Commit 0c826d5c authored by Kirill Smelkov's avatar Kirill Smelkov

BigArray: An ndarray-like on top of BigFile memory mappings

I.e. something like numpy.memmap for numpy.ndarray and OS files. The whole
bigarray cannot be used as a drop-in replacement for numpy arrays, but BigArray
_slices_ are real ndarrays and can be used everywhere ndarray can be used,
including in C/Fortran code. Slice size is limited by mapping-size (=
address-space size) limit, i.e. to ~ max 127TB on Linux/amd64.

Changes to bigarray memory are changes to bigfile memory mapping and as such
can be discarded or saved back to bigfile using mapping (= BigFileH) dirty
discard/writeout interface.

For the same reason the whole amount of changes to memory is limited by amount
of physical RAM.
parent 4174b84a
This diff is collapsed.
../../bigfile/tests/__init__.py
\ No newline at end of file
# Wendeling.core.bigarray | Basic tests
# Copyright (C) 2014-2015 Nexedi SA and Contributors.
# Kirill Smelkov <kirr@nexedi.com>
#
# This program is free software: you can Use, Study, Modify and Redistribute
# it under the terms of the GNU General Public License version 3, or (at your
# option) any later version, as published by the Free Software Foundation.
#
# You can also Link and Combine this program with other software covered by
# the terms of any of the Open Source Initiative approved licenses and Convey
# the resulting work. Corresponding source of such a combination shall include
# the source code for all other software used.
#
# This program is distributed WITHOUT ANY WARRANTY; without even the implied
# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#
# See COPYING file for full licensing terms.
from wendelin.bigarray import BigArray
from wendelin.bigfile import BigFile
from wendelin.lib.mem import memcpy
from numpy import ndarray, dtype, int32, uint32, uint8, all, arange, multiply, array_equal
from pytest import raises
# Synthetic bigfile that just loads zeros, and ignores writes (= a-la /dev/zero)
class BigFile_Zero(BigFile):
def loadblk(self, blk, buf):
# Nothing to do here - the memory buf obtained from OS comes pre-cleared
# XXX reenable once/if memory comes uninitialized here
return
def storeblk(self, blk, buf):
return
PS = 2*1024*1024 # FIXME hardcoded, TODO -> ram.pagesize
# basic ndarray-compatibility attributes of BigArray
def test_bigarray_basic():
Z = BigFile_Zero(PS)
Zh = Z.fileh_open()
A = BigArray((10,3), int32, Zh)
raises(TypeError, "A.data")
assert A.strides == (12, 4)
assert A.dtype == dtype(int32)
# XXX .flags?
# XXX .flat? (non-basic)
# XXX .imag? (non-basic)
# XXX .real? (non-basic)
assert A.size == 10*3
assert len(A) == 10
assert A.itemsize == 4
assert A.nbytes == 4*10*3
assert A.ndim == 2
assert A.shape == (10,3)
# XXX .ctypes (non-basic)
# TODO .base
# DoubleGet(obj1, obj2)[key] -> obj1[key], obj2[key]
class DoubleGet:
def __init__(self, obj1, obj2):
self.obj1 = obj1
self.obj2 = obj2
def __getitem__(self, key):
return self.obj1[key], self.obj2[key]
# getitem/setitem (1d case)
def test_bigarray_indexing_1d():
Z = BigFile_Zero(PS)
Zh = Z.fileh_open()
A = BigArray((10*PS,), uint8, Zh)
# ndarray of the same shape - we'll use it to get slices and compare result
# shape/stride against BigArray.__getitem__
A_= ndarray ((10*PS,), uint8)
# AA[key] -> A[key], A_[key]
AA = DoubleGet(A, A_)
# "empty" slices
assert A[10:5:1] .size == 0
assert A[5:10:-1] .size == 0
assert A[5:5] .size == 0
assert A[100*PS:200*PS] .size == 0
# whole array
a, _ = AA[:]
assert isinstance(a, ndarray)
assert a.dtype == dtype(uint8)
assert a.shape == _.shape
assert a.strides == _.strides
assert a[0] == 0
assert a[5*PS] == 0
assert a[10*PS-1] == 0
# overlaps with a
b, _ = AA[4*PS:]
assert isinstance(b, ndarray)
assert b.dtype == dtype(uint8)
assert b.shape == _.shape
assert b.strides == _.strides
assert b[0] == 0
assert b[1*PS] == 0
assert b[5*PS-1] == 0
# a <-> b
assert b[1*PS] == 0
a[5*PS] = 1
assert b[1*PS] == 1
# non-pagesize aligned slice
c, _ = AA[4*PS+3 : 9*PS-3]
assert isinstance(c, ndarray)
assert c.dtype == dtype(uint8)
assert c.shape == _.shape
assert c.strides == _.strides
assert c[0] == 0
assert c[-1] == 0
# a <-> b <-> c
assert b[3] == 0
assert a[4*PS+3] == 0
c[0] = 3
assert b[3] == 3
assert a[4*PS+3] == 3
assert b[5*PS-4] == 0
assert a[9*PS-4] == 0
c[-1] = 99
assert b[5*PS-4] == 99
assert a[9*PS-4] == 99
# negative stride
d, _ = AA[9*PS+1:4*PS-1:-1]
assert isinstance(d, ndarray)
assert d.dtype == dtype(uint8)
assert d.shape == _.shape
assert d.strides == _.strides
assert all(d[:5] == 0)
assert d[5] == 99 # c[-1]
assert all(d[6:-(PS+1)] == 0)
assert d[-(PS+1)] == 1 # a[5*PS]
assert all(d[-PS:-4] == 0)
assert d[-4] == 3 # c[0]
assert all(d[-3:] == 0)
# like c, but stride > 1
e, _ = AA [4*PS+3 : 9*PS-3 : 7]
assert isinstance(e, ndarray)
assert e.dtype == dtype(uint8)
assert e.shape == _.shape
assert e.strides == _.strides
c[0] = 4
assert e[0] == c[0]
c[0] = 5
assert e[0] == c[0]
c[7] = 7
assert e[1] == c[7]
c[7] = 8
assert e[1] == c[7]
# TODO check more
# like d, but stride < -1
f, _ = AA[9*PS+1:4*PS-1:-11]
assert isinstance(f, ndarray)
assert f.dtype == dtype(uint8)
assert f.shape == _.shape
assert f.strides == _.strides
d[0] = 11
assert f[0] == d[0]
d[0] = 12
assert f[0] == d[0]
d[11] = 13
assert f[1] == d[11]
d[11] = 14
assert f[1] == d[11]
# TODO check more
# setitem
A[2*PS+1:3*PS+2] = 5
assert all(a[2*PS+1 : 3*PS+2] == 5)
assert a[2*PS] == 0
assert a[3*PS+3] == 0
A[2*PS+2:2*PS+5] = [6,7,8]
assert a[2*PS+0] == 0
assert a[2*PS+1] == 5
assert a[2*PS+2] == 6
assert a[2*PS+3] == 7
assert a[2*PS+4] == 8
assert a[2*PS+5] == 5
assert a[2*PS+6] == 5
assert raises(ValueError, 'A[:4] = range(5)')
# given dimension length n, yield index variants to test
def indices_to_test(n):
# ":"
yield slice(None)
# int
yield 0
yield -1
yield n//2
# start:stop:stride
yield slice(1,-1)
yield slice(n//4+1, n*3//4-1, 2)
yield slice(n//5+1, n*4//5-1, 3)
# geven shape, yield all Nd idx variant, where every index iterates full indices_to_test
def idx_to_test(shape, idx_prefix=()):
leaf = len(shape) <= 1
for i in indices_to_test(shape[0]):
idx = idx_prefix + (i,)
if leaf:
yield idx
else:
# = yield from
for _ in idx_to_test(shape[1:], idx):
yield _
# getitem/setitem (Nd case)
def test_bigarray_indexing_Nd():
# shape of tested array - all primes, total size for uint32 ~ 7 2M pages
# XXX even less dimensions (to speed up tests)?
shape = tuple(reversed( (17,23,101,103) ))
# test data - all items are unique - so we can check array by content
# NOTE +PS so that BigFile_Data has no problem loading last blk
# (else data slice will be smaller than buf)
data = arange(multiply.reduce(shape) + PS, dtype=uint32)
# synthetic bigfile that loads data from `data`
class BigFile_Data(BigFile):
def loadblk(self, blk, buf):
datab = data.view(uint8)
x = datab[self.blksize * blk : self.blksize * (blk+1)]
memcpy(buf, x)
def storeblk(self, blk, buf):
raise RuntimeError('tests should not try to change test data')
f = BigFile_Data(PS)
fh = f.fileh_open()
A = BigArray(shape, uint32, fh) # bigarray with test data and shape
A_ = data[:multiply.reduce(shape)].reshape(shape) # ndarray ----//----
# now just go over combinations of various slice at each dimension, and see
# whether slicing result is the same ndarray would do.
for idx in idx_to_test(shape):
a = A [idx]
a_ = A_[idx]
assert array_equal(a, a_)
# TODO ... -> expanded (0,1,2,negative), rejected if many
# TODO newaxis
# TODO nidx < len(shape)
# TODO empty slice in major row, empty slice in secondary row
"""
# ellipsis - take some idx[a:b] and replace it by ...
for ellipsis in range(2): # 0 - no ellipsis
# newaxis - added after at some position(s)
for newaxis in range(3): # 0 - no newaxis
"""
...@@ -182,9 +182,9 @@ setup( ...@@ -182,9 +182,9 @@ setup(
packages = ['wendelin'] + ['wendelin.%s' % _ for _ in packages = ['wendelin'] + ['wendelin.%s' % _ for _ in
find_packages(exclude='3rdparty')], find_packages(exclude='3rdparty')],
install_requires = [ install_requires = [
'numpy', # lib/mem 'numpy', # BigArray + its children
# for ZBigFile # for ZBigFile / ZBigArray
# ( NOTE: ZODB3 3.11 just pulls in latest ZODB _4_, so this way # ( NOTE: ZODB3 3.11 just pulls in latest ZODB _4_, so this way
# specifying ZODB _3_ we allow external requirements to # specifying ZODB _3_ we allow external requirements to
# specify either to use e.g. ZODB3.10 or ZODB4 ) # specify either to use e.g. ZODB3.10 or ZODB4 )
......
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