rewrite local_mul_specialize to use alloc instead of fill_chain

theano/tensor/opt.py

@@ -1345,9 +1345,9 @@ register_specialize(local_pow_specialize)
 
 @gof.local_optimizer([T.mul])
 def local_mul_specialize(node):
-    def fill_chain(v):
-        return _fill_chain(v, node.inputs)
     #here, we are past the point of canonicalization, so we don't want to put in un-necessary fills.
+    #
+    # at this point [post canonicalize], mul() may have many inputs.
     if node.op == T.mul:
         #the idea here is that we have pow(x, y)
         neg = False
@@ -1365,6 +1365,7 @@ def local_mul_specialize(node):
             elif N.all(y == -1.0):
                 neg ^= True #toggles
             elif N.all(y == 0.0):
+                # if we find any zero, we just return right away
                 return [T.alloc(numpy.asarray(0, dtype=node.outputs[0].dtype),
                         *node.env.shape_feature.shape_of[node.outputs[0]])]
             else:
@@ -1374,26 +1375,29 @@ def local_mul_specialize(node):
             if new_inputs:
                 if len(new_inputs) == 1:
                     if neg:
-                        msg = -new_inputs[0]
+                        rval = -new_inputs[0]
                     else:
-                        msg = new_inputs[0]
-                    return fill_chain(msg)
+                        rval = new_inputs[0]
                 else:
                     if neg:
-                        msg = -T.mul(*new_inputs)
+                        rval = -T.mul(*new_inputs)
                     else:
-                        msg = T.mul(*new_inputs)
+                        rval = T.mul(*new_inputs)
 
-                return [T.alloc(T.cast(msg, node.outputs[0].dtype),
+                return [T.alloc(T.cast(rval, node.outputs[0].dtype),
                         *node.env.shape_feature.shape_of[node.outputs[0]])]
             else:
+                # there are no variable inputs to mul
+                # N.B. this could have been constant-folded...
                 if neg:
                     # return output's worth of -1
-                    return [T.alloc(numpy.asarray(-1, dtype=node.outputs[0].dtype),
+                    return [T.alloc(
+                        numpy.asarray(-1, dtype=node.outputs[0].dtype),
                             *node.env.shape_feature.shape_of[node.outputs[0]])]
                 else:
                     # return output's worth of 1
-                    return [T.alloc(numpy.asarray(1, dtype=node.outputs[0].dtype),
+                    return [T.alloc(
+                        numpy.asarray(1, dtype=node.outputs[0].dtype),
                             *node.env.shape_feature.shape_of[node.outputs[0]])]
 
 register_specialize(local_mul_specialize)

theano/tensor/tests/test_opt.py

@@ -1013,6 +1013,51 @@ class test_shapeoptimizer(unittest.TestCase):
         print f.maker.env.toposort()
         assert [] == f.maker.env.toposort()
 
+def test_local_mul_specialize():
+
+    # test a few cases to make sure that the basics are covered
+    # 
+
+    mode = theano.config.mode
+    if mode == 'FAST_COMPILE':
+        mode = 'FAST_RUN'
+    v = T.vector()
+    m = T.vector()
+
+    f = function([v,m], v*1, mode=mode)
+    nodes = [node.op for node in f.maker.env.toposort()]
+    print nodes
+    assert nodes == []
+
+    f = function([v,m], v*0, mode=mode)
+    nodes = [node.op for node in f.maker.env.toposort()]
+    print nodes
+    assert nodes == [Shape_i(0), T.alloc]
+
+    f = function([v,m], v*(-1), mode=mode)
+    nodes = [node.op for node in f.maker.env.toposort()]
+    print nodes
+    assert nodes == [T.neg]
+
+    f = function([v,m], v*1*(-m), mode=mode)
+    nodes = [node.op for node in f.maker.env.toposort()]
+    print nodes
+    theano.printing.debugprint(f)
+    assert nodes == [T.mul, inplace.neg_inplace]
+
+    f = function([v,m], v*0*(-m), mode=mode)
+    nodes = [node.op for node in f.maker.env.toposort()]
+    print nodes
+    theano.printing.debugprint(f)
+    assert nodes == [Shape_i(0), T.alloc]
+
+    f = function([v,m], v*(-1)*(-m), mode=mode)
+    nodes = [node.op for node in f.maker.env.toposort()]
+    print nodes
+    theano.printing.debugprint(f)
+    assert nodes == [T.mul]
+
+
 if __name__ == '__main__':
 #    unittest.main()
     test_fusion().tes_memory_leak()