Move the access to strides outside of the loop.

theano/tensor/nnet/nnet.py

@@ -396,6 +396,8 @@ class Softmax(gof.Op):
         #TODO: use this to accept float32 and int32: node.inputs[0].type.dtype_specs()[1]
         init_decl = """
         npy_intp* Nx = PyArray_DIMS(%(x)s);
+        npy_intp Sx1 = 0;
+        npy_intp Ssm1 = 0;
 
         if (PyArray_NDIM(%(x)s) != 2)
         {
@@ -422,6 +424,8 @@ class Softmax(gof.Op):
                 %(fail)s
             }
         }
+        Sx1 = PyArray_STRIDES(%(x)s)[1]/sizeof(dtype_%(x)s);
+        Ssm1 = PyArray_STRIDES(%(sm)s)[1]/sizeof(dtype_%(sm)s);
         """
 
         begin_row_loop = """
@@ -436,16 +440,13 @@ class Softmax(gof.Op):
         """
 
         inside_row_loop = """
-            npy_intp Sx = PyArray_STRIDES(%(x)s)[1]/sizeof(dtype_%(x)s);
-            npy_intp Ssm = PyArray_STRIDES(%(sm)s)[1]/sizeof(dtype_%(sm)s);
-
             size_t row_max_j=0;
             dtype_%(sm)s row_max = x_i[0];
             //std::cout << "0 " << row_max << "\\n";
             // Get the maximum value of the row
             for (j = 1; j < Nx[1]; ++j)
             {
-                dtype_%(sm)s row_ij = x_i[j * Sx] ;
+                dtype_%(sm)s row_ij = x_i[j * Sx1] ;
                 //std::cout << "1 " << row_ij << "\\n";
                 row_max_j = (row_ij > row_max) ? j : row_max_j;
                 row_max   = (row_ij > row_max) ? row_ij : row_max;
@@ -453,19 +454,19 @@ class Softmax(gof.Op):
 
             for (j = 0; j < Nx[1]; ++j)
             {
-                dtype_%(sm)s row_ij = x_i[j * Sx] ;
+                dtype_%(sm)s row_ij = x_i[j * Sx1] ;
                 //std::cout << "2 " << j << " " << row_ij << " " << row_max << "\\n";
                 dtype_%(sm)s sm_ij = exp(row_ij - row_max);
                 //std::cout << "3 " << j << " " << sm_ij << "\\n";
                 sum += sm_ij;
-                sm_i[j * Ssm] = sm_ij;
+                sm_i[j * Ssm1] = sm_ij;
             }
 
             //cblas_dscal(x.N, 1.0 / sum, &mat_at(s,i,0), s.n);
             double sum_inv = 1.0 / sum;
             for (j = 0; j < Nx[1]; ++j)
             {
-                sm_i[j * Ssm] *= sum_inv;
+                sm_i[j * Ssm1] *= sum_inv;
             }
 
         """