Ignore unused inputs in some tests.

theano/compile/tests/test_function_module.py

@@ -54,26 +54,45 @@ class T_function(unittest.TestCase):
     def test_missing_inputs(self):
 
         MissingInputException = TypeError
+        UnusedInputException = ValueError
 
         def fn():
             x,s = T.scalars('xs')
             fn = function([], [x])
         checkfor(self, fn, MissingInputException)
 
+        def fn():
+            x,s = T.scalars('xs')
+            # Ignore unused input s, as it hides the other error
+            fn = function([s], [x], on_unused_input='ignore')
+        checkfor(self, fn, MissingInputException)
+
         def fn():
             x,s = T.scalars('xs')
             fn = function([s], [x])
+        checkfor(self, fn, UnusedInputException)
+
+        def fn():
+            x,s = T.scalars('xs')
+            # Ignore unused input s, as it hides the other error
+            fn = function([s], x, on_unused_input='ignore')
         checkfor(self, fn, MissingInputException)
 
         def fn():
             x,s = T.scalars('xs')
             fn = function([s], x)
+        checkfor(self, fn, UnusedInputException)
+
+        def fn():
+            x,s = T.scalars('xs')
+            # Ignore unused input s, as it hides the other error
+            fn = function([s], Out(x), on_unused_input='ignore')
         checkfor(self, fn, MissingInputException)
 
         def fn():
             x,s = T.scalars('xs')
             fn = function([s], Out(x))
-        checkfor(self, fn, MissingInputException)
+        checkfor(self, fn, UnusedInputException)
 
         def fn():
             x,s = T.scalars('xs')
@@ -124,7 +143,8 @@ class T_function(unittest.TestCase):
         x,s = T.scalars('xs')
 
         #x's name is ignored because it is followed by anonymous parameter a.
-        f = function([x, a, s], a/s)
+        # Ignore unused input x, as it hides the other error
+        f = function([x, a, s], a/s, on_unused_input='ignore')
         self.assertTrue(f(9,1,2) == 0.5)
         self.assertTrue(f(9,2,1) == 2.0)
         self.assertTrue(f(9,2, s=1) == 2.0)
@@ -355,6 +375,20 @@ class T_function(unittest.TestCase):
         f(o+.1) #should clobber the memory used to store four
         assert not numpy.all(four==4)
 
+    def test_disconnected_input(self):
+        a = T.scalar('a')
+        v = T.vector('v')
+        self.assertRaises(ValueError, function, [a, v], v*2)
+        f = function([a, v], v*2, on_unused_input='ignore')
+
+    def test_masked_input(self):
+        m = T.matrix('m')
+        mt = m.T
+        mt.name = 'm.T'
+        self.assertRaises(ValueError, function, [m, mt], mt*2)
+        f = function([m, mt], mt*2, on_unused_input='ignore')
+
+
 class T_picklefunction(unittest.TestCase):
 
     def test_deepcopy(self):
@@ -631,7 +665,6 @@ class T_picklefunction(unittest.TestCase):
         assert blah.f1[blah.s] != blah2.f1[blah2.s]
 
 
-
 class SomethingToPickle(object):
     def __init__(self):
         a = T.scalar() # the a is for 'anonymous' (un-named).

theano/tensor/tests/test_basic.py

@@ -57,13 +57,15 @@ else:
 utt.seed_rng()
 
 
-def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False):
+def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False,
+        on_unused_input='raise'):
     if mode is None:
         mode = get_default_mode()
     return function(inputs, outputs,
             mode=mode,
             allow_input_downcast=allow_input_downcast,
-            accept_inplace=True)
+            accept_inplace=True,
+            on_unused_input=on_unused_input)
 
 
 def eval_outputs(outputs):

theano/tensor/tests/test_blas.py

@@ -399,7 +399,8 @@ def just_gemm(i, o, ishapes = [(4,3), (3,5), (4,5), (), ()], max_graphlen=0):
         f = inplace_func(
                 [Param(ii, mutable=True, allow_downcast=True) for ii in i],
                 o,
-                mode='FAST_RUN')
+                mode='FAST_RUN',
+                on_unused_input='ignore')
         at_least_one_gemm = False
         for node in f.maker.env.nodes:
             if node.op == T.dot:
@@ -410,7 +411,7 @@ def just_gemm(i, o, ishapes = [(4,3), (3,5), (4,5), (), ()], max_graphlen=0):
                 at_least_one_gemm = True
         assert at_least_one_gemm
         g = inplace_func(i, o, mode=compile.Mode(linker='py', optimizer=None),
-                allow_input_downcast=True)
+                allow_input_downcast=True, on_unused_input='ignore')
         for node in g.maker.env.nodes:
             if node.op == gemm_inplace:
                 raise Exception('gemm_inplace in original graph')
@@ -475,11 +476,12 @@ def test_gemm_opt_double_gemm():
         + gemm_inplace(Z, b, S.T, R.T, T.constant(1.0).astype('float64')))]
     try:
         f = inplace_func([Param(ii, mutable=True) for ii in i],o,
-                mode='FAST_RUN')
+                mode='FAST_RUN', on_unused_input='ignore')
         for node in f.maker.env.nodes:
             if node.op == T.dot: raise Failure('dot in graph')
             if node.op == _dot22: raise Failure('_dot22 in graph')
-        g = inplace_func(i, o, mode=compile.Mode(linker='py', optimizer=None))
+        g = inplace_func(i, o, mode=compile.Mode(linker='py', optimizer=None),
+                on_unused_input='ignore')
         #for node in g.maker.env.nodes:
         #    if node.op == gemm_inplace: raise Failure('gemm_inplace in graph')
 

theano/tensor/tests/test_opt.py

@@ -3256,7 +3256,8 @@ class T_local_sum_dimshuffle(unittest.TestCase):
         try:
             for i, s in enumerate(sums):
                 print i
-                f = theano.function([a, b, c, d], s, mode=self.mode)
+                f = theano.function([a, b, c, d], s, mode=self.mode,
+                        on_unused_input='ignore')
                 theano.printing.debugprint(f)
                 g = f.maker.env.toposort()
                 #print 'g =', g
@@ -3294,7 +3295,7 @@ def test_make_vector():
                             ]:
         mv = opt.MakeVector(dtype=dtype)(*inputs)
         assert mv.dtype == dtype
-        f = theano.function([b, i, d], mv)
+        f = theano.function([b, i, d], mv, on_unused_input='ignore')
         f_val = f(val[b], val[i], val[d])
         #print 'f_val =', f_val
 

theano/tests/test_rop.py

@@ -106,12 +106,12 @@ class RopLop_checker(unittest.TestCase):
         vv = numpy.asarray(self.rng.uniform(size=self.mat_in_shape),
                            theano.config.floatX)
         yv = tensor.Rop(y, self.mx, self.mv)
-        rop_f = function([self.mx, self.mv], yv)
+        rop_f = function([self.mx, self.mv], yv, on_unused_input='ignore')
         sy, _ = theano.scan(lambda i, y, x, v: \
                                 (tensor.grad(y[i], x) * v).sum(),
                            sequences=tensor.arange(y.shape[0]),
                            non_sequences=[y, self.mx, self.mv])
-        scan_f = function([self.mx, self.mv], sy)
+        scan_f = function([self.mx, self.mv], sy, on_unused_input='ignore')
 
         v1 = rop_f(vx, vv)
         v2 = scan_f(vx, vv)
@@ -146,13 +146,13 @@ class RopLop_checker(unittest.TestCase):
                            theano.config.floatX)
 
         yv = tensor.Rop(y, self.x, self.v)
-        rop_f = function([self.x, self.v], yv)
+        rop_f = function([self.x, self.v], yv, on_unused_input='ignore')
         J, _ = theano.scan(lambda i, y, x: tensor.grad(y[i], x),
                            sequences=tensor.arange(y.shape[0]),
                            non_sequences=[y, self.x])
         sy = tensor.dot(J, self.v)
 
-        scan_f = function([self.x, self.v], sy)
+        scan_f = function([self.x, self.v], sy, on_unused_input='ignore')
 
         v1 = rop_f(vx, vv)
         v2 = scan_f(vx, vv)
@@ -168,7 +168,7 @@ class RopLop_checker(unittest.TestCase):
                            theano.config.floatX)
 
         yv = tensor.Lop(y, self.x, self.v)
-        lop_f = function([self.x, self.v], yv)
+        lop_f = function([self.x, self.v], yv, on_unused_input='ignore')
         J, _ = theano.scan(lambda i, y, x: tensor.grad(y[i], x),
                            sequences=tensor.arange(y.shape[0]),
                            non_sequences=[y, self.x])