libgpuarray/pygpu/_array.py at lowerC99 · NeutralCode/libgpuarray · GitHub

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from __future__ import division

import numpy as np

from .elemwise import elemwise1, elemwise2, ielemwise2, compare, arg, GpuElemwise, as_argument

from .reduction import reduce1

from .dtypes import dtype_to_ctype, get_np_obj, get_common_dtype

from . import gpuarray

class ndgpuarray(gpuarray.GpuArray):

"""

Extension class for gpuarray.GpuArray to add numpy mathematical

operations between arrays. These operations are all performed on

the GPU but this is not the most efficient way since it will

involve the creation of temporaries (just like numpy) for all

intermediate results.

This class may help transition code from numpy to pygpu by acting

more like a drop-in replacement for numpy.ndarray than the raw

GpuArray class.

"""

# add

def __add__(self, other):

return elemwise2(self, '+', other, self, broadcast=True)

def __radd__(self, other):

return elemwise2(other, '+', self, self, broadcast=True)

def __iadd__(self, other):

return ielemwise2(self, '+', other, broadcast=True)

# sub

def __sub__(self, other):

return elemwise2(self, '-', other, self, broadcast=True)

def __rsub__(self, other):

return elemwise2(other, '-', self, self, broadcast=True)

def __isub__(self, other):

return ielemwise2(self, '-', other, broadcast=True)

# mul

def __mul__(self, other):

return elemwise2(self, '*', other, self, broadcast=True)

def __rmul__(self, other):

return elemwise2(other, '*', self, self, broadcast=True)

def __imul__(self, other):

return ielemwise2(self, '*', other, broadcast=True)

# div

def __div__(self, other):

return elemwise2(self, '/', other, self, broadcast=True)

def __rdiv__(self, other):

return elemwise2(other, '/', self, self, broadcast=True)

def __idiv__(self, other):

return ielemwise2(self, '/', other, broadcast=True)

# truediv

def __truediv__(self, other):

np1 = get_np_obj(self)

np2 = get_np_obj(other)

res = (np1.__truediv__(np2)).dtype

return elemwise2(self, '/', other, self, odtype=res, broadcast=True)

def __rtruediv__(self, other):

np1 = get_np_obj(self)

np2 = get_np_obj(other)

res = (np2.__truediv__(np1)).dtype

return elemwise2(other, '/', self, self, odtype=res, broadcast=True)

def __itruediv__(self, other):

np2 = get_np_obj(other)

kw = {'broadcast': True}

if self.dtype == np.float32 or np2.dtype == np.float32:

kw['op_tmpl'] = "a = (float)a / (float)b"

if self.dtype == np.float64 or np2.dtype == np.float64:

kw['op_tmpl'] = "a = (double)a / (double)b"

return ielemwise2(self, '/', other, **kw)

# floordiv

def __floordiv__(self, other):

out_dtype = get_common_dtype(self, other, True)

kw = {'broadcast': True}

if out_dtype.kind == 'f':

kw['op_tmpl'] = "res = floor((%(out_t)s)a / (%(out_t)s)b)"

return elemwise2(self, '/', other, self, odtype=out_dtype, **kw)

def __rfloordiv__(self, other):

out_dtype = get_common_dtype(other, self, True)

kw = {'broadcast': True}

if out_dtype.kind == 'f':

kw['op_tmpl'] = "res = floor((%(out_t)s)a / (%(out_t)s)b)"

return elemwise2(other, '/', self, self, odtype=out_dtype, **kw)

def __ifloordiv__(self, other):

out_dtype = self.dtype

kw = {'broadcast': True}

if out_dtype == np.float32:

kw['op_tmpl'] = "a = floor((float)a / (float)b)"

if out_dtype == np.float64:

kw['op_tmpl'] = "a = floor((double)a / (double)b)"

return ielemwise2(self, '/', other, **kw)

# mod

def __mod__(self, other):

out_dtype = get_common_dtype(self, other, True)

kw = {'broadcast': True}

if out_dtype.kind == 'f':

kw['op_tmpl'] = "res = fmod((%(out_t)s)a, (%(out_t)s)b)"

return elemwise2(self, '%', other, self, odtype=out_dtype, **kw)

def __rmod__(self, other):

out_dtype = get_common_dtype(other, self, True)

kw = {'broadcast': True}

if out_dtype.kind == 'f':

kw['op_tmpl'] = "res = fmod((%(out_t)s)a, (%(out_t)s)b)"

return elemwise2(other, '%', self, self, odtype=out_dtype, **kw)

def __imod__(self, other):

out_dtype = get_common_dtype(self, other, self.dtype == np.float64)

kw = {'broadcast': True}

if out_dtype == np.float32:

kw['op_tmpl'] = "a = fmod((float)a, (float)b)"

if out_dtype == np.float64:

kw['op_tmpl'] = "a = fmod((double)a, (double)b)"

return ielemwise2(self, '%', other, **kw)

# divmod

def __divmod__(self, other):

if not isinstance(other, gpuarray.GpuArray):

other = np.asarray(other)

odtype = get_common_dtype(self, other, True)

a_arg = as_argument(self, 'a', read=True)

b_arg = as_argument(other, 'b', read=True)

args = [arg('div', odtype, write=True), arg('mod', odtype, write=True), a_arg, b_arg]

div = self._empty_like_me(dtype=odtype)

mod = self._empty_like_me(dtype=odtype)

if odtype.kind == 'f':

tmpl = "div = floor((%(out_t)s)a / (%(out_t)s)b)," \

"mod = fmod((%(out_t)s)a, (%(out_t)s)b)"

else:

tmpl = "div = (%(out_t)s)a / (%(out_t)s)b," \

"mod = a %% b"

ksrc = tmpl % {'out_t': dtype_to_ctype(odtype)}

k = GpuElemwise(self.context, ksrc, args)

k(div, mod, self, other, broadcast=True)

return (div, mod)

def __rdivmod__(self, other):

if not isinstance(other, gpuarray.GpuArray):

other = np.asarray(other)

odtype = get_common_dtype(other, self, True)

a_arg = as_argument(other, 'a', read=True)

b_arg = as_argument(self, 'b', read=True)

args = [arg('div', odtype, write=True), arg('mod', odtype, write=True), a_arg, b_arg]

div = self._empty_like_me(dtype=odtype)

mod = self._empty_like_me(dtype=odtype)

if odtype.kind == 'f':

tmpl = "div = floor((%(out_t)s)a / (%(out_t)s)b)," \

"mod = fmod((%(out_t)s)a, (%(out_t)s)b)"

else:

tmpl = "div = (%(out_t)s)a / (%(out_t)s)b," \

"mod = a %% b"

ksrc = tmpl % {'out_t': dtype_to_ctype(odtype)}

k = GpuElemwise(self.context, ksrc, args)

k(div, mod, other, self, broadcast=True)

return (div, mod)

def __neg__(self):

return elemwise1(self, '-')

def __pos__(self):

return elemwise1(self, '+')

def __abs__(self):

if self.dtype.kind == 'u':

return self.copy()

if self.dtype.kind == 'f':

oper = "res = fabs(a)"

elif self.dtype.itemsize < 4:

# cuda 5.5 finds the c++ stdlib definition if we don't cast here.

oper = "res = abs((int)a)"

else:

oper = "res = abs(a)"

return elemwise1(self, None, oper=oper)

# richcmp

def __lt__(self, other):

return compare(self, '<', other, broadcast=True)

def __le__(self, other):

return compare(self, '<=', other, broadcast=True)

def __eq__(self, other):

return compare(self, '==', other, broadcast=True)

def __ne__(self, other):

return compare(self, '!=', other, broadcast=True)

def __ge__(self, other):

return compare(self, '>=', other, broadcast=True)

def __gt__(self, other):

return compare(self, '>', other, broadcast=True)

# misc other things

@property

def T(self):

if self.ndim < 2:

return self

return self.transpose()

"""

Since these functions are untested (thus probably wrong), we disable them.

def clip(self, a_min, a_max, out=None):

oper=('res = a > %(max)s ? %(max)s : '

'(a < %(min)s ? %(min)s : a)' % dict(min=a_min, max=a_max))

return elemwise1(self, '', oper=oper, out=out)

def fill(self, value):

self[...] = value

"""

# reductions

def all(self, axis=None, out=None):

if self.ndim == 0:

return self.copy()

return reduce1(self, '&&', '1', np.dtype('bool'),

axis=axis, out=out)

def any(self, axis=None, out=None):

if self.ndim == 0:

return self.copy()

return reduce1(self, '||', '0', np.dtype('bool'),

axis=axis, out=out)

def prod(self, axis=None, dtype=None, out=None):

if dtype is None:

dtype = self.dtype

# we only upcast integers that are smaller than the plaform default

if dtype.kind == 'i':

di = np.dtype('int')

if di.itemsize > dtype.itemsize:

dtype = di

if dtype.kind == 'u':

di = np.dtype('uint')

if di.itemsize > dtype.itemsize:

dtype = di

return reduce1(self, '*', '1', dtype, axis=axis, out=out)

# def max(self, axis=None, out=None);

# nd = self.ndim

# if nd == 0:

# return self.copy()

# idx = (0,) * nd

# n = str(self.__getitem__(idx).__array__())

# return reduce1(self, '', n, self.dtype, axis=axis, out=out,

# oper='max(a, b)')

# def min(self, axis=None, out=None):

# nd = self.ndim

# if nd == 0:

# return self.copy()

# idx = (0,) * nd

# n = str(self.__getitem__(idx).__array__())

# return reduce1(self, '', n, self.dtype, axis=axis, out=out,

# oper='min(a, b)')

def sum(self, axis=None, dtype=None, out=None):

if dtype is None:

dtype = self.dtype

# we only upcast integers that are smaller than the plaform default

if dtype.kind == 'i':

di = np.dtype('int')

if di.itemsize > dtype.itemsize:

dtype = di

if dtype.kind == 'u':

di = np.dtype('uint')

if di.itemsize > dtype.itemsize:

dtype = di

return reduce1(self, '+', '0', dtype, axis=axis, out=out)

libgpuarray/pygpu/_array.py at lowerC99 · NeutralCode/libgpuarray · GitHub

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