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体验新版 GitCode,发现更多精彩内容 >>
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effc5559
编写于
5月 29, 2019
作者:
J
Jiabin Yang
提交者:
GitHub
5月 29, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
test=develop, layz init Grad (#17653)
上级
33a791dd
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
100 addition
and
52 deletion
+100
-52
paddle/fluid/imperative/CMakeLists.txt
paddle/fluid/imperative/CMakeLists.txt
+0
-1
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+41
-26
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+39
-8
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+4
-7
python/paddle/fluid/dygraph/layers.py
python/paddle/fluid/dygraph/layers.py
+2
-1
python/paddle/fluid/tests/unittests/test_imperative_gnn.py
python/paddle/fluid/tests/unittests/test_imperative_gnn.py
+14
-9
未找到文件。
paddle/fluid/imperative/CMakeLists.txt
浏览文件 @
effc5559
...
...
@@ -4,6 +4,5 @@ cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context pybind profiler)
cc_library
(
engine SRCS engine.cc
)
cc_library
(
imperative_profiler SRCS profiler.cc
)
cc_library
(
nccl_context SRCS nccl_context.cc DEPS device_context
)
cc_test
(
nccl_context_test SRCS nccl_context_test.cc DEPS nccl_context
)
endif
()
paddle/fluid/imperative/layer.cc
浏览文件 @
effc5559
...
...
@@ -97,6 +97,13 @@ void AddTo(Variable* src, Variable* dst, platform::Place place) {
boost
::
apply_visitor
(
func
,
place
);
}
void
ZeroGrads
(
VarBase
*
vb
,
const
platform
::
Place
&
place
)
{
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
*
dev_ctx
=
pool
.
Get
(
place
);
auto
grad_t
=
vb
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
operators
::
math
::
set_constant
(
*
dev_ctx
,
grad_t
,
0.0
);
}
void
AddGradBySort
(
BackwardSumMap
*
bck_map
,
VarBase
*
target
)
{
PADDLE_ENFORCE
(
bck_map
->
find
(
target
)
!=
bck_map
->
end
(),
"Can't find %s in backward grad map"
,
target
->
Name
());
...
...
@@ -110,9 +117,9 @@ void AddGradBySort(BackwardSumMap* bck_map, VarBase* target) {
for
(
auto
&
var_pair
:
current
.
second
)
{
Variable
*
origin_grad
=
target
->
var_
.
get
();
Variable
*
grad_to_add
=
var_pair
.
second
->
var_
.
get
();
VLOG
(
2
)
<<
"add origin_grad: "
<<
target
->
Name
();
VLOG
(
2
)
<<
"added grad: "
<<
var_pair
.
second
->
Name
()
<<
" trace id is: "
<<
var_pair
.
first
;
VLOG
(
10
)
<<
"add origin_grad: "
<<
target
->
Name
();
VLOG
(
10
)
<<
"added grad: "
<<
var_pair
.
second
->
Name
()
<<
" trace id is: "
<<
var_pair
.
first
;
AddTo
(
grad_to_add
,
origin_grad
,
current
.
first
);
delete
var_pair
.
second
;
var_pair
.
second
=
nullptr
;
...
...
@@ -127,7 +134,7 @@ class Autograd {
if
(
var
->
IsStopGradient
())
{
return
;
}
VLOG
(
3
)
<<
"start autograd"
;
VLOG
(
2
)
<<
"start autograd"
;
BackwardSumMap
bck_map
;
GradientRef
grad_ref
;
std
::
deque
<
OpBase
*>
ready
;
...
...
@@ -195,7 +202,7 @@ class Autograd {
for
(
auto
it
:
candidate
->
pre_ops_
)
{
for
(
OpBase
*
pre_op
:
it
.
second
)
{
if
(
!
pre_op
)
continue
;
VLOG
(
2
)
<<
"op dep "
<<
candidate
->
Type
()
<<
" trace id "
VLOG
(
9
)
<<
"op dep "
<<
candidate
->
Type
()
<<
" trace id "
<<
candidate
->
trace_id_
<<
" <---- "
<<
it
.
first
<<
" <---- "
<<
pre_op
->
Type
()
<<
" trace id "
<<
pre_op
->
trace_id_
;
if
(
visited
.
find
(
pre_op
)
==
visited
.
end
())
{
...
...
@@ -267,9 +274,11 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad(
for
(
const
auto
&
it
:
grad_output_variable_map
)
{
auto
&
outputs
=
tmp_grad_outputs
[
k
][
it
.
first
];
outputs
.
reserve
(
it
.
second
.
size
());
for
(
size_t
i
=
0
;
i
<
it
.
second
.
size
();
++
i
)
{
VarBase
*
origin_grad_var_base
=
it
.
second
[
i
];
for
(
VarBase
*
origin_grad_var_base
:
it
.
second
)
{
if
(
!
origin_grad_var_base
->
IsInitialize
())
{
origin_grad_var_base
->
InitBuffer
();
ZeroGrads
(
origin_grad_var_base
,
place_
);
}
// Allocate a new variable
VarBase
*
tmp_grad_var_base
=
new
VarBase
(
string
::
Sprintf
(
"%s@IGrad"
,
origin_grad_var_base
->
Name
()),
...
...
@@ -304,11 +313,15 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad(
for
(
const
auto
&
it
:
grad_input_vars_
[
k
])
{
auto
&
grad_invars
=
grad_invars_map
[
it
.
first
];
grad_invars
.
reserve
(
it
.
second
.
size
());
for
(
const
VarBase
*
grad_inp
:
it
.
second
)
{
for
(
VarBase
*
grad_inp
:
it
.
second
)
{
PADDLE_ENFORCE_NOT_NULL
(
grad_inp
->
var_
,
"op %s input %s nullptr"
,
grad_op_desc
->
Type
(),
grad_inp
->
Name
());
grad_invars
.
emplace_back
(
grad_inp
->
var_
.
get
());
if
(
!
grad_inp
->
IsInitialize
())
{
grad_inp
->
InitBuffer
();
ZeroGrads
(
grad_inp
,
place_
);
}
const
VarBase
*
const_grad_inp
=
grad_inp
;
grad_invars
.
emplace_back
(
const_grad_inp
->
var_
.
get
());
}
}
...
...
@@ -343,22 +356,23 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad(
// track outputs used by sum
if
(
bck_stratedy
.
sorted_sum_gradient_
)
{
#ifndef PADDLE_WITH_CUDA
VLOG
(
2
)
<<
"origin_outputs is : "
<<
origin_outputs
[
i
]
->
Name
()
<<
" "
;
VLOG
(
2
)
<<
origin_outputs
[
i
]
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
()
->
data
<
float
>
()[
0
];
VLOG
(
2
)
<<
"outputs is : "
<<
outputs
[
i
]
->
Name
()
<<
" "
;
VLOG
(
2
)
<<
outputs
[
i
]
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
()
->
data
<
float
>
()[
0
];
VLOG
(
10
)
<<
"origin_outputs is : "
<<
origin_outputs
[
i
]
->
Name
()
<<
" "
;
VLOG
(
10
)
<<
origin_outputs
[
i
]
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
()
->
data
<
float
>
()[
0
];
VLOG
(
10
)
<<
"outputs is : "
<<
outputs
[
i
]
->
Name
()
<<
" "
;
VLOG
(
10
)
<<
outputs
[
i
]
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
()
->
data
<
float
>
()[
0
];
#endif
if
(
bck_map
->
find
(
origin_outputs
[
i
])
!=
bck_map
->
end
())
{
VLOG
(
2
)
<<
"add sub grad to "
<<
origin_outputs
[
i
]
->
Name
();
VLOG
(
10
)
<<
"add sub grad to "
<<
origin_outputs
[
i
]
->
Name
();
bck_map
->
at
(
origin_outputs
[
i
])
.
second
.
emplace_back
(
std
::
pair
<
int
,
VarBase
*>
(
this
->
trace_id_
,
outputs
[
i
]));
}
else
{
VLOG
(
2
)
<<
"insert new map for "
<<
origin_outputs
[
i
]
->
Name
();
VLOG
(
10
)
<<
"insert new map for "
<<
origin_outputs
[
i
]
->
Name
();
std
::
pair
<
platform
::
Place
,
std
::
vector
<
std
::
pair
<
int
,
VarBase
*>>>
tmp
(
place_
,
{
std
::
make_pair
(
this
->
trace_id_
,
outputs
[
i
])});
bck_map
->
insert
(
std
::
make_pair
(
origin_outputs
[
i
],
tmp
));
...
...
@@ -370,19 +384,19 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad(
PADDLE_ENFORCE
(
grad_ref
->
at
(
origin_outputs
[
i
])
>=
1
,
"Backward error when calculate grad reference"
);
if
(
grad_ref
->
at
(
origin_outputs
[
i
])
>
1
)
{
VLOG
(
2
)
<<
"remove ref for "
<<
origin_outputs
[
i
]
->
Name
();
VLOG
(
10
)
<<
"remove ref for "
<<
origin_outputs
[
i
]
->
Name
();
grad_ref
->
at
(
origin_outputs
[
i
])
--
;
}
else
{
VLOG
(
2
)
<<
"Add grad for: "
<<
origin_outputs
[
i
]
->
Name
();
VLOG
(
10
)
<<
"Add grad for: "
<<
origin_outputs
[
i
]
->
Name
();
AddGradBySort
(
bck_map
,
origin_outputs
[
i
]);
grad_ref
->
at
(
origin_outputs
[
i
])
--
;
}
}
else
{
framework
::
Variable
*
grad
=
outputs
[
i
]
->
var_
.
get
();
framework
::
Variable
*
orig_grad
=
origin_outputs
[
i
]
->
var_
.
get
();
VLOG
(
2
)
<<
"AddTo Called with orig_grad is: "
<<
origin_outputs
[
i
]
->
name_
<<
" Grad to be added is "
<<
outputs
[
i
]
->
name_
;
VLOG
(
10
)
<<
"AddTo Called with orig_grad is: "
<<
origin_outputs
[
i
]
->
name_
<<
" Grad to be added is "
<<
outputs
[
i
]
->
name_
;
AddTo
(
grad
,
orig_grad
,
place_
);
delete
outputs
[
i
];
}
...
...
@@ -413,6 +427,7 @@ void VarBase::RunBackward(const detail::BackwardStrategy& bck_stratedy) {
if
(
!
pre_op_
)
return
;
platform
::
RecordEvent
record_event
(
"Imperative Backward"
);
VLOG
(
3
)
<<
"start backward"
;
grads_
->
InitBuffer
();
auto
grads_t
=
grads_
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
operators
::
math
::
set_constant
(
*
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
effc5559
...
...
@@ -121,7 +121,7 @@ class VarBase {
:
VarBase
(
name
,
var
->
Get
<
framework
::
LoDTensor
>
().
type
(),
var
->
Get
<
framework
::
LoDTensor
>
().
dims
(),
var
->
Get
<
framework
::
LoDTensor
>
().
place
(),
nullptr
,
grad
,
stop_gradient
,
false
)
{
stop_gradient
,
false
,
true
)
{
var_
=
std
::
move
(
var
);
}
...
...
@@ -137,18 +137,27 @@ class VarBase {
const
framework
::
DDim
&
shape
,
const
platform
::
Place
&
place
,
bool
stop_gradient
,
bool
persistable
)
:
VarBase
(
name
,
dtype
,
shape
,
place
,
nullptr
,
nullptr
,
stop_gradient
,
persistable
)
{}
persistable
,
true
)
{}
// Grad used constructor
VarBase
(
const
std
::
string
&
name
,
const
framework
::
proto
::
VarType
::
Type
dtype
,
const
std
::
vector
<
int64_t
>&
shape
,
const
platform
::
Place
&
place
,
bool
stop_gradient
,
bool
persistable
,
bool
need_initialize
)
:
VarBase
(
name
,
dtype
,
framework
::
make_ddim
(
shape
),
place
,
nullptr
,
nullptr
,
stop_gradient
,
persistable
,
need_initialize
)
{}
private:
// TODO(minqiyang): need support SelectedRows
VarBase
(
const
std
::
string
&
name
,
framework
::
proto
::
VarType
::
Type
dtype
,
const
framework
::
DDim
&
shape
,
const
platform
::
Place
&
place
,
std
::
unique_ptr
<
framework
::
Variable
>
var
,
VarBase
*
grad
,
bool
stop_gradient
,
bool
persistable
)
bool
stop_gradient
,
bool
persistable
,
bool
need_initialize
)
:
name_
(
name
),
type_
(
framework
::
proto
::
VarType
::
LOD_TENSOR
),
place_
(
place
),
var_
(
std
::
move
(
var
)),
grads_
(
grad
),
dtype_
(
dtype
),
stop_gradient_
(
stop_gradient
),
persistable_
(
persistable
),
pre_op_
(
nullptr
),
...
...
@@ -159,9 +168,17 @@ class VarBase {
}
auto
tensor
=
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
tensor
->
Resize
(
shape
);
tensor
->
mutable_data
(
place
,
dtype
);
VLOG
(
10
)
<<
"create varbase: "
<<
name_
<<
" type: "
<<
dtype
<<
" place: "
<<
place
;
if
(
need_initialize
)
{
tensor
->
mutable_data
(
place
,
dtype
);
is_initialized_
=
true
;
VLOG
(
2
)
<<
"initialized varbase: "
<<
name_
<<
" type: "
<<
dtype
<<
" place: "
<<
place
;
}
else
{
is_initialized_
=
false
;
VLOG
(
2
)
<<
"not initialized varbase: "
<<
name_
;
}
VLOG
(
2
)
<<
"create varbase: "
<<
name_
<<
" type: "
<<
dtype
<<
" place: "
<<
place
;
}
public:
...
...
@@ -173,10 +190,12 @@ class VarBase {
pre_op_
=
nullptr
;
pre_op_out_idx_
=
-
1
;
VLOG
(
2
)
<<
"destruct varbase: "
<<
name_
;
}
inline
void
SetName
(
const
std
::
string
&
name
)
{
name_
=
name
;
}
inline
std
::
string
Name
()
const
{
return
name_
;
}
inline
bool
IsInitialize
()
const
{
return
is_initialized_
;
}
inline
std
::
vector
<
int64_t
>
Shape
()
const
{
if
(
var_
->
IsInitialized
())
{
...
...
@@ -211,7 +230,7 @@ class VarBase {
inline
void
SetPersistable
(
bool
persistable
)
{
persistable_
=
persistable
;
}
inline
bool
IsPersistable
()
const
{
return
persistable_
;
}
inline
platform
::
Place
GetPlace
()
{
return
place_
;
}
inline
OpBase
*
PreOp
()
const
{
return
pre_op_
;
}
inline
int
PreOpOutIdx
()
const
{
return
pre_op_out_idx_
;
}
...
...
@@ -225,6 +244,17 @@ class VarBase {
}
}
void
InitBuffer
()
{
if
(
!
is_initialized_
)
{
var_
->
GetMutable
<
framework
::
LoDTensor
>
()
->
mutable_data
(
place_
,
dtype_
);
is_initialized_
=
true
;
VLOG
(
2
)
<<
"initialized varbase: "
<<
name_
<<
" type: "
<<
dtype_
<<
" place: "
<<
place_
;
}
else
{
VLOG
(
2
)
<<
"var: "
<<
name_
<<
" has already been initialized "
;
}
}
void
TrackPreOp
(
OpBase
*
pre_op
,
const
std
::
string
&
pre_op_out_name
,
int
pre_op_out_idx
,
bool
pre_op_stop_gradient
)
{
pre_op_
=
pre_op
;
...
...
@@ -263,9 +293,10 @@ class VarBase {
VarBase
*
grads_
;
private:
framework
::
proto
::
VarType
::
Type
dtype_
;
bool
stop_gradient_
;
bool
persistable_
;
bool
is_initialized_
;
OpBase
*
pre_op_
;
std
::
string
pre_op_out_name_
;
int
pre_op_out_idx_
;
...
...
paddle/fluid/imperative/tracer.cc
浏览文件 @
effc5559
...
...
@@ -46,7 +46,7 @@ void CreateGradOp(const framework::OpDesc& op_desc,
}
}
void
Init
Grad
(
VarBase
*
var
,
platform
::
DeviceContext
*
dev_ctx
)
{
void
CreateNoBuffuer
Grad
(
VarBase
*
var
,
platform
::
DeviceContext
*
dev_ctx
)
{
PADDLE_ENFORCE_NOT_NULL
(
var
,
"Could not get valid var base"
);
PADDLE_ENFORCE_NOT_NULL
(
dev_ctx
,
"Could not get valid device from forward op"
);
...
...
@@ -55,9 +55,7 @@ void InitGrad(VarBase* var, platform::DeviceContext* dev_ctx) {
auto
&
var_t
=
var
->
var_
->
Get
<
framework
::
LoDTensor
>
();
var
->
grads_
=
new
VarBase
(
var
->
GradName
(),
framework
::
proto
::
VarType
::
FP32
,
framework
::
vectorize
(
var_t
.
dims
()),
dev_ctx
->
GetPlace
(),
true
,
false
);
auto
grad_t
=
var
->
grads_
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
operators
::
math
::
set_constant
(
*
dev_ctx
,
grad_t
,
0.0
);
dev_ctx
->
GetPlace
(),
true
,
false
,
false
);
}
}
...
...
@@ -261,7 +259,7 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
grad_in_vars
.
emplace_back
(
fwd_var_it
->
second
);
}
else
{
VarBase
*
var
=
current_vars_map
[
var_it
->
second
];
Init
Grad
(
var
,
prepared_op
.
GetDeviceContext
());
CreateNoBuffuer
Grad
(
var
,
prepared_op
.
GetDeviceContext
());
// Douts.
grad_in_vars
.
emplace_back
(
var
->
grads_
);
}
...
...
@@ -279,7 +277,7 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
"operator %s's stop gradient be True"
,
op
->
Type
());
VarBase
*
var
=
current_vars_map
[
var_it
->
second
];
Init
Grad
(
var
,
prepared_op
.
GetDeviceContext
());
CreateNoBuffuer
Grad
(
var
,
prepared_op
.
GetDeviceContext
());
grad_out_vars
.
push_back
(
var
->
grads_
);
VLOG
(
3
)
<<
"grads output var name: "
<<
var
->
name_
;
}
...
...
@@ -289,6 +287,5 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
return
vars_saved_for_backward
;
}
}
// namespace imperative
}
// namespace paddle
python/paddle/fluid/dygraph/layers.py
浏览文件 @
effc5559
...
...
@@ -147,7 +147,8 @@ class Layer(core.Layer):
def
clear_gradients
(
self
):
for
p
in
self
.
parameters
():
p
.
clear_gradient
()
if
p
.
trainable
:
p
.
clear_gradient
()
def
build_once
(
self
,
*
args
):
pass
...
...
python/paddle/fluid/tests/unittests/test_imperative_gnn.py
浏览文件 @
effc5559
...
...
@@ -101,11 +101,11 @@ class TestDygraphGNN(unittest.TestCase):
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
exe
.
run
(
startup
)
static_loss
=
exe
.
run
(
feed
=
{
'features'
:
np
.
zero
s
(
'features'
:
np
.
one
s
(
[
1
,
100
,
50
],
dtype
=
np
.
float32
),
'adj'
:
np
.
zero
s
(
'adj'
:
np
.
one
s
(
[
1
,
100
,
100
],
dtype
=
np
.
float32
),
'labels'
:
np
.
zero
s
(
'labels'
:
np
.
one
s
(
[
100
,
1
],
dtype
=
np
.
int64
)
},
fetch_list
=
[
loss
])[
0
]
...
...
@@ -117,10 +117,10 @@ class TestDygraphGNN(unittest.TestCase):
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
features
=
np
.
zero
s
([
1
,
100
,
50
],
dtype
=
np
.
float32
)
features
=
np
.
one
s
([
1
,
100
,
50
],
dtype
=
np
.
float32
)
# Use selected rows when it's supported.
adj
=
np
.
zero
s
([
1
,
100
,
100
],
dtype
=
np
.
float32
)
labels
=
np
.
zero
s
([
100
,
1
],
dtype
=
np
.
int64
)
adj
=
np
.
one
s
([
1
,
100
,
100
],
dtype
=
np
.
float32
)
labels
=
np
.
one
s
([
100
,
1
],
dtype
=
np
.
int64
)
model
=
GCN
(
'test_gcn'
,
50
)
logits
=
model
(
to_variable
(
features
),
to_variable
(
adj
))
...
...
@@ -130,17 +130,20 @@ class TestDygraphGNN(unittest.TestCase):
loss
=
fluid
.
layers
.
softmax_with_cross_entropy
(
logits
,
to_variable
(
labels
))
loss
=
fluid
.
layers
.
reduce_sum
(
loss
)
loss
.
backward
()
adam
=
AdamOptimizer
(
learning_rate
=
1e-3
)
adam
.
minimize
(
loss
)
model
.
clear_gradients
()
with
fluid
.
dygraph
.
guard
():
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
features2
=
np
.
zero
s
([
1
,
100
,
50
],
dtype
=
np
.
float32
)
features2
=
np
.
one
s
([
1
,
100
,
50
],
dtype
=
np
.
float32
)
# Use selected rows when it's supported.
adj2
=
np
.
zero
s
([
1
,
100
,
100
],
dtype
=
np
.
float32
)
labels2
=
np
.
zero
s
([
100
,
1
],
dtype
=
np
.
int64
)
adj2
=
np
.
one
s
([
1
,
100
,
100
],
dtype
=
np
.
float32
)
labels2
=
np
.
one
s
([
100
,
1
],
dtype
=
np
.
int64
)
model2
=
GCN
(
'test_gcn'
,
50
)
logits2
=
model2
(
to_variable
(
features2
),
to_variable
(
adj2
))
...
...
@@ -150,8 +153,10 @@ class TestDygraphGNN(unittest.TestCase):
loss2
=
fluid
.
layers
.
softmax_with_cross_entropy
(
logits2
,
to_variable
(
labels2
))
loss2
=
fluid
.
layers
.
reduce_sum
(
loss2
)
loss2
.
backward
()
adam2
=
AdamOptimizer
(
learning_rate
=
1e-3
)
adam2
.
minimize
(
loss2
)
model2
.
clear_gradients
()
self
.
assertEqual
(
static_loss
,
loss
.
numpy
())
self
.
assertTrue
(
np
.
allclose
(
static_weight
,
model
.
gc
.
weight
.
numpy
()))
...
...
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