- Only run local optimizers on the ops they are registered for.

theano/gof/opt.py

@@ -736,6 +736,14 @@ class LocalOptimizer(object):
             _optimizer_idx[0] += 1
         return self._optimizer_idx
 
+    def tracks(self):
+        """
+        Return the list of op classes that this opt applies to.
+
+        Return None to apply to all nodes.
+        """
+        return None
+
     def transform(self, node):
         """Transform a subgraph whose output is `node`.
 
@@ -791,9 +799,15 @@ class FromFunctionLocalOptimizer(LocalOptimizer):
                 id(self))
 
 
-def local_optimizer(*tracks):
+def local_optimizer(tracks):
     def decorator(f):
         """WRITEME"""
+        if tracks is not None:
+            if len(tracks) is 0:
+                raise ValueError, ("Use None instead of an empty list to apply to all nodes.", f.__module__, f.__name__)
+            for t in tracks:
+                if not (isinstance(t, type) or isinstance(t, op.Op)):
+                    raise ValueError, ("Tracks are op classes or instances", f.__module__, f.__name__)
         rval = FromFunctionLocalOptimizer(f, tracks)
         rval.__name__ = f.__name__
         return rval
@@ -870,7 +884,7 @@ class OpSub(LocalOptimizer):
         return self.op1
 
     def tracks(self):
-        return [[self.op1]]
+        return [self.op1]
 
     def transform(self, node):
         if node.op != self.op1:
@@ -901,7 +915,7 @@ class OpRemove(LocalOptimizer):
         return self.op
 
     def tracks(self):
-        return [[self.op]]
+        return [self.op]
 
     def transform(self, node):
         if node.op != self.op:
@@ -1500,12 +1514,17 @@ class EquilibriumOptimizer(NavigatorOptimizer):
             None,
             ignore_newtrees=True,
             failure_callback=failure_callback)
-        self.local_optimizers = []
+        self.local_optimizers_map = dict()
+        self.local_optimizers_all = []
         self.global_optimizers = []
 
         for opt in optimizers:
             if isinstance(opt, LocalOptimizer):
-                self.local_optimizers.append(opt)
+                if opt.tracks is None:
+                    self.local_optimizers_all.append(opt)
+                else:
+                    for c in opt.tracks():
+                        self.local_optimizers_map.setdefault(c, []).append(opt)
             else:
                 self.global_optimizers.append(opt)
         self.max_depth = max_depth
@@ -1513,10 +1532,21 @@ class EquilibriumOptimizer(NavigatorOptimizer):
         assert self.max_use_ratio is not None, (
                 'max_use_ratio has to be a number')
 
+    def get_local_optimizers(self):
+        for opt in self.local_optimizers_all:
+            yield opt
+        # if repeat is not a problem we can drop the set
+        s = set()
+        for lopt in self.local_optimizers_map.values():
+            for opt in lopt:
+                if opt not in s:
+                    yield opt
+                    s.add(opt)
+
     def add_requirements(self, fgraph):
         super(EquilibriumOptimizer, self).add_requirements(fgraph)
         fgraph.attach_feature(ChangeTracker())
-        for opt in self.local_optimizers:
+        for opt in self.get_local_optimizers():
             opt.add_requirements(fgraph)
         for opt in self.global_optimizers:
             opt.add_requirements(fgraph)
@@ -1542,7 +1572,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
         time_opts = {}
         io_toposort_timing = []
         nb_nodes = []
-        for opt in self.global_optimizers + self.local_optimizers:
+        for opt in self.global_optimizers + list(self.get_local_optimizers()):
             global_process_count.setdefault(opt, 0)
             time_opts.setdefault(opt, 0)
 
@@ -1595,7 +1625,9 @@ class EquilibriumOptimizer(NavigatorOptimizer):
                     node = q.pop()
                     current_node = node
 
-                    for lopt in self.local_optimizers:
+                    for lopt in (self.local_optimizers_all +
+                                 self.local_optimizers_map.get(type(node.op), []) +
+                                 self.local_optimizers_map.get(node.op, [])):
                         t_opt = time.time()
                         lopt_change = self.process_node(fgraph, node, lopt)
                         time_opts[lopt] += time.time() - t_opt
@@ -1634,7 +1666,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
         print >> stream, "%s%s %s id=%i" % (
                 (' ' * level), self.__class__.__name__, name, id(self))
         if depth != 0:
-            for lopt in self.local_optimizers:
+            for lopt in self.get_local_optimizers():
                 lopt.print_summary(stream, level=(level + 2),
                                    depth=(depth - 1))
 
@@ -1654,7 +1686,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
                 start_nb_nodes, end_nb_nodes, max_nb_nodes)
         print >> stream, blanc, "  time io_toposort %.3fs" % sum(
             io_toposort_timing)
-        s = sum([time_opts[o] for o in opt.local_optimizers])
+        s = sum([time_opts[o] for o in opt.get_local_optimizers()])
         print >> stream, blanc, "  time in local optimizers %.3fs" % s
         s = sum([time_opts[o] for o in opt.global_optimizers])
         print >> stream, blanc, "  time in global optimizers %.3fs" % s
@@ -1679,7 +1711,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
         not_used = 0
         not_used_time = 0
         process_count = {}
-        for o in opt.global_optimizers + opt.local_optimizers:
+        for o in opt.global_optimizers + opt.get_local_optimizers():
             process_count.setdefault(o, 0)
         for count in loop_process_count:
             for o, v in count.iteritems():
@@ -1707,8 +1739,8 @@ class EquilibriumOptimizer(NavigatorOptimizer):
         #(opt, loop_timing, loop_process_count, max_nb_nodes,
         # global_opt_timing, nb_nodes, time_opts, io_toposort_timing) = prof1
 
-        local_optimizers = set(prof1[0].local_optimizers).union(
-            prof2[0].local_optimizers)
+        local_optimizers = set(prof1[0].get_local_optimizers()).union(
+            prof2[0].get_local_optimizers())
         global_optimizers = set(prof1[0].global_optimizers).union(
             prof2[0].global_optimizers)
         new_opt = EquilibriumOptimizer(

theano/scan_module/scan_opt.py

@@ -49,7 +49,7 @@ def info(*msg):
     _logger.info('INFO theano.scan: ' + ' '.join(msg))
 
 
-@gof.local_optimizer([None])
+@gof.local_optimizer([scan_op.Scan])
 def remove_constants_and_unused_inputs_scan(node):
     '''
     Move constants into the inner graph, and remove unused inputs.
@@ -1337,7 +1337,7 @@ def make_equiv(lo, li):
     return left, right
 
 
-@gof.local_optimizer([None])
+@gof.local_optimizer([scan_op.Scan])
 def scan_merge_inouts(node):
     if not isinstance(node.op, scan_op.Scan):
         return False

theano/tensor/blas.py

@@ -1645,7 +1645,7 @@ class Dot22(GemmRelated):
 _dot22 = Dot22()
 
 
-@local_optimizer([T._dot])
+@local_optimizer([T.Dot])
 def local_dot_to_dot22(node):
     # This works for tensor.outer too because basic.outer is a macro that
     # produces a dot(dimshuffle,dimshuffle) of form 4 below
@@ -2025,7 +2025,7 @@ blas_optdb.register('local_dot22_to_dot22scalar',
 
 #from opt import register_specialize, register_canonicalize
 #@register_specialize
-@local_optimizer([])
+@local_optimizer([T.sub, T.add])
 def local_print_as_we_go_along(node):
     if node.op in (T.sub, T.add):
         debugprint(node)

theano/tensor/nnet/nnet.py

@@ -589,7 +589,7 @@ opt.local_mul_canonizer.add_simplifier(softmax_simplifier,
 
 if 0:
     @opt.register_specialize
-    @gof.local_optimizer([])
+    @gof.local_optimizer([tensor.add])
     def local_softmax_grad(node):
         '''dy*sm - DimShuffle{0,'x'}(sum{1}(dy*sm))*sm -> softmax_grad(dy,sm)'''
         #TODO what if the signs are changed?
@@ -1417,7 +1417,7 @@ def _is_const(z, val, approx=False):
 
 
 @opt.register_specialize
-@gof.local_optimizer([])
+@gof.local_optimizer([subtensor.AdvancedSubtensor])
 def local_advanced_indexing_crossentropy_onehot(node):
     log = None
     sm = None

theano/tensor/raw_random.py

@@ -816,7 +816,7 @@ def multinomial(random_state, size=None, n=1, pvals=[0.5, 0.5],
     return op(random_state, size, n, pvals)
 
 
-@gof.local_optimizer([None])
+@gof.local_optimizer([RandomFunction])
 def random_make_inplace(node):
     op = node.op
     if isinstance(op, RandomFunction) and not op.inplace: