Format

src/targets/gpu/jit/ck_gemm.cpp

@@ -113,11 +113,12 @@ static std::vector<tuning_entry> read_tuning(const std::string& s)
 static std::size_t get_tuning_for(const std::vector<shape>& inputs)
 {
     static auto tuning = read_tuning(string_value_of(MIGRAPHX_CK_TUNING{}, ""));
-    if (tuning.empty())
+    if(tuning.empty())
         std::cout << "*********** Warning: No CK tuning!" << std::endl;
     auto it = std::find_if(
         tuning.begin(), tuning.end(), [&](const auto& p) { return p.first == inputs; });
-    if(it == tuning.end()) {
+    if(it == tuning.end())
+    {
         std::cout << "*********** Warning: CK tuning missing for config!" << std::endl;
         return 4;
     }

src/targets/gpu/kernels/include/migraphx/kernels/ck.hpp

@@ -13,23 +13,23 @@
 namespace migraphx {
 
 namespace detail {
-template<class T>
+template <class T>
 struct to_ck_type_impl
 {
     using type = T;
 };
-template<>
+template <>
 struct to_ck_type_impl<migraphx::half>
 {
     using type = ck::half_t;
 };
 
-template<class Shape>
+template <class Shape>
 constexpr bool is_row_major()
 {
     constexpr auto strides = Shape{}.strides;
     MIGRAPHX_ASSERT(strides.size() >= 2);
-    if (strides.back() == 1)
+    if(strides.back() == 1)
     {
         MIGRAPHX_ASSERT(not Shape{}.is_trasnposed());
         return true;
@@ -41,18 +41,21 @@ constexpr bool is_row_major()
 
 } // namespace detail
 
-template<class T>
+template <class T>
 using to_ck_type = typename detail::to_ck_type_impl<T>::type;
 
-template<class Shape>
-using to_ck_gemm_layout = conditional_t<detail::is_row_major<get_shape_c<Shape>>(), ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::ColumnMajor>;
+template <class Shape>
+using to_ck_gemm_layout = conditional_t<detail::is_row_major<get_shape_c<Shape>>(),
+                                        ck::tensor_layout::gemm::RowMajor,
+                                        ck::tensor_layout::gemm::ColumnMajor>;
 
-template<class Tensor>
+template <class Tensor>
 constexpr auto to_ck_tensor()
 {
     constexpr auto s = get_shape_c<Tensor>{};
     return sequence(s.lens.size(), [](auto... is) {
-        return ck::make_naive_tensor_descriptor(ck::make_tuple(s.lens[is]...), ck::make_tuple(s.strides[is]...));
+        return ck::make_naive_tensor_descriptor(ck::make_tuple(s.lens[is]...),
+                                                ck::make_tuple(s.strides[is]...));
     });
 }
 

src/targets/gpu/kernels/include/migraphx/kernels/ck_gemm.hpp

@@ -33,7 +33,6 @@
 
 namespace migraphx {
 
-
 template <class G, class A, class B, class C>
 __device__ void ck_gemm(const A& a, const B& b, const C& c)
 {
@@ -48,11 +47,11 @@ __device__ void ck_gemm(const A& a, const B& b, const C& c)
     constexpr auto c_grid_desc_mblock_mperblock_nblock_nperblock =
         GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(c_desc);
 
-    constexpr auto shared_block_size =
-            GridwiseGemm::GetSharedMemoryNumberOfByte();
+    constexpr auto shared_block_size = GridwiseGemm::GetSharedMemoryNumberOfByte();
     __shared__ char p_shared_block[shared_block_size];
 
-    constexpr bool HasMainKBlockLoop = GridwiseGemm::CalculateHasMainKBlockLoop(A{}.get_shape().elements());
+    constexpr bool HasMainKBlockLoop =
+        GridwiseGemm::CalculateHasMainKBlockLoop(A{}.get_shape().elements());
     GridwiseGemm::template Run<HasMainKBlockLoop>(a.data(),
                                                   b.data(),
                                                   c.data(),
@@ -64,7 +63,6 @@ __device__ void ck_gemm(const A& a, const B& b, const C& c)
                                                   b_desc,
                                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                   block_2_ctile_map);
-
 }
 
 } // namespace migraphx

src/targets/gpu/kernels/include/migraphx/kernels/ck_gemm_includes.hpp

@@ -152,9 +152,8 @@ template <typename ALayout,
           ck::LoopScheduler LoopSched = ck::make_default_loop_scheduler()>
 struct CKDeviceGemm
 {
-    template<class AGridDesc_AK0_M_AK1, class BGridDesc_BK0_N_BK1, class CGridDesc_M_N>
-    using Make = 
-        ck::GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
+    template <class AGridDesc_AK0_M_AK1, class BGridDesc_BK0_N_BK1, class CGridDesc_M_N>
+    using Make = ck::GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
         ADataType, // TODO: distinguish A/B datatype
         GemmAccDataType,
         CShuffleDataType,
@@ -203,7 +202,7 @@ struct CKDeviceGemm
     static constexpr auto BOp() { return BElementwiseOperation{}; }
     static constexpr auto COp() { return CElementwiseOperation{}; }
     // return block_id to C matrix tile idx (m0, n0) mapping
-    template<class CGridDesc_M_N>
+    template <class CGridDesc_M_N>
     __host__ __device__ static constexpr auto
     MakeDefaultBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n)
     {