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7ce0d45e
编写于
7月 11, 2018
作者:
Q
qiaolongfei
浏览文件
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电子邮件补丁
差异文件
fix adam and adamax optimizer
上级
e91ecd5d
变更
1
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并排
Showing
1 changed file
with
61 addition
and
61 deletion
+61
-61
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+61
-61
未找到文件。
python/paddle/fluid/optimizer.py
浏览文件 @
7ce0d45e
...
@@ -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
],
...
@@ -566,25 +564,28 @@ class AdamOptimizer(Optimizer):
...
@@ -566,25 +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
:
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"
,
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_
beta1_pow_acc
},
inputs
=
{
"X"
:
beta1_pow_acc
},
outputs
=
{
"Out"
:
self
.
_
beta1_pow_acc
},
outputs
=
{
"Out"
:
beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
attrs
=
{
"scale"
:
self
.
_beta1
})
scale_beta2
=
main_block
.
append_op
(
main_block
.
append_op
(
type
=
"scale"
,
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_
beta2_pow_acc
},
inputs
=
{
"X"
:
beta2_pow_acc
},
outputs
=
{
"Out"
:
self
.
_
beta2_pow_acc
},
outputs
=
{
"Out"
:
beta2_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta2
})
attrs
=
{
"scale"
:
self
.
_beta2
})
return
[
scale_beta1
,
scale_beta2
]
class
AdamaxOptimizer
(
Optimizer
):
class
AdamaxOptimizer
(
Optimizer
):
"""
"""
...
@@ -626,6 +627,7 @@ class AdamaxOptimizer(Optimizer):
...
@@ -626,6 +627,7 @@ 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 +647,16 @@ class AdamaxOptimizer(Optimizer):
...
@@ -645,21 +647,16 @@ 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 +664,8 @@ class AdamaxOptimizer(Optimizer):
...
@@ -667,6 +664,8 @@ 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 +675,7 @@ class AdamaxOptimizer(Optimizer):
...
@@ -676,7 +675,7 @@ 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
],
...
@@ -691,19 +690,20 @@ class AdamaxOptimizer(Optimizer):
...
@@ -691,19 +690,20 @@ 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
:
beta1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
param
)
main_block
.
append_op
(
type
=
"scale"
,
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_
beta1_pow_acc
},
inputs
=
{
"X"
:
beta1_pow_acc
},
outputs
=
{
"Out"
:
self
.
_
beta1_pow_acc
},
outputs
=
{
"Out"
:
beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
attrs
=
{
"scale"
:
self
.
_beta1
})
return
[
scale_beta1
]
class
DecayedAdagradOptimizer
(
Optimizer
):
class
DecayedAdagradOptimizer
(
Optimizer
):
"""
"""
...
...
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