make local_subtensor_of_alloc detect the right broadcast pattern more frequently.

theano/tensor/opt.py

@@ -1998,7 +1998,14 @@ def local_subtensor_of_alloc(node):
             # That dimension is removed.
             pass
         else:
-            nw_dims += [T.ceil_intdiv((csl.stop - csl.start), csl.step)]
+            nw_dim = csl.stop - csl.start
+
+            if csl.step != 1:
+                # Do not add the ceil_intdiv() graphs in the graphs
+                # when this is not needed as it prevent detecting the
+                # correct broadcast pattern.
+                nw_dim = T.ceil_intdiv(nw_dim, csl.step)
+            nw_dims += [nw_dim]
 
     nw_val = val[tuple(val_slices)]
     nw_dims += dims[len(slices):]
@@ -2007,7 +2014,9 @@ def local_subtensor_of_alloc(node):
     rval = T.alloc(nw_val, *nw_dims)
     if type(rval) not in (list, tuple):
         rval = [rval]
-
+    if rval[0].type != node.outputs[0].type:
+        #This happen from time to time, we need to discover why
+        return
     return rval
 
 

theano/tensor/tests/test_opt.py

@@ -2366,11 +2366,13 @@ class Test_alloc_zero(unittest.TestCase):
 
 
 def test_local_subtensor_of_alloc():
-    x = tensor.matrix('x')
 
     # DebugMode should detect if something goes wrong.
     # test shape combination of odd and event shape.
-    for shape in [(3, 5), (4, 6), (3, 8), (4, 7)]:
+    for shape in [(3, 5), (4, 6), (3, 8), (4, 7),
+                  (1, 5), (5, 1)]:
+        x = tensor.tensor(dtype=theano.config.floatX,
+                          broadcastable=(shape[0] == 1, shape[1] == 1))
 
         xval = numpy.zeros(shape, dtype=config.floatX)
         yval = numpy.arange(shape[1], dtype=config.floatX)
@@ -2387,21 +2389,29 @@ def test_local_subtensor_of_alloc():
             # Only one column
             z_vec = yx[:, 3]
             assert z_vec.ndim == 1
-
-            for slices in [
-                # results are vector
-                (slice(None), 3),
-                (2, slice(None)),
-                # results are matrix
+            # results are vector
+            slicess = []
+            if shape[0] != 1:
+                slicess.append((2, slice(None)))
+            if shape[1] != 1:
+                slicess.append((slice(None), 3))
+
+            # results are matrix
+            slicess += [
                 (slice(None), slice(3, None)),
                 (slice(3, None), ),
                 (slice(3, None), slice(3, None)),
                 (slice(1, 3), slice(None, -1)),
                 (slice(None, None, 2)),
                 (slice(1, None, 2)),
-                ]:
+                ]
+            for slices in slicess:
                 z = yx.__getitem__(slices)
                 f = theano.function([x], z)
+                theano.printing.debugprint(f)
+#                if theano.config.mode != 'FAST_COMPILE':
+#                    assert not any([isinstance(node.op, Subtensor)
+#                                    for node in f.maker.fgraph.toposort()])
                 val = f(xval)
                 assert xval.__getitem__(slices).shape == val.shape