fixed bug in canonize related to output type-matching. added fill_chain…

theano/tensor/basic.py

@@ -252,7 +252,7 @@ class TensorType(Type):
 
     def __hash__(self):
         """Hash equal for same kinds of TensorType"""
-        return hash(self.dtype) ^ hash(self.broadcastable)
+        return hash(type(self)) ^ hash(self.dtype) ^ hash(self.broadcastable)
 
     ndim = property(lambda self: len(self.broadcastable), doc = "number of dimensions")
     """Number of dimensions

theano/tensor/opt.py

@@ -34,6 +34,11 @@ def in2out(*local_opts, **kwargs):
                              failure_callback=TopoOptimizer.warn_inplace,
                              **kwargs)
 
+def _fill_chain(new_out, orig_inputs):
+    for i in orig_inputs:
+        new_out = T.fill(i, new_out)
+    return [new_out]
+
 
 
 @gof.optimizer
@@ -692,9 +697,6 @@ class Canonizer(gof.LocalOptimizer):
         elif op == self.reciprocal:
             reorg = len(iops.intersection([self.inverse, self.reciprocal])) != 0
 
-        # just in case
-        assert len(node.outputs) == 1
-
         # Here we make the canonical version of the graph around this node
         # See the documentation of get_num_denum and simplify
         orig_num, orig_denum = self.get_num_denum(node.outputs[0])
@@ -715,17 +717,16 @@ class Canonizer(gof.LocalOptimizer):
             elem_op = T.Elemwise(scalar.Identity(scalar.specific_out(getattr(scalar, out.type.dtype))))
             new = elem_op(new)
 
-        if new.type != out.type:
-            for x in orig_num + orig_denum:
-                if x.type == out.type:
-                    new = T.fill(x, new)
-                    break
+        assert (new.type == out.type) == (not (new.type != out.type))
+
+        if not (new.type == out.type):
+            new = _fill_chain(new, node.inputs)[0]
 
-        if new.type != out.type:
+        if new.type == out.type:
+            return [new]
+        else:
             print >> sys.stderr, 'CANONIZE FAILED: new, out = ', new, ',', out, 'types', new.type, ',', out.type
             return False
-        else:
-            return [new]
 
     def __str__(self):
         return getattr(self, 'name', 'Canonizer(%s, %s, %s)' % (self.main, self.inverse, self.reciprocal))
@@ -817,6 +818,8 @@ 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.
     if node.op == T.mul:
         #the idea here is that we have pow(x, y)
@@ -829,20 +832,20 @@ def local_mul_specialize(node):
             elif N.all(y == -1.0):
                 neg ^= True #toggles
             elif N.all(y == 0.0):
-                return [input]
+                return fill_chain(input)
             else:
                 new_inputs.append(input)
         if len(new_inputs) < len(node.inputs):
             if len(new_inputs) == 0:
                 newval = -y.flatten()[0] if neg else y.flatten()[0]
-                return [T.TensorConstant(T.TensorType(dtype=node.outputs[0].type.dtype,
-                    broadcastable = [True] * node.outputs[0].ndim), N.asarray(newval))]
+                return fill_chain(T.TensorConstant(T.TensorType(dtype=node.outputs[0].type.dtype,
+                    broadcastable = [True] * node.outputs[0].ndim), N.asarray(newval)))
 
             if len(new_inputs) == 1:
-                return [-new_inputs[0]] if neg else new_inputs
+                return fill_chain(-new_inputs[0] if neg else new_inputs[0])
             else:
-                return [-T.mul(*new_inputs)] if neg else \
-                        [T.mul(*new_inputs)] 
+                return fill_chain(-T.mul(*new_inputs) if neg else \
+                        T.mul(*new_inputs))
     else:
         return False
 register_specialize(local_mul_specialize)
@@ -985,7 +988,7 @@ def local_greedy_distributor(node):
 
     rval = local_mul_canonizer.merge_num_denum(new_num, new_denum)
 
-    if rval.type != out.type:  
+    if not (rval.type == out.type):
         #WHY DOES THIS HAPPEN?
         return False