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08021979
编写于
8月 02, 2017
作者:
Z
Zhuoyuan
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
gather and scatter-update added
上级
cc6c33b8
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
233 addition
and
0 deletion
+233
-0
paddle/operators/gather_func.h
paddle/operators/gather_func.h
+114
-0
paddle/operators/scatter_func.h
paddle/operators/scatter_func.h
+119
-0
未找到文件。
paddle/operators/gather_func.h
0 → 100644
浏览文件 @
08021979
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 <cstring>
#include "paddle/framework/tensor.h"
#include "paddle/platform/place.h"
#include "paddle/framework/ddim.h"
/**
* Return a new tensor from source tensor, gathered according to index
* input[src]: type-T source Tensor
* input[Index]: type-int index Tensor (1-D)
* return: output tensor
*/
template
<
typename
place
,
typename
T
>
Tensor
*
Gather_func
(
Tensor
*
Src
,
Tensor
*
Index
)
{
// assert index is an int-type tensor?
// assert(Index->istype(int));
// check index of shape 1-D
assert
(
Index
->
dims
().
size
()
==
1
);
int
index_size
=
Index
->
dims
()[
0
];
// Source shape
auto
src_dims
=
Src
->
dims
();
DDim
output_dims
(
dims_src
);
// Create a tensor of shape [index_size, dim_src[1:]]
output_dims
[
0
]
=
index_size
;
Tensor
*
New_tensor
;
float
*
output
=
nullptr
;
/* slice size */
int
slice_size
=
1
;
for
(
unsigned
int
i
=
0
;
i
<
src_dims
.
size
();
++
i
)
slice_size
*=
src_dims
[
i
];
/* Gathering */
if
(
place
==
CPUPlace
())
{
// init for CPU
output
=
New_tensor
.
mutable_data
<
T
>
(
output_dims
,
CPUPlace
());
CPUGather
(
Src
->
data
(),
Index
->
data
(),
slice_size
,
new_tensor
->
mutable_data
());
}
else
{
// GPU
// init for GPU
output
=
New_tensor
.
mutable_data
<
T
>
(
output_dims
,
GPUPlace
());
/* how to specialize device??*/
GPUGather
(
d
,
Src
->
data
(),
Index
->
data
(),
slice_size
,
new_tensor
->
mutable_data
());
}
return
New_tensor
;
}
/* Implementation of CPU copy */
template
<
typename
T
>
void
CPUGather
(
const
T
*
params
,
const
int
*
indices
,
const
int
slice_size
,
const
int
index_size
,
T
*
output
)
{
const
size_t
slice_bytes
=
slice_size
*
sizeof
(
T
);
for
(
int
i
=
0
;
i
<
index_size
;
++
i
)
int
index_
=
indices
[
i
];
/* copy src[index_] to output[i] */
memcpy
(
output
+
i
*
slice_bytes
,
params
+
index_
*
slice_bytes
,
slice_bytes
);
}
/* Implementation of GPU copy:
I suppose the GPUDevice& d, contains gpu_id and thread_id
d = cuda_stream(gpu_id_, stream_id_);
*/
template
<
typename
T
>
void
GPUGather
(
const
GPUDevice
&
d
,
const
T
*
src
,
const
int
*
Index
,
const
int
slice_size
,
const
int
index_size
,
T
*
output
)
{
int
block_count
=
slice_size
*
index_size
;
int
thread_per_block
=
1024
;
GatherOpKernel
<
T
>
<<<
block_count
,
thread_per_block
,
0
,
d
.
stream
()
>>>
(
src
,
Index
,
output
,
slice_size
,
indices_size
,
slice_size
,
out_size
);
}
template
<
typename
T
>
__global__
void
GatherOpKernel
(
const
T
*
params
,
const
int
*
indices
,
T
*
out
,
int64
indices_size
,
int64
slice_size
,
int64
out_size
)
{
/* I suppose we have the following macro,
which I strongly suggest that we should put in cuda:
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \
i += blockDim.x * gridDim.x)
*/
CUDA_1D_KERNEL_LOOP
(
i
,
out_size
)
{
int
indices_i
=
i
/
slice_size
;
int
slice_i
=
i
-
indices_i
*
slice_size
;
// offset inside the slice
int
gather_i
=
indices
[
indices_i
];
int
params_i
=
gather_i
*
slice_size
+
slice_i
;
out
[
i
]
=
*
(
params
+
params_i
);
}
}
paddle/operators/scatter_func.h
0 → 100644
浏览文件 @
08021979
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 <cstring>
#include "paddle/framework/tensor.h"
#include "paddle/platform/place.h"
#include "paddle/framework/ddim.h"
/**
* Return a updated tensor from source tensor, scattered according to index:
* dst[i] += src[index[i]]
* input[src]: type-T source Tensor
* input[Index]: type-int index Tensor (1-D)
* return: output tensor
*/
template
<
typename
place
,
typename
T
>
void
ScatterUpdate_func
(
Tensor
*
Src
,
Tensor
*
Dst
,
Tensor
*
Index
)
{
// assert index is an int-type tensor
assert
(
Index
->
istype
(
int
));
// Source shape
auto
src_dims
=
Src
->
dims
();
auto
dst_dims
=
Dst
->
dims
();
DDim
output_dims
(
dims_src
);
// check Src shape and Dst shape should match
for
(
int
i
=
1
;
i
<
src_dims
.
size
();
i
++
)
assert
(
src_dims
[
i
]
==
dst_dims
[
i
]);
int
index_size
=
Index
->
dims
()[
0
];
/* slice size */
int
slice_size
=
1
;
for
(
unsigned
int
i
=
0
;
i
<
src_dims
.
size
();
++
i
)
slice_size
*=
src_dims
[
i
];
if
(
place
==
CPUPlace
())
{
// init
output
=
new_tensor
.
mutable_data
<
T
>
(
output_dims
,
CPUPlace
());
CPUScatterUpdate
(
src
->
data
(),
index
->
data
(),
slice_size
,
new_tensor
->
mutable_data
());
}
else
{
// GPU
// init
output
=
new_tensor
.
mutable_data
<
T
>
(
output_dims
,
GPUPlace
());
/* how to specialize device??*/
GPUScatterUpdate
(
d
,
src
->
data
(),
index
->
data
(),
slice_size
,
new_tensor
->
mutable_data
());
}
}
/* Implementation of CPU copy */
template
<
typename
T
>
void
CPUScatterUpdate
(
const
T
*
src
,
const
int
*
Index
,
const
int
slice_size
,
const
int
index_size
,
T
*
output
)
{
//const size_t slice_bytes = slice_size * sizeof(T);
for
(
int
i
=
0
;
i
<
index_size
;
++
i
)
int
index_
=
index
[
i
];
/* dst[index_] += src[index_]
add operation size: slice_size
*/
math
::
vAdd
<
T
>
(
slice_size
,
src
+
index_
*
slice_bytes
,
output
+
i
*
slice_bytes
,
output
+
i
*
slice_bytes
);
/* Scatter update, not just assign
memcpy(output + i * slice_bytes,
src + index_ * slice_bytes,
slice_bytes);
*/
}
/* Implementation of GPU scatter:
I suppose the GPUDevice& d, contains gpu_id and thread_id
d = cuda_stream(gpu_id_, stream_id_);
*/
template
<
typename
T
>
void
GPUScatterUpdate
(
const
GPUDevice
&
d
,
const
T
*
src
,
const
int
*
Index
,
const
int
slice_size
,
const
int
index_size
,
T
*
output
)
{
int
block_count
=
slice_size
*
index_size
;
int
thread_per_block
=
1024
;
ScatterOpKernel
<
T
>
<<<
block_count
,
thread_per_block
,
0
,
d
.
stream
()
>>>
(
src
,
Index
,
output
,
slice_size
,
indices_size
,
slice_size
,
out_size
);
}
template
<
typename
T
>
__global__
void
ScatterOpKernel
(
const
T
*
params
,
const
int
*
indices
,
T
*
out
,
int64
indices_size
,
int64
slice_size
,
int64
out_size
)
{
/* I suppose we have the following macro,
which I strongly suggest that we should put in cuda:
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \
i += blockDim.x * gridDim.x)
*/
CUDA_1D_KERNEL_LOOP
(
i
,
out_size
)
{
int
indices_i
=
i
/
slice_size
;
int
slice_i
=
i
-
indices_i
*
slice_size
;
// offset inside the slice
int
scatter_i
=
indices
[
indices_i
];
int
params_i
=
scatter_i
*
slice_size
+
slice_i
;
out
[
i
]
+=
*
(
params
+
params_i
);
}
}
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