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e2d56561
编写于
12月 18, 2018
作者:
Q
Qiao Longfei
提交者:
GitHub
12月 18, 2018
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差异文件
Merge pull request #14889 from jacquesqiao/optimize-adam
adam optimizer support lazy mode
上级
16c244bc
fe3995d3
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
84 addition
and
37 deletion
+84
-37
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/optimizers/adam_op.cc
paddle/fluid/operators/optimizers/adam_op.cc
+5
-0
paddle/fluid/operators/optimizers/adam_op.h
paddle/fluid/operators/optimizers/adam_op.h
+34
-12
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+12
-4
python/paddle/fluid/tests/unittests/test_adam_op.py
python/paddle/fluid/tests/unittests/test_adam_op.py
+32
-20
未找到文件。
paddle/fluid/API.spec
浏览文件 @
e2d56561
...
...
@@ -376,7 +376,7 @@ paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learnin
paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'
], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, Non
e))
paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'
, 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, Fals
e))
paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
...
...
paddle/fluid/operators/optimizers/adam_op.cc
浏览文件 @
e2d56561
...
...
@@ -109,6 +109,11 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
"(float, default 1.0e-8) "
"Constant for numerical stability"
)
.
SetDefault
(
1.0e-8
f
);
AddAttr
<
bool
>
(
"lazy_mode"
,
"(bool, default false) "
"only update the parameter that has gradient in sparse update"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Adam Optimizer.
...
...
paddle/fluid/operators/optimizers/adam_op.h
浏览文件 @
e2d56561
...
...
@@ -177,12 +177,13 @@ struct SparseAdamFunctor {
const
int64_t
*
rows_
;
int64_t
row_numel_
;
int64_t
row_count_
;
bool
lazy_mode_
;
SparseAdamFunctor
(
T
beta1
,
T
beta2
,
T
epsilon
,
const
T
*
beta1_pow
,
const
T
*
beta2_pow
,
const
T
*
mom1
,
T
*
mom1_out
,
const
T
*
mom2
,
T
*
mom2_out
,
const
T
*
lr
,
const
T
*
grad
,
const
T
*
param
,
T
*
param_out
,
const
int64_t
*
rows
,
int64_t
row_numel
,
int64_t
row_count
)
int64_t
row_numel
,
int64_t
row_count
,
bool
lazy_mode
)
:
beta1_
(
beta1
),
beta2_
(
beta2
),
epsilon_
(
epsilon
),
...
...
@@ -198,13 +199,10 @@ struct SparseAdamFunctor {
param_out_
(
param_out
),
rows_
(
rows
),
row_numel_
(
row_numel
),
row_count_
(
row_count
)
{}
inline
HOSTDEVICE
void
operator
()(
size_t
i
)
const
{
auto
row_idx
=
math
::
BinarySearch
<
int64_t
>
(
rows_
,
row_count_
,
i
/
row_numel_
);
T
g
=
row_idx
>=
0
?
grad_
[
row_idx
*
row_numel_
+
i
%
row_numel_
]
:
0
;
row_count_
(
row_count
),
lazy_mode_
(
lazy_mode
)
{}
inline
HOSTDEVICE
void
adam_update
(
size_t
i
,
T
g
)
const
{
// The following code is the same as dense
T
mom1
=
moment1_
[
i
];
T
mom2
=
moment2_
[
i
];
...
...
@@ -225,6 +223,17 @@ struct SparseAdamFunctor {
moment2_out_
[
i
]
=
mom2
;
param_out_
[
i
]
=
p
;
}
inline
HOSTDEVICE
void
operator
()(
size_t
i
)
const
{
auto
row_idx
=
math
::
BinarySearch
<
int64_t
>
(
rows_
,
row_count_
,
i
/
row_numel_
);
if
(
lazy_mode_
&&
row_idx
<
0
)
{
return
;
}
else
{
T
g
=
row_idx
>=
0
?
grad_
[
row_idx
*
row_numel_
+
i
%
row_numel_
]
:
0
;
adam_update
(
i
,
g
);
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
...
...
@@ -240,6 +249,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
using
paddle
::
framework
::
LoDTensor
;
using
paddle
::
operators
::
detail
::
Ref
;
bool
lazy_mode
=
ctx
.
Attr
<
bool
>
(
"lazy_mode"
);
T
beta1
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta1"
));
T
beta2
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta2"
));
T
epsilon
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
));
...
...
@@ -351,11 +361,23 @@ class AdamOpKernel : public framework::OpKernel<T> {
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
lr
.
template
data
<
T
>(),
grad_data
,
param
.
template
data
<
T
>(),
param_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
rows
,
row_numel
,
grad_merge
.
rows
().
size
());
platform
::
ForRange
<
DeviceContext
>
for_range
(
static_cast
<
const
DeviceContext
&>
(
ctx
.
device_context
()),
param
.
numel
());
for_range
(
functor
);
grad_merge
.
rows
().
size
(),
lazy_mode
);
VLOG
(
3
)
<<
"lazy_mode :"
<<
lazy_mode
;
if
(
lazy_mode
&&
platform
::
is_cpu_place
(
ctx
.
GetPlace
()))
{
size_t
row_count
=
grad_merge
.
rows
().
size
();
std
::
vector
<
int64_t
>
cpu_rows
(
grad_merge
.
rows
());
for
(
size_t
row_index
=
0
;
row_index
<
row_count
;
++
row_index
)
{
for
(
size_t
offset
=
0
;
offset
<
row_numel
;
++
offset
)
{
size_t
i
=
cpu_rows
[
row_index
]
*
row_numel
+
offset
;
functor
.
adam_update
(
i
,
grad_data
[
row_index
*
row_numel
+
offset
]);
}
}
}
else
{
platform
::
ForRange
<
DeviceContext
>
for_range
(
static_cast
<
const
DeviceContext
&>
(
ctx
.
device_context
()),
param
.
numel
());
for_range
(
functor
);
}
}
else
{
PADDLE_THROW
(
"Variable type not supported by adam_op"
);
}
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
e2d56561
...
...
@@ -641,9 +641,14 @@ class AdamOptimizer(Optimizer):
beta1 (float): The exponential decay rate for the 1st moment estimates.
beta2 (float): The exponential decay rate for the 2nd moment estimates.
epsilon (float): a small float value for numerical stability.
regularization: A Regularizer, such as
fluid.regularizer.L2DecayRegularizer.
regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer.
name: A optional name prefix.
lazy_mode(bool: false): The official Adam algorithm has two moving-average accumulators
the accumulators are updated at every step. Every element of the two moving-average is updated
in both dense mode and sparse mode. If the size of parameter is very large, then the update
may be very slow. The lazy mode only update the element that has gradient is the current
mini-batch, so it will be much more faster. But this mode has different semantics with the
original Adam algorithm and may lead to different result.
Examples:
.. code-block:: python
...
...
@@ -663,7 +668,8 @@ class AdamOptimizer(Optimizer):
beta2
=
0.999
,
epsilon
=
1e-8
,
regularization
=
None
,
name
=
None
):
name
=
None
,
lazy_mode
=
False
):
assert
learning_rate
is
not
None
assert
beta1
is
not
None
assert
beta2
is
not
None
...
...
@@ -676,6 +682,7 @@ class AdamOptimizer(Optimizer):
self
.
_beta1
=
beta1
self
.
_beta2
=
beta2
self
.
_epsilon
=
epsilon
self
.
_lazy_mode
=
lazy_mode
def
_create_accumulators
(
self
,
block
,
parameters
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
@@ -729,7 +736,8 @@ class AdamOptimizer(Optimizer):
attrs
=
{
"beta1"
:
self
.
_beta1
,
"beta2"
:
self
.
_beta2
,
"epsilon"
:
self
.
_epsilon
"epsilon"
:
self
.
_epsilon
,
"lazy_mode"
:
self
.
_lazy_mode
})
return
adam_op
...
...
python/paddle/fluid/tests/unittests/test_adam_op.py
浏览文件 @
e2d56561
...
...
@@ -194,7 +194,8 @@ def adam_step(inputs, attributes):
return
param_out
,
moment1_out
,
moment2_out
def
adam_step_sparse
(
inputs
,
attributes
,
height
,
rows
,
row_numel
,
np_grad
):
def
adam_step_sparse
(
inputs
,
attributes
,
height
,
rows
,
row_numel
,
np_grad
,
lazy_mode
):
'''
Simulate one step of the adam optimizer
:param inputs: dict of inputs
...
...
@@ -218,19 +219,30 @@ def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
moment2_out
=
np
.
zeros
(
shape
=
[
height
,
row_numel
])
param_out
=
np
.
zeros
(
shape
=
[
height
,
row_numel
])
for
idx
,
row_id
in
enumerate
(
rows
):
def
update_row
(
row_id
,
update_value
):
moment1_out
[
row_id
]
=
beta1
*
moment1
[
row_id
]
+
(
1
-
beta1
)
*
np_grad
[
idx
]
)
*
update_value
moment2_out
[
row_id
]
=
beta2
*
moment2
[
row_id
]
+
(
1
-
beta2
)
*
np
.
square
(
np_grad
[
idx
]
)
1
-
beta2
)
*
np
.
square
(
update_value
)
lr_t
=
lr
*
np
.
sqrt
(
1
-
beta2_pow
)
/
(
1
-
beta1_pow
)
param_out
[
row_id
]
=
param
[
row_id
]
-
lr_t
*
(
moment1_out
[
row_id
]
/
(
np
.
sqrt
(
moment2_out
[
row_id
])
+
epsilon
))
if
lazy_mode
:
for
idx
,
row_id
in
enumerate
(
rows
):
update_row
(
row_id
,
np_grad
[
idx
])
else
:
for
row_id
in
range
(
param_out
.
shape
[
0
]):
update_value
=
np
.
zeros
(
np_grad
[
0
].
shape
).
astype
(
"float32"
)
if
row_id
in
rows
:
update_value
=
np_grad
[
rows
.
index
(
row_id
)]
update_row
(
row_id
,
update_value
)
return
param_out
,
moment1_out
,
moment2_out
class
TestSparseAdamOp
(
unittest
.
TestCase
):
def
setup
(
self
,
scope
,
place
):
def
setup
(
self
,
scope
,
place
,
lazy_mode
):
beta1
=
0.78
beta2
=
0.836
epsilon
=
1e-4
...
...
@@ -248,6 +260,7 @@ class TestSparseAdamOp(unittest.TestCase):
'Beta2Pow'
:
np
.
array
([
beta2
**
10
]).
astype
(
"float32"
),
"LearningRate"
:
np
.
full
((
1
),
2.0
).
astype
(
"float32"
)
}
self
.
init_output
=
np
.
full
((
height
,
row_numel
),
0.0
).
astype
(
"float32"
)
self
.
attrs
=
{
'epsilon'
:
epsilon
,
'beta1'
:
beta1
,
'beta2'
:
beta2
}
grad_selected_rows
=
scope
.
var
(
'Grad'
).
get_selected_rows
()
...
...
@@ -262,19 +275,21 @@ class TestSparseAdamOp(unittest.TestCase):
self
.
sparse_inputs
=
[
"Grad"
]
param_out
,
mom1
,
mom2
=
adam_step_sparse
(
self
.
dense_inputs
,
self
.
attrs
,
height
,
rows
,
row_numel
,
np_array
)
param_out
,
mom1
,
mom2
=
adam_step_sparse
(
self
.
dense_inputs
,
self
.
attrs
,
height
,
rows
,
row_numel
,
np_array
,
lazy_mode
)
self
.
outputs
=
{
"ParamOut"
:
param_out
,
"Moment1Out"
:
mom1
,
"Moment2Out"
:
mom2
}
def
check_with_place
(
self
,
place
):
def
check_with_place
(
self
,
place
,
lazy_mode
):
scope
=
core
.
Scope
()
self
.
setup
(
scope
,
place
)
self
.
setup
(
scope
,
place
,
lazy_mode
)
op_args
=
dict
()
op_args
[
'lazy_mode'
]
=
lazy_mode
for
key
,
np_array
in
self
.
dense_inputs
.
items
():
var
=
scope
.
var
(
key
).
get_tensor
()
var
.
set
(
np_array
,
place
)
...
...
@@ -283,7 +298,7 @@ class TestSparseAdamOp(unittest.TestCase):
op_args
[
s
]
=
s
for
s
in
self
.
outputs
:
var
=
scope
.
var
(
s
).
get_tensor
()
var
.
set
(
self
.
outputs
[
s
]
,
place
)
var
.
set
(
self
.
init_output
,
place
)
op_args
[
s
]
=
s
for
k
in
self
.
attrs
:
op_args
[
k
]
=
self
.
attrs
[
k
]
...
...
@@ -297,20 +312,17 @@ class TestSparseAdamOp(unittest.TestCase):
actual
=
np
.
array
(
out_var
)
actual
=
actual
.
reshape
([
actual
.
size
])
np_array
=
np_array
.
reshape
([
np_array
.
size
])
for
idx
,
row_id
in
enumerate
(
self
.
rows
):
j
=
0
while
j
<
self
.
row_numel
:
pos
=
row_id
*
self
.
row_numel
+
j
self
.
assertLess
((
actual
[
pos
]
-
np_array
[
pos
])
/
actual
[
pos
],
0.00001
)
j
+=
1
def
test_sparse_sgd
(
self
):
for
i
in
range
(
np_array
.
size
):
self
.
assertLess
((
actual
[
i
]
-
np_array
[
i
]),
0.00001
)
def
test_sparse_adam
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
core
.
CUDAPlace
(
0
))
for
place
in
places
:
self
.
check_with_place
(
place
)
for
lazy_mode
in
(
True
,
False
):
self
.
check_with_place
(
place
,
lazy_mode
)
if
__name__
==
"__main__"
:
...
...
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