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体验新版 GitCode,发现更多精彩内容 >>
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822496f6
编写于
8月 16, 2018
作者:
T
tangwei12
浏览文件
操作
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电子邮件补丁
差异文件
merge cpu and gpu
上级
9f09d686
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
85 addition
and
133 deletion
+85
-133
paddle/fluid/operators/sampling_id_op.cc
paddle/fluid/operators/sampling_id_op.cc
+1
-55
paddle/fluid/operators/sampling_id_op.cu
paddle/fluid/operators/sampling_id_op.cu
+4
-78
paddle/fluid/operators/sampling_id_op.h
paddle/fluid/operators/sampling_id_op.h
+80
-0
未找到文件。
paddle/fluid/operators/sampling_id_op.cc
浏览文件 @
822496f6
...
...
@@ -12,67 +12,13 @@ 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 <iostream>
#include <iterator>
#include <random>
#include <sstream>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/sampling_id_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
SamplingIdKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"X"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
const
int
width
=
static_cast
<
int
>
(
input
->
dims
()[
1
]);
PADDLE_ENFORCE_GE
(
batch_size
,
0
,
"batch_size(dims[0]) must be nonnegative."
);
PADDLE_ENFORCE_GE
(
width
,
0
,
"width(dims[1]) must be nonnegative."
);
std
::
vector
<
T
>
ins_vector
;
framework
::
TensorToVector
(
*
input
,
context
.
device_context
(),
&
ins_vector
);
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
std
::
minstd_rand
engine
;
if
(
seed
==
0
)
{
seed
=
std
::
random_device
()();
}
engine
.
seed
(
seed
);
std
::
uniform_real_distribution
<
T
>
dist
(
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"min"
)),
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"max"
)));
std
::
vector
<
T
>
ids
(
batch_size
);
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
T
r
=
dist
(
engine
);
int
idx
=
width
-
1
;
for
(
int
j
=
0
;
j
<
width
;
++
j
)
{
if
((
r
-=
ins_vector
[
i
*
width
+
j
])
<
0
)
{
idx
=
j
;
break
;
}
}
ids
[
i
]
=
ins_vector
[
i
*
width
+
idx
];
}
std
::
vector
<
int64_t
>
out_dim
;
out_dim
.
push_back
(
static_cast
<
int64_t
>
(
batch_size
));
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Out"
);
output
->
Resize
(
framework
::
make_ddim
(
out_dim
));
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
framework
::
TensorFromVector
(
ids
,
context
.
device_context
(),
output
);
}
};
class
SamplingIdOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
paddle/fluid/operators/sampling_id_op.cu
浏览文件 @
822496f6
...
...
@@ -11,83 +11,9 @@
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 <thrust/random.h>
#include <thrust/transform.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
template
<
typename
T
>
struct
UniformGenerator
{
T
min_
,
max_
;
unsigned
int
seed_
;
#include "paddle/fluid/operators/sampling_id_op.h"
__host__
__device__
UniformGenerator
(
T
min
,
T
max
,
int
seed
)
:
min_
(
min
),
max_
(
max
),
seed_
(
seed
)
{}
__host__
__device__
T
operator
()(
const
unsigned
int
n
)
const
{
thrust
::
minstd_rand
rng
;
rng
.
seed
(
seed_
);
thrust
::
uniform_real_distribution
<
T
>
dist
(
min_
,
max_
);
rng
.
discard
(
n
);
return
dist
(
rng
);
}
};
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
SamplingIdGPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"X"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
const
int
width
=
static_cast
<
int
>
(
input
->
dims
()[
1
]);
PADDLE_ENFORCE_GE
(
batch_size
,
0
,
"batch_size(dims[0]) must be nonnegative."
);
PADDLE_ENFORCE_GE
(
width
,
0
,
"width(dims[1]) must be nonnegative."
);
std
::
vector
<
T
>
ins_vector
;
framework
::
TensorToVector
(
*
input
,
context
.
device_context
(),
&
ins_vector
);
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
if
(
seed
==
0
)
{
std
::
random_device
rd
;
seed
=
rd
();
}
T
min
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"min"
));
T
max
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"max"
));
UniformGenerator
<
T
>
gen
=
UniformGenerator
<
T
>
(
min
,
max
,
seed
);
std
::
vector
<
T
>
ids
(
batch_size
);
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
T
r
=
gen
(
0
);
int
idx
=
width
-
1
;
for
(
int
j
=
0
;
j
<
width
;
++
j
)
{
if
((
r
-=
ins_vector
[
i
*
width
+
j
])
<
0
)
{
idx
=
j
;
break
;
}
}
ids
[
i
]
=
ins_vector
[
i
*
width
+
idx
];
}
std
::
vector
<
int64_t
>
out_dim
;
out_dim
.
push_back
(
static_cast
<
int64_t
>
(
batch_size
));
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Out"
);
output
->
Resize
(
framework
::
make_ddim
(
out_dim
));
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
framework
::
TensorFromVector
(
ids
,
context
.
device_context
(),
output
);
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OP_CUDA_KERNEL
(
sampling_id
,
paddle
::
operators
::
SamplingIdGPUKernel
<
float
>
,
paddle
::
operators
::
SamplingIdGPUKernel
<
double
>
);
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
sampling_id
,
paddle
::
operators
::
SamplingIdKernel
<
float
>
,
paddle
::
operators
::
SamplingIdKernel
<
double
>
);
paddle/fluid/operators/sampling_id_op.h
0 → 100644
浏览文件 @
822496f6
/* 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 <algorithm>
#include <iostream>
#include <iterator>
#include <random>
#include <sstream>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
SamplingIdKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"X"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
const
int
width
=
static_cast
<
int
>
(
input
->
dims
()[
1
]);
PADDLE_ENFORCE_GE
(
batch_size
,
0
,
"batch_size(dims[0]) must be nonnegative."
);
PADDLE_ENFORCE_GE
(
width
,
0
,
"width(dims[1]) must be nonnegative."
);
std
::
vector
<
T
>
ins_vector
;
framework
::
TensorToVector
(
*
input
,
context
.
device_context
(),
&
ins_vector
);
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
std
::
minstd_rand
engine
;
if
(
seed
==
0
)
{
seed
=
std
::
random_device
()();
}
engine
.
seed
(
seed
);
std
::
uniform_real_distribution
<
T
>
dist
(
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"min"
)),
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"max"
)));
std
::
vector
<
T
>
ids
(
batch_size
);
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
T
r
=
dist
(
engine
);
int
idx
=
width
-
1
;
for
(
int
j
=
0
;
j
<
width
;
++
j
)
{
if
((
r
-=
ins_vector
[
i
*
width
+
j
])
<
0
)
{
idx
=
j
;
break
;
}
}
ids
[
i
]
=
ins_vector
[
i
*
width
+
idx
];
}
std
::
vector
<
int64_t
>
out_dim
;
out_dim
.
push_back
(
static_cast
<
int64_t
>
(
batch_size
));
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Out"
);
output
->
Resize
(
framework
::
make_ddim
(
out_dim
));
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
framework
::
TensorFromVector
(
ids
,
context
.
device_context
(),
output
);
}
};
}
// namespace operators
}
// namespace paddle
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