Allow to include/exclude/require optimizers for the MonitorMode.

theano/compile/monitormode.py

@@ -62,3 +62,21 @@ class MonitorMode(Mode):
         fn()
         if self.post_func is not None:
             self.post_func(i, node, fn)
+
+    def including(self, *tags):
+        ret = super(MonitorMode, self).including(*tags)
+        ret.pre_func = self.pre_func
+        ret.post_func = self.post_func
+        return ret
+
+    def excluding(self, *tags):
+        ret = super(MonitorMode, self).excluding(*tags)
+        ret.pre_func = self.pre_func
+        ret.post_func = self.post_func
+        return ret
+
+    def requiring(self, *tags):
+        ret = super(MonitorMode, self).requiring(*tags)
+        ret.pre_func = self.pre_func
+        ret.post_func = self.post_func
+        return ret

theano/compile/tests/test_monitormode.py

@@ -25,3 +25,32 @@ def test_detect_nan():
                             post_func=detect_nan))
     f(0)  # log(0) * 0 = -inf * 0 = NaN
     assert nan_detected[0]
+
+
+def test_optimizers():
+    """
+    Test that we can remove optimizers
+    """
+    nan_detected = [False]
+
+    def detect_nan(i, node, fn):
+        for output in fn.outputs:
+            if numpy.isnan(output[0]).any():
+                print '*** NaN detected ***'
+                theano.printing.debugprint(node)
+                print 'Inputs : %s' % [input[0] for input in fn.inputs]
+                print 'Outputs: %s' % [output[0] for output in fn.outputs]
+                nan_detected[0] = True
+                break
+
+    x = theano.tensor.dscalar('x')
+    mode = theano.compile.MonitorMode(post_func=detect_nan)
+    mode = mode.excluding('fusion')
+    f = theano.function([x], [theano.tensor.log(x) * x],
+                        mode=mode)
+    # Test that the fusion wasn't done
+    assert len(f.maker.fgraph.nodes) == 2
+    f(0)  # log(0) * 0 = -inf * 0 = NaN
+
+    # Test that we still detect the nan
+    assert nan_detected[0]