def find_cycles(system):
from pypy.tool.algo import graphlib
vertices = dict.fromkeys(system.modules)
edges = {}
for m in system.modules:
edges[m] = []
for n in system.modules[m]._imports:
edges[m].append(graphlib.Edge(m, n))
cycles = []
for component in graphlib.strong_components(vertices, edges):
random_vertex = component.iterkeys().next()
cycles.extend(graphlib.all_cycles(random_vertex, component, edges))
ncycles = []
for cycle in cycles:
packs = {}
for edge in cycle:
package = edge.source.rsplit('.', 1)[0]
packs[package] = True
if len(packs) > 1:
ncycles.append(cycle)
cycles = ncycles
for cycle in cycles:
l = len(cycle[0].source)
print cycle[0].source, '->', cycle[0].target
for edge in cycle[1:]:
print ' ' * l, '->', edge.target
print len(cycles), 'inter-package cycles'
def definestr_finalizer_order(cls):
import random
from pypy.tool.algo import graphlib
cls.finalizer_order_examples = examples = []
if cls.large_tests_ok:
letters = 'abcdefghijklmnopqrstuvwxyz'
COUNT = 20
else:
letters = 'abcdefghijklm'
COUNT = 2
for i in range(COUNT):
input = []
edges = {}
for c in letters:
edges[c] = []
# make up a random graph
for c in letters:
for j in range(random.randrange(0, 4)):
d = random.choice(letters)
edges[c].append(graphlib.Edge(c, d))
input.append((c, d))
# find the expected order in which destructors should be called
components = list(graphlib.strong_components(edges, edges))
head = {}
for component in components:
c = component.keys()[0]
for d in component:
assert d not in head
head[d] = c
assert len(head) == len(letters)
strict = []
for c, d in input:
if head[c] != head[d]:
strict.append((c, d))
examples.append((input, components, strict))
class State:
pass
state = State()
class A:
def __init__(self, key):
self.key = key
self.refs = []
def __del__(self):
assert state.age[self.key] == -1
state.age[self.key] = state.time
state.progress = True
def build_example(input):
state.time = 0
state.age = {}
vertices = {}
for c in letters:
vertices[c] = A(c)
state.age[c] = -1
for c, d in input:
vertices[c].refs.append(vertices[d])
def f(_):
i = 0
while i < len(examples):
input, components, strict = examples[i]
build_example(input)
while state.time < len(letters):
state.progress = False
llop.gc__collect(lltype.Void)
if not state.progress:
break
state.time += 1
# summarize the finalization order
lst = []
for c in letters:
lst.append('%s:%d' % (c, state.age[c]))
summary = ', '.join(lst)
# check that all instances have been finalized
if -1 in state.age.values():
return error(i, summary, "not all instances finalized")
# check that if a -> b and a and b are not in the same
# strong component, then a is finalized strictly before b
for c, d in strict:
if state.age[c] >= state.age[d]:
return error(i, summary,
"%s should be finalized before %s"
% (c, d))
# check that two instances in the same strong component
# are never finalized during the same collection
for component in components:
seen = {}
for c in component:
age = state.age[c]
if age in seen:
d = seen[age]
return error(i, summary,
"%s and %s should not be finalized"
" at the same time" % (c, d))
seen[age] = c
i += 1
return "ok"
def error(i, summary, msg):
return '%d\n%s\n%s' % (i, summary, msg)
return f
def find_inner_loops(graph, check_exitswitch_type=None):
"""Enumerate what look like the innermost loops of the graph.
Returns a list of non-overlapping Loop() instances.
"""
# Heuristic (thanks Stakkars for the idea):
# to find the "best" cycle to pick,
#
# * look for variables that don't change over the whole cycle
#
# * cycles with _more_ of them are _inside_ cycles with less of them,
# because any variable that doesn't change over an outer loop will
# not change over an inner loop either, and on the other hand the
# outer loop is likely to use and modify variables that remain
# constant over the inner loop.
#
# * if the numbers are the same, fall back to a more arbitrary
# measure: loops involving less blocks may be more important
# to optimize
#
# * in a cycle, which block is the head of the loop? Somewhat
# arbitrarily we pick the first Bool-switching block that has
# two exits. The "first" means the one closest to the
# startblock of the graph.
#
startdistance = {} # {block: distance-from-startblock}
pending = [graph.startblock]
edge_list = []
dist = 0
while pending:
newblocks = []
for block in pending:
if block not in startdistance:
startdistance[block] = dist
for link in block.exits:
newblocks.append(link.target)
edge = graphlib.Edge(block, link.target)
edge.link = link
edge_list.append(edge)
dist += 1
pending = newblocks
vertices = startdistance
edges = graphlib.make_edge_dict(edge_list)
cycles = graphlib.all_cycles(graph.startblock, vertices, edges)
loops = []
variable_families = None
for cycle in cycles:
# find the headblock
candidates = []
for i in range(len(cycle)):
block = cycle[i].source
v = block.exitswitch
if isinstance(v, Variable) and len(block.exits) == 2:
if getattr(v, 'concretetype', None) is check_exitswitch_type:
dist = startdistance[block]
candidates.append((dist, i))
if not candidates:
continue
_, i = min(candidates)
links = [edge.link for edge in cycle[i:] + cycle[:i]]
loop = Loop(cycle[i].source, links)
# count the variables that remain constant across the cycle,
# detected as having its SSA family present across all blocks.
if variable_families is None:
dffb = DataFlowFamilyBuilder(graph)
variable_families = dffb.get_variable_families()
num_loop_constants = 0
for v in loop.headblock.inputargs:
v = variable_families.find_rep(v)
for link in loop.links:
block1 = link.target
for v1 in block1.inputargs:
v1 = variable_families.find_rep(v1)
if v1 is v:
break # ok, found in this block
else:
break # not found in this block, fail
else:
# found in all blocks, this variable is a loop constant
num_loop_constants += 1
# smaller keys are "better"
key = (
-num_loop_constants, # maximize num_loop_constants
len(cycle)) # minimize len(cycle)
loops.append((key, loop))
loops.sort()
# returns 'loops' without overlapping blocks
result = []
blocks_seen = {}
for key, loop in loops:
for link in loop.links:
if link.target in blocks_seen:
break # overlapping
else:
# non-overlapping
result.append(loop)
for link in loop.links:
blocks_seen[link.target] = True
return result