That test was failing at some point, I do not remember when or why...

theano/tensor/nnet/tests/test_nnet.py

@@ -516,6 +516,51 @@ class T_CrossentropyCategorical1Hot(unittest.TestCase):
                 theano.printing.debugprint(g)
                 raise
 
+    def test_xent_thing_int32(self):
+        verbose = 0
+        mode = theano.compile.mode.get_default_mode()
+        if mode == theano.compile.mode.get_mode('FAST_COMPILE'):
+            mode = 'FAST_RUN'
+        rng = numpy.random.RandomState(utt.fetch_seed())
+        x_val = rng.randn(3,5)
+        b_val = rng.randn(5)
+        y_val = numpy.asarray([2,4,1], dtype='int64')
+
+        x = T.dmatrix('x')
+        b = T.dvector('b')
+        y = T.lvector('y')
+        yi = T.cast(y, 'int32')
+
+        expressions = [
+                T.sum(-T.log(softmax(x)[T.arange(yi.shape[0]), yi])),
+                -T.sum(T.log(softmax(x)[T.arange(yi.shape[0]), yi])),
+                -T.sum(T.log(softmax(x))[T.arange(yi.shape[0]), yi]),
+                T.sum(-T.log(softmax(x))[T.arange(yi.shape[0]), yi])
+                ]
+
+        for expr in expressions:
+            # Verify the optimizer worked on the expressions
+            f = theano.function([x,y], expr, mode=mode)
+            if verbose:
+                theano.printing.debugprint(f)
+            try:
+                assert len(f.maker.env.toposort()) == 5
+                f(x_val, y_val)
+            except:
+                theano.printing.debugprint(f)
+                raise
+
+            # Also verify the gradient wrt x
+            g = theano.function([x,y], T.grad(expr, x), mode=mode)
+            if verbose:
+                theano.printing.debugprint(g)
+            try:
+                assert len(g.maker.env.toposort()) == 5
+                g(x_val, y_val)
+            except:
+                theano.printing.debugprint(g)
+                raise
+
 
         ## Test that a biased softmax is optimized correctly
         bias_expressions = [
@@ -526,7 +571,8 @@ class T_CrossentropyCategorical1Hot(unittest.TestCase):
 
         for expr in bias_expressions:
             f = theano.function([x,b,y], expr, mode=mode)
-            if verbose: print_graph(f)
+            if verbose:
+                theano.printing.debugprint(f)
             try:
                 assert len(f.maker.env.toposort()) == 2 # [big_op, sum]
                 f(x_val, b_val, y_val)
@@ -535,7 +581,8 @@ class T_CrossentropyCategorical1Hot(unittest.TestCase):
                 raise
 
             g = theano.function([x,b,y], T.grad(expr, x), mode=mode)
-            if verbose: print_graph(g)
+            if verbose:
+                theano.printing.debugprint(g)
             try:
                 assert len(g.maker.env.toposort()) == 4
                 g(x_val, b_val, y_val)
@@ -552,7 +599,8 @@ class T_CrossentropyCategorical1Hot(unittest.TestCase):
 
         for expr in mean_expressions:
             f = theano.function([x,y], expr, mode=mode)
-            if verbose: print_graph(f)
+            if verbose:
+                theano.printing.debugprint(f)
             try:
                 assert len(f.maker.env.toposort()) == 6
                 f(x_val, y_val)
@@ -561,7 +609,8 @@ class T_CrossentropyCategorical1Hot(unittest.TestCase):
                 raise
 
             g = theano.function([x,y], T.grad(expr, x), mode=mode)
-            if verbose: print_graph(g)
+            if verbose:
+                theano.printing.debugprint(g)
             try:
                 assert len(g.maker.env.toposort()) in (6,7) #there's an extra dimshuffle in there
                 # but I can't think of a good rule to get rid of it
@@ -578,7 +627,8 @@ class T_CrossentropyCategorical1Hot(unittest.TestCase):
 
         for expr in mean_bias_expressions:
             f = theano.function([x,b,y], expr, mode=mode)
-            if verbose: print_graph(f)
+            if verbose:
+                theano.printing.debugprint(f)
             try:
                 assert len(f.maker.env.toposort()) == 4
             except:
@@ -586,7 +636,8 @@ class T_CrossentropyCategorical1Hot(unittest.TestCase):
                 raise
 
             g = theano.function([x,b,y], T.grad(expr, x), mode=mode)
-            if verbose: print_graph(g)
+            if verbose:
+                theano.printing.debugprint(g)
             try:
                 assert len(g.maker.env.toposort()) in (6,7)
                 g(x_val, b_val, y_val)