'f2b' : 'bool',
}
+def get_c_opcode(op):
+ if op in conv_opcode_types:
+ return 'nir_search_op_' + op
+ else:
+ return 'nir_op_' + op
+
+
if sys.version_info < (3, 0):
integer_types = (int, long)
string_type = unicode
return self.comm_exprs
def c_opcode(self):
- if self.opcode in conv_opcode_types:
- return 'nir_search_op_' + self.opcode
- else:
- return 'nir_op_' + self.opcode
+ return get_c_opcode(self.opcode)
def render(self, cache):
srcs = "\n".join(src.render(cache) for src in self.sources)
BitSizeValidator(varset).validate(self.search, self.replace)
+class TreeAutomaton(object):
+ """This class calculates a bottom-up tree automaton to quickly search for
+ the left-hand sides of tranforms. Tree automatons are a generalization of
+ classical NFA's and DFA's, where the transition function determines the
+ state of the parent node based on the state of its children. We construct a
+ deterministic automaton to match patterns, using a similar algorithm to the
+ classical NFA to DFA construction. At the moment, it only matches opcodes
+ and constants (without checking the actual value), leaving more detailed
+ checking to the search function which actually checks the leaves. The
+ automaton acts as a quick filter for the search function, requiring only n
+ + 1 table lookups for each n-source operation. The implementation is based
+ on the theory described in "Tree Automatons: Two Taxonomies and a Toolkit."
+ In the language of that reference, this is a frontier-to-root deterministic
+ automaton using only symbol filtering. The filtering is crucial to reduce
+ both the time taken to generate the tables and the size of the tables.
+ """
+ def __init__(self, transforms):
+ self.patterns = [t.search for t in transforms]
+ self._compute_items()
+ self._build_table()
+ #print('num items: {}'.format(len(set(self.items.values()))))
+ #print('num states: {}'.format(len(self.states)))
+ #for state, patterns in zip(self.states, self.patterns):
+ # print('{}: num patterns: {}'.format(state, len(patterns)))
+
+ class IndexMap(object):
+ """An indexed list of objects, where one can either lookup an object by
+ index or find the index associated to an object quickly using a hash
+ table. Compared to a list, it has a constant time index(). Compared to a
+ set, it provides a stable iteration order.
+ """
+ def __init__(self, iterable=()):
+ self.objects = []
+ self.map = {}
+ for obj in iterable:
+ self.add(obj)
+
+ def __getitem__(self, i):
+ return self.objects[i]
+
+ def __contains__(self, obj):
+ return obj in self.map
+
+ def __len__(self):
+ return len(self.objects)
+
+ def __iter__(self):
+ return iter(self.objects)
+
+ def clear(self):
+ self.objects = []
+ self.map.clear()
+
+ def index(self, obj):
+ return self.map[obj]
+
+ def add(self, obj):
+ if obj in self.map:
+ return self.map[obj]
+ else:
+ index = len(self.objects)
+ self.objects.append(obj)
+ self.map[obj] = index
+ return index
+
+ def __repr__(self):
+ return 'IndexMap([' + ', '.join(repr(e) for e in self.objects) + '])'
+
+ class Item(object):
+ """This represents an "item" in the language of "Tree Automatons." This
+ is just a subtree of some pattern, which represents a potential partial
+ match at runtime. We deduplicate them, so that identical subtrees of
+ different patterns share the same object, and store some extra
+ information needed for the main algorithm as well.
+ """
+ def __init__(self, opcode, children):
+ self.opcode = opcode
+ self.children = children
+ # These are the indices of patterns for which this item is the root node.
+ self.patterns = []
+ # This the set of opcodes for parents of this item. Used to speed up
+ # filtering.
+ self.parent_ops = set()
+
+ def __str__(self):
+ return '(' + ', '.join([self.opcode] + [str(c) for c in self.children]) + ')'
+
+ def __repr__(self):
+ return str(self)
+
+ def _compute_items(self):
+ """Build a set of all possible items, deduplicating them."""
+ # This is a map from (opcode, sources) to item.
+ self.items = {}
+
+ # The set of all opcodes used by the patterns. Used later to avoid
+ # building and emitting all the tables for opcodes that aren't used.
+ self.opcodes = self.IndexMap()
+
+ def get_item(opcode, children, pattern=None):
+ commutative = len(children) == 2 \
+ and "commutative" in opcodes[opcode].algebraic_properties
+ item = self.items.setdefault((opcode, children),
+ self.Item(opcode, children))
+ if commutative:
+ self.items[opcode, (children[1], children[0])] = item
+ if pattern is not None:
+ item.patterns.append(pattern)
+ return item
+
+ self.wildcard = get_item("__wildcard", ())
+ self.const = get_item("__const", ())
+
+ def process_subpattern(src, pattern=None):
+ if isinstance(src, Constant):
+ # Note: we throw away the actual constant value!
+ return self.const
+ elif isinstance(src, Variable):
+ if src.is_constant:
+ return self.const
+ else:
+ # Note: we throw away which variable it is here! This special
+ # item is equivalent to nu in "Tree Automatons."
+ return self.wildcard
+ else:
+ assert isinstance(src, Expression)
+ opcode = src.opcode
+ stripped = opcode.rstrip('0123456789')
+ if stripped in conv_opcode_types:
+ # Matches that use conversion opcodes with a specific type,
+ # like f2b1, are tricky. Either we construct the automaton to
+ # match specific NIR opcodes like nir_op_f2b1, in which case we
+ # need to create separate items for each possible NIR opcode
+ # for patterns that have a generic opcode like f2b, or we
+ # construct it to match the search opcode, in which case we
+ # need to map f2b1 to f2b when constructing the automaton. Here
+ # we do the latter.
+ opcode = stripped
+ self.opcodes.add(opcode)
+ children = tuple(process_subpattern(c) for c in src.sources)
+ item = get_item(opcode, children, pattern)
+ for i, child in enumerate(children):
+ child.parent_ops.add(opcode)
+ return item
+
+ for i, pattern in enumerate(self.patterns):
+ process_subpattern(pattern, i)
+
+ def _build_table(self):
+ """This is the core algorithm which builds up the transition table. It
+ is based off of Algorithm 5.7.38 "Reachability-based tabulation of Cl .
+ Comp_a and Filt_{a,i} using integers to identify match sets." It
+ simultaneously builds up a list of all possible "match sets" or
+ "states", where each match set represents the set of Item's that match a
+ given instruction, and builds up the transition table between states.
+ """
+ # Map from opcode + filtered state indices to transitioned state.
+ self.table = defaultdict(dict)
+ # Bijection from state to index. q in the original algorithm is
+ # len(self.states)
+ self.states = self.IndexMap()
+ # List of pattern matches for each state index.
+ self.state_patterns = []
+ # Map from state index to filtered state index for each opcode.
+ self.filter = defaultdict(list)
+ # Bijections from filtered state to filtered state index for each
+ # opcode, called the "representor sets" in the original algorithm.
+ # q_{a,j} in the original algorithm is len(self.rep[op]).
+ self.rep = defaultdict(self.IndexMap)
+
+ # Everything in self.states with a index at least worklist_index is part
+ # of the worklist of newly created states. There is also a worklist of
+ # newly fitered states for each opcode, for which worklist_indices
+ # serves a similar purpose. worklist_index corresponds to p in the
+ # original algorithm, while worklist_indices is p_{a,j} (although since
+ # we only filter by opcode/symbol, it's really just p_a).
+ self.worklist_index = 0
+ worklist_indices = defaultdict(lambda: 0)
+
+ # This is the set of opcodes for which the filtered worklist is non-empty.
+ # It's used to avoid scanning opcodes for which there is nothing to
+ # process when building the transition table. It corresponds to new_a in
+ # the original algorithm.
+ new_opcodes = self.IndexMap()
+
+ # Process states on the global worklist, filtering them for each opcode,
+ # updating the filter tables, and updating the filtered worklists if any
+ # new filtered states are found. Similar to ComputeRepresenterSets() in
+ # the original algorithm, although that only processes a single state.
+ def process_new_states():
+ while self.worklist_index < len(self.states):
+ state = self.states[self.worklist_index]
+
+ # Calculate pattern matches for this state. Each pattern is
+ # assigned to a unique item, so we don't have to worry about
+ # deduplicating them here. However, we do have to sort them so
+ # that they're visited at runtime in the order they're specified
+ # in the source.
+ patterns = list(sorted(p for item in state for p in item.patterns))
+ assert len(self.state_patterns) == self.worklist_index
+ self.state_patterns.append(patterns)
+
+ # calculate filter table for this state, and update filtered
+ # worklists.
+ for op in self.opcodes:
+ filt = self.filter[op]
+ rep = self.rep[op]
+ filtered = frozenset(item for item in state if \
+ op in item.parent_ops)
+ if filtered in rep:
+ rep_index = rep.index(filtered)
+ else:
+ rep_index = rep.add(filtered)
+ new_opcodes.add(op)
+ assert len(filt) == self.worklist_index
+ filt.append(rep_index)
+ self.worklist_index += 1
+
+ # There are two start states: one which can only match as a wildcard,
+ # and one which can match as a wildcard or constant. These will be the
+ # states of intrinsics/other instructions and load_const instructions,
+ # respectively. The indices of these must match the definitions of
+ # WILDCARD_STATE and CONST_STATE below, so that the runtime C code can
+ # initialize things correctly.
+ self.states.add(frozenset((self.wildcard,)))
+ self.states.add(frozenset((self.const,self.wildcard)))
+ process_new_states()
+
+ while len(new_opcodes) > 0:
+ for op in new_opcodes:
+ rep = self.rep[op]
+ table = self.table[op]
+ op_worklist_index = worklist_indices[op]
+ if op in conv_opcode_types:
+ num_srcs = 1
+ else:
+ num_srcs = opcodes[op].num_inputs
+
+ # Iterate over all possible source combinations where at least one
+ # is on the worklist.
+ for src_indices in itertools.product(range(len(rep)), repeat=num_srcs):
+ if all(src_idx < op_worklist_index for src_idx in src_indices):
+ continue
+
+ srcs = tuple(rep[src_idx] for src_idx in src_indices)
+
+ # Try all possible pairings of source items and add the
+ # corresponding parent items. This is Comp_a from the paper.
+ parent = set(self.items[op, item_srcs] for item_srcs in
+ itertools.product(*srcs) if (op, item_srcs) in self.items)
+
+ # We could always start matching something else with a
+ # wildcard. This is Cl from the paper.
+ parent.add(self.wildcard)
+
+ table[src_indices] = self.states.add(frozenset(parent))
+ worklist_indices[op] = len(rep)
+ new_opcodes.clear()
+ process_new_states()
+
_algebraic_pass_template = mako.template.Template("""
#include "nir.h"
#include "nir_builder.h"
unsigned condition_offset;
};
+struct per_op_table {
+ const uint16_t *filter;
+ unsigned num_filtered_states;
+ const uint16_t *table;
+};
+
+/* Note: these must match the start states created in
+ * TreeAutomaton._build_table()
+ */
+
+/* WILDCARD_STATE = 0 is set by zeroing the state array */
+static const uint16_t CONST_STATE = 1;
+
#endif
<% cache = {} %>
${xform.replace.render(cache)}
% endfor
-% for (opcode, xform_list) in sorted(opcode_xforms.items()):
-static const struct transform ${pass_name}_${opcode}_xforms[] = {
-% for xform in xform_list:
- { ${xform.search.c_ptr(cache)}, ${xform.replace.c_value_ptr(cache)}, ${xform.condition_index} },
+% for state_id, state_xforms in enumerate(automaton.state_patterns):
+static const struct transform ${pass_name}_state${state_id}_xforms[] = {
+% for i in state_xforms:
+ { ${xforms[i].search.c_ptr(cache)}, ${xforms[i].replace.c_value_ptr(cache)}, ${xforms[i].condition_index} },
% endfor
};
% endfor
+static const struct per_op_table ${pass_name}_table[nir_num_search_ops] = {
+% for op in automaton.opcodes:
+ [${get_c_opcode(op)}] = {
+ .filter = (uint16_t []) {
+ % for e in automaton.filter[op]:
+ ${e},
+ % endfor
+ },
+ <%
+ num_filtered = len(automaton.rep[op])
+ %>
+ .num_filtered_states = ${num_filtered},
+ .table = (uint16_t []) {
+ <%
+ num_srcs = len(next(iter(automaton.table[op])))
+ %>
+ % for indices in itertools.product(range(num_filtered), repeat=num_srcs):
+ ${automaton.table[op][indices]},
+ % endfor
+ },
+ },
+% endfor
+};
+
+static void
+${pass_name}_pre_block(nir_block *block, uint16_t *states)
+{
+ nir_foreach_instr(instr, block) {
+ switch (instr->type) {
+ case nir_instr_type_alu: {
+ nir_alu_instr *alu = nir_instr_as_alu(instr);
+ nir_op op = alu->op;
+ uint16_t search_op = nir_search_op_for_nir_op(op);
+ const struct per_op_table *tbl = &${pass_name}_table[search_op];
+ if (tbl->num_filtered_states == 0)
+ continue;
+
+ /* Calculate the index into the transition table. Note the index
+ * calculated must match the iteration order of Python's
+ * itertools.product(), which was used to emit the transition
+ * table.
+ */
+ uint16_t index = 0;
+ for (unsigned i = 0; i < nir_op_infos[op].num_inputs; i++) {
+ index *= tbl->num_filtered_states;
+ index += tbl->filter[states[alu->src[i].src.ssa->index]];
+ }
+ states[alu->dest.dest.ssa.index] = tbl->table[index];
+ break;
+ }
+
+ case nir_instr_type_load_const: {
+ nir_load_const_instr *load_const = nir_instr_as_load_const(instr);
+ states[load_const->def.index] = CONST_STATE;
+ break;
+ }
+
+ default:
+ break;
+ }
+ }
+}
+
static bool
${pass_name}_block(nir_builder *build, nir_block *block,
- const bool *condition_flags)
+ const uint16_t *states, const bool *condition_flags)
{
bool progress = false;
if (!alu->dest.dest.is_ssa)
continue;
- switch (alu->op) {
- % for opcode in sorted(opcode_xforms.keys()):
- case nir_op_${opcode}:
- for (unsigned i = 0; i < ARRAY_SIZE(${pass_name}_${opcode}_xforms); i++) {
- const struct transform *xform = &${pass_name}_${opcode}_xforms[i];
+ switch (states[alu->dest.dest.ssa.index]) {
+% for i in range(len(automaton.state_patterns)):
+ case ${i}:
+ for (unsigned i = 0; i < ARRAY_SIZE(${pass_name}_state${i}_xforms); i++) {
+ const struct transform *xform = &${pass_name}_state${i}_xforms[i];
if (condition_flags[xform->condition_offset] &&
nir_replace_instr(build, alu, xform->search, xform->replace)) {
progress = true;
}
}
break;
- % endfor
- default:
- break;
+% endfor
+ default: assert(0);
}
}
nir_builder build;
nir_builder_init(&build, impl);
+ /* Note: it's important here that we're allocating a zeroed array, since
+ * state 0 is the default state, which means we don't have to visit
+ * anything other than constants and ALU instructions.
+ */
+ uint16_t *states = calloc(impl->ssa_alloc, sizeof(*states));
+
+ nir_foreach_block(block, impl) {
+ ${pass_name}_pre_block(block, states);
+ }
+
nir_foreach_block_reverse(block, impl) {
- progress |= ${pass_name}_block(&build, block, condition_flags);
+ progress |= ${pass_name}_block(&build, block, states, condition_flags);
}
+ free(states);
+
if (progress) {
nir_metadata_preserve(impl, nir_metadata_block_index |
nir_metadata_dominance);
}
""")
+
+
class AlgebraicPass(object):
def __init__(self, pass_name, transforms):
self.xforms = []
else:
self.opcode_xforms[xform.search.opcode].append(xform)
+ self.automaton = TreeAutomaton(self.xforms)
+
if error:
sys.exit(1)
return _algebraic_pass_template.render(pass_name=self.pass_name,
xforms=self.xforms,
opcode_xforms=self.opcode_xforms,
- condition_list=condition_list)
+ condition_list=condition_list,
+ automaton=self.automaton,
+ get_c_opcode=get_c_opcode,
+ itertools=itertools)
projects / platform / upstream / mesa.git / commitdiff