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体验新版 GitCode,发现更多精彩内容 >>
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be273ea9
编写于
10月 19, 2022
作者:
L
Li-fAngyU
提交者:
GitHub
10月 19, 2022
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电子邮件补丁
差异文件
fix build warning: [Wsign-compare] on linux (#46644)
上级
ddf317ed
变更
4
展开全部
显示空白变更内容
内联
并排
Showing
4 changed file
with
244 addition
and
232 deletion
+244
-232
paddle/fluid/distributed/ps/table/common_graph_table.cc
paddle/fluid/distributed/ps/table/common_graph_table.cc
+85
-73
paddle/fluid/distributed/ps/table/memory_dense_table.cc
paddle/fluid/distributed/ps/table/memory_dense_table.cc
+22
-22
paddle/fluid/distributed/ps/table/memory_sparse_table.cc
paddle/fluid/distributed/ps/table/memory_sparse_table.cc
+92
-92
paddle/fluid/distributed/ps/table/sparse_sgd_rule.cc
paddle/fluid/distributed/ps/table/sparse_sgd_rule.cc
+45
-45
未找到文件。
paddle/fluid/distributed/ps/table/common_graph_table.cc
浏览文件 @
be273ea9
此差异已折叠。
点击以展开。
paddle/fluid/distributed/ps/table/memory_dense_table.cc
浏览文件 @
be273ea9
...
...
@@ -21,8 +21,8 @@ namespace distributed {
int
FLAGS_pslib_table_save_max_retry_dense
=
3
;
void
MemoryDenseTable
::
CreateInitializer
(
const
std
::
string
&
attr
,
const
std
::
string
&
name
)
{
void
MemoryDenseTable
::
CreateInitializer
(
const
std
::
string
&
attr
,
const
std
::
string
&
name
)
{
auto
slices
=
string
::
split_string
<
std
::
string
>
(
attr
,
"&"
);
if
(
slices
[
0
]
==
"gaussian_random"
)
{
...
...
@@ -60,14 +60,14 @@ int32_t MemoryDenseTable::InitializeValue() {
values_
.
resize
(
size
);
total_dim_
=
0
;
for
(
int
x
=
0
;
x
<
size
;
++
x
)
{
auto
&
varname
=
common
.
params
()[
x
];
auto
&
dim
=
common
.
dims
()[
x
];
auto
&
varname
=
common
.
params
()[
x
];
auto
&
dim
=
common
.
dims
()[
x
];
if
(
varname
==
"Param"
)
{
param_dim_
=
dim
;
param_idx_
=
x
;
}
auto
&
initializer
=
common
.
initializers
()[
x
];
auto
&
initializer
=
common
.
initializers
()[
x
];
total_dim_
+=
dim
;
CreateInitializer
(
initializer
,
varname
);
...
...
@@ -81,7 +81,7 @@ int32_t MemoryDenseTable::InitializeValue() {
fixed_len_params_dim_
=
0
;
for
(
int
x
=
0
;
x
<
size
;
++
x
)
{
auto
&
dim
=
common
.
dims
()[
x
];
auto
&
dim
=
common
.
dims
()[
x
];
if
(
static_cast
<
int
>
(
dim
)
!=
param_dim_
)
{
fixed_len_params_dim_
+=
dim
;
}
else
{
...
...
@@ -124,19 +124,19 @@ int32_t MemoryDenseTable::InitializeOptimizer() {
return
0
;
}
int32_t
MemoryDenseTable
::
SetGlobalLR
(
float
*
lr
)
{
int32_t
MemoryDenseTable
::
SetGlobalLR
(
float
*
lr
)
{
_global_lr
=
lr
;
optimizer_
->
SetGlobalLR
(
_global_lr
);
return
0
;
}
int32_t
MemoryDenseTable
::
Pull
(
TableContext
&
context
)
{
int32_t
MemoryDenseTable
::
Pull
(
TableContext
&
context
)
{
CHECK
(
context
.
value_type
==
Dense
);
float
*
pull_values
=
context
.
pull_context
.
values
;
float
*
pull_values
=
context
.
pull_context
.
values
;
return
PullDense
(
pull_values
,
context
.
num
);
}
int32_t
MemoryDenseTable
::
Push
(
TableContext
&
context
)
{
int32_t
MemoryDenseTable
::
Push
(
TableContext
&
context
)
{
CHECK
(
context
.
value_type
==
Dense
);
if
(
context
.
push_context
.
values
!=
nullptr
)
{
if
(
!
context
.
push_context
.
is_param
)
{
...
...
@@ -148,13 +148,13 @@ int32_t MemoryDenseTable::Push(TableContext& context) {
return
0
;
}
int32_t
MemoryDenseTable
::
PullDense
(
float
*
pull_values
,
size_t
num
)
{
int32_t
MemoryDenseTable
::
PullDense
(
float
*
pull_values
,
size_t
num
)
{
std
::
copy
(
values_
[
param_idx_
].
begin
(),
values_
[
param_idx_
].
end
(),
pull_values
);
return
0
;
}
int32_t
MemoryDenseTable
::
PushDenseParam
(
const
float
*
values
,
size_t
num
)
{
int32_t
MemoryDenseTable
::
PushDenseParam
(
const
float
*
values
,
size_t
num
)
{
PADDLE_ENFORCE_GE
(
num
,
param_dim_
,
...
...
@@ -171,7 +171,7 @@ int32_t MemoryDenseTable::Pour() {
return
0
;
}
int32_t
MemoryDenseTable
::
PushDense
(
const
float
*
values
,
size_t
num
)
{
int32_t
MemoryDenseTable
::
PushDense
(
const
float
*
values
,
size_t
num
)
{
if
(
sync
)
{
std
::
future
<
int
>
task
=
_shards_task_pool
[
0
]
->
enqueue
([
this
,
&
values
]()
->
int
{
...
...
@@ -185,7 +185,7 @@ int32_t MemoryDenseTable::PushDense(const float* values, size_t num) {
return
0
;
}
int32_t
MemoryDenseTable
::
_PushDense
(
const
float
*
values
,
size_t
num
)
{
int32_t
MemoryDenseTable
::
_PushDense
(
const
float
*
values
,
size_t
num
)
{
PADDLE_ENFORCE_GE
(
num
,
param_dim_
,
...
...
@@ -212,8 +212,8 @@ int32_t MemoryDenseTable::_PushDense(const float* values, size_t num) {
return
0
;
}
int32_t
MemoryDenseTable
::
Load
(
const
std
::
string
&
path
,
const
std
::
string
&
param
)
{
int32_t
MemoryDenseTable
::
Load
(
const
std
::
string
&
path
,
const
std
::
string
&
param
)
{
if
(
param_dim_
<=
0
)
{
return
0
;
}
...
...
@@ -249,7 +249,7 @@ int32_t MemoryDenseTable::Load(const std::string& path,
try
{
int
dim_idx
=
0
;
float
data_buffer
[
5
];
float
*
data_buff_ptr
=
data_buffer
;
float
*
data_buff_ptr
=
data_buffer
;
std
::
string
line_data
;
auto
common
=
_config
.
common
();
...
...
@@ -319,8 +319,8 @@ int32_t MemoryDenseTable::Load(const std::string& path,
return
0
;
}
int32_t
MemoryDenseTable
::
Save
(
const
std
::
string
&
path
,
const
std
::
string
&
param
)
{
int32_t
MemoryDenseTable
::
Save
(
const
std
::
string
&
path
,
const
std
::
string
&
param
)
{
int
save_param
=
atoi
(
param
.
c_str
());
uint32_t
feasign_size
;
VLOG
(
0
)
<<
"MemoryDenseTable::save path "
<<
path
;
...
...
@@ -353,7 +353,7 @@ int32_t MemoryDenseTable::Save(const std::string& path,
os
.
clear
();
os
.
str
(
""
);
os
<<
values_
[
param_col_ids_
[
0
]][
y
]
<<
" 0"
;
for
(
in
t
x
=
2
;
x
<
param_col_ids_
.
size
();
++
x
)
{
for
(
size_
t
x
=
2
;
x
<
param_col_ids_
.
size
();
++
x
)
{
os
<<
" "
;
os
<<
values_
[
param_col_ids_
[
x
]][
y
];
}
...
...
@@ -365,7 +365,7 @@ int32_t MemoryDenseTable::Save(const std::string& path,
os
.
clear
();
os
.
str
(
""
);
os
<<
values_
[
param_col_ids_
[
0
]][
y
];
for
(
in
t
x
=
1
;
x
<
param_col_ids_
.
size
();
++
x
)
{
for
(
size_
t
x
=
1
;
x
<
param_col_ids_
.
size
();
++
x
)
{
os
<<
" "
;
os
<<
values_
[
param_col_ids_
[
x
]][
y
];
}
...
...
@@ -383,7 +383,7 @@ int32_t MemoryDenseTable::Save(const std::string& path,
auto
write_channel
=
_afs_client
.
open_w
(
channel_config
,
1024
*
1024
*
40
,
&
err_no
);
for
(
auto
&
t
:
result_buffer_param
)
{
for
(
auto
&
t
:
result_buffer_param
)
{
if
(
0
!=
write_channel
->
write_line
(
t
))
{
++
retry_num
;
is_write_failed
=
true
;
...
...
paddle/fluid/distributed/ps/table/memory_sparse_table.cc
浏览文件 @
be273ea9
此差异已折叠。
点击以展开。
paddle/fluid/distributed/ps/table/sparse_sgd_rule.cc
浏览文件 @
be273ea9
...
...
@@ -23,7 +23,7 @@ DEFINE_bool(enable_show_scale_gradient, true, "enable show scale gradient");
namespace
paddle
{
namespace
distributed
{
void
SparseNaiveSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
void
SparseNaiveSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
size_t
emb_dim
)
{
_embedding_dim
=
emb_dim
;
auto
naive_param
=
param
.
naive
();
...
...
@@ -41,9 +41,9 @@ void SparseNaiveSGDRule::LoadConfig(const SparseCommonSGDRuleParameter& param,
}
}
void
SparseNaiveSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
push_value
,
void
SparseNaiveSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
push_value
,
float
scale
)
{
for
(
size_t
i
=
0
;
i
<
_embedding_dim
;
++
i
)
{
w
[
i
]
-=
learning_rate_
*
push_value
[
i
];
...
...
@@ -51,8 +51,8 @@ void SparseNaiveSGDRule::UpdateValueWork(float* w,
}
}
void
SparseNaiveSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
void
SparseNaiveSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
bool
zero_init
)
{
if
(
zero_init
)
{
for
(
size_t
i
=
0
;
i
<
_embedding_dim
;
++
i
)
{
...
...
@@ -68,7 +68,7 @@ void SparseNaiveSGDRule::InitValueWork(float* value,
}
}
}
void
SparseAdaGradSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
void
SparseAdaGradSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
size_t
emb_dim
)
{
_embedding_dim
=
emb_dim
;
auto
adagrad_param
=
param
.
adagrad
();
...
...
@@ -88,11 +88,11 @@ void SparseAdaGradSGDRule::LoadConfig(const SparseCommonSGDRuleParameter& param,
}
}
void
SparseAdaGradSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
void
SparseAdaGradSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
float
scale
)
{
float
&
g2sum
=
sgd
[
G2SumIndex
()];
float
&
g2sum
=
sgd
[
G2SumIndex
()];
double
add_g2sum
=
0
;
for
(
size_t
i
=
0
;
i
<
_embedding_dim
;
i
++
)
{
...
...
@@ -106,8 +106,8 @@ void SparseAdaGradSGDRule::UpdateValueWork(float* w,
g2sum
+=
add_g2sum
/
_embedding_dim
;
}
void
SparseAdaGradSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
void
SparseAdaGradSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
bool
zero_init
)
{
for
(
size_t
i
=
0
;
i
<
_embedding_dim
;
++
i
)
{
if
(
zero_init
)
{
...
...
@@ -125,7 +125,7 @@ void SparseAdaGradSGDRule::InitValueWork(float* value,
sgd
[
G2SumIndex
()]
=
0
;
}
void
StdAdaGradSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
void
StdAdaGradSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
size_t
emb_dim
)
{
_embedding_dim
=
emb_dim
;
auto
adagrad_param
=
param
.
adagrad
();
...
...
@@ -145,12 +145,12 @@ void StdAdaGradSGDRule::LoadConfig(const SparseCommonSGDRuleParameter& param,
}
}
void
StdAdaGradSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
void
StdAdaGradSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
float
scale
)
{
for
(
size_t
i
=
0
;
i
<
_embedding_dim
;
i
++
)
{
float
&
g2sum
=
sgd
[
G2SumIndex
()
+
i
];
float
&
g2sum
=
sgd
[
G2SumIndex
()
+
i
];
double
scaled_grad
=
grad
[
i
]
/
scale
;
w
[
i
]
-=
learning_rate_
*
scaled_grad
*
sqrt
(
_initial_g2sum
/
(
_initial_g2sum
+
g2sum
));
...
...
@@ -159,8 +159,8 @@ void StdAdaGradSGDRule::UpdateValueWork(float* w,
}
}
void
StdAdaGradSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
void
StdAdaGradSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
bool
zero_init
)
{
for
(
size_t
i
=
0
;
i
<
_embedding_dim
;
++
i
)
{
if
(
zero_init
)
{
...
...
@@ -178,7 +178,7 @@ void StdAdaGradSGDRule::InitValueWork(float* value,
}
}
void
SparseAdamSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
void
SparseAdamSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
size_t
emb_dim
)
{
_embedding_dim
=
emb_dim
;
auto
adam_param
=
param
.
adam
();
...
...
@@ -199,15 +199,15 @@ void SparseAdamSGDRule::LoadConfig(const SparseCommonSGDRuleParameter& param,
}
}
void
SparseAdamSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
void
SparseAdamSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
float
scale
)
{
float
*
gsum
=
sgd
+
GSumIndex
();
float
*
g2sum
=
sgd
+
G2SumIndex
();
float
*
beta1_pow
=
sgd
+
Beta1PowIndex
();
float
*
beta2_pow
=
sgd
+
Beta2PowIndex
();
const
float
*
g
=
grad
;
float
*
gsum
=
sgd
+
GSumIndex
();
float
*
g2sum
=
sgd
+
G2SumIndex
();
float
*
beta1_pow
=
sgd
+
Beta1PowIndex
();
float
*
beta2_pow
=
sgd
+
Beta2PowIndex
();
const
float
*
g
=
grad
;
float
lr
=
learning_rate_
;
float
beta1_pow_
=
*
beta1_pow
;
...
...
@@ -227,8 +227,8 @@ void SparseAdamSGDRule::UpdateValueWork(float* w,
(
*
beta2_pow
)
*=
_beta2_decay_rate
;
}
void
SparseAdamSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
void
SparseAdamSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
bool
zero_init
)
{
for
(
size_t
i
=
0
;
i
<
_embedding_dim
;
++
i
)
{
if
(
zero_init
)
{
...
...
@@ -253,7 +253,7 @@ void SparseAdamSGDRule::InitValueWork(float* value,
}
void
SparseSharedAdamSGDRule
::
LoadConfig
(
const
SparseCommonSGDRuleParameter
&
param
,
size_t
emb_dim
)
{
const
SparseCommonSGDRuleParameter
&
param
,
size_t
emb_dim
)
{
_embedding_dim
=
emb_dim
;
auto
adam_param
=
param
.
adam
();
learning_rate_
=
adam_param
.
learning_rate
();
...
...
@@ -273,15 +273,15 @@ void SparseSharedAdamSGDRule::LoadConfig(
}
}
void
SparseSharedAdamSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
void
SparseSharedAdamSGDRule
::
UpdateValueWork
(
float
*
w
,
float
*
sgd
,
const
float
*
grad
,
float
scale
)
{
float
*
gsum
=
sgd
+
GSumIndex
();
float
*
g2sum
=
sgd
+
G2SumIndex
();
float
*
beta1_pow
=
sgd
+
Beta1PowIndex
();
float
*
beta2_pow
=
sgd
+
Beta2PowIndex
();
const
float
*
g
=
grad
;
float
*
gsum
=
sgd
+
GSumIndex
();
float
*
g2sum
=
sgd
+
G2SumIndex
();
float
*
beta1_pow
=
sgd
+
Beta1PowIndex
();
float
*
beta2_pow
=
sgd
+
Beta2PowIndex
();
const
float
*
g
=
grad
;
float
lr
=
learning_rate_
;
float
beta1_pow_
=
*
beta1_pow
;
...
...
@@ -292,7 +292,7 @@ void SparseSharedAdamSGDRule::UpdateValueWork(float* w,
lr
*=
sqrt
(
1
-
beta2_pow_
)
/
(
1
-
beta1_pow_
);
double
sum_gsum
=
0.0
;
double
sum_g2sum
=
0.0
;
for
(
in
t
i
=
0
;
i
<
_embedding_dim
;
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
_embedding_dim
;
i
++
)
{
// Calculation
double
new_gsum
=
_beta1_decay_rate
*
gsum_
+
(
1
-
_beta1_decay_rate
)
*
g
[
i
];
...
...
@@ -310,10 +310,10 @@ void SparseSharedAdamSGDRule::UpdateValueWork(float* w,
(
*
beta2_pow
)
*=
_beta2_decay_rate
;
}
void
SparseSharedAdamSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
void
SparseSharedAdamSGDRule
::
InitValueWork
(
float
*
value
,
float
*
sgd
,
bool
zero_init
)
{
for
(
in
t
i
=
0
;
i
<
_embedding_dim
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
_embedding_dim
;
++
i
)
{
if
(
zero_init
)
{
value
[
i
]
=
0.0
;
BoundValue
(
value
[
i
]);
...
...
@@ -327,7 +327,7 @@ void SparseSharedAdamSGDRule::InitValueWork(float* value,
}
}
// init rule gsum and g2sum
for
(
in
t
i
=
GSumIndex
();
i
<
Beta1PowIndex
();
i
++
)
{
for
(
size_
t
i
=
GSumIndex
();
i
<
Beta1PowIndex
();
i
++
)
{
sgd
[
i
]
=
0.0
;
}
// init beta1_pow and beta2_pow
...
...
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