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提交 25df3acb authored 作者: Chiheb Trabelsi's avatar Chiheb Trabelsi
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elemwise.py has been modified in order to respect the flake8 style.

elemwise.py do not contain long lines.
上级 27c8da22
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正在显示 包含 178 行增加 和 102 行删除
theano/sandbox/cuda/elemwise.py
浏览文件 @ 25df3acb
......@@ -97,7 +97,7 @@ class NaiveAlgo(object):
self.scalar_op.__class__.__name__, nodename, nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i
for i in xrange(nd)), file=sio)
for i in xrange(nd)), file=sio)
# declare inputs
for ipos, i in enumerate(node.inputs):
s = ", ".join(["const float * i%i_data" % ipos] +
......@@ -108,8 +108,8 @@ class NaiveAlgo(object):
s = ", ".join(["float * o%i_data" % ipos] +
["int o%i_str_%i" % (ipos, d) for d in xrange(nd)])
print("\t,", s, file=sio)
#print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
#print >> sio, "\t,", "float * o%i_data" % ipos
# print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
# print >> sio, "\t,", "float * o%i_data" % ipos
print("\t)\n{", file=sio)
print(" const int idx = blockIdx.x * blockDim.x + threadIdx.x;", file=sio)
print(" const int numThreads = blockDim.x * gridDim.x;", file=sio)
......@@ -129,7 +129,7 @@ class NaiveAlgo(object):
print(" const float * ii_i%i_data = i%i_data;" % (ipos, ipos), file=sio)
for ipos, i in enumerate(node.outputs):
print(" float * ii_o%i_data = o%i_data;" % (ipos, ipos), file=sio)
for d in xrange(nd-1, -1, -1):
for d in xrange(nd - 1, -1, -1):
if d > 0:
print(" int pos%i = ii %% dim%i;" % (d, d), file=sio)
print(" ii = ii / dim%i;" % d, file=sio)
......@@ -161,9 +161,9 @@ class NaiveAlgo(object):
print("ii_o%i_data[0] = o%i_i;" % (ipos, ipos), file=sio)
print(" }", file=sio)
#indent = " "*(4*d+7)
# indent = " "*(4*d+7)
# for ipos, i in enumerate(node.inputs):
#print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
# print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
print("}", file=sio)
# print sio.getvalue()
......@@ -211,10 +211,11 @@ class NaiveAlgo(object):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(node.outputs):
print("// Output ", ipos, str(i.type), file=sio)
print("static __global__ void kernel_%s_%s_%s(unsigned int numEls" % (
self.scalar_op.__class__.__name__,
nodename,
'tiling%i'%nd), file=sio)
print(
"static __global__ void kernel_%s_%s_%s(unsigned int numEls" %
(self.scalar_op.__class__.__name__,
nodename,
'tiling%i' % nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i for i in xrange(nd)), file=sio)
# declare inputs
......@@ -225,15 +226,15 @@ class NaiveAlgo(object):
for ipos, i in enumerate(node.outputs):
s = ", ".join(["float * o%i_data" % ipos] + list("int o%i_str_%i" % (ipos, d) for d in xrange(nd)))
print("\t,", s, file=sio)
#print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
#print >> sio, "\t,", "float * o%i_data" % ipos
# print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
# print >> sio, "\t,", "float * o%i_data" % ipos
print("\t)\n{", file=sio)
# For each input that is a scalar which has been broadcasted to a tensor,
# load it into a local variable
print(" __shared__ float value0[%i];" % len(node.inputs), file=sio)
print(" __shared__ int shared_dims[%(nd)s];" % locals(), file=sio)
#print >> sio, " __shared__ int shared_i_str[%(n_in)s][%(nd)s]"
# print >> sio, " __shared__ int shared_i_str[%(n_in)s][%(nd)s]"
print(" if ((threadIdx.x == 0) && (threadIdx.y == 0)) {", file=sio)
for ipos, i in enumerate(node.inputs):
if _logical_scalar(i):
......@@ -274,15 +275,18 @@ class NaiveAlgo(object):
# perform the scalar operation on the input and output references
# TODO: What if the scalar_op needs support_code??
task_code = self.scalar_op.c_code(
Apply(self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs])
, nodename + '_scalar_'
, get_str_list_logical_scalar(node, value_str='value0[%i]')
, ['ii_o%i_data[0]'%ipos for ipos, i in enumerate(node.outputs)]
, sub=dict(fail='return;')) # TODO: set a failure code somehow!!!
Apply(
self.scalar_op,
[scalar.Scalar(
dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(
dtype=output.type.dtype).make_variable()
for output in node.outputs]),
nodename + '_scalar_',
get_str_list_logical_scalar(node, value_str='value0[%i]'),
['ii_o%i_data[0]' % ipos for ipos, i in enumerate(node.outputs)],
sub=dict(fail='return;')) # TODO: set a failure code somehow!!!
print(" ", task_code, file=sio)
print(" }" * nd, file=sio)
......@@ -290,9 +294,9 @@ class NaiveAlgo(object):
# TODO: insert runtime stride checks that select the best loop order either here, or in
# the host code that launched the kernel (host code probably better spot)
#indent = " "*(4*d+7)
# indent = " "*(4*d+7)
# for ipos, i in enumerate(node.inputs):
#print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
# print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
print("}", file=sio)
print(sio.getvalue())
......@@ -319,10 +323,11 @@ class NaiveAlgo(object):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(node.outputs):
print("// Output ", ipos, str(i.type), file=sio)
print("static __global__ void kernel_%s_%s_%s(unsigned int numEls" % (
self.scalar_op.__class__.__name__,
nodename,
'tiling%i_less_registers'%nd), file=sio)
print(
"static __global__ void kernel_%s_%s_%s(unsigned int numEls" %
(self.scalar_op.__class__.__name__,
nodename,
'tiling%i_less_registers' % nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i for i in xrange(nd)), file=sio)
# declare inputs
......@@ -333,8 +338,8 @@ class NaiveAlgo(object):
for ipos, i in enumerate(node.outputs):
s = ", ".join(["float * o%i_data_0" % ipos] + list("int o%i_str_%i" % (ipos, d) for d in xrange(nd)))
print("\t,", s, file=sio)
#print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
#print >> sio, "\t,", "float * o%i_data" % ipos
# print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
# print >> sio, "\t,", "float * o%i_data" % ipos
print("\t)\n{", file=sio)
# TODO: Setting these to true makes the function fail SOMETIMES. I don't know why yet.
......@@ -350,6 +355,7 @@ class NaiveAlgo(object):
return "s%s_str[%i][%i]" % (io, p, d)
else:
return "%s%i_str_%i" % (io, p, d)
def limits(d):
if use_shared_limits:
return "limits[%i]" % d
......@@ -417,15 +423,19 @@ class NaiveAlgo(object):
def task_code(d):
print(self.scalar_op.c_code(
Apply(self.scalar_op,
Apply(
self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs])
, nodename + '_scalar_'
, ['i%i_data_%i[0]'%(ipos, d) for ipos, i in enumerate(node.inputs)]
, ['o%i_data_%i[0]'%(ipos, d) for ipos, i in enumerate(node.outputs)]
, sub=dict(fail='return;')), file=sio) # TODO: set a failure code somehow!!!
for output in node.outputs]),
nodename + '_scalar_',
['i%i_data_%i[0]' % (ipos, d) for ipos,
i in enumerate(node.inputs)],
['o%i_data_%i[0]' % (ipos, d) for ipos,
i in enumerate(node.outputs)],
sub=dict(fail='return;')), file=sio)
# TODO: set a failure code somehow!!!
if nd == 4:
decl_shared_stride(n_in, n_out, nd)
......@@ -495,16 +505,19 @@ class NaiveAlgo(object):
for ipos, i in enumerate(node.outputs):
print("npy_%s o%d_i;" % (i.dtype, ipos), file=sio)
task_code = self.scalar_op.c_code(
Apply(self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs])
, nodename + '_scalar_'
#, ['i%i_data[i]'%ipos for ipos, i in enumerate(node.inputs)]
, get_str_list_logical_scalar(node, data_str='i%i_data[i]')
, ['o%i_i'%ipos for ipos, i in enumerate(node.outputs)]
, sub=dict(fail='return;')) # TODO: set a failure code somehow!!!
Apply(
self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs]),
nodename + '_scalar_',
# , ['i%i_data[i]'%ipos for ipos,
# i in enumerate(node.inputs)]
get_str_list_logical_scalar(node, data_str='i%i_data[i]'),
['o%i_i' % ipos for ipos, i in enumerate(node.outputs)],
sub=dict(fail='return;'))
# TODO: set a failure code somehow!!!
print(" ", task_code, file=sio)
for ipos, _ in enumerate(node.outputs):
print("o%i_data[i] = o%i_i;" % (ipos, ipos), file=sio)
......@@ -539,18 +552,21 @@ class NaiveAlgo(object):
nb_outputs = len(node.outputs)
d = dict()
# input_params and output_params go into the function declaration/definition
input_params = ", ".join("const float * i%i_data, const int * i%i_str"%(ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_params = ", ".join("float * o%i_data, const int * o%i_str"%(ipos, ipos)
for ipos in xrange(len(node.outputs)))
input_params = ", ".join(
"const float * i%i_data, const int * i%i_str" % (ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_params = ", ".join(
"float * o%i_data, const int * o%i_str" % (ipos, ipos)
for ipos in xrange(len(node.outputs)))
# input_args and output_args go into the recursive call.
input_args = ", ".join("i%i_data, i%i_str"%(ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_args = ", ".join("o%i_data, o%i_str"%(ipos, ipos)
for ipos in xrange(len(node.outputs)))
input_args = ", ".join("i%i_data, i%i_str" % (ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_args = ", ".join("o%i_data, o%i_str" % (ipos, ipos)
for ipos in xrange(len(node.outputs)))
prod_dims = '*'.join(["dims[%i]"%di for di in xrange(nd)]+['1'])
prod_dims = '*'.join(
["dims[%i]" % di for di in xrange(nd)] + ['1'])
scalar_op = self.scalar_op.__class__.__name__
......@@ -578,20 +594,30 @@ class NaiveAlgo(object):
print("""
std::cerr << "calling kernel_%(scalar_op)s_%(nodename)s w numEls" << numEls << " dims"<< d << "\\n";
""" % locals(), file=sio)
print('std::cerr << ' + " << ' ' << ".join(['" "']+list("dims[%i]"%di
for di in xrange(nd)) + ["'\\n';"]), file=sio)
print(
'std::cerr << ' + " << ' ' << ".join(
['" "'] +
list("dims[%i]" % di for di in xrange(nd)) +
["'\\n';"]),
file=sio)
if self.verbose > 1:
for ipos in xrange(len(node.inputs)):
istrings = [
"i%s_str[%i]" % (ipos, di) for di in xrange(nd)]
ipositions = " << ' ' << ".join(
["i%s_data" % ipos] + istrings)
print("""
std::cerr << " %(ipos)s data strides" <<
""" % locals() + " << ' ' << ".join(["i%s_data"%ipos]
+ list("i%s_str[%i]"%(ipos, di) for di in xrange(nd))) + ''' << "\\n"; ''', file=sio)
std::cerr << " %(ipos)s data strides" << %(ipositions)s << "\\n";
""" % dict(ipos=ipos, ipositions=ipositions), file=sio)
for ipos in xrange(len(node.outputs)):
print("""
std::cerr << " %(ipos)s data strides" <<
""" % locals() + " << ' ' << ".join(["o%s_data"%ipos]
+ list("o%s_str[%i]"%(ipos, di) for di in xrange(nd))) + ''' << "\\n"; ''', file=sio)
""" % locals() + " << ' ' << ".join(
["o%s_data" % ipos] +
list(
"o%s_str[%i]" % (ipos, di) for di in xrange(nd)
)) +
''' << "\\n"; ''', file=sio)
# collapse dimension that are broadcast in all inputs.
# need to be done before contiguous collapse as it will break it.
# do the dimensions and the strides
......@@ -636,11 +662,19 @@ class NaiveAlgo(object):
print('std::cerr << "\\n";', file=sio)
if nd > 0:
for ipos in xrange(len(node.inputs)):
print('std::cerr << " local_str inputs %(ipos)s: " <<'%locals() + \
' << " " << '.join(["local_str[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_str inputs %(ipos)s: " <<' % locals() +
' << " " << '.join(["local_str[%s][%s]" % (ipos, x)
for x in xrange(nd)]) +
'<<"\\n";', file=sio)
for ipos in xrange(len(node.outputs)):
print('std::cerr << " local_ostr inputs %(ipos)s: " <<'%locals() + \
' << " " << '.join(["local_ostr[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_ostr inputs %(ipos)s: " <<' %
locals() +
' << " " << '.join(
["local_ostr[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
print("""
for(int id=0;id<nd_collapse;id++){
......@@ -668,35 +702,51 @@ class NaiveAlgo(object):
nd_collapse--; id--;
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
if self.verbose > 2:
print('std::cerr <<"after broadcast collapse\\n";', file=sio)
print('std::cerr<< "nd_collapse "<< nd_collapse << "\\n"; ', file=sio)
print('std::cerr << "local_dims";', file=sio)
for d in xrange(nd):
print('std::cerr << " " << local_dims[%(d)s]; '%locals(), file=sio)
print('std::cerr << " " << local_dims[%(d)s]; ' %
locals(), file=sio)
print('std::cerr << "\\n";', file=sio)
if nd > 0:
for ipos in xrange(len(node.inputs)):
print('std::cerr << " local_str %(ipos)s: " <<'%locals()+' << " " << '.join(["local_str[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print('std::cerr << " local_str %(ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_str[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
for ipos in xrange(len(node.outputs)):
print('std::cerr << " local_ostr %(ipos)s: " <<'%locals()+' << " " << '.join(["local_ostr[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_ostr %(ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_ostr[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
# collapse contiguous dimensions (ignoring scalars, generic version(collapse any dimensions, right, left, middle))
# this is a good idea because we make less index calculation in the gpu.
if nd > 0:
print("int nd_collapse_[%(nd)s] = {"%locals() + ','.join(['1' for x in xrange(nd)]) + "};", file=sio)
print("int nd_collapse_[%(nd)s] = {" %
locals() + ','.join(
['1' for x in xrange(nd)]) + "};", file=sio)
else:
print("int *nd_collapse_ = NULL;", file=sio)
for ipos in xrange(len(node.inputs)):
if not _logical_scalar(node.inputs[ipos]):
if nd > 0:
print("""
int nd_collapse_%(ipos)s[%(nd)s] = {"""%locals() + ','.join(['1' for x in xrange(nd)]) + "};", file=sio)
int nd_collapse_%(ipos)s[%(nd)s] = {""" %
locals() +
','.join(['1' for x in xrange(nd)]) +
"};", file=sio)
else:
print("""
int *nd_collapse_%(ipos)s = NULL;"""%locals(), file=sio)
int * nd_collapse_%(ipos)s = NULL;""" %
locals(), file=sio)
print("""
can_collapse_%(nodename)s(nd_collapse, local_dims, local_str[%(ipos)s], nd_collapse_%(ipos)s);
for(int i=0;i<nd_collapse;i++){
......@@ -707,8 +757,10 @@ nd_collapse_[i]=0;
if self.verbose > 1:
print("""
std::cerr<< "nd_collapse_%(ipos)s "<<
"""%locals(), file=sio)
print(' << " " << '.join(["nd_collapse_%s[" % ipos + str(i)+"]" for i in xrange(nd)]), file=sio)
""" % locals(), file=sio)
print(' << " " << '.join(["nd_collapse_ %s[" %
ipos + str(i) + "]" for i in xrange(nd)]),
file=sio)
print('<< "\\n";', file=sio)
# update the local stride.
......@@ -721,7 +773,7 @@ nd_collapse_[i]=0;
local_str[%(ipos)s][j-1]=local_str[%(ipos)s][j];
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
for ipos in xrange(len(node.outputs)):
print("""
......@@ -732,7 +784,7 @@ nd_collapse_[i]=0;
local_ostr[%(ipos)s][j-1]=local_ostr[%(ipos)s][j];
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
# update the local dims.
print("""
......@@ -743,16 +795,20 @@ nd_collapse_[i]=0;
local_dims[j-1]=local_dims[j];
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
# update the new number of dim
print("""
for(int i=1, end=nd_collapse;i<end;i++){
if(nd_collapse_[i]==1)nd_collapse--;
}
if(nd_collapse == 1 """%locals(), file=sio)
l = ["local_str[%s][nd_collapse-1]==1 "%ipos for ipos in xrange(len(node.inputs)) if not _logical_scalar(node.inputs[ipos])]
l += ["local_ostr[%s][nd_collapse-1]==1 "%ipos for ipos in xrange(len(node.outputs)) if not _logical_scalar(node.outputs[ipos])]
if(nd_collapse == 1 """ % locals(), file=sio)
l = ["local_str[%s][nd_collapse-1]==1 " %
ipos for ipos in xrange(len(node.inputs)) if not
_logical_scalar(node.inputs[ipos])]
l += ["local_ostr[%s][nd_collapse-1]==1 " %
ipos for ipos in xrange(len(node.outputs)) if not
_logical_scalar(node.outputs[ipos])]
if len(l) > 0:
print(" && ", " && ".join(l), file=sio)
print("""){nd_collapse=0;} """, file=sio)
......@@ -762,20 +818,31 @@ nd_collapse_[i]=0;
print("""std::cerr << "nd_collapse " << nd_collapse << "\\n"; """ % locals(), file=sio)
if self.verbose > 1:
for d in xrange(nd):
print('std::cerr << " " << local_dims[%(d)s]; '%locals(), file=sio)
print('std::cerr << " " << local_dims[%(d)s]; ' %
locals(),
file=sio)
print('std::cerr << "\\n";', file=sio)
if nd > 0:
for ipos in xrange(len(node.inputs)):
print('std::cerr << " local_str %(ipos)s: " <<'%locals()+' << " " << '.join(["local_str[%s][%s]"%(ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_str % (ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_str[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
for ipos in xrange(len(node.outputs)):
print('std::cerr << " local_ostr %(ipos)s: " <<'%locals()+' << " " << '.join(["local_ostr[%s][%s]"%(ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print('std::cerr << " local_ostr % (ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_ostr[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
def launch_Ccontiguous(nodename, scalar_op, sync=True):
kernel_call_args = ["numEls"]
for ipos in xrange(len(node.inputs)):
kernel_call_args.append("i%i_data"%ipos)
kernel_call_args.append("i%i_data" % ipos)
for ipos in xrange(len(node.outputs)):
kernel_call_args.append("o%i_data"%ipos)
kernel_call_args.append("o%i_data" % ipos)
kernel_call_args = ", ".join(kernel_call_args)
verb = ""
if self.verbose:
......@@ -817,20 +884,27 @@ nd_collapse_[i]=0;
# kernel_call_args are used to invoke the cuda kernel
local = "local_"
kernel_call_args = ["numEls"]
kernel_call_args.extend(local+"dims[%i]"%di for di in xrange(force_nd))
kernel_call_args.extend(
local + "dims[%i]" %
di for di in xrange(force_nd))
for ipos in xrange(len(node.inputs)):
kernel_call_args += ["i%i_data"%ipos] + list(local+"str[%i][%i]"%(ipos, di) for di in xrange(force_nd))
#strides = ", ".join("i%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
#kernel_call_args.append( "%s, i%i_data" % (strides, ipos))
kernel_call_args += ["i%i_data" % ipos] + list(
local + "str[%i][%i]" %
(ipos, di) for di in xrange(force_nd))
# strides = ", ".join("i%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
# kernel_call_args.append( "%s, i%i_data" % (strides, ipos))
for ipos in xrange(len(node.outputs)):
kernel_call_args += ["o%i_data"%ipos] + list(local+"ostr[%i][%i]"%(ipos, di) for di in xrange(force_nd))
#strides = ", ".join("o%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
#kernel_call_args.append( "%s, o%i_data" % (strides, ipos))
kernel_call_args += ["o%i_data" % ipos] + list(
local + "ostr[%i][%i]" %
(ipos, di) for di in xrange(force_nd))
# strides = ", ".join("o%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
# kernel_call_args.append( "%s, o%i_data" % (strides, ipos))
if self.verbose:
print("""
std::cerr << " Running general version with %(force_nd)s dims\\n";
"""%locals(), file=sio)
print("std::cerr << " + ' << " " << '.join(kernel_call_args)+' << "\\n";', file=sio)
""" % locals(), file=sio)
print("std::cerr << " + ' << " " << '.join(
kernel_call_args) + ' << "\\n";', file=sio)
# std::cerr << numEls << dims[0] << i0_data, i0_str[0] << o0_data, o0_str[0]\n;
kernel_call_args = ", ".join(kernel_call_args)
......@@ -866,12 +940,13 @@ nd_collapse_[i]=0;
else:
print(" return 0; " % locals(), file=sio)
print("if(numEls==0) return 0;", file=sio)
print("switch (nd_collapse==0?0:min(%(nd)s,nd_collapse)) {"%locals(), file=sio)
print("switch (nd_collapse==0?0:min(%(nd)s,nd_collapse)) {" %
locals(), file=sio)
print("case 0: {", file=sio)
launch_Ccontiguous(nodename, scalar_op, self.sync)
print(" } break;", file=sio)
for i in xrange(1, nd+1):
print("case "+str(i)+": {", file=sio)
for i in xrange(1, nd + 1):
print("case " + str(i) + ": {", file=sio)
launch_General(nodename, scalar_op, i, self.sync)
print(" } break;", file=sio)
......@@ -889,9 +964,10 @@ nd_collapse_[i]=0;
#define INTMOD_POW2(a, b) (a & ((1<<b)-1))
"""
kernels = "".join(
[self.c_src_kernel(node, nodename, x) for x in xrange(1, nd + 1)]
+ [self.c_src_kernel_Ccontiguous(node, nodename)]
+ [self.c_src_callkernel(node, nodename)])
[self.c_src_kernel(node, nodename, x)
for x in xrange(1, nd + 1)] +
[self.c_src_kernel_Ccontiguous(node, nodename)] +
[self.c_src_callkernel(node, nodename)])
return defines + kernels
def c_support_code(self):
......
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