Add the optimization tag cxx_only.

doc/extending/optimization.txt

@@ -19,7 +19,11 @@ In this section we will define a couple optimizations on doubles.
    Later, the rest is more useful for when that decorator syntax type thing
    doesn't work. (There are optimizations that don't fit that model).
 
+.. note::
 
+   There is the optimization tag `cxx_only` that tell this
+   optimization will insert Op that only have c code. So we should not
+   run them when we don't have a c++ compiler.
 
 Global and local optimizations
 ==============================

theano/compile/mode.py

@@ -83,10 +83,13 @@ def register_linker(name, linker):
 # If a string is passed as the optimizer argument in the constructor
 # for Mode, it will be used as the key to retrieve the real optimizer
 # in this dictionary
-OPT_FAST_RUN = gof.Query(include=['fast_run'])
+exclude=[]
+if not theano.config.cxx:
+    exclude = ['cxx_only']
+OPT_FAST_RUN = gof.Query(include=['fast_run'], exclude=exclude)
 OPT_FAST_RUN_STABLE = OPT_FAST_RUN.requiring('stable')
-OPT_FAST_COMPILE = gof.Query(include=['fast_compile'])
-OPT_STABILIZE = gof.Query(include=['fast_run'])
+OPT_FAST_COMPILE = gof.Query(include=['fast_compile'], exclude=exclude)
+OPT_STABILIZE = gof.Query(include=['fast_run'], exclude=exclude)
 OPT_STABILIZE.position_cutoff = 1.5000001
 OPT_FAST_RUN.name = 'OPT_FAST_RUN'
 OPT_FAST_RUN_STABLE.name = 'OPT_FAST_RUN_STABLE'

theano/sparse/opt.py

@@ -686,7 +686,7 @@ register_specialize(local_usmm, name="local_usmm")
 def local_usmm_csc_dense_inplace(node):
     if node.op == usmm_csc_dense:
         return [usmm_csc_dense_inplace(*node.inputs)]
-register_specialize(local_usmm_csc_dense_inplace, 'inplace')
+register_specialize(local_usmm_csc_dense_inplace, 'cxx_only', 'inplace')
 
 
 # This is tested in tests/test_basic.py:UsmmTests
@@ -714,7 +714,7 @@ 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)
+sparse.register_specialize(local_usmm_csx, 'cxx_only')
 
 
 class CSMGradC(gof.Op):
@@ -850,7 +850,7 @@ def local_csm_grad_c(node):
     if node.op == csm_grad(None):
         return [csm_grad_c(*node.inputs)]
     return False
-register_specialize(local_csm_grad_c)
+register_specialize(local_csm_grad_c, 'cxx_only')
 
 
 class MulSDCSC(gof.Op):
@@ -1117,7 +1117,7 @@ def local_mul_s_d(node):
                     sparse.csm_shape(svar))]
 
     return False
-sparse.register_specialize(local_mul_s_d)
+sparse.register_specialize(local_mul_s_d, 'cxx_only')
 
 
 class MulSVCSR(gof.Op):
@@ -1259,7 +1259,7 @@ 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)
+sparse.register_specialize(local_mul_s_v, 'cxx_only')
 
 
 class StructuredAddSVCSR(gof.Op):
@@ -1416,7 +1416,7 @@ 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)
+sparse.register_specialize(local_structured_add_s_v, 'cxx_only')
 
 
 class SamplingDotCSR(gof.Op):
@@ -1656,4 +1656,5 @@ 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')