framework
::
ToTypeIndex
(
desc
.
data_type
()));
if
(
platform
::
is_gpu_place
(
dev_ctx
.
GetPlace
()))
{
#ifdef PADDLE_WITH_CUDA
Tensor
cpu_tensor
;
cpu_tensor
.
Resize
(
framework
::
make_ddim
(
dims
));
framework
::
VisitDataType
(
desc
.
data_type
(),
DeserializedDataFunctor
(
&
buf
,
&
cpu_tensor
,
ctx
.
GetPlace
()));
is
.
read
(
static_cast
<
char
*>
(
buf
),
size
);
auto
dst_place
=
dev_ctx
.
GetPlace
();
framework
::
TensorCopy
(
cpu_tensor
,
dst_place
,
dev_ctx
,
tensor
);
#else
PADDLE_THROW
(
"Unexpected branch"
);
#endif
}
else
{
framework
::
VisitDataType
(
desc
.
data_type
(),
DeserializedDataFunctor
(
&
buf
,
tensor
,
ctx
.
GetPlace
()));
is
.
read
(
static_cast
<
char
*>
(
buf
),
size
);
}
}
}
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/tensor_util.cu
0 → 120000
浏览文件 @
49710960
tensor_util
.
cc
\ No newline at end of file
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