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193aeaea
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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(
}
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.
...
...
@@ -131,7 +131,7 @@ ParallelExecutor::ParallelExecutor(
member_
->
places_
,
std
::
move
(
member_
->
executor_
)));
}
void
ParallelExecutor
::
BCastParamsTo
GPU
s
(
void
ParallelExecutor
::
BCastParamsTo
Dev
s
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
{
// the the initializing bcast, all vars would be bcast from device(0),
// otherwise
...
...
@@ -202,7 +202,11 @@ void ParallelExecutor::BCastParamsToGPUs(
#endif
}
else
{
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
*
t
=
local_scope
->
Var
(
var
)
->
GetMutable
<
LoDTensor
>
();
t
->
Resize
(
dims
);
...
...
paddle/fluid/framework/parallel_executor.h
浏览文件 @
193aeaea
...
...
@@ -66,7 +66,7 @@ class ParallelExecutor {
void
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
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:
ParallelExecutorPrivate
*
member_
;
...
...
paddle/fluid/framework/reader.h
浏览文件 @
193aeaea
...
...
@@ -29,11 +29,11 @@ enum ReaderStatus { kRunning, kStopped };
class
ReaderBase
{
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
// they are readers just before read op.
...
...
@@ -42,7 +42,7 @@ class ReaderBase {
virtual
~
ReaderBase
();
protected:
virtual
void
ReadNextImpl
(
std
::
vector
<
LoDTensor
>*
out
)
=
0
;
virtual
void
ReadNextImpl
(
std
::
vector
<
LoDTensor
>*
out
)
{}
virtual
void
ShutdownImpl
()
{}
...
...
paddle/fluid/operators/reader/blocking_queue.h
浏览文件 @
193aeaea
...
...
@@ -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
()
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
mutex_
);
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 {
queue_
=
queue
;
}
void
ReadNext
Impl
(
std
::
vector
<
framework
::
LoDTensor
>*
out
)
override
{
void
ReadNext
(
std
::
vector
<
framework
::
LoDTensor
>*
out
)
override
{
bool
success
;
*
out
=
queue_
->
Pop
(
&
success
);
if
(
!
success
)
out
->
clear
();
}
private:
void
ShutdownImpl
()
override
{
/* TODO */
}
void
Shutdown
()
override
{
queue_
->
Close
();
}
void
StartImpl
()
override
{
/* TODO */
}
void
Start
()
override
{
queue_
->
ReOpen
();
}
private:
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 {
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
();
}
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
(),
"Expect input size is %d but found %s"
,
dims_
.
size
(),
lod_tensor_vec
.
size
());
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
193aeaea
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#include <Python.h>
#include <algorithm>
#include <map>
#include <memory>
#include <mutex> // NOLINT // for call_once
#include <string>
#include <unordered_map>
...
...
@@ -86,37 +87,37 @@ PYBIND11_PLUGIN(core) {
py
::
class_
<
Tensor
>
(
m
,
"Tensor"
,
py
::
buffer_protocol
())
.
def_buffer
(
[](
Tensor
&
self
)
->
py
::
buffer_info
{
return
CastToPyBuffer
(
self
);
})
.
def
(
"get_dims"
,
.
def
(
"
_
get_dims"
,
[](
const
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
})
.
def
(
"set_dims"
,
.
def
(
"
_
set_dims"
,
[](
Tensor
&
self
,
const
std
::
vector
<
int64_t
>
&
dim
)
{
self
.
Resize
(
make_ddim
(
dim
));
})
.
def
(
"set_layout"
,
.
def
(
"
_
set_layout"
,
[](
Tensor
&
self
,
const
std
::
string
&
layout
)
{
self
.
set_layout
(
StringToDataLayout
(
layout
));
})
.
def
(
"alloc_float"
,
.
def
(
"
_
alloc_float"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
})
.
def
(
"alloc_float"
,
.
def
(
"
_
alloc_float"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
})
.
def
(
"alloc_int"
,
.
def
(
"
_
alloc_int"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
})
.
def
(
"alloc_int"
,
.
def
(
"
_
alloc_int"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
})
.
def
(
"alloc_int"
,
.
def
(
"
_
alloc_int"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPinnedPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
})
.
def
(
"alloc_float"
,
.
def
(
"
_
alloc_float"
,
[](
Tensor
&
self
,
paddle
::
platform
::
CUDAPinnedPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
})
...
...
@@ -144,11 +145,11 @@ PYBIND11_PLUGIN(core) {
.
def
(
"set"
,
PyCUDAPinnedTensorSetFromArray
<
uint8_t
>
)
#endif
.
def
(
"shape"
,
[](
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
})
.
def
(
"set_float_element"
,
TensorSetElement
<
float
>
)
.
def
(
"get_float_element"
,
TensorGetElement
<
float
>
)
.
def
(
"set_double_element"
,
TensorSetElement
<
double
>
)
.
def
(
"get_double_element"
,
TensorGetElement
<
double
>
)
.
def
(
"dtype"
,
[](
Tensor
&
self
)
{
return
ToDataType
(
self
.
type
());
});
.
def
(
"
_
set_float_element"
,
TensorSetElement
<
float
>
)
.
def
(
"
_
get_float_element"
,
TensorGetElement
<
float
>
)
.
def
(
"
_
set_double_element"
,
TensorSetElement
<
double
>
)
.
def
(
"
_
get_double_element"
,
TensorGetElement
<
double
>
)
.
def
(
"
_
dtype"
,
[](
Tensor
&
self
)
{
return
ToDataType
(
self
.
type
());
});
py
::
class_
<
LoDTensor
,
Tensor
>
(
m
,
"LoDTensor"
)
.
def_buffer
(
...
...
@@ -310,7 +311,8 @@ All parameter, weight, gradient are variables in Paddle.
::
paddle
::
operators
::
reader
::
LoDTensorBlockingQueue
;
using
LoDTensorBlockingQueueHolder
=
::
paddle
::
operators
::
reader
::
LoDTensorBlockingQueueHolder
;
py
::
class_
<
LoDTensorBlockingQueue
>
(
m
,
"LoDTensorBlockingQueue"
,
""
)
py
::
class_
<
LoDTensorBlockingQueue
,
std
::
shared_ptr
<
LoDTensorBlockingQueue
>>
(
m
,
"LoDTensorBlockingQueue"
,
""
)
.
def
(
"push"
,
[](
LoDTensorBlockingQueue
&
self
,
const
std
::
vector
<
framework
::
LoDTensor
>
&
lod_tensor_vec
)
{
...
...
@@ -325,7 +327,7 @@ All parameter, weight, gradient are variables in Paddle.
m
.
def
(
"init_lod_tensor_blocking_queue"
,
[](
Variable
&
var
,
size_t
capacity
,
const
std
::
vector
<
std
::
vector
<
int64_t
>>
&
shapes
)
->
LoDTensorBlockingQueue
*
{
->
std
::
shared_ptr
<
LoDTensorBlockingQueue
>
{
std
::
vector
<
DDim
>
dims
(
shapes
.
size
());
std
::
transform
(
shapes
.
begin
(),
shapes
.
end
(),
dims
.
begin
(),
[](
const
std
::
vector
<
int64_t
>
&
shape
)
{
...
...
@@ -333,9 +335,9 @@ All parameter, weight, gradient are variables in Paddle.
});
auto
*
holder
=
var
.
GetMutable
<
LoDTensorBlockingQueueHolder
>
();
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"
,
""
)
.
def
(
"var"
,
...
...
@@ -543,6 +545,8 @@ All parameter, weight, gradient are variables in Paddle.
});
py
::
class_
<
LoDTensorArray
>
(
m
,
"LoDTensorArray"
)
.
def
(
"__init__"
,
[](
LoDTensorArray
&
instance
)
{
new
(
&
instance
)
LoDTensorArray
();
})
.
def
(
"__getitem__"
,
[](
LoDTensorArray
&
self
,
size_t
i
)
{
return
&
self
.
at
(
i
);
},
py
::
return_value_policy
::
reference
)
...
...
@@ -665,7 +669,7 @@ All parameter, weight, gradient are variables in Paddle.
const
std
::
string
&
,
Scope
*
,
std
::
vector
<
Scope
*>
&
,
const
ExecutionStrategy
&
,
const
BuildStrategy
&
,
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.
// 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*
...
...
paddle/legacy/capi/Arguments.cpp
浏览文件 @
193aeaea
...
...
@@ -66,6 +66,17 @@ paddle_error paddle_arguments_get_value(paddle_arguments args,
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
,
uint64_t
ID
,
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,
uint64_t
ID
,
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
* index is `ID`.
...
...
python/paddle/fluid/__init__.py
浏览文件 @
193aeaea
...
...
@@ -44,7 +44,7 @@ import metrics
import
transpiler
from
param_attr
import
ParamAttr
,
WeightNormParamAttr
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
,
\
memory_optimize
,
release_memory
from
concurrency
import
(
Go
,
make_channel
,
channel_send
,
channel_recv
,
...
...
@@ -72,6 +72,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
'backward'
,
'regularizer'
,
'LoDTensor'
,
'LoDTensorArray'
,
'CPUPlace'
,
'CUDAPlace'
,
'CUDAPinnedPlace'
,
...
...
python/paddle/fluid/clip.py
浏览文件 @
193aeaea
...
...
@@ -31,7 +31,7 @@ class BaseErrorClipAttr(object):
def
__str__
(
self
):
raise
NotImplementedError
()
def
append_clip_op
(
self
,
block
,
grad_name
):
def
_
append_clip_op
(
self
,
block
,
grad_name
):
raise
NotImplementedError
()
...
...
@@ -67,7 +67,7 @@ class ErrorClipByValue(BaseErrorClipAttr):
def
__str__
(
self
):
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
.
set_type
(
"clip"
)
clip_op_desc
.
set_input
(
"X"
,
[
grad_name
])
...
...
@@ -90,17 +90,17 @@ def error_clip_callback(block, context):
"Variable's error_clip should be an instance of BaseErrorClipAttr or 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
):
def
__str__
(
self
):
raise
NotImplementedError
()
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
raise
NotImplementedError
()
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
raise
NotImplementedError
()
...
...
@@ -108,10 +108,10 @@ class NullGradientClipAttr(BaseGradientClipAttr):
def
__str__
(
self
):
return
"Null"
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
pass
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
return
param
,
grad
...
...
@@ -153,10 +153,10 @@ class GradientClipByValue(BaseGradientClipAttr):
def
__str__
(
self
):
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
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
new_grad
=
layers
.
clip
(
x
=
grad
,
min
=
self
.
min
,
max
=
self
.
max
)
return
param
,
new_grad
...
...
@@ -199,10 +199,10 @@ class GradientClipByNorm(BaseGradientClipAttr):
def
__str__
(
self
):
return
"ByNorm, clip_norm=%f"
%
self
.
clip_norm
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
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
)
return
param
,
new_grad
...
...
@@ -257,7 +257,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
return
"ByGlobalNorm, group_name=%s, clip_norm=%f"
%
(
self
.
group_name
,
self
.
clip_norm
)
def
process_context
(
self
,
context
,
param
,
grad
):
def
_
process_context
(
self
,
context
,
param
,
grad
):
if
self
.
group_name
not
in
context
:
context
[
self
.
group_name
]
=
[]
context
[
self
.
group_name
+
"_clip_value"
]
=
self
.
clip_norm
...
...
@@ -274,7 +274,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
self
.
context
=
context
def
create_operators
(
self
,
param
,
grad
):
def
_
create_operators
(
self
,
param
,
grad
):
group_scale_name
=
self
.
group_name
+
"_scale"
if
group_scale_name
not
in
self
.
context
:
group_norm_var
=
layers
.
sums
(
input
=
self
.
context
[
self
.
group_name
])
...
...
@@ -336,12 +336,12 @@ def append_gradient_clip_ops(param_grad):
"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
=
[]
for
p
,
g
in
param_grad
:
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
...
...
python/paddle/fluid/layer_helper.py
浏览文件 @
193aeaea
...
...
@@ -68,11 +68,11 @@ class LayerHelper(object):
@
property
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
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
):
param_attr
=
self
.
param_attr
...
...
@@ -262,11 +262,11 @@ class LayerHelper(object):
g_param
=
self
.
startup_program
.
global_block
().
create_parameter
(
dtype
=
dtype
,
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
(
dtype
=
dtype
,
shape
=
v_param_shape
,
**
v_param_attr
.
to_kwargs
(
with_initializer
=
True
))
**
v_param_attr
.
_
to_kwargs
(
with_initializer
=
True
))
__norm_except_dim
(
x
=
v_param
,
out
=
g_param
,
...
...
@@ -275,9 +275,9 @@ class LayerHelper(object):
# Add weight normalization to main_program
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
(
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
)
return
w_param
...
...
@@ -296,11 +296,11 @@ class LayerHelper(object):
if
default_initializer
is
None
and
attr
.
initializer
is
None
:
if
is_bias
:
attr
.
set_default_bias_initializer
()
attr
.
_
set_default_bias_initializer
()
else
:
attr
.
set_default_param_initializer
()
attr
.
_
set_default_param_initializer
()
else
:
attr
.
set_default_initializer
(
default_initializer
)
attr
.
_
set_default_initializer
(
default_initializer
)
# If weight normalization is set, insert extra parameters and ops.
# Refer to https://arxiv.org/pdf/1602.07868.pdf
...
...
@@ -310,9 +310,9 @@ class LayerHelper(object):
return
param
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
(
dtype
=
dtype
,
shape
=
shape
,
**
attr
.
to_kwargs
())
dtype
=
dtype
,
shape
=
shape
,
**
attr
.
_
to_kwargs
())
def
get_parameter
(
self
,
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
__all__
=
[
'data'
,
'BlockGuardServ'
,
'ListenAndServ'
,
'Send'
,
'Recv'
,
'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):
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
,
shapes
,
lod_levels
,
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
193aeaea
...
...
@@ -123,7 +123,7 @@ class Optimizer(object):
"""
pass
def
_finish_update
(
self
,
block
):
def
_finish_update
(
self
,
block
,
parameters
):
"""Finish any custom updates needed
before completing an optimization step
...
...
@@ -132,7 +132,7 @@ class Optimizer(object):
parameters: list of parameter variables for the optimizer
Returns:
list of finish ops or
None
None
"""
pass
...
...
@@ -236,7 +236,8 @@ class Optimizer(object):
# Get custom finish ops for subclasses
# 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
)
return
global_block
.
slice_ops
(
start
,
end
)
...
...
@@ -486,6 +487,8 @@ class AdamOptimizer(Optimizer):
"""
_moment1_acc_str
=
"moment1"
_moment2_acc_str
=
"moment2"
_beta1_pow_acc_str
=
"beta1_pow_acc"
_beta2_pow_acc_str
=
"beta2_pow_acc"
def
__init__
(
self
,
learning_rate
=
0.001
,
...
...
@@ -507,32 +510,22 @@ class AdamOptimizer(Optimizer):
def
_create_accumulators
(
self
,
block
,
parameters
):
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
for
p
in
parameters
:
self
.
_add_accumulator
(
self
.
_moment1_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
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
@@ -541,6 +534,11 @@ class AdamOptimizer(Optimizer):
param_and_grad
[
0
])
moment2
=
self
.
_get_accumulator
(
self
.
_moment2_acc_str
,
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
adam_op
=
block
.
append_op
(
type
=
self
.
type
,
...
...
@@ -550,8 +548,8 @@ class AdamOptimizer(Optimizer):
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
"Moment1"
:
moment1
,
"Moment2"
:
moment2
,
"Beta1Pow"
:
self
.
_
beta1_pow_acc
,
"Beta2Pow"
:
self
.
_
beta2_pow_acc
"Beta1Pow"
:
beta1_pow_acc
,
"Beta2Pow"
:
beta2_pow_acc
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
...
...
@@ -566,24 +564,28 @@ class AdamOptimizer(Optimizer):
return
adam_op
def
_finish_update
(
self
,
block
):
def
_finish_update
(
self
,
block
,
parameters
):
"""Update Beta1 and Beta2 Power accumulators
"""
assert
isinstance
(
block
,
framework
.
Block
)
main_block
=
block
.
program
.
global_block
()
scale_beta1
=
main_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_beta1_pow_acc
},
outputs
=
{
"Out"
:
self
.
_beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
scale_beta2
=
main_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_beta2_pow_acc
},
outputs
=
{
"Out"
:
self
.
_beta2_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta2
})
return
[
scale_beta1
,
scale_beta2
]
for
param
in
parameters
:
with
param
.
block
.
program
.
optimized_guard
(
param
):
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
param
)
beta2_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta2_pow_acc_str
,
param
)
main_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
beta1_pow_acc
},
outputs
=
{
"Out"
:
beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
main_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
beta2_pow_acc
},
outputs
=
{
"Out"
:
beta2_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta2
})
class
AdamaxOptimizer
(
Optimizer
):
...
...
@@ -626,6 +628,7 @@ class AdamaxOptimizer(Optimizer):
"""
_moment_acc_str
=
"moment"
_inf_norm_acc_str
=
"inf_norm"
_beta1_pow_acc_str
=
"beta1_pow_acc"
def
__init__
(
self
,
learning_rate
=
0.001
,
...
...
@@ -645,21 +648,16 @@ class AdamaxOptimizer(Optimizer):
self
.
_epsilon
=
epsilon
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
for
p
in
parameters
:
self
.
_add_accumulator
(
self
.
_moment_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
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
@@ -667,6 +665,8 @@ class AdamaxOptimizer(Optimizer):
moment
=
self
.
_get_accumulator
(
self
.
_moment_acc_str
,
param_and_grad
[
0
])
inf_norm
=
self
.
_get_accumulator
(
self
.
_inf_norm_acc_str
,
param_and_grad
[
0
])
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
param_and_grad
[
0
])
# create the adamax optimize op
adamax_op
=
block
.
append_op
(
type
=
self
.
type
,
...
...
@@ -676,7 +676,7 @@ class AdamaxOptimizer(Optimizer):
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
"Moment"
:
moment
,
"InfNorm"
:
inf_norm
,
"Beta1Pow"
:
self
.
_
beta1_pow_acc
"Beta1Pow"
:
beta1_pow_acc
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
...
...
@@ -691,18 +691,20 @@ class AdamaxOptimizer(Optimizer):
return
adamax_op
def
_finish_update
(
self
,
block
):
def
_finish_update
(
self
,
block
,
parameters
):
"""Update Beta1 Power accumulator
"""
assert
isinstance
(
block
,
framework
.
Block
)
main_block
=
block
.
program
.
global_block
()
scale_beta1
=
main_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_beta1_pow_acc
},
outputs
=
{
"Out"
:
self
.
_beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
return
[
scale_beta1
]
for
param
in
parameters
:
with
param
.
block
.
program
.
optimized_guard
(
param
):
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
param
)
main_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
beta1_pow_acc
},
outputs
=
{
"Out"
:
beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
class
DecayedAdagradOptimizer
(
Optimizer
):
...
...
@@ -1156,7 +1158,8 @@ class ModelAverage(Optimizer):
self
.
params_grads
.
append
((
param
,
grad
))
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
()
block
=
self
.
apply_program
.
global_block
()
...
...
python/paddle/fluid/param_attr.py
浏览文件 @
193aeaea
...
...
@@ -67,7 +67,7 @@ class ParamAttr(object):
self
.
gradient_clip
=
gradient_clip
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,
Uniform, Normal, Xavier, MSRA.
...
...
@@ -88,7 +88,7 @@ class ParamAttr(object):
self
.
initializer
=
initializer
def
set_default_param_initializer
(
self
):
def
_
set_default_param_initializer
(
self
):
"""
Set the default initializer for the parameter with Xavier.
...
...
@@ -98,9 +98,9 @@ class ParamAttr(object):
Returns:
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).
...
...
@@ -110,10 +110,10 @@ class ParamAttr(object):
Returns:
None.
"""
self
.
set_default_initializer
(
Constant
(
0.0
))
self
.
_
set_default_initializer
(
Constant
(
0.0
))
@
staticmethod
def
to_attr
(
arg
):
def
_
to_attr
(
arg
):
"""
Create ParamAttr[s].
...
...
@@ -131,7 +131,7 @@ class ParamAttr(object):
if
arg
is
None
:
return
ParamAttr
()
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
):
return
arg
elif
isinstance
(
arg
,
str
)
or
isinstance
(
arg
,
unicode
):
...
...
@@ -141,11 +141,11 @@ class ParamAttr(object):
elif
isinstance
(
arg
,
WeightDecayRegularizer
):
return
ParamAttr
(
regularizer
=
arg
)
elif
isinstance
(
arg
,
bool
):
return
ParamAttr
.
to_attr
(
None
)
if
arg
else
False
return
ParamAttr
.
_
to_attr
(
None
)
if
arg
else
False
else
:
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.
...
...
python/paddle/fluid/regularizer.py
浏览文件 @
193aeaea
...
...
@@ -15,10 +15,7 @@
import
framework
from
.
import
core
__all__
=
[
'append_regularization_ops'
,
'L1Decay'
,
'L2Decay'
,
'L1DecayRegularizer'
,
'L2DecayRegularizer'
]
__all__
=
[
'L1Decay'
,
'L2Decay'
,
'L1DecayRegularizer'
,
'L2DecayRegularizer'
]
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,
return
np
.
array
(
sum
).
mean
()
tensor_to_check
=
scope
.
find_var
(
input_to_check
).
get_tensor
()
tensor_size
=
product
(
tensor_to_check
.
get_dims
())
tensor_to_check_dtype
=
tensor_to_check
.
dtype
()
tensor_size
=
product
(
tensor_to_check
.
shape
())
tensor_to_check_dtype
=
tensor_to_check
.
_
dtype
()
if
tensor_to_check_dtype
==
core
.
VarDesc
.
VarType
.
FP32
:
tensor_to_check_dtype
=
np
.
float32
elif
tensor_to_check_dtype
==
core
.
VarDesc
.
VarType
.
FP64
:
...
...
@@ -74,15 +74,15 @@ def get_numeric_gradient(place,
def
__get_elem__
(
tensor
,
i
):
if
tensor_to_check_dtype
==
np
.
float32
:
return
tensor
.
get_float_element
(
i
)
return
tensor
.
_
get_float_element
(
i
)
else
:
return
tensor
.
get_double_element
(
i
)
return
tensor
.
_
get_double_element
(
i
)
def
__set_elem__
(
tensor
,
i
,
e
):
if
tensor_to_check_dtype
==
np
.
float32
:
tensor
.
set_float_element
(
i
,
e
)
tensor
.
_
set_float_element
(
i
,
e
)
else
:
tensor
.
set_double_element
(
i
,
e
)
tensor
.
_
set_double_element
(
i
,
e
)
# we only compute gradient of one element each time.
# we use a for loop to compute the gradient of every element.
...
...
@@ -107,7 +107,7 @@ def get_numeric_gradient(place,
__set_elem__
(
tensor_to_check
,
i
,
origin
)
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
):
...
...
@@ -125,7 +125,7 @@ class OpTest(unittest.TestCase):
@
classmethod
def
tearDownClass
(
cls
):
'''Restore random seeds'''
"""Restore random seeds"""
np
.
random
.
set_state
(
cls
.
_np_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):
if
var
is
not
None
:
assert
isinstance
(
var
,
np
.
ndarray
)
tensor
.
set_recursive_sequence_lengths
([])
tensor
.
set_dims
(
var
.
shape
)
tensor
.
set
(
var
,
place
)
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):
return
self
.
_lodtensor_to_ndarray
(
fetch_outs
[
0
])
def
_lodtensor_to_ndarray
(
self
,
lod_tensor
):
dims
=
lod_tensor
.
get_dims
()
dims
=
lod_tensor
.
shape
()
ndarray
=
np
.
zeros
(
shape
=
dims
).
astype
(
'float32'
)
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
()
def
build_graph
(
self
,
only_forward
=
False
):
...
...
@@ -185,19 +185,19 @@ class TestDyRnnStaticInput(unittest.TestCase):
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'
)
# calculate numeric gradients
tensor_size
=
np
.
product
(
static_input_shape
)
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
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
]
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
]
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
self
.
assertTrue
(
np
.
allclose
(
actual_gradients
,
numeric_gradients
,
0.001
))
self
.
assertTrue
(
...
...
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @
193aeaea
...
...
@@ -287,7 +287,7 @@ class TestAdamOptimizer(unittest.TestCase):
# Check 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_moment2_str
()
in
accumulators
)
moment1_acc
=
accumulators
[
adam_optimizer
.
get_moment1_str
()]
...
...
@@ -354,7 +354,7 @@ class TestAdamaxOptimizer(unittest.TestCase):
# Check 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_inf_norm_str
()
in
accumulators
)
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):
# compare tensor
self
.
assertAlmostEqual
(
2.0
,
selected_rows
.
get_tensor
().
get_float_element
(
0
))
selected_rows
.
get_tensor
().
_
get_float_element
(
0
))
self
.
assertAlmostEqual
(
1.0
,
selected_rows
.
get_tensor
().
get_float_element
(
1
))
selected_rows
.
get_tensor
().
_
get_float_element
(
1
))
self
.
assertAlmostEqual
(
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__"
:
...
...
python/paddle/fluid/tests/unittests/test_shrink_rnn_memory.py
浏览文件 @
193aeaea
...
...
@@ -45,8 +45,8 @@ class TestShrinkRNNMemoryBase(unittest.TestCase):
def
sum_lodtensor
(
self
,
tensor
):
sum_res
=
0.0
for
i
in
xrange
(
np
.
product
(
tensor
.
get_dims
())):
sum_res
+=
tensor
.
get_float_element
(
i
)
for
i
in
xrange
(
np
.
product
(
tensor
.
shape
())):
sum_res
+=
tensor
.
_
get_float_element
(
i
)
return
sum_res
...
...
python/paddle/fluid/tests/unittests/test_tensor.py
浏览文件 @
193aeaea
...
...
@@ -25,8 +25,8 @@ class TestTensor(unittest.TestCase):
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
1000
,
784
])
tensor
.
alloc_int
(
place
)
tensor
.
_
set_dims
([
1000
,
784
])
tensor
.
_
alloc_int
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
tensor_array
[
3
,
9
]
=
1
...
...
@@ -44,8 +44,8 @@ class TestTensor(unittest.TestCase):
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
1000
,
784
])
tensor
.
alloc_float
(
place
)
tensor
.
_
set_dims
([
1000
,
784
])
tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
...
...
@@ -63,8 +63,8 @@ class TestTensor(unittest.TestCase):
var_lod
=
scope
.
var
(
"test_lod_tensor"
)
lod_tensor
=
var_lod
.
get_tensor
()
lod_tensor
.
set_dims
([
4
,
4
,
6
])
lod_tensor
.
alloc_int
(
place
)
lod_tensor
.
_
set_dims
([
4
,
4
,
6
])
lod_tensor
.
_
alloc_int
(
place
)
array
=
numpy
.
array
(
lod_tensor
)
array
[
0
,
0
,
0
]
=
3
array
[
3
,
3
,
5
]
=
10
...
...
@@ -84,8 +84,8 @@ class TestTensor(unittest.TestCase):
var_lod
=
scope
.
var
(
"test_lod_tensor"
)
lod_tensor
=
var_lod
.
get_tensor
()
lod_tensor
.
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
alloc_float
(
place
)
lod_tensor
.
_
set_dims
([
5
,
2
,
3
,
4
])
lod_tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
self
.
assertEqual
((
5
,
2
,
3
,
4
),
tensor_array
.
shape
)
...
...
@@ -104,14 +104,13 @@ class TestTensor(unittest.TestCase):
self
.
assertListEqual
(
lod_py
,
lod
)
def
test_lod_tensor_init
(
self
):
scope
=
core
.
Scope
()
place
=
core
.
CPUPlace
()
lod_py
=
[[
2
,
1
],
[
1
,
2
,
2
]]
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
.
alloc_float
(
place
)
lod_tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
tensor_array
[
0
,
0
,
0
,
0
]
=
1.0
tensor_array
[
0
,
0
,
0
,
1
]
=
2.0
...
...
@@ -129,9 +128,9 @@ class TestTensor(unittest.TestCase):
lod_py
=
[[
2
,
1
],
[
1
,
2
,
2
]]
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
.
alloc_float
(
place
)
lod_tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
lod_tensor
)
tensor_array
[
0
,
0
,
0
,
0
]
=
1.0
tensor_array
[
0
,
0
,
0
,
1
]
=
2.0
...
...
@@ -149,15 +148,15 @@ class TestTensor(unittest.TestCase):
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
0
,
1
])
tensor
.
alloc_float
(
place
)
tensor
.
_
set_dims
([
0
,
1
])
tensor
.
_
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
0
,
1
),
tensor_array
.
shape
)
if
core
.
is_compiled_with_cuda
():
gpu_place
=
core
.
CUDAPlace
(
0
)
tensor
.
alloc_float
(
gpu_place
)
tensor
.
_
alloc_float
(
gpu_place
)
tensor_array
=
numpy
.
array
(
tensor
)
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):
if
isinstance
(
var
,
tuple
):
tensor
.
set_recursive_sequence_lengths
(
var
[
1
])
var
=
var
[
0
]
tensor
.
set_dims
(
var
.
shape
)
tensor
.
_
set_dims
(
var
.
shape
)
tensor
.
set
(
var
,
place
)
elif
isinstance
(
var
,
float
):
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):
# append it into the sub program.
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
,
lr_ops
):
...
...
@@ -1289,26 +1284,8 @@ class DistributeTranspiler(object):
# If one op's input is another op's output or
# one op's output is another op's input, we say
# the two operator is connected.
def
_append_inname_remove_beta
(
varname_list
):
op_input_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
):
if
set
(
op1
.
desc
.
output_arg_names
())
&
set
(
op2
.
desc
.
input_arg_names
())
or
\
set
(
op1
.
desc
.
input_arg_names
())
&
set
(
op2
.
desc
.
output_arg_names
()):
return
True
return
False
...
...
@@ -1413,7 +1390,7 @@ class DistributeTranspiler(object):
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:
opt_ops (list): optimize operators.
params_grads (dict): paramter->gradient.
...
...
@@ -1436,20 +1413,6 @@ class DistributeTranspiler(object):
origin_var_dict
[
param_name
],
origin_var_dict
[
input_name
]
])
elif
self
.
_is_adam_connected_op
(
op
):
opt_ops
.
append
(
op
)
else
:
pass
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():
def is_taged():
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()
except:
return False
if
git_tag
.replace('v', '') == '@PADDLE_VERSION@':
if
str(git_tag)
.replace('v', '') == '@PADDLE_VERSION@':
return True
else:
return False
...
...
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