/usr/lib/python2.7/dist-packages/pyopencl/compyte/ndarray/gen_elemwise.py is in python-pyopencl 2017.2.2-1.
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This file implement 1 version of the elemwise op on the gpu.
The elemwise fct are also used with scalar operation! So it can happen
that ndim is 0 as with all scalar type.
"""
from __future__ import absolute_import
from __future__ import print_function
import numpy
import StringIO
import pygpu_ndarray as gpu_ndarray
from six.moves import map
from six.moves import range
_CL_MODE = hasattr(gpu_ndarray, "set_opencl_context")
if _CL_MODE:
# THIS IS NOT FINISHED
import pyopencl as cl
import pyopencl.array as cl_array
from pyopencl.tools import dtype_to_ctype
# import pyopencl._mymako as mako
from pyopencl._cluda import CLUDA_PREAMBLE
# TODO: use mako to get rid of the %if
CLUDA_PREAMBLE = CLUDA_PREAMBLE[:455]
CLUDA_PREAMBLE += """
#define LDIM_0 get_local_size(0)
#define LDIM_1 get_local_size(1)
#define LDIM_2 get_local_size(2)
#define GDIM_0 get_num_groups(0)
#define GDIM_1 get_num_groups(1)
#define GDIM_2 get_num_groups(2)
"""
# TODO, reuse the same context as the use used to create the memory.
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
else:
import pycuda.autoinit
import pycuda.driver as driver
from pycuda.compiler import SourceModule
from pycuda.tools import dtype_to_ctype
# import pycuda._mymako as mako
from pycuda._cluda import CLUDA_PREAMBLE
CLUDA_PREAMBLE += """
#define LDIM_0 blockDim.x
#define LDIM_1 blockDim.y
#define LDIM_2 blockDim.z
#define GDIM_0 gridDim.x
#define GDIM_1 gridDim.y
#define GDIM_2 gridDim.z
"""
from theano import Apply
from theano import scalar
from theano.tensor import TensorType
import theano
import logging
_logger_name = 'compyte.gen_elemwise'
_logger = logging.getLogger(_logger_name)
_logger.setLevel(logging.INFO)
_logger.addHandler(logging.StreamHandler()) # TO REMOVE
def warning(*msg):
_logger.warning(_logger_name + 'WARNING: ' + ' '.join(str(m) for m in msg))
def info(*msg):
_logger.info(_logger_name + 'INFO: ' + ' '.join(str(m) for m in msg))
def debug(*msg):
_logger.debug(_logger_name + 'DEBUG: ' + ' '.join(str(m) for m in msg))
if _CL_MODE:
gpu_ndarray.set_opencl_context(ctx.obj_ptr)
cast_int = numpy.intc
cast_uint = numpy.uintc
def _logical_scalar(x):
return numpy.all(x.type.broadcastable)
def get_str_list_logical_scalar(inputs, value_str='ii_i%i_value',
data_str='ii_i%i_data[0]'):
l = []
for ipos, i in enumerate(inputs):
if _logical_scalar(i):
l += [value_str % ipos]
else:
l += [data_str % ipos]
return l
class WrapOpenCLFunction(object):
def __init__(self, fct):
self.fct = fct
def _param_wrap(self, p):
if isinstance(p, MyGpuNdArray):
p = p.gpu_nd_array
if isinstance(p, gpu_ndarray.GpuNdArrayObject):
p = cl.MemoryObject.from_cl_mem_as_int(p.bytes)
return p
def set_block_shape(self, *shape):
self.local_size = shape
def param_set(self, *param):
self.param = [self._param_wrap(p) for p in param]
def launch_grid(self, *global_shape):
global_size = global_shape + (1,)
d = {"g_times_l": True}
return self.fct(queue, global_size, self.local_size,
*self.param, **d)
def compile_gpu_code(code, fct_name):
if _CL_MODE:
# Compile the gpu function with pyopencl
prg = cl.Program(ctx, code).build()
fct2 = getattr(prg, fct_name)
fct = WrapOpenCLFunction(fct2)
else:
# Compile the gpu function with pycuda
mod = SourceModule(code)
fct = mod.get_function(fct_name)
return fct
class ElemwiseAlgo(object):
verbose = 0 # 1, 2 or 3 for more verbose output.
cache_version = ()
cache_version = ('debug', 14, verbose)
def __init__(self, scalar_op, inplace_pattern={}):
"""
:param scalar_op: the scalar operation to execute on each element.
"""
self.scalar_op = scalar_op
self.inplace_pattern = inplace_pattern
def task_code(self, inputs, outputs, sio,
nodename, iname=None, oname=None):
if iname == None:
iname = get_str_list_logical_scalar(inputs)
if oname == None:
oname = ['ii_o%i_data[0]' % ipos for ipos, i in enumerate(outputs)]
print(self.scalar_op.c_code(
Apply(self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype)()
for input in inputs],
[scalar.Scalar(dtype=output.type.dtype)()
for output in outputs]),
nodename + '_scalar_',
iname,
oname,
sub=dict(fail='return;')), file=sio) # TODO: set a failure code somehow!!!
def c_src_kernel(self, inputs, outputs, nodename, nd, static="static"):
sio = StringIO.StringIO()
#print 'C_SRC_KERNEL', sio.getvalue()
for ipos, i in enumerate(inputs):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(outputs):
print("// Output ", ipos, str(i.type), file=sio)
print(static, (
"KERNEL void kernel_%s_%s(unsigned int numEls" % (nodename, nd)), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i
for i in range(nd)), file=sio)
#declare inputs
for ipos, i in enumerate(inputs):
s = ", ".join(["GLOBAL_MEM const %s * i%i_data" % (
dtype_to_ctype(i.dtype), ipos)] +
list("int i%i_str_%i" % (ipos, d)
for d in range(nd)))
print("\t,", s, file=sio)
#declare outputs
for ipos, i in enumerate(outputs):
s = ", ".join(["GLOBAL_MEM %s * o%i_data" % (
dtype_to_ctype(i.dtype), ipos)]
+ list("int o%i_str_%i" % (ipos, d)
for d in range(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("\t)\n{", file=sio)
print(" const int idx = GID_0 * LDIM_0 + LID_0;", file=sio)
print(" const int numThreads = LDIM_0 * GDIM_0;", file=sio)
# For each input that is a scalar which has been broadcasted
# to a tensor, load it into a local variable
for ipos, i in enumerate(inputs):
if _logical_scalar(i):
print(" const %s ii_i%i_value = i%i_data[0];" % (
dtype_to_ctype(i.dtype), ipos, ipos), file=sio)
#loop over the elements to be treated by this kernel call
print(" for (int i = idx; i < numEls; i += numThreads) {", file=sio)
# calculate the data pointers for all arguments
print(" int ii = i;", file=sio)
for ipos, i in enumerate(inputs):
if not _logical_scalar(i):
print((" GLOBAL_MEM const "
"%s * ii_i%i_data = i%i_data;" % (
dtype_to_ctype(i.dtype), ipos, ipos)), file=sio)
for ipos, i in enumerate(outputs):
print(" GLOBAL_MEM %s * ii_o%i_data = o%i_data;" % (
dtype_to_ctype(i.dtype), ipos, ipos), file=sio)
for d in range(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)
else:
print(" int pos%i = ii;" % d, file=sio)
for ipos, i in enumerate(inputs):
if not _logical_scalar(i):
print((" ii_i"
"%i_data += pos%i * i%i_str_%i;" % (ipos, d, ipos, d)), file=sio)
for ipos, i in enumerate(outputs):
print(" ii_o%i_data += pos%i * o%i_str_%i;" % (
ipos, d, ipos, d), file=sio)
# perform the scalar operation on the input and output references
#TODO: What if the scalar_op needs support_code??
self.task_code(inputs, outputs, sio, nodename)
print(" }", file=sio)
#indent = " "*(4*d+7)
#for ipos, i in enumerate(inputs):
#print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
print("}", file=sio)
#print sio.getvalue()
return sio.getvalue()
def c_src_kernel_Ccontiguous(self, inputs, outputs,
nodename, static="static"):
nd = outputs[0].type.ndim
sio = StringIO.StringIO()
#print 'C_SRC_KERNEL', sio.getvalue()
for ipos, i in enumerate(inputs):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(outputs):
print("// Output ", ipos, str(i.type), file=sio)
print(static, ("KERNEL void kernel_%s_Ccontiguous"
" (unsigned int numEls" % (nodename)), file=sio)
#declare inputs
for ipos, i in enumerate(inputs):
print("\t,", "GLOBAL_MEM const %s * i%i_data" % (
dtype_to_ctype(i.dtype), ipos), file=sio)
#declare outputs
for ipos, i in enumerate(outputs):
print("\t,", "GLOBAL_MEM %s * o%i_data" % (
dtype_to_ctype(i.dtype), ipos), file=sio)
print("\t)\n{", file=sio)
print(" const int idx = GID_0 * LDIM_0 + LID_0;", file=sio)
print(" const int numThreads = LDIM_0 * GDIM_0;", file=sio)
# For each input that is a scalar which has been broadcasted
# to a tensor, load it into a local variable
for ipos, i in enumerate(inputs):
if _logical_scalar(i):
print(" const %s ii_i%i_value = i%i_data[0];" % (
dtype_to_ctype(i.dtype), ipos, ipos), file=sio)
#loop over the elements to be treated by this kernel call
print(" for (int i = idx; i < numEls; i += numThreads) {", file=sio)
# perform the scalar operation on the input and output references
#TODO: What if the scalar_op needs support_code??
self.task_code(inputs, outputs, sio, nodename,
iname=get_str_list_logical_scalar(
inputs, data_str='i%i_data[i]'),
oname=['o%i_data[i]' % ipos
for ipos, i in enumerate(outputs)])
print(" }", file=sio)
print("}", file=sio)
#print sio.getvalue()
return sio.getvalue()
def c_src_callkernel(self, inputs, outputs, nodename):
#
# This function serves three main goals:
#
# The first is stride unpacking:
# it accepts input and output arguments as
# float * , int*
# pairs, and it constructs a kernel function call where inputs
# and arguments are named like
# float *, int, int, int ...
#
# The second is to recognize when any dimensions can be collapsed as
# being contiguous. That mean that we can merge that dimensions with
# another one for all inputs/outputs and have the same retusuls
# (confusing... read code)
#
# The thrid is to make a special case for scalar element. We allow
# the collapsing of them. In the ccontiguous and not contiguous case,
# we use registers to lower the number of memory access.
# TODO: make a special case for broadcasting, to store the
# data in shared memory.
nd = outputs[0].type.ndim
nb_inputs = len(inputs)
nb_outputs = len(outputs)
d = dict()
# input_params and output_params go into the function
# declaration/definition
input_params = ", ".join("const %s * i%i_data, const int * i%i_str" % (
dtype_to_ctype(inputs[i].dtype), ipos, ipos)
for ipos in range(len(inputs)))
output_params = ", ".join("%s * o%i_data, const int * o%i_str" % (
dtype_to_ctype(outputs[i].dtype),
ipos, ipos)
for ipos in range(len(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 range(len(inputs)))
output_args = ", ".join("o%i_data, o%i_str" % (ipos, ipos)
for ipos in range(len(outputs)))
prod_dims = '*'.join(["dims[%i]" % di for di in range(nd)] + ['1'])
sio = StringIO.StringIO()
print("""
static void can_collapse_%(nodename)s(int nd, const int * dims,
const int * strides,
int collapse[])
{
//can we collapse dims[i] and dims[i-1]
for(int i=nd-1;i>0;i--){
if(strides[i]*dims[i]==strides[i-1]){
//the dims nd-1 are not strided again dimension nd
collapse[i]=1;
}else collapse[i]=0;
}
}
""" % locals(), file=sio)
print("""
static int callkernel_%(nodename)s(unsigned int numEls, const int d,
const int * dims,
%(input_params)s,
%(output_params)s)
{
numEls = %(prod_dims)s;
""" % locals(), file=sio)
if self.verbose:
print("""
std::cerr << "calling kernel_%(nodename)s w numEls" << numEls << " dims"<< d << "\\n";
""" % locals(), file=sio)
print('std::cerr << ' + " << ' ' << ".join(['" "']+list("dims[%i]"%di
for di in range(nd)) + ["'\\n';"]), file=sio)
if self.verbose > 1:
for ipos in range(len(inputs)):
print("""
std::cerr << " %(ipos)s data strides" <<
""" % locals() + " << ' ' << ".join(["i%s_data" % ipos]
+ list("i%s_str[%i]" % (ipos, di)
for di in range(nd))) + ''' << "\\n"; ''', file=sio)
for ipos in range(len(outputs)):
print("""
std::cerr << " %(ipos)s data strides" <<
""" % locals() + " << ' ' << ".join(["o%s_data" % ipos]
+ list("o%s_str[%i]" % (ipos, di)
for di in range(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
print("""
int local_dims[%(nd)s];
int local_str[%(nb_inputs)s][%(nd)s];
int local_ostr[%(nb_inputs)s][%(nd)s];
int nd_collapse = %(nd)s;
for(int i=0;i<%(nd)s;i++){//init new dim
local_dims[i]=dims[i];
}
""" % locals(), file=sio)
for ipos in range(len(inputs)):
print("""
for(int i=0;i<%(nd)s;i++){//init new strides
local_str[%(ipos)s][i]=i%(ipos)s_str[i];
}
""" % locals(), file=sio)
for ipos in range(len(outputs)):
print("""
for(int i=0;i<%(nd)s;i++){//init new strides
local_ostr[%(ipos)s][i]=o%(ipos)s_str[i];
}
""" % locals(), file=sio)
if self.verbose > 2:
print('std::cerr <<"before 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 range(nd):
print('std::cerr << " " << local_dims[%(d)s]; ' % locals(), file=sio)
print('std::cerr << "\\n";', file=sio)
for ipos in range(len(inputs)):
print('std::cerr << " local_str inputs %(ipos)s: " <<' % locals()+' << " " << '.join(["local_str[%(ipos)s][%(x)s]"%locals() for x in range(nd)])+'<<"\\n";', file=sio)
for ipos in range(len(outputs)):
print('std::cerr << " local_ostr inputs %(ipos)s: " <<' % locals()+' << " " << '.join(["local_ostr[%(ipos)s][%(x)s]"%locals() for x in range(nd)])+'<<"\\n";', file=sio)
print("""
for(int id=0;id<nd_collapse;id++){
bool all_broadcast=true;
for(int input_id=0;input_id<%(nb_inputs)s;input_id++){
if(local_str[input_id][id]!=0 || local_dims[id]!=1) all_broadcast= false;
}
for(int input_id=0;input_id<%(nb_outputs)s;input_id++){
if(local_ostr[input_id][id]!=0 || local_dims[id]!=1) all_broadcast= false;
}
if(all_broadcast){
for(int j=id+1;j<nd_collapse;j++)//remove dims i from the array
local_dims[j-1]=local_dims[j];
for(int input_id=0;input_id<%(nb_inputs)s;input_id++){
for(int j=id+1;j<nd_collapse;j++){//remove dims i from the array
local_str[input_id][j-1]=local_str[input_id][j];
}
}
for(int output_id=0;output_id<%(nb_outputs)s;output_id++){
for(int j=id+1;j<nd_collapse;j++){//remove dims i from the array
local_ostr[output_id][j-1]=local_ostr[output_id][j];
}
}
nd_collapse--; id--;
}
}
""" % 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 range(nd):
print('std::cerr << " " << local_dims[%(d)s]; ' % locals(), file=sio)
print('std::cerr << "\\n";', file=sio)
for ipos in range(len(inputs)):
print('std::cerr << " local_str %(ipos)s: " <<' % locals()+' << " " << '.join(["local_str[%(ipos)s][%(x)s]"%locals() for x in range(nd)])+'<<"\\n";', file=sio)
for ipos in range(len(outputs)):
print('std::cerr << " local_ostr %(ipos)s: " <<' % locals()+' << " " << '.join(["local_ostr[%(ipos)s][%(x)s]"%locals() for x in range(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.
print("int nd_collapse_[%(nd)s] = {" % locals() +','.join(['1' for x in range(nd)]) +"};", file=sio)
for ipos in range(len(inputs)):
if not _logical_scalar(inputs[ipos]):
print("""
int nd_collapse_%(ipos)s[%(nd)s] = {""" % locals() +','.join(['1' for x in range(nd)]) +"};", 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++){
if(nd_collapse_%(ipos)s[i]==0)
nd_collapse_[i]=0;
}
""" % locals(), file=sio)
if self.verbose > 1:
print("""
std::cerr<< "nd_collapse_%(ipos)s "<<
""" % locals(), file=sio)
print(' << " " << '.join(
["nd_collapse_%(ipos)s[" % locals() + str(i) + "]"
for i in range(nd)]), file=sio)
print('<< "\\n";', file=sio)
print("""
std::cerr<< "nd_collapse_ "<<
""" % locals(), file=sio)
print(' << " " << '.join(
["nd_collapse_[" % locals() + str(i) + "]"
for i in range(nd)]), file=sio)
print('<< "\\n";', file=sio)
# update the local stride.
for ipos in range(len(inputs)):
print("""
for(int i=nd_collapse-1;i>0;i--){
if(nd_collapse_[i]==1){
local_str[%(ipos)s][i-1]=local_str[%(ipos)s][i];//set new strides
for(int j=i+1;j<nd_collapse;j++)//remove stride i from the array
local_str[%(ipos)s][j-1]=local_str[%(ipos)s][j];
}
}
""" % locals(), file=sio)
for ipos in range(len(outputs)):
print("""
for(int i=nd_collapse-1;i>0;i--){
if(nd_collapse_[i]==1){
local_ostr[%(ipos)s][i-1]=local_ostr[%(ipos)s][i];//set new strides
for(int j=i+1;j<nd_collapse;j++)//remove stride i from the array
local_ostr[%(ipos)s][j-1]=local_ostr[%(ipos)s][j];
}
}
""" % locals(), file=sio)
# update the local dims.
print("""
for(int i=nd_collapse-1;i>0;i--){
if(nd_collapse_[i]==1){
local_dims[i-1]*=local_dims[i];//set new dims
for(int j=i+1;j<nd_collapse;j++)//remove dims i from the array
local_dims[j-1]=local_dims[j];
}
}
""" % 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[%(ipos)s][nd_collapse-1]==1 " % locals()
for ipos in range(len(inputs))
if not _logical_scalar(inputs[ipos])]
l += ["local_ostr[%(ipos)s][nd_collapse-1]==1 " % locals()
for ipos in range(len(outputs))
if not _logical_scalar(outputs[ipos])]
if len(l) > 0:
print(" && ", " && ".join(l), file=sio)
print("""){nd_collapse=0;} """, file=sio)
if self.verbose:
print('std::cerr <<"after can_collapse\\n";', file=sio)
print("""std::cerr << "nd_collapse " << nd_collapse << "\\n"; """ % locals(), file=sio)
if self.verbose > 1:
for d in range(nd):
print('std::cerr << " " << local_dims[%(d)s]; ' % locals(), file=sio)
print('std::cerr << "\\n";', file=sio)
for ipos in range(len(inputs)):
print(('std::cerr << " local_str %(ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_str[%(ipos)s][%(x)s]" % locals()
for x in range(nd)]) + '<<"\\n";'), file=sio)
for ipos in range(len(outputs)):
print(('std::cerr << " local_ostr %(ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_ostr[%(ipos)s][%(x)s]" % locals()
for x in range(nd)]) + '<<"\\n";'), file=sio)
def launch_Ccontiguous(nodename, scalar_op):
kernel_call_args = ["numEls"]
for ipos in range(len(inputs)):
kernel_call_args.append("i%i_data" % ipos)
for ipos in range(len(outputs)):
kernel_call_args.append("o%i_data" % ipos)
kernel_call_args = ", ".join(kernel_call_args)
verb = ""
if self.verbose:
verb = 'std::cerr << " Running ccontiguous version\\n";'
print("""
//first use at least a full warp
int threads_per_block = std::min(numEls, (unsigned int)32); //WARP SIZE
//next start adding multiprocessors
int n_blocks = std::min(numEls/threads_per_block + (numEls %% threads_per_block?1:0), (unsigned int)30); // UP TO NUMBER OF MULTIPROCESSORS
// next start adding more warps per multiprocessor
if (threads_per_block * n_blocks < numEls)
threads_per_block = std::min(numEls/n_blocks, (unsigned int)NUM_VECTOR_OP_THREADS_PER_BLOCK);
kernel_%(nodename)s_Ccontiguous<<<n_blocks, threads_per_block>>>(%(kernel_call_args)s);
//std::cerr << "calling callkernel returned\\n";
""" % locals(), file=sio)
print("""
CNDA_THREAD_SYNC;
cudaError_t err = cudaGetLastError();
if( cudaSuccess != err)
{
PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s.\\n n_blocks=%%i threads_per_block=%%i\\n Call: %%s\\n",
"GpuElemwise %(nodename)s", cudaGetErrorString(err),
n_blocks, threads_per_block,
"kernel_%(nodename)s_Ccontiguous<<<n_blocks, threads_per_block>>>(%(kernel_call_args)s)");
return -1;
}
%(verb)s
return 0;
""" % locals(), file=sio)
def launch_General(nodename, scalar_op, force_nd):
# 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 range(force_nd))
for ipos in range(len(inputs)):
kernel_call_args += ["i%i_data" % ipos] + list(
local + "str[%i][%i]" % (ipos, di)
for di in range(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 range(len(outputs)):
kernel_call_args += ["o%i_data" % ipos] + list(
local + "ostr[%i][%i]" % (ipos, di)
for di in range(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)
#std::cerr << numEls << dims[0] << i0_data, i0_str[0] << o0_data, o0_str[0]\n;
kernel_call_args = ", ".join(kernel_call_args)
print("""
//first use at least a full warp
int threads_per_block = std::min(numEls, (unsigned int)32); //WARP SIZE
//next start adding multiprocessors
int n_blocks = std::min(numEls/threads_per_block + (numEls %% threads_per_block?1:0), (unsigned int)30); // UP TO NUMBER OF MULTIPROCESSORS
// next start adding more warps per multiprocessor
if (threads_per_block * n_blocks < numEls)
threads_per_block = std::min(numEls/n_blocks, (unsigned int)NUM_VECTOR_OP_THREADS_PER_BLOCK);
kernel_%(nodename)s_%(force_nd)s<<<n_blocks, threads_per_block>>>(%(kernel_call_args)s);
""" % locals(), file=sio)
print("""
CNDA_THREAD_SYNC;
cudaError_t err = cudaGetLastError();
if( cudaSuccess != err)
{
PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s.\\n n_blocks=%%i threads_per_block=%%i\\n Call: %%s\\n",
"GpuElemwise %(nodename)s", cudaGetErrorString(err),
n_blocks, threads_per_block,
"kernel_%(nodename)s_Ccontiguous<<<n_blocks, threads_per_block>>>(%(kernel_call_args)s)");
return -1;
}
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("case 0: {", file=sio)
launch_Ccontiguous(nodename, scalar_op)
print(" } break;", file=sio)
for i in range(1, nd + 1):
print("case " + str(i) + ": {", file=sio)
launch_General(nodename, scalar_op, i)
print(" } break;", file=sio)
print("}", file=sio) # end case
print("return -2;", file=sio) # should not get to this point
print("}", file=sio) # end fct
#N.B. cudaGetLastError is called by c_code
return sio.getvalue()
def c_support_code_apply(self, inputs, outputs, nodename):
nd = outputs[0].type.ndim
return "".join(
CLUDA_PREAMBLE,
[self.c_src_kernel(inputs, outputs, nodename, x)
for x in range(1, nd + 1)] +
[self.c_src_kernel_Ccontiguous(inputs, outputs, nodename),
self.c_src_callkernel(inputs, outputs, nodename),
])
def c_code(self, ninputs, noutputs, nodename, inputs, outputs, sub):
d = dict(sub)
nd = noutputs[0].type.ndim
d.update(locals())
sio = StringIO.StringIO()
nin = len(inputs)
nout = len(outputs)
fail = sub['fail']
opname = str(self.scalar_op)
initial_dims = ','.join('1' for i in range(nd))
if 1 or self.scalar_op == scalar.pow:
print("""
//std::cerr << "C_CODE %(opname)s START\\n";
//standard elemwise size checks
""" % locals(), file=sio)
print("""
int dims[%(nd)s] = {%(initial_dims)s};
""" % locals(), file=sio)
#check that all inputs have valid dimensions
emitted_inames = {}
for id, iname in enumerate(inputs):
if iname in emitted_inames:
assert emitted_inames[iname] is ninputs[id]
continue
broadcasts = ', '.join(map(str, list(map(int,
ninputs[id].broadcastable))))
nd = ninputs[id].ndim
print("""
int broadcasts_%(iname)s[%(nd)s] = {%(broadcasts)s};
""" % locals(), file=sio)
emitted_inames[iname] = ninputs[id]
#check that all inputs have valid dimensions
emitted_inames = {}
for id, iname in enumerate(inputs):
if iname in emitted_inames:
continue
print("""
//std::cerr << "C_CODE %(opname)s checking input %(iname)s\\n";
if (%(nd)s != %(iname)s->nd)
{
PyErr_Format(PyExc_TypeError, "need %(nd)s dims, not %%i", %(iname)s->nd);
%(fail)s;
}
for (int i = 0; i< %(nd)s; ++i)
{
dims[i] = (dims[i] == 1) ? CudaNdarray_HOST_DIMS(%(iname)s)[i] : dims[i];
if ((!(broadcasts_%(iname)s[i] && CudaNdarray_HOST_DIMS(%(iname)s)[i] == 1))&& (dims[i] != CudaNdarray_HOST_DIMS(%(iname)s)[i]))
{
//std::cerr << "C_CODE %(opname)s checking input %(iname)s failed\\n";
PyErr_Format(PyExc_ValueError, "GpuElemwise. Input dimension mis-match. One of your inputs has shape[%%i] == %%i, but the output's size on that axis is %%i.",
i,
CudaNdarray_HOST_DIMS(%(iname)s)[i],
dims[i]
);
%(fail)s;
}
}
""" % locals(), file=sio)
emitted_inames[iname] = True
#check that all outputs have valid dimensions
for idx, oname in enumerate(outputs):
if idx not in list(self.inplace_pattern.keys()):
print("""
for (int i = 0; (i< %(nd)s) && (%(oname)s); ++i) {
if (dims[i] != CudaNdarray_HOST_DIMS(%(oname)s)[i])
{
Py_DECREF(%(oname)s);
%(oname)s = NULL;
}
}
if (NULL == %(oname)s)
{
%(oname)s = (CudaNdarray*)CudaNdarray_New();
if (!%(oname)s)
{
//error string already set
%(fail)s;
}
if (CudaNdarray_alloc_contiguous(%(oname)s, %(nd)s, dims))
{
//error string already set
Py_DECREF(%(oname)s);
%(oname)s = NULL;
%(fail)s;
}
}
//std::cerr << "ELEMWISE NEW %(oname)s nd" << %(oname)s->nd << "\\n";
//std::cerr << "ELEMWISE NEW %(oname)s data" << %(oname)s->devdata << "\\n";
""" % locals(), file=sio)
else:
input_idx = self.inplace_pattern[idx]
iname = inputs[input_idx]
print("""
Py_XDECREF(%(oname)s);
%(oname)s = %(iname)s;
Py_INCREF(%(oname)s);
for (int i = 0; (i< %(nd)s) && (%(oname)s); ++i) {
if (dims[i] != CudaNdarray_HOST_DIMS(%(oname)s)[i])
{
Py_DECREF(%(oname)s);
%(oname)s = NULL;
%(fail)s;
}
}
//std::cerr << "ELEMWISE NEW %(oname)s nd" << %(oname)s->nd << "\\n";
//std::cerr << "ELEMWISE NEW %(oname)s data" << %(oname)s->devdata << "\\n";
""" % locals(), file=sio)
print("""
{
//new block so that failure gotos don't skip over variable initialization
//std::cerr << "calling callkernel\\n";
if (callkernel_%(nodename)s(1, 0, dims
""" % locals(), file=sio)
for iname in inputs:
print("""
, CudaNdarray_DEV_DATA(%(iname)s), CudaNdarray_HOST_STRIDES(%(iname)s)
""" % locals(), file=sio)
for oname in outputs:
print("""
, CudaNdarray_DEV_DATA(%(oname)s), CudaNdarray_HOST_STRIDES(%(oname)s)
""" % locals(), file=sio)
print("""
))
{
// error
""", file=sio)
for oname in outputs:
print("""
Py_DECREF(%(oname)s);
%(oname)s = NULL;
""" % locals(), file=sio)
print("""
%(fail)s;
}
else // no error
{
}
}
//std::cerr << "C_CODE %(opname)s END\\n";
""" % locals(), file=sio)
#print sio.getvalue()
return sio.getvalue()
def c_support_code(self):
return """
#define INTDIV_POW2(a, b) (a >> b)
#define INTMOD_POW2(a, b) (a & ((1<<b)-1))
"""
def dummy_holder_for_code_not_used():
def c_src_kernel_tiling(self, inputs, outputs, nodename):
""" The kernel applies to problems with <= 5 dimensions """
#The kernel is intended to be structured roughly like this:
"""
static __global__ void kernel()
{
for (int v = blockIdx.y; v < dim0; v += gridDim.x)
{
for (int w = blockIdx.y; w < dim1; w += gridDim.y)
{
for (int x = threadIdx.x; x < dim2; x += blockDim.x)
{
for (int y = threadIdx.y; y < dim3; y += blockDim.y)
{
for (int z = threadIdx.z; z < dim4; z += blockDim.z)
{
out[v * out_stride[0] + ...] = f(in1[...], in2[...])
}
}
}
}
}
}
"""
nd = outputs[0].type.ndim
sio = StringIO.StringIO()
#print 'C_SRC_KERNEL', sio.getvalue()
if nd in (4,):
# print some leading comments to make the code easier to read
for ipos, i in enumerate(inputs):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(outputs):
print("// Output ", ipos, str(i.type), file=sio)
print("""static __global__ void kernel_%s_%s(
unsigned int numEls""" % (
nodename,
'tiling%i' % nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i
for i in range(nd)), file=sio)
#declare inputs
for ipos, i in enumerate(inputs):
s = ", ".join(["const float * i%i_data" % ipos] + list(
"int i%i_str_%i" % (ipos, d) for d in range(nd)))
print("\t,", s, file=sio)
#declare outputs
for ipos, i in enumerate(outputs):
s = ", ".join(["float * o%i_data" % ipos] + list(
"int o%i_str_%i" % (ipos, d) for d in range(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("\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(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(" if ((threadIdx.x == 0) && (threadIdx.y == 0)) {", file=sio)
for ipos, i in enumerate(inputs):
if _logical_scalar(i):
print(" value0[%i] = i%i_data[0];" % (ipos,
ipos), file=sio)
for ipos in range(nd):
print(" shared_dims[%i] = dim%i;" % (ipos, ipos), file=sio)
print(" }", file=sio)
print(" __syncthreads();", file=sio)
if (nd == 4):
print("""
for (int pos0 = blockIdx.x; pos0 < shared_dims[0]; pos0 += gridDim.x)
{
for (int pos1 = blockIdx.y; pos1 < shared_dims[1]; pos1 += gridDim.y)
{
//for (int pos2 = threadIdx.x; pos2 < shared_dims[2]; pos2 += blockDim.x)
for (int pos2 = threadIdx.y; pos2 < shared_dims[2]; pos2 += blockDim.y)
{
//for (int pos3 = threadIdx.y; pos3 < shared_dims[3]; pos3 += blockDim.y)
for (int pos3 = threadIdx.x; pos3 < shared_dims[3]; pos3 += blockDim.x)
{
""", file=sio)
else:
raise NotImplementedError()
for ipos, i in enumerate(inputs):
if not _logical_scalar(i):
print(" const float * ii_i%i_data = i%i_data;" % (ipos, ipos), file=sio)
for ipos, i in enumerate(outputs):
print(" float * ii_o%i_data = o%i_data;" % (ipos, ipos), file=sio)
for d in range(nd):
for ipos, i in enumerate(inputs):
if not _logical_scalar(i):
print(" ii_i%i_data += pos%i * i%i_str_%i;" % (ipos, d, ipos, d), file=sio)
for ipos, i in enumerate(outputs):
print(" ii_o%i_data += pos%i * o%i_str_%i;" % (ipos, d, ipos, d), file=sio)
# perform the scalar operation on the input and output references
#TODO: What if the scalar_op needs support_code??
self.task_code(inputs, outputs, sio, nodename,
iname=get_str_list_logical_scalar(
inputs, value_str='value0[%i]'))
print(" }" * nd, file=sio)
#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)
#for ipos, i in enumerate(inputs):
#print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
print("}", file=sio)
print(sio.getvalue())
return sio.getvalue()
def c_src_kernel_tiling_less_registers(self, inputs, outputs, nodename):
""" The kernel applies to problems with <= 5 dimensions """
nd = outputs[0].type.ndim
n_in = len(inputs)
n_out = len(outputs)
sio = StringIO.StringIO()
if nd not in (2,):
return sio.getvalue()
# print some leading comments to make the code easier to read
for ipos, i in enumerate(inputs):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(outputs):
print("// Output ", ipos, str(i.type), file=sio)
print("static __global__ void kernel_%s_%s(unsigned int numEls" %(
nodename,
'tiling%i_less_registers'%nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i
for i in range(nd)), file=sio)
#declare inputs
for ipos, i in enumerate(inputs):
s = ", ".join(["const float * i%i_data_0" % ipos] + list(
"int i%i_str_%i" % (ipos, d) for d in range(nd)))
print("\t,", s, file=sio)
#declare outputs
for ipos, i in enumerate(outputs):
s = ", ".join(["float * o%i_data_0" % ipos] + list(
"int o%i_str_%i" % (ipos, d) for d in range(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("\t)\n{", file=sio)
# TODO: Setting these to true makes the function fail SOMETIMES. I don't know why yet.
use_shared_stride = False
use_shared_limits = False
def decl_limits(nd):
if use_shared_limits:
print("__shared__ float * limits[%(nd)s];" % locals(), file=sio)
def stride(io, p, d):
if use_shared_stride:
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
else:
return "limits%i" % d
def decl_shared_stride(nin, nout, nd):
if not use_shared_stride:
return
print("""
__shared__ int si_str[%(nin)s][%(nd)s];
__shared__ int so_str[%(nout)s][%(nd)s];
if ((threadIdx.x == 0) && (threadIdx.y == 0)) {
""" % locals(), file=sio)
for i in range(nin):
for d in range(nd):
print("si_str[%(i)s][%(d)s] = i%(i)s_str_%(d)s;" % locals(), file=sio)
for i in range(n_out):
for d in range(nd):
print("so_str[%(i)s][%(d)s] = o%(i)s_str_%(d)s;" % locals(), file=sio)
print("} __syncthreads();", file=sio)
def calc_limit(d):
s = stride('o', 0, d)
lname = limits(d)
if use_shared_limits:
print("if ((threadIdx.x == 0) && (threadIdx.y == 0)) {", file=sio)
if d == 0:
print("%(lname)s = o0_data_0 + dim%(d)s * %(s)s;" % locals(), file=sio)
else:
dm1 = d - 1
print("%(lname)s = o0_data_%(dm1)s + dim%(d)s * %(s)s;" % locals(), file=sio)
print("} __syncthreads();", file=sio)
else:
if d == 0:
print("const float * %(lname)s = o0_data_0 + dim%(d)s * %(s)s;" % locals(), file=sio)
else:
dm1 = d - 1
print("const float * %(lname)s = o0_data_%(dm1)s + dim%(d)s * %(s)s;" % locals(), file=sio)
def decl_ptrs(d, offset):
dm1 = d - 1
assert dm1 >= 0
for i in range(n_in):
s = stride('i', i, d)
print("const float * i%(i)s_data_%(d)s = i%(i)s_data_%(dm1)s + %(offset)s * %(s)s;" % locals(), file=sio)
for i in range(n_out):
s = stride('o', i, d)
print("float * o%(i)s_data_%(d)s = o%(i)s_data_%(dm1)s + %(offset)s * %(s)s;" % locals(), file=sio)
def inc_ptrs(d, amt):
for i in range(n_in):
s = stride('i', i, d)
print("i%(i)s_data_%(d)s += %(amt)s * %(s)s;" % locals(), file=sio)
for i in range(n_out):
s = stride('o', i, d)
print("o%(i)s_data_%(d)s += %(amt)s * %(s)s;" % locals(), file=sio)
def while_limit(d):
lname = limits(d)
print("while (o0_data_%(d)s < %(lname)s) { " % locals(), file=sio)
def end_while(d):
print("}", file=sio)
def task_code(d):
self.task_code(inputs, outputs, sio, nodename,
iname=['i%i_data_%i[0]' % (ipos, d)
for ipos, i in enumerate(inputs)],
oname=['o%i_data_%i[0]' % (ipos, d)
for ipos, i in enumerate(outputs)])
if nd == 4:
decl_shared_stride(n_in, n_out, nd)
decl_limits(nd)
calc_limit(0)
inc_ptrs(0, 'blockIdx.x')
while_limit(0)
if 1:
calc_limit(1)
decl_ptrs(1, 'blockIdx.y')
while_limit(1)
if 1:
calc_limit(2)
decl_ptrs(2, 'threadIdx.y')
while_limit(2)
if 1:
calc_limit(3)
decl_ptrs(3, 'threadIdx.x')
while_limit(3)
if 1:
task_code(3)
inc_ptrs(3, 'blockDim.x')
end_while(3)
inc_ptrs(2, 'blockDim.y')
end_while(2)
inc_ptrs(1, 'gridDim.y')
end_while(1)
inc_ptrs(0, 'gridDim.x')
end_while(0)
print("}", file=sio)
print(sio.getvalue())
return sio.getvalue()
def elemwise_collapses(inputs, outputs, out_shape=None, verbose=0):
"""
This collapse dimensions that are not needed when computing
elemwise. This is usefull as it lower the indexing computation
that is heavier on gpu then on cpu.
This is a generic version. It collapse dimensions at any place in
the shape. It handle broadcasted dimensions correctly.
There is no special handling needed for broadcasted scalar at this level.
@return: ndims, tuple(dims, strides) after collapsing.
"""
in_out = inputs + outputs
del inputs
if out_shape is not None:
local_dims = tuple(out_shape)
else:
# TODO, use the right algo here or make the parameter not optional
# We should always have the same shape for all outputs
# If there is more then one outputs
local_dims = tuple(outputs[0].shape)
del outputs
nd_orig = len(local_dims)
if nd_orig == 1:
# This have a lower overhead
all_c_contig = True
for inp in in_out:
if not inp.flags['C_CONTIGUOUS'] or inp.shape != local_dims:
all_c_contig = False
break
if all_c_contig:
return 0, (local_dims, [])
collapsable = [1] * nd_orig
local_str = [None] * len(in_out)
nd_collapse = nd_orig
for ipos in range(len(in_out)):
inp = in_out[ipos]
assert len(inp.shape) == nd_orig, "All inputs/outputs must have the same number of dimensions. You must broadcast before calling elemwise_collapse"
local_str[ipos] = list(inp.strides)
# We set the strides of broacastable dims to 0
# This make indexing in gpu simpler and is needed
# For collapsing the dimensions.
for dim_pos in range(inp.ndim):
if inp.shape[dim_pos] == 1:
local_str[ipos][dim_pos] = 0
if nd_orig == 1:
# We already covered the contiguous case before
# So we are sure it is not contiguous
# TODO: Add a test that f contiguous are also collapsed by the first case.
# I think that for 1d array when the flags f contiguous is true, c contiguous is also true.
return 1, (local_dims, local_str)
if verbose > 2:
print("before broadcast collapse")
print(" nd_collapse", nd_collapse)
print(" local_dims", local_dims)
for ipos in range(len(local_str)):
print(" local_str inputs", ipos, local_str[ipos])
local_dims = list(local_dims)
# Collapse dimension that are broadcast in all inputs.
# need to be done before contiguous collapse as it will break it.
# Update the dimensions and the strides
for id in range(nd_collapse):
if local_dims[id] == 1:
# remove dims i from the array
for j in range(id + 1, nd_collapse):
local_dims[j - 1] = local_dims[j]
# remove dims i from the array
for input_id in range(len(in_out)):
for j in range(id + 1, nd_collapse):
local_str[input_id][j - 1] = local_str[input_id][j]
nd_collapse -= 1
id -= 1 # TODO: what is this? How this work?
if verbose > 2:
print("after broadcast collapse")
print(" nd_collapse", nd_collapse)
print(" local_dims", local_dims)
for ipos in range(len(local_str)):
print(" local_str inputs", ipos, local_str[ipos])
nd_collapse_ = [1] * nd_orig
for ipos in range(len(local_str)):
# Can we collapse dims[i] and dims[i-1]?
strides = local_str[ipos]
for i in range(nd_collapse - 1, 0, -1):
if strides[i] * local_dims[i] != strides[i - 1]:
# The dims nd-1 are not strided again dimension nd
nd_collapse_[i] = 0
if verbose > 1:
print("nd_collapse_", nd_collapse_)
nd_collapse2 = nd_collapse
for i in range(nd_collapse - 1, 0, -1):
if nd_collapse_[i] == 1:
# update the local dims.
local_dims[i - 1] *= local_dims[i]
for j in range(i + 1, nd_collapse):
local_dims[j - 1] = local_dims[j]
# update the local stride.
for ipos in range(len(local_str)):
local_str[ipos][i - 1] = local_str[ipos][i] # set new strides
# remove stride i from the array
for j in range(i + 1, nd_collapse):
local_str[ipos][j - 1] = local_str[ipos][j]
# update the new number of dim
nd_collapse2 -= 1
nd_collapse = nd_collapse2
if nd_collapse == 1:
l = [local_str[ipos][nd_collapse - 1] == in_out[ipos].itemsize
for ipos in range(len(local_str))]
if all(l):
nd_collapse = 0
if verbose:
print("end collapsing")
print(" nd_collapse", nd_collapse)
if verbose > 1:
print(" local_dims", local_dims)
for ipos in range(len(local_str)):
print(" local_str inputs", ipos, local_str[ipos])
return nd_collapse, (local_dims, local_str)
def reduction_collapses(inout, axis, verbose=0):
"""
This collapse dimensions that are not needed when computing
reduction. This is usefull as it lower the indexing computation
that is heavier on gpu then on cpu.
This is a generic version. It collapse dimensions at any place in
the shape.
@param: inout: tuple(input, output)
@param: axis: None, interger, list of 1 interger
The axis over witch we will do reduction.
@return: (ndims, (input dims, input strides, input pattern), out strides)
after collapsing.
:note: we suppose that we can always collapse the output dimensions.
"""
input = inout[0]
out = inout[1]
# Some quick check. It is faster then the full version.
if axis is None:
# The output size is always 1, so we don't care about this strides
if (input.flags['C_CONTIGUOUS'] or input.flags['F_CONTIGUOUS']):
return 0, ((input.size,), (input.itemsize,), axis), (0,)
if input.ndim == 1:
assert axis == [0] or axis == 0 or axis is None
# not c contiguous as the first if should have catched it.
return 1, (input.shape, input.strides, axis), (0,)
if not isinstance(axis, (list, tuple)):
local_axis = [axis]
else:
local_axis = list(axis)
# This is needed for the computing of the output strides
assert axis is None or len(local_axis) == 1
local_dims = list(input.shape)
local_str = list(input.strides)
out_strides = list(out.strides)
nd_orig = len(local_dims)
collapsable = [1] * nd_orig
nd_collapse = nd_orig
if verbose > 2:
print("before broadcast collapse")
print(" nd_collapse", nd_collapse)
print(" local_dims", local_dims)
print(" local_str inputs", local_str)
print(" local_axis", local_axis)
# Collapse dimension that are broadcast in all inputs.
# need to be done before contiguous collapse as it will break it.
# Update the dimensions and the strides
for id in range(nd_collapse):
if local_dims[id] == 1:
for j in range(id + 1, nd_collapse):
# remove dims i from the array
local_dims[j - 1] = local_dims[j]
# remove strides i from the array
local_str[j - 1] = local_str[j]
# remove output strides i from the array
if axis is not None:
out_strides[j - 2] = out_strides[j - 1]
if id in local_axis:
local_axis.remove(id)
for axis_pos in range(len(local_axis)):
if local_axis[axis_pos] > id:
local_axis[axis_pos] -= 1
nd_collapse -= 1
id -= 1 # TODO: how this work?
if verbose > 2:
print("after broadcast collapse")
print(" nd_collapse", nd_collapse)
print(" local_dims", local_dims)
print(" local_str inputs", local_str)
print(" local_axis", local_axis)
print(" out_strides", out_strides)
nd_collapse_ = [1] * nd_orig
# Can we collapse dims[i] and dims[i-1]?
for i in range(nd_collapse - 1, 0, -1):
if ((local_str[i] * local_dims[i] != local_str[i - 1])):
# The dims nd-1 are not strided again dimension nd
nd_collapse_[i] = 0
elif (i in local_axis) != ((i - 1) in local_axis):
nd_collapse_[i] = 0
if verbose > 1:
print("nd_collapse_", nd_collapse_)
nd_collapse2 = nd_collapse
for i in range(nd_collapse - 1, 0, -1):
if nd_collapse_[i] == 1:
# update the local dims.
local_dims[i - 1] *= local_dims[i]
# set new strides
local_str[i - 1] = local_str[i]
#remove the old dims and strides
for j in range(i + 1, nd_collapse):
local_dims[j - 1] = local_dims[j]
local_str[j - 1] = local_str[j]
if axis is not None:
out_strides[j - 2] = out_strides[j - 1]
if i in local_axis:
local_axis.remove(i)
for axis_pos in range(len(local_axis)):
if local_axis[axis_pos] > i:
local_axis[axis_pos] -= 1
# update the new number of dim
nd_collapse2 -= 1
nd_collapse = nd_collapse2
if nd_collapse == 1:
if local_str[nd_collapse - 1] == input.itemsize:
nd_collapse = 0
if verbose:
print("end collapsing")
print(" nd_collapse", nd_collapse)
if verbose > 1:
print(" local_dims", local_dims)
print(" local_str inputs", local_str)
print(" local_axis", local_axis)
print(" out_strides", out_strides)
#print input.shape, input.strides
#print nd_collapse, (local_dims, local_str, local_axis)
local_dims = local_dims[:nd_collapse]
local_str = local_str[:nd_collapse]
out_strides = out_strides[:nd_collapse]
return nd_collapse, (local_dims, local_str, local_axis), out_strides
def call_elemwise(fct, input_vals, block=None, grid=None, out=None,
out_shape=None,
strides=None):
""" Call an elemwise gpu function with gived inputs and block size.
:param fct: The gpu function to call
:param input_vals: a list of inputs to pass to fct
:param block: int, the size of the block wanted
:param grid: int, the size of the grid wanted
:param out: Optional, the preallocated output. Must have the right shape
and dtype.
:param out_shape: Optional, if provided, we will suppose that the output,
have this shape event if it is not true.
:param strides: Optional, if provided, we will use those strides for
the inputs and outputs.
:note: param out_shape and strides are used for the collapsing of
dimensions.
"""
inp = input_vals[0]
# Get the output and output shape to us
if out_shape is None and out is None:
out_shape = list(inp.shape)
for i in input_vals[1:]:
# dtype checked by pycuda before gpu call
for s_i in range(len(inp.shape)):
assert (inp.shape[s_i] == i.shape[s_i]
or inp.shape[s_i] == 1
or i.shape[s_i] == 1)
out_shape[s_i] = max(out_shape[s_i], inp.shape[s_i],
i.shape[s_i])
if out is None:
out = gpu_ndarray.empty(out_shape, dtype=inp.dtype)
elif out_shape is None:
out_shape = out.shape
# Arg: nb element
args = [cast_uint(out.size)]
# Arg: output shape to the arguments.
for i in range(len(out_shape)):
args.append(cast_int(out_shape[i]))
# for each inputs and the output
# add its ptr and strides
nd = len(out_shape)
idx = 0
for i in list(input_vals) + [out]:
itemsize = i.dtype.itemsize
args.append(i)
for j in range(nd):
# We force a stride of 0 for broadcastable dimensions
# This lower the index computation in the kernel.
if strides is not None:
# strides should have a strides of 0 for broadcasting.
args.append(cast_int(strides[idx][j] / itemsize))
elif i.shape[j] == 1:
args.append(cast_int(0))
else:
args.append(cast_int(i.strides[j] / itemsize))
idx += 1
out_size = out.size
# First use at least a full warp
if block is None:
block_ = min(32, out_size)
else:
block_ = block
# Next start adding multiprocessors
if grid is None:
grid_ = min(out_size / block_ + (out_size % block_ != 0), 60)
else:
grid_ = grid
# Next start adding more warps per multiprocessor
if block is None:
if block_ * grid_ < out_size:
block_ = min(out_size / grid_, 512)
# We bypass the pycuda wrapper gpu function call.
# by calling directly the gpu function.
# This is faster and lower the overhead.
# Here is code that allow you to use the pycuda fct call.
# d = {"block":(block_,1,1), "grid":(grid_,1)}
# fct(*args, **d)
fct.set_block_shape(block_, 1, 1) # time_kernel
fct.param_set(*args)
fct.launch_grid(grid_, 1)
return out
class MyGpuNdArray():
_compiled_fct = {}
def __init__(self, gpu_nd_array):
#assert isinstance(gpu_nd_array, gpu_ndarray.GpuNdArrayObject)
self.gpu_nd_array = gpu_nd_array
self.ctype = dtype_to_ctype(self.gpu_nd_array.dtype)
@staticmethod
def gen_fct(op, inputs, nd, nodename="TestNodeName",
collapse=True):
if _CL_MODE:
npy_ty = "typedef float npy_float32;\n"
else:
npy_ty = "typedef double npy_float64;\n typedef float npy_float32;\n"
# Generate the gpu functions
nb_in = len(inputs)
fcts = [None]
for nd in range(1, nd + 1): # 1 to nd
out = op(*[TensorType(i.gpu_nd_array.dtype,
(False,) * nd)() for i in inputs])
out_dtype = out.dtype
node = out.owner
elemwise_algo = ElemwiseAlgo(node.op.scalar_op)
code = (CLUDA_PREAMBLE +
npy_ty +
elemwise_algo.c_src_kernel(node.inputs,
node.outputs,
nodename, nd,
static=""))
fct_name = "kernel_%s_%d" % (nodename, nd)
fct = compile_gpu_code(code, fct_name)
fcts.append(fct)
# All inputs/outputs C contiguous case
code = (npy_ty +
CLUDA_PREAMBLE +
elemwise_algo.c_src_kernel_Ccontiguous(
node.inputs, node.outputs, nodename, static=""))
fct_name = "kernel_%s_Ccontiguous" % nodename
fcts[0] = compile_gpu_code(code, fct_name)
def call_fct2(inputs, out=None):
" Do dimensions collapsing before call the gpu code "
assert len(inputs) == nb_in
# dtype checked by pycuda
# TODO: assert nb dim?
inp = inputs[0]
# Compute the output shape.
out_shape = list(inp.shape)
for i in inputs[1:]:
for s_i in range(len(inp.shape)):
assert (inp.shape[s_i] == i.shape[s_i]
or inp.shape[s_i] == 1
or i.shape[s_i] == 1)
out_shape[s_i] = max(out_shape[s_i], i.shape[s_i])
# Create the output object
if (out is None
or out.dtype != out_dtype
or out.shape != tuple(out_shape)):
out = MyGpuNdArray(gpu_ndarray.empty(out_shape,
dtype=out_dtype))
if collapse:
# Do the collapsing.
nd_col, info = elemwise_collapses(list(inputs), [out])
# The two next line are usefull to force a call to the
# c contiguous version:
#nd_col = 0
#info = [[],[]]
out = call_elemwise(fcts[nd_col], inputs,
out=out, out_shape=info[0][:nd_col],
strides=info[1])
else:
out = call_elemwise(fcts[-1], inputs, out=out,
out_shape=out_shape)
return out
return call_fct2
def __elemwise2__(self, other, name, op):
""" Call this code on this op with 2 inputs """
nd = len(self.gpu_nd_array.shape) # self.gpu_nd_array.ndim
assert nd == len(other.gpu_nd_array.shape) # ndim
tag = (name + '_' + str(self.gpu_nd_array.dtype)
+ str(self.gpu_nd_array.ndim))
tag += ('_' + str(other.gpu_nd_array.dtype)
+ str(other.gpu_nd_array.ndim))
fct = self._compiled_fct.get(tag, None)
if fct is None:
# print "compile", tag
fct = MyGpuNdArray.gen_fct(op, [self, other], nd)
self._compiled_fct[tag] = fct
return fct((self, other))
@classmethod
def __elemwise__(cls, inputs, name, op, out=None):
""" Call this code on this op with * inputs """
nd = len(inputs[0].gpu_nd_array.shape) # self.gpu_nd_array.ndim
for i in inputs[1:]:
assert nd == len(i.gpu_nd_array.shape) # ndim
nb = len(inputs)
tag = name + "_".join([str(i.gpu_nd_array.dtype) +
str(i.gpu_nd_array.ndim) for i in inputs])
fct = cls._compiled_fct.get(tag, None)
if fct is None:
# print "compile", tag
fct = MyGpuNdArray.gen_fct(op, inputs, nd)
cls._compiled_fct[tag] = fct
return fct(inputs, out=out)
base = property(lambda self: self.gpu_nd_array.base)
bytes = property(lambda self: self.gpu_nd_array.bytes)
dtype = property(lambda self: self.gpu_nd_array.dtype)
flags = property(lambda self: self.gpu_nd_array.flags)
itemsize = property(lambda self: self.gpu_nd_array.itemsize)
ndim = property(lambda self: self.gpu_nd_array.ndim,
doc="number of dimensions")
offset = property(lambda self: self.gpu_nd_array.offset)
shape = property(lambda self: self.gpu_nd_array.shape)
size = property(lambda self: self.gpu_nd_array.size)
strides = property(lambda self: self.gpu_nd_array.strides)
def __array__(self):
return numpy.asarray(self.gpu_nd_array)
def copy(self):
return MyGpuNdArray(self.gpu_nd_array.copy())
def view(self):
return MyGpuNdArray(self.gpu_nd_array.view())
def __copy__(self):
return MyGpuNdArray(self.gpu_nd_array.__copy__())
def __deepcopy__(self):
return MyGpuNdArray(self.gpu_nd_array.__deepcopy__())
@property
def gpudata(self):
# TODO: Add this assert when PyCUDA/PyOpenCL can use the bytes
# attributes. Without this assert old code that don't support
# strides can receive as input object that are strided and no
# error will be gived
#assert (self.gpu_nd_array.flags['C_CONTIGUOUS'] or
# self.gpu_nd_array.flags['F_CONTIGUOUS'])
# TODO: find a way to pass to a pycuda/pyopencl function the
# bytes + offset directly.
return self.bytes + self.offset
def __getitem__(self, *inputs):
return MyGpuNdArray(self.gpu_nd_array.__getitem__(*inputs))
def __add__(self, other):
return self.__elemwise2__(other, "add", theano.tensor.add)
def __sub__(self, other):
return self.__elemwise2__(other, "sub", theano.tensor.sub)
def __mul__(self, other):
return self.__elemwise2__(other, "mul", theano.tensor.mul)
def __div__(self, other):
assert (str(self.gpu_nd_array.dtype).startswith("float") or
str(other.gpu_nd_array.dtype).startswith("float"))
return self.__elemwise2__(other, "true_div", theano.tensor.true_div)
@classmethod
def add(cls, x, y, out=None):
""" add all inputs togethers element-wise """
return cls.__elemwise__([x, y], "add", theano.tensor.add, out=out)
@classmethod
def adds(cls, *inputs):
""" add all inputs togethers element-wise """
return cls.__elemwise__(inputs, "add", theano.tensor.add)
@classmethod
def multiplys(cls, *inputs):
""" multiply all inputs togethers element-wise """
return cls.__elemwise__(inputs, "mul", theano.tensor.mul)
def sum(self, axis=None, collapse=True):
from . import gen_reduction
max_thread_per_block = 512
max_block = 4096
if isinstance(axis, (list, tuple)):
if len(axis) == 1:
axis = axis[0]
else:
assert len(axis) == self.ndim
axis.sort()
assert axis == list(range(self.ndim))
axis = None
# TODO: Why this?
if self.size == 0:
make_out = gpu_ndarray.zeros
else:
make_out = gpu_ndarray.empty
if axis is None:
out = make_out((), self.dtype)
out = MyGpuNdArray(out)
else:
out_shape = [self.shape[i] for i in range(self.ndim)
if i != axis]
out = make_out(out_shape, self.dtype)
out = MyGpuNdArray(out)
if self.size == 0:
return out
args_set = False
if collapse:
coll_ndim, (coll_shape, coll_strides, coll_axis), coll_out_str = (
reduction_collapses([self, out], axis))
else:
coll_ndim = self.ndim
coll_shape = self.shape
coll_strides = self.strides
coll_axis = [axis]
coll_out_str = out.strides
if axis is not None:
coll_axis = coll_axis[0]
args_set = False
if coll_ndim == 0:
sum_op = gen_reduction.GpuSum([1], self.dtype)
c_code = sum_op.c_support_code_apply("nodename", contig=True)
fctname = "kernel_reduce_sum_ccontig_nodename"
fct = compile_gpu_code(c_code, fctname)
block_ = min(coll_shape[0], max_thread_per_block)
block = (block_, 1, 1)
grid = (1, 1)
shared_ = self.dtype.itemsize * block_
args = [cast_int(coll_shape[0]), self, out]
args_set = True
elif axis is None:
pattern = [1] * coll_ndim
str_pattern = [str(i) for i in pattern]
sum_op = gen_reduction.GpuSum(pattern, self.dtype)
c_code = sum_op.c_support_code_apply("nodename")
if not c_code:
raise NotImplementedError(
"GpuNdArray sum case not implemented")
fctname = "kernel_reduce_sum_" + "".join(str_pattern) + "_nodename"
fct = compile_gpu_code(c_code, fctname)
if coll_ndim == 1:
bx = min(max_thread_per_block, coll_shape[0])
block = (bx, 1, 1)
block_ = bx
elif coll_ndim == 2:
bx = min(max_thread_per_block, coll_shape[1])
by = min(max_thread_per_block // coll_shape[1], coll_shape[0])
by = max(by, 1)
block = (bx, by, 1)
block_ = bx * by
elif coll_ndim == 3:
bx = min(max_thread_per_block, coll_shape[2])
by = min(max_thread_per_block // bx, coll_shape[1])
bz = min(max_thread_per_block // (bx * by), coll_shape[0])
by = max(by, 1)
bz = min(max(bz, 1), 64)
block = (bx, by, bz)
block_ = bx * by * bz
elif coll_ndim == 4:
bx = min(max_thread_per_block, coll_shape[3])
by = min(max_thread_per_block // bx, coll_shape[2])
bz = min(max_thread_per_block // (bx * by), coll_shape[1])
by = max(by, 1)
bz = min(max(bz, 1), 64)
block = (bx, by, bz)
block_ = bx * by * bz
grid = (1, 1)
shared_ = self.dtype.itemsize * block_
elif coll_ndim in [1, 2, 3]:
if coll_ndim == 1:
assert coll_axis == 0
# pattern 1
sum_op = gen_reduction.GpuSum([1], self.dtype)
fctname = "kernel_reduce_sum_1_nodename"
grid = (1, 1)
block_ = min(max_thread_per_block, coll_shape[0])
block = (block_, 1, 1)
elif coll_ndim == 3 and coll_axis == 0:
# pattern 100
sum_op = gen_reduction.GpuSum([1, 0, 0], self.dtype)
fctname = "kernel_reduce_sum_100_nodename"
gx = min(coll_shape[1], max_block)
gy = min(max_block // (gx * coll_shape[2]), coll_shape[2])
gy = max(gy, 1)
grid = (gx, gy)
block_ = min(max_thread_per_block, coll_shape[0])
block = (block_, 1, 1)
elif coll_ndim == 3 and coll_axis == 1:
# pattern 010
sum_op = gen_reduction.GpuSum([0, 1, 0], self.dtype)
fctname = "kernel_reduce_sum_010_AD_nodename"
A = coll_shape[0]
B = coll_shape[1]
C = coll_shape[2]
D = C / 32
if (32 * D < C):
D += 1
assert ((C <= 32 * D) and (32 * D < C + 32))
shared_ = 0
gx = min(A, max_block)
gy = min(max_block // (D * A), D)
gy = max(gy, 1)
grid = (gx, gy)
block = (32, 1, 1)
block_ = 32
args_set = True
# input shape
args = [cast_int(A), cast_int(B),
cast_int(C), cast_int(D)]
# input
args.append(self)
# input strides
args += [cast_int(i / self.dtype.itemsize)
for i in coll_strides]
# output
args.append(out)
# output strides
args.append(cast_int(coll_out_str[0] / out.dtype.itemsize))
args.append(cast_int(coll_out_str[1] / out.dtype.itemsize))
elif coll_ndim == 3 and coll_axis == 2:
# pattern 001
sum_op = gen_reduction.GpuSum([0, 0, 1], self.dtype)
fctname = "kernel_reduce_sum_001_nodename"
gx = min(coll_shape[0], max_block)
gy = min(max_block // (gx * coll_shape[1]), coll_shape[1])
gy = max(gy, 1)
grid = (gx, gy)
block_ = min(max_thread_per_block, coll_shape[2])
block = (block_, 1, 1)
elif coll_axis == 0:
# pattern 10
sum_op = gen_reduction.GpuSum([1, 0], self.dtype)
fctname = "kernel_reduce_sum_010_nodename"
block_ = min(coll_shape[1], max_thread_per_block)
block = (block_, 1, 1)
grid = (1, coll_shape[0])
args_set = True
# input shape
args = [cast_int(1)]
args += [cast_int(i) for i in coll_shape]
# input
args.append(self)
# input strides
args.append(cast_int(1))
args += [cast_int(i / self.dtype.itemsize)
for i in coll_strides]
# output
args.append(out)
# output strides
args.append(cast_int(1))
# We must take the last dimensions in the case of
# dimensions collapsing.
args.append(cast_int(coll_out_str[-1] / out.dtype.itemsize))
elif coll_axis == 1:
# pattern 01
sum_op = gen_reduction.GpuSum([0, 1], self.dtype)
fctname = "kernel_reduce_sum_01_nodename"
block_ = min(coll_shape[1], max_thread_per_block)
block = (block_, 1, 1)
grid = (1, min(coll_shape[0], max_block))
else:
raise Exception("Bad axis")
c_code = sum_op.c_support_code_apply("nodename")
fct = compile_gpu_code(c_code, fctname)
shared_ = self.dtype.itemsize * block_
else:
raise Exception("Not implemented")
if not args_set:
# input shape
args = [cast_int(i) for i in coll_shape]
# input
args.append(self)
# input strides
args += [cast_int(i / self.dtype.itemsize)
for i in coll_strides]
# output
args.append(out)
# output strides
args += [cast_int(i / self.dtype.itemsize)
for i in coll_out_str]
pycuda._driver.Context.synchronize()
#print fctname, block, grid, shared_, axis
#print self.ndim, self.shape, self.strides, axis, out.strides
#print coll_ndim, coll_shape, coll_strides, coll_axis, coll_out_str
#print args
if False:
d = {"block": block,
"shared": shared_,
"grid": grid}
fct(*args, **d)
else:
# We bypass the pycuda wrapper gpu function call.
# by calling directly the gpu function.
# This is faster and lower the overhead.
fct.set_block_shape(*block)
fct.set_shared_size(shared_)
fct.param_set(*args)
fct.launch_grid(*grid)
return out
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