Use another mechanism that make sure all instance will use the scipy nfunc.

theano/scalar/basic_scipy.py

@@ -26,6 +26,7 @@ except (ImportError, ValueError):
 
 
 class Erf(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.erf', 1, 1)
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erf(x)
@@ -58,6 +59,8 @@ erf = Erf(upgrade_to_float, name='erf')
 
 
 class Erfc(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.erfc', 1, 1)
+
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfc(x)
@@ -105,6 +108,7 @@ class Erfcx(UnaryScalarOp):
     running on GPU an optimization will replace it with a gpu version.
 
     """
+    nfunc_spec = ('scipy.special.erfcx', 1, 1)
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfcx(x)
@@ -140,6 +144,7 @@ class Erfinv(UnaryScalarOp):
 
     (TODO) Find a C implementation of erfinv for CPU.
     """
+    nfunc_spec = ('scipy.special.erfinv', 1, 1)
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfinv(x)
@@ -173,6 +178,8 @@ erfinv = Erfinv(upgrade_to_float_no_complex, name='erfinv')
 
 
 class Erfcinv(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.erfcinv', 1, 1)
+
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfcinv(x)
@@ -206,6 +213,8 @@ erfcinv = Erfcinv(upgrade_to_float_no_complex, name='erfcinv')
 
 
 class Gamma(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.gamma', 1, 1)
+
     @staticmethod
     def st_impl(x):
         return scipy.special.gamma(x)
@@ -243,6 +252,8 @@ class GammaLn(UnaryScalarOp):
     Log gamma function.
 
     """
+    nfunc_spec = ('scipy.special.gammaln', 1, 1)
+
     @staticmethod
     def st_impl(x):
         return scipy.special.gammaln(x)
@@ -287,6 +298,8 @@ class Psi(UnaryScalarOp):
     Derivative of log gamma function.
 
     """
+    nfunc_spec = ('scipy.special.psi', 1, 1)
+
     @staticmethod
     def st_impl(x):
         return scipy.special.psi(x)
@@ -472,6 +485,7 @@ class Chi2SF(BinaryScalarOp):
     https://github.com/Theano/Theano_lgpl.git
 
     """
+    nfunc_spec = ('scipy.stats.chi2.sf', 2, 1)
 
     @staticmethod
     def st_impl(x, k):
@@ -489,6 +503,7 @@ class Jv(BinaryScalarOp):
     """
     Bessel function of the first kind of order v (real).
     """
+    nfunc_spec = ('scipy.special.jv', 2, 1)
 
     @staticmethod
     def st_impl(v, x):
@@ -513,6 +528,7 @@ class J1(UnaryScalarOp):
     """
     Bessel function of the first kind of order 1.
     """
+    nfunc_spec = ('scipy.special.j1', 1, 1)
 
     @staticmethod
     def st_impl(x):
@@ -544,6 +560,7 @@ class J0(UnaryScalarOp):
     """
     Bessel function of the first kind of order 0.
     """
+    nfunc_spec = ('scipy.special.j0', 1, 1)
 
     @staticmethod
     def st_impl(x):
@@ -575,6 +592,7 @@ class Iv(BinaryScalarOp):
     """
     Modified Bessel function of the first kind of order v (real).
     """
+    nfunc_spec = ('scipy.special.iv', 2, 1)
 
     @staticmethod
     def st_impl(v, x):
@@ -599,6 +617,7 @@ class I1(UnaryScalarOp):
     """
     Modified Bessel function of the first kind of order 1.
     """
+    nfunc_spec = ('scipy.special.i1', 1, 1)
 
     @staticmethod
     def st_impl(x):
@@ -622,6 +641,7 @@ class I0(UnaryScalarOp):
     """
     Modified Bessel function of the first kind of order 0.
     """
+    nfunc_spec = ('scipy.special.i0', 1, 1)
 
     @staticmethod
     def st_impl(x):

theano/tensor/basic.py

@@ -2342,42 +2342,42 @@ def arctanh(a):
     """hyperbolic arc tangent of a"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.erf', 1, 1)
+@_scal_elemwise
 def erf(a):
     """error function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.erfc', 1, 1)
+@_scal_elemwise
 def erfc(a):
     """complementary error function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.erfcx', 1, 1)
+@_scal_elemwise
 def erfcx(a):
     """scaled complementary error function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.erfinv', 1, 1)
+@_scal_elemwise
 def erfinv(a):
     """inverse error function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.erfcinv', 1, 1)
+@_scal_elemwise
 def erfcinv(a):
     """inverse complementary error function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.gamma', 1, 1)
+@_scal_elemwise
 def gamma(a):
     """gamma function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.gammaln', 1, 1)
+@_scal_elemwise
 def gammaln(a):
     """log gamma function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.psi', 1, 1)
+@_scal_elemwise
 def psi(a):
     """derivative of log gamma function"""
 
@@ -2387,37 +2387,37 @@ def tri_gamma(a):
     """second derivative of the log gamma function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.stats.chi2.sf', 2, 1)
+@_scal_elemwise
 def chi2sf(x, k):
     """chi squared survival function"""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.j0', 1, 1)
+@_scal_elemwise
 def j0(x):
     """Bessel function of the first kind of order 0."""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.j1', 1, 1)
+@_scal_elemwise
 def j1(x):
     """Bessel function of the first kind of order 1."""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.jv', 2, 1)
+@_scal_elemwise
 def jv(v, x):
     """Bessel function of the first kind of order v (real)."""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.i0', 1, 1)
+@_scal_elemwise
 def i0(x):
     """Modified Bessel function of the first kind of order 0."""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.i1', 1, 1)
+@_scal_elemwise
 def i1(x):
     """Modified Bessel function of the first kind of order 1."""
 
 
-@_scal_elemwise_with_nfunc('scipy.special.iv', 2, 1)
+@_scal_elemwise
 def iv(v, x):
     """Modified Bessel function of the first kind of order v (real)."""