Do not loop over broadcastable dimensions

theano/compile/debugmode.py

@@ -1067,22 +1067,44 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
     # We assume that the different outputs of a same Op will behave
     # independently, and there is no need to test over all combinations
     # of outputs (the time taken is prohibitive).
+    # When all outputs on a certain dimension are broadcastable, the Op
+    # can assume that the shape is 1 on that dimension, and stride testing
+    # is less relevant.
     max_ndim = 0
+    out_broadcast_pattern = [True] * max_ndim
     for r in node.outputs:
         if isinstance(r.type, (TensorType, CudaNdarrayType)):
-            max_ndim = max(max_ndim, r.ndim)
+            if max_ndim < r.ndim:
+                out_broadcast_pattern += [True] * (r.ndim - max_ndim)
+                max_ndim = r.ndim
+            assert len(out_broadcast_pattern) == max_ndim
+
+            for i, b in enumerate(r.broadcastable):
+                out_broadcast_pattern[i] = out_broadcast_pattern[i] and b
 
     if 'strided' in prealloc_modes or 'ALL' in prealloc_modes:
         # Initial allocation
         init_strided = {}
         for r in node.outputs:
             if isinstance(r.type, (TensorType, CudaNdarrayType)):
-                # Create a buffer twice as large in every dimension
-                new_buf = r.type.value_zeros(
-                        [(s * 2) for s in r_vals[r].shape])
+                # Create a buffer twice as large in every dimension,
+                # except if broadcastable
+                buf_shape = []
+                for s, b in zip(r_vals[r].shape, r.broadcastable):
+                    if b:
+                        buf_shape.append(s)
+                    else:
+                        buf_shape.append(s * 2)
+                new_buf = r.type.value_zeros(buf_shape)
                 init_strided[r] = new_buf
 
-        for step_signs in itertools_product((-1, 1), repeat=max_ndim):
+        step_signs_list = []
+        for b in out_broadcast_pattern:
+            if b:
+                step_signs_list.append((1,))
+            else:
+                step_signs_list.append((-1, 1))
+        for step_signs in itertools_product(*step_signs_list):
             for step_size in (1, 2):
                 strided = {}
                 steps = [s * step_size for s in step_signs]
@@ -1111,7 +1133,11 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
 
     if 'wrong_size' in prealloc_modes or 'ALL' in prealloc_modes:
         # For each dimension, try size-1, size, size+1
-        for dim in xrange(max_ndim):
+        for dim, b in enumerate(out_broadcast_pattern):
+            if b:
+                # The shape has to be 1
+                continue
+
             shape_diff = [0] * max_ndim
             for diff in (-1, 1):
                 shape_diff[dim] = diff