added unittests checking for stack trace for nnet.py optimizations

theano/tensor/nnet/tests/test_nnet.py

@@ -139,6 +139,14 @@ class T_SoftmaxWithBias(utt.InferShapeTester):
         f([0, 1, 0])
         # print f.maker.fgraph.toposort()
 
+    def test_softmax_with_bias_trace(self):
+        a = theano.shared(
+            numpy.random.randn(3).astype(config.floatX))
+        b = theano.shared(numpy.float32(numpy.random.randn()))
+        sm = T.softmax(a + b)
+        f = theano.function([], sm)
+        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+
     def test_infer_shape(self):
         admat = matrix()
         advec = vector()
@@ -242,6 +250,7 @@ class T_LogSoftmax(utt.InferShapeTester):
         sm = tensor.nnet.softmax(x)
         logsm = tensor.log(sm)
         f = theano.function([x], logsm)
+        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
         assert isinstance(f.maker.fgraph.outputs[0].owner.op,
                           theano.tensor.nnet.nnet.LogSoftmax)
 
@@ -265,6 +274,8 @@ class T_LogSoftmax(utt.InferShapeTester):
             return logsm
         # We set step to 0.1 because for big values we need a big epsilon
         utt.verify_grad(myfunc, [a], eps=0.1, mode=m)
+        f = theano.function([], myfunc(a))
+        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
 
 
 class T_SoftmaxGrad(utt.InferShapeTester):
@@ -642,6 +653,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         fgraph = gof.FunctionGraph(
                 [x, one_of_n],
                 [g_x])
+        self.assertTrue(hasattr(fgraph.outputs[0].tag, 'trace'))
 
         # print 'BEFORE'
         # for node in fgraph.toposort():
@@ -737,6 +749,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         for expr in expressions:
             # Verify the optimizer worked on the expressions
             f = theano.function([x, y], expr, mode=mode)
+            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -752,6 +765,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
             # Also verify the gradient wrt x
             g = theano.function([x, y], T.grad(expr, x), mode=mode)
+            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -774,6 +788,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         for expr in bias_expressions:
             f = theano.function([x, b, y], expr, mode=mode)
+            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -785,6 +800,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 theano.printing.debugprint(f)
                 raise
             g = theano.function([x, b, y], T.grad(expr, x), mode=mode)
+            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -807,6 +823,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         for expr in mean_expressions:
             f = theano.function([x, y], expr, mode=mode)
+            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -821,6 +838,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 raise
 
             g = theano.function([x, y], T.grad(expr, x), mode=mode)
+            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -844,6 +862,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         for expr in mean_bias_expressions:
             f = theano.function([x, b, y], expr, mode=mode)
+            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -856,6 +875,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 theano.printing.debugprint(f)
                 raise
             g = theano.function([x, b, y], T.grad(expr, x), mode=mode)
+            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -1269,6 +1289,7 @@ def test_argmax_pushdown():
         fgraph = gof.FunctionGraph(
                 [x],
                 [out])
+        assert hasattr(fgraph.outputs[0].tag, 'trace')
 
         backup = config.warn.argmax_pushdown_bug
         config.warn.argmax_pushdown_bug = False
@@ -1621,7 +1642,6 @@ def test_h_softmax():
     #############
     x_mat = numpy.random.normal(size=(batch_size, input_size)).astype(floatX)
     y_mat = numpy.random.randint(0, output_size, batch_size).astype('int32')
-
     tg_output = fun_output_tg(x_mat, y_mat)
     all_outputs = fun_output(x_mat)