def test_find_loop_blocks3():
import os
def ps(loops):
return 42.0, 42.1
def f(loops):
benchtime, stones = ps(abs(loops))
s = "" # annotator happiness
if loops >= 0:
s = (
"RPystone(%s) time for %d passes = %f" % (23, loops, benchtime)
+ "\n"
+ ("This machine benchmarks at %f pystones/second" % stones)
)
os.write(1, s)
if loops == 12345:
f(loops - 1)
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert backedges == []
loop_blocks = find_loop_blocks(graph)
assert len(loop_blocks) == 0
def test_find_loop_blocks3():
import os
def ps(loops):
return 42.0, 42.1
def f(loops):
benchtime, stones = ps(abs(loops))
s = '' # annotator happiness
if loops >= 0:
s = ("RPystone(%s) time for %d passes = %f" %
(23, loops, benchtime) + '\n' +
("This machine benchmarks at %f pystones/second" % stones))
os.write(1, s)
if loops == 12345:
f(loops - 1)
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert backedges == []
loop_blocks = find_loop_blocks(graph)
assert len(loop_blocks) == 0
def test_find_loop_blocks_simple():
def f(a):
if a <= 0:
return 1
return f(a - 1)
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert backedges == []
loop_blocks = find_loop_blocks(graph)
assert len(loop_blocks) == 0
def test_find_loop_blocks_simple():
def f(a):
if a <= 0:
return 1
return f(a - 1)
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert backedges == []
loop_blocks = find_loop_blocks(graph)
assert len(loop_blocks) == 0
def test_find_backedges():
def f(k):
result = 0
for i in range(k):
result += 1
for j in range(k):
result += 1
return result
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert len(backedges) == 2
def test_find_backedges():
def f(k):
result = 0
for i in range(k):
result += 1
for j in range(k):
result += 1
return result
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert len(backedges) == 2
def test_find_loop_blocks2():
class A:
pass
def f(n):
a1 = A()
a1.x = 1
a2 = A()
a2.x = 2
if n > 0:
a = a1
else:
a = a2
return a.x
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert backedges == []
loop_blocks = find_loop_blocks(graph)
assert len(loop_blocks) == 0
def test_find_loop_blocks2():
class A:
pass
def f(n):
a1 = A()
a1.x = 1
a2 = A()
a2.x = 2
if n > 0:
a = a1
else:
a = a2
return a.x
t = TranslationContext()
t.buildannotator().build_types(f, [int])
t.buildrtyper().specialize()
graph = graphof(t, f)
backedges = find_backedges(graph)
assert backedges == []
loop_blocks = find_loop_blocks(graph)
assert len(loop_blocks) == 0
def look_inside_graph(self, graph):
from rpython.translator.backendopt.support import find_backedges
contains_loop = bool(find_backedges(graph))
try:
func = graph.func
except AttributeError:
see_function = True
else:
if hasattr(func, '_jit_look_inside_'):
see_function = func._jit_look_inside_ # override guessing
else:
see_function = (self.look_inside_function(func) and not
self._reject_function(func))
contains_loop = contains_loop and not getattr(
func, '_jit_unroll_safe_', False)
res = see_function and not contains_unsupported_variable_type(graph,
self.supports_floats,
self.supports_longlong,
self.supports_singlefloats)
if res and contains_loop:
self.unsafe_loopy_graphs.add(graph)
res = res and not contains_loop
if (see_function and not res and
getattr(graph, "access_directly", False)):
# This happens when we have a function which has an argument with
# the access_directly flag, and the annotator has determined we will
# see the function. (See
# pypy/annotation/specialize.py:default_specialize) However,
# look_inside_graph just decided that we will not see it. (It has a
# loop or unsupported variables.) If we return False, the call will
# be turned into a residual call, but the graph is access_directly!
# If such a function is called and accesses a virtualizable, the JIT
# will not notice, and the virtualizable will fall out of sync. So,
# we fail loudly now.
raise ValueError("access_directly on a function which we don't see %s" % graph)
return res
def look_inside_graph(self, graph):
from rpython.translator.backendopt.support import find_backedges
contains_loop = bool(find_backedges(graph))
try:
func = graph.func
except AttributeError:
see_function = True
else:
if hasattr(func, '_jit_look_inside_'):
see_function = func._jit_look_inside_ # override guessing
else:
see_function = (self.look_inside_function(func) and not
self._reject_function(func))
contains_loop = contains_loop and not getattr(
func, '_jit_unroll_safe_', False)
res = see_function and not contains_unsupported_variable_type(graph,
self.supports_floats,
self.supports_longlong,
self.supports_singlefloats)
if res and contains_loop:
self.unsafe_loopy_graphs.add(graph)
res = res and not contains_loop
if (see_function and not res and
getattr(graph, "access_directly", False)):
# This happens when we have a function which has an argument with
# the access_directly flag, and the annotator has determined we will
# see the function. (See
# pypy/annotation/specialize.py:default_specialize) However,
# look_inside_graph just decided that we will not see it. (It has a
# loop or unsupported variables.) If we return False, the call will
# be turned into a residual call, but the graph is access_directly!
# If such a function is called and accesses a virtualizable, the JIT
# will not notice, and the virtualizable will fall out of sync. So,
# we fail loudly now.
raise ValueError("access_directly on a function which we don't see %s" % graph)
return res
def partial_escape(translator, graph):
"""
Main function.
Blocks, which we'll work on, are in a dequeue, called "worklist", and are
indexing link-state tuples in "statemap".
"""
insert_links(graph)
worklist = deque([graph.startblock])
statemap = defaultdict(list)
statemap[graph.startblock] = [(None, {})]
finished = set()
entrymap = mkentrymap(graph)
backedges = find_backedges(graph)
number_getfield_removed = 0
while worklist:
block = worklist.popleft()
must_be_materialized = block.is_final_block()
for link in entrymap[block]:
if link in backedges:
must_be_materialized = True
state = get_current_state(statemap[block],
must_be_materialized=must_be_materialized)
if block.is_final_block():
continue
new_operations = []
# Going through the operations
for op in block.operations:
if op.opname == 'malloc':
# Create new entry for every allocation that is not returned
if can_remove(op):
vobj = VirtualObject(op.result.concretetype, op.args)
state[op.result] = vobj
vobj.aliases.add(op.result)
else:
new_operations.append(op)
elif op.opname == 'cast_pointer':
if op.args[0] in state:
# Creating something like an 'alias' for the casting
state[op.result] = vobj = state[op.args[0]]
vobj.aliases.add(op.result)
else:
new_operations.append(op)
elif op.opname == 'setfield':
if op.args[0] in state:
state[op.args[0]].vars[op.args[1].value,
op.args[0].concretetype] = op.args[2]
else:
materialize_object(op.args[2], state, new_operations)
new_operations.append(op)
elif op.opname == 'getfield':
key = op.args[1].value, op.args[0].concretetype
if op.args[0] in state and key in state[op.args[0]].vars:
targ = state[op.args[0]].vars[key]
number_getfield_removed += 1
if targ in state:
state[op.result] = vobj = state[targ]
state[targ].aliases.add(vobj)
else:
new_operations.append(SpaceOperation('same_as',
[targ],
op.result))
else:
materialize_object(op.args[0], state, new_operations)
new_operations.append(op)
else:
for arg in op.args:
materialize_object(arg, state, new_operations)
new_operations.append(op)
# for all backedges, materialize all arguments (loops aren't supported
# properly yet)
for exit in block.exits:
if exit in backedges or exit.target.is_final_block():
for arg in exit.args:
materialize_object(arg, state, new_operations)
block.operations = new_operations
# We're done with the internals of the block. Editing the lists:
finished.add(block)
for exit in block.exits:
# Only adding to the worklist if all its ancestors are processed
for lnk in entrymap[exit.target]:
if lnk.prevblock not in finished and lnk not in backedges:
break
else:
if exit.target not in finished and exit.target not in worklist: # XXX
worklist.append(exit.target)
# setting statemaps:
statemap[exit.target].append((exit, state))
if number_getfield_removed:
if translator.config.translation.verbose:
log.cse("partial escape analysis removed %s getfields in graph %s" % (number_getfield_removed, graph))
else:
log.dot()
# Done. Cleaning up.
remove_same_as(graph)
transform_dead_op_vars(graph)
eliminate_empty_blocks(graph)
join_blocks(graph)
checkgraph(graph)
return number_getfield_removed
