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体验新版 GitCode,发现更多精彩内容 >>
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0b8630b9
编写于
4月 19, 2018
作者:
Y
Yancey
提交者:
GitHub
4月 19, 2018
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差异文件
Merge pull request #9897 from Yancey1989/auto_grwon_sparse_table
Auto-grown sparse table
上级
6db5309b
f12b3f36
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
204 addition
and
8 deletion
+204
-8
paddle/fluid/framework/selected_rows.cc
paddle/fluid/framework/selected_rows.cc
+107
-0
paddle/fluid/framework/selected_rows.h
paddle/fluid/framework/selected_rows.h
+56
-5
paddle/fluid/framework/selected_rows_test.cc
paddle/fluid/framework/selected_rows_test.cc
+36
-1
paddle/fluid/operators/lookup_table_op.h
paddle/fluid/operators/lookup_table_op.h
+2
-1
paddle/fluid/operators/sgd_op.h
paddle/fluid/operators/sgd_op.h
+3
-1
未找到文件。
paddle/fluid/framework/selected_rows.cc
浏览文件 @
0b8630b9
...
...
@@ -17,6 +17,52 @@ limitations under the License. */
namespace
paddle
{
namespace
framework
{
struct
ReAllocateVisitor
{
ReAllocateVisitor
(
framework
::
Tensor
*
tensor
,
const
framework
::
DDim
&
dims
)
:
tensor_
(
tensor
),
dims_
(
dims
)
{}
template
<
typename
T
>
void
operator
()()
const
{
framework
::
Tensor
cpu_tensor
;
platform
::
CPUPlace
cpu
;
T
*
ptr
=
cpu_tensor
.
mutable_data
<
T
>
(
dims_
,
cpu
);
const
T
*
old_ptr
=
tensor_
->
memory_size
()
==
0
?
nullptr
:
tensor_
->
data
<
T
>
();
if
(
old_ptr
!=
nullptr
)
{
std
::
copy
(
old_ptr
,
old_ptr
+
tensor_
->
numel
(),
ptr
);
}
tensor_
->
ShareDataWith
(
cpu_tensor
);
}
framework
::
Tensor
*
tensor_
;
framework
::
DDim
dims_
;
};
struct
TensorCopyVisitor
{
TensorCopyVisitor
(
framework
::
Tensor
*
dst
,
int64_t
dst_offset
,
const
framework
::
Tensor
src
,
int64_t
src_offset
,
int64_t
size
)
:
dst_
(
dst
),
dst_offset_
(
dst_offset
),
src_
(
src
),
src_offset_
(
src_offset
),
size_
(
size
)
{}
template
<
typename
T
>
void
operator
()()
const
{
// TODO(Yancey1989): support other place
platform
::
CPUPlace
cpu
;
memory
::
Copy
(
cpu
,
dst_
->
mutable_data
<
T
>
(
cpu
)
+
dst_offset_
,
cpu
,
src_
.
data
<
T
>
()
+
src_offset_
,
size_
*
sizeof
(
T
));
}
framework
::
Tensor
*
dst_
;
int64_t
dst_offset_
;
framework
::
Tensor
src_
;
int64_t
src_offset_
;
int64_t
size_
;
};
void
SerializeToStream
(
std
::
ostream
&
os
,
const
SelectedRows
&
selected_rows
,
const
platform
::
DeviceContext
&
dev_ctx
)
{
{
// the 1st field, uint32_t version
...
...
@@ -69,5 +115,66 @@ void DeserializeFromStream(std::istream& is, SelectedRows* selected_rows,
TensorFromStream
(
is
,
selected_rows
->
mutable_value
(),
dev_ctx
);
}
bool
SelectedRows
::
HasKey
(
int64_t
key
)
const
{
return
std
::
find
(
rows_
.
begin
(),
rows_
.
end
(),
key
)
==
rows_
.
end
()
?
false
:
true
;
}
std
::
vector
<
int64_t
>
SelectedRows
::
Get
(
std
::
vector
<
int64_t
>
keys
,
framework
::
Tensor
*
value
)
const
{
PADDLE_ENFORCE
(
value
->
IsInitialized
(),
"The value tensor should be initialized."
);
std
::
vector
<
int64_t
>
non_keys
;
int64_t
value_width
=
value_
->
numel
()
/
value_
->
dims
()[
0
];
PADDLE_ENFORCE_EQ
(
value_width
,
value
->
numel
()
/
value
->
dims
()[
0
],
"output tensor should have the same shape with table "
"execpt the dims[0]."
);
for
(
size_t
i
=
0
;
i
<
keys
.
size
();
++
i
)
{
int64_t
index
=
Index
(
keys
[
i
]);
if
(
index
==
-
1
)
{
non_keys
.
push_back
(
keys
[
i
]);
}
else
{
framework
::
VisitDataType
(
framework
::
ToDataType
(
value_
->
type
()),
TensorCopyVisitor
(
value
,
i
*
value_width
,
*
value_
.
get
(),
index
*
value_width
,
value_width
));
}
}
return
non_keys
;
}
bool
SelectedRows
::
Set
(
int64_t
key
,
const
framework
::
Tensor
&
value
)
{
PADDLE_ENFORCE
(
value
.
IsInitialized
(),
"The value should be initialized."
);
if
(
value_
->
IsInitialized
())
{
PADDLE_ENFORCE_EQ
(
value
.
type
(),
value_
->
type
(),
"The type of the value should be same with the original value"
);
}
PADDLE_ENFORCE_EQ
(
value
.
dims
()[
0
],
static_cast
<
size_t
>
(
1
),
"The first dim of value should be 1."
);
auto
index
=
Index
(
key
);
bool
is_new_key
=
false
;
if
(
index
==
-
1
)
{
rows_
.
push_back
(
key
);
index
=
rows_
.
size
()
-
1
;
is_new_key
=
true
;
// whether need to resize the table
if
(
static_cast
<
int64_t
>
(
rows_
.
size
())
>
value_
->
dims
()[
0
])
{
auto
dims
=
value_
->
dims
();
dims
[
0
]
=
(
dims
[
0
]
+
1
)
<<
1
;
framework
::
VisitDataType
(
framework
::
ToDataType
(
value
.
type
()),
ReAllocateVisitor
(
value_
.
get
(),
dims
));
}
}
framework
::
VisitDataType
(
framework
::
ToDataType
(
value
.
type
()),
TensorCopyVisitor
(
value_
.
get
(),
index
*
value_
->
numel
()
/
value_
->
dims
()[
0
],
value
,
static_cast
<
int64_t
>
(
0
),
value
.
numel
()));
return
is_new_key
;
}
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/selected_rows.h
浏览文件 @
0b8630b9
...
...
@@ -14,15 +14,33 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/memory/memcpy.h"
namespace
paddle
{
namespace
framework
{
class
SelectedRows
{
/*
* @brief We can use the SelectedRows structure to reproduce a sparse table.
* A sparse table is a key-value structure that the key is an `int64_t`
* number,
* and the value is a Tensor which the first dimension is 0.
* You can use the following interface to operate the sparse table, and you
* can find
* some detail information from the comments of each interface:
*
* HasKey(key), whether the sparse table has the specified key.
* Set(key, value), set a key-value pair into the sparse table.
* Get(keys, value*), get value by given key list and apply it to the given
* value pointer
* with the specified offset.
*
*/
public:
SelectedRows
(
const
std
::
vector
<
int64_t
>&
rows
,
const
int64_t
&
height
)
:
rows_
(
rows
),
height_
(
height
)
{
...
...
@@ -50,12 +68,45 @@ class SelectedRows {
void
set_rows
(
const
Vector
<
int64_t
>&
rows
)
{
rows_
=
rows
;
}
/**
* get the index of id in rows
/*
* @brief wheter has the specified key in the table.
*
* @return true if the key is exists.
*/
bool
HasKey
(
int64_t
key
)
const
;
/*
* @brief Get value by the key list, if the
*
* @return a list of keys which does not exists in table
*/
std
::
vector
<
int64_t
>
Get
(
std
::
vector
<
int64_t
>
keys
,
framework
::
Tensor
*
tensor
)
const
;
/*
* @brief Set a key-value pair into the table.
* This function will double the value memory if it's not engouth.
*
* @note:
* 1. The first dim of the value should be 1
* 2. The value should be initialized and the data type
* should be the same with the table.
*
* @return true if the key is a new one, otherwise false
*
*/
bool
Set
(
int64_t
key
,
const
Tensor
&
value
);
/*
* @brief Get the index of key in rows
*
* @return -1 if the key does not exists.
*/
int64_t
index
(
int64_t
id
)
const
{
auto
it
=
std
::
find
(
rows_
.
begin
(),
rows_
.
end
(),
id
);
PADDLE_ENFORCE
(
it
!=
rows_
.
end
(),
"id should be in rows"
);
int64_t
Index
(
int64_t
key
)
const
{
auto
it
=
std
::
find
(
rows_
.
begin
(),
rows_
.
end
(),
key
);
if
(
it
==
rows_
.
end
())
{
return
static_cast
<
int64_t
>
(
-
1
);
}
return
static_cast
<
int64_t
>
(
std
::
distance
(
rows_
.
begin
(),
it
));
}
...
...
paddle/fluid/framework/selected_rows_test.cc
浏览文件 @
0b8630b9
...
...
@@ -17,7 +17,7 @@ namespace framework {
class
SelectedRowsTester
:
public
::
testing
::
Test
{
public:
v
irtual
v
oid
SetUp
()
override
{
void
SetUp
()
override
{
std
::
vector
<
int64_t
>
rows
{
0
,
4
,
7
};
int64_t
height
=
10
;
int64_t
row_numel
=
100
;
...
...
@@ -59,5 +59,40 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
ASSERT_EQ
(
selected_rows_
->
GetCompleteDims
(),
dst_tensor
.
GetCompleteDims
());
}
TEST_F
(
SelectedRowsTester
,
Table
)
{
platform
::
CPUPlace
cpu
;
SelectedRows
table
;
// initialize a sparse table
table
.
mutable_value
()
->
Resize
(
framework
::
make_ddim
({
1
,
100
}));
table
.
mutable_value
()
->
mutable_data
<
float
>
(
cpu
);
table
.
mutable_rows
()
->
push_back
(
1
);
int64_t
key
=
10000
;
int64_t
non_key
=
999
;
framework
::
Tensor
value
;
value
.
Resize
(
framework
::
make_ddim
({
1
,
100
}));
auto
ptr
=
value
.
mutable_data
<
float
>
(
cpu
);
ptr
[
0
]
=
static_cast
<
float
>
(
10
);
ASSERT_EQ
(
table
.
rows
().
size
(),
static_cast
<
size_t
>
(
1
));
ASSERT_EQ
(
table
.
HasKey
(
key
),
false
);
table
.
Set
(
key
,
value
);
ASSERT_EQ
(
table
.
rows
().
size
(),
static_cast
<
size_t
>
(
2
));
ASSERT_EQ
(
table
.
HasKey
(
key
),
true
);
// check re-allocate
ASSERT_EQ
(
table
.
value
().
dims
()[
0
],
static_cast
<
int64_t
>
(
4
));
framework
::
Tensor
get_value
;
get_value
.
mutable_data
<
float
>
(
framework
::
make_ddim
({
2
,
100
}),
cpu
);
std
::
vector
<
int64_t
>
keys
({
non_key
,
key
});
auto
non_keys
=
table
.
Get
(
keys
,
&
get_value
);
ASSERT_EQ
(
get_value
.
data
<
float
>
()[
100
],
static_cast
<
float
>
(
10
));
ASSERT_EQ
(
non_keys
.
size
(),
static_cast
<
size_t
>
(
1
));
ASSERT_EQ
(
non_keys
[
0
],
non_key
);
}
}
// namespace framework
}
// namespace paddle
paddle/fluid/operators/lookup_table_op.h
浏览文件 @
0b8630b9
...
...
@@ -103,7 +103,8 @@ class LookupTableKernel : public framework::OpKernel<T> {
memset
(
output
+
i
*
row_width
,
0
,
row_width
*
sizeof
(
T
));
}
else
{
PADDLE_ENFORCE_GE
(
ids
[
i
],
0
);
auto
id_index
=
table_t
.
index
(
ids
[
i
]);
auto
id_index
=
table_t
.
Index
(
ids
[
i
]);
PADDLE_ENFORCE_GE
(
id_index
,
0
,
"the input key should be exists."
);
memcpy
(
output
+
i
*
row_width
,
table
+
id_index
*
row_width
,
row_width
*
sizeof
(
T
));
}
...
...
paddle/fluid/operators/sgd_op.h
浏览文件 @
0b8630b9
...
...
@@ -107,7 +107,9 @@ class SGDOpKernel : public framework::OpKernel<T> {
for
(
size_t
i
=
0
;
i
<
grad
.
rows
().
size
();
i
++
)
{
PADDLE_ENFORCE
(
grad
.
rows
()[
i
]
<
grad
.
height
(),
"Input rows index should less than height"
);
int64_t
id_index
=
param
.
index
(
grad
.
rows
()[
i
]);
int64_t
id_index
=
param
.
Index
(
grad
.
rows
()[
i
]);
PADDLE_ENFORCE_GE
(
id_index
,
static_cast
<
int64_t
>
(
0
),
"id should be in the table"
);
for
(
size_t
j
=
0
;
j
<
grad_row_width
;
j
++
)
{
out_data
[
id_index
*
grad_row_width
+
j
]
-=
lr
[
0
]
*
grad_data
[
i
*
grad_row_width
+
j
];
...
...
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