def test_r_hat():
array = Partition((3500, 3500, 3500), name='array')
in_blocks = Partition((875, 875, 875), array=array, name='in')
out_blocks = Partition((700, 875, 700), array=array, name='out')
r_hat = keep.get_r_hat(in_blocks, out_blocks)
assert (r_hat == (875, 875, 875))
def test_r_hat_1():
array = Partition((20, 20, 20), name='array')
in_blocks = Partition((20, 20, 20), array=array, name='in')
out_blocks = Partition((20, 10, 2), array=array, name='out')
r_hat = keep.get_r_hat(in_blocks, out_blocks)
assert (r_hat == (20, 20, 20))
def test_find_shape_with_constraint():
array = Partition((100, 100, 100), name='array', fill='random')
in_blocks = Partition((10, 10, 10), name='out', array=array)
out_blocks = Partition((50, 50, 50), name='out', array=array)
shape, mc = keep.find_shape_with_constraint(in_blocks, out_blocks, None)
assert ((shape, mc) == ((50, 50, 50), 125000))
shape, mc = keep.find_shape_with_constraint(in_blocks, out_blocks, 3000)
assert ((shape, mc) == ((1, 50, 50), 2500))
def test_seek_model():
array = Partition((2, 2, 2), name='array', fill='random')
out_blocks = Partition((2, 1, 2), name='out', array=array)
_, _, seeks, _ = keep.keep(array, out_blocks, None, array)
assert (seeks == 3)
_, _, seeks, _ = keep.baseline(out_blocks, array, None, array)
assert (seeks == 6)
def test_get_f_blocks_1():
array = Partition((12, 12, 12), name='array', fill='random')
in_blocks = Partition((4, 4, 4), name='in', array=array)
fblocks = keep.get_F_blocks(array.blocks[(0, 0, 0)], in_blocks)
assert ([str(b) for b in fblocks] == [
'Block: origin (0, 0, 0); shape (12, 12, 12); data in mem: 0B', 'None',
'None', 'None', 'None', 'None', 'None', 'None'
])
def test_partition_to_end_coords():
d = 12
array = Partition((d, d, d), name='array')
in_blocks = Partition((4, 4, 4), name='in', array=array)
coords = keep.partition_to_end_coords(in_blocks)
assert (coords == ([3, 7, 11], [3, 7, 11], [3, 7, 11]))
d = 3500
array = Partition((d, d, d), name='array')
in_blocks = Partition((500, 500, 500), name='in', array=array)
coords = keep.partition_to_end_coords(in_blocks)
assert (coords == ([499, 999, 1499, 1999, 2499, 2999,
3499], [499, 999, 1499, 1999, 2499, 2999, 3499],
[499, 999, 1499, 1999, 2499, 2999, 3499]))
def test_repartition_baseline_2(cleanup_blocks):
array = Partition((10, 20, 30), name='array')
in_blocks = Partition((10, 20, 30), name='in', array=array, fill='random')
in_blocks.blocks[(0, 0, 0)].read()
in_data = in_blocks.blocks[(0, 0, 0)].data.bytes()
out_blocks = Partition((10, 10, 15), name='out', array=array)
in_blocks.repartition(out_blocks, None, keep.baseline)
rein_blocks = Partition((10, 20, 30), name='rein', array=array)
out_blocks.repartition(rein_blocks, None, keep.baseline)
rein_blocks.blocks[(0, 0, 0)].read()
rein_data = rein_blocks.blocks[(0, 0, 0)].data.bytes()
assert (rein_data == in_data)
def create_write_blocks(read_blocks, out_blocks):
'''
read_block: partition
out_blocks: partition
'''
match = {}
moved_f_blocks = [[] for i in range(len(read_blocks.blocks))]
for i, r in enumerate(read_blocks.blocks):
f_blocks = get_F_blocks(read_blocks.blocks[r],
out_blocks,
get_data=False)
moved_f_blocks[i] += [f_blocks[0]] # don't move F0
match[(r, 0)] = i
for f in range(1, 8):
if not f_blocks[f] is None:
destF0 = destination_F0(read_blocks, i, f)
moved_f_blocks[destF0] += [f_blocks[f]]
match[(r, f)] = destF0
merged_blocks = [merge_blocks(blocks) for blocks in moved_f_blocks]
match = {k: merged_blocks[match[k]] for k in match}
blocks = {m.origin: m for m in merged_blocks}
# Warning: write_blocks are a partition but a non-uniform one
# This may have side effects. This is also the reason for the
# weird create_blocks param
write_blocks = Partition((1, 1, 1),
name='write_blocks',
array=read_blocks.array,
create_blocks=False)
write_blocks.blocks = blocks
cache = KeepCache(out_blocks, match)
return write_blocks, cache
def test_repartition_keep_1(cleanup_blocks):
array = Partition((5, 6, 7), name='array', fill='random')
out_blocks = Partition((5, 3, 7), name='out', array=array)
array.repartition(out_blocks, None, keep.keep)
array.blocks[(0, 0, 0)].read()
out_blocks.blocks[(0, 0, 0)].read()
out_blocks.blocks[(0, 3, 0)].read()
assert (array.blocks[(0, 0, 0)].data.bytes()[:20] == out_blocks.blocks[(
0, 0, 0)].data.bytes()[:20])
assert (array.blocks[(0, 0, 0)].data.bytes()[-20:] == out_blocks.blocks[(
0, 3, 0)].data.bytes()[-20:])
rein_blocks = Partition((5, 6, 7), name='rein')
out_blocks.repartition(rein_blocks, None, keep.keep)
rein_blocks.blocks[(0, 0, 0)].read()
assert (array.blocks[(0, 0, 0)].data.bytes() == rein_blocks.blocks[(
0, 0, 0)].data.bytes())
def test_get_f_blocks():
array = Partition((12, 12, 12), name='array', fill='random')
in_blocks = Partition((4, 4, 4), name='in', array=array)
out_blocks = Partition((3, 3, 3), name='out', array=array)
fblocks = keep.get_F_blocks(in_blocks.blocks[(0, 0, 0)], out_blocks)
assert ([str(b) for b in fblocks] == [('Block: origin (0, 0, 0); shape'
' (3, 3, 3); data in mem: 0B'),
('Block: origin (0, 0, 3); shape'
' (3, 3, 1); data in mem: 0B'),
('Block: origin (0, 3, 0); shape'
' (3, 1, 3); data in mem: 0B'),
('Block: origin (0, 3, 3); shape'
' (3, 1, 1); data in mem: 0B'),
('Block: origin (3, 0, 0); shape'
' (1, 3, 3); data in mem: 0B'),
('Block: origin (3, 0, 3); shape'
' (1, 3, 1); data in mem: 0B'),
('Block: origin (3, 3, 0); shape'
' (1, 1, 3); data in mem: 0B'),
('Block: origin (3, 3, 3); shape'
' (1, 1, 1); data in mem: 0B')])
def test_repartition_keep(cleanup_blocks):
array = Partition((2, 2, 2), name='array', fill='random')
out_blocks = Partition((2, 1, 2), name='out', array=array)
array.repartition(out_blocks, None, keep.keep)
array.blocks[(0, 0, 0)].read()
out_blocks.blocks[(0, 0, 0)].read()
out_blocks.blocks[(0, 1, 0)].read()
assert (array.blocks[(0, 0, 0)].data.bytes()[:2] == out_blocks.blocks[(
0, 0, 0)].data.bytes()[:2])
assert (array.blocks[(0, 0, 0)].data.bytes()[2:4] == out_blocks.blocks[(
0, 1, 0)].data.bytes()[:2])
assert (array.blocks[(0, 0, 0)].data.bytes()[4:6] == out_blocks.blocks[(
0, 0, 0)].data.bytes()[2:4])
assert (array.blocks[(0, 0, 0)].data.bytes()[6:8] == out_blocks.blocks[(
0, 1, 0)].data.bytes()[2:4])
rein_blocks = Partition((2, 2, 2), name='rein')
out_blocks.repartition(rein_blocks, None, keep.keep)
rein_blocks.blocks[(0, 0, 0)].read()
def baseline(in_blocks, out_blocks, m, array):
'''
Implements get_read_blocks_and_cache(in_blocks, out_blocks, m, array)
used in Partition.repartition. It provides a baseline repartitioning
algorithm where read blocks are input blocks.
Arguments:
in_blocks: input partition, to be repartitioned
out_blocks: output partition, to be written to disk
m: max memory to be used by the repartitioning. This parameter is
here for type consistency in Partition.repartition but it is
ignored in this baseline implementation.
array: partitioned array. This parameter is here for type
consistency in Partition.repartition but it is ignored in this
baseline implementation.
'''
# TODO: creating a new partition makes memory estimates correct,
# but it adds an in-memory copy, this could be fixed
return (Partition(in_blocks.shape, 'read_blocks', array), BaselineCache(),
baseline_seek_count(in_blocks,
out_blocks), math.prod(in_blocks.shape))
def keep(in_blocks, out_blocks, m, array):
'''
Implements get_read_blocks_and_cache(in_blocks, out_blocks, m, array)
used in Partition.repartition. Implements the keep heuristic (Algorithm
2 in the paper).
Arguments:
in_blocks: input partition, to be repartitioned
out_blocks: output partition, to be written to disk
m: max memory to be used by the repartitioning. If None, memory
constraint is ignored.
array: partitioned array. Doesn't need to contain data, used just
to get total dimensions of the array.
'''
r, peak_mem = find_shape_with_constraint(in_blocks, out_blocks, m)
read_blocks = Partition(r, 'read_blocks', array=array)
write_blocks, cache = create_write_blocks(read_blocks, out_blocks)
# Technically this count is not necessary
seeks = keep_seek_count(in_blocks, read_blocks, write_blocks, out_blocks)
return read_blocks, cache, seeks, peak_mem
def peak_memory(read_shape, in_blocks, out_blocks):
'''
Return the estimated amount of memory required to repartition in_blocks
into out_blocks, using read_blocks and write_blocks.
'''
# To estimate the amount of memory required, we run a dry run of the
# repartitioning
read_blocks = Partition(read_shape, 'read_blocks', array=in_blocks.array)
_, cache = create_write_blocks(read_blocks, out_blocks)
def local_get_read_blocks_and_cache(in_blocks, out_blocks, m, array):
return read_blocks, cache, None, None
(_, _, peak_mem, _,
_) = in_blocks.repartition(out_blocks,
None,
local_get_read_blocks_and_cache,
dry_run=True)
in_blocks.clear()
out_blocks.clear()
return peak_mem
def test_partition_clear(cleanup_blocks):
array = Partition((12, 12, 12), name='array', fill='random')
array.clear()
def test_repartition_keep_3(cleanup_blocks):
array = Partition((6, 6, 6), name='array', fill='random')
array.clear()
in_blocks = Partition((3, 3, 3), name='in', array=array)
array.repartition(in_blocks, None, keep.keep)
in_blocks.clear()
out_blocks = Partition((2, 2, 2), name='out', array=array)
in_blocks.repartition(out_blocks, None, keep.keep)
array = Partition((12, 12, 12), name='array', fill='random')
array.clear()
in_blocks = Partition((4, 4, 4), name='in', array=array)
array.repartition(in_blocks, None, keep.keep)
in_blocks.clear()
out_blocks = Partition((3, 3, 3), name='out', array=array)
in_blocks.repartition(out_blocks, None, keep.keep)
rein_blocks = Partition((12, 12, 12), name='rein', array=array)
out_blocks.repartition(rein_blocks, None, keep.keep)
rein_blocks.blocks[(0, 0, 0)].read()
rein_data = rein_blocks.blocks[(0, 0, 0)].data.bytes()
array.blocks[(0, 0, 0)].read()
array_data = array.blocks[(0, 0, 0)].data.bytes()
assert (rein_data == array_data)
def main(args=None):
parser = ArgumentParser()
parser.add_argument("A",
action="store",
help="shape of the reconstructed array")
parser.add_argument(
"I",
action="store",
help="shape of the input blocks. Input blocks "
"called 'in...' must be stored on disk",
)
parser.add_argument(
"O",
action="store",
help="shape of the outut blocks. Output blocks"
" called 'out...' will be created on disk",
)
commands = parser.add_mutually_exclusive_group()
commands.add_argument(
"--create",
action="store_true",
help="create input blocks on disk"
" before repartitioning.",
)
commands.add_argument(
"--repartition",
action="store_true",
help="repartition input blocks to output block dimensions",
)
commands.add_argument(
"--delete",
action="store_true",
help="delete output blocks after repartitioning.",
)
commands.add_argument(
"--test-data",
action="store_true",
help="reconstruct array from input blocks, "
"reconstruct array from output blocks, "
"check that data is identical in both "
"reconstructions.",
)
parser.add_argument("--max-mem",
action="store",
help="max memory to use, in bytes")
parser.add_argument(
"method",
action="store",
help="repartitioning method to use",
choices=["baseline", "keep"],
)
args, params = parser.parse_known_args(args)
mem = args.max_mem
if mem is not None:
mem = int(mem)
repart_func = {"baseline": keep.baseline, "keep": keep.keep}
array = Partition(make_tuple(args.A), name="array")
if args.create:
fill = "random"
log("Creating input blocks", 1)
else:
fill = None
log("Using existing input blocks", 1)
in_blocks = Partition(make_tuple(args.I),
name="in",
array=array,
fill=fill)
in_blocks.clear()
if not args.create:
out_blocks = Partition(make_tuple(args.O), name="out", array=array)
# Repartitioning
if args.repartition:
log("Repartitioning input blocks into output blocks", 1)
out_blocks.delete()
out_blocks.clear() # shouldn't be necessary but just in case
start = time.time()
(
total_bytes,
seeks,
peak_mem,
read_time,
write_time,
) = in_blocks.repartition(out_blocks, mem,
repart_func[args.method])
end = time.time()
total_time = end - start
assert total_time > read_time + write_time
assert total_bytes == 2 * math.prod(array.shape)
log(
f"Seeks, peak memory (B), read time (s),"
f" write time (s), elapsed time (s):" + os.linesep +
f"{seeks},{peak_mem},{round(read_time,2)},"
f"{round(write_time,2)},{round(total_time,2)}",
2,
)
if args.test_data:
log("Testing data", 1)
in_blocks.repartition(array, mem, repart_func[args.method])
with open(array.blocks[(0, 0, 0)].file_name, "rb") as f:
in_data = f.read()
array.delete()
out_blocks.repartition(array, mem, repart_func[args.method])
with open(array.blocks[(0, 0, 0)].file_name, "rb") as f:
out_data = f.read()
assert in_data == out_data
if args.delete:
log("Deleting output blocks", 1)
out_blocks.delete()
def test_r_hat_2():
array = Partition((10, 10, 10), name='array')
in_blocks = Partition((2, 2, 2), array=array, name='in')
out_blocks = Partition((5, 5, 5), array=array, name='out')
with pytest.raises(Exception):
r_hat = keep.get_r_hat(in_blocks, out_blocks)