local_mul_specialize now don't introcude neg node when this will add more node then needed.

theano/tensor/opt.py

@@ -3600,7 +3600,15 @@ def local_pow_specialize_device(node):
 
 @gof.local_optimizer([T.mul])
 def local_mul_specialize(node):
-    """Remove special-case constants from mul arguments
+    """Remove special-case constants from mul arguments and useless neg in inputs.
+
+    mul(-1, x) -> neg(x)
+    mul(1, x, y) -> mul(x, y)
+    mul(0, ...) -> alloc(0, shapes...)
+
+    This is not done if we would add more nodes in the graph, like with:
+
+    mul(-1, x, y) -/-> neg(mul(x, y))
     """
     # here, we are past the point of canonicalization, so we don't
     # want to put in un-necessary fills.
@@ -3610,19 +3618,23 @@ def local_mul_specialize(node):
         #the idea here is that we have pow(x, y)
         neg = False
         new_inputs = []
+        nb_neg_node = 0
+        nb_cst = 0
         for input in node.inputs:
             # remove any neg arguments
             while input.owner and input.owner.op == T.neg:
                 neg ^= True
                 input = input.owner.inputs[0]
+                nb_neg_node += 1
 
             # remove special case arguments of 1, -1 or 0
             y = local_mul_canonizer.get_constant(input)
-            if N.all(y == 1.0):
-                continue
-            elif N.all(y == -1.0):
+            if y == 1.0:
+                nb_cst += 1
+            elif y == -1.0:
+                nb_cst += 1
                 neg ^= True  # toggles
-            elif N.all(y == 0.0):
+            elif y == 0.0:
                 # if we find any zero, we just return right away
                 return [broadcast_like(0, node.outputs[0], node.fgraph)]
             else:
@@ -3636,10 +3648,17 @@ def local_mul_specialize(node):
                     else:
                         rval = new_inputs[0]
                 else:
-                    if neg:
-                        rval = -T.mul(*new_inputs)
-                    else:
-                        rval = T.mul(*new_inputs)
+                    # The next case would cause a replace by an equivalent case.
+                    if (neg and
+                        nb_neg_node == 0 and
+                        nb_cst == 1):
+                        return
+                    elif neg:
+                        # Don't add an extra neg node as we can't
+                        # fully replace this mul by a neg.
+                        m1 = numpy.asarray(-1, dtype=node.outputs[0].dtype)
+                        new_inputs = [m1] + new_inputs
+                    rval = T.mul(*new_inputs)
 
                 return [broadcast_like(rval, node.outputs[0], node.fgraph)]
             else:

theano/tensor/tests/test_opt.py

@@ -2838,7 +2838,7 @@ def test_local_mul_specialize():
     nodes = [node.op for node in f.maker.fgraph.toposort()]
     print nodes
     theano.printing.debugprint(f)
-    assert nodes == [T.mul, inplace.neg_inplace]
+    assert nodes == [T.mul]
 
     f = function([v, m], v * 0 * (-m), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
@@ -2852,6 +2852,12 @@ def test_local_mul_specialize():
     theano.printing.debugprint(f)
     assert nodes == [T.mul]
 
+    f = function([v, m], v * (-1) * m, mode=mode)
+    nodes = [node.op for node in f.maker.fgraph.toposort()]
+    print nodes
+    theano.printing.debugprint(f)
+    assert nodes == [T.mul]
+
 
 def speed_local_pow_specialize_range():
     val = numpy.random.rand(1e7)