small code cleanup.

theano/sparse/basic.py

@@ -25,12 +25,6 @@ from theano.sparse.type import SparseType, _is_sparse
 sparse_formats = ['csc', 'csr']
 
 
-# TODO: move this decorator to the compile submodule
-def register_specialize(lopt, *tags, **kwargs):
-    compile.optdb['specialize'].register((kwargs and kwargs.pop('name')) or
-                                         lopt.__name__, lopt, 'fast_run',
-                                         *tags)
-
 """ Types of sparse matrices to use for testing """
 _mtypes = [scipy.sparse.csc_matrix, scipy.sparse.csr_matrix]
 #_mtypes = [sparse.csc_matrix, sparse.csr_matrix, sparse.dok_matrix,

theano/sparse/opt.py

@@ -6,8 +6,8 @@ import scipy
 import theano
 from theano import gof, scalar, tensor
 from theano.tensor import blas
+from theano.tensor.opt import register_specialize
 from theano.sparse import (CSC, CSR, csm_properties,
-                           register_specialize,
                            csm_grad, usmm, csm_indices, csm_indptr,
                            csm_data)
 from theano.sparse import basic as sparse
@@ -29,7 +29,7 @@ def local_csm_properties_csm(node):
             return ret_var
 
     return False
-sparse.register_specialize(local_csm_properties_csm)
+register_specialize(local_csm_properties_csm)
 
 
 # This is tested in tests/test_basic.py:test_remove0
@@ -861,7 +861,7 @@ def local_usmm_csx(node):
                 return [usmm_csc_dense(alpha, x_val, x_ind, x_ptr,
                                        x_nsparse, y, z)]
     return False
-sparse.register_specialize(local_usmm_csx, 'cxx_only')
+register_specialize(local_usmm_csx, 'cxx_only')
 
 
 class CSMGradC(gof.Op):
@@ -1272,7 +1272,7 @@ def local_mul_s_d(node):
                     sparse.csm_shape(svar))]
 
     return False
-sparse.register_specialize(local_mul_s_d, 'cxx_only')
+register_specialize(local_mul_s_d, 'cxx_only')
 
 
 class MulSVCSR(gof.Op):
@@ -1414,7 +1414,7 @@ def local_mul_s_v(node):
         return [CSx(c_data, s_ind, s_ptr, s_shape)]
 
     return False
-sparse.register_specialize(local_mul_s_v, 'cxx_only')
+register_specialize(local_mul_s_v, 'cxx_only')
 
 
 class StructuredAddSVCSR(gof.Op):
@@ -1573,7 +1573,7 @@ def local_structured_add_s_v(node):
         return [CSx(c_data, s_ind, s_ptr, s_shape)]
 
     return False
-sparse.register_specialize(local_structured_add_s_v, 'cxx_only')
+register_specialize(local_structured_add_s_v, 'cxx_only')
 
 
 class SamplingDotCSR(gof.Op):
@@ -1822,6 +1822,6 @@ def local_sampling_dot_csr(node):
             return [sparse.CSR(z_data, z_ind, z_ptr, p_shape)]
     return False
 
-sparse.register_specialize(local_sampling_dot_csr,
-                           'cxx_only',
-                           name='local_sampling_dot_csr')
+register_specialize(local_sampling_dot_csr,
+                    'cxx_only',
+                    name='local_sampling_dot_csr')