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44224f4b
编写于
9月 18, 2017
作者:
W
wanghaoshuang
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import
unittest
import
numpy
import
itertools
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
__all__
=
[
'get_numeric_gradient'
]
def
create_op
(
op_type
):
# TODO need to set attrs
kwargs
=
dict
()
for
in_name
in
Operator
.
get_op_input_names
(
op_type
):
kwargs
[
in_name
]
=
in_name
for
out_name
in
Operator
.
get_op_output_names
(
op_type
):
kwargs
[
out_name
]
=
out_name
return
Operator
(
op_type
,
**
kwargs
)
def
grad_var_name
(
var_name
):
return
var_name
+
"@GRAD"
def
empty_var_name
():
return
"@EMPTY@"
def
get_numeric_gradient
(
op
,
input_values
,
output_name
,
input_to_check
,
delta
=
0.005
,
local_scope
=
None
,
in_place
=
False
):
"""
Get Numeric Gradient for an operator's input.
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
variable name. Value is numpy array.
:param output_name: The final output variable name.
:param input_to_check: The input variable need to get gradient.
:param delta: The perturbation value for numeric gradient method. The
smaller delta is, the more accurate result will get. But if that delta is
too small, it could occur numerical stability problem.
:param local_scope: The local scope used for get_numeric_gradient.
:return: The gradient array in numpy format.
"""
if
local_scope
is
None
:
local_scope
=
core
.
Scope
()
# Create all input variable in local_scope
for
var_name
in
input_values
:
var
=
local_scope
.
new_var
(
var_name
)
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
(
input_values
[
var_name
].
shape
)
tensor
.
alloc_float
(
core
.
CPUPlace
())
tensor
.
set
(
input_values
[
var_name
],
core
.
CPUPlace
())
# Create all output variable in local_scope
opts
=
op
.
outputs
()
for
key
in
opts
:
for
output
in
opts
[
key
]:
if
local_scope
.
find_var
(
output
)
is
None
:
local_scope
.
new_var
(
output
).
get_tensor
()
op
.
infer_shape
(
local_scope
)
# allocate output memory
for
key
in
opts
:
for
output
in
opts
[
key
]:
local_scope
.
find_var
(
output
).
get_tensor
().
alloc_float
(
core
.
CPUPlace
(
))
cpu_ctx
=
core
.
DeviceContext
.
create
(
core
.
CPUPlace
())
def
get_output
():
op
.
run
(
local_scope
,
cpu_ctx
)
return
numpy
.
array
(
local_scope
.
find_var
(
output_name
).
get_tensor
()).
sum
()
def
product
(
dim
):
return
reduce
(
lambda
a
,
b
:
a
*
b
,
dim
,
1
)
def
restore_inputs
():
for
var_name
in
input_values
:
tensor_
=
local_scope
.
find_var
(
var_name
).
get_tensor
()
tensor_
.
set
(
numpy
.
copy
(
input_values
[
var_name
]),
core
.
CPUPlace
())
# get the input tensor that we want to get it's numeric gradient.
tensor_to_check
=
local_scope
.
find_var
(
input_to_check
).
get_tensor
()
tensor_size
=
product
(
tensor_to_check
.
get_dims
())
# prepare a numpy array to store the gradient.
gradient_flat
=
numpy
.
zeros
(
shape
=
(
tensor_size
,
),
dtype
=
'float32'
)
# we only compute gradient of one element each time.
# we use a for loop to compute the gradient of every element.
for
i
in
xrange
(
tensor_size
):
if
in_place
:
restore_inputs
()
# get one input element throw it's index i.
origin
=
tensor_to_check
.
get_float_element
(
i
)
# add delta to it, run op and then get the sum of the result tensor.
x_pos
=
origin
+
delta
tensor_to_check
.
set_float_element
(
i
,
x_pos
)
y_pos
=
get_output
()
# plus delta to this element, run op and get the sum of the result tensor.
if
in_place
:
restore_inputs
()
x_neg
=
origin
-
delta
tensor_to_check
.
set_float_element
(
i
,
x_neg
)
y_neg
=
get_output
()
# restore old value
tensor_to_check
.
set_float_element
(
i
,
origin
)
# compute the gradient of this element and store it into a numpy array.
gradient_flat
[
i
]
=
(
y_pos
-
y_neg
)
/
delta
/
2
# reshape the gradient result to the shape of the source tensor.
return
gradient_flat
.
reshape
(
tensor_to_check
.
get_dims
())
class
GradientChecker
(
unittest
.
TestCase
):
def
__get_gradient
(
self
,
forward_op
,
backward_op
,
input_value
,
grad_names
,
place
):
"""Get the input gradients after running forward and backward operators
on the given places.
:param forward_op: forward operator
:type forward_op: Operator
:param backward_op: backward operator
:type backward_op: Operator
:param input_value: input values.
:type input_value: dict{string:numpy.array}
:param grad_names: the names of returned input gradients.
:type input_value: a list of string
:param place: the device type.
:type place: CPUPlace or GPUPlace
:return: the input grdients of given grad_names.
:rtype: a list of numpy.array
"""
scope
=
core
.
Scope
()
ctx
=
core
.
DeviceContext
.
create
(
place
)
inputs
=
forward_op
.
inputs
()
in_names
=
[
item
for
k
in
inputs
for
item
in
inputs
[
k
]]
outputs
=
forward_op
.
outputs
()
out_names
=
[
item
for
k
in
outputs
for
item
in
outputs
[
k
]]
# create input var and set value
for
name
,
value
in
input_value
.
iteritems
():
if
name
not
in
in_names
:
raise
ValueError
(
name
+
"does not exist in Op's inputs."
)
var
=
scope
.
new_var
(
name
).
get_tensor
()
var
.
set_dims
(
value
.
shape
)
var
.
set
(
value
,
place
)
# run forward op
for
out_name
in
out_names
:
scope
.
new_var
(
out_name
)
forward_op
.
infer_shape
(
scope
)
forward_op
.
run
(
scope
,
ctx
)
# set output var's shape
# set output grad to ones
for
name
in
out_names
:
out_tensor
=
scope
.
find_var
(
name
).
get_tensor
()
grad_tensor
=
scope
.
new_var
(
grad_var_name
(
name
)).
get_tensor
()
grad_tensor
.
set_dims
(
out_tensor
.
shape
())
data
=
numpy
.
ones
(
out_tensor
.
shape
(),
dtype
=
numpy
.
float32
)
grad_tensor
.
set
(
data
,
place
)
# run backward op
backward_outs
=
backward_op
.
outputs
()
backward_names
=
[
item
for
key
in
backward_outs
for
item
in
backward_outs
[
key
]
]
for
name
in
backward_names
:
scope
.
new_var
(
name
)
backward_op
.
infer_shape
(
scope
)
backward_op
.
run
(
scope
,
ctx
)
outs
=
[
numpy
.
array
(
scope
.
find_var
(
name
).
get_tensor
())
for
name
in
grad_names
]
return
outs
def
compare_grad
(
self
,
forward_op
,
input_value
,
no_grad_set
=
None
):
""" Compare the input gradients between CPU and GPU for the given forward
operator.
:param forward_op: forward operator
:type forward_op: Operator
:param input_value: input values.
:type input_value: dict{string:numpy.array}
:param no_grad_set: the set of variables names without gradients.
:type no_grad_set: a set of string
:raises: AssertionError, there is different gradient value.
"""
if
no_grad_set
is
None
:
no_grad_set
=
set
()
backward_op
=
core
.
Operator
.
backward
(
forward_op
,
no_grad_set
)
# return if not compile with GPU or not implementing GPU kernel
if
not
(
core
.
is_compile_gpu
()
and
backward_op
.
support_gpu
()):
return
outputs
=
backward_op
.
outputs
()
out_names
=
[
item
for
k
in
outputs
for
item
in
outputs
[
k
]]
out_names
=
filter
(
lambda
x
:
x
!=
empty_var_name
(),
out_names
)
cpu_grads
=
self
.
__get_gradient
(
forward_op
,
backward_op
,
input_value
,
out_names
,
core
.
CPUPlace
())
gpu_grads
=
self
.
__get_gradient
(
forward_op
,
backward_op
,
input_value
,
out_names
,
core
.
GPUPlace
(
0
))
for
c_grad
,
g_grad
,
name
in
itertools
.
izip
(
cpu_grads
,
gpu_grads
,
out_names
):
self
.
assertTrue
(
numpy
.
allclose
(
c_grad
,
g_grad
,
atol
=
1e-4
),
"output name: "
+
name
+
" has diff"
)
def
__assert_is_close
(
self
,
numeric_grads
,
analytic_grads
,
names
,
max_relative_error
,
msg_prefix
):
"""Use relative error for the comparison.
:param numeric_grads: the numerical graidents.
:type numeric_grads: a list of numpy.array
:param analytic_grads: the analytical graidents.
:type analytic_grads: a list of numpy.array
:param name: the names of gradients, used to print for debug.
:type names: a list of string
:param msg_prefix: string info, used to print for debug.
:type msf_prefix: string
"""
for
a
,
b
,
name
in
itertools
.
izip
(
numeric_grads
,
analytic_grads
,
names
):
print
"a=%s ; b=%s"
%
(
a
,
b
)
abs_a
=
numpy
.
abs
(
a
)
# if abs_a is nearly zero, then use abs error for a, not relative
# error.
abs_a
[
abs_a
<
1e-3
]
=
1
diff_mat
=
numpy
.
abs
(
a
-
b
)
/
abs_a
max_diff
=
numpy
.
max
(
diff_mat
)
def
err_msg
():
offset
=
numpy
.
argmax
(
diff_mat
>
max_relative_error
)
return
"%s Variable %s max gradient diff %f over limit %f, the first "
\
"error element is %d"
%
(
msg_prefix
,
name
,
max_diff
,
max_relative_error
,
offset
)
self
.
assertLessEqual
(
max_diff
,
max_relative_error
,
err_msg
())
def
check_grad
(
self
,
forward_op
,
input_vars
,
inputs_to_check
,
output_name
,
no_grad_set
=
None
,
only_cpu
=
False
,
in_place
=
False
,
max_relative_error
=
0.005
):
"""
:param forward_op: used to create backward_op
:param input_vars: numpy value of input variable. The following
computation will use these variables.
:param inputs_to_check: inputs var names that should check gradient.
:param output_name: the output variable name of forward network.
:param max_relative_error: The relative tolerance parameter.
:param no_grad_set: used when create backward ops
:param only_cpu: only compute and check gradient on cpu kernel.
:return:
"""
if
no_grad_set
is
None
:
no_grad_set
=
set
()
no_tmp_out
=
forward_op
.
no_intermediate_outputs
()
if
len
(
no_tmp_out
)
!=
1
:
raise
ValueError
(
"non temp out_names should be 1"
)
inputs
=
forward_op
.
inputs
()
in_names
=
[
item
for
k
in
inputs
for
item
in
inputs
[
k
]]
for
no_grad
in
no_grad_set
:
if
no_grad
not
in
in_names
:
raise
ValueError
(
"no_grad should be in in_names"
)
if
no_grad
in
inputs_to_check
:
raise
ValueError
(
"no_grad should not be in inputs_to_check"
)
backward_op
=
core
.
Operator
.
backward
(
forward_op
,
no_grad_set
)
places
=
[
core
.
CPUPlace
()]
if
not
only_cpu
and
core
.
is_compile_gpu
()
and
backward_op
.
support_gpu
():
places
.
append
(
core
.
GPUPlace
(
0
))
# get numerical gradients
numeric_grads
=
[
get_numeric_gradient
(
forward_op
,
input_vars
,
output_name
,
name
,
in_place
=
in_place
)
for
name
in
inputs_to_check
]
check_names
=
[
grad_var_name
(
name
)
for
name
in
inputs_to_check
]
for
place
in
places
:
analytic_grads
=
self
.
__get_gradient
(
forward_op
,
backward_op
,
input_vars
,
check_names
,
place
)
self
.
__assert_is_close
(
numeric_grads
,
analytic_grads
,
check_names
,
max_relative_error
,
"Gradient Check On %s"
%
str
(
place
))
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