Merge pull request #2864 from dwf/tuple_params

theano/gof/sched.py

@@ -36,8 +36,9 @@ def memodict(f):
 
 def make_depends():
     @memodict
-    def depends((a, b)):
+    def depends(pair):
         """ Returns True if a depends on b """
+        a, b = pair
         return (any(bout in a.inputs for bout in b.outputs)
                 or any(depends((ainp.owner, b)) for ainp in a.inputs
                        if ainp.owner))

theano/sandbox/rng_mrg.py

@@ -84,7 +84,9 @@ class DotModulo(Op):
     def make_node(self, A, s, m, A2, s2, m2):
         return Apply(self, [A, s, m, A2, s2, m2], [s.type()])
 
-    def perform(self, node, (A, s, m, A2, s2, m2), (out, )):
+    def perform(self, node, inputs, outputs):
+        (A, s, m, A2, s2, m2) = inputs
+        (out,) = outputs
         o1 = matVecModM(A, s, m)
         o2 = matVecModM(A2, s2, m2)
         out[0] = numpy.concatenate((o1, o2))
@@ -92,7 +94,9 @@ class DotModulo(Op):
     def c_code_cache_version(self):
         return (6,)
 
-    def c_code(self, node, name, (_A, _s, _m, _A2, _s2, _m2), (_z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (_A, _s, _m, _A2, _s2, _m2) = inputs
+        (_z,) = outputs
         return """
         int osize = -1;
         if (PyArray_NDIM(%(_A)s) != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(A) != 2"); %(fail)s;}

theano/scalar/basic.py

@@ -925,7 +925,9 @@ class UnaryScalarOp(ScalarOp):
     amd_float32 = None
     amd_float64 = None
 
-    def c_code_contiguous(self, node, name, (x, ), (z, ), sub):
+    def c_code_contiguous(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if (not theano.config.lib.amdlibm or
             # We compare the dtype AND the broadcast flag
             # as this function do not broadcast
@@ -1008,7 +1010,9 @@ class LT(LogicalComparison):
         # built-in < don't support complex
         return numpy.less(x, y)
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = (%(x)s < %(y)s);" % locals()
@@ -1024,7 +1028,9 @@ class GT(LogicalComparison):
         # built-in > don't support complex
         return numpy.greater(x, y)
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = (%(x)s > %(y)s);" % locals()
@@ -1040,7 +1046,9 @@ class LE(LogicalComparison):
         # built-in <= don't support complex
         return numpy.less_equal(x, y)
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = (%(x)s <= %(y)s);" % locals()
@@ -1056,7 +1064,9 @@ class GE(LogicalComparison):
         # built-in >= don't support complex
         return numpy.greater_equal(x, y)
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = (%(x)s >= %(y)s);" % locals()
@@ -1071,7 +1081,9 @@ class EQ(LogicalComparison):
     def impl(self, x, y):
         return x == y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = (%(x)s == %(y)s);" % locals()
@@ -1086,7 +1098,9 @@ class NEQ(LogicalComparison):
     def impl(self, x, y):
         return x != y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = (%(x)s != %(y)s);" % locals()
@@ -1097,7 +1111,9 @@ class IsNan(FixedLogicalComparison):
     def impl(self, x):
         return numpy.isnan(x)
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = isnan(%(x)s);" % locals()
@@ -1108,7 +1124,9 @@ class IsInf(FixedLogicalComparison):
     def impl(self, x):
         return numpy.isinf(x)
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         # Note that the C isinf returns -1 for -Inf and +1 for +Inf, while
@@ -1136,7 +1154,9 @@ class InRange(LogicalComparison):
             return False
         return True
 
-    def c_code(self, node, name, (x, low, hi), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, low, hi) = inputs
+        (z,) = outputs
         if self.openlow:
             cmp1 = '>'
         else:
@@ -1165,7 +1185,9 @@ class InRange(LogicalComparison):
         else:
             return elem.zeros_like()
 
-    def grad(self, (x, low, hi), (gz, )):
+    def grad(self, inputs, gout):
+        (x, low, hi) = inputs
+        (gz,) = gout
         grads = []
         for elem in [x, low, hi]:
             grads.append(get_grad(elem))
@@ -1186,10 +1208,14 @@ class Switch(ScalarOp):
 
             # backport
             # return ift if cond else iff
-    def c_code(self, node, name, (cond, ift, iff), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (cond, ift, iff) = inputs
+        (z,) = outputs
         return "%(z)s = %(cond)s ? %(ift)s : %(iff)s;" % locals()
 
-    def grad(self, (cond, ift, iff), (gz, )):
+    def grad(self, inputs, gout):
+        (cond, ift, iff) = inputs
+        (gz,) = gout
         first_part = switch(cond, gz, 0.)
         second_part = switch(cond, 0., gz)
 
@@ -1205,7 +1231,8 @@ class Switch(ScalarOp):
 
         return (condition_grad, first_part, second_part)
 
-    def output_types(self, (cond_t, ift_t, iff_t)):
+    def output_types(self, types):
+        (cond_t, ift_t, iff_t) = types
         return upcast_out(ift_t, iff_t)
 switch = Switch()
 
@@ -1249,7 +1276,9 @@ class OR(BinaryBitOp):
     def impl(self, x, y):
         return x | y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         return "%(z)s = (%(x)s | %(y)s);" % locals()
 or_ = OR()
 
@@ -1262,7 +1291,9 @@ class XOR(BinaryBitOp):
     def impl(self, x, y):
         return x ^ y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         return "%(z)s = (%(x)s ^ %(y)s);" % locals()
 xor = XOR()
 
@@ -1275,7 +1306,9 @@ class AND(BinaryBitOp):
     def impl(self, x, y):
         return x & y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         return "%(z)s = (%(x)s & %(y)s);" % locals()
 and_ = AND()
 
@@ -1284,7 +1317,9 @@ class Invert(UnaryBitOp):
     def impl(self, x):
         return ~x
 
-    def c_code(self, node, name, (x,), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = (~%(x)s);" % locals()
 invert = Invert()
 
@@ -1300,14 +1335,18 @@ class Maximum(BinaryScalarOp):
         # The built-in max function don't support complex type
         return numpy.maximum(*inputs)
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if any([i.type in complex_types for i in node.inputs]):
             raise NotImplementedError()
         # Test for both y>x and x>=y to detect NaN
         return ('%(z)s = ((%(y)s)>(%(x)s)? (%(y)s): '
                 '((%(x)s)>=(%(y)s)? (%(x)s): nan("")));' % locals())
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             # max is currently defined for complex_types,
             # but the gradient for complex is not.
@@ -1334,13 +1373,17 @@ class Minimum(BinaryScalarOp):
         # The built-in min function don't support complex type
         return numpy.minimum(*inputs)
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if any([i.type in complex_types for i in node.inputs]):
             raise NotImplementedError()
         return ('%(z)s = ((%(y)s)<(%(x)s)? (%(y)s): '
                 '((%(x)s)<=(%(y)s)? (%(x)s): nan("")));' % locals())
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             # min is currently defined for complex_types,
             # but the gradient for complex is not.
@@ -1364,13 +1407,15 @@ class Add(ScalarOp):
     def impl(self, *inputs):
         return sum(inputs)
 
-    def c_code(self, node, name, inputs, (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (z,) = outputs
         if not inputs:
             return z + " = 0;"
         else:
             return z + " = " + " + ".join(inputs) + ";"
 
-    def grad(self, inputs, (gz, )):
+    def grad(self, inputs, gout):
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(*inputs).type in discrete_types:
@@ -1400,13 +1445,15 @@ class Mul(ScalarOp):
     def impl(self, *inputs):
         return numpy.product(inputs)
 
-    def c_code(self, node, name, inputs, (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (z,) = outputs
         if not inputs:
             return z + " = 1;"
         else:
             return z + " = " + " * ".join(inputs) + ";"
 
-    def grad(self, inputs, (gz, )):
+    def grad(self, inputs, gout):
+        (gz,) = gout
         retval = []
 
         # The following 3 lines verify that gz is complex when the
@@ -1448,10 +1495,14 @@ class Sub(BinaryScalarOp):
     def impl(self, x, y):
         return x - y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         return "%(z)s = %(x)s - %(y)s;" % locals()
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
 
@@ -1528,8 +1579,10 @@ class TrueDiv(BinaryScalarOp):
         else:
             return x / y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
         # we generate good c code only when both are complex!
+        (x, y) = inputs
+        (z,) = outputs
         if sum([node.inputs[0].type in complex_types,
                 node.inputs[1].type in complex_types]) == 1:
             raise NotImplementedError('type not supported', type)
@@ -1538,8 +1591,10 @@ class TrueDiv(BinaryScalarOp):
             return "%(z)s = ((double)%(x)s) / %(y)s;" % locals()
         return "%(z)s = %(x)s / %(y)s;" % locals()
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
 
+        (x, y) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
 
@@ -1578,7 +1633,9 @@ class IntDiv(BinaryScalarOp):
         # of string formatting.
         return "#define THEANO_MACRO_MOD(x,y) (x % y)"
 
-    def c_code(self, node, name, (x, y), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         t = node.inputs[0].type.upcast(*[i.type for i in node.inputs[1:]])
         if t in imap(str, discrete_types):
             x_div_y_pp = '(%(x)s / %(y)s)' % locals()
@@ -1666,13 +1723,13 @@ class Mod(BinaryScalarOp):
         # of string formatting.
         return "#define THEANO_MACRO_MOD(x,y) (x % y)"
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
         """
         We want the result to have the same sign as python, not the other
         implementation of mod.
         """
-        # raise NotImplementedError("Unlike Python, C's modulo returns negative
-        # modulo on negative dividend (to implement)")
+        (x, y) = inputs
+        (z,) = outputs
         t = node.inputs[0].type.upcast(*[i.type for i in node.inputs[1:]])
         if (str(t) in imap(str, discrete_types) or
                 t in ['uint8', 'int8', 'uint16', 'int16'] or
@@ -1716,7 +1773,9 @@ class Mod(BinaryScalarOp):
             }
             """) % locals()
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         z = self(x, y)
         if z.type.dtype in discrete_types:
             # The gradient does not flow in if the output is discrete
@@ -1732,13 +1791,17 @@ class Pow(BinaryScalarOp):
     def impl(self, x, y):
         return x ** y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if (node.inputs[0].type in complex_types or
             node.inputs[1].type in complex_types):
             raise NotImplementedError('type not supported', type)
         return "%(z)s = pow(%(x)s, %(y)s);" % locals()
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
 
@@ -1753,7 +1816,9 @@ class Pow(BinaryScalarOp):
 
         return (first_part, second_part)
 
-    def c_code_contiguous(self, node, name, (x, y), (z, ), sub):
+    def c_code_contiguous(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         if not theano.config.lib.amdlibm:
             raise theano.gof.utils.MethodNotDefined()
 
@@ -1807,10 +1872,14 @@ class Clip(ScalarOp):
         else:
             return x
 
-    def c_code(self, node, name, (x, min, max), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, min, max) = inputs
+        (z,) = outputs
         return "%(z)s = %(x)s < %(min)s ? %(min)s : %(x)s > %(max)s ? %(max)s : %(x)s;" % locals()
 
-    def grad(self, (x, mn, mx), (gz, )):
+    def grad(self, inputs, gout):
+        (x, mn, mx) = inputs
+        (gz,) = gout
         assert gz.type not in complex_types
         gx = ((x >= mn) & (x <= mx)) * gz
         gmn = (x < mn) * gz
@@ -1834,7 +1903,9 @@ class Second(BinaryScalarOp):
     def impl(self, x, y):
         return y
 
-    def c_code(self, node, name, (x, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x, y) = inputs
+        (z,) = outputs
         return "%(z)s = %(y)s;" % locals()
 
     def connection_pattern(self, node):
@@ -1844,8 +1915,10 @@ class Second(BinaryScalarOp):
 
         return [[False], [True]]
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
 
+        (x, y) = inputs
+        (gz,) = gout
         if y.type in continuous_types:
             # x is disconnected because the elements of x are not used
             return DisconnectedType()(), gz
@@ -1863,10 +1936,14 @@ class Identity(UnaryScalarOp):
     def impl(self, input):
         return input
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = %(x)s;" % locals()
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in continuous_types:
             return gz,
         else:
@@ -1889,10 +1966,14 @@ class Cast(UnaryScalarOp):
     def impl(self, input):
         return self.ctor(input)
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%s = (%s)%s;" % (z, node.outputs[0].type.dtype_specs()[1], x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if self.o_type in continuous_types:
             return [gz]
         else:
@@ -1962,7 +2043,9 @@ class Abs(UnaryScalarOp):
     def impl(self, x):
         return numpy.abs(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if self(x).type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
@@ -1971,7 +2054,9 @@ class Abs(UnaryScalarOp):
 
         return gz * x / abs(x),  # formula works for complex and real
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         type = node.inputs[0].type
         if type in int_types:
             return "%(z)s = abs(%(x)s);" % locals()
@@ -1988,8 +2073,9 @@ class Sgn(UnaryScalarOp):
         # casting to output type is handled by filter
         return numpy.sign(x)
 
-    def grad(self, (x, ), (gz, )):
-
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         rval = x.zeros_like()
 
         if rval.type.dtype in discrete_types:
@@ -1997,9 +2083,11 @@ class Sgn(UnaryScalarOp):
 
         return [rval]
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
         # casting is done by compiler
         # TODO: use copysign
+        (x,) = inputs
+        (z,) = outputs
         type = node.inputs[0].type
         if type in float_types:
             return "%(z)s = (%(x)s >= 0) ? (%(x)s == 0) ? 0.0 : 1.0 : -1.0;" % locals()
@@ -2020,7 +2108,9 @@ class Ceil(UnaryScalarOp):
     def impl(self, x):
         return numpy.ceil(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         rval = x.zeros_like()
 
         if rval.type.dtype in discrete_types:
@@ -2028,7 +2118,9 @@ class Ceil(UnaryScalarOp):
 
         return [rval]
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = ceil(%(x)s);" % locals()
 ceil = Ceil(same_out_nocomplex, name='ceil')
 
@@ -2037,7 +2129,9 @@ class Floor(UnaryScalarOp):
     def impl(self, x):
         return numpy.floor(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         rval = x.zeros_like()
 
         if rval.type.dtype in discrete_types:
@@ -2045,7 +2139,9 @@ class Floor(UnaryScalarOp):
 
         return [rval]
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = floor(%(x)s);" % locals()
 floor = Floor(same_out_nocomplex, name='floor')
 
@@ -2054,10 +2150,14 @@ class Trunc(UnaryScalarOp):
     def impl(self, x):
         return numpy.trunc(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         return [x.zeros_like().astype(theano.config.floatX)]
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = %(x)s >= 0? floor(%(x)s): -floor(-%(x)s);" % locals()
 trunc = Trunc(same_out_nocomplex, name='trunc')
 
@@ -2072,7 +2172,9 @@ class RoundHalfToEven(UnaryScalarOp):
     def impl(self, x):
         return numpy.round(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         rval = x.zeros_like()
 
         if rval.type.dtype in discrete_types:
@@ -2080,7 +2182,9 @@ class RoundHalfToEven(UnaryScalarOp):
 
         return [rval]
 
-    def c_code___(self, node, name, (x, ), (z, ), sub):
+    def c_code___(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         typ = node.outputs[0].type.dtype
         if not typ in ['float32', 'float64']:
             Exception("The output should be float32 or float64")
@@ -2164,7 +2268,9 @@ class RoundHalfAwayFromZero(UnaryScalarOp):
     def impl(self, x):
         return round_half_away_from_zero_vec(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         rval = x.zeros_like()
 
         if rval.type.dtype in discrete_types:
@@ -2172,7 +2278,9 @@ class RoundHalfAwayFromZero(UnaryScalarOp):
 
         return [rval]
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.outputs[0].type.dtype in ['float32', 'float64']:
             return "%(z)s = round(%(x)s);" % locals()
         else:
@@ -2184,7 +2292,9 @@ class Neg(UnaryScalarOp):
     def impl(self, x):
         return -x
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if self(x).type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
@@ -2193,7 +2303,9 @@ class Neg(UnaryScalarOp):
 
         return -gz,
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = -%(x)s;" % locals()
 neg = Neg(same_out, name='neg')
 
@@ -2212,7 +2324,9 @@ class Inv(UnaryScalarOp):
     def impl(self, x):
         return numpy.float32(1.0) / x
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2223,7 +2337,9 @@ class Inv(UnaryScalarOp):
 
         return -gz / (x * x),
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError()
         return "%(z)s = 1.0 / %(x)s;" % locals()
@@ -2243,7 +2359,9 @@ class Log(UnaryScalarOp):
             return numpy.log(x, sig='f')
         return numpy.log(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2254,10 +2372,12 @@ class Log(UnaryScalarOp):
 
         return gz / x,
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
         # todo: the version using log2 seems to be very slightly faster
         # on some machines for some reason, check if it's worth switching
         # return "%(z)s = log2(%(x)s) * 0.69314718055994529;" % locals()
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = log(%(x)s);" % locals()
@@ -2277,7 +2397,9 @@ class Log2(UnaryScalarOp):
             return numpy.log2(x, sig='f')
         return numpy.log2(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2288,7 +2410,9 @@ class Log2(UnaryScalarOp):
 
         return gz / (x * math.log(2.0)),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = log2(%(x)s);" % locals()
@@ -2308,7 +2432,9 @@ class Log10(UnaryScalarOp):
             return numpy.log10(x, sig='f')
         return numpy.log10(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2319,7 +2445,9 @@ class Log10(UnaryScalarOp):
 
         return gz / (x * numpy.log(10.0)),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = log10(%(x)s);" % locals()
@@ -2336,7 +2464,9 @@ class Log1p(UnaryScalarOp):
             return numpy.log1p(x, sig='f')
         return numpy.log1p(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2347,7 +2477,9 @@ class Log1p(UnaryScalarOp):
 
         return [gz / (1 + x)]
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = log1p(%(x)s);" % locals()
@@ -2366,7 +2498,9 @@ class Exp(UnaryScalarOp):
             return numpy.exp(x, sig='f')
         return numpy.exp(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2377,7 +2511,9 @@ class Exp(UnaryScalarOp):
 
         return gz * exp(x),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = exp(%(x)s);" % locals()
@@ -2393,7 +2529,9 @@ class Exp2(UnaryScalarOp):
             return numpy.exp2(x, sig='f')
         return numpy.exp2(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2404,7 +2542,9 @@ class Exp2(UnaryScalarOp):
 
         return gz * exp2(x) * log(numpy.cast[x.type](2)),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = exp2(%(x)s);" % locals()
@@ -2420,7 +2560,9 @@ class Expm1(UnaryScalarOp):
             return numpy.expm1(x, sig='f')
         return numpy.expm1(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2431,7 +2573,9 @@ class Expm1(UnaryScalarOp):
 
         return gz * exp(x),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = expm1(%(x)s);" % locals()
@@ -2445,7 +2589,9 @@ class Sqr(UnaryScalarOp):
     def impl(self, x):
         return x * x
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2456,7 +2602,9 @@ class Sqr(UnaryScalarOp):
 
         return gz * x * 2,
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = %(x)s * %(x)s;" % locals()
 sqr = Sqr(same_out, name='sqr')
 
@@ -2470,7 +2618,9 @@ class Sqrt(UnaryScalarOp):
             return numpy.sqrt(x, sig='f')
         return numpy.sqrt(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2481,7 +2631,9 @@ class Sqrt(UnaryScalarOp):
 
         return (gz * 0.5) / sqrt(x),
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = sqrt(%(x)s);" % locals()
@@ -2497,7 +2649,9 @@ class Deg2Rad(UnaryScalarOp):
             return numpy.deg2rad(x, sig='f')
         return numpy.deg2rad(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2508,7 +2662,9 @@ class Deg2Rad(UnaryScalarOp):
 
         return gz * numpy.asarray(numpy.pi / 180, gz.type),
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = %(x)s * (M_PI / 180.0);" % locals()
@@ -2524,7 +2680,9 @@ class Rad2Deg(UnaryScalarOp):
             return numpy.rad2deg(x, sig='f')
         return numpy.rad2deg(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2535,7 +2693,9 @@ class Rad2Deg(UnaryScalarOp):
 
         return gz * numpy.asarray(180. / numpy.pi, gz.type),
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = %(x)s * (180.0 / M_PI);" % locals()
@@ -2554,7 +2714,9 @@ class Cos(UnaryScalarOp):
             return numpy.cos(x, sig='f')
         return numpy.cos(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2565,7 +2727,9 @@ class Cos(UnaryScalarOp):
 
         return -gz * sin(x),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = cos(%(x)s);" % locals()
@@ -2581,7 +2745,9 @@ class ArcCos(UnaryScalarOp):
             return numpy.arccos(x, sig='f')
         return numpy.arccos(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2592,7 +2758,9 @@ class ArcCos(UnaryScalarOp):
 
         return - gz / sqrt(numpy.cast[x.type](1) - sqr(x)),
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = acos(%(x)s);" % locals()
@@ -2611,7 +2779,9 @@ class Sin(UnaryScalarOp):
             return numpy.sin(x, sig='f')
         return numpy.sin(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2622,7 +2792,9 @@ class Sin(UnaryScalarOp):
 
         return gz * cos(x),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = sin(%(x)s);" % locals()
@@ -2638,7 +2810,9 @@ class ArcSin(UnaryScalarOp):
             return numpy.arcsin(x, sig='f')
         return numpy.arcsin(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2649,7 +2823,9 @@ class ArcSin(UnaryScalarOp):
 
         return gz / sqrt(numpy.cast[x.type](1) - sqr(x)),
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = asin(%(x)s);" % locals()
@@ -2665,7 +2841,9 @@ class Tan(UnaryScalarOp):
             return numpy.tan(x, sig='f')
         return numpy.tan(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2676,7 +2854,9 @@ class Tan(UnaryScalarOp):
 
         return gz / sqr(cos(x)),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = tan(%(x)s);" % locals()
@@ -2692,7 +2872,9 @@ class ArcTan(UnaryScalarOp):
             return numpy.arctan(x, sig='f')
         return numpy.arctan(x)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2703,7 +2885,9 @@ class ArcTan(UnaryScalarOp):
 
         return gz / (numpy.cast[x.type](1) + sqr(x)),
 
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = atan(%(x)s);" % locals()
@@ -2721,7 +2905,9 @@ class ArcTan2(BinaryScalarOp):
                 return numpy.arctan2(y, x, sig='f')
         return numpy.arctan2(y, x)
 
-    def grad(self, (y, x), (gz,)):
+    def grad(self, inputs, gout):
+        (y, x) = inputs
+        (gz,) = gout
         if gz.type in complex_types:
             raise NotImplementedError()
         else:
@@ -2741,7 +2927,9 @@ class ArcTan2(BinaryScalarOp):
             return [gz * x / (sqr(x) + sqr(y)),
                     gz * neg(y) / (sqr(x) + sqr(y))]
 
-    def c_code(self, node, name, (y, x), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (y, x) = inputs
+        (z,) = outputs
         if (node.inputs[0].type in complex_types or
             node.inputs[1].type in complex_types):
             raise NotImplementedError('type not supported', type)
@@ -2761,7 +2949,9 @@ class Cosh(UnaryScalarOp):
             return numpy.cosh(x, sig='f')
         return numpy.cosh(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2772,7 +2962,9 @@ class Cosh(UnaryScalarOp):
 
         return gz * sinh(x),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = cosh(%(x)s);" % locals()
@@ -2788,7 +2980,9 @@ class ArcCosh(UnaryScalarOp):
             return numpy.arccosh(x, sig='f')
         return numpy.arccosh(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2799,7 +2993,9 @@ class ArcCosh(UnaryScalarOp):
 
         return gz / sqrt(sqr(x) - numpy.cast[x.type](1)),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = acosh(%(x)s);" % locals()
@@ -2818,7 +3014,9 @@ class Sinh(UnaryScalarOp):
             return numpy.sinh(x, sig='f')
         return numpy.sinh(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2829,7 +3027,9 @@ class Sinh(UnaryScalarOp):
 
         return gz * cosh(x),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = sinh(%(x)s);" % locals()
@@ -2845,7 +3045,9 @@ class ArcSinh(UnaryScalarOp):
             return numpy.arcsinh(x, sig='f')
         return numpy.arcsinh(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2856,7 +3058,9 @@ class ArcSinh(UnaryScalarOp):
 
         return gz / sqrt(sqr(x) + numpy.cast[x.type](1)),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = asinh(%(x)s);" % locals()
@@ -2876,7 +3080,9 @@ class Tanh(UnaryScalarOp):
             return numpy.tanh(x, sig='f')
         return numpy.tanh(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2887,7 +3093,9 @@ class Tanh(UnaryScalarOp):
 
         return gz * (1 - sqr(tanh(x))),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = tanh(%(x)s);" % locals()
@@ -2903,7 +3111,9 @@ class ArcTanh(UnaryScalarOp):
             return numpy.arctanh(x, sig='f')
         return numpy.arctanh(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -2914,7 +3124,9 @@ class ArcTanh(UnaryScalarOp):
 
         return gz / (numpy.cast[x.type](1) - sqr(x)),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in complex_types:
             raise NotImplementedError('type not supported', type)
         return "%(z)s = atanh(%(x)s);" % locals()
@@ -2926,7 +3138,9 @@ class Real(UnaryScalarOp):
     def impl(self, x):
         return numpy.real(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         return [complex(gz, 0)]
 
 real = Real(real_out, name='real')
@@ -2936,7 +3150,9 @@ class Imag(UnaryScalarOp):
     def impl(self, x):
         return numpy.imag(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             return [complex(0, gz)]
         elif x.type in float_types:
@@ -2951,7 +3167,7 @@ class Angle(UnaryScalarOp):
     def impl(self, x):
         return numpy.angle(x)
 
-    def grad(self, (c, ), (gtheta, )):
+    def grad(self, inputs, gout):
         # y = x.imag
         # r = sqrt(y**2 + x.real**2)
         # g = y/r
@@ -2962,6 +3178,8 @@ class Angle(UnaryScalarOp):
         # else:
         #     theta = -numpy.arcsin(g)+numpy.pi
 
+        (c,) = inputs
+        (gtheta,) = gout
         x = real(c)
         y = imag(c)
         r = abs(c)
@@ -2996,7 +3214,9 @@ class Complex(BinaryScalarOp):
     def impl(self, x, y):
         return numpy.complex(x, y)
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         return [cast(real(gz), x.type.dtype),
                 cast(imag(gz), y.type.dtype)]
 complex = Complex(name='complex')
@@ -3023,7 +3243,9 @@ class ComplexFromPolar(BinaryScalarOp):
         else:
             return numpy.complex128(numpy.complex(x, y))
 
-    def grad(self, (r, theta), (gz,)):
+    def grad(self, inputs, gout):
+        (r, theta) = inputs
+        (gz,) = gout
         gr = gz * complex_from_polar(1, theta)
         gtheta = gz * complex_from_polar(r, -theta)
         return [gr, gtheta]

theano/scalar/basic_scipy.py

@@ -171,7 +171,9 @@ class Gamma(UnaryScalarOp):
         else:
             super(Gamma, self).impl(x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
         if self(x).type in discrete_types:
@@ -182,7 +184,9 @@ class Gamma(UnaryScalarOp):
 
         return gz * gamma(x) * psi(x),
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         if node.inputs[0].type in float_types:
             return """%(z)s = tgamma(%(x)s);""" % locals()
         raise NotImplementedError('only floating point is implemented')

theano/sparse/basic.py

@@ -490,7 +490,8 @@ class CSMProperties(gof.Op):
         return gof.Apply(self, [csm],
                 [data, tensor.ivector(), tensor.ivector(), tensor.ivector()])
 
-    def perform(self, node, (csm,), out):
+    def perform(self, node, inputs, out):
+        (csm,) = inputs
         if self.kmap is None:
             out[0][0] = csm.data
         else:
@@ -503,7 +504,7 @@ class CSMProperties(gof.Op):
         out[2][0] = theano._asarray(csm.indptr, dtype='int32')
         out[3][0] = theano._asarray(csm.shape, dtype='int32')
 
-    def grad(self, (csm,), g):
+    def grad(self, inputs, g):
 
         # g[1:] is all integers, so their Jacobian in this op
         # is 0. We thus don't need to worry about what their values
@@ -513,6 +514,7 @@ class CSMProperties(gof.Op):
         # any gradient anywhere. but we know that at least one of
         # g[1:] is connected, or this grad method wouldn't have been
         # called, so we should report zeros
+        (csm,) = inputs
         if isinstance(g[0].type, DisconnectedType):
             return [csm.zeros_like()]
 
@@ -644,8 +646,10 @@ class CSM(gof.Op):
                          [SparseType(dtype=data.type.dtype,
                                      format=self.format).make_variable()])
 
-    def perform(self, node, (data, indices, indptr, shape), (out,)):
+    def perform(self, node, inputs, outputs):
         # for efficiency, if remap does nothing, then do not apply it
+        (data, indices, indptr, shape) = inputs
+        (out,) = outputs
         if self.kmap is not None:
             data = data[self.kmap]
 
@@ -672,7 +676,9 @@ class CSM(gof.Op):
     def connection_pattern(self, node):
         return [[True], [False], [False], [False]]
 
-    def grad(self, (x_data, x_indices, x_indptr, x_shape), (g_out,)):
+    def grad(self, inputs, gout):
+        (x_data, x_indices, x_indptr, x_shape) = inputs
+        (g_out,) = gout
         g_data, g_indices, g_indptr, g_shape = csm_properties(g_out)
         # unpack the data vector and wrap it as a 1d TensorType
         g_data = csm_grad(self.kmap)(x_data, x_indices, x_indptr, x_shape,
@@ -773,8 +779,10 @@ class CSMGrad(gof.op.Op):
         return gof.Apply(self, [x_data, x_indices, x_indptr, x_shape,
             g_data, g_indices, g_indptr, g_shape], [gout_data])
 
-    def perform(self, node, (x_data, x_indices, x_indptr, x_shape,
-                g_data, g_indices, g_indptr, g_shape), (g_out,)):
+    def perform(self, node, inputs, outputs):
+        (x_data, x_indices, x_indptr, x_shape,
+                g_data, g_indices, g_indptr, g_shape) = inputs
+        (g_out,) = outputs
         if len(x_indptr) - 1 == x_shape[0]:
             sp_dim = x_shape[1]
         else:
@@ -826,7 +834,9 @@ class Cast(gof.op.Op):
             self, [x],
             [SparseType(dtype=self.out_type, format=x.format).make_variable()])
 
-    def perform(self, node, (x, ), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (out,) = outputs
         assert _is_sparse(x)
         out[0] = x.astype(self.out_type)
 
@@ -909,7 +919,9 @@ class DenseFromSparse(gof.op.Op):
                                             broadcastable=(False, False)
                                            ).make_variable()])
 
-    def perform(self, node, (x, ), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (out,) = outputs
         if _is_dense(x):
             print >> sys.stderr, (
                 "WARNING: You just called DenseFromSparse on a dense matrix."
@@ -919,7 +931,9 @@ class DenseFromSparse(gof.op.Op):
             out[0] = x.toarray()
         assert _is_dense(out[0])
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if self.sparse_grad:
             left = sp_ones_like(x)
             right = gz
@@ -989,10 +1003,14 @@ class SparseFromDense(gof.op.Op):
                                      format=self.format
                                     ).make_variable()])
 
-    def perform(self, node, (x, ), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (out,) = outputs
         out[0] = SparseType.format_cls[self.format](x)
 
-    def grad(self, (x, ), (gz, )):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         gx = dense_from_sparse(gz)
         gx = tensor.patternbroadcast(gx, x.broadcastable)
         return gx,
@@ -1035,7 +1053,8 @@ class GetItemList(gof.op.Op):
 
         return gof.Apply(self, [x, ind], [x.type()])
 
-    def perform(self, node, inp, (out, )):
+    def perform(self, node, inp, outputs):
+        (out,) = outputs
         x = inp[0]
         indices = inp[1]
         assert _is_sparse(x)
@@ -1090,7 +1109,8 @@ class GetItemListGrad(gof.op.Op):
 
         return gof.Apply(self, [x, ind, gz], [x.type()])
 
-    def perform(self, node, inp, (out, )):
+    def perform(self, node, inp, outputs):
+        (out,) = outputs
         x = inp[0]
         indices = inp[1]
         gz = inp[2]
@@ -1129,7 +1149,8 @@ class GetItem2Lists(gof.op.Op):
         return gof.Apply(self, [x, ind1, ind2],
                          [theano.tensor.vector()])
 
-    def perform(self, node, inp, (out, )):
+    def perform(self, node, inp, outputs):
+        (out,) = outputs
         x = inp[0]
         ind1 = inp[1]
         ind2 = inp[2]
@@ -1184,7 +1205,8 @@ class GetItem2ListsGrad(gof.op.Op):
 
         return gof.Apply(self, [x, ind1, ind2, gz], [x.type()])
 
-    def perform(self, node, inp, (out, )):
+    def perform(self, node, inp, outputs):
+        (out,) = outputs
         x = inp[0]
         ind1 = inp[1]
         ind2 = inp[2]
@@ -1292,7 +1314,9 @@ class GetItem2d(gof.op.Op):
 
         return gof.Apply(self, input_op, [x.type()])
 
-    def perform(self, node, (x, start1, stop1, step1, start2, stop2, step2), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, start1, stop1, step1, start2, stop2, step2) = inputs
+        (out,) = outputs
         assert _is_sparse(x)
         out[0] = x[start1:stop1:step1, start2:stop2:step2]
 
@@ -1364,7 +1388,9 @@ class GetItemScalar(gof.op.Op):
 
         return gof.Apply(self, input_op, [tensor.scalar(dtype=x.dtype)])
 
-    def perform(self, node, (x, ind1, ind2), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, ind1, ind2) = inputs
+        (out,) = outputs
         assert _is_sparse(x)
         out[0] = theano._asarray(x[ind1, ind2], x.dtype)
 
@@ -1413,11 +1439,15 @@ class Transpose(gof.op.Op):
                                      format=self.format_map[x.type.format]
                                  ).make_variable()])
 
-    def perform(self, node, (x, ), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (out,) = outputs
         assert _is_sparse(x)
         out[0] = x.transpose()
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) and _is_sparse_variable(gz)
         return transpose(gz),
 
@@ -1454,11 +1484,15 @@ class Neg(gof.op.Op):
         assert x.format in ["csr", "csc"]
         return gof.Apply(self, [x], [x.type()])
 
-    def perform(self, node, (x, ), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (out,) = outputs
         assert _is_sparse(x)
         out[0] = -x
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) and _is_sparse_variable(gz)
         return -gz,
 
@@ -1500,7 +1534,9 @@ class ColScaleCSC(gof.op.Op):
             raise ValueError('x was not a csc matrix')
         return gof.Apply(self, [x, s], [x.type()])
 
-    def perform(self, node, (x, s), (z,)):
+    def perform(self, node, inputs, outputs):
+        (x, s) = inputs
+        (z,) = outputs
         M, N = x.shape
         assert x.format == 'csc'
         assert s.shape == (N, )
@@ -1512,7 +1548,9 @@ class ColScaleCSC(gof.op.Op):
 
         z[0] = y
 
-    def grad(self, (x, s), (gz,)):
+    def grad(self, inputs, gout):
+        (x, s) = inputs
+        (gz,) = gout
         return [col_scale(gz, s), sp_sum(x * gz, axis=0)]
 
     def infer_shape(self, node, ins_shapes):
@@ -1549,10 +1587,12 @@ class RowScaleCSC(gof.op.Op):
         assert x.format in ["csr", "csc"]
         return gof.Apply(self, [x, s], [x.type()])
 
-    def perform(self, node, (x, s), (z,)):
+    def perform(self, node, inputs, outputs):
+        (x, s) = inputs
+        (z,) = outputs
         M, N = x.shape
         assert x.format == 'csc'
-        assert s.shape == (M, )
+        assert s.shape == (M,)
 
         indices = x.indices
         indptr = x.indptr
@@ -1565,7 +1605,9 @@ class RowScaleCSC(gof.op.Op):
 
         z[0] = scipy.sparse.csc_matrix((y_data, indices, indptr), (M, N))
 
-    def grad(self, (x, s), (gz,)):
+    def grad(self, inputs, gout):
+        (x, s) = inputs
+        (gz,) = gout
         return [row_scale(gz, s), sp_sum(x * gz, axis=1)]
 
     def infer_shape(self, node, ins_shapes):
@@ -1650,13 +1692,17 @@ class SpSum(gof.op.Op):
         z = tensor.TensorType(broadcastable=b, dtype=x.dtype)()
         return gof.Apply(self, [x], [z])
 
-    def perform(self, node, (x,), (z,)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         if self.axis is None:
             z[0] = numpy.asarray(x.sum())
         else:
             z[0] = numpy.asarray(x.sum(self.axis)).ravel()
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         if x.dtype not in continuous_dtypes:
             return [x.zeros_like(dtype=theano.config.floatX)]
         if self.structured:
@@ -1738,13 +1784,17 @@ class Diag(gof.op.Op):
         return gof.Apply(self, [x], [tensor.tensor(broadcastable=(False,),
                                                    dtype=x.dtype)])
 
-    def perform(self, node, (x,), (z,)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         N, M = x.shape
         if N != M:
             raise ValueError('Diag only apply on square matrix')
         z[0] = x.diagonal()
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         return [square_diagonal(gz)]
 
     def infer_shape(self, nodes, shapes):
@@ -1782,7 +1832,8 @@ class SquareDiagonal(gof.op.Op):
         return gof.Apply(self, [diag],
                 [SparseType(dtype=diag.dtype, format='csc')()])
 
-    def perform(self, node, inputs, (z,)):
+    def perform(self, node, inputs, outputs):
+        (z,) = outputs
         diag, o_shape = inputs[0], inputs[0].shape * 2
 
         N = len(diag)
@@ -1793,7 +1844,8 @@ class SquareDiagonal(gof.op.Op):
 
         z[0] = scipy.sparse.csc_matrix(tup, copy=True)
 
-    def grad(self, inputs, (gz,)):
+    def grad(self, inputs, gout):
+        (gz,) = gout
         return [diag(gz)]
 
     def infer_shape(self, nodes, shapes):
@@ -1831,7 +1883,9 @@ class EnsureSortedIndices(gof.op.Op):
         assert x.format in ["csr", "csc"]
         return gof.Apply(self, [x], [x.type()])
 
-    def perform(self, node, (x, ), (z, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         if self.inplace:
             z[0] = x.sort_indices()
         else:
@@ -1906,12 +1960,16 @@ class AddSS(gof.op.Op):
                                      format=x.type.format
                                     ).make_variable()])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x) and _is_sparse(y)
         assert x.shape == y.shape
         out[0] = x + y
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) and _is_sparse_variable(y)
         assert _is_sparse_variable(gz)
         return gz, gz
@@ -1943,14 +2001,17 @@ class AddSSData(gof.op.Op):
                          [SparseType(dtype=x.type.dtype,
                                  format=x.type.format).make_variable()])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x) and _is_sparse(y)
         assert x.shape == y.shape
         assert x.data.shape == y.data.shape
         out[0] = x.copy()
         out[0].data += y.data
 
-    def grad(self, inputs, (gz, )):
+    def grad(self, inputs, gout):
+        (gz,) = gout
         is_continuous = [(i.dtype in continuous_dtypes)
                          for i in inputs]
         derivative = {True: gz, False: None}
@@ -2006,14 +2067,18 @@ class AddSD(gof.op.Op):
                                             broadcastable=y.type.broadcastable
                                            ).make_variable()])
 
-    def perform(self, node, (x,  y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x,  y) = inputs
+        (out,) = outputs
         assert _is_dense(y)
 
         # The asarray is needed as in some case, this return a
         # numpy.matrixlib.defmatrix.matrix object and not an ndarray.
         out[0] = theano._asarray(x + y, dtype=node.outputs[0].type.dtype)
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) and _is_dense_variable(y)
         assert _is_dense_variable(gz)
         return sp_ones_like(x) * gz, gz
@@ -2045,12 +2110,16 @@ class StructuredAddSV(gof.op.Op):
                          [SparseType(dtype=x.type.dtype,
                                  format=x.type.format).make_variable()])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x) and not _is_sparse(y)
         assert x.shape[1] == y.shape[0]
         out[0] = x.__class__(x + (x.toarray() != 0) * y)
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) and not _is_sparse_variable(y)
         assert _is_sparse_variable(gz)
         return gz, sp_sum(gz, axis=0, sparse_grad=True)
@@ -2156,7 +2225,9 @@ class MulSS(gof.op.Op):
                                      format=x.type.format
                                     )()])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x) and _is_sparse(y)
         assert len(x.shape) == 2
         assert y.shape == x.shape
@@ -2164,7 +2235,9 @@ class MulSS(gof.op.Op):
         # x * y calls dot...
         out[0] = x.multiply(y)
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         return y * gz, x * gz
 
     def infer_shape(self, node, shapes):
@@ -2202,7 +2275,9 @@ class MulSD(gof.op.Op):
                          format=x.type.format)()
         return gof.Apply(self, [x, y], [out])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x) and _is_dense(y)
         if len(y.shape) == 0:
             out_dtype = node.outputs[0].dtype
@@ -2258,7 +2333,9 @@ class MulSD(gof.op.Op):
                 ), x.format
                 out[0] = type(x)(x.toarray() * y)
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) and _is_dense_variable(y)
         assert _is_sparse_variable(gz)
         return y * gz, dense_from_sparse(x * gz)
@@ -2291,12 +2368,16 @@ class MulSV(gof.op.Op):
                          [SparseType(dtype=x.type.dtype,
                                  format=x.type.format).make_variable()])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x) and not _is_sparse(y)
         assert x.shape[1] == y.shape[0]
         out[0] = x.__class__(x.toarray() * y)
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) and _is_dense_variable(y)
         assert _is_sparse_variable(gz)
 
@@ -2402,7 +2483,9 @@ class __ComparisonOpSS(gof.op.Op):
                          [SparseType(dtype='uint8',
                                  format=x.type.format).make_variable()])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x) and _is_sparse(y)
         assert x.shape == y.shape
         out[0] = self.comparison(x, y).astype('uint8')
@@ -2444,7 +2527,9 @@ class __ComparisonOpSD(gof.op.Op):
                          [SparseType(dtype='uint8',
                                  format=x.type.format).make_variable()])
 
-    def perform(self, node, (x, y), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, y) = inputs
+        (out,) = outputs
         assert _is_sparse(x)
         assert x.shape == y.shape
         assert _is_dense(y)
@@ -2682,7 +2767,8 @@ class HStack(gof.op.Op):
             self, var,
             [SparseType(dtype=self.dtype, format=self.format).make_variable()])
 
-    def perform(self, node, block, (out, )):
+    def perform(self, node, block, outputs):
+        (out,) = outputs
         for b in block:
             assert _is_sparse(b)
         out[0] = scipy.sparse.hstack(block, format=self.format,
@@ -2692,7 +2778,8 @@ class HStack(gof.op.Op):
         if out[0].dtype != self.dtype:
             out[0] = out[0].astype(self.dtype)
 
-    def grad(self, inputs, (gz, )):
+    def grad(self, inputs, gout):
+        (gz,) = gout
         is_continuous = [(inputs[i].dtype in tensor.continuous_dtypes)
                          for i in range(len(inputs))]
 
@@ -2749,7 +2836,8 @@ def hstack(blocks, format=None, dtype=None):
 
 class VStack(HStack):
     # See doc in instance of this Op or function after this class definition.
-    def perform(self, node, block, (out, )):
+    def perform(self, node, block, outputs):
+        (out,) = outputs
         for b in block:
             assert _is_sparse(b)
         out[0] = scipy.sparse.vstack(block, format=self.format,
@@ -2759,7 +2847,8 @@ class VStack(HStack):
         if out[0].dtype != self.dtype:
             out[0] = out[0].astype(self.dtype)
 
-    def grad(self, inputs, (gz, )):
+    def grad(self, inputs, gout):
+        (gz,) = gout
         is_continuous = [(inputs[i].dtype in tensor.continuous_dtypes)
                         for i in range(len(inputs))]
 
@@ -2836,7 +2925,9 @@ class Remove0(gof.Op):
         assert x.format in ["csr", "csc"]
         return gof.Apply(self, [x], [x.type()])
 
-    def perform(self, node, (x,), (z,)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         if self.inplace:
             c = x
         else:
@@ -2844,7 +2935,9 @@ class Remove0(gof.Op):
         c.eliminate_zeros()
         z[0] = c
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         return [gz]
 
     def infer_shape(self, node, i0_shapes):
@@ -3157,7 +3250,9 @@ class TrueDot(gof.op.Op):
                                                shape, copy=False)
         out[0] = rval
 
-    def grad(self, (x, y), (gz, )):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(gz)
         assert _is_sparse_variable(x)
 
@@ -3246,7 +3341,9 @@ class StructuredDot(gof.Op):
                              [tensor.tensor(dtype_out,
                                             (False, b.type.broadcastable[1]))])
 
-    def perform(self, node, (a, b), (out,)):
+    def perform(self, node, inputs, outputs):
+        (a, b) = inputs
+        (out,) = outputs
         if a.shape[1] != b.shape[0]:
             raise ValueError('shape mismatch in StructuredDot.perform',
                              (a.shape, b.shape))
@@ -3287,10 +3384,12 @@ class StructuredDot(gof.Op):
         # theano._asarray function documentation.
         out[0] = theano._asarray(variable, str(variable.dtype))
 
-    def grad(self, (a, b), (g_out,)):
+    def grad(self, inputs, gout):
         # a is sparse, b is dense, g_out is dense
         # ga = g_out x b.T
         # gb = a.T x g_out
+        (a, b) = inputs
+        (g_out,) = gout
         return [structured_dot_grad(a, b, g_out), structured_dot(a.T, g_out)]
 
     def infer_shape(self, node, shapes):
@@ -3367,7 +3466,9 @@ class StructuredDotGradCSC(gof.Op):
         return gof.Apply(self, [a_indices, a_indptr, b, g_ab],
                                [tensor.tensor(g_ab.dtype, (False,))])
 
-    def perform(self, node, (a_indices, a_indptr, b, g_ab), (out,)):
+    def perform(self, node, inputs, outputs):
+        (a_indices, a_indptr, b, g_ab) = inputs
+        (out,) = outputs
         g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
         for j in xrange(len(a_indptr) - 1):
             ind0 = a_indptr[j]
@@ -3386,8 +3487,10 @@ class StructuredDotGradCSC(gof.Op):
     def c_code_cache_version(self):
         return (1,)
 
-    def c_code(self, node, name, (_indices, _indptr, _d, _g), (_zout, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
 
+        (_indices, _indptr, _d, _g) = inputs
+        (_zout,) = outputs
         if node.inputs[2].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for b')
         if node.inputs[3].type.dtype in ('complex64', 'complex128'):
@@ -3501,7 +3604,9 @@ class StructuredDotGradCSR(gof.Op):
         return gof.Apply(self, [a_indices, a_indptr, b, g_ab],
                          [tensor.tensor(b.dtype, (False,))])
 
-    def perform(self, node, (a_indices, a_indptr, b, g_ab), (out,)):
+    def perform(self, node, inputs, outputs):
+        (a_indices, a_indptr, b, g_ab) = inputs
+        (out,) = outputs
         g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
         for i in xrange(len(a_indptr) - 1):  # loop over rows
             ind0 = a_indptr[i]
@@ -3522,8 +3627,10 @@ class StructuredDotGradCSR(gof.Op):
     def c_code_cache_version(self):
         return (1,)
 
-    def c_code(self, node, name, (_indices, _indptr, _d, _g), (_zout, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
 
+        (_indices, _indptr, _d, _g) = inputs
+        (_zout,) = outputs
         if node.inputs[2].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for b')
         if node.inputs[3].type.dtype in ('complex64', 'complex128'):
@@ -3651,7 +3758,9 @@ class SamplingDot(gof.op.Op):
 
         return gof.Apply(self, [x, y, p], [p.type()])
 
-    def perform(self, node, (x, y, p), (out,)):
+    def perform(self, node, inputs, outputs):
+        (x, y, p) = inputs
+        (out,) = outputs
         if _is_sparse(x):
             raise TypeError(x)
 
@@ -3663,7 +3772,9 @@ class SamplingDot(gof.op.Op):
 
         out[0] = p.__class__(p.multiply(numpy.dot(x, y.T)))
 
-    def grad(self, (x, y, p), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y, p) = inputs
+        (gz,) = gout
         rval = [
             dot(p * gz, y),
             dot((p * gz).T, x),
@@ -3787,7 +3898,9 @@ class Dot(gof.op.Op):
 
         out[0] = theano._asarray(rval, dtype=node.outputs[0].dtype)
 
-    def grad(self, (x, y), (gz,)):
+    def grad(self, inputs, gout):
+        (x, y) = inputs
+        (gz,) = gout
         assert _is_sparse_variable(x) or _is_sparse_variable(y)
         rval = []
 
@@ -3876,7 +3989,9 @@ class Usmm(gof.op.Op):
                          [tensor.tensor(dtype=dtype_out,
                                         broadcastable=(False, False))])
 
-    def perform(self, node, (alpha, x, y, z), (out, )):
+    def perform(self, node, inputs, outputs):
+        (alpha, x, y, z) = inputs
+        (out,) = outputs
         x_is_sparse = _is_sparse(x)
         y_is_sparse = _is_sparse(y)
 

theano/sparse/opt.py

@@ -105,7 +105,9 @@ class AddSD_ccode(gof.op.Op):
                          [data, indices, indptr, y],
                          [out])
 
-    def c_code(self, node, name, (_data, _indices, _indptr, y), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (_data, _indices, _indptr, y) = inputs
+        (z,) = outputs
         inplace = int(self.inplace)
         format = {'csc': 0, 'csr': 1}[self.format]
         out_typenum = node.outputs[0].type.dtype_specs()[2]
@@ -236,7 +238,9 @@ class StructuredDotCSC(gof.Op):
                 [tensor.tensor(dtype_out, (False, b.type.broadcastable[1]))])
         return r
 
-    def perform(self, node, (a_val, a_ind, a_ptr, a_nrows, b), (out,)):
+    def perform(self, node, inputs, outputs):
+        (a_val, a_ind, a_ptr, a_nrows, b) = inputs
+        (out,) = outputs
         a = scipy.sparse.csc_matrix((a_val, a_ind, a_ptr),
                 (a_nrows, b.shape[0]),
                 copy=False)
@@ -244,7 +248,7 @@ class StructuredDotCSC(gof.Op):
         out[0] = theano._asarray(a * b, dtype=node.outputs[0].type.dtype)
         assert _is_dense(out[0])  # scipy 0.7 automatically converts to dense
 
-    def c_code(self, node, name, (a_val, a_ind, a_ptr, a_nrows, b), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
         # C-implementation of the dot product of the sparse matrix A and matrix
         # B.
         # @param a_val: non-zero values of the sparse matrix
@@ -257,6 +261,8 @@ class StructuredDotCSC(gof.Op):
         # @param z: return value
         # @param sub: TODO, not too sure, something to do with weave probably
 
+        (a_val, a_ind, a_ptr, a_nrows, b) = inputs
+        (z,) = outputs
         if node.inputs[0].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for a_val')
         if node.inputs[4].type.dtype in ('complex64', 'complex128'):
@@ -426,7 +432,9 @@ class StructuredDotCSR(gof.Op):
                                                 b.type.broadcastable[1]))])
         return r
 
-    def perform(self, node, (a_val, a_ind, a_ptr, b), (out,)):
+    def perform(self, node, inputs, outputs):
+        (a_val, a_ind, a_ptr, b) = inputs
+        (out,) = outputs
         a = scipy.sparse.csr_matrix((a_val, a_ind, a_ptr),
                 (len(a_ptr) - 1, b.shape[0]),
                 copy=True)  # use view_map before setting this to False
@@ -435,7 +443,7 @@ class StructuredDotCSR(gof.Op):
         # scipy 0.7 automatically converts to dense, but not .6 sometimes
         assert _is_dense(out[0])
 
-    def c_code(self, node, name, (a_val, a_ind, a_ptr, b), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
         """
         C-implementation of the dot product of the sparse matrix A and matrix
         B.
@@ -449,7 +457,8 @@ class StructuredDotCSR(gof.Op):
         @param z: return value
         @param sub: TODO, not too sure, something to do with weave probably
         """
-        # retrieve dtype number
+        (a_val, a_ind, a_ptr, b) = inputs
+        (z,) = outputs
         typenum_z = tensor.TensorType(self.dtype_out, []).dtype_specs()[2]
         if node.inputs[0].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for a_val')
@@ -890,9 +899,11 @@ class CSMGradC(gof.Op):
         return gof.Apply(self, [a_val, a_ind, a_ptr, a_dim,
              b_val, b_ind, b_ptr, b_dim], [b_val.type()])
 
-    def c_code(self, node, name, (a_val, a_ind, a_ptr, a_dim,
-                        b_val, b_ind, b_ptr, b_dim), (z,), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
         # retrieve dtype number
+        (a_val, a_ind, a_ptr, a_dim,
+         b_val, b_ind, b_ptr, b_dim) = inputs
+        (z,) = outputs
         typenum_z = node.outputs[0].type.dtype_specs()[2]
         if node.inputs[0].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for a_val')
@@ -1047,9 +1058,10 @@ class MulSDCSC(gof.Op):
     # def perform(self, node, (a_data, a_indices, a_indptr, b), (out,)):
     #    return NotImplementedError()
 
-    def c_code(self, node, name, (_data, _indices, _indptr, _b,),
-               (_zout, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
 
+        (_data, _indices, _indptr, _b,) = inputs
+        (_zout,) = outputs
         if node.inputs[0].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for a')
         if node.inputs[3].type.dtype in ('complex64', 'complex128'):
@@ -1163,9 +1175,10 @@ class MulSDCSR(gof.Op):
     # def perform(self, node, (a_data, a_indices, a_indptr, b), (out,)):
     #    return NotImplemented()
 
-    def c_code(self, node, name, (_data, _indices, _indptr, _b,),
-               (_zout, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
 
+        (_data, _indices, _indptr, _b,) = inputs
+        (_zout,) = outputs
         if node.inputs[0].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for a')
         if node.inputs[3].type.dtype in ('complex64', 'complex128'):

theano/sparse/sandbox/sp.py

@@ -42,18 +42,20 @@ class ConvolutionIndices(Op):
     """
 
     @staticmethod
-    def sparse_eval(inshp, kshp, nkern, (dx, dy)=(1, 1), mode='valid'):
+    def sparse_eval(inshp, kshp, nkern, strides=(1, 1), mode='valid'):
+        (dx, dy) = strides
         return convolution_indices.evaluate(inshp, kshp, (dx, dy),
                                             nkern, mode=mode, ws=False)
 
     @staticmethod
-    def conv_eval(inshp, kshp, (dx, dy)=(1, 1), mode='valid'):
+    def conv_eval(inshp, kshp, strides=(1, 1), mode='valid'):
+        (dx, dy) = strides
         return convolution_indices.evaluate(inshp, kshp, (dx, dy),
                                             mode=mode, ws=True)
 
     # img_shape and ker_shape are (height,width)
     @staticmethod
-    def evaluate(inshp, kshp, (dx, dy)=(1, 1), nkern=1, mode='valid', ws=True):
+    def evaluate(inshp, kshp, strides=(1, 1), nkern=1, mode='valid', ws=True):
         """Build a sparse matrix which can be used for performing...
         * convolution: in this case, the dot product of this matrix
         with the input images will generate a stack of images
@@ -79,6 +81,7 @@ class ConvolutionIndices(Op):
         :returns: the structure of a sparse matrix, and the logical dimensions
                   of the image which will be the result of filtering.
         """
+        (dx, dy) = strides
         N = numpy
 
         # inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
@@ -251,8 +254,9 @@ class ConvolutionIndices(Op):
 
         return rval
 
-    def perform(self, node, (inshp, kshp),\
-                (out_indices, out_indptr, spmat_shape)):
+    def perform(self, node, inputs, outputs):
+        (inshp, kshp) = inputs
+        (out_indices, out_indptr, spmat_shape) = outputs
         indices, indptr, spmatshp, outshp = self.evaluate(inshp, kshp)
         out_indices[0] = indices
         out_indptr[0] = indptr

theano/sparse/sandbox/sp2.py

@@ -71,7 +71,9 @@ class Poisson(gof.op.Op):
         x = as_sparse_variable(x)
         return gof.Apply(self, [x], [x.type()])
 
-    def perform(self, node, (x, ), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (out,) = outputs
         assert _is_sparse(x)
         assert x.format in ["csr", "csc"]
         out[0] = x.copy()
@@ -130,7 +132,9 @@ class Binomial(gof.op.Op):
                          [SparseType(dtype=self.dtype,
                                      format=self.format).make_variable()])
 
-    def perform(self, node, (n, p, shape, ), (out, )):
+    def perform(self, node, inputs, outputs):
+        (n, p, shape) = inputs
+        (out,) = outputs
         binomial = numpy.random.binomial(n, p, size=shape)
         csx_matrix = getattr(scipy.sparse, self.format + '_matrix')
         out[0] = csx_matrix(binomial, dtype=self.dtype)
@@ -138,7 +142,9 @@ class Binomial(gof.op.Op):
     def connection_pattern(self, node):
         return [[True], [True], [False]]
 
-    def grad(self, (n, p, shape, ), (gz,)):
+    def grad(self, inputs, gout):
+        (n, p, shape) = inputs
+        (gz,) = gout
         comment_n = "No gradient exists for the number of samples in class\
                      Binomial of theano/sparse/sandbox/sp2.py"
         comment_p = "No gradient exists for the prob of success in class\
@@ -196,7 +202,9 @@ class Multinomial(gof.op.Op):
 
         return gof.Apply(self, [n, p], [p.type()])
 
-    def perform(self, node, (n, p), (out, )):
+    def perform(self, node, inputs, outputs):
+        (n, p) = inputs
+        (out,) = outputs
         assert _is_sparse(p)
 
         if p.format != 'csr':

theano/sparse/tests/test_basic.py

@@ -186,11 +186,15 @@ class T_verify_grad_sparse(unittest.TestCase):
             x = as_sparse_variable(x)
             return gof.Apply(self, [x], [x.type()])
 
-        def perform(self, node, (x, ), (out, )):
+        def perform(self, node, inputs, outputs):
+            (x,) = inputs
+            (out,) = outputs
             assert _is_sparse(x)
             out[0] = -x
 
-        def grad(self, (x,), (gz,)):
+        def grad(self, inputs, gout):
+            (x,) = inputs
+            (gz,) = gout
             assert _is_sparse_variable(x) and _is_sparse_variable(gz)
             if self.structured:
                 return sp_ones_like(x) * dense_from_sparse(gz),

theano/tensor/basic.py

@@ -5159,10 +5159,14 @@ class Diagonal(Op):
         return Apply(self, [x], [tensor(dtype=x.dtype,
                                         broadcastable=[False] * (x.ndim - 1))])
 
-    def perform(self, node, (x,), (z,)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         z[0] = x.diagonal(self.offset, self.axis1, self.axis2)
 
-    def grad(self, (x,), (gz,)):
+    def grad(self, inputs, gout):
+        (x,) = inputs
+        (gz,) = gout
         return [grad_not_implemented(self, 0, x)]
 
     def infer_shape(self, node, shapes):
@@ -5207,10 +5211,12 @@ class Diag(Op):
 
         return Apply(self, [diag], [matrix(dtype=diag.dtype)])
 
-    def perform(self, node, inputs, (z,)):
+    def perform(self, node, inputs, outputs):
+        (z,) = outputs
         z[0] = numpy.diag(inputs[0])
 
-    def grad(self, inputs, (gz,)):
+    def grad(self, inputs, gout):
+        (gz,) = gout
         return [diagonal(gz)]
 
     def infer_shape(self, nodes, shapes):
@@ -5435,7 +5441,8 @@ class Choose(Op):
         o = TensorType(choice.dtype, bcast)
         return Apply(self, [a, choice], [o()])
 
-    def perform(self, node, inputs, (z, )):
+    def perform(self, node, inputs, outputs):
+        (z,) = outputs
         a = inputs[0]
         choice = inputs[1]
         # TODO reuse out?

theano/tensor/extra_ops.py

@@ -593,7 +593,9 @@ class RepeatOp(theano.Op):
 
         return [[True], [False]]
 
-    def grad(self, (x, repeats), (gz, )):
+    def grad(self, inputs, gout):
+        (x, repeats) = inputs
+        (gz,) = gout
         if repeats.ndim == 0:
             if self.axis is None:
                 axis = x.ndim

theano/tensor/nlinalg.py

@@ -42,7 +42,9 @@ class MatrixPinv(Op):
         assert x.ndim == 2
         return Apply(self, [x], [x.type()])
 
-    def perform(self, node, (x,), (z, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         z[0] = numpy.linalg.pinv(x).astype(x.dtype)
 
 pinv = MatrixPinv()
@@ -69,7 +71,9 @@ class MatrixInverse(Op):
         assert x.ndim == 2
         return Apply(self, [x], [x.type()])
 
-    def perform(self, node, (x,), (z, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         z[0] = numpy.linalg.inv(x).astype(x.dtype)
 
     def grad(self, inputs, g_outputs):
@@ -149,7 +153,9 @@ class AllocDiag(Op):
     def grad(self, inputs, g_outputs):
         return [extract_diag(g_outputs[0])]
 
-    def perform(self, node, (x,), (z,)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         if x.ndim != 1:
             raise TypeError(x)
         z[0] = numpy.diag(x)
@@ -264,7 +270,9 @@ class Det(Op):
         o = theano.tensor.scalar(dtype=x.dtype)
         return Apply(self, [x], [o])
 
-    def perform(self, node, (x,), (z, )):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (z,) = outputs
         try:
             z[0] = numpy.asarray(numpy.linalg.det(x), dtype=x.dtype)
         except Exception:
@@ -298,7 +306,9 @@ class Eig(Op):
         v = theano.tensor.matrix(dtype=x.dtype)
         return Apply(self, [x], [w, v])
 
-    def perform(self, node, (x,), (w, v)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (w, v) = outputs
         w[0], v[0] = [z.astype(x.dtype) for z in self._numop(x)]
 
     def infer_shape(self, node, shapes):
@@ -333,7 +343,9 @@ class Eigh(Eig):
         v = theano.tensor.matrix(dtype=x.dtype)
         return Apply(self, [x], [w, v])
 
-    def perform(self, node, (x,), (w, v)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (w, v) = outputs
         w[0], v[0] = self._numop(x, self.UPLO)
 
     def grad(self, inputs, g_outputs):
@@ -466,7 +478,9 @@ class QRFull(Op):
 
         return Apply(self, [x], [q, r])
 
-    def perform(self, node, (x,), (q, r)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (q, r) = outputs
         assert x.ndim == 2, "The input of qr function should be a matrix."
         q[0], r[0] = self._numop(x, self.mode)
 
@@ -489,7 +503,9 @@ class QRIncomplete(Op):
         q = theano.tensor.matrix(dtype=x.dtype)
         return Apply(self, [x], [q])
 
-    def perform(self, node, (x,), (q,)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (q,) = outputs
         assert x.ndim == 2, "The input of qr function should be a matrix."
         q[0] = self._numop(x,
                            self.mode)
@@ -594,7 +610,9 @@ class SVD(Op):
         v = theano.tensor.matrix(dtype=x.dtype)
         return Apply(self, [x], [w, u, v])
 
-    def perform(self, node, (x,), (w, u, v)):
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (w, u, v) = outputs
         assert x.ndim == 2, "The input of svd function should be a matrix."
         w[0], u[0], v[0] = self._numop(x,
                                        self.full_matrices,

theano/tensor/slinalg.py

@@ -232,7 +232,8 @@ class Eigvalsh(Op):
             w = theano.tensor.vector(dtype=out_dtype)
             return Apply(self, [a, b], [w])
 
-    def perform(self, node, inputs, (w,)):
+    def perform(self, node, inputs, outputs):
+        (w,) = outputs
         if len(inputs) == 2:
             w[0] = scipy.linalg.eigvalsh(a=inputs[0], b=inputs[1], lower=self.lower)
         else:
@@ -288,7 +289,8 @@ class EigvalshGrad(Op):
         out2 = theano.tensor.matrix(dtype=out_dtype)
         return Apply(self, [a, b, gw], [out1, out2])
 
-    def perform(self, node, (a, b, gw), outputs):
+    def perform(self, node, inputs, outputs):
+        (a, b, gw) = inputs
         w, v = scipy.linalg.eigh(a, b, lower=self.lower)
         gA = v.dot(numpy.diag(gw).dot(v.T))
         gB = - v.dot(numpy.diag(gw*w).dot(v.T))
@@ -353,10 +355,14 @@ class Expm(Op):
         expm = theano.tensor.matrix(dtype=A.dtype)
         return Apply(self, [A, ], [expm, ])
 
-    def perform(self, node, (A,), (expm,)):
+    def perform(self, node, inputs, outputs):
+        (A,) = inputs
+        (expm,) = outputs
         expm[0] = scipy.linalg.expm(A)
 
-    def grad(self, (A,), (g_out,)):
+    def grad(self, inputs, outputs):
+        (A,) = inputs
+        (g_out,) = outputs
         return [ExpmGrad()(A, g_out)]
 
     def infer_shape(self, node, shapes):
@@ -378,10 +384,12 @@ class ExpmGrad(Op):
     def infer_shape(self, node, shapes):
         return [shapes[0]]
 
-    def perform(self, node, (A, gA), (out,)):
+    def perform(self, node, inputs, outputs):
         # Kalbfleisch and Lawless, J. Am. Stat. Assoc. 80 (1985) Equation 3.4
         # Kind of... You need to do some algebra from there to arrive at
         # this expression.
+        (A, gA) = inputs
+        (out,) = outputs
         w, V = scipy.linalg.eig(A, right=True)
         U = scipy.linalg.inv(V).T
 

theano/tensor/tests/test_elemwise.py

@@ -1233,7 +1233,9 @@ def test_not_implemented_elemwise_grad():
         def impl(self, n, x):
             return x * n
 
-        def grad(self, (n, x), (gz,)):
+        def grad(self, inputs, gout):
+            (n, x) = inputs
+            (gz,) = gout
             dy_dx = n
             return [theano.gradient.grad_not_implemented(self, 0, n),
                     gz * dy_dx]

theano/tensor/tests/test_opt.py

@@ -1421,7 +1421,9 @@ class TimesN(theano.scalar.basic.UnaryScalarOp):
         float %(nodename)s_timesn(float x) { return x * %(n)s; }
         """ % locals()
 
-    def c_code(self, node, name, (x, ), (z, ), sub):
+    def c_code(self, node, name, inputs, outputs, sub):
+        (x,) = inputs
+        (z,) = outputs
         return "%(z)s = %(name)s_timesn(%(x)s);" % locals()
 
 

theano/typed_list/basic.py

@@ -80,7 +80,9 @@ class GetItem(Op):
         else:
             raise TypeError('Expected scalar or slice as index.')
 
-    def perform(self, node, (x, index), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, index) = inputs
+        (out,) = outputs
         if not isinstance(index, slice):
             index = int(index)
         out[0] = x[index]
@@ -137,7 +139,9 @@ class Append(Op):
         assert x.ttype == toAppend.type, (x.ttype, toAppend.type)
         return Apply(self, [x, toAppend], [x.type()])
 
-    def perform(self, node, (x, toAppend), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, toAppend) = inputs
+        (out,) = outputs
         if not self.inplace:
             out[0] = list(x)
         else:
@@ -209,7 +213,9 @@ class Extend(Op):
         assert x.type == toAppend.type
         return Apply(self, [x, toAppend], [x.type()])
 
-    def perform(self, node, (x, toAppend), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, toAppend) = inputs
+        (out,) = outputs
         if not self.inplace:
             out[0] = list(x)
         else:
@@ -292,7 +298,9 @@ class Insert(Op):
             assert isinstance(index, T.TensorVariable) and index.ndim == 0
         return Apply(self, [x, index, toInsert], [x.type()])
 
-    def perform(self, node, (x, index, toInsert), (out, )):
+    def perform(self, node, inputs, outputs):
+        (x, index, toInsert) = inputs
+        (out,) = outputs
         if not self.inplace:
             out[0] = list(x)
         else:
@@ -360,8 +368,9 @@ class Remove(Op):
         assert x.ttype == toRemove.type
         return Apply(self, [x, toRemove], [x.type()])
 
-    def perform(self, node, (x, toRemove), (out, )):
-
+    def perform(self, node, inputs, outputs):
+        (x, toRemove) = inputs
+        (out,) = outputs
         if not self.inplace:
             out[0] = list(x)
         else:
@@ -413,8 +422,8 @@ class Reverse(Op):
         assert isinstance(x.type, TypedListType)
         return Apply(self, [x], [x.type()])
 
-    def perform(self, node, inp, (out, )):
-
+    def perform(self, node, inp, outputs):
+        (out,) = outputs
         if not self.inplace:
             out[0] = list(inp[0])
         else:
@@ -470,12 +479,14 @@ class Index(Op):
         assert x.ttype == elem.type
         return Apply(self, [x, elem], [T.scalar()])
 
-    def perform(self, node, (x, elem), (out, )):
+    def perform(self, node, inputs, outputs):
         """
         inelegant workaround for ValueError: The truth value of an
         array with more than one element is ambiguous. Use a.any() or a.all()
         being thrown when trying to remove a matrix from a matrices list
         """
+        (x, elem) = inputs
+        (out,) = outputs
         for y in range(len(x)):
             if node.inputs[0].ttype.values_eq(x[y], elem):
                 out[0] = numpy.asarray(y, dtype=theano.config.floatX)
@@ -500,12 +511,14 @@ class Count(Op):
         assert x.ttype == elem.type
         return Apply(self, [x, elem], [T.scalar()])
 
-    def perform(self, node, (x, elem), (out, )):
+    def perform(self, node, inputs, outputs):
         """
         inelegant workaround for ValueError: The truth value of an
         array with more than one element is ambiguous. Use a.any() or a.all()
         being thrown when trying to remove a matrix from a matrices list
         """
+        (x, elem) = inputs
+        (out,) = outputs
         out[0] = 0
         for y in range(len(x)):
             if node.inputs[0].ttype.values_eq(x[y], elem):
@@ -543,7 +556,8 @@ class Length(Op):
         assert isinstance(x.type, TypedListType)
         return Apply(self, [x], [T.scalar(dtype='int64')])
 
-    def perform(self, node, x, (out, )):
+    def perform(self, node, x, outputs):
+        (out,) = outputs
         out[0] = numpy.asarray(len(x[0]), 'int64')
 
     def __str__(self):
@@ -593,7 +607,8 @@ class MakeList(Op):
 
         return Apply(self, a2, [tl])
 
-    def perform(self, node, inputs, (out, )):
+    def perform(self, node, inputs, outputs):
+        (out,) = outputs
         out[0] = list(inputs)
 
 make_list = MakeList()