2
0
mirror of https://gitlab.com/apparmor/apparmor synced 2025-08-22 10:07:12 +00:00

862 lines
25 KiB
C++
Raw Normal View History

2007-02-27 02:29:16 +00:00
/*
2007-04-11 08:12:51 +00:00
* (C) 2006, 2007 Andreas Gruenbacher <agruen@suse.de>
* Copyright (c) 2003-2008 Novell, Inc. (All rights reserved)
* Copyright 2009-2010 Canonical Ltd.
2007-02-27 02:29:16 +00:00
*
* The libapparmor library is licensed under the terms of the GNU
* Lesser General Public License, version 2.1. Please see the file
* COPYING.LGPL.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
*
* Base of implementation based on the Lexical Analysis chapter of:
* Alfred V. Aho, Ravi Sethi, Jeffrey D. Ullman:
* Compilers: Principles, Techniques, and Tools (The "Dragon Book"),
* Addison-Wesley, 1986.
*/
2007-02-27 02:29:16 +00:00
#include <list>
#include <vector>
#include <stack>
#include <set>
#include <map>
#include <ostream>
#include <iostream>
#include <fstream>
2007-02-27 02:29:16 +00:00
#include "expr-tree.h"
#include "hfa.h"
2007-02-27 02:29:16 +00:00
#include "../immunix.h"
ostream &operator<<(ostream &os, const State &state)
2010-11-09 11:14:55 -08:00
{
/* dump the state label */
2010-11-09 11:14:55 -08:00
os << '{';
os << state.label;
os << '}';
return os;
}
State *DFA::add_new_state(NodeMap &nodemap,
pair<unsigned long, NodeSet *> index,
NodeSet *nodes, dfa_stats_t &stats)
{
State *state = new State(nodemap.size(), nodes);
states.push_back(state);
nodemap.insert(make_pair(index, state));
stats.proto_sum += nodes->size();
if (nodes->size() > stats.proto_max)
stats.proto_max = nodes->size();
return state;
}
2010-11-09 11:14:55 -08:00
State *DFA::find_target_state(NodeMap &nodemap, list<State *> &work_queue,
NodeSet *nodes, dfa_stats_t &stats)
{
State *target;
pair<unsigned long, NodeSet *> index = make_pair(hash_NodeSet(nodes), nodes);
map<pair<unsigned long, NodeSet *>, State *, deref_less_than>::iterator x = nodemap.find(index);
if (x == nodemap.end()) {
/* set of nodes isn't known so create new state, and nodes to
* state mapping
*/
target = add_new_state(nodemap, index, nodes, stats);
work_queue.push_back(target);
} else {
/* set of nodes already has a mapping so free this one */
stats.duplicates++;
delete(nodes);
target = x->second;
}
return target;
}
2010-11-09 11:14:55 -08:00
void DFA::update_state_transitions(NodeMap &nodemap, list<State *> &work_queue,
State *state, dfa_stats_t &stats)
{
/* Compute possible transitions for state->nodes. This is done by
* iterating over all the nodes in state->nodes and combining the
* transitions.
*
* The resultant transition set is a mapping of characters to
* sets of nodes.
*/
NodeCases cases;
for (NodeSet::iterator i = state->nodes->begin(); i != state->nodes->end(); i++)
(*i)->follow(cases);
/* Now for each set of nodes in the computed transitions, make
* sure that there is a state that maps to it, and add the
* matching case to the state.
*/
/* check the default transition first */
if (cases.otherwise)
state->cases.otherwise = find_target_state(nodemap, work_queue,
cases.otherwise,
stats);;
/* For each transition from *from, check if the set of nodes it
* transitions to already has been mapped to a state
*/
for (NodeCases::iterator j = cases.begin(); j != cases.end(); j++) {
State *target;
target = find_target_state(nodemap, work_queue, j->second, stats);
/* Don't insert transition that the default transition
* already covers
*/
if (target != state->cases.otherwise)
state->cases.cases[j->first] = target;
}
}
/* WARNING: This routine can only be called from within DFA creation as
* the nodes value is only valid during dfa construction.
*/
void DFA::dump_node_to_dfa(void)
{
cerr << "Mapping of States to expr nodes\n"
" State <= Nodes\n"
"-------------------\n";
for (Partition::iterator i = states.begin(); i != states.end(); i++)
cerr << " " << (*i)->label << " <= " << *(*i)->nodes << "\n";
}
2007-02-27 02:29:16 +00:00
/**
* Construct a DFA from a syntax tree.
*/
DFA::DFA(Node *root, dfaflags_t flags): root(root)
2007-02-27 02:29:16 +00:00
{
dfa_stats_t stats = { 0, 0, 0 };
int i = 0;
2010-11-09 11:14:55 -08:00
if (flags & DFA_DUMP_PROGRESS)
fprintf(stderr, "Creating dfa:\r");
2010-11-09 11:14:55 -08:00
for (depth_first_traversal i(root); i; i++) {
(*i)->compute_nullable();
(*i)->compute_firstpos();
(*i)->compute_lastpos();
}
2010-11-09 11:14:55 -08:00
if (flags & DFA_DUMP_PROGRESS)
fprintf(stderr, "Creating dfa: followpos\r");
for (depth_first_traversal i(root); i; i++) {
(*i)->compute_followpos();
}
NodeMap nodemap;
NodeSet *emptynode = new NodeSet;
nonmatching = add_new_state(nodemap,
make_pair(hash_NodeSet(emptynode), emptynode),
emptynode, stats);
2010-11-09 11:14:55 -08:00
NodeSet *first = new NodeSet(root->firstpos);
start = add_new_state(nodemap, make_pair(hash_NodeSet(first), first),
first, stats);
2010-11-09 11:14:55 -08:00
/* the work_queue contains the states that need to have their
* transitions computed. This could be done with a recursive
* algorithm instead of a work_queue, but it would be slightly slower
* and consume more memory.
2010-11-09 11:14:55 -08:00
*
* TODO: currently the work_queue is treated in a breadth first
* search manner. Test using the work_queue in a depth first
* manner, this may help reduce the number of entries on the
* work_queue at any given time, thus reducing peak memory use.
*/
list<State *> work_queue;
work_queue.push_back(start);
2007-02-27 02:29:16 +00:00
2010-11-09 11:14:55 -08:00
while (!work_queue.empty()) {
if (i % 1000 == 0 && (flags & DFA_DUMP_PROGRESS))
fprintf(stderr, "\033[2KCreating dfa: queue %ld\tstates %ld\teliminated duplicates %d\r",
work_queue.size(), states.size(),
stats.duplicates);
2010-11-09 11:14:55 -08:00
i++;
2007-02-27 02:29:16 +00:00
State *from = work_queue.front();
2010-11-09 11:14:55 -08:00
work_queue.pop_front();
2007-02-27 02:29:16 +00:00
/* Update 'from's transitions, and if it transitions to any
* unknown State create it and add it to the work_queue
2010-11-09 11:14:55 -08:00
*/
update_state_transitions(nodemap, work_queue, from, stats);
2010-11-09 11:14:55 -08:00
} /* while (!work_queue.empty()) */
2010-11-09 11:14:55 -08:00
/* cleanup Sets of nodes used computing the DFA as they are no longer
* needed.
*/
for (depth_first_traversal i(root); i; i++) {
(*i)->firstpos.clear();
(*i)->lastpos.clear();
(*i)->followpos.clear();
2007-02-27 02:29:16 +00:00
}
if (flags & DFA_DUMP_NODE_TO_DFA)
dump_node_to_dfa();
2010-11-09 11:14:55 -08:00
for (NodeMap::iterator i = nodemap.begin(); i != nodemap.end(); i++)
delete i->first.second;
2010-11-09 11:14:55 -08:00
nodemap.clear();
2010-11-09 11:14:55 -08:00
if (flags & (DFA_DUMP_STATS))
fprintf(stderr, "\033[2KCreated dfa: states %ld,\teliminated duplicates %d,\tprotostate sets: longest %u, avg %u\n",
states.size(), stats.duplicates, stats.proto_max,
(unsigned int)(stats.proto_sum / states.size()));
2007-02-27 02:29:16 +00:00
}
DFA::~DFA()
{
for (Partition::iterator i = states.begin(); i != states.end(); i++)
delete *i;
2007-02-27 02:29:16 +00:00
}
void DFA::dump_uniq_perms(const char *s)
{
set<pair<uint32_t, uint32_t> > uniq;
for (Partition::iterator i = states.begin(); i != states.end(); i++)
uniq.insert(make_pair((*i)->accept, (*i)->audit));
cerr << "Unique Permission sets: " << s << " (" << uniq.size() << ")\n";
cerr << "----------------------\n";
for (set<pair<uint32_t, uint32_t> >::iterator i = uniq.begin();
i != uniq.end(); i++) {
cerr << " " << hex << i->first << " " << i->second << dec << "\n";
}
}
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
/* Remove dead or unreachable states */
void DFA::remove_unreachable(dfaflags_t flags)
{
set<State *> reachable;
list<State *> work_queue;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
/* find the set of reachable states */
reachable.insert(nonmatching);
work_queue.push_back(start);
while (!work_queue.empty()) {
State *from = work_queue.front();
work_queue.pop_front();
reachable.insert(from);
2010-11-09 11:14:55 -08:00
if (from->cases.otherwise &&
(reachable.find(from->cases.otherwise) == reachable.end()))
work_queue.push_back(from->cases.otherwise);
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
for (Cases::iterator j = from->cases.begin(); j != from->cases.end(); j++) {
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (reachable.find(j->second) == reachable.end())
work_queue.push_back(j->second);
}
}
/* walk the set of states and remove any that aren't reachable */
if (reachable.size() < states.size()) {
int count = 0;
2010-11-09 11:14:55 -08:00
Partition::iterator i;
Partition::iterator next;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
for (i = states.begin(); i != states.end(); i = next) {
next = i;
next++;
if (reachable.find(*i) == reachable.end()) {
if (flags & DFA_DUMP_UNREACHABLE) {
cerr << "unreachable: " << **i;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (*i == start)
cerr << " <==";
2010-11-09 11:14:55 -08:00
if ((*i)->accept) {
cerr << " (0x" << hex
<< (*i)->accept << " "
<< (*i)->audit << dec
<< ')';
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
cerr << "\n";
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
2010-11-09 11:14:55 -08:00
State *current = *i;
states.erase(i);
delete(current);
count++;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
}
if (count && (flags & DFA_DUMP_STATS))
cerr << "DFA: states " << states.size() << " removed "
<< count << " unreachable states\n";
}
}
/* test if two states have the same transitions under partition_map */
bool DFA::same_mappings(State *s1, State *s2)
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
{
if (s1->cases.otherwise && s1->cases.otherwise != nonmatching) {
if (!s2->cases.otherwise || s2->cases.otherwise == nonmatching)
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
return false;
Partition *p1 = s1->cases.otherwise->partition;
Partition *p2 = s2->cases.otherwise->partition;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (p1 != p2)
return false;
} else if (s2->cases.otherwise && s2->cases.otherwise != nonmatching) {
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
return false;
}
2010-11-09 11:14:55 -08:00
if (s1->cases.cases.size() != s2->cases.cases.size())
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
return false;
for (Cases::iterator j1 = s1->cases.begin(); j1 != s1->cases.end(); j1++) {
2010-11-09 11:14:55 -08:00
Cases::iterator j2 = s2->cases.cases.find(j1->first);
if (j2 == s2->cases.end())
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
return false;
Partition *p1 = j1->second->partition;
Partition *p2 = j2->second->partition;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (p1 != p2)
return false;
}
return true;
}
/* Do simple djb2 hashing against a States transition cases
* this provides a rough initial guess at state equivalence as if a state
* has a different number of transitions or has transitions on different
* cases they will never be equivalent.
* Note: this only hashes based off of the alphabet (not destination)
* as different destinations could end up being equiv
*/
size_t DFA::hash_trans(State *s)
{
unsigned long hash = 5381;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
for (Cases::iterator j = s->cases.begin(); j != s->cases.end(); j++) {
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
hash = ((hash << 5) + hash) + j->first;
2010-11-09 11:14:55 -08:00
State *k = j->second;
hash = ((hash << 5) + hash) + k->cases.cases.size();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
2010-11-09 11:14:55 -08:00
if (s->cases.otherwise && s->cases.otherwise != nonmatching) {
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
hash = ((hash << 5) + hash) + 5381;
2010-11-09 11:14:55 -08:00
State *k = s->cases.otherwise;
hash = ((hash << 5) + hash) + k->cases.cases.size();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
2010-11-09 11:14:55 -08:00
hash = (hash << 8) | s->cases.cases.size();
return hash;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
/* minimize the number of dfa states */
void DFA::minimize(dfaflags_t flags)
{
map<pair<uint64_t, size_t>, Partition *> perm_map;
list<Partition *> partitions;
/* Set up the initial partitions
* minimium of - 1 non accepting, and 1 accepting
* if trans hashing is used the accepting and non-accepting partitions
* can be further split based on the number and type of transitions
* a state makes.
* If permission hashing is enabled the accepting partitions can
* be further divided by permissions. This can result in not
* obtaining a truely minimized dfa but comes close, and can speedup
* minimization.
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
*/
int accept_count = 0;
int final_accept = 0;
2010-11-09 11:14:55 -08:00
for (Partition::iterator i = states.begin(); i != states.end(); i++) {
uint64_t perm_hash = 0;
if (flags & DFA_CONTROL_MINIMIZE_HASH_PERMS) {
/* make every unique perm create a new partition */
perm_hash = ((uint64_t) (*i)->audit) << 32 |
(uint64_t) (*i)->accept;
} else if ((*i)->audit || (*i)->accept) {
/* combine all perms together into a single parition */
perm_hash = 1;
} /* else not an accept state so 0 for perm_hash */
size_t trans_hash = 0;
if (flags & DFA_CONTROL_MINIMIZE_HASH_TRANS)
trans_hash = hash_trans(*i);
pair<uint64_t, size_t> group = make_pair(perm_hash, trans_hash);
map<pair<uint64_t, size_t>, Partition *>::iterator p = perm_map.find(group);
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (p == perm_map.end()) {
Partition *part = new Partition();
part->push_back(*i);
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
perm_map.insert(make_pair(group, part));
partitions.push_back(part);
(*i)->partition = part;
if (perm_hash)
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
accept_count++;
} else {
(*i)->partition = p->second;
p->second->push_back(*i);
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
2010-11-09 11:14:55 -08:00
if ((flags & DFA_DUMP_PROGRESS) && (partitions.size() % 1000 == 0))
cerr << "\033[2KMinimize dfa: partitions "
<< partitions.size() << "\tinit " << partitions.size()
<< " (accept " << accept_count << ")\r";
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
/* perm_map is no longer needed so free the memory it is using.
* Don't remove - doing it manually here helps reduce peak memory usage.
*/
perm_map.clear();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
int init_count = partitions.size();
if (flags & DFA_DUMP_PROGRESS)
cerr << "\033[2KMinimize dfa: partitions " << partitions.size()
<< "\tinit " << init_count << " (accept "
<< accept_count << ")\r";
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
/* Now do repartitioning until each partition contains the set of
* states that are the same. This will happen when the partition
* splitting stables. With a worse case of 1 state per partition
* ie. already minimized.
*/
Partition *new_part;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
int new_part_count;
do {
new_part_count = 0;
for (list<Partition *>::iterator p = partitions.begin();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
p != partitions.end(); p++) {
new_part = NULL;
State *rep = *((*p)->begin());
Partition::iterator next;
for (Partition::iterator s = ++(*p)->begin(); s != (*p)->end();) {
if (same_mappings(rep, *s)) {
++s;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
continue;
}
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (!new_part) {
new_part = new Partition;
list<Partition *>::iterator tmp = p;
partitions.insert(++tmp, new_part);
new_part_count++;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
new_part->push_back(*s);
s = (*p)->erase(s);
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
/* remapping partition_map for new_part entries
* Do not do this above as it messes up same_mappings
*/
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (new_part) {
for (Partition::iterator m = new_part->begin();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
m != new_part->end(); m++) {
(*m)->partition = new_part;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
}
if ((flags & DFA_DUMP_PROGRESS) && (partitions.size() % 100 == 0))
cerr << "\033[2KMinimize dfa: partitions "
<< partitions.size() << "\tinit "
<< init_count << " (accept "
<< accept_count << ")\r";
}
} while (new_part_count);
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (partitions.size() == states.size()) {
if (flags & DFA_DUMP_STATS)
cerr << "\033[2KDfa minimization no states removed: partitions "
<< partitions.size() << "\tinit " << init_count
<< " (accept " << accept_count << ")\n";
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
goto out;
}
/* Remap the dfa so it uses the representative states
* Use the first state of a partition as the representative state
* At this point all states with in a partion have transitions
* to states within the same partitions, however this can slow
* down compressed dfa compression as there are more states,
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
*/
for (list<Partition *>::iterator p = partitions.begin();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
p != partitions.end(); p++) {
/* representative state for this partition */
State *rep = *((*p)->begin());
/* update representative state's transitions */
2010-11-09 11:14:55 -08:00
if (rep->cases.otherwise) {
Partition *partition = rep->cases.otherwise->partition;
2010-11-09 11:14:55 -08:00
rep->cases.otherwise = *partition->begin();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
for (Cases::iterator c = rep->cases.begin(); c != rep->cases.end(); c++) {
Partition *partition = c->second->partition;
2010-11-09 11:14:55 -08:00
c->second = *partition->begin();
}
//if ((*p)->size() > 1)
//cerr << rep->label << ": ";
/* clear the state label for all non representative states,
* and accumulate permissions */
2010-11-09 11:14:55 -08:00
for (Partition::iterator i = ++(*p)->begin(); i != (*p)->end(); i++) {
//cerr << " " << (*i)->label;
(*i)->label = -1;
rep->accept |= (*i)->accept;
rep->audit |= (*i)->audit;
2010-11-09 11:14:55 -08:00
}
if (rep->accept || rep->audit)
final_accept++;
2010-11-09 11:14:55 -08:00
//if ((*p)->size() > 1)
//cerr << "\n";
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
if (flags & DFA_DUMP_STATS)
cerr << "\033[2KMinimized dfa: final partitions "
<< partitions.size() << " (accept " << final_accept
<< ")" << "\tinit " << init_count << " (accept "
<< accept_count << ")\n";
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
/* make sure nonmatching and start state are up to date with the
* mappings */
{
Partition *partition = nonmatching->partition;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (*partition->begin() != nonmatching) {
nonmatching = *partition->begin();
}
partition = start->partition;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
if (*partition->begin() != start) {
start = *partition->begin();
}
}
2010-11-09 11:14:55 -08:00
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
/* Now that the states have been remapped, remove all states
2010-11-09 11:14:55 -08:00
* that are not the representive states for their partition, they
* will have a label == -1
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
*/
for (Partition::iterator i = states.begin(); i != states.end();) {
2010-11-09 11:14:55 -08:00
if ((*i)->label == -1) {
State *s = *i;
2010-11-09 11:14:55 -08:00
i = states.erase(i);
delete(s);
2010-11-09 11:14:55 -08:00
} else
i++;
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
}
out:
/* Cleanup */
while (!partitions.empty()) {
Partition *p = partitions.front();
Dfa minimization and unreachable state removal Add basic Hopcroft based dfa minimization. It currently does a simple straight state comparison that can be quadratic in time to split partitions. This is offset however by using hashing to setup the initial partitions so that the number of states within a partition are relative few. The hashing of states for initial partition setup is linear in time. This means the closer the initial partition set is to the final set, the closer the algorithm is to completing in a linear time. The hashing works as follows: For each state we know the number of transitions that are not the default transition. For each of of these we hash the set of letters it can transition on using a simple djb2 hash algorithm. This creates a unique hash based on the number of transitions and the input it can transition on. If a state does not have the same hash we know it can not the same as another because it either has a different number of transitions or or transitions on a different set. To further distiguish states, the number of transitions of each transitions target state are added into the hash. This serves to further distiguish states as a transition to a state with a different number of transitions can not possibly be reduced to an equivalent state. A further distinction of states is made for accepting states in that we know each state with a unique set of accept permissions must be in its own partition to ensure the unique accept permissions are in the final dfa. The unreachable state removal is a basic walk of the dfa from the start state marking all states that are reached. It then sweeps any state not reached away. This does not do dead state removal where a non accepting state gets into a loop that will never result in an accepting state.
2010-01-20 03:32:34 -08:00
partitions.pop_front();
delete(p);
}
}
2007-02-27 02:29:16 +00:00
/**
* text-dump the DFA (for debugging).
*/
void DFA::dump(ostream & os)
2007-02-27 02:29:16 +00:00
{
for (Partition::iterator i = states.begin(); i != states.end(); i++) {
if (*i == start || (*i)->accept) {
os << **i;
if (*i == start)
os << " <==";
if ((*i)->accept) {
os << " (0x" << hex << (*i)->accept << " "
<< (*i)->audit << dec << ')';
}
os << "\n";
}
2007-02-27 02:29:16 +00:00
}
os << "\n";
for (Partition::iterator i = states.begin(); i != states.end(); i++) {
if ((*i)->cases.otherwise)
os << **i << " -> " << (*i)->cases.otherwise << "\n";
for (Cases::iterator j = (*i)->cases.begin();
j != (*i)->cases.end(); j++) {
os << **i << " -> " << j->second << ": "
<< j->first << "\n";
}
2007-02-27 02:29:16 +00:00
}
os << "\n";
2007-02-27 02:29:16 +00:00
}
/**
* Create a dot (graphviz) graph from the DFA (for debugging).
*/
void DFA::dump_dot_graph(ostream & os)
2007-02-27 02:29:16 +00:00
{
os << "digraph \"dfa\" {" << "\n";
2007-02-27 02:29:16 +00:00
for (Partition::iterator i = states.begin(); i != states.end(); i++) {
if (*i == nonmatching)
continue;
2007-02-27 02:29:16 +00:00
os << "\t\"" << **i << "\" [" << "\n";
if (*i == start) {
os << "\t\tstyle=bold" << "\n";
}
uint32_t perms = (*i)->accept;
if (perms) {
os << "\t\tlabel=\"" << **i << "\\n("
<< perms << ")\"" << "\n";
}
os << "\t]" << "\n";
2007-02-27 02:29:16 +00:00
}
for (Partition::iterator i = states.begin(); i != states.end(); i++) {
Cases &cases = (*i)->cases;
Chars excluded;
for (Cases::iterator j = cases.begin(); j != cases.end(); j++) {
if (j->second == nonmatching)
excluded.insert(j->first);
else {
os << "\t\"" << **i << "\" -> \"" << j->second
<< "\" [" << "\n";
os << "\t\tlabel=\"" << j-> first << "\"\n";
os << "\t]" << "\n";
}
}
if (cases.otherwise && cases.otherwise != nonmatching) {
os << "\t\"" << **i << "\" -> \"" << cases.otherwise
<< "\" [" << "\n";
if (!excluded.empty()) {
os << "\t\tlabel=\"[^";
for (Chars::iterator i = excluded.begin();
i != excluded.end(); i++) {
os << *i;
}
os << "]\"" << "\n";
}
os << "\t]" << "\n";
2007-02-27 02:29:16 +00:00
}
}
os << '}' << "\n";
2007-02-27 02:29:16 +00:00
}
/**
* Compute character equivalence classes in the DFA to save space in the
* transition table.
*/
map<uchar, uchar> DFA::equivalence_classes(dfaflags_t flags)
2007-02-27 02:29:16 +00:00
{
map<uchar, uchar> classes;
uchar next_class = 1;
for (Partition::iterator i = states.begin(); i != states.end(); i++) {
Cases & cases = (*i)->cases;
/* Group edges to the same next state together */
map<const State *, Chars> node_sets;
for (Cases::iterator j = cases.begin(); j != cases.end(); j++)
node_sets[j->second].insert(j->first);
for (map<const State *, Chars>::iterator j = node_sets.begin();
j != node_sets.end(); j++) {
/* Group edges to the same next state together by class */
map<uchar, Chars> node_classes;
bool class_used = false;
for (Chars::iterator k = j->second.begin();
k != j->second.end(); k++) {
pair<map<uchar, uchar>::iterator, bool> x = classes.insert(make_pair(*k, next_class));
if (x.second)
class_used = true;
pair<map<uchar, Chars>::iterator, bool> y = node_classes.insert(make_pair(x.first->second, Chars()));
y.first->second.insert(*k);
}
if (class_used) {
next_class++;
class_used = false;
}
for (map<uchar, Chars>::iterator k = node_classes.begin();
k != node_classes.end(); k++) {
/**
* If any other characters are in the same class, move
* the characters in this class into their own new
* class
*/
map<uchar, uchar>::iterator l;
for (l = classes.begin(); l != classes.end(); l++) {
if (l->second == k->first &&
k->second.find(l->first) == k->second.end()) {
class_used = true;
break;
}
}
if (class_used) {
for (Chars::iterator l = k->second.begin();
l != k->second.end(); l++) {
classes[*l] = next_class;
}
next_class++;
class_used = false;
}
}
2007-02-27 02:29:16 +00:00
}
}
if (flags & DFA_DUMP_EQUIV_STATS)
fprintf(stderr, "Equiv class reduces to %d classes\n",
next_class - 1);
return classes;
2007-02-27 02:29:16 +00:00
}
/**
* Text-dump the equivalence classes (for debugging).
*/
void dump_equivalence_classes(ostream &os, map<uchar, uchar> &eq)
2007-02-27 02:29:16 +00:00
{
map<uchar, Chars> rev;
for (map<uchar, uchar>::iterator i = eq.begin(); i != eq.end(); i++) {
Chars &chars = rev.insert(make_pair(i->second, Chars())).first->second;
chars.insert(i->first);
}
os << "(eq):" << "\n";
for (map<uchar, Chars>::iterator i = rev.begin(); i != rev.end(); i++) {
os << (int)i->first << ':';
Chars &chars = i->second;
for (Chars::iterator j = chars.begin(); j != chars.end(); j++) {
os << ' ' << *j;
}
os << "\n";
2007-02-27 02:29:16 +00:00
}
}
/**
* Replace characters with classes (which are also represented as
* characters) in the DFA transition table.
*/
void DFA::apply_equivalence_classes(map<uchar, uchar> &eq)
2007-02-27 02:29:16 +00:00
{
/**
* Note: We only transform the transition table; the nodes continue to
* contain the original characters.
*/
for (Partition::iterator i = states.begin(); i != states.end(); i++) {
map<uchar, State *> tmp;
tmp.swap((*i)->cases.cases);
for (Cases::iterator j = tmp.begin(); j != tmp.end(); j++)
(*i)->cases.cases.
insert(make_pair(eq[j->first], j->second));
}
2007-02-27 02:29:16 +00:00
}
2007-03-30 15:20:57 +00:00
#if 0
typedef set <ImportantNode *>AcceptNodes;
map<ImportantNode *, AcceptNodes> dominance(DFA & dfa)
2007-02-27 02:29:16 +00:00
{
map<ImportantNode *, AcceptNodes> is_dominated;
2007-02-27 02:29:16 +00:00
for (States::iterator i = dfa.states.begin(); i != dfa.states.end(); i++) {
AcceptNodes set1;
for (State::iterator j = (*i)->begin(); j != (*i)->end(); j++) {
if (AcceptNode * accept = dynamic_cast<AcceptNode *>(*j))
set1.insert(accept);
}
for (AcceptNodes::iterator j = set1.begin(); j != set1.end(); j++) {
pair<map<ImportantNode *, AcceptNodes>::iterator, bool> x = is_dominated.insert(make_pair(*j, set1));
if (!x.second) {
AcceptNodes & set2(x.first->second), set3;
for (AcceptNodes::iterator l = set2.begin();
l != set2.end(); l++) {
if (set1.find(*l) != set1.end())
set3.insert(*l);
}
set3.swap(set2);
}
2007-02-27 02:29:16 +00:00
}
}
return is_dominated;
2007-02-27 02:29:16 +00:00
}
2007-03-30 15:20:57 +00:00
#endif
2007-02-27 02:29:16 +00:00
2007-11-16 09:27:34 +00:00
static inline int diff_qualifiers(uint32_t perm1, uint32_t perm2)
{
2008-04-16 04:44:21 +00:00
return ((perm1 & AA_EXEC_TYPE) && (perm2 & AA_EXEC_TYPE) &&
(perm1 & AA_EXEC_TYPE) != (perm2 & AA_EXEC_TYPE));
2007-11-16 09:27:34 +00:00
}
/**
* Compute the permission flags that this state corresponds to. If we
* have any exact matches, then they override the execute and safe
* execute flags.
*/
2010-11-09 11:14:55 -08:00
uint32_t accept_perms(NodeSet *state, uint32_t *audit_ctl, int *error)
{
uint32_t perms = 0, exact_match_perms = 0;
uint32_t audit = 0, exact_audit = 0, quiet = 0, deny = 0;
if (error)
*error = 0;
for (NodeSet::iterator i = state->begin(); i != state->end(); i++) {
MatchFlag *match;
if (!(match = dynamic_cast<MatchFlag *>(*i)))
continue;
if (dynamic_cast<ExactMatchFlag *>(match)) {
/* exact match only ever happens with x */
if (!is_merged_x_consistent(exact_match_perms,
match->flag) && error)
*error = 1;;
exact_match_perms |= match->flag;
exact_audit |= match->audit;
} else if (dynamic_cast<DenyMatchFlag *>(match)) {
deny |= match->flag;
quiet |= match->audit;
} else {
if (!is_merged_x_consistent(perms, match->flag)
&& error)
*error = 1;
perms |= match->flag;
audit |= match->audit;
}
}
Add Audit control to AppArmor through, the use of audit and deny key words. Deny is also used to subtract permissions from the profiles permission set. the audit key word can be prepended to any file, network, or capability rule, to force a selective audit when that rule is matched. Audit permissions accumulate just like standard permissions. eg. audit /bin/foo rw, will force an audit message when the file /bin/foo is opened for read or write. audit /etc/shadow w, /etc/shadow r, will force an audit message when /etc/shadow is opened for writing. The audit message is per permission bit so only opening the file for read access will not, force an audit message. audit can also be used in block form instead of prepending audit to every rule. audit { /bin/foo rw, /etc/shadow w, } /etc/shadow r, # don't audit r access to /etc/shadow the deny key word can be prepended to file, network and capability rules, to result in a denial of permissions when matching that rule. The deny rule specifically does 3 things - it gives AppArmor the ability to remember what has been denied so that the tools don't prompt for what has been denied in previous profiling sessions. - it subtracts globally from the allowed permissions. Deny permissions accumulate in the the deny set just as allow permissions accumulate then, the deny set is subtracted from the allow set. - it quiets known rejects. The default audit behavior of deny rules is to quiet known rejects so that audit logs are not flooded with already known rejects. To have known rejects logged prepend the audit keyword to the deny rule. Deny rules do not have a block form. eg. deny /foo/bar rw, audit deny /etc/shadow w, audit { deny owner /blah w, deny other /foo w, deny /etc/shadow w, }
2008-03-13 17:39:03 +00:00
//if (audit || quiet)
//fprintf(stderr, "perms: 0x%x, audit: 0x%x exact: 0x%x eaud: 0x%x deny: 0x%x quiet: 0x%x\n", perms, audit, exact_match_perms, exact_audit, deny, quiet);
perms |= exact_match_perms & ~(AA_USER_EXEC_TYPE | AA_OTHER_EXEC_TYPE);
2007-11-16 09:35:57 +00:00
if (exact_match_perms & AA_USER_EXEC_TYPE) {
perms = (exact_match_perms & AA_USER_EXEC_TYPE) |
(perms & ~AA_USER_EXEC_TYPE);
audit = (exact_audit & AA_USER_EXEC_TYPE) |
(audit & ~AA_USER_EXEC_TYPE);
}
if (exact_match_perms & AA_OTHER_EXEC_TYPE) {
perms = (exact_match_perms & AA_OTHER_EXEC_TYPE) |
(perms & ~AA_OTHER_EXEC_TYPE);
audit = (exact_audit & AA_OTHER_EXEC_TYPE) |
(audit & ~AA_OTHER_EXEC_TYPE);
}
if (perms & AA_USER_EXEC & deny)
perms &= ~AA_USER_EXEC_TYPE;
2007-11-16 09:35:57 +00:00
if (perms & AA_OTHER_EXEC & deny)
perms &= ~AA_OTHER_EXEC_TYPE;
Add Audit control to AppArmor through, the use of audit and deny key words. Deny is also used to subtract permissions from the profiles permission set. the audit key word can be prepended to any file, network, or capability rule, to force a selective audit when that rule is matched. Audit permissions accumulate just like standard permissions. eg. audit /bin/foo rw, will force an audit message when the file /bin/foo is opened for read or write. audit /etc/shadow w, /etc/shadow r, will force an audit message when /etc/shadow is opened for writing. The audit message is per permission bit so only opening the file for read access will not, force an audit message. audit can also be used in block form instead of prepending audit to every rule. audit { /bin/foo rw, /etc/shadow w, } /etc/shadow r, # don't audit r access to /etc/shadow the deny key word can be prepended to file, network and capability rules, to result in a denial of permissions when matching that rule. The deny rule specifically does 3 things - it gives AppArmor the ability to remember what has been denied so that the tools don't prompt for what has been denied in previous profiling sessions. - it subtracts globally from the allowed permissions. Deny permissions accumulate in the the deny set just as allow permissions accumulate then, the deny set is subtracted from the allow set. - it quiets known rejects. The default audit behavior of deny rules is to quiet known rejects so that audit logs are not flooded with already known rejects. To have known rejects logged prepend the audit keyword to the deny rule. Deny rules do not have a block form. eg. deny /foo/bar rw, audit deny /etc/shadow w, audit { deny owner /blah w, deny other /foo w, deny /etc/shadow w, }
2008-03-13 17:39:03 +00:00
perms &= ~deny;
Add Audit control to AppArmor through, the use of audit and deny key words. Deny is also used to subtract permissions from the profiles permission set. the audit key word can be prepended to any file, network, or capability rule, to force a selective audit when that rule is matched. Audit permissions accumulate just like standard permissions. eg. audit /bin/foo rw, will force an audit message when the file /bin/foo is opened for read or write. audit /etc/shadow w, /etc/shadow r, will force an audit message when /etc/shadow is opened for writing. The audit message is per permission bit so only opening the file for read access will not, force an audit message. audit can also be used in block form instead of prepending audit to every rule. audit { /bin/foo rw, /etc/shadow w, } /etc/shadow r, # don't audit r access to /etc/shadow the deny key word can be prepended to file, network and capability rules, to result in a denial of permissions when matching that rule. The deny rule specifically does 3 things - it gives AppArmor the ability to remember what has been denied so that the tools don't prompt for what has been denied in previous profiling sessions. - it subtracts globally from the allowed permissions. Deny permissions accumulate in the the deny set just as allow permissions accumulate then, the deny set is subtracted from the allow set. - it quiets known rejects. The default audit behavior of deny rules is to quiet known rejects so that audit logs are not flooded with already known rejects. To have known rejects logged prepend the audit keyword to the deny rule. Deny rules do not have a block form. eg. deny /foo/bar rw, audit deny /etc/shadow w, audit { deny owner /blah w, deny other /foo w, deny /etc/shadow w, }
2008-03-13 17:39:03 +00:00
if (audit_ctl)
*audit_ctl = PACK_AUDIT_CTL(audit, quiet & deny);
Add Audit control to AppArmor through, the use of audit and deny key words. Deny is also used to subtract permissions from the profiles permission set. the audit key word can be prepended to any file, network, or capability rule, to force a selective audit when that rule is matched. Audit permissions accumulate just like standard permissions. eg. audit /bin/foo rw, will force an audit message when the file /bin/foo is opened for read or write. audit /etc/shadow w, /etc/shadow r, will force an audit message when /etc/shadow is opened for writing. The audit message is per permission bit so only opening the file for read access will not, force an audit message. audit can also be used in block form instead of prepending audit to every rule. audit { /bin/foo rw, /etc/shadow w, } /etc/shadow r, # don't audit r access to /etc/shadow the deny key word can be prepended to file, network and capability rules, to result in a denial of permissions when matching that rule. The deny rule specifically does 3 things - it gives AppArmor the ability to remember what has been denied so that the tools don't prompt for what has been denied in previous profiling sessions. - it subtracts globally from the allowed permissions. Deny permissions accumulate in the the deny set just as allow permissions accumulate then, the deny set is subtracted from the allow set. - it quiets known rejects. The default audit behavior of deny rules is to quiet known rejects so that audit logs are not flooded with already known rejects. To have known rejects logged prepend the audit keyword to the deny rule. Deny rules do not have a block form. eg. deny /foo/bar rw, audit deny /etc/shadow w, audit { deny owner /blah w, deny other /foo w, deny /etc/shadow w, }
2008-03-13 17:39:03 +00:00
// if (perms & AA_ERROR_BIT) {
// fprintf(stderr, "error bit 0x%x\n", perms);
// exit(255);
//}
//if (perms & AA_EXEC_BITS)
//fprintf(stderr, "accept perm: 0x%x\n", perms);
/*
if (perms & ~AA_VALID_PERMS)
yyerror(_("Internal error accumulated invalid perm 0x%llx\n"), perms);
*/
//if (perms & AA_CHANGE_HAT)
// fprintf(stderr, "change_hat 0x%x\n", perms);
if (*error)
fprintf(stderr, "profile has merged rule with conflicting x modifiers\n");
return perms;
}