add reviewers sugestions

example/15_grouped_gemm/grouped_gemm_multiple_d_splitk_xdl_fp16.cpp

@@ -18,7 +18,6 @@
 
 #include "ck/library/utility/check_err.hpp"
 #include "ck/library/utility/device_memory.hpp"
-#include "ck/library/utility/fill.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
 #include "ck/library/utility/literals.hpp"

include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_splitk_xdl_cshuffle_two_stage.hpp

@@ -20,7 +20,6 @@
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/device_grouped_gemm_multiple_d_splitk.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_elementwise_dynamic_vector_dims.hpp"
-
 #include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_splitk_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include <ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp>
@@ -237,12 +236,13 @@ struct DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage
 
     using Block2ETileMapKSplit =
         BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc_M_N>;
+    using Block2TileMap = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
     using GridwiseElementwise =
         GridwiseElementwise<CDGridDesc_M_N,
                             ck::Tuple<EGridDesc_M_N>,
                             CDDataTypes,
                             ck::Tuple<EDataType*>,
-                            Block2ETileMapKSplit,
+                            Block2TileMap,
                             CDEElementwiseOperation,
                             BlockSize,
                             MPerBlock,
@@ -737,7 +737,7 @@ struct DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage
                                                                ck::Tuple<EGridDesc_M_N>,
                                                                CDDataTypes,
                                                                ck::Tuple<EDataType*>,
-                                                               Block2ETileMapKSplit,
+                                                               Block2TileMap,
                                                                CDEElementwiseOperation>;
             return LaunchKernel(gemm_kernel,
                                 elementwise_kernel,
@@ -791,8 +791,8 @@ struct DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage
                     concat_tuple(make_tuple(arg.gemm_kernel_args_[i].karg_.p_c_grid),
                                  arg.ds_grid_pointer_[i]),
                     type_convert<EDataType*>(arg.e_ptrs_[i]),
-                    Block2ETileMapKSplit{
-                        arg.elementwise_c_grid_descs_m_n_[i], B2E_M01, arg.K_BATCH},
+                    Block2TileMap{arg.elementwise_c_grid_descs_m_n_[i].GetLength(I0),
+                                  arg.elementwise_c_grid_descs_m_n_[i].GetLength(I1)},
                     arg.cde_element_op_);
             }
             return time;

library/include/ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp

@@ -206,6 +206,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
     {
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
 
+#if defined(CK_ENABLE_FP16)
         if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
                      is_same_v<EDataType, half_t>)
         {
@@ -238,6 +239,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
             }
         }
+#endif
+#if defined(CK_ENABLE_FP16) && defined(CK_ENABLE_FP8)
         else if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
                           is_same_v<EDataType, half_t>)
         {
@@ -256,6 +259,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 add_device_grouped_gemm_xdl_splitk_f8_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
             }
         }
+#endif
+#if defined(CK_ENABLE_BF16) && defined(CK_ENABLE_INT8)
         else if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, int8_t> &&
                           is_same_v<EDataType, bhalf_t>)
         {
@@ -266,6 +271,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                     op_ptrs);
             }
         }
+#endif
         return op_ptrs;
     }
 };

profiler/src/profile_grouped_gemm_two_stage.cpp

@@ -99,7 +99,6 @@ int profile_grouped_gemm_two_stage(int argc, char* argv[])
         n_iter   = std::stoi(argv[17]);
     }
 
-#ifdef CK_ENABLE_FP16
     if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
     {
         ck::profiler::profile_grouped_gemm_two_stage_impl<ck::half_t,
@@ -150,7 +149,6 @@ int profile_grouped_gemm_two_stage(int argc, char* argv[])
     {
         throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
     }
-#endif
     return 0;
 }