Merge pull request #1179 from IntelPython/refactoring/dpctl-tensor-type-dispatch-namespace

Refactoring/dpctl tensor type dispatch namespace

Author: oleksandr-pavlyk

dpctl/tensor/libtensor/include/kernels/boolean_advanced_indexing.hpp

@@ -763,10 +763,6 @@ sycl::event masked_place_some_slices_strided_impl(
     return comp_ev;
 }
 
-static masked_place_all_slices_strided_impl_fn_ptr_t
-    masked_place_all_slices_strided_impl_dispatch_vector
-        [dpctl::tensor::detail::num_types];
-
 template <typename fnT, typename T> struct MaskPlaceAllSlicesStridedFactory
 {
     fnT get()
@@ -776,10 +772,6 @@ template <typename fnT, typename T> struct MaskPlaceAllSlicesStridedFactory
     }
 };
 
-static masked_place_some_slices_strided_impl_fn_ptr_t
-    masked_place_some_slices_strided_impl_dispatch_vector
-        [dpctl::tensor::detail::num_types];
-
 template <typename fnT, typename T> struct MaskPlaceSomeSlicesStridedFactory
 {
     fnT get()

dpctl/tensor/libtensor/include/utils/type_dispatch.hpp

@@ -33,7 +33,7 @@ namespace dpctl
 namespace tensor
 {
 
-namespace detail
+namespace type_dispatch
 {
 
 enum class typenum_t : int
@@ -164,7 +164,7 @@ struct usm_ndarray_types
 
     int typenum_to_lookup_id(int typenum) const
     {
-        using typenum_t = dpctl::tensor::detail::typenum_t;
+        using typenum_t = ::dpctl::tensor::type_dispatch::typenum_t;
         auto const &api = ::dpctl::detail::dpctl_capi::get();
 
         if (typenum == api.UAR_DOUBLE_) {
@@ -250,7 +250,7 @@ struct usm_ndarray_types
     }
 };
 
-} // namespace detail
+} // namespace type_dispatch
 
 } // namespace tensor
 } // namespace dpctl

dpctl/tensor/libtensor/source/boolean_advanced_indexing.cpp

@@ -93,27 +93,27 @@ void _split_iteration_space(const shT &shape_vec,
 
 // Computation of positions of masked elements
 
+namespace td_ns = dpctl::tensor::type_dispatch;
+
 using dpctl::tensor::kernels::indexing::mask_positions_contig_impl_fn_ptr_t;
 static mask_positions_contig_impl_fn_ptr_t
-    mask_positions_contig_dispatch_vector[dpctl::tensor::detail::num_types];
+    mask_positions_contig_dispatch_vector[td_ns::num_types];
 
 using dpctl::tensor::kernels::indexing::mask_positions_strided_impl_fn_ptr_t;
 static mask_positions_strided_impl_fn_ptr_t
-    mask_positions_strided_dispatch_vector[dpctl::tensor::detail::num_types];
+    mask_positions_strided_dispatch_vector[td_ns::num_types];
 
 void populate_mask_positions_dispatch_vectors(void)
 {
     using dpctl::tensor::kernels::indexing::MaskPositionsContigFactory;
-    dpctl::tensor::detail::DispatchVectorBuilder<
-        mask_positions_contig_impl_fn_ptr_t, MaskPositionsContigFactory,
-        dpctl::tensor::detail::num_types>
+    td_ns::DispatchVectorBuilder<mask_positions_contig_impl_fn_ptr_t,
+                                 MaskPositionsContigFactory, td_ns::num_types>
         dvb1;
     dvb1.populate_dispatch_vector(mask_positions_contig_dispatch_vector);
 
     using dpctl::tensor::kernels::indexing::MaskPositionsStridedFactory;
-    dpctl::tensor::detail::DispatchVectorBuilder<
-        mask_positions_strided_impl_fn_ptr_t, MaskPositionsStridedFactory,
-        dpctl::tensor::detail::num_types>
+    td_ns::DispatchVectorBuilder<mask_positions_strided_impl_fn_ptr_t,
+                                 MaskPositionsStridedFactory, td_ns::num_types>
         dvb2;
     dvb2.populate_dispatch_vector(mask_positions_strided_dispatch_vector);
 
@@ -158,14 +158,13 @@ size_t py_mask_positions(dpctl::tensor::usm_ndarray mask,
     const char *mask_data = mask.get_data();
     char *cumsum_data = cumsum.get_data();
 
-    auto const &array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto const &array_types = td_ns::usm_ndarray_types();
 
     int mask_typeid = array_types.typenum_to_lookup_id(mask_typenum);
     int cumsum_typeid = array_types.typenum_to_lookup_id(cumsum_typenum);
 
     // cumsum must be int64_t only
-    constexpr int int64_typeid =
-        static_cast<int>(dpctl::tensor::detail::typenum_t::INT64);
+    constexpr int int64_typeid = static_cast<int>(td_ns::typenum_t::INT64);
     if (cumsum_typeid != int64_typeid) {
         throw py::value_error(
             "Cumulative sum array must have int64 data-type.");
@@ -244,30 +243,28 @@ using dpctl::tensor::kernels::indexing::
     masked_extract_all_slices_strided_impl_fn_ptr_t;
 
 static masked_extract_all_slices_strided_impl_fn_ptr_t
-    masked_extract_all_slices_strided_impl_dispatch_vector
-        [dpctl::tensor::detail::num_types];
+    masked_extract_all_slices_strided_impl_dispatch_vector[td_ns::num_types];
 
 using dpctl::tensor::kernels::indexing::
     masked_extract_some_slices_strided_impl_fn_ptr_t;
 
 static masked_extract_some_slices_strided_impl_fn_ptr_t
-    masked_extract_some_slices_strided_impl_dispatch_vector
-        [dpctl::tensor::detail::num_types];
+    masked_extract_some_slices_strided_impl_dispatch_vector[td_ns::num_types];
 
 void populate_masked_extract_dispatch_vectors(void)
 {
     using dpctl::tensor::kernels::indexing::MaskExtractAllSlicesStridedFactory;
-    dpctl::tensor::detail::DispatchVectorBuilder<
+    td_ns::DispatchVectorBuilder<
         masked_extract_all_slices_strided_impl_fn_ptr_t,
-        MaskExtractAllSlicesStridedFactory, dpctl::tensor::detail::num_types>
+        MaskExtractAllSlicesStridedFactory, td_ns::num_types>
         dvb1;
     dvb1.populate_dispatch_vector(
         masked_extract_all_slices_strided_impl_dispatch_vector);
 
     using dpctl::tensor::kernels::indexing::MaskExtractSomeSlicesStridedFactory;
-    dpctl::tensor::detail::DispatchVectorBuilder<
+    td_ns::DispatchVectorBuilder<
         masked_extract_some_slices_strided_impl_fn_ptr_t,
-        MaskExtractSomeSlicesStridedFactory, dpctl::tensor::detail::num_types>
+        MaskExtractSomeSlicesStridedFactory, td_ns::num_types>
         dvb2;
     dvb2.populate_dispatch_vector(
         masked_extract_some_slices_strided_impl_dispatch_vector);
@@ -359,13 +356,12 @@ py_extract(dpctl::tensor::usm_ndarray src,
     int dst_typenum = dst.get_typenum();
     int cumsum_typenum = cumsum.get_typenum();
 
-    auto const &array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto const &array_types = td_ns::usm_ndarray_types();
     int src_typeid = array_types.typenum_to_lookup_id(src_typenum);
     int dst_typeid = array_types.typenum_to_lookup_id(dst_typenum);
     int cumsum_typeid = array_types.typenum_to_lookup_id(cumsum_typenum);
 
-    constexpr int int64_typeid =
-        static_cast<int>(dpctl::tensor::detail::typenum_t::INT64);
+    constexpr int int64_typeid = static_cast<int>(td_ns::typenum_t::INT64);
     if (cumsum_typeid != int64_typeid) {
         throw py::value_error(
             "Unexact data type of cumsum array, expecting 'int64'");
@@ -557,30 +553,28 @@ using dpctl::tensor::kernels::indexing::
     masked_place_all_slices_strided_impl_fn_ptr_t;
 
 static masked_place_all_slices_strided_impl_fn_ptr_t
-    masked_place_all_slices_strided_impl_dispatch_vector
-        [dpctl::tensor::detail::num_types];
+    masked_place_all_slices_strided_impl_dispatch_vector[td_ns::num_types];
 
 using dpctl::tensor::kernels::indexing::
     masked_place_some_slices_strided_impl_fn_ptr_t;
 
 static masked_place_some_slices_strided_impl_fn_ptr_t
-    masked_place_some_slices_strided_impl_dispatch_vector
-        [dpctl::tensor::detail::num_types];
+    masked_place_some_slices_strided_impl_dispatch_vector[td_ns::num_types];
 
 void populate_masked_place_dispatch_vectors(void)
 {
     using dpctl::tensor::kernels::indexing::MaskPlaceAllSlicesStridedFactory;
-    dpctl::tensor::detail::DispatchVectorBuilder<
-        masked_place_all_slices_strided_impl_fn_ptr_t,
-        MaskPlaceAllSlicesStridedFactory, dpctl::tensor::detail::num_types>
+    td_ns::DispatchVectorBuilder<masked_place_all_slices_strided_impl_fn_ptr_t,
+                                 MaskPlaceAllSlicesStridedFactory,
+                                 td_ns::num_types>
         dvb1;
     dvb1.populate_dispatch_vector(
         masked_place_all_slices_strided_impl_dispatch_vector);
 
     using dpctl::tensor::kernels::indexing::MaskPlaceSomeSlicesStridedFactory;
-    dpctl::tensor::detail::DispatchVectorBuilder<
-        masked_place_some_slices_strided_impl_fn_ptr_t,
-        MaskPlaceSomeSlicesStridedFactory, dpctl::tensor::detail::num_types>
+    td_ns::DispatchVectorBuilder<masked_place_some_slices_strided_impl_fn_ptr_t,
+                                 MaskPlaceSomeSlicesStridedFactory,
+                                 td_ns::num_types>
         dvb2;
     dvb2.populate_dispatch_vector(
         masked_place_some_slices_strided_impl_dispatch_vector);
@@ -673,13 +667,12 @@ py_place(dpctl::tensor::usm_ndarray dst,
     int rhs_typenum = rhs.get_typenum();
     int cumsum_typenum = cumsum.get_typenum();
 
-    auto const &array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto const &array_types = td_ns::usm_ndarray_types();
     int dst_typeid = array_types.typenum_to_lookup_id(dst_typenum);
     int rhs_typeid = array_types.typenum_to_lookup_id(rhs_typenum);
     int cumsum_typeid = array_types.typenum_to_lookup_id(cumsum_typenum);
 
-    constexpr int int64_typeid =
-        static_cast<int>(dpctl::tensor::detail::typenum_t::INT64);
+    constexpr int int64_typeid = static_cast<int>(td_ns::typenum_t::INT64);
     if (cumsum_typeid != int64_typeid) {
         throw py::value_error(
             "Unexact data type of cumsum array, expecting 'int64'");
@@ -913,15 +906,14 @@ std::pair<sycl::event, sycl::event> py_nonzero(
     py::ssize_t nz_elems = indexes_shape[1];
 
     int indexes_typenum = indexes.get_typenum();
-    auto const &array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto const &array_types = td_ns::usm_ndarray_types();
     int indexes_typeid = array_types.typenum_to_lookup_id(indexes_typenum);
 
     int cumsum_typenum = cumsum.get_typenum();
     int cumsum_typeid = array_types.typenum_to_lookup_id(cumsum_typenum);
 
     // cumsum must be int64_t only
-    constexpr int int64_typeid =
-        static_cast<int>(dpctl::tensor::detail::typenum_t::INT64);
+    constexpr int int64_typeid = static_cast<int>(td_ns::typenum_t::INT64);
     if (cumsum_typeid != int64_typeid || indexes_typeid != int64_typeid) {
         throw py::value_error(
             "Cumulative sum array and index array must have int64 data-type");

dpctl/tensor/libtensor/source/copy_and_cast_usm_to_usm.cpp

@@ -49,18 +49,18 @@ namespace tensor
 namespace py_internal
 {
 
-namespace _ns = dpctl::tensor::detail;
+namespace td_ns = dpctl::tensor::type_dispatch;
 
 using dpctl::tensor::kernels::copy_and_cast::copy_and_cast_1d_fn_ptr_t;
 using dpctl::tensor::kernels::copy_and_cast::copy_and_cast_contig_fn_ptr_t;
 using dpctl::tensor::kernels::copy_and_cast::copy_and_cast_generic_fn_ptr_t;
 
 static copy_and_cast_generic_fn_ptr_t
-    copy_and_cast_generic_dispatch_table[_ns::num_types][_ns::num_types];
+    copy_and_cast_generic_dispatch_table[td_ns::num_types][td_ns::num_types];
 static copy_and_cast_1d_fn_ptr_t
-    copy_and_cast_1d_dispatch_table[_ns::num_types][_ns::num_types];
+    copy_and_cast_1d_dispatch_table[td_ns::num_types][td_ns::num_types];
 static copy_and_cast_contig_fn_ptr_t
-    copy_and_cast_contig_dispatch_table[_ns::num_types][_ns::num_types];
+    copy_and_cast_contig_dispatch_table[td_ns::num_types][td_ns::num_types];
 
 namespace py = pybind11;
 
@@ -121,7 +121,7 @@ copy_usm_ndarray_into_usm_ndarray(dpctl::tensor::usm_ndarray src,
     int src_typenum = src.get_typenum();
     int dst_typenum = dst.get_typenum();
 
-    auto array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto array_types = td_ns::usm_ndarray_types();
     int src_type_id = array_types.typenum_to_lookup_id(src_typenum);
     int dst_type_id = array_types.typenum_to_lookup_id(dst_typenum);
 
@@ -277,7 +277,7 @@ copy_usm_ndarray_into_usm_ndarray(dpctl::tensor::usm_ndarray src,
 
 void init_copy_and_cast_usm_to_usm_dispatch_tables(void)
 {
-    using namespace dpctl::tensor::detail;
+    using namespace td_ns;
 
     using dpctl::tensor::kernels::copy_and_cast::CopyAndCastContigFactory;
     DispatchTableBuilder<copy_and_cast_contig_fn_ptr_t,

dpctl/tensor/libtensor/source/copy_for_reshape.cpp

@@ -39,14 +39,14 @@ namespace tensor
 namespace py_internal
 {
 
-namespace _ns = dpctl::tensor::detail;
+namespace td_ns = dpctl::tensor::type_dispatch;
 
 using dpctl::tensor::kernels::copy_and_cast::copy_for_reshape_fn_ptr_t;
 using dpctl::utils::keep_args_alive;
 
 // define static vector
 static copy_for_reshape_fn_ptr_t
-    copy_for_reshape_generic_dispatch_vector[_ns::num_types];
+    copy_for_reshape_generic_dispatch_vector[td_ns::num_types];
 
 /*
  * Copies src into dst (same data type) of different shapes by using flat
@@ -121,7 +121,7 @@ copy_usm_ndarray_for_reshape(dpctl::tensor::usm_ndarray src,
     int src_nd = src.get_ndim();
     int dst_nd = dst.get_ndim();
 
-    auto array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto array_types = td_ns::usm_ndarray_types();
     int type_id = array_types.typenum_to_lookup_id(src_typenum);
 
     auto fn = copy_for_reshape_generic_dispatch_vector[type_id];
@@ -172,7 +172,7 @@ copy_usm_ndarray_for_reshape(dpctl::tensor::usm_ndarray src,
 
 void init_copy_for_reshape_dispatch_vectors(void)
 {
-    using namespace dpctl::tensor::detail;
+    using namespace td_ns;
     using dpctl::tensor::kernels::copy_and_cast::CopyForReshapeGenericFactory;
 
     DispatchVectorBuilder<copy_for_reshape_fn_ptr_t,

dpctl/tensor/libtensor/source/copy_numpy_ndarray_into_usm_ndarray.cpp

@@ -36,7 +36,7 @@
 #include "simplify_iteration_space.hpp"
 
 namespace py = pybind11;
-namespace _ns = dpctl::tensor::detail;
+namespace td_ns = dpctl::tensor::type_dispatch;
 
 namespace dpctl
 {
@@ -49,8 +49,8 @@ using dpctl::tensor::kernels::copy_and_cast::
     copy_and_cast_from_host_blocking_fn_ptr_t;
 
 static copy_and_cast_from_host_blocking_fn_ptr_t
-    copy_and_cast_from_host_blocking_dispatch_table[_ns::num_types]
-                                                   [_ns::num_types];
+    copy_and_cast_from_host_blocking_dispatch_table[td_ns::num_types]
+                                                   [td_ns::num_types];
 
 void copy_numpy_ndarray_into_usm_ndarray(
     py::array npy_src,
@@ -111,7 +111,7 @@ void copy_numpy_ndarray_into_usm_ndarray(
         py::detail::array_descriptor_proxy(npy_src.dtype().ptr())->type_num;
     int dst_typenum = dst.get_typenum();
 
-    auto array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto array_types = td_ns::usm_ndarray_types();
     int src_type_id = array_types.typenum_to_lookup_id(src_typenum);
     int dst_type_id = array_types.typenum_to_lookup_id(dst_typenum);
 
@@ -239,11 +239,11 @@ void copy_numpy_ndarray_into_usm_ndarray(
 
 void init_copy_numpy_ndarray_into_usm_ndarray_dispatch_tables(void)
 {
-    using namespace dpctl::tensor::detail;
+    using namespace td_ns;
     using dpctl::tensor::kernels::copy_and_cast::CopyAndCastFromHostFactory;
 
     DispatchTableBuilder<copy_and_cast_from_host_blocking_fn_ptr_t,
-                         CopyAndCastFromHostFactory, _ns::num_types>
+                         CopyAndCastFromHostFactory, num_types>
         dtb_copy_from_numpy;
 
     dtb_copy_from_numpy.populate_dispatch_table(

dpctl/tensor/libtensor/source/eye_ctor.cpp

@@ -34,7 +34,7 @@
 #include "utils/type_dispatch.hpp"
 
 namespace py = pybind11;
-namespace _ns = dpctl::tensor::detail;
+namespace td_ns = dpctl::tensor::type_dispatch;
 
 namespace dpctl
 {
@@ -46,7 +46,7 @@ namespace py_internal
 using dpctl::utils::keep_args_alive;
 
 using dpctl::tensor::kernels::constructors::eye_fn_ptr_t;
-static eye_fn_ptr_t eye_dispatch_vector[_ns::num_types];
+static eye_fn_ptr_t eye_dispatch_vector[td_ns::num_types];
 
 std::pair<sycl::event, sycl::event>
 usm_ndarray_eye(py::ssize_t k,
@@ -66,7 +66,7 @@ usm_ndarray_eye(py::ssize_t k,
                               "allocation queue");
     }
 
-    auto array_types = dpctl::tensor::detail::usm_ndarray_types();
+    auto array_types = td_ns::usm_ndarray_types();
     int dst_typenum = dst.get_typenum();
     int dst_typeid = array_types.typenum_to_lookup_id(dst_typenum);
 
@@ -118,7 +118,7 @@ usm_ndarray_eye(py::ssize_t k,
 
 void init_eye_ctor_dispatch_vectors(void)
 {
-    using namespace dpctl::tensor::detail;
+    using namespace td_ns;
     using dpctl::tensor::kernels::constructors::EyeFactory;
 
     DispatchVectorBuilder<eye_fn_ptr_t, EyeFactory, num_types> dvb;