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提交 bcccce7a authored 作者: Brandon T. Willard's avatar Brandon T. Willard
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Apply pyupgrade to theano.gpuarray

上级 2e3f17cb
隐藏空白字符变更
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正在显示 包含 100 行增加 和 116 行删除
theano/gpuarray/__init__.py
浏览文件 @ bcccce7a
......@@ -152,7 +152,7 @@ def init_dev(dev, name=None, preallocate=None):
file=sys.stderr,
)
if preallocate < 0:
print("Disabling allocation cache on %s" % (dev,))
print("Disabling allocation cache on {}".format(dev))
elif preallocate > 0:
if preallocate <= 1:
gmem = min(preallocate, 0.95) * context.total_gmem
......
theano/gpuarray/basic_ops.py
浏览文件 @ bcccce7a
......@@ -4,7 +4,6 @@ import re
from collections import deque
import numpy as np
from six import string_types
import theano
from theano import Apply, Op, Type, Variable, config, tensor
......@@ -129,7 +128,7 @@ def gpuarray_helper_inc_dir():
return os.path.join(os.path.dirname(__file__), "c_code")
class Kernel(object):
class Kernel:
"""
This class groups together all the attributes of a gpu kernel.
......@@ -214,14 +213,14 @@ class Kernel(object):
@staticmethod
def get_flags(*types):
def get_dtype(t):
if isinstance(t, string_types):
if isinstance(t, str):
return np.dtype(t)
elif isinstance(t, Type):
return t.dtype
elif isinstance(t, Variable):
return t.type.dtype
else:
raise TypeError("can't get a dtype from %s" % (type(t),))
raise TypeError("can't get a dtype from {}".format(type(t)))
dtypes = [get_dtype(t) for t in types]
flags = dict()
......@@ -291,7 +290,7 @@ def get_ctype(dtype):
return "npy_" + dtype.name
class GpuKernelBase(object):
class GpuKernelBase:
"""
Base class for operations that need to compile kernels.
......@@ -334,7 +333,7 @@ class GpuKernelBase(object):
if isinstance(self.params_type, ParamsType) and self.params_type.has_type(
gpu_context_type
):
return "(%s->%s)" % (
return "({}->{})".format(
params_c_name,
self.params_type.get_field(gpu_context_type),
)
......@@ -351,14 +350,14 @@ class GpuKernelBase(object):
def c_headers(self):
try:
o = super(GpuKernelBase, self).c_headers()
o = super().c_headers()
except MethodNotDefined:
o = []
return o + ["gpuarray/types.h", "numpy/npy_common.h"]
def c_header_dirs(self):
try:
o = super(GpuKernelBase, self).c_header_dirs()
o = super().c_header_dirs()
except MethodNotDefined:
o = []
# We rely on the input types for the directory to gpuarray includes
......@@ -577,7 +576,7 @@ class CGpuKernelBase(COp, GpuKernelBase):
kcode = split[n + 1]
splt2 = kspec.split(":")
if len(splt2) != 3:
raise ValueError("Bad kernel spec: %s" % (kspec,))
raise ValueError("Bad kernel spec: {}".format(kspec))
kname = splt2[0].strip()
ktypes = [get_dtype(s.strip()) for s in splt2[1].split(",")]
kflags = splt2[2].strip()
......@@ -697,7 +696,7 @@ class GpuFromHost(Op):
self.context_name = context_name
def __str__(self):
return "GpuFromHost<%s>" % (self.context_name,)
return "GpuFromHost<{}>".format(self.context_name)
def make_node(self, x):
if not isinstance(x.type, tensor.TensorType):
......@@ -800,7 +799,7 @@ class GpuToGpu(Op):
self.context_name = context_name
def __str__(self):
return "GpuToGpu<%s>" % (self.context_name,)
return "GpuToGpu<{}>".format(self.context_name)
def make_node(self, x):
if not isinstance(x.type, GpuArrayType):
......@@ -893,7 +892,7 @@ class GpuAlloc(HideC, Alloc):
m = "{memset_0=True}"
else:
m = ""
return "%s<%s>%s" % (self.__class__.__name__, self.context_name, m)
return "{}<{}>{}".format(self.__class__.__name__, self.context_name, m)
def make_node(self, value, *shape):
value = as_gpuarray_variable(value, context_name=self.context_name)
......@@ -1436,7 +1435,7 @@ class GpuJoin(HideC, Join):
view = self.view
non_empty_tensor = tensors[view]
for i, inp in enumerate(tensors):
copy_to_list.append("als[%s] = &%s->ga;" % (i, inp))
copy_to_list.append("als[{}] = &{}->ga;".format(i, inp))
n = len(tensors)
fail = sub["fail"]
......@@ -1507,7 +1506,7 @@ class GpuSplit(HideC, Split):
_f16_ok = True
def __init__(self, len_splits):
super(GpuSplit, self).__init__(len_splits)
super().__init__(len_splits)
# The GPU version of Split returns splits as views of the input.
self.view_map = {}
for i in range(self.len_splits):
......
theano/gpuarray/blas.py
浏览文件 @ bcccce7a
from six import integer_types
import theano
from theano import Apply, Op
from theano.compile import optdb
......@@ -446,7 +444,7 @@ class GpuGemmBatch(BlasOp):
return Apply(self, [C, alpha, A, B, beta], [C.type()])
def c_headers(self):
return super(GpuGemmBatch, self).c_headers() + ["<gpuarray/blas.h>"]
return super().c_headers() + ["<gpuarray/blas.h>"]
def c_code(self, node, name, inp, out, sub):
vars = dict(
......@@ -544,7 +542,7 @@ class BaseGpuCorrMM(CGpuKernelBase):
num_groups=1,
unshared=False,
):
if isinstance(border_mode, integer_types):
if isinstance(border_mode, int):
if border_mode < 0:
raise ValueError(
"invalid border_mode {}, which must be a "
......@@ -595,7 +593,7 @@ class BaseGpuCorrMM(CGpuKernelBase):
return ((0, 0),) * 2
def __str__(self):
return "%s{%s, %s, %s, %s, %s}" % (
return "{}{{{}, {}, {}, {}, {}}}".format(
self.__class__.__name__,
self.border_mode,
str(self.subsample),
......@@ -1071,9 +1069,7 @@ class GpuCorrMM(BaseGpuCorrMM):
num_groups=1,
unshared=False,
):
super(GpuCorrMM, self).__init__(
border_mode, subsample, filter_dilation, num_groups, unshared
)
super().__init__(border_mode, subsample, filter_dilation, num_groups, unshared)
def make_node(self, img, kern):
ctx_name = infer_context_name(img, kern)
......@@ -1108,9 +1104,7 @@ class GpuCorrMM(BaseGpuCorrMM):
bottom, weights = inp
(top,) = out_
direction = "forward"
return super(GpuCorrMM, self).c_code_helper(
bottom, weights, top, direction, sub
)
return super().c_code_helper(bottom, weights, top, direction, sub)
def grad(self, inp, grads):
bottom, weights = inp
......@@ -1152,9 +1146,7 @@ class GpuCorrMM_gradWeights(BaseGpuCorrMM):
num_groups=1,
unshared=False,
):
super(GpuCorrMM_gradWeights, self).__init__(
border_mode, subsample, filter_dilation, num_groups, unshared
)
super().__init__(border_mode, subsample, filter_dilation, num_groups, unshared)
def make_node(self, img, topgrad, shape=None):
ctx_name = infer_context_name(img, topgrad)
......@@ -1207,7 +1199,7 @@ class GpuCorrMM_gradWeights(BaseGpuCorrMM):
height, width = inp[2:] or (None, None)
(weights,) = out_
direction = "backprop weights"
return super(GpuCorrMM_gradWeights, self).c_code_helper(
return super().c_code_helper(
bottom, weights, top, direction, sub, height, width
)
......@@ -1260,9 +1252,7 @@ class GpuCorrMM_gradInputs(BaseGpuCorrMM):
num_groups=1,
unshared=False,
):
super(GpuCorrMM_gradInputs, self).__init__(
border_mode, subsample, filter_dilation, num_groups, unshared
)
super().__init__(border_mode, subsample, filter_dilation, num_groups, unshared)
def make_node(self, kern, topgrad, shape=None):
ctx_name = infer_context_name(kern, topgrad)
......@@ -1311,7 +1301,7 @@ class GpuCorrMM_gradInputs(BaseGpuCorrMM):
height, width = inp[2:] or (None, None)
(bottom,) = out_
direction = "backprop inputs"
return super(GpuCorrMM_gradInputs, self).c_code_helper(
return super().c_code_helper(
bottom, weights, top, direction, sub, height, width
)
......@@ -1376,7 +1366,7 @@ class BaseGpuCorr3dMM(CGpuKernelBase):
filter_dilation=(1, 1, 1),
num_groups=1,
):
if isinstance(border_mode, integer_types):
if isinstance(border_mode, int):
border_mode = (border_mode, border_mode, border_mode)
if isinstance(border_mode, tuple):
pad_h, pad_w, pad_d = map(int, border_mode)
......@@ -1409,7 +1399,7 @@ class BaseGpuCorr3dMM(CGpuKernelBase):
return (0, 0, 0)
def __str__(self):
return "%s{%s, %s, %s, %s}" % (
return "{}{{{}, {}, {}, {}}}".format(
self.__class__.__name__,
self.border_mode,
str(self.subsample),
......@@ -1842,9 +1832,7 @@ class GpuCorr3dMM(BaseGpuCorr3dMM):
filter_dilation=(1, 1, 1),
num_groups=1,
):
super(GpuCorr3dMM, self).__init__(
border_mode, subsample, filter_dilation, num_groups
)
super().__init__(border_mode, subsample, filter_dilation, num_groups)
def make_node(self, img, kern):
ctx_name = infer_context_name(img, kern)
......@@ -1876,9 +1864,7 @@ class GpuCorr3dMM(BaseGpuCorr3dMM):
bottom, weights = inp
(top,) = out_
direction = "forward"
return super(GpuCorr3dMM, self).c_code_helper(
bottom, weights, top, direction, sub
)
return super().c_code_helper(bottom, weights, top, direction, sub)
def grad(self, inp, grads):
bottom, weights = inp
......@@ -1911,9 +1897,7 @@ class GpuCorr3dMM_gradWeights(BaseGpuCorr3dMM):
filter_dilation=(1, 1, 1),
num_groups=1,
):
super(GpuCorr3dMM_gradWeights, self).__init__(
border_mode, subsample, filter_dilation, num_groups
)
super().__init__(border_mode, subsample, filter_dilation, num_groups)
def make_node(self, img, topgrad, shape=None):
ctx_name = infer_context_name(img, topgrad)
......@@ -1958,7 +1942,7 @@ class GpuCorr3dMM_gradWeights(BaseGpuCorr3dMM):
height, width, depth = inp[2:] or (None, None, None)
(weights,) = out_
direction = "backprop weights"
return super(GpuCorr3dMM_gradWeights, self).c_code_helper(
return super().c_code_helper(
bottom, weights, top, direction, sub, height, width, depth
)
......@@ -2002,9 +1986,7 @@ class GpuCorr3dMM_gradInputs(BaseGpuCorr3dMM):
filter_dilation=(1, 1, 1),
num_groups=1,
):
super(GpuCorr3dMM_gradInputs, self).__init__(
border_mode, subsample, filter_dilation, num_groups
)
super().__init__(border_mode, subsample, filter_dilation, num_groups)
def make_node(self, kern, topgrad, shape=None):
ctx_name = infer_context_name(kern, topgrad)
......@@ -2051,7 +2033,7 @@ class GpuCorr3dMM_gradInputs(BaseGpuCorr3dMM):
height, width, depth = inp[2:] or (None, None, None)
(bottom,) = out_
direction = "backprop inputs"
return super(GpuCorr3dMM_gradInputs, self).c_code_helper(
return super().c_code_helper(
bottom, weights, top, direction, sub, height, width, depth
)
......
theano/gpuarray/cudnn_defs.py
浏览文件 @ bcccce7a
......@@ -48,7 +48,7 @@ def is_double_config(dtype, precision):
# exclude them from lists of supported algorithms.
class CuDNNV51(object):
class CuDNNV51:
version = 5
cudnnConvolutionMode_t = CEnumType(
......@@ -319,7 +319,7 @@ class CuDNNV6(CuDNNV51):
)
def fwd_algo_supports_dtype_config(self, algo, dtype, precision, ndim):
is_supported = super(CuDNNV6, self).fwd_algo_supports_dtype_config(
is_supported = super().fwd_algo_supports_dtype_config(
algo, dtype, precision, ndim
)
if not is_supported:
......@@ -339,7 +339,7 @@ class CuDNNV6(CuDNNV51):
return is_supported
def bwd_filter_algo_supports_dtype_config(self, algo, dtype, precision, ndim):
is_supported = super(CuDNNV6, self).bwd_filter_algo_supports_dtype_config(
is_supported = super().bwd_filter_algo_supports_dtype_config(
algo, dtype, precision, ndim
)
if not is_supported:
......@@ -354,7 +354,7 @@ class CuDNNV6(CuDNNV51):
return is_supported
def bwd_data_algo_supports_dtype_config(self, algo, dtype, precision, ndim):
is_supported = super(CuDNNV6, self).bwd_data_algo_supports_dtype_config(
is_supported = super().bwd_data_algo_supports_dtype_config(
algo, dtype, precision, ndim
)
if not is_supported:
......
theano/gpuarray/dnn.py
浏览文件 @ bcccce7a
......@@ -5,7 +5,6 @@ import warnings
from functools import reduce
import numpy as np
from six import integer_types
import theano
import theano.pathparse
......@@ -155,19 +154,19 @@ if ((err = cudnnCreate(&_handle)) != CUDNN_STATUS_SUCCESS) {
path_wrapper = '"' if os.name == "nt" else ""
params = ["-l", "cudnn"]
params.extend(
["-I%s%s%s" % (path_wrapper, gpuarray_helper_inc_dir(), path_wrapper)]
["-I{}{}{}".format(path_wrapper, gpuarray_helper_inc_dir(), path_wrapper)]
)
if config.dnn.include_path:
params.extend(
["-I%s%s%s" % (path_wrapper, config.dnn.include_path, path_wrapper)]
["-I{}{}{}".format(path_wrapper, config.dnn.include_path, path_wrapper)]
)
if config.cuda.include_path:
params.extend(
["-I%s%s%s" % (path_wrapper, config.cuda.include_path, path_wrapper)]
["-I{}{}{}".format(path_wrapper, config.cuda.include_path, path_wrapper)]
)
if config.dnn.library_path:
params.extend(
["-L%s%s%s" % (path_wrapper, config.dnn.library_path, path_wrapper)]
["-L{}{}{}".format(path_wrapper, config.dnn.library_path, path_wrapper)]
)
# Do not run here the test program. It would run on the
# default gpu, not the one selected by the user. If mixed
......@@ -462,7 +461,7 @@ class DnnBase(COp):
return []
def c_code_cache_version(self):
return (super(DnnBase, self).c_code_cache_version(), version(), 4)
return (super().c_code_cache_version(), version(), 4)
class GpuDnnConvDesc(COp):
......@@ -542,7 +541,7 @@ class GpuDnnConvDesc(COp):
if version() < 6000 and any([d != 1 for d in dilation]):
raise RuntimeError("Dilation > 1 not supported for cuDNN version < 6.")
if isinstance(border_mode, integer_types):
if isinstance(border_mode, int):
border_mode = (border_mode,) * len(subsample)
if isinstance(border_mode, tuple):
assert len(border_mode) == len(subsample)
......@@ -621,7 +620,7 @@ class GpuDnnConvDesc(COp):
nb_dims = property(lambda self: len(self.subsample))
def c_code_cache_version(self):
return (super(GpuDnnConvDesc, self).c_code_cache_version(), version())
return (super().c_code_cache_version(), version())
def __setstate__(self, d):
self.__dict__.update(d)
......@@ -646,7 +645,7 @@ def ensure_dt(val, default, name, dtype):
if hasattr(val, "ndim") and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
raise TypeError("{}: expected a scalar value".format(name))
if not val.type.dtype == dtype:
val = val.astype(dtype)
return val
......@@ -2971,7 +2970,9 @@ class GpuDnnRNNOp(DnnBase):
elif direction_mode == "unidirectional":
self.num_dirs = 1
else:
raise ValueError("direction_mode is invalid (got %s)" % (direction_mode,))
raise ValueError(
"direction_mode is invalid (got {})".format(direction_mode)
)
def dnn_context(self, node):
return node.outputs[1].type.context_name
......@@ -3114,7 +3115,7 @@ class GpuDnnRNNGradWeights(DnnBase):
return Apply(self, inputs, outputs)
class RNNBlock(object):
class RNNBlock:
"""
An object that allow us to use CuDNN RNN implementation.
TODO: make an example how to use. You can check Theano tests
......
theano/gpuarray/elemwise.py
浏览文件 @ bcccce7a
......@@ -103,7 +103,7 @@ class GpuElemwise(HideC, Elemwise):
if self.name is not None:
return self.name
items = str(sorted(self.inplace_pattern.items()))
return "GpuElemwise{%s}%s<gpuarray>" % (self.scalar_op, items)
return "GpuElemwise{{{}}}{}<gpuarray>".format(self.scalar_op, items)
def max_inputs(self, node_or_outputs):
return max_inputs_to_GpuElemwise(node_or_outputs)
......@@ -233,7 +233,7 @@ class GpuElemwise(HideC, Elemwise):
args[%(n)s].typecode = %(typecode)s;
args[%(n)s].flags = GE_READ;
""" % dict(
n=n, name='"%s"' % (name,), typecode=i.type.typecode
n=n, name='"{}"'.format(name), typecode=i.type.typecode
)
p = len(inps)
......@@ -249,7 +249,7 @@ class GpuElemwise(HideC, Elemwise):
args[%(n)s].typecode = %(typecode)s;
args[%(n)s].flags = GE_WRITE;
""" % dict(
n=p, name='"%s"' % (outs[n],), typecode=o.type.typecode
n=p, name='"{}"'.format(outs[n]), typecode=o.type.typecode
)
p += 1
......@@ -572,8 +572,8 @@ class GpuCAReduceCuda(GpuKernelBase, HideC, CAReduceDtype):
pre = "pre=%s,red=" % str(self.pre_scalar_op)
ax = ""
if self.axis is not None:
ax = "{%s}" % (", ".join(str(x) for x in self.axis),)
return "GpuCAReduceCuda{%s%s}%s" % (pre, str(self.scalar_op), ax)
ax = "{{{}}}".format(", ".join(str(x) for x in self.axis))
return "GpuCAReduceCuda{{{}{}}}{}".format(pre, str(self.scalar_op), ax)
def __setstate__(self, d):
self.__dict__.update(d)
......@@ -585,7 +585,7 @@ class GpuCAReduceCuda(GpuKernelBase, HideC, CAReduceDtype):
x = as_gpuarray_variable(x, infer_context_name(x))
if x.type.context.kind != b"cuda":
raise TypeError("GpuCAReduceCuda doesn't work for non-cuda devices")
ret = super(GpuCAReduceCuda, self).make_node(x)
ret = super().make_node(x)
self = copy.copy(self)
self.axis = ret.op.axis
if self.pre_scalar_op:
......@@ -3056,8 +3056,8 @@ class GpuCAReduceCPY(GpuKernelBase, HideC, CAReduceDtype):
def __str__(self):
ax = ""
if self.axis is not None:
ax = "{%s}" % (", ".join(str(x) for x in self.axis),)
return "GpuReduce{%s}%s" % (self.scalar_op, ax)
ax = "{{{}}}".format(", ".join(str(x) for x in self.axis))
return "GpuReduce{{{}}}{}".format(self.scalar_op, ax)
def make_node(self, input):
ctx_name = infer_context_name(input)
......
theano/gpuarray/extra_ops.py
浏览文件 @ bcccce7a
......@@ -73,7 +73,7 @@ class GpuCumOp(GpuKernelBase, Op):
)
if self.axis >= x.ndim or self.axis < -x.ndim:
raise ValueError("axis(={0}) out of bounds".format(self.axis))
raise ValueError("axis(={}) out of bounds".format(self.axis))
return Apply(self, [x], [x.type()])
def gpu_kernels(self, node, nodename):
......@@ -500,7 +500,7 @@ class GpuCumOp(GpuKernelBase, Op):
"""
% locals()
)
return super(GpuCumOp, self).c_support_code_struct(node, nodename) + code
return super().c_support_code_struct(node, nodename) + code
# GpuCumsumOp exists only to serve backward compatibility.
......
theano/gpuarray/kernel_codegen.py
浏览文件 @ bcccce7a
......@@ -27,8 +27,7 @@ def nvcc_kernel(name, params, body):
def flatbody():
for b in body:
if isinstance(b, (list, tuple)):
for bb in b:
yield bb
yield from b
else:
yield b
......@@ -94,8 +93,8 @@ def inline_reduce(N, buf, pos, count, manner_fn):
rest of the buffer is trashed by this function.
"""
loop_line = manner_fn("%s[%s]" % (buf, pos), "%s[i]" % (buf))
r_n = manner_fn("%s[%s]" % (buf, pos), "%s[%s+_n]" % (buf, pos))
loop_line = manner_fn("{}[{}]".format(buf, pos), "%s[i]" % (buf))
r_n = manner_fn("{}[{}]".format(buf, pos), "{}[{}+_n]".format(buf, pos))
return (
"""
......@@ -124,22 +123,22 @@ def inline_reduce(N, buf, pos, count, manner_fn):
@code_version(inline_reduce.code_version)
def inline_reduce_max(N, buf, pos, count):
return inline_reduce(N, buf, pos, count, lambda a, b: "max(%s, %s)" % (a, b))
return inline_reduce(N, buf, pos, count, lambda a, b: "max({}, {})".format(a, b))
@code_version(inline_reduce.code_version)
def inline_reduce_sum(N, buf, pos, count):
return inline_reduce(N, buf, pos, count, lambda a, b: "%s + %s" % (a, b))
return inline_reduce(N, buf, pos, count, lambda a, b: "{} + {}".format(a, b))
@code_version(inline_reduce.code_version)
def inline_reduce_min(N, buf, pos, count):
return inline_reduce(N, buf, pos, count, lambda a, b: "min(%s, %s)" % (a, b))
return inline_reduce(N, buf, pos, count, lambda a, b: "min({}, {})".format(a, b))
@code_version(inline_reduce.code_version)
def inline_reduce_prod(N, buf, pos, count):
return inline_reduce(N, buf, pos, count, lambda a, b: "%s * %s" % (a, b))
return inline_reduce(N, buf, pos, count, lambda a, b: "{} * {}".format(a, b))
@code_version((2,) + inline_reduce_max.code_version + inline_reduce_sum.code_version)
......@@ -275,8 +274,8 @@ def inline_reduce_fixed_shared(
loop_line = manner_fn(
"red", manner_init("%(load_x)s(%(x)s[i * %(stride_x)s])" % locals())
)
loop_line2 = manner_fn("%s[%s]" % (buf, pos), "%s[i]" % buf)
r_n = manner_fn("%s[%s]" % (buf, pos), "%s[%s+_n]" % (buf, pos))
loop_line2 = manner_fn("{}[{}]".format(buf, pos), "%s[i]" % buf)
r_n = manner_fn("{}[{}]".format(buf, pos), "{}[{}+_n]".format(buf, pos))
ctype = gpuarray.dtype_to_ctype(dtype)
return (
......@@ -331,7 +330,7 @@ def inline_reduce_fixed_shared_max(
load_x,
pos,
count,
lambda a, b: "max(%s, %s)" % (a, b),
lambda a, b: "max({}, {})".format(a, b),
lambda a: a,
b,
stride_b,
......@@ -431,7 +430,7 @@ def inline_softmax_fixed_shared(
load_x,
threadPos,
threadCount,
lambda a, b: "%s + %s" % (a, b),
lambda a, b: "{} + {}".format(a, b),
lambda a: "exp(%s - row_max)" % a,
b,
stride_b,
......
theano/gpuarray/linalg.py
浏览文件 @ bcccce7a
......@@ -137,7 +137,7 @@ class GpuCusolverSolve(Op):
if self.inplace:
self.destroy_map = {0: [0]}
assert A_structure in MATRIX_STRUCTURES_SOLVE
super(GpuCusolverSolve, self).__init__()
super().__init__()
def make_node(self, inp1, inp2):
if not cusolver_available:
......@@ -358,7 +358,7 @@ class GpuCublasTriangularSolve(Op):
def __init__(self, lower=True, trans="N"):
self.trans = trans
self.lower = lower
super(GpuCublasTriangularSolve, self).__init__()
super().__init__()
def make_node(self, inp1, inp2):
if not cublas_available:
......@@ -541,7 +541,7 @@ class GpuCholesky(Op):
self.inplace = inplace
if self.inplace:
self.destroy_map = {0: [0]}
super(GpuCholesky, self).__init__()
super().__init__()
def clone_inplace(self):
return self.__class__(lower=self.lower, inplace=True)
......@@ -788,7 +788,7 @@ class GpuMagmaSVD(GpuMagmaBase):
)
def prepare_node(self, node, storage_map, compute_map, impl):
super(GpuMagmaSVD, self).prepare_node(node, storage_map, compute_map, impl)
super().prepare_node(node, storage_map, compute_map, impl)
# Check node to prevent eventual errors with old pickled nodes.
if self.compute_uv:
A, B, C = node.outputs
......
theano/gpuarray/multinomial.py
浏览文件 @ bcccce7a
......@@ -534,4 +534,4 @@ class GPUAMultinomialWOReplacementFromUniform(GPUAChoiceFromUniform):
DeprecationWarning,
stacklevel=2,
)
super(GPUAMultinomialWOReplacementFromUniform, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
theano/gpuarray/opt.py
浏览文件 @ bcccce7a
......@@ -484,7 +484,7 @@ class GraphToGPU(Optimizer):
for (t, o) in not_used[::-1]:
if t > 0:
# Skip opt that have 0 times, they probably wasn't even tried.
print(blanc + " ", " %.3fs - %s" % (t, o), file=stream)
print(blanc + " ", " {:.3f}s - {}".format(t, o), file=stream)
print(file=stream)
@staticmethod
......@@ -2182,7 +2182,7 @@ def local_abstractconv3d_gradinputs_gemm_alt(node):
class ConvMetaOptimizer(LocalMetaOptimizer):
def __init__(self):
super(ConvMetaOptimizer, self).__init__()
super().__init__()
def time_call(self, fn):
start = time.time()
......
theano/gpuarray/rng_mrg.py
浏览文件 @ bcccce7a
......@@ -59,7 +59,7 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
return op(rstate, v_size)
def c_headers(self):
return super(GPUA_mrg_uniform, self).c_headers() + ["numpy_compat.h"]
return super().c_headers() + ["numpy_compat.h"]
def gpu_kernels(self, node, name):
write = write_w(self.output_type.dtype)
......
theano/gpuarray/sort.py
浏览文件 @ bcccce7a
......@@ -225,7 +225,7 @@ class GpuTopKOp(GpuKernelBase, TopKOp):
prep_output = ""
if self.return_values:
def_dvstrides = "const ssize_t *dvstrides = PyGpuArray_STRIDES(%s)" % yv
params_dv = "%s->ga.data, %s->ga.offset,\n" % (yv, yv)
params_dv = "{}->ga.data, {}->ga.offset,\n".format(yv, yv)
params_dv += "".join("dvstrides[%d], " % i for i in reordered_axes)
prep_output += (
"""
......@@ -241,7 +241,7 @@ class GpuTopKOp(GpuKernelBase, TopKOp):
if self.return_indices:
def_distrides = "const ssize_t *distrides = PyGpuArray_STRIDES(%s)" % yi
params_di = "%s->ga.data, %s->ga.offset,\n" % (yi, yi)
params_di = "{}->ga.data, {}->ga.offset,\n".format(yi, yi)
params_di += "".join("distrides[%d], " % i for i in reordered_axes)
prep_output += (
"""
......
theano/gpuarray/subtensor.py
浏览文件 @ bcccce7a
import numpy as np
from six import integer_types
from six.moves import StringIO
import theano.tensor as tt
......@@ -157,7 +156,7 @@ class GpuSubtensor(HideC, Subtensor):
def fix_idx(idx):
if idx is None:
return "0", 1
elif isinstance(idx, (np.integer, integer_types)):
elif isinstance(idx, (np.integer, int)):
return str(idx), 0
elif isinstance(idx, gof.Type):
return indices.pop(0), 0
......@@ -196,7 +195,7 @@ class GpuSubtensor(HideC, Subtensor):
else:
if isinstance(idx, gof.Type):
start = indices.pop(0)
elif isinstance(idx, (np.integer, integer_types)):
elif isinstance(idx, (np.integer, int)):
start = idx
else:
assert 0, idx
......@@ -454,7 +453,7 @@ int sub_setarray(GpuArray *dst, GpuArray *src) {
)
def c_code_cache_version(self):
parent_version = super(GpuIncSubtensor, self).c_code_cache_version()
parent_version = super().c_code_cache_version()
if not parent_version:
return
return parent_version + (10,)
......@@ -576,7 +575,7 @@ def check_and_convert_boolean_masks(input, idx_list):
return out_idx_list
class BaseGpuAdvancedSubtensor(object):
class BaseGpuAdvancedSubtensor:
def perform(self, node, inputs, out_):
(out,) = out_
x = inputs[0]
......@@ -703,7 +702,7 @@ class GpuAdvancedSubtensor(HideC, BaseGpuAdvancedSubtensor, AdvancedSubtensor):
return gof.Apply(self, [x] + rval.inputs[1:], [otype()])
class BaseGpuAdvancedIncSubtensor(object):
class BaseGpuAdvancedIncSubtensor:
def perform(self, node, inp, out_):
(out,) = out_
x = inp[0]
......@@ -1133,7 +1132,7 @@ class GpuAdvancedIncSubtensor1_dev20(GpuKernelBase, HideC, GpuAdvancedIncSubtens
return gof.Apply(self, [x_, y_, ilist_], [x_.type()])
def perform(self, node, inp, out, params):
return super(GpuAdvancedIncSubtensor1_dev20, self).perform(node, inp, out)
return super().perform(node, inp, out)
def c_code_cache_version(self):
return (14,)
......@@ -1269,9 +1268,7 @@ if (GpuArray_vector_add_fast(%(out)s, %(y)s, %(ind)s, %(params)s->set_instead_of
def c_support_code_struct(self, node, nodename):
return (
super(GpuAdvancedIncSubtensor1_dev20, self).c_support_code_struct(
node, nodename
)
super().c_support_code_struct(node, nodename)
+ """
int GpuArray_vector_add_fast(PyGpuArrayObject* py_self,
PyGpuArrayObject* py_other,
......
theano/gpuarray/type.py
浏览文件 @ bcccce7a
......@@ -80,7 +80,7 @@ def reg_context(name, ctx):
"""
if name in _context_reg:
raise ValueError("context name %s is already defined" % (name,))
raise ValueError("context name {} is already defined".format(name))
if not isinstance(ctx, gpuarray.GpuContext):
raise TypeError("context is not GpuContext")
_context_reg[name] = ctx
......@@ -101,7 +101,7 @@ def get_context(name):
"""
if name not in _context_reg:
raise ContextNotDefined("context name %s not defined" % (name,))
raise ContextNotDefined("context name {} not defined".format(name))
return _context_reg[name]
......@@ -189,7 +189,9 @@ class GpuArrayType(Type):
self.typecode = gpuarray.dtype_to_typecode(self.dtype)
except gpuarray.GpuArrayException:
raise TypeError(
"Unsupported dtype for %s: %s" % (self.__class__.__name__, self.dtype)
"Unsupported dtype for {}: {}".format(
self.__class__.__name__, self.dtype
)
)
def clone(self, dtype=None, broadcastable=None):
......@@ -233,7 +235,9 @@ class GpuArrayType(Type):
bcast = str(b)
else:
bcast = "%iD" % len(b)
return "GpuArrayType<%s>(%s, %s)" % (self.context_name, self.dtype, bcast)
return "GpuArrayType<{}>({}, {})".format(
self.context_name, self.dtype, bcast
)
def filter(self, data, strict=False, allow_downcast=None):
return self.filter_inplace(
......@@ -450,7 +454,9 @@ class GpuArrayType(Type):
}[self.dtype]
except KeyError:
raise TypeError(
"Unsupported dtype for %s: %s" % (self.__class__.__name__, self.dtype)
"Unsupported dtype for {}: {}".format(
self.__class__.__name__, self.dtype
)
)
def get_shape_info(self, obj):
......@@ -474,7 +480,7 @@ class GpuArrayType(Type):
)
def c_init(self, name, sub):
return "%s = NULL;" % (name,)
return "{} = NULL;".format(name)
def c_extract(self, name, sub, check_input=True):
# TODO I don't check broadcast stuff for now.
......@@ -499,7 +505,7 @@ class GpuArrayType(Type):
}
def c_cleanup(self, name, sub):
return "Py_XDECREF(%(name)s); %(name)s = NULL;" % {"name": name}
return "Py_XDECREF({name}); {name} = NULL;".format(name=name)
def c_sync(self, name, sub):
return """
......@@ -914,10 +920,10 @@ class GpuContextType(Type):
return a == b
def c_declare(self, name, sub, check_input=True):
return "PyGpuContextObject *%s;" % (name,)
return "PyGpuContextObject *{};".format(name)
def c_init(self, name, sub):
return "%s = NULL;" % (name,)
return "{} = NULL;".format(name)
def c_extract(self, name, sub, check_input=True):
if check_input:
......
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