Fix the optim to replace the gradient of log softmax which didnt check true_div explicitly

theano/tensor/nnet/nnet.py

@@ -773,7 +773,7 @@ def local_logsoftmax_grad(node):
     if (isinstance(node.op, SoftmaxGrad) and
         len(node.inputs) == 2 and
         node.inputs[0].owner is not None and
-        isinstance(node.inputs[0].owner.op, tensor.Elemwise) and
+        node.inputs[0].owner.op == tensor.true_div and
         len(node.inputs[0].owner.inputs) >= 2 and
         node.inputs[0].owner.inputs[1].owner is not None and
         node.inputs[0].owner.inputs[1].owner.op == softmax_op and

theano/tensor/nnet/tests/test_nnet.py

@@ -287,6 +287,30 @@ class T_LogSoftmax(utt.InferShapeTester):
         f = theano.function([], myfunc(sa))
         self.assertTrue(check_stack_trace(f, ops_to_check='all'))
 
+    def test_logsoftmax_grad_true_div_elemwise(self):
+        # Checks that the gradient of an expression similar to a log(softmax)
+        # but with a different elemwise operation than true_div is not
+        # optimized.
+
+        x = T.matrix('x')
+        y = T.log(T.nnet.softmax(x))
+        g = T.grad(y.sum(), x)
+
+        softmax_grad_node = g.owner
+        assert softmax_grad_node.op == softmax_grad
+        true_div_node = softmax_grad_node.inputs[0].owner
+        assert true_div_node.op == tensor.true_div
+
+        # We replace the elemwise true_div op by an elemwise add.
+        new_g = softmax_grad(tensor.add(*true_div_node.inputs),
+                             softmax_grad_node.inputs[1])
+
+        fgraph = gof.FunctionGraph([x], [new_g])
+        theano.compile.mode.optdb.query(
+            theano.compile.mode.OPT_FAST_RUN).optimize(fgraph)
+
+        assert softmax_grad in [n.op for n in fgraph.toposort()]
+
 
 class T_SoftmaxGrad(utt.InferShapeTester):