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体验新版 GitCode,发现更多精彩内容 >>
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c59cdf3a
编写于
12月 13, 2018
作者:
D
dongdaxiang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine executor_thread_worker.h and executor_thread_worker.cc code style
上级
c4cb4142
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
243 addition
and
213 deletion
+243
-213
paddle/fluid/framework/executor_thread_worker.cc
paddle/fluid/framework/executor_thread_worker.cc
+192
-172
paddle/fluid/framework/executor_thread_worker.h
paddle/fluid/framework/executor_thread_worker.h
+51
-41
未找到文件。
paddle/fluid/framework/executor_thread_worker.cc
浏览文件 @
c59cdf3a
...
...
@@ -58,7 +58,8 @@ bool DensePullThread::check_update_param(uint64_t table_id) {
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
_mutex_for_version
);
auto
&
version
=
_training_versions
[
table_id
];
_current_version
[
table_id
]
=
*
(
std
::
min_element
(
version
.
begin
(),
version
.
end
()));
_current_version
[
table_id
]
=
*
(
std
::
min_element
(
version
.
begin
(),
version
.
end
()));
}
if
(
_current_version
[
table_id
]
-
_last_versions
[
table_id
]
<
_threshold
)
{
return
false
;
...
...
@@ -93,7 +94,8 @@ void DensePullThread::wait_all() {
t
.
wait
();
auto
status
=
t
.
get
();
if
(
status
!=
0
)
{
LOG
(
WARNING
)
<<
"pull dense failed times:"
<<
++
_pull_dense_fail_times
;
LOG
(
WARNING
)
<<
"pull dense failed times:"
<<
++
_pull_dense_fail_times
;
}
}
...
...
@@ -105,7 +107,8 @@ void DensePullThread::wait_all() {
_pull_dense_status
.
resize
(
0
);
}
void
DensePullThread
::
increase_thread_version
(
int
thread_id
,
uint64_t
table_id
)
{
void
DensePullThread
::
increase_thread_version
(
int
thread_id
,
uint64_t
table_id
)
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
_mutex_for_version
);
_training_versions
[
table_id
][
thread_id
]
++
;
}
...
...
@@ -169,10 +172,6 @@ void ExecutorThreadWorker::SetFetchVarNames(
fetch_var_names
.
end
());
}
void
ExecutorThreadWorker
::
SetPSlibPtr
(
std
::
shared_ptr
<
paddle
::
distributed
::
PSlib
>
pslib_ptr
)
{
}
void
ExecutorThreadWorker
::
SetDevice
()
{
#if defined _WIN32 || defined __APPLE__
...
...
@@ -332,10 +331,12 @@ void AsyncExecutorThreadWorker::TrainFiles() {
}
// end while ()
}
void
AsyncExecutorThreadWorker
::
SetPSlibPtr
(
std
::
shared_ptr
<
paddle
::
distributed
::
PSlib
>
pslib_ptr
)
{
void
AsyncExecutorThreadWorker
::
SetPSlibPtr
(
std
::
shared_ptr
<
paddle
::
distributed
::
PSlib
>
pslib_ptr
)
{
_pslib_ptr
=
pslib_ptr
;
}
void
AsyncExecutorThreadWorker
::
SetPullDenseThread
(
std
::
shared_ptr
<
DensePullThread
>
dpt
)
{
void
AsyncExecutorThreadWorker
::
SetPullDenseThread
(
std
::
shared_ptr
<
DensePullThread
>
dpt
)
{
_pull_dense_thread
=
dpt
;
}
void
AsyncExecutorThreadWorker
::
TrainOneNetwork
()
{
...
...
@@ -347,7 +348,8 @@ void AsyncExecutorThreadWorker::TrainOneNetwork() {
}
bool
need_skip
=
false
;
for
(
auto
t
=
0u
;
t
<
_param_config
->
skip_op
.
size
();
++
t
)
{
if
(
op
->
Type
().
find
(
_param_config
->
skip_op
[
t
])
!=
std
::
string
::
npos
)
{
if
(
op
->
Type
().
find
(
_param_config
->
skip_op
[
t
])
!=
std
::
string
::
npos
)
{
need_skip
=
true
;
break
;
}
...
...
@@ -359,13 +361,13 @@ void AsyncExecutorThreadWorker::TrainOneNetwork() {
UpdateParams
();
}
void
AsyncExecutorThreadWorker
::
SetParamConfig
(
AsyncWorkerParamConfig
*
param_config
)
{
void
AsyncExecutorThreadWorker
::
SetParamConfig
(
AsyncWorkerParamConfig
*
param_config
)
{
_param_config
=
param_config
;
}
void
AsyncExecutorThreadWorker
::
PrepareParams
()
{
for
(
auto
table_id
:
_param_config
->
sparse_table_id
)
{
for
(
auto
table_id
:
_param_config
->
sparse_table_id
)
{
PullSparse
(
table_id
);
for
(
auto
&
t
:
_pull_sparse_status
)
{
t
.
wait
();
...
...
@@ -378,7 +380,7 @@ void AsyncExecutorThreadWorker::PrepareParams() {
}
_pull_sparse_status
.
resize
(
0
);
for
(
auto
table_id
:
_param_config
->
sparse_table_id
)
{
for
(
auto
table_id
:
_param_config
->
sparse_table_id
)
{
FillSparse
(
table_id
);
}
}
...
...
@@ -440,180 +442,198 @@ void AsyncExecutorThreadWorker::PushDense(int table_id) {
void
AsyncExecutorThreadWorker
::
PullSparse
(
int
table_id
)
{
auto
&
features
=
_features
[
table_id
];
auto
&
feature_value
=
_feature_value
[
table_id
];
auto
fea_dim
=
_param_config
->
fea_dim
;
// slot id starts from 1
features
.
clear
();
features
.
resize
(
0
);
features
.
reserve
(
MAX_FEASIGN_NUM
);
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
// slot_idx = 0 is label TODO
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
int
len
=
tensor
->
numel
();
for
(
auto
i
=
0u
;
i
<
len
;
++
i
)
{
//todo: current trick - filter feasign=use_slot_mod(bug: datafeed fill use_slot_mod for empty slot)
if
(
ids
[
i
]
==
0u
)
{
continue
;
}
features
.
push_back
(
static_cast
<
uint64_t
>
(
ids
[
i
]));
}
}
check_pull_push_memory
(
features
,
feature_value
,
fea_dim
);
std
::
vector
<
float
*>
pull_feature_value
;
for
(
auto
i
=
0u
;
i
<
features
.
size
();
++
i
)
{
pull_feature_value
.
push_back
(
feature_value
[
i
].
data
());
}
for
(
int
i
=
0
;
i
<
features
.
size
();
++
i
)
{
auto
&
features
=
_features
[
table_id
];
auto
&
feature_value
=
_feature_value
[
table_id
];
auto
fea_dim
=
_param_config
->
fea_dim
;
// slot id starts from 1
features
.
clear
();
features
.
resize
(
0
);
features
.
reserve
(
MAX_FEASIGN_NUM
);
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
// slot_idx = 0 is label TODO
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
int
len
=
tensor
->
numel
();
for
(
auto
i
=
0u
;
i
<
len
;
++
i
)
{
// todo(colourful-tree): current trick - filter feasign=use_slot_mod(
// bug: datafeed fill use_slot_mod for empty slot)
if
(
ids
[
i
]
==
0u
)
{
continue
;
}
features
.
push_back
(
static_cast
<
uint64_t
>
(
ids
[
i
]));
}
auto
status
=
_pslib_ptr
->
_worker_ptr
->
pull_sparse
(
pull_feature_value
.
data
(),
table_id
,
features
.
data
(),
features
.
size
());
_pull_sparse_status
.
push_back
(
std
::
move
(
status
));
auto
&
push_g
=
_feature_push_value
[
table_id
];
check_pull_push_memory
(
features
,
push_g
,
fea_dim
);
collect_feasign_info
(
table_id
);
}
check_pull_push_memory
(
features
,
feature_value
,
fea_dim
);
std
::
vector
<
float
*>
pull_feature_value
;
for
(
auto
i
=
0u
;
i
<
features
.
size
();
++
i
)
{
pull_feature_value
.
push_back
(
feature_value
[
i
].
data
());
}
auto
status
=
_pslib_ptr
->
_worker_ptr
->
pull_sparse
(
pull_feature_value
.
data
(),
table_id
,
features
.
data
(),
features
.
size
());
_pull_sparse_status
.
push_back
(
std
::
move
(
status
));
auto
&
push_g
=
_feature_push_value
[
table_id
];
check_pull_push_memory
(
features
,
push_g
,
fea_dim
);
collect_feasign_info
(
table_id
);
}
void
AsyncExecutorThreadWorker
::
FillSparse
(
int
table_id
)
{
auto
slot_dim
=
_param_config
->
slot_dim
;
auto
fea_dim
=
_param_config
->
fea_dim
;
auto
&
features
=
_features
[
table_id
];
auto
&
fea_value
=
_feature_value
[
table_id
];
CHECK
(
features
.
size
()
>
0
)
<<
"feature size check failed"
;
auto
fea_idx
=
0u
;
std
::
vector
<
float
>
init_value
(
fea_dim
);
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
// slot_idx = 0 is label TODO
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
int
len
=
tensor
->
numel
();
Variable
*
var_emb
=
thread_scope_
->
FindVar
(
_param_config
->
slot_input_vec
[
table_id
][
slot_idx
-
1
]);
LoDTensor
*
tensor_emb
=
var_emb
->
GetMutable
<
LoDTensor
>
();
float
*
ptr
=
tensor_emb
->
mutable_data
<
float
>
({
len
,
slot_dim
},
platform
::
CPUPlace
());
memset
(
ptr
,
0
,
sizeof
(
float
)
*
len
*
slot_dim
);
auto
&
tensor_lod
=
tensor
->
lod
()[
0
];
LoD
data_lod
{
tensor_lod
};
tensor_emb
->
set_lod
(
data_lod
);
for
(
auto
index
=
0u
;
index
<
len
;
++
index
){
if
(
ids
[
index
]
==
0u
)
{
memcpy
(
ptr
+
slot_dim
*
index
,
init_value
.
data
()
+
2
,
sizeof
(
float
)
*
slot_dim
);
continue
;
}
memcpy
(
ptr
+
slot_dim
*
index
,
fea_value
[
fea_idx
].
data
()
+
2
,
sizeof
(
float
)
*
slot_dim
);
fea_idx
++
;
}
auto
slot_dim
=
_param_config
->
slot_dim
;
auto
fea_dim
=
_param_config
->
fea_dim
;
auto
&
features
=
_features
[
table_id
];
auto
&
fea_value
=
_feature_value
[
table_id
];
CHECK
(
features
.
size
()
>
0
)
<<
"feature size check failed"
;
auto
fea_idx
=
0u
;
std
::
vector
<
float
>
init_value
(
fea_dim
);
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
// slot_idx = 0 is label TODO
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
int
len
=
tensor
->
numel
();
Variable
*
var_emb
=
thread_scope_
->
FindVar
(
_param_config
->
slot_input_vec
[
table_id
][
slot_idx
-
1
]);
LoDTensor
*
tensor_emb
=
var_emb
->
GetMutable
<
LoDTensor
>
();
float
*
ptr
=
tensor_emb
->
mutable_data
<
float
>
(
{
len
,
slot_dim
},
platform
::
CPUPlace
());
memset
(
ptr
,
0
,
sizeof
(
float
)
*
len
*
slot_dim
);
auto
&
tensor_lod
=
tensor
->
lod
()[
0
];
LoD
data_lod
{
tensor_lod
};
tensor_emb
->
set_lod
(
data_lod
);
for
(
auto
index
=
0u
;
index
<
len
;
++
index
)
{
if
(
ids
[
index
]
==
0u
)
{
memcpy
(
ptr
+
slot_dim
*
index
,
init_value
.
data
()
+
2
,
sizeof
(
float
)
*
slot_dim
);
continue
;
}
memcpy
(
ptr
+
slot_dim
*
index
,
fea_value
[
fea_idx
].
data
()
+
2
,
sizeof
(
float
)
*
slot_dim
);
fea_idx
++
;
}
}
}
void
AsyncExecutorThreadWorker
::
PushSparse
(
int
table_id
)
{
auto
slot_dim
=
_param_config
->
slot_dim
;
auto
fea_dim
=
_param_config
->
fea_dim
;
auto
&
features
=
_features
[
table_id
];
CHECK
(
features
.
size
()
<
1000000
)
<<
"features size is too big, may be wrong:"
<<
features
.
size
();
auto
&
push_g
=
_feature_push_value
[
table_id
];
check_pull_push_memory
(
features
,
push_g
,
fea_dim
);
CHECK
(
push_g
.
size
()
==
features
.
size
()
+
1
)
<<
"push_g size:"
<<
push_g
.
size
()
<<
" features size:"
<<
features
.
size
();
uint64_t
fea_idx
=
0u
;
auto
&
fea_info
=
_fea_info
[
table_id
];
int
offset
=
2
;
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
// slot_idx = 0 is label
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
if
(
_param_config
->
slot_alias_to_table
.
find
(
feed_vec
[
slot_idx
])
==
_param_config
->
slot_alias_to_table
.
end
())
{
LOG
(
ERROR
)
<<
"ERROR slot_idx:"
<<
slot_idx
<<
" name:"
<<
feed_vec
[
slot_idx
];
}
else
if
(
_param_config
->
slot_alias_to_table
[
feed_vec
[
slot_idx
]]
!=
table_id
)
{
continue
;
}
Variable
*
g_var
=
thread_scope_
->
FindVar
(
_param_config
->
gradient_var
[
table_id
][
slot_idx
-
1
]);
CHECK
(
g_var
!=
nullptr
)
<<
"var["
<<
_param_config
->
gradient_var
[
table_id
][
slot_idx
-
1
]
<<
"] not found"
;
LoDTensor
*
g_tensor
=
g_var
->
GetMutable
<
LoDTensor
>
();
if
(
g_tensor
==
NULL
)
{
LOG
(
ERROR
)
<<
"var["
<<
_param_config
->
gradient_var
[
table_id
][
slot_idx
-
1
]
<<
"] not found"
;
exit
(
-
1
);
}
float
*
g
=
g_tensor
->
data
<
float
>
();
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
CHECK
(
var
!=
nullptr
)
<<
"var["
<<
feed_vec
[
slot_idx
]
<<
"] not found"
;
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
if
(
tensor
==
NULL
)
{
LOG
(
ERROR
)
<<
"var["
<<
feed_vec
[
slot_idx
]
<<
"] not found"
;
exit
(
-
1
);
}
int
len
=
tensor
->
numel
();
CHECK
(
slot_dim
*
len
==
g_tensor
->
numel
())
<<
"len:"
<<
len
<<
" g_numel:"
<<
g_tensor
->
numel
();
CHECK
(
len
==
tensor
->
numel
())
<<
"len:"
<<
len
<<
"t_numel:"
<<
tensor
->
numel
();
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
for
(
auto
id_idx
=
0u
;
id_idx
<
len
;
++
id_idx
){
if
(
ids
[
id_idx
]
==
0
)
{
g
+=
slot_dim
;
continue
;
}
memcpy
(
push_g
[
fea_idx
].
data
()
+
offset
,
g
,
sizeof
(
float
)
*
slot_dim
);
push_g
[
fea_idx
][
0
]
=
1.0
f
;
CHECK
(
fea_idx
<
fea_info
.
size
())
<<
"fea_idx:"
<<
fea_idx
<<
" size:"
<<
fea_info
.
size
();
push_g
[
fea_idx
][
1
]
=
static_cast
<
float
>
(
fea_info
[
fea_idx
].
label
);
g
+=
slot_dim
;
fea_idx
++
;
}
auto
slot_dim
=
_param_config
->
slot_dim
;
auto
fea_dim
=
_param_config
->
fea_dim
;
auto
&
features
=
_features
[
table_id
];
auto
&
push_g
=
_feature_push_value
[
table_id
];
check_pull_push_memory
(
features
,
push_g
,
fea_dim
);
CHECK
(
push_g
.
size
()
==
features
.
size
()
+
1
)
<<
"push_g size:"
<<
push_g
.
size
()
<<
" features size:"
<<
features
.
size
();
uint64_t
fea_idx
=
0u
;
auto
&
fea_info
=
_fea_info
[
table_id
];
int
offset
=
2
;
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
// slot_idx = 0 is label
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
if
(
_param_config
->
slot_alias_to_table
.
find
(
feed_vec
[
slot_idx
])
==
_param_config
->
slot_alias_to_table
.
end
())
{
LOG
(
ERROR
)
<<
"ERROR slot_idx:"
<<
slot_idx
<<
" name:"
<<
feed_vec
[
slot_idx
];
}
else
if
(
_param_config
->
slot_alias_to_table
[
feed_vec
[
slot_idx
]]
!=
table_id
)
{
continue
;
}
CHECK
(
fea_idx
==
features
.
size
())
<<
"fea_idx:"
<<
fea_idx
<<
" features size:"
<<
features
.
size
();
CHECK
(
features
.
size
()
>
0
);
std
::
vector
<
float
*>
push_g_vec
;
for
(
auto
i
=
0u
;
i
<
features
.
size
();
++
i
)
{
push_g_vec
.
push_back
(
push_g
[
i
].
data
());
Variable
*
g_var
=
thread_scope_
->
FindVar
(
_param_config
->
gradient_var
[
table_id
][
slot_idx
-
1
]);
CHECK
(
g_var
!=
nullptr
)
<<
"var["
<<
_param_config
->
gradient_var
[
table_id
][
slot_idx
-
1
]
<<
"] not found"
;
LoDTensor
*
g_tensor
=
g_var
->
GetMutable
<
LoDTensor
>
();
if
(
g_tensor
==
NULL
)
{
LOG
(
ERROR
)
<<
"var["
<<
_param_config
->
gradient_var
[
table_id
][
slot_idx
-
1
]
<<
"] not found"
;
exit
(
-
1
);
}
float
*
g
=
g_tensor
->
data
<
float
>
();
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
CHECK
(
var
!=
nullptr
)
<<
"var["
<<
feed_vec
[
slot_idx
]
<<
"] not found"
;
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
if
(
tensor
==
NULL
)
{
LOG
(
ERROR
)
<<
"var["
<<
feed_vec
[
slot_idx
]
<<
"] not found"
;
exit
(
-
1
);
}
int
len
=
tensor
->
numel
();
CHECK
(
slot_dim
*
len
==
g_tensor
->
numel
())
<<
"len:"
<<
len
<<
" g_numel:"
<<
g_tensor
->
numel
();
CHECK
(
len
==
tensor
->
numel
())
<<
"len:"
<<
len
<<
"t_numel:"
<<
tensor
->
numel
();
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
for
(
auto
id_idx
=
0u
;
id_idx
<
len
;
++
id_idx
)
{
if
(
ids
[
id_idx
]
==
0
)
{
g
+=
slot_dim
;
continue
;
}
memcpy
(
push_g
[
fea_idx
].
data
()
+
offset
,
g
,
sizeof
(
float
)
*
slot_dim
);
push_g
[
fea_idx
][
0
]
=
1.0
f
;
CHECK
(
fea_idx
<
fea_info
.
size
())
<<
"fea_idx:"
<<
fea_idx
<<
" size:"
<<
fea_info
.
size
();
push_g
[
fea_idx
][
1
]
=
static_cast
<
float
>
(
fea_info
[
fea_idx
].
label
);
g
+=
slot_dim
;
fea_idx
++
;
}
auto
status
=
_pslib_ptr
->
_worker_ptr
->
push_sparse
(
table_id
,
features
.
data
(),
(
const
float
**
)
push_g_vec
.
data
(),
features
.
size
());
_push_sparse_status
.
push_back
(
std
::
move
(
status
));
}
CHECK
(
fea_idx
==
features
.
size
())
<<
"fea_idx:"
<<
fea_idx
<<
" features size:"
<<
features
.
size
();
CHECK_GT
(
features
.
size
(),
0
);
std
::
vector
<
float
*>
push_g_vec
;
for
(
auto
i
=
0u
;
i
<
features
.
size
();
++
i
)
{
push_g_vec
.
push_back
(
push_g
[
i
].
data
());
}
auto
status
=
_pslib_ptr
->
_worker_ptr
->
push_sparse
(
table_id
,
features
.
data
(),
(
const
float
**
)
push_g_vec
.
data
(),
features
.
size
());
_push_sparse_status
.
push_back
(
std
::
move
(
status
));
}
void
AsyncExecutorThreadWorker
::
collect_feasign_info
(
int
table_id
)
{
auto
&
fea_info
=
_fea_info
[
table_id
];
auto
&
feature
=
_features
[
table_id
];
fea_info
.
resize
(
feature
.
size
());
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
0
]);
int
table_id
)
{
auto
&
fea_info
=
_fea_info
[
table_id
];
auto
&
feature
=
_features
[
table_id
];
fea_info
.
resize
(
feature
.
size
());
const
std
::
vector
<
std
::
string
>&
feed_vec
=
thread_reader_
->
GetUseSlotAlias
();
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
0
]);
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
int64_t
*
label
=
tensor
->
data
<
int64_t
>
();
int
global_index
=
0
;
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
int64_t
*
label
=
tensor
->
data
<
int64_t
>
();
int
global_index
=
0
;
for
(
auto
slot_idx
=
1u
;
slot_idx
<
feed_vec
.
size
();
++
slot_idx
)
{
Variable
*
var
=
thread_scope_
->
FindVar
(
feed_vec
[
slot_idx
]);
LoDTensor
*
tensor
=
var
->
GetMutable
<
LoDTensor
>
();
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
int
fea_idx
=
0
;
for
(
auto
ins_idx
=
1u
;
ins_idx
<
tensor
->
lod
()[
0
].
size
();
++
ins_idx
)
{
for
(;
fea_idx
<
tensor
->
lod
()[
0
][
ins_idx
];
++
fea_idx
)
{
if
(
ids
[
fea_idx
]
==
0u
)
{
continue
;
}
FeasignInfo
info
{
slot_idx
,
ins_idx
,
label
[
ins_idx
-
1
]};
fea_info
[
global_index
++
]
=
std
::
move
(
info
);
}
int64_t
*
ids
=
tensor
->
data
<
int64_t
>
();
int
fea_idx
=
0
;
for
(
auto
ins_idx
=
1u
;
ins_idx
<
tensor
->
lod
()[
0
].
size
();
++
ins_idx
)
{
for
(;
fea_idx
<
tensor
->
lod
()[
0
][
ins_idx
];
++
fea_idx
)
{
if
(
ids
[
fea_idx
]
==
0u
)
{
continue
;
}
FeasignInfo
info
{
slot_idx
,
ins_idx
,
label
[
ins_idx
-
1
]};
fea_info
[
global_index
++
]
=
std
::
move
(
info
);
}
}
CHECK
(
global_index
==
feature
.
size
())
<<
"expect fea info size:"
<<
feature
.
size
()
<<
" real:"
<<
global_index
;
}
CHECK
(
global_index
==
feature
.
size
())
<<
"expect fea info size:"
<<
feature
.
size
()
<<
" real:"
<<
global_index
;
}
void
AsyncExecutorThreadWorker
::
check_pull_push_memory
(
...
...
paddle/fluid/framework/executor_thread_worker.h
浏览文件 @
c59cdf3a
...
...
@@ -35,21 +35,22 @@ const static uint32_t MAX_FEASIGN_NUM = 1000 * 100 * 100;
void
CreateTensor
(
Variable
*
var
,
proto
::
VarType
::
Type
var_type
);
struct
AsyncWorkerParamConfig
{
int
slot_dim
;
int
fea_dim
;
int32_t
tmp_push_dense_wait_times
;
int32_t
tmp_push_sparse_wait_times
;
std
::
vector
<
std
::
string
>
skip_op
;
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
dense_variable_name
;
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
dense_gradient_variable_name
;
std
::
vector
<
int
>
dense_table_id
;
std
::
vector
<
uint32_t
>
dense_table_size
;
// fea_dim for each dense table
std
::
vector
<
int
>
sparse_table_id
;
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
slot_input_vec
;
//6048slot 6050slot //name
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
gradient_var
;
//6048slot_embed
std
::
map
<
std
::
string
,
uint64_t
>
slot_alias_to_table
;
//TODO done
int
slot_dim
;
int
fea_dim
;
int32_t
tmp_push_dense_wait_times
;
int32_t
tmp_push_sparse_wait_times
;
std
::
vector
<
std
::
string
>
skip_op
;
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
dense_variable_name
;
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
dense_gradient_variable_name
;
std
::
vector
<
int
>
dense_table_id
;
// fea_dim for each dense table
std
::
vector
<
uint32_t
>
dense_table_size
;
std
::
vector
<
int
>
sparse_table_id
;
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
slot_input_vec
;
std
::
map
<
uint64_t
,
std
::
vector
<
std
::
string
>>
gradient_var
;
std
::
map
<
std
::
string
,
uint64_t
>
slot_alias_to_table
;
};
struct
DensePullThreadParam
{
...
...
@@ -62,8 +63,8 @@ struct DensePullThreadParam {
};
class
DensePullThread
{
public:
DensePullThread
(
DensePullThreadParam
&
param
)
:
public:
explicit
DensePullThread
(
const
DensePullThreadParam
&
param
)
:
_running
(
false
)
{
_ps_client
=
param
.
ps_client
;
_threshold
=
param
.
threshold
;
...
...
@@ -96,11 +97,11 @@ public:
void
pull_dense2
(
uint64_t
table_id
);
void
wait_all
();
private:
private:
void
run
();
bool
check_update_param
(
uint64_t
table_id
);
private:
private:
std
::
shared_ptr
<
paddle
::
ps
::
PSClient
>
_ps_client
;
int
_thread_num
;
int
_threshold
;
...
...
@@ -153,9 +154,13 @@ class ExecutorThreadWorker {
virtual
void
TrainFiles
();
// set fetch variable names from python interface assigned by users
void
SetFetchVarNames
(
const
std
::
vector
<
std
::
string
>&
fetch_var_names
);
virtual
void
SetPSlibPtr
(
std
::
shared_ptr
<
paddle
::
distributed
::
PSlib
>
pslib_ptr
);
virtual
void
SetPullDenseThread
(
std
::
shared_ptr
<
DensePullThread
>
dpt
)
{};
virtual
void
SetParamConfig
(
AsyncWorkerParamConfig
*
param_config
)
{};
virtual
void
SetPSlibPtr
(
std
::
shared_ptr
<
paddle
::
distributed
::
PSlib
>
pslib_ptr
);
virtual
void
SetPullDenseThread
(
std
::
shared_ptr
<
DensePullThread
>
dpt
)
{}
virtual
void
SetParamConfig
(
AsyncWorkerParamConfig
*
param_config
)
{}
private:
void
CreateThreadScope
(
const
framework
::
ProgramDesc
&
program
);
void
CreateThreadOperators
(
const
framework
::
ProgramDesc
&
program
);
...
...
@@ -178,32 +183,37 @@ class ExecutorThreadWorker {
Scope
*
root_scope_
;
// a thread scope, father scope is global score which is shared
Scope
*
thread_scope_
;
//private:
std
::
vector
<
std
::
string
>
fetch_var_names_
;
std
::
vector
<
std
::
vector
<
float
>>
fetch_values_
;
bool
debug_
;
};
class
AsyncExecutorThreadWorker
:
public
ExecutorThreadWorker
{
public:
AsyncExecutorThreadWorker
(){};
virtual
~
AsyncExecutorThreadWorker
()
{}
void
SetPSlibPtr
(
std
::
shared_ptr
<
paddle
::
distributed
::
PSlib
>
pslib_ptr
);
void
SetPullDenseThread
(
std
::
shared_ptr
<
DensePullThread
>
dpt
);
void
SetParamConfig
(
AsyncWorkerParamConfig
*
param_config
);
void
TrainFiles
();
void
TrainOneNetwork
();
void
PrepareParams
();
void
UpdateParams
();
void
PullSparse
(
int
table_id
);
void
FillSparse
(
int
table_id
);
void
PushSparse
(
int
table_id
);
void
PushDense
(
int
table_id
);
void
check_pull_push_memory
(
std
::
vector
<
uint64_t
>&
features
,
std
::
vector
<
float
*>&
push_g
,
int
dim
);
void
check_pull_push_memory
(
std
::
vector
<
uint64_t
>&
features
,
std
::
vector
<
std
::
vector
<
float
>>&
push_g
,
int
dim
);
public:
AsyncExecutorThreadWorker
()
{}
virtual
~
AsyncExecutorThreadWorker
()
{}
void
SetPSlibPtr
(
std
::
shared_ptr
<
paddle
::
distributed
::
PSlib
>
pslib_ptr
);
void
SetPullDenseThread
(
std
::
shared_ptr
<
DensePullThread
>
dpt
);
void
SetParamConfig
(
AsyncWorkerParamConfig
*
param_config
);
void
TrainFiles
();
void
TrainOneNetwork
();
void
PrepareParams
();
void
UpdateParams
();
void
PullSparse
(
int
table_id
);
void
FillSparse
(
int
table_id
);
void
PushSparse
(
int
table_id
);
void
PushDense
(
int
table_id
);
void
check_pull_push_memory
(
const
std
::
vector
<
uint64_t
>&
features
,
std
::
vector
<
float
*>&
push_g
,
int
dim
);
void
check_pull_push_memory
(
const
std
::
vector
<
uint64_t
>&
features
,
std
::
vector
<
std
::
vector
<
float
>>&
push_g
,
int
dim
);
void
collect_feasign_info
(
int
table_id
);
private:
private:
struct
FeasignInfo
{
uint32_t
slot
;
uint32_t
ins
;
...
...
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