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pytensor
Commits
afcb5350
提交
afcb5350
authored
6月 29, 2015
作者:
Iban Harlouchet
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
flake8 for tensor/blas.py; two E left
上级
715c215a
隐藏空白字符变更
内嵌
并排
正在显示
2 个修改的文件
包含
65 行增加
和
68 行删除
+65
-68
blas.py
theano/tensor/blas.py
+65
-67
test_flake8.py
theano/tests/test_flake8.py
+0
-1
没有找到文件。
theano/tensor/blas.py
浏览文件 @
afcb5350
...
...
@@ -164,7 +164,7 @@ def default_blas_ldflags():
global
numpy
try
:
if
(
hasattr
(
numpy
.
distutils
,
'__config__'
)
and
numpy
.
distutils
.
__config__
):
numpy
.
distutils
.
__config__
):
# If the old private interface is available use it as it
# don't print information to the user.
blas_info
=
numpy
.
distutils
.
__config__
.
blas_opt_info
...
...
@@ -319,8 +319,8 @@ SOMEPATH/Canopy_64bit/User/lib/python2.7/site-packages/numpy/distutils/system_in
AddConfigVar
(
'blas.ldflags'
,
"lib[s] to include for [Fortran] level-3 blas implementation"
,
StrParam
(
default_blas_ldflags
))
"lib[s] to include for [Fortran] level-3 blas implementation"
,
StrParam
(
default_blas_ldflags
))
try
:
...
...
@@ -333,12 +333,10 @@ try:
# `scipy.linalg.blas.fblas` with `scipy.linalg.blas`.
# See http://github.com/scipy/scipy/pull/358
fblas
=
scipy
.
linalg
.
blas
_blas_gemv_fns
=
{
numpy
.
dtype
(
'float32'
):
fblas
.
sgemv
,
numpy
.
dtype
(
'float64'
):
fblas
.
dgemv
,
numpy
.
dtype
(
'complex64'
):
fblas
.
cgemv
,
numpy
.
dtype
(
'complex128'
):
fblas
.
zgemv
,
}
_blas_gemv_fns
=
{
numpy
.
dtype
(
'float32'
):
fblas
.
sgemv
,
numpy
.
dtype
(
'float64'
):
fblas
.
dgemv
,
numpy
.
dtype
(
'complex64'
):
fblas
.
cgemv
,
numpy
.
dtype
(
'complex128'
):
fblas
.
zgemv
}
except
ImportError
as
e
:
have_fblas
=
False
# This is used in Gemv and ScipyGer. We use CGemv and CGer
...
...
@@ -400,8 +398,8 @@ class Gemv(Op):
# The following is not grounds for error because as long as
# sizes are 1 at time of perform() there is no problem
# if x.broadcastable[0] != A.broadcastable[1]:
# raise TypeError('broadcastable mismatch between x and A',
#
(x.type, A.type))
# raise TypeError('broadcastable mismatch between x and A',
#
(x.type, A.type))
return
Apply
(
self
,
[
y
,
alpha
,
A
,
x
,
beta
],
[
y
.
type
()])
def
perform
(
self
,
node
,
inputs
,
out_storage
):
...
...
@@ -411,9 +409,10 @@ class Gemv(Op):
gemv
=
_blas_gemv_fns
[
y
.
dtype
]
if
(
A
.
shape
[
0
]
!=
y
.
shape
[
0
]
or
A
.
shape
[
1
]
!=
x
.
shape
[
0
]):
raise
ValueError
(
'Incompatible shapes for gemv '
'(beta * y + alpha * dot(A, x)). y:
%
s, A:
%
s, x:
%
s '
%
(
y
.
shape
,
A
.
shape
,
x
.
shape
))
raise
ValueError
(
'Incompatible shapes for gemv '
'(beta * y + alpha * dot(A, x)). y:
%
s, A:
%
s, x:
%
s '
%
(
y
.
shape
,
A
.
shape
,
x
.
shape
))
# Here I suppose that A is in c order. If we don't make it
# explicitly as fortran order, scipy 0.7.2 seam to create
...
...
@@ -479,7 +478,7 @@ class Ger(Op):
alpha
=
T
.
as_tensor_variable
(
alpha
)
if
len
(
set
([
A
.
dtype
,
alpha
.
dtype
,
x
.
dtype
,
y
.
dtype
]))
!=
1
:
raise
TypeError
(
'ger requires matching dtypes'
,
(
A
.
dtype
,
alpha
.
dtype
,
x
.
dtype
,
y
.
dtype
))
(
A
.
dtype
,
alpha
.
dtype
,
x
.
dtype
,
y
.
dtype
))
if
alpha
.
ndim
!=
0
:
raise
TypeError
(
'ger requires scalar alpha'
,
alpha
.
type
)
if
A
.
ndim
!=
2
:
...
...
@@ -567,13 +566,14 @@ def _ldflags(ldflags_str, libs, flags, libs_dir, include_dir):
for
d
in
dirs
:
for
f
in
os
.
listdir
(
d
):
if
(
f
.
endswith
(
'.so'
)
or
f
.
endswith
(
'.dylib'
)
or
f
.
endswith
(
'.dll'
)):
f
.
endswith
(
'.dll'
)):
if
any
([
f
.
find
(
ll
)
>=
0
for
ll
in
l
]):
found_dyn
=
True
if
not
found_dyn
and
dirs
:
_logger
.
warning
(
"We did not found a dynamic library into the "
"library_dir of the library we use for blas. If you use "
"ATLAS, make sure to compile it with dynamics library."
)
_logger
.
warning
(
"We did not found a dynamic library into the "
"library_dir of the library we use for blas. If you use "
"ATLAS, make sure to compile it with dynamics library."
)
for
t
in
ldflags_str
.
split
():
# Remove extra quote.
...
...
@@ -644,7 +644,7 @@ class GemmRelated(Op):
return
ldflags
()
# code_cache_version is built by subclasses from
#
build_gemm_version
# build_gemm_version
def
c_compile_args
(
self
):
return
ldflags
(
libs
=
False
,
flags
=
True
)
...
...
@@ -673,7 +673,7 @@ class GemmRelated(Op):
int sx_0, sx_1, sy_0, sy_1, sz_0, sz_1;
"""
#setup_z_Nz_Sz = None
#
setup_z_Nz_Sz = None
check_xyz_rank2
=
"""
if (PyArray_NDIM(
%(_x)
s) != 2) {
...
...
@@ -823,7 +823,7 @@ class GemmRelated(Op):
{
"""
#case_float_ab_constants = None
#
case_float_ab_constants = None
case_float_gemm
=
"""
float* x = (float*)PyArray_DATA(
%(_x)
s);
...
...
@@ -856,7 +856,7 @@ class GemmRelated(Op):
{
"""
#case_double_ab_constants = None
#
case_double_ab_constants = None
case_double_gemm
=
"""
double* x = (double*)PyArray_DATA(
%(_x)
s);
...
...
@@ -1028,10 +1028,10 @@ class Gemm(GemmRelated):
if
not
(
z
.
dtype
==
a
.
dtype
==
x
.
dtype
==
y
.
dtype
==
b
.
dtype
):
raise
TypeError
(
Gemm
.
E_mixed
,
(
z
.
dtype
,
a
.
dtype
,
x
.
dtype
,
y
.
dtype
,
b
.
dtype
))
(
z
.
dtype
,
a
.
dtype
,
x
.
dtype
,
y
.
dtype
,
b
.
dtype
))
if
(
not
z
.
dtype
.
startswith
(
'float'
)
and
not
z
.
dtype
.
startswith
(
'complex'
)):
if
(
not
z
.
dtype
.
startswith
(
'float'
)
and
not
z
.
dtype
.
startswith
(
'complex'
)):
raise
TypeError
(
Gemm
.
E_float
,
(
z
.
dtype
))
output
=
z
.
type
()
...
...
@@ -1173,8 +1173,8 @@ class Gemm(GemmRelated):
_z
,
_a
,
_x
,
_y
,
_b
=
inp
_zout
,
=
out
if
node
.
inputs
[
0
]
.
type
.
dtype
.
startswith
(
'complex'
):
raise
utils
.
MethodNotDefined
(
'
%
s.c_code'
\
%
self
.
__class__
.
__name__
)
raise
utils
.
MethodNotDefined
(
'
%
s.c_code'
%
self
.
__class__
.
__name__
)
if
not
config
.
blas
.
ldflags
:
return
super
(
Gemm
,
self
)
.
c_code
(
node
,
name
,
(
_z
,
_a
,
_x
,
_y
,
_b
),
(
_zout
,
),
...
...
@@ -1203,9 +1203,9 @@ def res_is_a(node, op, maxclients=None):
else
:
retval
=
True
return
node
.
owner
\
and
node
.
owner
.
op
==
op
\
and
retval
return
(
node
.
owner
and
node
.
owner
.
op
==
op
and
retval
)
def
_as_scalar
(
res
,
dtype
=
None
):
...
...
@@ -1235,16 +1235,16 @@ def _as_scalar(res, dtype=None):
def
_is_real_matrix
(
res
):
return
res
.
type
.
dtype
in
(
'float32'
,
'float64'
)
\
and
res
.
type
.
ndim
==
2
\
and
res
.
type
.
broadcastable
[
0
]
==
False
\
and
res
.
type
.
broadcastable
[
1
]
==
False
# cope with tuple vs. list
return
(
res
.
type
.
dtype
in
(
'float32'
,
'float64'
)
and
res
.
type
.
ndim
==
2
and
res
.
type
.
broadcastable
[
0
]
==
False
and
res
.
type
.
broadcastable
[
1
]
==
False
)
# cope with tuple vs. list
def
_is_real_vector
(
res
):
return
res
.
type
.
dtype
in
(
'float32'
,
'float64'
)
\
and
res
.
type
.
ndim
==
1
\
and
res
.
type
.
broadcastable
[
0
]
==
False
return
(
res
.
type
.
dtype
in
(
'float32'
,
'float64'
)
and
res
.
type
.
ndim
==
1
and
res
.
type
.
broadcastable
[
0
]
==
False
)
def
_beta_L_plus_alpha_M
(
beta
,
L
,
alpha
,
M
,
recurse_flip
=
True
):
...
...
@@ -1262,8 +1262,8 @@ def _beta_L_plus_alpha_M(beta, L, alpha, M, recurse_flip=True):
# it also might be the case that there is a dimshuffle between the +
# and the dot22. local_dot_to_dot22 in particular will put in such things.
if
(
M
.
owner
and
isinstance
(
M
.
owner
.
op
,
T
.
DimShuffle
)
and
M
.
owner
.
inputs
[
0
]
.
owner
and
isinstance
(
M
.
owner
.
inputs
[
0
]
.
owner
.
op
,
Dot22
)):
M
.
owner
.
inputs
[
0
]
.
owner
and
isinstance
(
M
.
owner
.
inputs
[
0
]
.
owner
.
op
,
Dot22
)):
MM
=
M
.
owner
.
inputs
[
0
]
if
M
.
owner
.
op
.
new_order
==
(
0
,):
# it is making a column MM into a vector
...
...
@@ -1493,7 +1493,7 @@ def _gemm_from_factored_list(lst):
assert
len
(
gemm_of_sM_list
)
==
1
add_inputs
=
[
item_to_var
(
input
)
for
k
,
input
in
enumerate
(
lst
)
if
k
not
in
(
i
,
j
)]
for
k
,
input
in
enumerate
(
lst
)
if
k
not
in
(
i
,
j
)]
add_inputs
.
extend
(
gemm_of_sM_list
)
if
len
(
add_inputs
)
>
1
:
rval
=
[
T
.
add
(
*
add_inputs
)]
...
...
@@ -1583,7 +1583,7 @@ class GemmOptimizer(Optimizer):
(
theano
.
scalar
.
Add
,
theano
.
scalar
.
Sub
,
theano
.
scalar
.
Neg
,
theano
.
scalar
.
Mul
))):
continue
if
no
t
node
in
fgraph
.
apply_nodes
:
if
no
de
not
in
fgraph
.
apply_nodes
:
# This mean that we already removed this node from
# the graph
continue
...
...
@@ -1725,8 +1725,8 @@ class Dot22(GemmRelated):
_x
,
_y
=
inp
_zout
,
=
out
if
node
.
inputs
[
0
]
.
type
.
dtype
.
startswith
(
'complex'
):
raise
utils
.
MethodNotDefined
(
'
%
s.c_code'
\
%
self
.
__class__
.
__name__
)
raise
utils
.
MethodNotDefined
(
'
%
s.c_code'
%
self
.
__class__
.
__name__
)
if
len
(
self
.
c_libraries
())
<=
0
:
return
super
(
Dot22
,
self
)
.
c_code
(
node
,
name
,
(
_x
,
_y
),
(
_zout
,
),
sub
)
...
...
@@ -1895,17 +1895,16 @@ blas_optdb.register('local_dot_to_dot22',
in2out
(
local_dot_to_dot22
),
0
,
'fast_run'
,
'fast_compile'
)
blas_optdb
.
register
(
'gemm_optimizer'
,
GemmOptimizer
(),
10
,
'fast_run'
)
GemmOptimizer
(),
10
,
'fast_run'
)
blas_optdb
.
register
(
'local_gemm_to_gemv'
,
EquilibriumOptimizer
([
local_gemm_to_gemv
,
local_gemm_to_ger
,
local_dot22_to_ger_or_gemv
,
local_dimshuffle_lift
],
max_use_ratio
=
5
,
ignore_newtrees
=
False
),
15
,
'fast_run'
)
EquilibriumOptimizer
([
local_gemm_to_gemv
,
local_gemm_to_ger
,
local_dot22_to_ger_or_gemv
,
local_dimshuffle_lift
],
max_use_ratio
=
5
,
ignore_newtrees
=
False
),
15
,
'fast_run'
)
# After destroyhandler(49.5) but before we try to make elemwise things
...
...
@@ -1936,12 +1935,12 @@ class Dot22Scalar(GemmRelated):
if
not
(
a
.
dtype
==
x
.
dtype
==
y
.
dtype
):
raise
TypeError
(
'Dot22Scalar requires matching dtypes'
,
(
a
.
dtype
,
x
.
dtype
,
y
.
dtype
))
(
a
.
dtype
,
x
.
dtype
,
y
.
dtype
))
if
(
not
a
.
dtype
.
startswith
(
'float'
)
and
not
a
.
dtype
.
startswith
(
'complex'
)):
if
(
not
a
.
dtype
.
startswith
(
'float'
)
and
not
a
.
dtype
.
startswith
(
'complex'
)):
raise
TypeError
(
'Dot22Scalar requires float or complex args'
,
a
.
dtype
)
a
.
dtype
)
bz
=
[
x
.
type
.
broadcastable
[
0
],
y
.
type
.
broadcastable
[
1
]]
outputs
=
[
T
.
tensor
(
x
.
type
.
dtype
,
bz
)]
...
...
@@ -1992,8 +1991,8 @@ class Dot22Scalar(GemmRelated):
_x
,
_y
,
_a
=
inp
_zout
,
=
out
if
node
.
inputs
[
0
]
.
type
.
dtype
.
startswith
(
'complex'
):
raise
utils
.
MethodNotDefined
(
'
%
s.c_code'
\
%
self
.
__class__
.
__name__
)
raise
utils
.
MethodNotDefined
(
'
%
s.c_code'
%
self
.
__class__
.
__name__
)
if
len
(
self
.
c_libraries
())
<=
0
:
return
super
(
Dot22Scalar
,
self
)
.
c_code
(
node
,
name
,
(
_x
,
_y
),
(
_zout
,
),
sub
)
...
...
@@ -2051,7 +2050,7 @@ def local_dot22_to_dot22scalar(node):
# The canonizer should have merged those mul together.
i_mul
=
[
x
.
owner
and
x
.
owner
.
op
==
T
.
mul
and
any
([
_as_scalar
(
x_i
,
dtype
=
d
.
dtype
)
for
x_i
in
x
.
owner
.
inputs
])
for
x_i
in
x
.
owner
.
inputs
])
for
x
in
node
.
inputs
]
if
not
any
(
i_mul
):
# no scalar in input and no multiplication
...
...
@@ -2065,8 +2064,7 @@ def local_dot22_to_dot22scalar(node):
scalar_idx
=
-
1
for
i
,
x
in
enumerate
(
m
.
owner
.
inputs
):
if
_as_scalar
(
x
,
dtype
=
d
.
dtype
)
and
(
theano
.
scalar
.
upcast
(
x
.
type
.
dtype
,
d
.
type
.
dtype
)
==
d
.
type
.
dtype
):
x
.
type
.
dtype
,
d
.
type
.
dtype
)
==
d
.
type
.
dtype
):
scalar_idx
=
i
break
...
...
@@ -2103,8 +2101,8 @@ def local_dot22_to_dot22scalar(node):
break
if
scalar_idx
<
0
:
_logger
.
info
(
'Not optimizing dot22 with inputs
%
s
%
s, as the type '
'of the scalar cannot be upcasted to the matrix type'
,
node
.
inputs
,
[
x
.
type
for
x
in
node
.
inputs
])
'of the scalar cannot be upcasted to the matrix type'
,
node
.
inputs
,
[
x
.
type
for
x
in
node
.
inputs
])
return
False
assert
scalar_idx
<
len
(
node
.
inputs
)
s
=
node
.
inputs
[
scalar_idx
]
...
...
@@ -2128,8 +2126,8 @@ blas_optdb.register('local_dot22_to_dot22scalar',
11
,
'fast_run'
)
#from opt import register_specialize, register_canonicalize
#@register_specialize
#
from opt import register_specialize, register_canonicalize
#
@register_specialize
@local_optimizer
([
T
.
sub
,
T
.
add
])
def
local_print_as_we_go_along
(
node
):
if
node
.
op
in
(
T
.
sub
,
T
.
add
):
...
...
theano/tests/test_flake8.py
浏览文件 @
afcb5350
...
...
@@ -58,7 +58,6 @@ whitelist_flake8 = [
"typed_list/tests/test_opt.py"
,
"typed_list/tests/test_basic.py"
,
"tensor/__init__.py"
,
"tensor/blas.py"
,
"tensor/extra_ops.py"
,
"tensor/nlinalg.py"
,
"tensor/blas_c.py"
,
...
...
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