Merge pull request #2597 from lamblin/fix_bart

theano/tensor/opt.py

@@ -13,6 +13,7 @@ import operator
 import sys
 import time
 import traceback
+import warnings
 
 import numpy
 import numpy as N  # guys... please don't do this in the library :(
@@ -3901,10 +3902,14 @@ def local_reduce_join(node):
             # The reduction do something about the dtype.
             return
 
+        reduce_axis = node.op.axis
+        if reduce_axis is None:
+            reduce_axis = tuple(xrange(node.inputs[0].ndim))
+
         # I put this warning late to don't add extra warning.
-        if len(node.op.axis) != 1 or 0 not in node.op.axis:
+        if len(reduce_axis) != 1 or 0 not in reduce_axis:
             if theano.config.warn.reduce_join:
-                _logger.warn((
+                warnings.warn((
                     'Your current code is fine, but Theano versions '
                     'prior to 0.7 (or this development version Sept 2014) '
                     'might have given an incorrect result for this code. '
@@ -3920,7 +3925,7 @@ def local_reduce_join(node):
         # We add the new check late to don't add extra warning.
         try:
             join_axis = get_scalar_constant_value(join.inputs[0])
-            if join_axis != node.op.axis[0]:
+            if join_axis != reduce_axis[0]:
                 return
         except NotScalarConstantError:
             return

theano/tensor/tests/test_opt.py

@@ -4447,7 +4447,8 @@ class T_local_reduce(unittest.TestCase):
             assert isinstance(topo[-1].op, T.Elemwise), out
 
         # Test different axis for the join and the reduction
-        # We must force the dtype, of otherwise, this tests will fail in 32 bit system
+        # We must force the dtype, of otherwise, this tests will fail
+        # on 32 bit systems
         A = theano.shared(numpy.array([1, 2, 3, 4, 5], dtype='int64'))
 
         f = theano.function([], T.sum(T.stack(A, A), axis=0), mode=self.mode)
@@ -4482,6 +4483,17 @@ class T_local_reduce(unittest.TestCase):
         topo = f.maker.fgraph.toposort()
         assert not isinstance(topo[-1].op, T.Elemwise)
 
+        # Test that the optimization does not crash in one case where it
+        # is not applied.  Reported at
+        # https://groups.google.com/d/topic/theano-users/EDgyCU00fFA/discussion
+        old = theano.config.warn.reduce_join
+        try:
+            theano.config.warn.reduce_join = False
+            out = tensor.sum([vx, vy, vz], axis=None)
+            f = theano.function([vx, vy, vz], out)
+        finally:
+            theano.config.warn.reduce_join = old
+
 
 class T_local_sum_dimshuffle(unittest.TestCase):
     def setUp(self):