Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
193aeaea
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
12 个月 前同步成功
通知
692
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
193aeaea
编写于
7月 13, 2018
作者:
L
Luo Tao
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' into demo
上级
b5a6c4d8
76086df4
变更
28
隐藏空白更改
内联
并排
Showing
28 changed file
with
629 addition
and
220 deletion
+629
-220
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+7
-3
paddle/fluid/framework/parallel_executor.h
paddle/fluid/framework/parallel_executor.h
+1
-1
paddle/fluid/framework/reader.h
paddle/fluid/framework/reader.h
+4
-4
paddle/fluid/operators/reader/blocking_queue.h
paddle/fluid/operators/reader/blocking_queue.h
+9
-0
paddle/fluid/operators/reader/create_py_reader_op.cc
paddle/fluid/operators/reader/create_py_reader_op.cc
+4
-6
paddle/fluid/operators/reader/lod_tensor_blocking_queue.h
paddle/fluid/operators/reader/lod_tensor_blocking_queue.h
+5
-2
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+23
-19
paddle/legacy/capi/Arguments.cpp
paddle/legacy/capi/Arguments.cpp
+11
-0
paddle/legacy/capi/arguments.h
paddle/legacy/capi/arguments.h
+12
-0
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+2
-1
python/paddle/fluid/clip.py
python/paddle/fluid/clip.py
+15
-15
python/paddle/fluid/layer_helper.py
python/paddle/fluid/layer_helper.py
+11
-11
python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
+84
-1
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+65
-62
python/paddle/fluid/param_attr.py
python/paddle/fluid/param_attr.py
+9
-9
python/paddle/fluid/regularizer.py
python/paddle/fluid/regularizer.py
+1
-4
python/paddle/fluid/tests/unittests/op_test.py
python/paddle/fluid/tests/unittests/op_test.py
+8
-8
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
+0
-1
python/paddle/fluid/tests/unittests/test_dynrnn_static_input.py
.../paddle/fluid/tests/unittests/test_dynrnn_static_input.py
+7
-7
python/paddle/fluid/tests/unittests/test_optimizer.py
python/paddle/fluid/tests/unittests/test_optimizer.py
+2
-2
python/paddle/fluid/tests/unittests/test_py_reader_push_pop.py
...n/paddle/fluid/tests/unittests/test_py_reader_push_pop.py
+99
-0
python/paddle/fluid/tests/unittests/test_py_reader_using_executor.py
...le/fluid/tests/unittests/test_py_reader_using_executor.py
+224
-0
python/paddle/fluid/tests/unittests/test_selected_rows.py
python/paddle/fluid/tests/unittests/test_selected_rows.py
+3
-3
python/paddle/fluid/tests/unittests/test_shrink_rnn_memory.py
...on/paddle/fluid/tests/unittests/test_shrink_rnn_memory.py
+2
-2
python/paddle/fluid/tests/unittests/test_tensor.py
python/paddle/fluid/tests/unittests/test_tensor.py
+15
-16
python/paddle/fluid/tests/unittests/testsuite.py
python/paddle/fluid/tests/unittests/testsuite.py
+1
-1
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+3
-40
python/setup.py.in
python/setup.py.in
+2
-2
未找到文件。
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
193aeaea
...
@@ -95,7 +95,7 @@ ParallelExecutor::ParallelExecutor(
...
@@ -95,7 +95,7 @@ ParallelExecutor::ParallelExecutor(
}
}
if
(
member_
->
local_scopes_
.
size
()
!=
1
&&
local_scopes
.
empty
())
{
if
(
member_
->
local_scopes_
.
size
()
!=
1
&&
local_scopes
.
empty
())
{
BCastParamsTo
GPU
s
(
bcast_vars
);
BCastParamsTo
Dev
s
(
bcast_vars
);
}
}
// Startup Program has been run. All local scopes has correct parameters.
// Startup Program has been run. All local scopes has correct parameters.
...
@@ -131,7 +131,7 @@ ParallelExecutor::ParallelExecutor(
...
@@ -131,7 +131,7 @@ ParallelExecutor::ParallelExecutor(
member_
->
places_
,
std
::
move
(
member_
->
executor_
)));
member_
->
places_
,
std
::
move
(
member_
->
executor_
)));
}
}
void
ParallelExecutor
::
BCastParamsTo
GPU
s
(
void
ParallelExecutor
::
BCastParamsTo
Dev
s
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
{
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
{
// the the initializing bcast, all vars would be bcast from device(0),
// the the initializing bcast, all vars would be bcast from device(0),
// otherwise
// otherwise
...
@@ -202,7 +202,11 @@ void ParallelExecutor::BCastParamsToGPUs(
...
@@ -202,7 +202,11 @@ void ParallelExecutor::BCastParamsToGPUs(
#endif
#endif
}
else
{
}
else
{
platform
::
CPUPlace
cpu
;
platform
::
CPUPlace
cpu
;
for
(
size_t
i
=
1
;
i
<
member_
->
places_
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
member_
->
places_
.
size
();
++
i
)
{
if
((
initializing
&&
i
==
0
)
||
(
!
initializing
&&
static_cast
<
int
>
(
i
)
==
var_dev_id
))
continue
;
auto
local_scope
=
member_
->
local_scopes_
[
i
];
auto
local_scope
=
member_
->
local_scopes_
[
i
];
auto
*
t
=
local_scope
->
Var
(
var
)
->
GetMutable
<
LoDTensor
>
();
auto
*
t
=
local_scope
->
Var
(
var
)
->
GetMutable
<
LoDTensor
>
();
t
->
Resize
(
dims
);
t
->
Resize
(
dims
);
...
...
paddle/fluid/framework/parallel_executor.h
浏览文件 @
193aeaea
...
@@ -66,7 +66,7 @@ class ParallelExecutor {
...
@@ -66,7 +66,7 @@ class ParallelExecutor {
void
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
void
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
const
std
::
string
&
fetched_var_name
);
const
std
::
string
&
fetched_var_name
);
void
BCastParamsTo
GPU
s
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
;
void
BCastParamsTo
Dev
s
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
;
private:
private:
ParallelExecutorPrivate
*
member_
;
ParallelExecutorPrivate
*
member_
;
...
...
paddle/fluid/framework/reader.h
浏览文件 @
193aeaea
...
@@ -29,11 +29,11 @@ enum ReaderStatus { kRunning, kStopped };
...
@@ -29,11 +29,11 @@ enum ReaderStatus { kRunning, kStopped };
class
ReaderBase
{
class
ReaderBase
{
public:
public:
void
ReadNext
(
std
::
vector
<
LoDTensor
>*
out
);
v
irtual
v
oid
ReadNext
(
std
::
vector
<
LoDTensor
>*
out
);
void
Shutdown
();
v
irtual
v
oid
Shutdown
();
void
Start
();
v
irtual
v
oid
Start
();
// Return the readers which are the end of decorating chain. Basically
// Return the readers which are the end of decorating chain. Basically
// they are readers just before read op.
// they are readers just before read op.
...
@@ -42,7 +42,7 @@ class ReaderBase {
...
@@ -42,7 +42,7 @@ class ReaderBase {
virtual
~
ReaderBase
();
virtual
~
ReaderBase
();
protected:
protected:
virtual
void
ReadNextImpl
(
std
::
vector
<
LoDTensor
>*
out
)
=
0
;
virtual
void
ReadNextImpl
(
std
::
vector
<
LoDTensor
>*
out
)
{}
virtual
void
ShutdownImpl
()
{}
virtual
void
ShutdownImpl
()
{}
...
...
paddle/fluid/operators/reader/blocking_queue.h
浏览文件 @
193aeaea
...
@@ -81,6 +81,15 @@ class BlockingQueue {
...
@@ -81,6 +81,15 @@ class BlockingQueue {
}
}
}
}
void
ReOpen
()
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
mutex_
);
closed_
=
false
;
std
::
deque
<
T
>
new_deque
;
queue_
.
swap
(
new_deque
);
send_cv_
.
notify_all
();
receive_cv_
.
notify_all
();
}
void
Close
()
{
void
Close
()
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
mutex_
);
std
::
lock_guard
<
std
::
mutex
>
lock
(
mutex_
);
closed_
=
true
;
closed_
=
true
;
...
...
paddle/fluid/operators/reader/create_py_reader_op.cc
浏览文件 @
193aeaea
...
@@ -27,19 +27,17 @@ class PyReader : public framework::FileReader {
...
@@ -27,19 +27,17 @@ class PyReader : public framework::FileReader {
queue_
=
queue
;
queue_
=
queue
;
}
}
void
ReadNext
Impl
(
std
::
vector
<
framework
::
LoDTensor
>*
out
)
override
{
void
ReadNext
(
std
::
vector
<
framework
::
LoDTensor
>*
out
)
override
{
bool
success
;
bool
success
;
*
out
=
queue_
->
Pop
(
&
success
);
*
out
=
queue_
->
Pop
(
&
success
);
if
(
!
success
)
out
->
clear
();
if
(
!
success
)
out
->
clear
();
}
}
private:
void
Shutdown
()
override
{
queue_
->
Close
();
}
void
ShutdownImpl
()
override
{
/* TODO */
}
void
StartImpl
()
override
{
/* TODO */
void
Start
()
override
{
queue_
->
ReOpen
();
}
}
private:
std
::
shared_ptr
<
LoDTensorBlockingQueue
>
queue_
;
std
::
shared_ptr
<
LoDTensorBlockingQueue
>
queue_
;
};
};
...
...
paddle/fluid/operators/reader/lod_tensor_blocking_queue.h
浏览文件 @
193aeaea
...
@@ -58,12 +58,15 @@ class LoDTensorBlockingQueue {
...
@@ -58,12 +58,15 @@ class LoDTensorBlockingQueue {
inline
size_t
Size
()
const
{
return
queue_
.
Size
();
}
inline
size_t
Size
()
const
{
return
queue_
.
Size
();
}
inline
void
Close
()
{
return
queue_
.
Close
();
}
inline
void
ReOpen
()
{
queue_
.
ReOpen
();
}
inline
void
Close
()
{
queue_
.
Close
();
}
inline
bool
IsClosed
()
const
{
return
queue_
.
IsClosed
();
}
inline
bool
IsClosed
()
const
{
return
queue_
.
IsClosed
();
}
private:
private:
void
CheckDims
(
const
std
::
vector
<
framework
::
LoDTensor
>&
lod_tensor_vec
)
{
void
CheckDims
(
const
std
::
vector
<
framework
::
LoDTensor
>&
lod_tensor_vec
)
const
{
PADDLE_ENFORCE
(
dims_
.
size
()
==
lod_tensor_vec
.
size
(),
PADDLE_ENFORCE
(
dims_
.
size
()
==
lod_tensor_vec
.
size
(),
"Expect input size is %d but found %s"
,
dims_
.
size
(),
"Expect input size is %d but found %s"
,
dims_
.
size
(),
lod_tensor_vec
.
size
());
lod_tensor_vec
.
size
());
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
193aeaea
...
@@ -14,6 +14,7 @@ limitations under the License. */
...
@@ -14,6 +14,7 @@ limitations under the License. */
#include <Python.h>
#include <Python.h>
#include <algorithm>
#include <algorithm>
#include <map>
#include <map>
#include <memory>
#include <mutex> // NOLINT // for call_once
#include <mutex> // NOLINT // for call_once
#include <string>
#include <string>
#include <unordered_map>
#include <unordered_map>
...
@@ -86,37 +87,37 @@ PYBIND11_PLUGIN(core) {
...
@@ -86,37 +87,37 @@ PYBIND11_PLUGIN(core) {
py
::
class_
<
Tensor
>
(
m
,
"Tensor"
,
py
::
buffer_protocol
())
py
::
class_
<
Tensor
>
(
m
,
"Tensor"
,
py
::
buffer_protocol
())
.
def_buffer
(
.
def_buffer
(
[](
Tensor
&
self
)
->
py
::
buffer_info
{
return
CastToPyBuffer
(
self
);
})
[](
Tensor
&
self
)
->
py
::
buffer_info
{
return
CastToPyBuffer
(
self
);
})
.
def
(
"get_dims"
,
.
def
(
"
_
get_dims"
,
[](
const
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
})
[](
const
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
})
.
def
(
"set_dims"
,
.
def
(
"
_
set_dims"
,
[](
Tensor
&
self
,
const
std
::
vector
<
int64_t
>
&
dim
)
{
[](
Tensor
&
self
,
const
std
::
vector
<
int64_t
>
&
dim
)
{
self
.
Resize
(
make_ddim
(
dim
));
self
.
Resize
(
make_ddim
(
dim
));
})
})
.
def
(
"set_layout"
,
.
def
(
"
_
set_layout"
,
[](
Tensor
&
self
,
const
std
::
string
&
layout
)
{
[](
Tensor
&
self
,
const
std
::
string
&
layout
)
{
self
.
set_layout
(
StringToDataLayout
(
layout
));
self
.
set_layout
(
StringToDataLayout
(
layout
));
})
})
.
def
(
"alloc_float"
,
.
def
(
"
_
alloc_float"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPlace
&
place
)
{
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
self
.
mutable_data
<
float
>
(
place
);
})
})
.
def
(
"alloc_float"
,
.
def
(
"
_
alloc_float"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
[](
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
self
.
mutable_data
<
float
>
(
place
);
})
})
.
def
(
"alloc_int"
,
.
def
(
"
_
alloc_int"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
[](
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
self
.
mutable_data
<
int
>
(
place
);
})
})
.
def
(
"alloc_int"
,
.
def
(
"
_
alloc_int"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPlace
&
place
)
{
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
self
.
mutable_data
<
int
>
(
place
);
})
})
.
def
(
"alloc_int"
,
.
def
(
"
_
alloc_int"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPinnedPlace
&
place
)
{
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPinnedPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
self
.
mutable_data
<
int
>
(
place
);
})
})
.
def
(
"alloc_float"
,
.
def
(
"
_
alloc_float"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPinnedPlace
&
place
)
{
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPinnedPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
self
.
mutable_data
<
float
>
(
place
);
})
})
...
@@ -144,11 +145,11 @@ PYBIND11_PLUGIN(core) {
...
@@ -144,11 +145,11 @@ PYBIND11_PLUGIN(core) {
.
def
(
"set"
,
PyCUDAPinnedTensorSetFromArray
<
uint8_t
>
)
.
def
(
"set"
,
PyCUDAPinnedTensorSetFromArray
<
uint8_t
>
)
#endif
#endif
.
def
(
"shape"
,
[](
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
})
.
def
(
"shape"
,
[](
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
})
.
def
(
"set_float_element"
,
TensorSetElement
<
float
>
)
.
def
(
"
_
set_float_element"
,
TensorSetElement
<
float
>
)
.
def
(
"get_float_element"
,
TensorGetElement
<
float
>
)
.
def
(
"
_
get_float_element"
,
TensorGetElement
<
float
>
)
.
def
(
"set_double_element"
,
TensorSetElement
<
double
>
)
.
def
(
"
_
set_double_element"
,
TensorSetElement
<
double
>
)
.
def
(
"get_double_element"
,
TensorGetElement
<
double
>
)
.
def
(
"
_
get_double_element"
,
TensorGetElement
<
double
>
)
.
def
(
"dtype"
,
[](
Tensor
&
self
)
{
return
ToDataType
(
self
.
type
());
});
.
def
(
"
_
dtype"
,
[](
Tensor
&
self
)
{
return
ToDataType
(
self
.
type
());
});
py
::
class_
<
LoDTensor
,
Tensor
>
(
m
,
"LoDTensor"
)
py
::
class_
<
LoDTensor
,
Tensor
>
(
m
,
"LoDTensor"
)
.
def_buffer
(
.
def_buffer
(
...
@@ -310,7 +311,8 @@ All parameter, weight, gradient are variables in Paddle.
...
@@ -310,7 +311,8 @@ All parameter, weight, gradient are variables in Paddle.
::
paddle
::
operators
::
reader
::
LoDTensorBlockingQueue
;
::
paddle
::
operators
::
reader
::
LoDTensorBlockingQueue
;
using
LoDTensorBlockingQueueHolder
=
using
LoDTensorBlockingQueueHolder
=
::
paddle
::
operators
::
reader
::
LoDTensorBlockingQueueHolder
;
::
paddle
::
operators
::
reader
::
LoDTensorBlockingQueueHolder
;
py
::
class_
<
LoDTensorBlockingQueue
>
(
m
,
"LoDTensorBlockingQueue"
,
""
)
py
::
class_
<
LoDTensorBlockingQueue
,
std
::
shared_ptr
<
LoDTensorBlockingQueue
>>
(
m
,
"LoDTensorBlockingQueue"
,
""
)
.
def
(
"push"
,
.
def
(
"push"
,
[](
LoDTensorBlockingQueue
&
self
,
[](
LoDTensorBlockingQueue
&
self
,
const
std
::
vector
<
framework
::
LoDTensor
>
&
lod_tensor_vec
)
{
const
std
::
vector
<
framework
::
LoDTensor
>
&
lod_tensor_vec
)
{
...
@@ -325,7 +327,7 @@ All parameter, weight, gradient are variables in Paddle.
...
@@ -325,7 +327,7 @@ All parameter, weight, gradient are variables in Paddle.
m
.
def
(
"init_lod_tensor_blocking_queue"
,
m
.
def
(
"init_lod_tensor_blocking_queue"
,
[](
Variable
&
var
,
size_t
capacity
,
[](
Variable
&
var
,
size_t
capacity
,
const
std
::
vector
<
std
::
vector
<
int64_t
>>
&
shapes
)
const
std
::
vector
<
std
::
vector
<
int64_t
>>
&
shapes
)
->
LoDTensorBlockingQueue
*
{
->
std
::
shared_ptr
<
LoDTensorBlockingQueue
>
{
std
::
vector
<
DDim
>
dims
(
shapes
.
size
());
std
::
vector
<
DDim
>
dims
(
shapes
.
size
());
std
::
transform
(
shapes
.
begin
(),
shapes
.
end
(),
dims
.
begin
(),
std
::
transform
(
shapes
.
begin
(),
shapes
.
end
(),
dims
.
begin
(),
[](
const
std
::
vector
<
int64_t
>
&
shape
)
{
[](
const
std
::
vector
<
int64_t
>
&
shape
)
{
...
@@ -333,9 +335,9 @@ All parameter, weight, gradient are variables in Paddle.
...
@@ -333,9 +335,9 @@ All parameter, weight, gradient are variables in Paddle.
});
});
auto
*
holder
=
var
.
GetMutable
<
LoDTensorBlockingQueueHolder
>
();
auto
*
holder
=
var
.
GetMutable
<
LoDTensorBlockingQueueHolder
>
();
holder
->
InitOnce
(
capacity
,
dims
);
holder
->
InitOnce
(
capacity
,
dims
);
return
holder
->
GetQueue
()
.
get
()
;
return
holder
->
GetQueue
();
},
},
py
::
return_value_policy
::
reference
);
py
::
return_value_policy
::
copy
);
py
::
class_
<
Scope
>
(
m
,
"Scope"
,
""
)
py
::
class_
<
Scope
>
(
m
,
"Scope"
,
""
)
.
def
(
"var"
,
.
def
(
"var"
,
...
@@ -543,6 +545,8 @@ All parameter, weight, gradient are variables in Paddle.
...
@@ -543,6 +545,8 @@ All parameter, weight, gradient are variables in Paddle.
});
});
py
::
class_
<
LoDTensorArray
>
(
m
,
"LoDTensorArray"
)
py
::
class_
<
LoDTensorArray
>
(
m
,
"LoDTensorArray"
)
.
def
(
"__init__"
,
[](
LoDTensorArray
&
instance
)
{
new
(
&
instance
)
LoDTensorArray
();
})
.
def
(
"__getitem__"
,
.
def
(
"__getitem__"
,
[](
LoDTensorArray
&
self
,
size_t
i
)
{
return
&
self
.
at
(
i
);
},
[](
LoDTensorArray
&
self
,
size_t
i
)
{
return
&
self
.
at
(
i
);
},
py
::
return_value_policy
::
reference
)
py
::
return_value_policy
::
reference
)
...
@@ -665,7 +669,7 @@ All parameter, weight, gradient are variables in Paddle.
...
@@ -665,7 +669,7 @@ All parameter, weight, gradient are variables in Paddle.
const
std
::
string
&
,
Scope
*
,
std
::
vector
<
Scope
*>
&
,
const
std
::
string
&
,
Scope
*
,
std
::
vector
<
Scope
*>
&
,
const
ExecutionStrategy
&
,
const
BuildStrategy
&
,
size_t
,
const
ExecutionStrategy
&
,
const
BuildStrategy
&
,
size_t
,
size_t
>
())
size_t
>
())
.
def
(
"bcast_params"
,
&
ParallelExecutor
::
BCastParamsTo
GPU
s
)
.
def
(
"bcast_params"
,
&
ParallelExecutor
::
BCastParamsTo
Dev
s
)
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
// We still cannot get local_scope from this vector, since the element
// We still cannot get local_scope from this vector, since the element
// of vec<Scope*> will be freed by Python GC. We can only return Scope*
// of vec<Scope*> will be freed by Python GC. We can only return Scope*
...
...
paddle/legacy/capi/Arguments.cpp
浏览文件 @
193aeaea
...
@@ -66,6 +66,17 @@ paddle_error paddle_arguments_get_value(paddle_arguments args,
...
@@ -66,6 +66,17 @@ paddle_error paddle_arguments_get_value(paddle_arguments args,
return
kPD_NO_ERROR
;
return
kPD_NO_ERROR
;
}
}
PD_API
paddle_error
paddle_arguments_get_prob
(
paddle_arguments
args
,
uint64_t
ID
,
paddle_matrix
mat
)
{
if
(
args
==
nullptr
||
mat
==
nullptr
)
return
kPD_NULLPTR
;
auto
m
=
paddle
::
capi
::
cast
<
paddle
::
capi
::
CMatrix
>
(
mat
);
auto
a
=
castArg
(
args
);
if
(
ID
>=
a
->
args
.
size
())
return
kPD_OUT_OF_RANGE
;
m
->
mat
=
a
->
args
[
ID
].
in
;
return
kPD_NO_ERROR
;
}
paddle_error
paddle_arguments_get_ids
(
paddle_arguments
args
,
paddle_error
paddle_arguments_get_ids
(
paddle_arguments
args
,
uint64_t
ID
,
uint64_t
ID
,
paddle_ivector
ids
)
{
paddle_ivector
ids
)
{
...
...
paddle/legacy/capi/arguments.h
浏览文件 @
193aeaea
...
@@ -87,6 +87,18 @@ PD_API paddle_error paddle_arguments_get_value(paddle_arguments args,
...
@@ -87,6 +87,18 @@ PD_API paddle_error paddle_arguments_get_value(paddle_arguments args,
uint64_t
ID
,
uint64_t
ID
,
paddle_matrix
mat
);
paddle_matrix
mat
);
/**
* @brief paddle_arguments_get_prob Get the prob matrix of beam search, which
* slot ID is `ID`
* @param [in] args arguments array
* @param [in] ID array index
* @param [out] mat matrix pointer
* @return paddle_error
*/
PD_API
paddle_error
paddle_arguments_get_prob
(
paddle_arguments
args
,
uint64_t
ID
,
paddle_matrix
mat
);
/**
/**
* @brief PDArgsGetIds Get the integer vector of one argument in array, which
* @brief PDArgsGetIds Get the integer vector of one argument in array, which
* index is `ID`.
* index is `ID`.
...
...
python/paddle/fluid/__init__.py
浏览文件 @
193aeaea
...
@@ -44,7 +44,7 @@ import metrics
...
@@ -44,7 +44,7 @@ import metrics
import
transpiler
import
transpiler
from
param_attr
import
ParamAttr
,
WeightNormParamAttr
from
param_attr
import
ParamAttr
,
WeightNormParamAttr
from
data_feeder
import
DataFeeder
from
data_feeder
import
DataFeeder
from
core
import
LoDTensor
,
CPUPlace
,
CUDAPlace
,
CUDAPinnedPlace
,
Scope
from
core
import
LoDTensor
,
LoDTensorArray
,
CPUPlace
,
CUDAPlace
,
CUDAPinnedPlace
,
Scope
from
transpiler
import
DistributeTranspiler
,
InferenceTranspiler
,
\
from
transpiler
import
DistributeTranspiler
,
InferenceTranspiler
,
\
memory_optimize
,
release_memory
memory_optimize
,
release_memory
from
concurrency
import
(
Go
,
make_channel
,
channel_send
,
channel_recv
,
from
concurrency
import
(
Go
,
make_channel
,
channel_send
,
channel_recv
,
...
@@ -72,6 +72,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
...
@@ -72,6 +72,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
'backward'
,
'backward'
,
'regularizer'
,
'regularizer'
,
'LoDTensor'
,
'LoDTensor'
,
'LoDTensorArray'
,
'CPUPlace'
,
'CPUPlace'
,
'CUDAPlace'
,
'CUDAPlace'
,
'CUDAPinnedPlace'
,
'CUDAPinnedPlace'
,
...
...
python/paddle/fluid/clip.py
浏览文件 @
193aeaea
...
@@ -31,7 +31,7 @@ class BaseErrorClipAttr(object):
...
@@ -31,7 +31,7 @@ class BaseErrorClipAttr(object):
def
__str__
(
self
):
def
__str__
(
self
):
raise
NotImplementedError
()
raise
NotImplementedError
()
def
append_clip_op
(
self
,
block
,
grad_name
):
def
_
append_clip_op
(
self
,
block
,
grad_name
):
raise
NotImplementedError
()
raise
NotImplementedError
()
...
@@ -67,7 +67,7 @@ class ErrorClipByValue(BaseErrorClipAttr):
...
@@ -67,7 +67,7 @@ class ErrorClipByValue(BaseErrorClipAttr):
def
__str__
(
self
):
def
__str__
(
self
):
return
"ByValue, min=%f, max=%f"
%
(
self
.
min
,
self
.
max
)
return
"ByValue, min=%f, max=%f"
%
(
self
.
min
,
self
.
max
)
def
append_clip_op
(
self
,
block
,
grad_name
):
def
_
append_clip_op
(
self
,
block
,
grad_name
):
clip_op_desc
=
block
.
desc
.
append_op
()
clip_op_desc
=
block
.
desc
.
append_op
()
clip_op_desc
.
set_type
(
"clip"
)
clip_op_desc
.
set_type
(
"clip"
)
clip_op_desc
.
set_input
(
"X"
,
[
grad_name
])
clip_op_desc
.
set_input
(
"X"
,
[
grad_name
])
...
@@ -90,17 +90,17 @@ def error_clip_callback(block, context):
...
@@ -90,17 +90,17 @@ def error_clip_callback(block, context):
"Variable's error_clip should be an instance of BaseErrorClipAttr or None."
"Variable's error_clip should be an instance of BaseErrorClipAttr or None."
)
)
if
error_clip
is
not
None
:
if
error_clip
is
not
None
:
error_clip
.
append_clip_op
(
block
,
grad_n
)
error_clip
.
_
append_clip_op
(
block
,
grad_n
)
class
BaseGradientClipAttr
(
object
):
class
BaseGradientClipAttr
(
object
):
def
__str__
(
self
):
def
__str__
(
self
):
raise
NotImplementedError
()
raise
NotImplementedError
()
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
raise
NotImplementedError
()
raise
NotImplementedError
()
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
raise
NotImplementedError
()
raise
NotImplementedError
()
...
@@ -108,10 +108,10 @@ class NullGradientClipAttr(BaseGradientClipAttr):
...
@@ -108,10 +108,10 @@ class NullGradientClipAttr(BaseGradientClipAttr):
def
__str__
(
self
):
def
__str__
(
self
):
return
"Null"
return
"Null"
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
pass
pass
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
return
param
,
grad
return
param
,
grad
...
@@ -153,10 +153,10 @@ class GradientClipByValue(BaseGradientClipAttr):
...
@@ -153,10 +153,10 @@ class GradientClipByValue(BaseGradientClipAttr):
def
__str__
(
self
):
def
__str__
(
self
):
return
"ByValue, min=%f, max=%f"
%
(
self
.
min
,
self
.
max
)
return
"ByValue, min=%f, max=%f"
%
(
self
.
min
,
self
.
max
)
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
pass
pass
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
new_grad
=
layers
.
clip
(
x
=
grad
,
min
=
self
.
min
,
max
=
self
.
max
)
new_grad
=
layers
.
clip
(
x
=
grad
,
min
=
self
.
min
,
max
=
self
.
max
)
return
param
,
new_grad
return
param
,
new_grad
...
@@ -199,10 +199,10 @@ class GradientClipByNorm(BaseGradientClipAttr):
...
@@ -199,10 +199,10 @@ class GradientClipByNorm(BaseGradientClipAttr):
def
__str__
(
self
):
def
__str__
(
self
):
return
"ByNorm, clip_norm=%f"
%
self
.
clip_norm
return
"ByNorm, clip_norm=%f"
%
self
.
clip_norm
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
pass
pass
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
new_grad
=
layers
.
clip_by_norm
(
x
=
grad
,
max_norm
=
self
.
clip_norm
)
new_grad
=
layers
.
clip_by_norm
(
x
=
grad
,
max_norm
=
self
.
clip_norm
)
return
param
,
new_grad
return
param
,
new_grad
...
@@ -257,7 +257,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
...
@@ -257,7 +257,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
return
"ByGlobalNorm, group_name=%s, clip_norm=%f"
%
(
self
.
group_name
,
return
"ByGlobalNorm, group_name=%s, clip_norm=%f"
%
(
self
.
group_name
,
self
.
clip_norm
)
self
.
clip_norm
)
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
if
self
.
group_name
not
in
context
:
if
self
.
group_name
not
in
context
:
context
[
self
.
group_name
]
=
[]
context
[
self
.
group_name
]
=
[]
context
[
self
.
group_name
+
"_clip_value"
]
=
self
.
clip_norm
context
[
self
.
group_name
+
"_clip_value"
]
=
self
.
clip_norm
...
@@ -274,7 +274,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
...
@@ -274,7 +274,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
self
.
context
=
context
self
.
context
=
context
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
group_scale_name
=
self
.
group_name
+
"_scale"
group_scale_name
=
self
.
group_name
+
"_scale"
if
group_scale_name
not
in
self
.
context
:
if
group_scale_name
not
in
self
.
context
:
group_norm_var
=
layers
.
sums
(
input
=
self
.
context
[
self
.
group_name
])
group_norm_var
=
layers
.
sums
(
input
=
self
.
context
[
self
.
group_name
])
...
@@ -336,12 +336,12 @@ def append_gradient_clip_ops(param_grad):
...
@@ -336,12 +336,12 @@ def append_gradient_clip_ops(param_grad):
"clip attribute should be an instance of BaseGradientClipAttr"
"clip attribute should be an instance of BaseGradientClipAttr"
)
)
clip_attr
.
process_context
(
context
=
context
,
param
=
p
,
grad
=
g
)
clip_attr
.
_
process_context
(
context
=
context
,
param
=
p
,
grad
=
g
)
res
=
[]
res
=
[]
for
p
,
g
in
param_grad
:
for
p
,
g
in
param_grad
:
with
p
.
block
.
program
.
optimized_guard
(
p
):
with
p
.
block
.
program
.
optimized_guard
(
p
):
res
.
append
(
clip_attr
.
create_operators
(
param
=
p
,
grad
=
g
))
res
.
append
(
clip_attr
.
_
create_operators
(
param
=
p
,
grad
=
g
))
return
res
return
res
...
...
python/paddle/fluid/layer_helper.py
浏览文件 @
193aeaea
...
@@ -68,11 +68,11 @@ class LayerHelper(object):
...
@@ -68,11 +68,11 @@ class LayerHelper(object):
@
property
@
property
def
param_attr
(
self
):
def
param_attr
(
self
):
return
ParamAttr
.
to_attr
(
self
.
kwargs
.
get
(
'param_attr'
,
None
))
return
ParamAttr
.
_
to_attr
(
self
.
kwargs
.
get
(
'param_attr'
,
None
))
@
property
@
property
def
bias_attr
(
self
):
def
bias_attr
(
self
):
return
ParamAttr
.
to_attr
(
self
.
kwargs
.
get
(
'bias_attr'
,
None
))
return
ParamAttr
.
_
to_attr
(
self
.
kwargs
.
get
(
'bias_attr'
,
None
))
def
multiple_param_attr
(
self
,
length
):
def
multiple_param_attr
(
self
,
length
):
param_attr
=
self
.
param_attr
param_attr
=
self
.
param_attr
...
@@ -262,11 +262,11 @@ class LayerHelper(object):
...
@@ -262,11 +262,11 @@ class LayerHelper(object):
g_param
=
self
.
startup_program
.
global_block
().
create_parameter
(
g_param
=
self
.
startup_program
.
global_block
().
create_parameter
(
dtype
=
dtype
,
dtype
=
dtype
,
shape
=
g_param_shape
,
shape
=
g_param_shape
,
**
g_param_attr
.
to_kwargs
(
with_initializer
=
False
))
**
g_param_attr
.
_
to_kwargs
(
with_initializer
=
False
))
v_param
=
self
.
startup_program
.
global_block
().
create_parameter
(
v_param
=
self
.
startup_program
.
global_block
().
create_parameter
(
dtype
=
dtype
,
dtype
=
dtype
,
shape
=
v_param_shape
,
shape
=
v_param_shape
,
**
v_param_attr
.
to_kwargs
(
with_initializer
=
True
))
**
v_param_attr
.
_
to_kwargs
(
with_initializer
=
True
))
__norm_except_dim
(
__norm_except_dim
(
x
=
v_param
,
x
=
v_param
,
out
=
g_param
,
out
=
g_param
,
...
@@ -275,9 +275,9 @@ class LayerHelper(object):
...
@@ -275,9 +275,9 @@ class LayerHelper(object):
# Add weight normalization to main_program
# Add weight normalization to main_program
g_param
=
self
.
main_program
.
global_block
().
create_parameter
(
g_param
=
self
.
main_program
.
global_block
().
create_parameter
(
dtype
=
dtype
,
shape
=
g_param_shape
,
**
g_param_attr
.
to_kwargs
())
dtype
=
dtype
,
shape
=
g_param_shape
,
**
g_param_attr
.
_
to_kwargs
())
v_param
=
self
.
main_program
.
global_block
().
create_parameter
(
v_param
=
self
.
main_program
.
global_block
().
create_parameter
(
dtype
=
dtype
,
shape
=
v_param_shape
,
**
v_param_attr
.
to_kwargs
())
dtype
=
dtype
,
shape
=
v_param_shape
,
**
v_param_attr
.
_
to_kwargs
())
w_param
=
__weight_normalize
(
g_param
,
v_param
,
dim
=
attr
.
dim
)
w_param
=
__weight_normalize
(
g_param
,
v_param
,
dim
=
attr
.
dim
)
return
w_param
return
w_param
...
@@ -296,11 +296,11 @@ class LayerHelper(object):
...
@@ -296,11 +296,11 @@ class LayerHelper(object):
if
default_initializer
is
None
and
attr
.
initializer
is
None
:
if
default_initializer
is
None
and
attr
.
initializer
is
None
:
if
is_bias
:
if
is_bias
:
attr
.
set_default_bias_initializer
()
attr
.
_
set_default_bias_initializer
()
else
:
else
:
attr
.
set_default_param_initializer
()
attr
.
_
set_default_param_initializer
()
else
:
else
:
attr
.
set_default_initializer
(
default_initializer
)
attr
.
_
set_default_initializer
(
default_initializer
)
# If weight normalization is set, insert extra parameters and ops.
# If weight normalization is set, insert extra parameters and ops.
# Refer to https://arxiv.org/pdf/1602.07868.pdf
# Refer to https://arxiv.org/pdf/1602.07868.pdf
...
@@ -310,9 +310,9 @@ class LayerHelper(object):
...
@@ -310,9 +310,9 @@ class LayerHelper(object):
return
param
return
param
self
.
startup_program
.
global_block
().
create_parameter
(
self
.
startup_program
.
global_block
().
create_parameter
(
dtype
=
dtype
,
shape
=
shape
,
**
attr
.
to_kwargs
(
with_initializer
=
True
))
dtype
=
dtype
,
shape
=
shape
,
**
attr
.
_
to_kwargs
(
with_initializer
=
True
))
return
self
.
main_program
.
global_block
().
create_parameter
(
return
self
.
main_program
.
global_block
().
create_parameter
(
dtype
=
dtype
,
shape
=
shape
,
**
attr
.
to_kwargs
())
dtype
=
dtype
,
shape
=
shape
,
**
attr
.
_
to_kwargs
())
def
get_parameter
(
self
,
name
):
def
get_parameter
(
self
,
name
):
param
=
self
.
main_program
.
global_block
().
var
(
name
)
param
=
self
.
main_program
.
global_block
().
var
(
name
)
...
...
python/paddle/fluid/layers/io.py
浏览文件 @
193aeaea
...
@@ -24,7 +24,8 @@ from layer_function_generator import generate_layer_fn, templatedoc
...
@@ -24,7 +24,8 @@ from layer_function_generator import generate_layer_fn, templatedoc
__all__
=
[
__all__
=
[
'data'
,
'BlockGuardServ'
,
'ListenAndServ'
,
'Send'
,
'Recv'
,
'data'
,
'BlockGuardServ'
,
'ListenAndServ'
,
'Send'
,
'Recv'
,
'open_recordio_file'
,
'open_files'
,
'read_file'
,
'shuffle'
,
'batch'
,
'open_recordio_file'
,
'open_files'
,
'read_file'
,
'shuffle'
,
'batch'
,
'double_buffer'
,
'random_data_generator'
,
'Preprocessor'
,
'load'
'double_buffer'
,
'random_data_generator'
,
'py_reader'
,
'Preprocessor'
,
'load'
]
]
...
@@ -445,6 +446,88 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
...
@@ -445,6 +446,88 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
return
monkey_patch_reader_methods
(
main_prog_var
)
return
monkey_patch_reader_methods
(
main_prog_var
)
def
py_reader
(
capacity
,
shapes
,
dtypes
,
lod_levels
=
None
):
"""
Create a reader and blocking queue for data feeding in Python
This layer returns a Reader Variable and a BlockingQueue.
The BlockingQueue provides `push()` method to push a `LoDTensorArray`
object into the queue in Python side. In C++ side, the Reader
Variable would invoke `pop()` method of the queue to retrieve the
feeding data. The process of feeding data in Python side and fetching
data in C++ side can run in parallel. The BlockingQueue should be closed
using `close()` method when unused.
Args:
capacity(int): The maximum capacity of the BlockingQueue.
shapes(list): List of tuples which declaring data shapes.
dtypes(list): List of strs which declaring data type.
lod_levels(list): List of ints which declaring data lod_level.
Returns:
tuple(Variable, BlockingQueue):
A Reader Variable from which we can get feeding data.
A BlockingQueue object for data feeding.
Examples:
.. code-block:: python
reader, queue = fluid.layers.py_reader(
capacity=10,
shapes=[[-1,3,224,224], [-1,1]],
dtypes=['float32', 'int64'])
# Via the reader, we can use 'read_file' layer to get data:
image, label = fluid.layers.read_file(reader)
# Via the blocking queue, we can feed data using threads
def feed_data(queue, feed_images, feed_labels):
for feed_image, feed_label in zip(feed_images, feed_labels):
data = core.LoDTensorArray()
data.append(feed_image)
data.append(feed_label)
queue.push(data)
thread = threading.Thread(target=feed_data, args=(queue, feed_images, feed_labels))
thread.start()
"""
dtypes
=
[
convert_np_dtype_to_dtype_
(
dt
)
for
dt
in
dtypes
]
shape_concat
=
[]
ranks
=
[]
for
shape
in
shapes
:
shape_concat
.
extend
(
shape
)
ranks
.
append
(
len
(
shape
))
if
lod_levels
is
None
:
lod_levels
=
[
0
]
*
len
(
shapes
)
queue_name
=
unique_name
(
'lod_tensor_blocking_queue'
)
var
=
global_scope
().
var
(
queue_name
)
feed_queue
=
core
.
init_lod_tensor_blocking_queue
(
var
,
capacity
,
shapes
)
startup_blk
=
default_startup_program
().
current_block
()
startup_var
=
startup_blk
.
create_var
(
name
=
unique_name
(
'create_py_reader'
))
startup_blk
.
append_op
(
type
=
'create_py_reader'
,
inputs
=
{
'blocking_queue'
:
queue_name
},
outputs
=
{
'Out'
:
[
startup_var
]},
attrs
=
{
'shape_concat'
:
shape_concat
,
'lod_levels'
:
lod_levels
,
'ranks'
:
ranks
})
startup_var
.
desc
.
set_dtypes
(
dtypes
)
startup_var
.
persistable
=
True
main_prog_var
=
_copy_reader_var_
(
default_main_program
().
current_block
(),
startup_var
)
return
monkey_patch_reader_methods
(
main_prog_var
),
feed_queue
def
open_files
(
filenames
,
def
open_files
(
filenames
,
shapes
,
shapes
,
lod_levels
,
lod_levels
,
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
193aeaea
...
@@ -123,7 +123,7 @@ class Optimizer(object):
...
@@ -123,7 +123,7 @@ class Optimizer(object):
"""
"""
pass
pass
def
_finish_update
(
self
,
block
):
def
_finish_update
(
self
,
block
,
parameters
):
"""Finish any custom updates needed
"""Finish any custom updates needed
before completing an optimization step
before completing an optimization step
...
@@ -132,7 +132,7 @@ class Optimizer(object):
...
@@ -132,7 +132,7 @@ class Optimizer(object):
parameters: list of parameter variables for the optimizer
parameters: list of parameter variables for the optimizer
Returns:
Returns:
list of finish ops or
None
None
"""
"""
pass
pass
...
@@ -236,7 +236,8 @@ class Optimizer(object):
...
@@ -236,7 +236,8 @@ class Optimizer(object):
# Get custom finish ops for subclasses
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
# FIXME: Need to fix this once we figure out how to handle dependencies
self
.
_finish_update
(
loss
.
block
)
self
.
_finish_update
(
loss
.
block
,
[
p
[
0
]
for
p
in
parameters_and_grads
])
end
=
len
(
global_block
.
ops
)
end
=
len
(
global_block
.
ops
)
return
global_block
.
slice_ops
(
start
,
end
)
return
global_block
.
slice_ops
(
start
,
end
)
...
@@ -486,6 +487,8 @@ class AdamOptimizer(Optimizer):
...
@@ -486,6 +487,8 @@ class AdamOptimizer(Optimizer):
"""
"""
_moment1_acc_str
=
"moment1"
_moment1_acc_str
=
"moment1"
_moment2_acc_str
=
"moment2"
_moment2_acc_str
=
"moment2"
_beta1_pow_acc_str
=
"beta1_pow_acc"
_beta2_pow_acc_str
=
"beta2_pow_acc"
def
__init__
(
self
,
def
__init__
(
self
,
learning_rate
=
0.001
,
learning_rate
=
0.001
,
...
@@ -507,32 +510,22 @@ class AdamOptimizer(Optimizer):
...
@@ -507,32 +510,22 @@ class AdamOptimizer(Optimizer):
def
_create_accumulators
(
self
,
block
,
parameters
):
def
_create_accumulators
(
self
,
block
,
parameters
):
assert
isinstance
(
block
,
framework
.
Block
)
assert
isinstance
(
block
,
framework
.
Block
)
main_block
=
block
.
program
.
global_block
()
# Create beta1 and beta2 power tensors
beta_shape
=
[
1
]
self
.
_beta1_pow_acc
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
.
generate
(
'beta1_pow_acc'
),
dtype
=
'float32'
if
self
.
_dtype
==
None
else
self
.
_dtype
,
shape
=
beta_shape
,
lod_level
=
0
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
self
.
_beta1_pow_acc
,
initializer
=
Constant
(
self
.
_beta1
))
self
.
_beta2_pow_acc
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
.
generate
(
'beta2_pow_acc'
),
dtype
=
'float32'
if
self
.
_dtype
==
None
else
self
.
_dtype
,
shape
=
beta_shape
,
lod_level
=
0
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
self
.
_beta2_pow_acc
,
initializer
=
Constant
(
self
.
_beta2
))
# Create accumulator tensors for first and second moments
# Create accumulator tensors for first and second moments
for
p
in
parameters
:
for
p
in
parameters
:
self
.
_add_accumulator
(
self
.
_moment1_acc_str
,
p
)
self
.
_add_accumulator
(
self
.
_moment1_acc_str
,
p
)
self
.
_add_accumulator
(
self
.
_moment2_acc_str
,
p
)
self
.
_add_accumulator
(
self
.
_moment2_acc_str
,
p
)
self
.
_add_accumulator
(
name
=
self
.
_beta1_pow_acc_str
,
param
=
p
,
dtype
=
'float32'
,
fill_value
=
self
.
_beta1
,
shape
=
[
1
])
self
.
_add_accumulator
(
name
=
self
.
_beta2_pow_acc_str
,
param
=
p
,
dtype
=
'float32'
,
fill_value
=
self
.
_beta2
,
shape
=
[
1
])
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
assert
isinstance
(
block
,
framework
.
Block
)
assert
isinstance
(
block
,
framework
.
Block
)
...
@@ -541,6 +534,11 @@ class AdamOptimizer(Optimizer):
...
@@ -541,6 +534,11 @@ class AdamOptimizer(Optimizer):
param_and_grad
[
0
])
param_and_grad
[
0
])
moment2
=
self
.
_get_accumulator
(
self
.
_moment2_acc_str
,
moment2
=
self
.
_get_accumulator
(
self
.
_moment2_acc_str
,
param_and_grad
[
0
])
param_and_grad
[
0
])
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
param_and_grad
[
0
])
beta2_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta2_pow_acc_str
,
param_and_grad
[
0
])
# create the adam optimize op
# create the adam optimize op
adam_op
=
block
.
append_op
(
adam_op
=
block
.
append_op
(
type
=
self
.
type
,
type
=
self
.
type
,
...
@@ -550,8 +548,8 @@ class AdamOptimizer(Optimizer):
...
@@ -550,8 +548,8 @@ class AdamOptimizer(Optimizer):
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
"Moment1"
:
moment1
,
"Moment1"
:
moment1
,
"Moment2"
:
moment2
,
"Moment2"
:
moment2
,
"Beta1Pow"
:
self
.
_
beta1_pow_acc
,
"Beta1Pow"
:
beta1_pow_acc
,
"Beta2Pow"
:
self
.
_
beta2_pow_acc
"Beta2Pow"
:
beta2_pow_acc
},
},
outputs
=
{
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"ParamOut"
:
param_and_grad
[
0
],
...
@@ -566,24 +564,28 @@ class AdamOptimizer(Optimizer):
...
@@ -566,24 +564,28 @@ class AdamOptimizer(Optimizer):
return
adam_op
return
adam_op
def
_finish_update
(
self
,
block
):
def
_finish_update
(
self
,
block
,
parameters
):
"""Update Beta1 and Beta2 Power accumulators
"""Update Beta1 and Beta2 Power accumulators
"""
"""
assert
isinstance
(
block
,
framework
.
Block
)
assert
isinstance
(
block
,
framework
.
Block
)
main_block
=
block
.
program
.
global_block
()
main_block
=
block
.
program
.
global_block
()
scale_beta1
=
main_block
.
append_op
(
for
param
in
parameters
:
type
=
"scale"
,
with
param
.
block
.
program
.
optimized_guard
(
param
):
inputs
=
{
"X"
:
self
.
_beta1_pow_acc
},
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
outputs
=
{
"Out"
:
self
.
_beta1_pow_acc
},
param
)
attrs
=
{
"scale"
:
self
.
_beta1
})
beta2_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta2_pow_acc_str
,
param
)
scale_beta2
=
main_block
.
append_op
(
main_block
.
append_op
(
type
=
"scale"
,
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_beta2_pow_acc
},
inputs
=
{
"X"
:
beta1_pow_acc
},
outputs
=
{
"Out"
:
self
.
_beta2_pow_acc
},
outputs
=
{
"Out"
:
beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta2
})
attrs
=
{
"scale"
:
self
.
_beta1
})
return
[
scale_beta1
,
scale_beta2
]
main_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
beta2_pow_acc
},
outputs
=
{
"Out"
:
beta2_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta2
})
class
AdamaxOptimizer
(
Optimizer
):
class
AdamaxOptimizer
(
Optimizer
):
...
@@ -626,6 +628,7 @@ class AdamaxOptimizer(Optimizer):
...
@@ -626,6 +628,7 @@ class AdamaxOptimizer(Optimizer):
"""
"""
_moment_acc_str
=
"moment"
_moment_acc_str
=
"moment"
_inf_norm_acc_str
=
"inf_norm"
_inf_norm_acc_str
=
"inf_norm"
_beta1_pow_acc_str
=
"beta1_pow_acc"
def
__init__
(
self
,
def
__init__
(
self
,
learning_rate
=
0.001
,
learning_rate
=
0.001
,
...
@@ -645,21 +648,16 @@ class AdamaxOptimizer(Optimizer):
...
@@ -645,21 +648,16 @@ class AdamaxOptimizer(Optimizer):
self
.
_epsilon
=
epsilon
self
.
_epsilon
=
epsilon
def
_create_accumulators
(
self
,
block
,
parameters
):
def
_create_accumulators
(
self
,
block
,
parameters
):
# Create beta1 power accumulator tensor
beta_shape
=
[
1
]
self
.
_beta1_pow_acc
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
.
generate
(
'beta1_pow_acc'
),
dtype
=
'float32'
if
self
.
_dtype
==
None
else
self
.
_dtype
,
shape
=
beta_shape
,
lod_level
=
0
,
persistable
=
True
)
self
.
helper
.
set_variable_initializer
(
self
.
_beta1_pow_acc
,
initializer
=
Constant
(
self
.
_beta1
))
# Create accumulator tensors for first moment and infinity norm
# Create accumulator tensors for first moment and infinity norm
for
p
in
parameters
:
for
p
in
parameters
:
self
.
_add_accumulator
(
self
.
_moment_acc_str
,
p
)
self
.
_add_accumulator
(
self
.
_moment_acc_str
,
p
)
self
.
_add_accumulator
(
self
.
_inf_norm_acc_str
,
p
)
self
.
_add_accumulator
(
self
.
_inf_norm_acc_str
,
p
)
self
.
_add_accumulator
(
name
=
self
.
_beta1_pow_acc_str
,
param
=
p
,
dtype
=
'float32'
,
fill_value
=
self
.
_beta1
,
shape
=
[
1
])
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
assert
isinstance
(
block
,
framework
.
Block
)
assert
isinstance
(
block
,
framework
.
Block
)
...
@@ -667,6 +665,8 @@ class AdamaxOptimizer(Optimizer):
...
@@ -667,6 +665,8 @@ class AdamaxOptimizer(Optimizer):
moment
=
self
.
_get_accumulator
(
self
.
_moment_acc_str
,
param_and_grad
[
0
])
moment
=
self
.
_get_accumulator
(
self
.
_moment_acc_str
,
param_and_grad
[
0
])
inf_norm
=
self
.
_get_accumulator
(
self
.
_inf_norm_acc_str
,
inf_norm
=
self
.
_get_accumulator
(
self
.
_inf_norm_acc_str
,
param_and_grad
[
0
])
param_and_grad
[
0
])
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
param_and_grad
[
0
])
# create the adamax optimize op
# create the adamax optimize op
adamax_op
=
block
.
append_op
(
adamax_op
=
block
.
append_op
(
type
=
self
.
type
,
type
=
self
.
type
,
...
@@ -676,7 +676,7 @@ class AdamaxOptimizer(Optimizer):
...
@@ -676,7 +676,7 @@ class AdamaxOptimizer(Optimizer):
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
"Moment"
:
moment
,
"Moment"
:
moment
,
"InfNorm"
:
inf_norm
,
"InfNorm"
:
inf_norm
,
"Beta1Pow"
:
self
.
_
beta1_pow_acc
"Beta1Pow"
:
beta1_pow_acc
},
},
outputs
=
{
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"ParamOut"
:
param_and_grad
[
0
],
...
@@ -691,18 +691,20 @@ class AdamaxOptimizer(Optimizer):
...
@@ -691,18 +691,20 @@ class AdamaxOptimizer(Optimizer):
return
adamax_op
return
adamax_op
def
_finish_update
(
self
,
block
):
def
_finish_update
(
self
,
block
,
parameters
):
"""Update Beta1 Power accumulator
"""Update Beta1 Power accumulator
"""
"""
assert
isinstance
(
block
,
framework
.
Block
)
assert
isinstance
(
block
,
framework
.
Block
)
main_block
=
block
.
program
.
global_block
()
main_block
=
block
.
program
.
global_block
()
scale_beta1
=
main_block
.
append_op
(
for
param
in
parameters
:
type
=
"scale"
,
with
param
.
block
.
program
.
optimized_guard
(
param
):
inputs
=
{
"X"
:
self
.
_beta1_pow_acc
},
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
outputs
=
{
"Out"
:
self
.
_beta1_pow_acc
},
param
)
attrs
=
{
"scale"
:
self
.
_beta1
})
main_block
.
append_op
(
type
=
"scale"
,
return
[
scale_beta1
]
inputs
=
{
"X"
:
beta1_pow_acc
},
outputs
=
{
"Out"
:
beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
class
DecayedAdagradOptimizer
(
Optimizer
):
class
DecayedAdagradOptimizer
(
Optimizer
):
...
@@ -1156,7 +1158,8 @@ class ModelAverage(Optimizer):
...
@@ -1156,7 +1158,8 @@ class ModelAverage(Optimizer):
self
.
params_grads
.
append
((
param
,
grad
))
self
.
params_grads
.
append
((
param
,
grad
))
for
param
,
grad
in
self
.
params_grads
:
for
param
,
grad
in
self
.
params_grads
:
self
.
_append_average_accumulate_op
(
param
)
with
param
.
block
.
program
.
optimized_guard
(
param
):
self
.
_append_average_accumulate_op
(
param
)
self
.
apply_program
=
Program
()
self
.
apply_program
=
Program
()
block
=
self
.
apply_program
.
global_block
()
block
=
self
.
apply_program
.
global_block
()
...
...
python/paddle/fluid/param_attr.py
浏览文件 @
193aeaea
...
@@ -67,7 +67,7 @@ class ParamAttr(object):
...
@@ -67,7 +67,7 @@ class ParamAttr(object):
self
.
gradient_clip
=
gradient_clip
self
.
gradient_clip
=
gradient_clip
self
.
model_average
=
do_model_average
self
.
model_average
=
do_model_average
def
set_default_initializer
(
self
,
initializer
):
def
_
set_default_initializer
(
self
,
initializer
):
"""
"""
Set the default initializer, the initializer should be Constant,
Set the default initializer, the initializer should be Constant,
Uniform, Normal, Xavier, MSRA.
Uniform, Normal, Xavier, MSRA.
...
@@ -88,7 +88,7 @@ class ParamAttr(object):
...
@@ -88,7 +88,7 @@ class ParamAttr(object):
self
.
initializer
=
initializer
self
.
initializer
=
initializer
def
set_default_param_initializer
(
self
):
def
_
set_default_param_initializer
(
self
):
"""
"""
Set the default initializer for the parameter with Xavier.
Set the default initializer for the parameter with Xavier.
...
@@ -98,9 +98,9 @@ class ParamAttr(object):
...
@@ -98,9 +98,9 @@ class ParamAttr(object):
Returns:
Returns:
None.
None.
"""
"""
self
.
set_default_initializer
(
Xavier
())
self
.
_
set_default_initializer
(
Xavier
())
def
set_default_bias_initializer
(
self
):
def
_
set_default_bias_initializer
(
self
):
"""
"""
Set the default initializer for the bias with Constant(0.0).
Set the default initializer for the bias with Constant(0.0).
...
@@ -110,10 +110,10 @@ class ParamAttr(object):
...
@@ -110,10 +110,10 @@ class ParamAttr(object):
Returns:
Returns:
None.
None.
"""
"""
self
.
set_default_initializer
(
Constant
(
0.0
))
self
.
_
set_default_initializer
(
Constant
(
0.0
))
@
staticmethod
@
staticmethod
def
to_attr
(
arg
):
def
_
to_attr
(
arg
):
"""
"""
Create ParamAttr[s].
Create ParamAttr[s].
...
@@ -131,7 +131,7 @@ class ParamAttr(object):
...
@@ -131,7 +131,7 @@ class ParamAttr(object):
if
arg
is
None
:
if
arg
is
None
:
return
ParamAttr
()
return
ParamAttr
()
elif
isinstance
(
arg
,
list
)
or
isinstance
(
arg
,
tuple
):
elif
isinstance
(
arg
,
list
)
or
isinstance
(
arg
,
tuple
):
return
[
ParamAttr
.
to_attr
(
a
)
for
a
in
arg
]
return
[
ParamAttr
.
_
to_attr
(
a
)
for
a
in
arg
]
elif
isinstance
(
arg
,
ParamAttr
):
elif
isinstance
(
arg
,
ParamAttr
):
return
arg
return
arg
elif
isinstance
(
arg
,
str
)
or
isinstance
(
arg
,
unicode
):
elif
isinstance
(
arg
,
str
)
or
isinstance
(
arg
,
unicode
):
...
@@ -141,11 +141,11 @@ class ParamAttr(object):
...
@@ -141,11 +141,11 @@ class ParamAttr(object):
elif
isinstance
(
arg
,
WeightDecayRegularizer
):
elif
isinstance
(
arg
,
WeightDecayRegularizer
):
return
ParamAttr
(
regularizer
=
arg
)
return
ParamAttr
(
regularizer
=
arg
)
elif
isinstance
(
arg
,
bool
):
elif
isinstance
(
arg
,
bool
):
return
ParamAttr
.
to_attr
(
None
)
if
arg
else
False
return
ParamAttr
.
_
to_attr
(
None
)
if
arg
else
False
else
:
else
:
raise
TypeError
(
"{0} cast to ParamAttr"
.
format
(
type
(
arg
)))
raise
TypeError
(
"{0} cast to ParamAttr"
.
format
(
type
(
arg
)))
def
to_kwargs
(
self
,
with_initializer
=
False
):
def
_
to_kwargs
(
self
,
with_initializer
=
False
):
"""
"""
Returns the attributes of this parameter.
Returns the attributes of this parameter.
...
...
python/paddle/fluid/regularizer.py
浏览文件 @
193aeaea
...
@@ -15,10 +15,7 @@
...
@@ -15,10 +15,7 @@
import
framework
import
framework
from
.
import
core
from
.
import
core
__all__
=
[
__all__
=
[
'L1Decay'
,
'L2Decay'
,
'L1DecayRegularizer'
,
'L2DecayRegularizer'
]
'append_regularization_ops'
,
'L1Decay'
,
'L2Decay'
,
'L1DecayRegularizer'
,
'L2DecayRegularizer'
]
def
append_regularization_ops
(
parameters_and_grads
,
regularization
=
None
):
def
append_regularization_ops
(
parameters_and_grads
,
regularization
=
None
):
...
...
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @
193aeaea
...
@@ -60,8 +60,8 @@ def get_numeric_gradient(place,
...
@@ -60,8 +60,8 @@ def get_numeric_gradient(place,
return
np
.
array
(
sum
).
mean
()
return
np
.
array
(
sum
).
mean
()
tensor_to_check
=
scope
.
find_var
(
input_to_check
).
get_tensor
()
tensor_to_check
=
scope
.
find_var
(
input_to_check
).
get_tensor
()
tensor_size
=
product
(
tensor_to_check
.
get_dims
())
tensor_size
=
product
(
tensor_to_check
.
shape
())
tensor_to_check_dtype
=
tensor_to_check
.
dtype
()
tensor_to_check_dtype
=
tensor_to_check
.
_
dtype
()
if
tensor_to_check_dtype
==
core
.
VarDesc
.
VarType
.
FP32
:
if
tensor_to_check_dtype
==
core
.
VarDesc
.
VarType
.
FP32
:
tensor_to_check_dtype
=
np
.
float32
tensor_to_check_dtype
=
np
.
float32
elif
tensor_to_check_dtype
==
core
.
VarDesc
.
VarType
.
FP64
:
elif
tensor_to_check_dtype
==
core
.
VarDesc
.
VarType
.
FP64
:
...
@@ -74,15 +74,15 @@ def get_numeric_gradient(place,
...
@@ -74,15 +74,15 @@ def get_numeric_gradient(place,
def
__get_elem__
(
tensor
,
i
):
def
__get_elem__
(
tensor
,
i
):
if
tensor_to_check_dtype
==
np
.
float32
:
if
tensor_to_check_dtype
==
np
.
float32
:
return
tensor
.
get_float_element
(
i
)
return
tensor
.
_
get_float_element
(
i
)
else
:
else
:
return
tensor
.
get_double_element
(
i
)
return
tensor
.
_
get_double_element
(
i
)
def
__set_elem__
(
tensor
,
i
,
e
):
def
__set_elem__
(
tensor
,
i
,
e
):
if
tensor_to_check_dtype
==
np
.
float32
:
if
tensor_to_check_dtype
==
np
.
float32
:
tensor
.
set_float_element
(
i
,
e
)
tensor
.
_
set_float_element
(
i
,
e
)
else
:
else
:
tensor
.
set_double_element
(
i
,
e
)
tensor
.
_
set_double_element
(
i
,
e
)
# we only compute gradient of one element each time.
# we only compute gradient of one element each time.
# we use a for loop to compute the gradient of every element.
# we use a for loop to compute the gradient of every element.
...
@@ -107,7 +107,7 @@ def get_numeric_gradient(place,
...
@@ -107,7 +107,7 @@ def get_numeric_gradient(place,
__set_elem__
(
tensor_to_check
,
i
,
origin
)
__set_elem__
(
tensor_to_check
,
i
,
origin
)
gradient_flat
[
i
]
=
(
y_pos
-
y_neg
)
/
delta
/
2
gradient_flat
[
i
]
=
(
y_pos
-
y_neg
)
/
delta
/
2
return
gradient_flat
.
reshape
(
tensor_to_check
.
get_dims
())
return
gradient_flat
.
reshape
(
tensor_to_check
.
shape
())
class
OpTest
(
unittest
.
TestCase
):
class
OpTest
(
unittest
.
TestCase
):
...
@@ -125,7 +125,7 @@ class OpTest(unittest.TestCase):
...
@@ -125,7 +125,7 @@ class OpTest(unittest.TestCase):
@
classmethod
@
classmethod
def
tearDownClass
(
cls
):
def
tearDownClass
(
cls
):
'''Restore random seeds'''
"""Restore random seeds"""
np
.
random
.
set_state
(
cls
.
_np_rand_state
)
np
.
random
.
set_state
(
cls
.
_np_rand_state
)
random
.
setstate
(
cls
.
_py_rand_state
)
random
.
setstate
(
cls
.
_py_rand_state
)
...
...
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
浏览文件 @
193aeaea
...
@@ -129,7 +129,6 @@ def create_or_get_tensor(scope, var_name, var, place):
...
@@ -129,7 +129,6 @@ def create_or_get_tensor(scope, var_name, var, place):
if
var
is
not
None
:
if
var
is
not
None
:
assert
isinstance
(
var
,
np
.
ndarray
)
assert
isinstance
(
var
,
np
.
ndarray
)
tensor
.
set_recursive_sequence_lengths
([])
tensor
.
set_recursive_sequence_lengths
([])
tensor
.
set_dims
(
var
.
shape
)
tensor
.
set
(
var
,
place
)
tensor
.
set
(
var
,
place
)
return
tensor
return
tensor
...
...
python/paddle/fluid/tests/unittests/test_dynrnn_static_input.py
浏览文件 @
193aeaea
...
@@ -65,10 +65,10 @@ class TestDyRnnStaticInput(unittest.TestCase):
...
@@ -65,10 +65,10 @@ class TestDyRnnStaticInput(unittest.TestCase):
return
self
.
_lodtensor_to_ndarray
(
fetch_outs
[
0
])
return
self
.
_lodtensor_to_ndarray
(
fetch_outs
[
0
])
def
_lodtensor_to_ndarray
(
self
,
lod_tensor
):
def
_lodtensor_to_ndarray
(
self
,
lod_tensor
):
dims
=
lod_tensor
.
get_dims
()
dims
=
lod_tensor
.
shape
()
ndarray
=
np
.
zeros
(
shape
=
dims
).
astype
(
'float32'
)
ndarray
=
np
.
zeros
(
shape
=
dims
).
astype
(
'float32'
)
for
i
in
xrange
(
np
.
product
(
dims
)):
for
i
in
xrange
(
np
.
product
(
dims
)):
ndarray
.
ravel
()[
i
]
=
lod_tensor
.
get_float_element
(
i
)
ndarray
.
ravel
()[
i
]
=
lod_tensor
.
_
get_float_element
(
i
)
return
ndarray
,
lod_tensor
.
recursive_sequence_lengths
()
return
ndarray
,
lod_tensor
.
recursive_sequence_lengths
()
def
build_graph
(
self
,
only_forward
=
False
):
def
build_graph
(
self
,
only_forward
=
False
):
...
@@ -185,19 +185,19 @@ class TestDyRnnStaticInput(unittest.TestCase):
...
@@ -185,19 +185,19 @@ class TestDyRnnStaticInput(unittest.TestCase):
actual_gradients
,
actual_lod
=
self
.
fetch_value
(
static_input_grad
)
actual_gradients
,
actual_lod
=
self
.
fetch_value
(
static_input_grad
)
static_input_shape
=
self
.
static_input_tensor
.
get_dims
()
static_input_shape
=
self
.
static_input_tensor
.
shape
()
numeric_gradients
=
np
.
zeros
(
shape
=
static_input_shape
).
astype
(
'float32'
)
numeric_gradients
=
np
.
zeros
(
shape
=
static_input_shape
).
astype
(
'float32'
)
# calculate numeric gradients
# calculate numeric gradients
tensor_size
=
np
.
product
(
static_input_shape
)
tensor_size
=
np
.
product
(
static_input_shape
)
for
i
in
xrange
(
tensor_size
):
for
i
in
xrange
(
tensor_size
):
origin
=
self
.
static_input_tensor
.
get_float_element
(
i
)
origin
=
self
.
static_input_tensor
.
_
get_float_element
(
i
)
x_pos
=
origin
+
self
.
_delta
x_pos
=
origin
+
self
.
_delta
self
.
static_input_tensor
.
set_float_element
(
i
,
x_pos
)
self
.
static_input_tensor
.
_
set_float_element
(
i
,
x_pos
)
y_pos
=
self
.
fetch_value
(
loss
)[
0
][
0
]
y_pos
=
self
.
fetch_value
(
loss
)[
0
][
0
]
x_neg
=
origin
-
self
.
_delta
x_neg
=
origin
-
self
.
_delta
self
.
static_input_tensor
.
set_float_element
(
i
,
x_neg
)
self
.
static_input_tensor
.
_
set_float_element
(
i
,
x_neg
)
y_neg
=
self
.
fetch_value
(
loss
)[
0
][
0
]
y_neg
=
self
.
fetch_value
(
loss
)[
0
][
0
]
self
.
static_input_tensor
.
set_float_element
(
i
,
origin
)
self
.
static_input_tensor
.
_
set_float_element
(
i
,
origin
)
numeric_gradients
.
ravel
()[
i
]
=
(
y_pos
-
y_neg
)
/
self
.
_delta
/
2
numeric_gradients
.
ravel
()[
i
]
=
(
y_pos
-
y_neg
)
/
self
.
_delta
/
2
self
.
assertTrue
(
np
.
allclose
(
actual_gradients
,
numeric_gradients
,
0.001
))
self
.
assertTrue
(
np
.
allclose
(
actual_gradients
,
numeric_gradients
,
0.001
))
self
.
assertTrue
(
self
.
assertTrue
(
...
...
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @
193aeaea
...
@@ -287,7 +287,7 @@ class TestAdamOptimizer(unittest.TestCase):
...
@@ -287,7 +287,7 @@ class TestAdamOptimizer(unittest.TestCase):
# Check accumulators
# Check accumulators
accumulators
=
adam_optimizer
.
get_accumulators
()
accumulators
=
adam_optimizer
.
get_accumulators
()
self
.
assertEqual
(
len
(
accumulators
),
2
)
self
.
assertEqual
(
len
(
accumulators
),
4
)
self
.
assertTrue
(
adam_optimizer
.
get_moment1_str
()
in
accumulators
)
self
.
assertTrue
(
adam_optimizer
.
get_moment1_str
()
in
accumulators
)
self
.
assertTrue
(
adam_optimizer
.
get_moment2_str
()
in
accumulators
)
self
.
assertTrue
(
adam_optimizer
.
get_moment2_str
()
in
accumulators
)
moment1_acc
=
accumulators
[
adam_optimizer
.
get_moment1_str
()]
moment1_acc
=
accumulators
[
adam_optimizer
.
get_moment1_str
()]
...
@@ -354,7 +354,7 @@ class TestAdamaxOptimizer(unittest.TestCase):
...
@@ -354,7 +354,7 @@ class TestAdamaxOptimizer(unittest.TestCase):
# Check accumulators
# Check accumulators
accumulators
=
adamax_optimizer
.
get_accumulators
()
accumulators
=
adamax_optimizer
.
get_accumulators
()
self
.
assertEqual
(
len
(
accumulators
),
2
)
self
.
assertEqual
(
len
(
accumulators
),
3
)
self
.
assertTrue
(
adamax_optimizer
.
get_moment_str
()
in
accumulators
)
self
.
assertTrue
(
adamax_optimizer
.
get_moment_str
()
in
accumulators
)
self
.
assertTrue
(
adamax_optimizer
.
get_inf_norm_str
()
in
accumulators
)
self
.
assertTrue
(
adamax_optimizer
.
get_inf_norm_str
()
in
accumulators
)
moment_acc
=
accumulators
[
adamax_optimizer
.
get_moment_str
()]
moment_acc
=
accumulators
[
adamax_optimizer
.
get_moment_str
()]
...
...
python/paddle/fluid/tests/unittests/test_py_reader_push_pop.py
0 → 100644
浏览文件 @
193aeaea
# 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.
import
unittest
import
paddle.fluid
as
fluid
import
numpy
as
np
from
threading
import
Thread
def
feed_data
(
feed_queue
,
inputs
):
for
in_data
in
inputs
:
feed_queue
.
push
(
in_data
)
class
TestPyReader
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
capacity
=
10
self
.
batch_size_min
=
10
self
.
batch_size_max
=
20
self
.
shapes
=
[(
-
1
,
3
,
2
,
1
),
(
-
1
,
1
)]
self
.
lod_levels
=
[
0
,
0
]
self
.
dtypes
=
[
'float32'
,
'int64'
]
self
.
iterations
=
20
def
test_single_thread_main
(
self
):
self
.
main
(
use_thread
=
False
)
def
test_multiple_thread_main
(
self
):
self
.
main
(
use_thread
=
True
)
def
main
(
self
,
use_thread
=
False
):
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
executor
=
fluid
.
Executor
(
place
)
data_file
,
feed_queue
=
fluid
.
layers
.
py_reader
(
capacity
=
self
.
capacity
,
dtypes
=
self
.
dtypes
,
lod_levels
=
self
.
lod_levels
,
shapes
=
self
.
shapes
)
read_out_data
=
fluid
.
layers
.
read_file
(
data_file
)
self
.
inputs
=
[]
for
i
in
range
(
self
.
iterations
):
in_data
=
fluid
.
LoDTensorArray
()
batch_size
=
np
.
random
.
random_integers
(
self
.
batch_size_min
,
self
.
batch_size_max
)
for
shape
,
dtype
in
zip
(
self
.
shapes
,
self
.
dtypes
):
next_data
=
np
.
random
.
uniform
(
low
=
0
,
high
=
1000
,
size
=
(
batch_size
,
)
+
shape
[
1
:]).
astype
(
dtype
)
in_data
.
append
(
executor
.
as_lodtensor
(
next_data
))
self
.
inputs
.
append
(
in_data
)
executor
.
run
(
fluid
.
default_startup_program
())
self
.
outputs
=
[]
if
use_thread
:
thread
=
Thread
(
target
=
feed_data
,
args
=
(
feed_queue
,
self
.
inputs
))
thread
.
start
()
for
in_data
in
self
.
inputs
:
self
.
outputs
.
append
(
executor
.
run
(
fetch_list
=
list
(
read_out_data
)))
else
:
for
in_data
in
self
.
inputs
:
feed_queue
.
push
(
in_data
)
self
.
outputs
.
append
(
executor
.
run
(
fetch_list
=
list
(
read_out_data
)))
feed_queue
.
close
()
self
.
validate
()
def
validate
(
self
):
self
.
assertEqual
(
len
(
self
.
inputs
),
len
(
self
.
outputs
))
for
in_data_list
,
out_data_list
in
zip
(
self
.
inputs
,
self
.
outputs
):
self
.
assertEqual
(
len
(
in_data_list
),
len
(
out_data_list
))
in_data_list_np
=
[
np
.
array
(
in_lod_tensor
)
for
in_lod_tensor
in
in_data_list
]
for
in_data
,
out_data
in
zip
(
in_data_list_np
,
out_data_list
):
self
.
assertTrue
((
in_data
==
out_data
).
all
())
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_py_reader_using_executor.py
0 → 100644
浏览文件 @
193aeaea
# 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.
import
unittest
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
numpy
as
np
import
threading
import
multiprocessing
import
os
def
as_tensor
(
np_array_or_tensor
,
place
=
None
):
if
isinstance
(
np_array_or_tensor
,
fluid
.
LoDTensor
):
return
np_array_or_tensor
if
place
is
None
:
place
=
fluid
.
CPUPlace
()
tensor
=
fluid
.
LoDTensor
()
tensor
.
set
(
np_array_or_tensor
,
place
)
return
tensor
def
as_numpy
(
tensor_or_numpy
):
return
tensor_or_numpy
if
isinstance
(
tensor_or_numpy
,
np
.
ndarray
)
else
np
.
array
(
tensor_or_numpy
)
def
feed_data
(
feed_queue
,
reader
):
data_generator
=
reader
()
while
True
:
data
=
next
(
data_generator
,
None
)
if
data
is
None
or
not
feed_queue
.
push
(
data
):
break
def
simple_fc_net
(
in_size
,
class_num
,
hidden_sizes
,
batch_size
,
queue_capacity
,
use_double_buffer
=
False
):
reader
,
feed_queue
=
fluid
.
layers
.
py_reader
(
capacity
=
queue_capacity
,
shapes
=
[[
-
1
,
in_size
],
[
-
1
,
1
]],
lod_levels
=
[
0
,
0
],
dtypes
=
[
'float32'
,
'int64'
])
reader
=
fluid
.
layers
.
batch
(
reader
,
batch_size
=
batch_size
)
if
use_double_buffer
:
reader
=
fluid
.
layers
.
double_buffer
(
reader
)
in_data
,
label
=
fluid
.
layers
.
read_file
(
reader
)
hidden
=
in_data
for
hidden_size
in
hidden_sizes
:
hidden
=
fluid
.
layers
.
fc
(
hidden
,
size
=
hidden_size
,
act
=
'tanh'
,
bias_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
1.0
)))
predict_label
=
fluid
.
layers
.
fc
(
hidden
,
size
=
class_num
,
act
=
'softmax'
)
loss
=
fluid
.
layers
.
mean
(
fluid
.
layers
.
cross_entropy
(
input
=
predict_label
,
label
=
label
))
optimizer
=
fluid
.
optimizer
.
Adam
()
optimizer
.
minimize
(
loss
)
return
in_data
,
label
,
loss
,
optimizer
,
feed_queue
class
TestPyReaderUsingExecutor
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
in_size
=
1000
self
.
hidden_sizes
=
[
50
,
30
,
20
]
self
.
class_num
=
10
self
.
batch_size
=
32
self
.
iterations
=
10
self
.
queue_capacity
=
50
def
test
(
self
):
for
use_cuda
in
[
False
,
True
]:
for
use_parallel_executor
in
[
False
,
True
]:
for
use_double_buffer
in
[
False
,
True
]:
print
(
'Test Parameters:'
),
print
({
'use_cuda'
:
use_cuda
,
'use_parallel_executor'
:
use_parallel_executor
,
'use_double_buffer'
:
use_double_buffer
})
self
.
main
(
use_cuda
,
use_parallel_executor
,
use_double_buffer
)
def
random_reader
(
self
):
def
reader
():
self
.
inputs
=
[]
cnt
=
0
while
True
:
tensors
=
fluid
.
LoDTensorArray
()
in_data
=
np
.
random
.
uniform
(
low
=
0
,
high
=
1
,
size
=
(
1
,
self
.
in_size
)).
astype
(
'float32'
)
tensors
.
append
(
as_tensor
(
in_data
))
label
=
np
.
random
.
random_integers
(
low
=
0
,
high
=
self
.
class_num
-
1
,
size
=
(
1
,
1
)).
astype
(
'int64'
)
tensors
.
append
(
as_tensor
(
label
))
if
cnt
<
self
.
iterations
*
self
.
batch_size
*
self
.
batch_size_times
:
if
cnt
%
(
self
.
batch_size
*
self
.
batch_size_times
)
==
0
:
self
.
inputs
.
append
([
in_data
,
label
])
else
:
self
.
inputs
[
-
1
][
0
]
=
np
.
concatenate
(
(
self
.
inputs
[
-
1
][
0
],
in_data
),
axis
=
0
)
self
.
inputs
[
-
1
][
1
]
=
np
.
concatenate
(
(
self
.
inputs
[
-
1
][
1
],
label
),
axis
=
0
)
elif
not
self
.
use_double_buffer
:
break
yield
tensors
cnt
+=
1
yield
None
return
reader
def
main
(
self
,
use_cuda
=
True
,
use_parallel_executor
=
False
,
use_double_buffer
=
False
):
assert
not
use_cuda
or
use_cuda
and
core
.
is_compiled_with_cuda
()
self
.
use_cuda
=
use_cuda
self
.
use_parallel_executor
=
use_parallel_executor
self
.
use_double_buffer
=
use_double_buffer
startup_program
=
fluid
.
Program
()
main_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
in_data
,
label
,
loss
,
optimizer
,
feed_queue
=
simple_fc_net
(
in_size
=
self
.
in_size
,
class_num
=
self
.
class_num
,
hidden_sizes
=
self
.
hidden_sizes
,
batch_size
=
self
.
batch_size
,
queue_capacity
=
self
.
queue_capacity
,
use_double_buffer
=
self
.
use_double_buffer
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
startup_exe
=
fluid
.
Executor
(
place
)
startup_exe
.
run
(
startup_program
)
if
use_parallel_executor
:
main_exe
=
fluid
.
ParallelExecutor
(
use_cuda
,
loss_name
=
loss
.
name
)
if
use_cuda
:
self
.
batch_size_times
=
core
.
get_cuda_device_count
()
else
:
self
.
batch_size_times
=
int
(
os
.
environ
.
get
(
'CPU_NUM'
,
multiprocessing
.
cpu_count
()))
else
:
main_exe
=
startup_exe
self
.
batch_size_times
=
1
reader
=
self
.
random_reader
()
thread
=
threading
.
Thread
(
target
=
feed_data
,
args
=
(
feed_queue
,
reader
))
thread
.
start
()
self
.
outputs
=
[]
for
_
in
range
(
self
.
iterations
):
fetches
=
main_exe
.
run
(
fetch_list
=
[
in_data
.
name
,
label
.
name
])
fetches
=
[
as_numpy
(
fetch
)
for
fetch
in
fetches
]
self
.
outputs
.
append
(
fetches
)
feed_queue
.
close
()
self
.
validate
()
def
validate
(
self
):
self
.
assertEqual
(
len
(
self
.
inputs
),
len
(
self
.
outputs
))
for
batch_in
,
batch_out
in
zip
(
self
.
inputs
,
self
.
outputs
):
self
.
assertEqual
(
len
(
batch_in
),
len
(
batch_out
))
if
self
.
use_parallel_executor
and
not
self
.
use_double_buffer
:
self
.
validate_unordered_batch
(
batch_in
,
batch_out
)
else
:
for
in_data
,
out_data
in
zip
(
batch_in
,
batch_out
):
self
.
assertEqual
(
in_data
.
shape
,
out_data
.
shape
)
if
not
self
.
use_parallel_executor
:
self
.
assertTrue
((
in_data
==
out_data
).
all
())
def
validate_unordered_batch
(
self
,
batch_in
,
batch_out
):
out_index_left_set
=
set
(
range
(
self
.
batch_size
*
self
.
batch_size_times
))
mapping_num
=
0
for
i
in
range
(
self
.
batch_size
*
self
.
batch_size_times
):
for
j
in
out_index_left_set
:
flag
=
True
for
k
in
range
(
len
(
batch_in
)):
in_data
=
batch_in
[
k
][
i
]
out_data
=
batch_out
[
k
][
j
]
if
(
in_data
!=
out_data
).
any
():
flag
=
False
break
if
flag
:
out_index_left_set
.
remove
(
j
)
mapping_num
+=
1
break
self
.
assertEqual
(
mapping_num
,
self
.
batch_size
*
self
.
batch_size_times
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_selected_rows.py
浏览文件 @
193aeaea
...
@@ -40,12 +40,12 @@ class TestSelectedRows(unittest.TestCase):
...
@@ -40,12 +40,12 @@ class TestSelectedRows(unittest.TestCase):
# compare tensor
# compare tensor
self
.
assertAlmostEqual
(
2.0
,
self
.
assertAlmostEqual
(
2.0
,
selected_rows
.
get_tensor
().
get_float_element
(
0
))
selected_rows
.
get_tensor
().
_
get_float_element
(
0
))
self
.
assertAlmostEqual
(
1.0
,
self
.
assertAlmostEqual
(
1.0
,
selected_rows
.
get_tensor
().
get_float_element
(
1
))
selected_rows
.
get_tensor
().
_
get_float_element
(
1
))
self
.
assertAlmostEqual
(
self
.
assertAlmostEqual
(
4.0
,
4.0
,
selected_rows
.
get_tensor
().
get_float_element
(
2
*
row_numel
+
8
))
selected_rows
.
get_tensor
().
_
get_float_element
(
2
*
row_numel
+
8
))
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/test_shrink_rnn_memory.py
浏览文件 @
193aeaea
...
@@ -45,8 +45,8 @@ class TestShrinkRNNMemoryBase(unittest.TestCase):
...
@@ -45,8 +45,8 @@ class TestShrinkRNNMemoryBase(unittest.TestCase):
def
sum_lodtensor
(
self
,
tensor
):
def
sum_lodtensor
(
self
,
tensor
):
sum_res
=
0.0
sum_res
=
0.0
for
i
in
xrange
(
np
.
product
(
tensor
.
get_dims
())):
for
i
in
xrange
(
np
.
product
(
tensor
.
shape
())):
sum_res
+=
tensor
.
get_float_element
(
i
)
sum_res
+=
tensor
.
_
get_float_element
(
i
)
return
sum_res
return
sum_res
...
...
python/paddle/fluid/tests/unittests/test_tensor.py
浏览文件 @
193aeaea
...
@@ -25,8 +25,8 @@ class TestTensor(unittest.TestCase):
...
@@ -25,8 +25,8 @@ class TestTensor(unittest.TestCase):
tensor
=
var
.
get_tensor
()
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
1000
,
784
])
tensor
.
_
set_dims
([
1000
,
784
])
tensor
.
alloc_int
(
place
)
tensor
.
_
alloc_int
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
tensor_array
[
3
,
9
]
=
1
tensor_array
[
3
,
9
]
=
1
...
@@ -44,8 +44,8 @@ class TestTensor(unittest.TestCase):
...
@@ -44,8 +44,8 @@ class TestTensor(unittest.TestCase):
tensor
=
var
.
get_tensor
()
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
1000
,
784
])
tensor
.
_
set_dims
([
1000
,
784
])
tensor
.
alloc_float
(
place
)
tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
...
@@ -63,8 +63,8 @@ class TestTensor(unittest.TestCase):
...
@@ -63,8 +63,8 @@ class TestTensor(unittest.TestCase):
var_lod
=
scope
.
var
(
"test_lod_tensor"
)
var_lod
=
scope
.
var
(
"test_lod_tensor"
)
lod_tensor
=
var_lod
.
get_tensor
()
lod_tensor
=
var_lod
.
get_tensor
()
lod_tensor
.
set_dims
([
4
,
4
,
6
])
lod_tensor
.
_
set_dims
([
4
,
4
,
6
])
lod_tensor
.
alloc_int
(
place
)
lod_tensor
.
_
alloc_int
(
place
)
array
=
numpy
.
array
(
lod_tensor
)
array
=
numpy
.
array
(
lod_tensor
)
array
[
0
,
0
,
0
]
=
3
array
[
0
,
0
,
0
]
=
3
array
[
3
,
3
,
5
]
=
10
array
[
3
,
3
,
5
]
=
10
...
@@ -84,8 +84,8 @@ class TestTensor(unittest.TestCase):
...
@@ -84,8 +84,8 @@ class TestTensor(unittest.TestCase):
var_lod
=
scope
.
var
(
"test_lod_tensor"
)
var_lod
=
scope
.
var
(
"test_lod_tensor"
)
lod_tensor
=
var_lod
.
get_tensor
()
lod_tensor
=
var_lod
.
get_tensor
()
lod_tensor
.
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
_
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
alloc_float
(
place
)
lod_tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
self
.
assertEqual
((
5
,
2
,
3
,
4
),
tensor_array
.
shape
)
self
.
assertEqual
((
5
,
2
,
3
,
4
),
tensor_array
.
shape
)
...
@@ -104,14 +104,13 @@ class TestTensor(unittest.TestCase):
...
@@ -104,14 +104,13 @@ class TestTensor(unittest.TestCase):
self
.
assertListEqual
(
lod_py
,
lod
)
self
.
assertListEqual
(
lod_py
,
lod
)
def
test_lod_tensor_init
(
self
):
def
test_lod_tensor_init
(
self
):
scope
=
core
.
Scope
()
place
=
core
.
CPUPlace
()
place
=
core
.
CPUPlace
()
lod_py
=
[[
2
,
1
],
[
1
,
2
,
2
]]
lod_py
=
[[
2
,
1
],
[
1
,
2
,
2
]]
lod_tensor
=
core
.
LoDTensor
()
lod_tensor
=
core
.
LoDTensor
()
lod_tensor
.
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
_
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
set_recursive_sequence_lengths
(
lod_py
)
lod_tensor
.
set_recursive_sequence_lengths
(
lod_py
)
lod_tensor
.
alloc_float
(
place
)
lod_tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
tensor_array
[
0
,
0
,
0
,
0
]
=
1.0
tensor_array
[
0
,
0
,
0
,
0
]
=
1.0
tensor_array
[
0
,
0
,
0
,
1
]
=
2.0
tensor_array
[
0
,
0
,
0
,
1
]
=
2.0
...
@@ -129,9 +128,9 @@ class TestTensor(unittest.TestCase):
...
@@ -129,9 +128,9 @@ class TestTensor(unittest.TestCase):
lod_py
=
[[
2
,
1
],
[
1
,
2
,
2
]]
lod_py
=
[[
2
,
1
],
[
1
,
2
,
2
]]
lod_tensor
=
core
.
LoDTensor
()
lod_tensor
=
core
.
LoDTensor
()
lod_tensor
.
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
_
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
set_recursive_sequence_lengths
(
lod_py
)
lod_tensor
.
set_recursive_sequence_lengths
(
lod_py
)
lod_tensor
.
alloc_float
(
place
)
lod_tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
tensor_array
[
0
,
0
,
0
,
0
]
=
1.0
tensor_array
[
0
,
0
,
0
,
0
]
=
1.0
tensor_array
[
0
,
0
,
0
,
1
]
=
2.0
tensor_array
[
0
,
0
,
0
,
1
]
=
2.0
...
@@ -149,15 +148,15 @@ class TestTensor(unittest.TestCase):
...
@@ -149,15 +148,15 @@ class TestTensor(unittest.TestCase):
tensor
=
var
.
get_tensor
()
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
0
,
1
])
tensor
.
_
set_dims
([
0
,
1
])
tensor
.
alloc_float
(
place
)
tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
0
,
1
),
tensor_array
.
shape
)
self
.
assertEqual
((
0
,
1
),
tensor_array
.
shape
)
if
core
.
is_compiled_with_cuda
():
if
core
.
is_compiled_with_cuda
():
gpu_place
=
core
.
CUDAPlace
(
0
)
gpu_place
=
core
.
CUDAPlace
(
0
)
tensor
.
alloc_float
(
gpu_place
)
tensor
.
_
alloc_float
(
gpu_place
)
tensor_array
=
numpy
.
array
(
tensor
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
0
,
1
),
tensor_array
.
shape
)
self
.
assertEqual
((
0
,
1
),
tensor_array
.
shape
)
...
...
python/paddle/fluid/tests/unittests/testsuite.py
浏览文件 @
193aeaea
...
@@ -75,7 +75,7 @@ def set_input(scope, op, inputs, place):
...
@@ -75,7 +75,7 @@ def set_input(scope, op, inputs, place):
if
isinstance
(
var
,
tuple
):
if
isinstance
(
var
,
tuple
):
tensor
.
set_recursive_sequence_lengths
(
var
[
1
])
tensor
.
set_recursive_sequence_lengths
(
var
[
1
])
var
=
var
[
0
]
var
=
var
[
0
]
tensor
.
set_dims
(
var
.
shape
)
tensor
.
_
set_dims
(
var
.
shape
)
tensor
.
set
(
var
,
place
)
tensor
.
set
(
var
,
place
)
elif
isinstance
(
var
,
float
):
elif
isinstance
(
var
,
float
):
scope
.
find_var
(
var_name
).
set_float
(
var
)
scope
.
find_var
(
var_name
).
set_float
(
var
)
...
...
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
193aeaea
...
@@ -377,11 +377,6 @@ class DistributeTranspiler(object):
...
@@ -377,11 +377,6 @@ class DistributeTranspiler(object):
# append it into the sub program.
# append it into the sub program.
global_ops
=
[]
global_ops
=
[]
# HACK: optimization global ops only used to scale beta1 and beta2
# replace it with dependency engine.
for
op
in
self
.
optimize_ops
:
if
self
.
_is_adam_connected_op
(
op
):
global_ops
.
append
(
op
)
def
__append_optimize_op__
(
op
,
block
,
grad_to_block_id
,
merged_var
,
def
__append_optimize_op__
(
op
,
block
,
grad_to_block_id
,
merged_var
,
lr_ops
):
lr_ops
):
...
@@ -1289,26 +1284,8 @@ class DistributeTranspiler(object):
...
@@ -1289,26 +1284,8 @@ class DistributeTranspiler(object):
# If one op's input is another op's output or
# If one op's input is another op's output or
# one op's output is another op's input, we say
# one op's output is another op's input, we say
# the two operator is connected.
# the two operator is connected.
def
_append_inname_remove_beta
(
varname_list
):
if
set
(
op1
.
desc
.
output_arg_names
())
&
set
(
op2
.
desc
.
input_arg_names
())
or
\
op_input_names
=
[]
set
(
op1
.
desc
.
input_arg_names
())
&
set
(
op2
.
desc
.
output_arg_names
()):
for
in_name
in
varname_list
:
# HACK: remove beta1 and beta2 to avoid let all
# ops connected.
if
in_name
.
startswith
(
"beta2_pow_acc"
)
or
\
in_name
.
startswith
(
"beta1_pow_acc"
):
continue
else
:
op_input_names
.
append
(
in_name
)
return
op_input_names
op1_input_names
=
_append_inname_remove_beta
(
op1
.
desc
.
input_arg_names
())
op1_output_names
=
op1
.
desc
.
output_arg_names
()
op2_input_names
=
_append_inname_remove_beta
(
op2
.
desc
.
input_arg_names
())
op2_output_names
=
op2
.
desc
.
output_arg_names
()
if
set
(
op1_output_names
)
&
set
(
op2_input_names
)
or
\
set
(
op1_input_names
)
&
set
(
op2_output_names
):
return
True
return
True
return
False
return
False
...
@@ -1413,7 +1390,7 @@ class DistributeTranspiler(object):
...
@@ -1413,7 +1390,7 @@ class DistributeTranspiler(object):
def
_get_optimize_pass
(
self
):
def
_get_optimize_pass
(
self
):
"""
"""
Get optimizer operators, paramters and gradients from origin_program
Get optimizer operators, param
e
ters and gradients from origin_program
Returns:
Returns:
opt_ops (list): optimize operators.
opt_ops (list): optimize operators.
params_grads (dict): paramter->gradient.
params_grads (dict): paramter->gradient.
...
@@ -1436,20 +1413,6 @@ class DistributeTranspiler(object):
...
@@ -1436,20 +1413,6 @@ class DistributeTranspiler(object):
origin_var_dict
[
param_name
],
origin_var_dict
[
param_name
],
origin_var_dict
[
input_name
]
origin_var_dict
[
input_name
]
])
])
elif
self
.
_is_adam_connected_op
(
op
):
opt_ops
.
append
(
op
)
else
:
else
:
pass
pass
return
opt_ops
,
params_grads
return
opt_ops
,
params_grads
def
_is_adam_connected_op
(
self
,
op
):
"""
A hack function to determinate whether the input operator
is connected to optimize operator.
"""
if
op
.
type
==
"scale"
:
for
in_name
in
op
.
input_arg_names
:
if
in_name
.
startswith
(
"beta1_pow_acc"
)
or
\
in_name
.
startswith
(
"beta2_pow_acc"
):
return
True
return
False
python/setup.py.in
浏览文件 @
193aeaea
...
@@ -42,12 +42,12 @@ def get_patch():
...
@@ -42,12 +42,12 @@ def get_patch():
def is_taged():
def is_taged():
try:
try:
cmd = ['git', 'describe', '--exact-match', '--tags']
cmd = ['git', 'describe', '--exact-match', '--tags'
, 'HEAD', '2>/dev/null'
]
git_tag = subprocess.Popen(cmd, stdout = subprocess.PIPE).communicate()[0].strip()
git_tag = subprocess.Popen(cmd, stdout = subprocess.PIPE).communicate()[0].strip()
except:
except:
return False
return False
if
git_tag
.replace('v', '') == '@PADDLE_VERSION@':
if
str(git_tag)
.replace('v', '') == '@PADDLE_VERSION@':
return True
return True
else:
else:
return False
return False
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录