move kenel_batched_gemm to file gridwise_gemm.hpp; formatting

include/ck/tensor_operation/gpu/device/device_batched_gemm_xdl.hpp

@@ -16,102 +16,6 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 
-/*
- * \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
- *
- * \tparam ComputePtrOffsetOfBatch Class that computes the base pointer offsets of A, B, C matrix
- * given the batch. For example, ComputePtrOffsetOfStridedBatch() computes the offsets of evenly
- * strided batched, but we can easily extend to other layouts. The returned offset can be either \p
- * index_t or \p long_index_t. If it returns \p long_index_t, we are not subject to the 2GB
- * limitations.
- *
- * \tparam Block2CTileMap Block2CTileMap::CalculateBottomIndex() takes in id of a workgroup and
- * returns the 2D index of the tile that it computes. \see
- * GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3::Run().
- *
- * \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2
- * tiles from different matrices. Keep in mind that these 2 matrices can share the same grid
- * descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link
- * device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for \link
- * DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the computing of
- * pointer offset into \p ComputePtrOffsetOfStridedBatch.
- *
- * \note \p Block2CTileMap allows customized mapping between a workgroup and the C-tile it computes.
- * Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to
- * realize BatchedGemm and GroupedGemm (and the corresponding GEMM fusion).
- *
- */
-template <typename GridwiseGemm,
-          typename FloatAB,
-          typename FloatC,
-          typename AGridDesc_K0_M_K1,
-          typename BGridDesc_K0_N_K1,
-          typename CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          typename ComputePtrOffsetOfBatch,
-          typename Block2CTileMap,
-          bool HasMainKBlockLoop>
-__global__ void
-#if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
-#endif
-        kernel_batched_gemm_xdlops_v2r3(
-            const FloatAB* __restrict__ p_a_grid,
-            const FloatAB* __restrict__ p_b_grid,
-            FloatC* __restrict__ p_c_grid,
-            const index_t batch_count,
-            const AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1,
-            const BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1,
-            const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
-            const AElementwiseOperation a_element_op,
-            const BElementwiseOperation b_element_op,
-            const CElementwiseOperation c_element_op,
-            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
-            const Block2CTileMap block_2_ctile_map)
-{
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
-    const index_t num_blocks_per_batch =
-        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
-    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
-
-    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
-    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
-    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
-
-    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
-
-    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
-                                                  p_b_grid + b_batch_offset,
-                                                  p_c_grid + c_batch_offset,
-                                                  p_shared,
-                                                  a_grid_desc_k0_m_k1,
-                                                  b_grid_desc_k0_n_k1,
-                                                  c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
-                                                  a_element_op,
-                                                  b_element_op,
-                                                  c_element_op,
-                                                  block_2_ctile_map);
-#else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = batch_count;
-    ignore = a_grid_desc_k0_m_k1;
-    ignore = b_grid_desc_k0_n_k1;
-    ignore = c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = compute_ptr_offset_of_batch;
-    ignore = block_2_ctile_map;
-#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
-}
-
 template <typename ADataType,
           typename BDataType,
           typename CDataType,

include/ck/tensor_operation/gpu/device/device_gemm_xdl_splitk.hpp

@@ -17,31 +17,6 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 
-/*
- * \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
- *
- * \tparam ComputePtrOffsetOfBatch Class that computes the base pointer offsets of A, B, C matrix
- * given the batch. For example, ComputePtrOffsetOfStridedBatch() computes the offsets of evenly
- * strided batched, but we can easily extend to other layouts. The returned offset can be either \p
- * index_t or \p long_index_t. If it returns \p long_index_t, we are not subject to the 2GB
- * limitations.
- *
- * \tparam Block2CTileMap Block2CTileMap::CalculateBottomIndex() takes in id of a workgroup and
- * returns the 2D index of the tile that it computes. \see
- * GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3::Run().
- *
- * \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2
- * tiles from different matrices. Keep in mind that these 2 matrices can share the same grid
- * descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link
- * device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for \link
- * DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the computing of
- * pointer offset into \p ComputePtrOffsetOfStridedBatch.
- *
- * \note \p Block2CTileMap allows customized mapping between a workgroup and the C-tile it computes.
- * Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to
- * realize BatchedGemm and GroupedGemm (and the corresponding GEMM fusion).
- *
- */
 template <typename GridwiseGemm,
           typename FloatAB,
           typename FloatC,
@@ -349,49 +324,6 @@ struct DeviceGemmXdlSplitK
         // index_t BatchStrideC_; // always zero
     };
 
-    // GridwiseGemm
-    // using GridwiseGemm =
-    //     GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
-    //                                             ADataType, // TODO: distinguish A/B datatype
-    //                                             AccDataType,
-    //                                             CDataType,
-    //                                             InMemoryDataOperationEnum::Set,
-    //                                             AGridDesc_K0_M_K1,
-    //                                             BGridDesc_K0_N_K1,
-    //                                             CGridDesc_M_N,
-    //                                             AElementwiseOperation,
-    //                                             BElementwiseOperation,
-    //                                             CElementwiseOperation,
-    //                                             MPerBlock,
-    //                                             NPerBlock,
-    //                                             K0PerBlock,
-    //                                             MPerXDL,
-    //                                             NPerXDL,
-    //                                             K1,
-    //                                             MXdlPerWave,
-    //                                             NXdlPerWave,
-    //                                             ABlockTransferThreadClusterLengths_K0_M_K1,
-    //                                             ABlockTransferThreadClusterArrangeOrder,
-    //                                             ABlockTransferSrcAccessOrder,
-    //                                             ABlockTransferSrcVectorDim,
-    //                                             ABlockTransferSrcScalarPerVector,
-    //                                             ABlockTransferDstScalarPerVector_K1,
-    //                                             false, //
-    //                                             AThreadTransferSrcResetCoordinateAfterRun,
-    //                                             ABlockLdsAddExtraM,
-    //                                             BBlockTransferThreadClusterLengths_K0_N_K1,
-    //                                             BBlockTransferThreadClusterArrangeOrder,
-    //                                             BBlockTransferSrcAccessOrder,
-    //                                             BBlockTransferSrcVectorDim,
-    //                                             BBlockTransferSrcScalarPerVector,
-    //                                             BBlockTransferDstScalarPerVector_K1,
-    //                                             false, //
-    //                                             BThreadTransferSrcResetCoordinateAfterRun,
-    //                                             BBlockLdsAddExtraN,
-    //                                             Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
-    //                                             CThreadTransferSrcDstVectorDim,
-    //                                             CThreadTransferDstScalarPerVector>;
-
     using GridwiseGemm =
         GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
                                                 ADataType, // TODO: distinguish A/B datatype
@@ -765,4 +697,3 @@ struct DeviceGemmXdlSplitK
 } // namespace tensor_operation
 } // namespace ck
 #endif
-

include/ck/tensor_operation/gpu/device/device_gemm_xdl_splitk_c_shuffle.hpp

@@ -18,103 +18,6 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 
-/*
- * \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
- *
- * \tparam ComputePtrOffsetOfBatch Class that computes the base pointer offsets of A, B, C matrix
- * given the batch. For example, ComputePtrOffsetOfStridedBatch() computes the offsets of evenly
- * strided batched, but we can easily extend to other layouts. The returned offset can be either \p
- * index_t or \p long_index_t. If it returns \p long_index_t, we are not subject to the 2GB
- * limitations.
- *
- * \tparam Block2CTileMap Block2CTileMap::CalculateBottomIndex() takes in id of a workgroup and
- * returns the 2D index of the tile that it computes. \see
- * GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3::Run().
- *
- * \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2
- * tiles from different matrices. Keep in mind that these 2 matrices can share the same grid
- * descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link
- * device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for \link
- * DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the computing of
- * pointer offset into \p ComputePtrOffsetOfStridedBatch.
- *
- * \note \p Block2CTileMap allows customized mapping between a workgroup and the C-tile it computes.
- * Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to
- * realize BatchedGemm and GroupedGemm (and the corresponding GEMM fusion).
- *
- */
-template <typename GridwiseGemm,
-          typename FloatAB,
-          typename FloatC,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          typename AGridDesc_AK0_M_AK1,
-          typename BGridDesc_BK0_N_BK1,
-          typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename ComputePtrOffsetOfBatch,
-          typename Block2CTileMap,
-          bool HasMainKBlockLoop>
-__global__ void
-#if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
-#endif
-        kernel_batched_gemm_xdl_cshuffle_v1(
-            const FloatAB* __restrict__ p_a_grid,
-            const FloatAB* __restrict__ p_b_grid,
-            FloatC* __restrict__ p_c_grid,
-            const index_t batch_count,
-            const AElementwiseOperation a_element_op,
-            const BElementwiseOperation b_element_op,
-            const CElementwiseOperation c_element_op,
-            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
-            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
-            const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
-                c_grid_desc_mblock_mperblock_nblock_nperblock,
-            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
-            const Block2CTileMap block_2_ctile_map)
-{
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
-    const index_t num_blocks_per_batch =
-        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
-    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
-
-    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
-    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
-    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
-
-    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
-
-    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
-                                                  p_b_grid + b_batch_offset,
-                                                  p_c_grid + c_batch_offset,
-                                                  p_shared,
-                                                  a_element_op,
-                                                  b_element_op,
-                                                  c_element_op,
-                                                  a_grid_desc_ak0_m_ak1,
-                                                  b_grid_desc_bk0_n_bk1,
-                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                  block_2_ctile_map);
-#else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = batch_count;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = a_grid_desc_ak0_m_ak1;
-    ignore = b_grid_desc_bk0_n_bk1;
-    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = compute_ptr_offset_of_batch;
-    ignore = block_2_ctile_map;
-#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
-}
-
 template <typename ALayout,
           typename BLayout,
           typename CLayout,
@@ -586,9 +489,11 @@ struct DeviceGemmXdlSplitKCShuffle
             const auto AKSplitted = AKPad / k_batch;
             const auto BKSplitted = BKPad / k_batch;
 
-            a_grid_desc_ak0_m_ak1_ = DeviceOp::MakeAGridDescriptor_AK0_M_AK1(MRaw, AKSplitted, StrideA);
-            b_grid_desc_bk0_n_bk1_ = DeviceOp::MakeBGridDescriptor_BK0_N_BK1(BKSplitted, NRaw, StrideB);
-            c_grid_desc_m_n_       = DeviceOp::MakeCGridDescriptor_M_N(MRaw, NRaw, StrideC);
+            a_grid_desc_ak0_m_ak1_ =
+                DeviceOp::MakeAGridDescriptor_AK0_M_AK1(MRaw, AKSplitted, StrideA);
+            b_grid_desc_bk0_n_bk1_ =
+                DeviceOp::MakeBGridDescriptor_BK0_N_BK1(BKSplitted, NRaw, StrideB);
+            c_grid_desc_m_n_ = DeviceOp::MakeCGridDescriptor_M_N(MRaw, NRaw, StrideC);
 
             if(GridwiseGemm::CheckValidity(
                    a_grid_desc_ak0_m_ak1_, b_grid_desc_bk0_n_bk1_, c_grid_desc_m_n_))
@@ -919,4 +824,3 @@ struct DeviceGemmXdlSplitKCShuffle
 } // namespace tensor_operation
 } // namespace ck
 #endif
-

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v1.hpp

@@ -66,6 +66,82 @@ __global__ void
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
 
+/*
+ * \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
+ *
+ * \see \link device_batched_gemm_xdl.hpp kernel_batched_gemm_xdlops_v2r3
+ */
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatC,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename ComputePtrOffsetOfBatch,
+          typename Block2CTileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_batched_gemm_xdl_cshuffle_v1(
+            const FloatAB* __restrict__ p_a_grid,
+            const FloatAB* __restrict__ p_b_grid,
+            FloatC* __restrict__ p_c_grid,
+            const index_t batch_count,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const CElementwiseOperation c_element_op,
+            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
+            const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
+            const Block2CTileMap block_2_ctile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    const index_t num_blocks_per_batch =
+        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+
+    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
+    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
+    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
+
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
+                                                  p_b_grid + b_batch_offset,
+                                                  p_c_grid + c_batch_offset,
+                                                  p_shared,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  c_element_op,
+                                                  a_grid_desc_ak0_m_ak1,
+                                                  b_grid_desc_bk0_n_bk1,
+                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  block_2_ctile_map);
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_c_grid;
+    ignore = batch_count;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = c_element_op;
+    ignore = a_grid_desc_ak0_m_ak1;
+    ignore = b_grid_desc_bk0_n_bk1;
+    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = compute_ptr_offset_of_batch;
+    ignore = block_2_ctile_map;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
 template <typename FloatAB,
           typename FloatGemmAcc,
           typename FloatCShuffle,

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp

@@ -67,6 +67,102 @@ __global__ void
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
 
+/*
+ * \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
+ *
+ * \tparam ComputePtrOffsetOfBatch Class that computes the base pointer offsets of A, B, C matrix
+ * given the batch. For example, ComputePtrOffsetOfStridedBatch() computes the offsets of evenly
+ * strided batched, but we can easily extend to other layouts. The returned offset can be either \p
+ * index_t or \p long_index_t. If it returns \p long_index_t, we are not subject to the 2GB
+ * limitations.
+ *
+ * \tparam Block2CTileMap Block2CTileMap::CalculateBottomIndex() takes in id of a workgroup and
+ * returns the 2D index of the tile that it computes. \see
+ * GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3::Run().
+ *
+ * \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2
+ * tiles from different matrices. Keep in mind that these 2 matrices can share the same grid
+ * descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link
+ * device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for \link
+ * DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the computing of
+ * pointer offset into \p ComputePtrOffsetOfStridedBatch.
+ *
+ * \note \p Block2CTileMap allows customized mapping between a workgroup and the C-tile it computes.
+ * Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to
+ * realize BatchedGemm and GroupedGemm (and the corresponding GEMM fusion).
+ *
+ */
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatC,
+          typename AGridDesc_K0_M_K1,
+          typename BGridDesc_K0_N_K1,
+          typename CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename ComputePtrOffsetOfBatch,
+          typename Block2CTileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_batched_gemm_xdlops_v2r3(
+            const FloatAB* __restrict__ p_a_grid,
+            const FloatAB* __restrict__ p_b_grid,
+            FloatC* __restrict__ p_c_grid,
+            const index_t batch_count,
+            const AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1,
+            const BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1,
+            const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const CElementwiseOperation c_element_op,
+            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
+            const Block2CTileMap block_2_ctile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    const index_t num_blocks_per_batch =
+        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+
+    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
+    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
+    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
+
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
+                                                  p_b_grid + b_batch_offset,
+                                                  p_c_grid + c_batch_offset,
+                                                  p_shared,
+                                                  a_grid_desc_k0_m_k1,
+                                                  b_grid_desc_k0_n_k1,
+                                                  c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  c_element_op,
+                                                  block_2_ctile_map);
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_c_grid;
+    ignore = batch_count;
+    ignore = a_grid_desc_k0_m_k1;
+    ignore = b_grid_desc_k0_n_k1;
+    ignore = c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = c_element_op;
+    ignore = compute_ptr_offset_of_batch;
+    ignore = block_2_ctile_map;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
 template <typename GridwiseGemm,
           typename FloatAB,
           typename FloatC,

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r4.hpp

-#ifndef CK_GRIDWISE_GEMM_XDLOPS_V2R4_HPP
-#define CK_GRIDWISE_GEMM_XDLOPS_V2R4_HPP
-
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "blockwise_tensor_slice_transfer_v4r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-
-namespace ck {
-
-template <typename GridwiseGemm,
-          typename FloatAB,
-          typename FloatC,
-          typename ABK0MK1GridDesc,
-          typename BBK0NK1GridDesc,
-          typename CM0N0M1N1M2M3M4N2GridDesc,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          typename CBlockClusterAdaptor,
-          bool HasMainKBlockLoop>
-__global__ void
-#if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
-#endif
-        kernel_gemm_xdlops_v2r4(const FloatAB* __restrict__ p_a_grid,
-                                const FloatAB* __restrict__ p_b_grid,
-                                FloatC* __restrict__ p_c_grid,
-                                const ABK0MK1GridDesc a_b_k0_m_k1_grid_desc,
-                                const BBK0NK1GridDesc b_b_k0_n_k1_grid_desc,
-                                const CM0N0M1N1M2M3M4N2GridDesc c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
-                                const AElementwiseOperation a_element_op,
-                                const BElementwiseOperation b_element_op,
-                                const CElementwiseOperation c_element_op,
-                                const CBlockClusterAdaptor c_block_cluster_adaptor)
-{
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
-    constexpr index_t shared_block_size =
-        GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
-
-    __shared__ FloatAB p_shared_block[shared_block_size];
-
-    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
-                                                  p_b_grid,
-                                                  p_c_grid,
-                                                  p_shared_block,
-                                                  a_b_k0_m_k1_grid_desc,
-                                                  b_b_k0_n_k1_grid_desc,
-                                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
-                                                  a_element_op,
-                                                  b_element_op,
-                                                  c_element_op,
-                                                  c_block_cluster_adaptor);
-#else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = a_b_k0_m_k1_grid_desc;
-    ignore = b_b_k0_n_k1_grid_desc;
-    ignore = c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = c_block_cluster_adaptor;
-#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
-}
-
-template <index_t BlockSize,
-          typename FloatAB,
-          typename FloatAcc,
-          typename FloatC,
-          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
-          typename ABK0MK1GridDesc,
-          typename BBK0NK1GridDesc,
-          typename CMNGridDesc,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          index_t MPerBlock,
-          index_t NPerBlock,
-          index_t K0PerBlock,
-          index_t MPerXDL,
-          index_t NPerXDL,
-          index_t K1Value,
-          index_t MRepeat,
-          index_t NRepeat,
-          typename ABlockTransferThreadClusterLengths_K0_M_K1,
-          typename ABlockTransferThreadClusterArrangeOrder,
-          typename ABlockTransferSrcAccessOrder,
-          index_t ABlockTransferSrcVectorDim,
-          index_t ABlockTransferSrcScalarPerVector,
-          index_t ABlockTransferDstScalarPerVector_K1,
-          bool AThreadTransferSrcResetCoordinateAfterRun,
-          bool ABlockLdsExtraM,
-          typename BBlockTransferThreadClusterLengths_K0_N_K1,
-          typename BBlockTransferThreadClusterArrangeOrder,
-          typename BBlockTransferSrcAccessOrder,
-          index_t BBlockTransferSrcVectorDim,
-          index_t BBlockTransferSrcScalarPerVector,
-          index_t BBlockTransferDstScalarPerVector_K1,
-          bool BThreadTransferSrcResetCoordinateAfterRun,
-          bool BBlockLdsExtraN,
-          typename CThreadTransferSrcDstAccessOrder,
-          index_t CThreadTransferSrcDstVectorDim,
-          index_t CThreadTransferDstScalarPerVector>
-struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4
-{
-    static constexpr auto I0 = Number<0>{};
-    static constexpr auto I1 = Number<1>{};
-    static constexpr auto I2 = Number<2>{};
-    static constexpr auto I3 = Number<3>{};
-    static constexpr auto I4 = Number<4>{};
-    static constexpr auto I5 = Number<5>{};
-    static constexpr auto I6 = Number<6>{};
-    static constexpr auto I7 = Number<7>{};
-
-    // K1 should be Number<...>
-    static constexpr auto K1 = Number<K1Value>{};
-
-    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
-    {
-        constexpr auto max_lds_align = K1;
-
-        // A matrix in LDS memory, dst of blockwise copy
-        constexpr auto a_k0_m_k1_block_desc = [&]() {
-            if constexpr(ABlockLdsExtraM)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        // B matrix in LDS memory, dst of blockwise copy
-        constexpr auto b_k0_n_k1_block_desc = [&]() {
-            if constexpr(BBlockLdsExtraN)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        // LDS allocation for A and B: be careful of alignment
-        constexpr auto a_block_space_size =
-            math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
-
-        constexpr auto b_block_space_size =
-            math::integer_least_multiple(b_k0_n_k1_block_desc.GetElementSpaceSize(), max_lds_align);
-
-        return (a_block_space_size + b_block_space_size) * sizeof(FloatAB);
-    }
-
-    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
-    __host__ __device__ static constexpr bool
-    CheckValidity(const ABK0MK1GridDesc& a_b_k0_m_k1_grid_desc,
-                  const BBK0NK1GridDesc& b_b_k0_n_k1_grid_desc,
-                  const CMNGridDesc& c_m_n_grid_desc,
-                  index_t M01,
-                  index_t N01)
-    {
-        static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
-                      "wrong! K1 need to be known at compile-time");
-
-        static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
-                          (NPerBlock % (NRepeat * NPerXDL)) == 0,
-                      "Invalid tuning param!");
-
-        const auto M      = a_b_k0_m_k1_grid_desc.GetLength(I2);
-        const auto N      = b_b_k0_n_k1_grid_desc.GetLength(I2);
-        const auto K0     = a_b_k0_m_k1_grid_desc.GetLength(I1);
-        const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);
-
-        if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
-             K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
-             K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
-             K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
-             KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
-            return false;
-
-        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
-            return false;
-
-        // check M01, N01
-        constexpr auto M1 = Number<MPerBlock>{};
-        constexpr auto N1 = Number<NPerBlock>{};
-
-        const auto M0 = M / M1;
-        const auto N0 = N / N1;
-
-        if(!(M0 % M01 == 0 && N0 % N01 == 0))
-            return false;
-
-        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
-        return true;
-    }
-
-    __host__ __device__ static constexpr index_t
-    CalculateGridSize(const CMNGridDesc& c_m_n_grid_desc, index_t KBatch)
-    {
-        const auto M = c_m_n_grid_desc.GetLength(I0);
-        const auto N = c_m_n_grid_desc.GetLength(I1);
-
-        const index_t grid_size = (M / MPerBlock) * (N / NPerBlock) * KBatch;
-
-        return grid_size;
-    }
-
-    __host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
-    {
-        const bool has_main_k0_block_loop = K0 > K0PerBlock;
-
-        return has_main_k0_block_loop;
-    }
-
-    __host__ __device__ static constexpr auto
-    MakeCM0N0M1N1M2M3M4N2GridDescriptor(const CMNGridDesc& c_m_n_grid_desc)
-    {
-        constexpr auto max_lds_align = K1;
-
-        // A matrix in LDS memory, dst of blockwise copy
-        constexpr auto a_k0_m_k1_block_desc = [&]() {
-            if constexpr(ABlockLdsExtraM)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        // B matrix in LDS memory, dst of blockwise copy
-        constexpr auto b_k0_n_k1_block_desc = [&]() {
-            if constexpr(BBlockLdsExtraN)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        using BlockwiseGemm =
-            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
-                                                                FloatAB,
-                                                                FloatAcc,
-                                                                decltype(a_k0_m_k1_block_desc),
-                                                                decltype(b_k0_n_k1_block_desc),
-                                                                MPerXDL,
-                                                                NPerXDL,
-                                                                MRepeat,
-                                                                NRepeat,
-                                                                K1>;
-
-        return BlockwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_m_n_grid_desc);
-    }
-
-    // return block_id to C matrix tile idx (m0, n0) mapping
-    __host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
-        const CMNGridDesc& c_m_n_grid_desc, index_t M01, index_t N01, index_t KBatch)
-    {
-        const auto M = c_m_n_grid_desc.GetLength(I0);
-        const auto N = c_m_n_grid_desc.GetLength(I1);
-
-        constexpr auto M1 = Number<MPerBlock>{};
-        constexpr auto N1 = Number<NPerBlock>{};
-
-        const auto M0 = M / M1;
-        const auto N0 = N / N1;
-
-        const auto M00 = M0 / M01;
-        const auto N00 = N0 / N01;
-
-        const auto kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
-            make_single_stage_tensor_adaptor(
-                make_tuple(make_pass_through_transform(KBatch),
-                           make_unmerge_transform(make_tuple(M00, M01)),
-                           make_unmerge_transform(make_tuple(N00, N01))),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
-                make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
-
-        const auto cblockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor =
-            make_single_stage_tensor_adaptor(
-                make_tuple(make_merge_transform(make_tuple(KBatch, M00, N00, M01, N01))),
-                make_tuple(Sequence<0, 1, 2, 3, 4>{}),
-                make_tuple(Sequence<0>{}));
-
-        const auto cblockid_to_kbatch_m0_n0_block_cluster_adaptor =
-            chain_tensor_adaptors(kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
-                                  cblockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor);
-
-        return cblockid_to_kbatch_m0_n0_block_cluster_adaptor;
-    }
-
-    using CM0N0M1N1M2M3M4N2GridDesc = decltype(MakeCM0N0M1N1M2M3M4N2GridDescriptor(CMNGridDesc{}));
-    using CBlockClusterAdaptor      = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));
-
-    template <bool HasMainKBlockLoop>
-    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
-                               const FloatAB* __restrict__ p_b_grid,
-                               FloatC* __restrict__ p_c_grid,
-                               FloatAB* __restrict__ p_shared_block,
-                               const ABK0MK1GridDesc& a_b_k0_m_k1_grid_desc,
-                               const BBK0NK1GridDesc& b_b_k0_n_k1_grid_desc,
-                               const CM0N0M1N1M2M3M4N2GridDesc& c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
-                               const AElementwiseOperation& a_element_op,
-                               const BElementwiseOperation& b_element_op,
-                               const CElementwiseOperation& c_element_op,
-                               const CBlockClusterAdaptor& c_block_cluster_adaptor)
-    {
-        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
-        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_b_grid, b_b_k0_n_k1_grid_desc.GetElementSpaceSize());
-        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_c_grid, c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc.GetElementSpaceSize());
-
-        const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
-
-        // divide block work by [M, N]
-        const auto block_work_idx =
-            c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
-
-        const index_t k_batch_id = block_work_idx[I0];
-
-        // HACK: this force m/n_block_data_idx_on_grid into SGPR
-        const index_t m_block_data_idx_on_grid =
-            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
-
-        const index_t n_block_data_idx_on_grid =
-            __builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
-
-        // lds max alignment
-        constexpr auto max_lds_align = K1;
-
-        // A matrix in LDS memory, dst of blockwise copy
-        constexpr auto a_k0_m_k1_block_desc = [&]() {
-            if constexpr(ABlockLdsExtraM)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        constexpr auto a_b_k0_m_k1_block_desc = [&]() {
-            if constexpr(ABlockLdsExtraM)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    make_tuple(Number<K0PerBlock>{} * Number<MPerBlock + 1>{} * K1,
-                               Number<MPerBlock + 1>{} * K1,
-                               K1,
-                               I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    max_lds_align);
-            }
-        }();
-        // B matrix in LDS memory, dst of blockwise copy
-        constexpr auto b_k0_n_k1_block_desc = [&]() {
-            if constexpr(BBlockLdsExtraN)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        constexpr auto b_b_k0_n_k1_block_desc = [&]() {
-            if constexpr(BBlockLdsExtraN)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    make_tuple(Number<K0PerBlock>{} * Number<NPerBlock + 1>{} * K1,
-                               Number<NPerBlock + 1>{} * K1,
-                               K1,
-                               I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    max_lds_align);
-            }
-        }();
-        // A matrix blockwise copy
-        auto a_blockwise_copy =
-            BlockwiseTensorSliceTransfer_v4r1<BlockSize,
-                                              AElementwiseOperation,
-                                              ck::tensor_operation::element_wise::PassThrough,
-                                              InMemoryDataOperationEnum::Set,
-                                              Sequence<1, K0PerBlock, MPerBlock, K1>,
-                                              ABlockTransferThreadClusterLengths_K0_M_K1,
-                                              ABlockTransferThreadClusterArrangeOrder,
-                                              FloatAB,
-                                              FloatAB,
-                                              decltype(a_b_k0_m_k1_grid_desc),
-                                              decltype(a_b_k0_m_k1_block_desc),
-                                              ABlockTransferSrcAccessOrder,
-                                              Sequence<0, 2, 1, 3>,
-                                              ABlockTransferSrcVectorDim,
-                                              3,
-                                              ABlockTransferSrcScalarPerVector,
-                                              ABlockTransferDstScalarPerVector_K1,
-                                              1,
-                                              1,
-                                              AThreadTransferSrcResetCoordinateAfterRun,
-                                              true>(
-                a_b_k0_m_k1_grid_desc,
-                make_multi_index(k_batch_id, 0, m_block_data_idx_on_grid, 0),
-                a_element_op,
-                a_b_k0_m_k1_block_desc,
-                make_multi_index(0, 0, 0, 0),
-                ck::tensor_operation::element_wise::PassThrough{});
-
-        // B matrix blockwise copy
-        auto b_blockwise_copy =
-            BlockwiseTensorSliceTransfer_v4r1<BlockSize,
-                                              BElementwiseOperation,
-                                              ck::tensor_operation::element_wise::PassThrough,
-                                              InMemoryDataOperationEnum::Set,
-                                              Sequence<1, K0PerBlock, NPerBlock, K1>,
-                                              BBlockTransferThreadClusterLengths_K0_N_K1,
-                                              BBlockTransferThreadClusterArrangeOrder,
-                                              FloatAB,
-                                              FloatAB,
-                                              decltype(b_b_k0_n_k1_grid_desc),
-                                              decltype(b_b_k0_n_k1_block_desc),
-                                              BBlockTransferSrcAccessOrder,
-                                              Sequence<0, 2, 1, 3>,
-                                              BBlockTransferSrcVectorDim,
-                                              3,
-                                              BBlockTransferSrcScalarPerVector,
-                                              BBlockTransferDstScalarPerVector_K1,
-                                              1,
-                                              1,
-                                              BThreadTransferSrcResetCoordinateAfterRun,
-                                              true>(
-                b_b_k0_n_k1_grid_desc,
-                make_multi_index(k_batch_id, 0, n_block_data_idx_on_grid, 0),
-                b_element_op,
-                b_b_k0_n_k1_block_desc,
-                make_multi_index(0, 0, 0, 0),
-                ck::tensor_operation::element_wise::PassThrough{});
-
-        // GEMM definition
-        //   c_mtx += transpose(a_mtx) * b_mtx
-        //     a_mtx[K0PerBlock, MPerBlock] is in LDS
-        //     b_mtx[K0PerBlock, NPerBlock] is in LDS
-        //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
-        //       register
-        // sanity check
-
-        auto blockwise_gemm =
-            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
-                                                                FloatAB,
-                                                                FloatAcc,
-                                                                decltype(a_k0_m_k1_block_desc),
-                                                                decltype(b_k0_n_k1_block_desc),
-                                                                MPerXDL,
-                                                                NPerXDL,
-                                                                MRepeat,
-                                                                NRepeat,
-                                                                K1>{};
-
-        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
-
-        // LDS allocation for A and B: be careful of alignment
-        constexpr auto a_block_space_size =
-            math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
-
-        FloatAB* p_a_block = p_shared_block;
-        FloatAB* p_b_block = p_shared_block + a_block_space_size;
-
-        constexpr auto a_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
-        constexpr auto b_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
-
-        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
-        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            p_b_block, b_k0_n_k1_block_desc.GetElementSpaceSize());
-
-        // preload data into LDS
-        {
-            a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
-            b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
-
-            a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
-            b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
-        }
-
-        // Initialize C
-        c_thread_buf.Clear();
-
-        // main body
-        if constexpr(HasMainKBlockLoop)
-        {
-            index_t k0_block_data_begin = 0;
-
-            do
-            {
-                a_blockwise_copy.MoveSrcSliceWindow(a_b_k0_m_k1_grid_desc, a_block_slice_copy_step);
-                b_blockwise_copy.MoveSrcSliceWindow(b_b_k0_n_k1_grid_desc, b_block_slice_copy_step);
-
-                a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
-
-                block_sync_lds();
-
-                b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
-
-                blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
-
-                block_sync_lds();
-
-                a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
-                b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
-
-                k0_block_data_begin += K0PerBlock;
-            } while(k0_block_data_begin < (K0 - K0PerBlock));
-        }
-
-        // tail
-        {
-            block_sync_lds();
-
-            blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
-        }
-
-        // output: register to global memory
-        {
-            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc =
-                blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
-
-            constexpr auto M0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I0);
-            constexpr auto N0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I1);
-            constexpr auto M1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I2);
-            constexpr auto N1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I3);
-            constexpr auto M2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I4);
-            constexpr auto M3 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I5);
-            constexpr auto M4 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I6);
-            constexpr auto N2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I7);
-
-            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(
-                    Number<M0>{}, Number<N0>{}, I1, I1, Number<M2>{}, I1, Number<M4>{}, I1));
-
-            // calculate origin of thread output tensor on global memory
-            //     blockwise GEMM c matrix starting index
-            const auto c_thread_mtx_on_block =
-                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
-
-            const index_t m_thread_data_on_grid =
-                m_block_data_idx_on_grid + c_thread_mtx_on_block[I0];
-
-            const index_t n_thread_data_on_grid =
-                n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
-
-            const auto m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor =
-                make_single_stage_tensor_adaptor(
-                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
-                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
-                    make_tuple(Sequence<0>{}));
-
-            const auto m_thread_data_on_grid_idx =
-                m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
-                    make_multi_index(m_thread_data_on_grid));
-
-            const auto n_thread_data_on_grid_to_n0_n1_n2_adaptor = make_single_stage_tensor_adaptor(
-                make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
-                make_tuple(Sequence<0, 1, 2>{}),
-                make_tuple(Sequence<0>{}));
-
-            const auto n_thread_data_on_grid_idx =
-                n_thread_data_on_grid_to_n0_n1_n2_adaptor.CalculateBottomIndex(
-                    make_multi_index(n_thread_data_on_grid));
-
-            auto c_thread_copy =
-                ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
-                                                   FloatC,
-                                                   decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc),
-                                                   decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc),
-                                                   CElementwiseOperation,
-                                                   Sequence<M0, N0, I1, I1, M2, I1, M4, I1>,
-                                                   CThreadTransferSrcDstAccessOrder,
-                                                   CThreadTransferSrcDstVectorDim,
-                                                   CThreadTransferDstScalarPerVector,
-                                                   CGlobalMemoryDataOperation,
-                                                   1,
-                                                   true>{
-
-                    c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
-                    make_multi_index(m_thread_data_on_grid_idx[I0],
-                                     n_thread_data_on_grid_idx[I0],
-                                     m_thread_data_on_grid_idx[I1],
-                                     n_thread_data_on_grid_idx[I1],
-                                     m_thread_data_on_grid_idx[I2],
-                                     m_thread_data_on_grid_idx[I3],
-                                     m_thread_data_on_grid_idx[I4],
-                                     n_thread_data_on_grid_idx[I2]),
-                    c_element_op};
-
-            c_thread_copy.Run(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
-                              make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
-                              c_thread_buf,
-                              c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
-                              c_grid_buf);
-        }
-    }
-}; // namespace ck
-
-} // namespace ck
-#endif

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r4r2.hpp

-#ifndef CK_GRIDWISE_GEMM_XDLOPS_V2R4R2_HPP
-#define CK_GRIDWISE_GEMM_XDLOPS_V2R4R2_HPP
-
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "blockwise_tensor_slice_transfer_v4r1.hpp"
-#include "blockwise_tensor_slice_transfer_v6r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-
-namespace ck {
-
-template <typename GridwiseGemm,
-          typename FloatAB,
-          typename FloatC,
-          typename AGridDesc_B_K0_M_K1,
-          typename BGridDesc_B_K0_N_K1,
-          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          typename CBlockClusterAdaptor,
-          bool HasMainKBlockLoop>
-__global__ void
-#if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
-#endif
-        kernel_gemm_xdlops_v2r4r2(const FloatAB* __restrict__ p_a_grid,
-                                  const FloatAB* __restrict__ p_b_grid,
-                                  FloatC* __restrict__ p_c_grid,
-                                  const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
-                                  const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
-                                  const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
-                                      c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                  const AElementwiseOperation a_element_op,
-                                  const BElementwiseOperation b_element_op,
-                                  const CElementwiseOperation c_element_op,
-                                  const CBlockClusterAdaptor c_block_cluster_adaptor)
-{
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
-    constexpr index_t shared_block_size =
-        GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
-
-    __shared__ FloatAB p_shared_block[shared_block_size];
-
-    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
-                                                  p_b_grid,
-                                                  p_c_grid,
-                                                  p_shared_block,
-                                                  a_b_k0_m_k1_grid_desc,
-                                                  b_b_k0_n_k1_grid_desc,
-                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                  a_element_op,
-                                                  b_element_op,
-                                                  c_element_op,
-                                                  c_block_cluster_adaptor);
-#else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = a_b_k0_m_k1_grid_desc;
-    ignore = b_b_k0_n_k1_grid_desc;
-    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = c_block_cluster_adaptor;
-#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
-}
-
-template <index_t BlockSize,
-          typename FloatAB,
-          typename FloatAcc,
-          typename FloatC,
-          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
-          typename AGridDesc_B_K0_M_K1,
-          typename BGridDesc_B_K0_N_K1,
-          typename CMNGridDesc,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          index_t MPerBlock,
-          index_t NPerBlock,
-          index_t K0PerBlock,
-          index_t MPerXDL,
-          index_t NPerXDL,
-          index_t K1Value,
-          index_t MRepeat,
-          index_t NRepeat,
-          typename ABlockTransferThreadClusterLengths_K0_M_K1,
-          typename ABlockTransferThreadClusterArrangeOrder,
-          typename ABlockTransferSrcAccessOrder,
-          index_t ABlockTransferSrcVectorDim,
-          index_t ABlockTransferSrcScalarPerVector,
-          index_t ABlockTransferDstScalarPerVector_K1,
-          bool AThreadTransferSrcResetCoordinateAfterRun,
-          bool ABlockLdsExtraM,
-          typename BBlockTransferThreadClusterLengths_K0_N_K1,
-          typename BBlockTransferThreadClusterArrangeOrder,
-          typename BBlockTransferSrcAccessOrder,
-          index_t BBlockTransferSrcVectorDim,
-          index_t BBlockTransferSrcScalarPerVector,
-          index_t BBlockTransferDstScalarPerVector_K1,
-          bool BThreadTransferSrcResetCoordinateAfterRun,
-          bool BBlockLdsExtraN,
-          index_t CShuffleMRepeatPerShuffle,
-          index_t CShuffleNRepeatPerShuffle,
-          index_t CBlockTransferScalarPerVector_NWaveNPerXDL,
-          typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>
-struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
-{
-    static constexpr auto I0 = Number<0>{};
-    static constexpr auto I1 = Number<1>{};
-    static constexpr auto I2 = Number<2>{};
-    static constexpr auto I3 = Number<3>{};
-    static constexpr auto I4 = Number<4>{};
-    static constexpr auto I5 = Number<5>{};
-    static constexpr auto I6 = Number<6>{};
-    static constexpr auto I7 = Number<7>{};
-
-    // K1 should be Number<...>
-    static constexpr auto K1 = Number<K1Value>{};
-
-    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
-    {
-        constexpr auto max_lds_align = K1;
-
-        // A matrix in LDS memory, dst of blockwise copy
-        constexpr auto a_k0_m_k1_block_desc = [&]() {
-            if constexpr(ABlockLdsExtraM)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        // B matrix in LDS memory, dst of blockwise copy
-        constexpr auto b_k0_n_k1_block_desc = [&]() {
-            if constexpr(BBlockLdsExtraN)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        // LDS allocation for A and B: be careful of alignment
-        constexpr auto a_block_space_size =
-            math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
-
-        constexpr auto b_block_space_size =
-            math::integer_least_multiple(b_k0_n_k1_block_desc.GetElementSpaceSize(), max_lds_align);
-
-        constexpr auto c_block_size =
-            GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock().GetElementSpaceSize();
-
-        return math::max((a_block_space_size + b_block_space_size) * sizeof(FloatAB),
-                         c_block_size * sizeof(FloatC));
-    }
-
-    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
-    __host__ __device__ static constexpr bool
-    CheckValidity(const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
-                  const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
-                  const CMNGridDesc& c_m_n_grid_desc,
-                  index_t M01,
-                  index_t N01)
-    {
-        static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
-                      "wrong! K1 need to be known at compile-time");
-
-        static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
-                          (NPerBlock % (NRepeat * NPerXDL)) == 0,
-                      "Invalid tuning param!");
-
-        const auto M      = a_b_k0_m_k1_grid_desc.GetLength(I2);
-        const auto N      = b_b_k0_n_k1_grid_desc.GetLength(I2);
-        const auto K0     = a_b_k0_m_k1_grid_desc.GetLength(I1);
-        const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);
-
-        if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
-             K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
-             K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
-             K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
-             KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
-            return false;
-
-        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
-            return false;
-
-        // check M01, N01
-        constexpr auto M1 = Number<MPerBlock>{};
-        constexpr auto N1 = Number<NPerBlock>{};
-
-        const auto M0 = M / M1;
-        const auto N0 = N / N1;
-
-        if(!(M0 % M01 == 0 && N0 % N01 == 0))
-            return false;
-
-        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
-        return true;
-    }
-
-    __host__ __device__ static constexpr index_t
-    CalculateGridSize(const CMNGridDesc& c_m_n_grid_desc, index_t KBatch)
-    {
-        const auto M = c_m_n_grid_desc.GetLength(I0);
-        const auto N = c_m_n_grid_desc.GetLength(I1);
-
-        const index_t grid_size = (M / MPerBlock) * (N / NPerBlock) * KBatch;
-
-        return grid_size;
-    }
-
-    __host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
-    {
-        const bool has_main_k0_block_loop = K0 > K0PerBlock;
-
-        return has_main_k0_block_loop;
-    }
-
-    __host__ __device__ static constexpr auto
-    MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CMNGridDesc& c_m_n_grid_desc)
-    {
-        const auto M = c_m_n_grid_desc.GetLength(I0);
-        const auto N = c_m_n_grid_desc.GetLength(I1);
-
-        const auto MBlock = M / MPerBlock;
-        const auto NBlock = N / NPerBlock;
-
-        return transform_tensor_descriptor(
-            c_m_n_grid_desc,
-            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
-                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
-            make_tuple(Sequence<0>{}, Sequence<1>{}),
-            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
-    }
-
-    // return block_id to C matrix tile idx (m0, n0) mapping
-    __host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
-        const CMNGridDesc& c_m_n_grid_desc, index_t M01, index_t N01, index_t KBatch)
-    {
-        const auto M = c_m_n_grid_desc.GetLength(I0);
-        const auto N = c_m_n_grid_desc.GetLength(I1);
-
-        constexpr auto M1 = Number<MPerBlock>{};
-        constexpr auto N1 = Number<NPerBlock>{};
-
-        const auto M0 = M / M1;
-        const auto N0 = N / N1;
-
-        const auto M00 = M0 / M01;
-        const auto N00 = N0 / N01;
-
-        const auto kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
-            make_single_stage_tensor_adaptor(
-                make_tuple(make_pass_through_transform(KBatch),
-                           make_unmerge_transform(make_tuple(M00, M01)),
-                           make_unmerge_transform(make_tuple(N00, N01))),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
-                make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
-
-        const auto c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor =
-            make_single_stage_tensor_adaptor(
-                make_tuple(make_merge_transform(make_tuple(KBatch, M00, N00, M01, N01))),
-                make_tuple(Sequence<0, 1, 2, 3, 4>{}),
-                make_tuple(Sequence<0>{}));
-
-        const auto c_blockid_to_kbatch_m0_n0_block_cluster_adaptor =
-            chain_tensor_adaptors(kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
-                                  c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor);
-
-        return c_blockid_to_kbatch_m0_n0_block_cluster_adaptor;
-    }
-
-    __host__ __device__ static constexpr auto
-    GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
-    {
-        constexpr index_t MWave = MPerBlock / (MRepeat * MPerXDL);
-        constexpr index_t NWave = NPerBlock / (NRepeat * NPerXDL);
-
-        return make_naive_tensor_descriptor_packed(
-            make_tuple(I1,
-                       Number<CShuffleMRepeatPerShuffle * MWave * MPerXDL>{},
-                       I1,
-                       Number<CShuffleNRepeatPerShuffle * NWave * NPerXDL>{}));
-    }
-
-    using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
-        decltype(MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CMNGridDesc{}));
-    using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));
-
-    template <bool HasMainKBlockLoop>
-    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
-                               const FloatAB* __restrict__ p_b_grid,
-                               FloatC* __restrict__ p_c_grid,
-                               FloatAB* __restrict__ p_shared_block,
-                               const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
-                               const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
-                               const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
-                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
-                               const AElementwiseOperation& a_element_op,
-                               const BElementwiseOperation& b_element_op,
-                               const CElementwiseOperation& c_element_op,
-                               const CBlockClusterAdaptor& c_block_cluster_adaptor)
-    {
-        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
-        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_b_grid, b_b_k0_n_k1_grid_desc.GetElementSpaceSize());
-        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
-
-        const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
-
-        // divide block work by [M, N]
-        const auto block_work_idx =
-            c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
-
-        const index_t k_batch_id = block_work_idx[I0];
-
-        // HACK: this force m/n_block_data_idx_on_grid into SGPR
-        const index_t m_block_data_idx_on_grid =
-            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
-
-        const index_t n_block_data_idx_on_grid =
-            __builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
-
-        // lds max alignment
-        constexpr auto max_lds_align = K1;
-
-        // A matrix in LDS memory, dst of blockwise copy
-        constexpr auto a_k0_m_k1_block_desc = [&]() {
-            if constexpr(ABlockLdsExtraM)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        constexpr auto a_b_k0_m_k1_block_desc = [&]() {
-            if constexpr(ABlockLdsExtraM)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    make_tuple(Number<K0PerBlock>{} * Number<MPerBlock + 1>{} * K1,
-                               Number<MPerBlock + 1>{} * K1,
-                               K1,
-                               I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
-                    max_lds_align);
-            }
-        }();
-        // B matrix in LDS memory, dst of blockwise copy
-        constexpr auto b_k0_n_k1_block_desc = [&]() {
-            if constexpr(BBlockLdsExtraN)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
-            }
-        }();
-
-        constexpr auto b_b_k0_n_k1_block_desc = [&]() {
-            if constexpr(BBlockLdsExtraN)
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    make_tuple(Number<K0PerBlock>{} * Number<NPerBlock + 1>{} * K1,
-                               Number<NPerBlock + 1>{} * K1,
-                               K1,
-                               I1));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_aligned(
-                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
-                    max_lds_align);
-            }
-        }();
-        // A matrix blockwise copy
-        auto a_blockwise_copy =
-            BlockwiseTensorSliceTransfer_v4r1<BlockSize,
-                                              AElementwiseOperation,
-                                              ck::tensor_operation::element_wise::PassThrough,
-                                              InMemoryDataOperationEnum::Set,
-                                              Sequence<1, K0PerBlock, MPerBlock, K1>,
-                                              ABlockTransferThreadClusterLengths_K0_M_K1,
-                                              ABlockTransferThreadClusterArrangeOrder,
-                                              FloatAB,
-                                              FloatAB,
-                                              decltype(a_b_k0_m_k1_grid_desc),
-                                              decltype(a_b_k0_m_k1_block_desc),
-                                              ABlockTransferSrcAccessOrder,
-                                              Sequence<0, 2, 1, 3>,
-                                              ABlockTransferSrcVectorDim,
-                                              3,
-                                              ABlockTransferSrcScalarPerVector,
-                                              ABlockTransferDstScalarPerVector_K1,
-                                              1,
-                                              1,
-                                              AThreadTransferSrcResetCoordinateAfterRun,
-                                              true>(
-                a_b_k0_m_k1_grid_desc,
-                make_multi_index(k_batch_id, 0, m_block_data_idx_on_grid, 0),
-                a_element_op,
-                a_b_k0_m_k1_block_desc,
-                make_multi_index(0, 0, 0, 0),
-                ck::tensor_operation::element_wise::PassThrough{});
-
-        // B matrix blockwise copy
-        auto b_blockwise_copy =
-            BlockwiseTensorSliceTransfer_v4r1<BlockSize,
-                                              BElementwiseOperation,
-                                              ck::tensor_operation::element_wise::PassThrough,
-                                              InMemoryDataOperationEnum::Set,
-                                              Sequence<1, K0PerBlock, NPerBlock, K1>,
-                                              BBlockTransferThreadClusterLengths_K0_N_K1,
-                                              BBlockTransferThreadClusterArrangeOrder,
-                                              FloatAB,
-                                              FloatAB,
-                                              decltype(b_b_k0_n_k1_grid_desc),
-                                              decltype(b_b_k0_n_k1_block_desc),
-                                              BBlockTransferSrcAccessOrder,
-                                              Sequence<0, 2, 1, 3>,
-                                              BBlockTransferSrcVectorDim,
-                                              3,
-                                              BBlockTransferSrcScalarPerVector,
-                                              BBlockTransferDstScalarPerVector_K1,
-                                              1,
-                                              1,
-                                              BThreadTransferSrcResetCoordinateAfterRun,
-                                              true>(
-                b_b_k0_n_k1_grid_desc,
-                make_multi_index(k_batch_id, 0, n_block_data_idx_on_grid, 0),
-                b_element_op,
-                b_b_k0_n_k1_block_desc,
-                make_multi_index(0, 0, 0, 0),
-                ck::tensor_operation::element_wise::PassThrough{});
-
-        // GEMM definition
-        //   c_mtx += transpose(a_mtx) * b_mtx
-        //     a_mtx[K0PerBlock, MPerBlock] is in LDS
-        //     b_mtx[K0PerBlock, NPerBlock] is in LDS
-        //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
-        //       register
-        // sanity check
-
-        auto blockwise_gemm =
-            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
-                                                                FloatAB,
-                                                                FloatAcc,
-                                                                decltype(a_k0_m_k1_block_desc),
-                                                                decltype(b_k0_n_k1_block_desc),
-                                                                MPerXDL,
-                                                                NPerXDL,
-                                                                MRepeat,
-                                                                NRepeat,
-                                                                K1>{};
-
-        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
-
-        // LDS allocation for A and B: be careful of alignment
-        constexpr auto a_block_space_size =
-            math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
-
-        FloatAB* p_a_block = p_shared_block;
-        FloatAB* p_b_block = p_shared_block + a_block_space_size;
-
-        constexpr auto a_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
-        constexpr auto b_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
-
-        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
-        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            p_b_block, b_k0_n_k1_block_desc.GetElementSpaceSize());
-
-        // preload data into LDS
-        {
-            a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
-            b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
-
-            a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
-            b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
-        }
-
-        // Initialize C
-        c_thread_buf.Clear();
-
-        // main body
-        if constexpr(HasMainKBlockLoop)
-        {
-            index_t k0_block_data_begin = 0;
-
-            do
-            {
-                a_blockwise_copy.MoveSrcSliceWindow(a_b_k0_m_k1_grid_desc, a_block_slice_copy_step);
-                b_blockwise_copy.MoveSrcSliceWindow(b_b_k0_n_k1_grid_desc, b_block_slice_copy_step);
-
-                a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
-
-                block_sync_lds();
-
-                b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
-
-                blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
-
-                block_sync_lds();
-
-                a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
-                b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
-
-                k0_block_data_begin += K0PerBlock;
-            } while(k0_block_data_begin < (K0 - K0PerBlock));
-        }
-
-        // tail
-        {
-            block_sync_lds();
-
-            blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
-        }
-
-        // output: register to global memory
-        {
-            constexpr index_t MWave = MPerBlock / (MRepeat * MPerXDL);
-            constexpr index_t NWave = NPerBlock / (NRepeat * NPerXDL);
-
-            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc =
-                blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
-
-            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc =
-                blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
-
-            constexpr auto M0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I0);
-            constexpr auto N0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I1);
-            constexpr auto M1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I2);
-            constexpr auto N1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I3);
-            constexpr auto M2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I4);
-            constexpr auto M3 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I5);
-            constexpr auto M4 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I6);
-            constexpr auto N2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I7);
-
-            constexpr auto c_block_desc_mblock_mperblock_nblock_nperblock =
-                GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
-
-            auto c_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-                static_cast<FloatC*>(p_shared_block),
-                c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
-
-            static_assert(M1 == MWave, "");
-            static_assert(N1 == NWave, "");
-            static_assert(M2 * M3 * M4 == MPerXDL, "");
-            static_assert(N2 == NPerXDL, "");
-
-            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
-                c_block_desc_mblock_mperblock_nblock_nperblock,
-                make_tuple(
-                    make_freeze_transform(I0), // freeze mblock
-                    make_unmerge_transform(make_tuple(CShuffleMRepeatPerShuffle,
-                                                      M1,
-                                                      M2,
-                                                      M3,
-                                                      M4)), // M1 = MWave, M2 * M3 * M4 = MPerXDL
-                    make_freeze_transform(I0),              // freeze nblock
-                    make_unmerge_transform(make_tuple(CShuffleNRepeatPerShuffle,
-                                                      N1,
-                                                      N2))), // M1 = MWave, M2 * M3 * M4 = MPerXDL
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                make_tuple(
-                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
-
-            // calculate origin of thread output tensor on global memory
-            //     blockwise GEMM c matrix starting index
-            const auto c_thread_mtx_on_block =
-                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
-
-            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
-            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
-
-            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
-                make_single_stage_tensor_adaptor(
-                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
-                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
-                    make_tuple(Sequence<0>{}));
-
-            const auto m_thread_data_on_block_idx =
-                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
-                    make_multi_index(m_thread_data_on_block));
-
-            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
-                make_single_stage_tensor_adaptor(
-                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
-                    make_tuple(Sequence<0, 1, 2>{}),
-                    make_tuple(Sequence<0>{}));
-
-            const auto n_thread_data_on_block_idx =
-                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
-                    make_multi_index(n_thread_data_on_block));
-
-            // VGPR to LDS
-            auto c_thread_copy_vgpr_to_lds =
-                ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
-                                                   FloatC,
-                                                   decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc),
-                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
-                                                   ck::tensor_operation::element_wise::PassThrough,
-                                                   Sequence<CShuffleMRepeatPerShuffle,
-                                                            CShuffleNRepeatPerShuffle,
-                                                            I1,
-                                                            I1,
-                                                            M2,
-                                                            I1,
-                                                            M4,
-                                                            I1>,
-                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
-                                                   7,
-                                                   1,
-                                                   InMemoryDataOperationEnum::Set,
-                                                   1,
-                                                   true>{
-                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
-                    make_multi_index(0,
-                                     0,
-                                     m_thread_data_on_block_idx[I1],
-                                     n_thread_data_on_block_idx[I1],
-                                     m_thread_data_on_block_idx[I2],
-                                     m_thread_data_on_block_idx[I3],
-                                     m_thread_data_on_block_idx[I4],
-                                     n_thread_data_on_block_idx[I2]),
-                    ck::tensor_operation::element_wise::PassThrough{}};
-
-            // LDS to global
-            auto c_block_copy_lds_to_global = BlockwiseTensorSliceTransfer_v6r1<
-                BlockSize,                  // index_t BlockSize,
-                CElementwiseOperation,      // ElementwiseOperation,
-                CGlobalMemoryDataOperation, // DstInMemOp,
-                Sequence<1,
-                         CShuffleMRepeatPerShuffle * MWave * MPerXDL,
-                         1,
-                         CShuffleNRepeatPerShuffle * NWave * NPerXDL>, // BlockSliceLengths,
-                CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
-                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
-                FloatC,               // typename SrcData,
-                FloatC,               // typename DstData,
-                decltype(c_block_desc_mblock_mperblock_nblock_nperblock),
-                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
-                Sequence<0, 1, 2, 3>,                       // typename DimAccessOrder,
-                3,                                          // index_t VectorDim,
-                CBlockTransferScalarPerVector_NWaveNPerXDL, // index_t ScalarPerVector,
-                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
-                false> // bool ThreadTransferDstResetCoordinateAfterRun
-                {c_block_desc_mblock_mperblock_nblock_nperblock,
-                 make_multi_index(0, 0, 0, 0),
-                 c_grid_desc_mblock_mperblock_nblock_nperblock,
-                 make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0),
-                 c_element_op};
-
-            constexpr auto mxdlperwave_forward_step =
-                make_multi_index(0, CShuffleMRepeatPerShuffle * MWave * MPerXDL, 0, 0);
-            constexpr auto nxdlperwave_forward_step =
-                make_multi_index(0, 0, 0, CShuffleNRepeatPerShuffle * NWave * NPerXDL);
-            constexpr auto nxdlperwave_backward_step =
-                make_multi_index(0, 0, 0, -CShuffleNRepeatPerShuffle * NWave * NPerXDL);
-
-            static_for<0, MRepeat, CShuffleMRepeatPerShuffle>{}([&](auto mxdlperwave_iter) {
-                constexpr auto mxdlperwave = mxdlperwave_iter;
-
-                static_for<0, NRepeat, CShuffleNRepeatPerShuffle>{}([&](auto nxdlperwave_iter) {
-                    constexpr bool nxdlperwave_forward_sweep =
-                        (mxdlperwave % (2 * CShuffleMRepeatPerShuffle) == 0);
-
-                    constexpr index_t nxdlperwave_value =
-                        nxdlperwave_forward_sweep
-                            ? nxdlperwave_iter
-                            : (NRepeat - nxdlperwave_iter - CShuffleNRepeatPerShuffle);
-
-                    constexpr auto nxdlperwave = Number<nxdlperwave_value>{};
-
-                    // make sure it's safe to do ds_write
-                    block_sync_lds();
-
-                    // VGPR to LDS
-                    c_thread_copy_vgpr_to_lds.Run(
-                        c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
-                        make_tuple(mxdlperwave, nxdlperwave, I0, I0, I0, I0, I0, I0),
-                        c_thread_buf,
-                        c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
-                        c_block_buf);
-
-                    // make sure it's safe to do ds_read
-                    block_sync_lds();
-
-                    // LDS to global
-                    c_block_copy_lds_to_global.Run(c_block_desc_mblock_mperblock_nblock_nperblock,
-                                                   c_block_buf,
-                                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                   c_grid_buf);
-
-                    // move on nxdlperwave dimension
-                    if constexpr(nxdlperwave_forward_sweep &&
-                                 (nxdlperwave < NRepeat - CShuffleNRepeatPerShuffle))
-                    {
-                        c_block_copy_lds_to_global.MoveDstSliceWindow(
-                            c_grid_desc_mblock_mperblock_nblock_nperblock,
-                            nxdlperwave_forward_step);
-                    }
-                    else if constexpr((!nxdlperwave_forward_sweep) && (nxdlperwave > 0))
-                    {
-                        c_block_copy_lds_to_global.MoveDstSliceWindow(
-                            c_grid_desc_mblock_mperblock_nblock_nperblock,
-                            nxdlperwave_backward_step);
-                    }
-                });
-
-                // move on mxdlperwave dimension
-                if constexpr(mxdlperwave < MRepeat - CShuffleMRepeatPerShuffle)
-                {
-                    c_block_copy_lds_to_global.MoveDstSliceWindow(
-                        c_grid_desc_mblock_mperblock_nblock_nperblock, mxdlperwave_forward_step);
-                }
-            });
-        }
-    }
-}; // namespace ck
-
-} // namespace ck
-#endif

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_kn_mn_instance.cpp

@@ -23,40 +23,40 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[k, m] * b[k, n] = c[m, n]
-using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_kn_mn_instances = std::tuple<
-    // clang-format off
-        //#####################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
-        //#####################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
-        //#####################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
-        //#####################|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   2,   2,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   2,   2,   32,   32,    2,    4,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   2,   2,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   2,   2,   32,   32,    2,    2,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   2,   2,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 4>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   2,   2,   32,   32,    2,    2,     S<8, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   2,   2,   32,   32,    2,    1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              1,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   2,   2,   32,   32,    1,    2,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>
-    // clang-format on
-    >;
+using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_kn_mn_instances =
+    std::tuple<
+        // clang-format off
+        //#########################| ALayout|  BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+        //#########################|        |         |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+        //#########################|        |         |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+        //#########################|        |         |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   2,   2,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   2,   2,   32,   32,    2,    4,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   2,   2,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   2,   2,   32,   32,    2,    2,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   2,   2,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 4>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   2,   2,   32,   32,    2,    2,     S<8, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   2,   2,   32,   32,    2,    1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              1,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   2,   2,   32,   32,    1,    2,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,      Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>
+        // clang-format on
+        >;
 
 void add_device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_kn_mn_instances(
     std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
 {
-    add_device_operation_instances(instances,
-                                   device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_kn_mn_instances{});
+    add_device_operation_instances(
+        instances, device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_kn_mn_instances{});
 }
 
 } // namespace device_gemm_instance
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
-

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_nk_mn_instance.cpp

@@ -23,40 +23,40 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[k, m] * b[n, k] = c[m, n]
-using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_nk_mn_instances = std::tuple<
-    // clang-format off
-        //#####################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
-        //#####################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
-        //#####################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
-        //#####################|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   2,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   2,   8,   32,   32,    2,    4,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   2,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   2,   8,   32,   32,    2,    2,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   2,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   2,   8,   32,   32,    2,    2,     S<8, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   2,   8,   32,   32,    2,    1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   2,   8,   32,   32,    1,    2,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Col,      Col,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>
-    // clang-format on
-    >;
+using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_nk_mn_instances =
+    std::tuple<
+        // clang-format off
+        //#########################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+        //#########################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+        //#########################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+        //#########################|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   2,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   2,   8,   32,   32,    2,    4,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   2,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   2,   8,   32,   32,    2,    2,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   2,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   2,   8,   32,   32,    2,    2,     S<8, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   2,   8,   32,   32,    2,    1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   2,   8,   32,   32,    1,    2,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              2,         0,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>
+        // clang-format on
+        >;
 
 void add_device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_nk_mn_instances(
     std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
 {
-    add_device_operation_instances(instances,
-                                   device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_nk_mn_instances{});
+    add_device_operation_instances(
+        instances, device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_km_nk_mn_instances{});
 }
 
 } // namespace device_gemm_instance
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
-

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_kn_mn_instance.cpp

@@ -23,40 +23,40 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[m, k] * b[k, n] = c[m, n]
-using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_kn_mn_instances = std::tuple<
-    // clang-format off
-        //#####################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
-        //#####################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
-        //#####################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
-        //#####################|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   2,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   2,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   2,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   2,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   2,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 4>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   2,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   2,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              1,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   2,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
-        DeviceGemmXdlSplitKCShuffle<     Row,      Row,    Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>
-    // clang-format on
-    >;
+using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_kn_mn_instances =
+    std::tuple<
+        // clang-format off
+        //#########################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+        //#########################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+        //#########################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+        //#########################|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   2,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   2,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   2,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   2,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   2,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 4>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,    64,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 4>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   2,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    64,   128,    32,   8,   8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   2,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<16,16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              1,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   2,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           1,               S<1, 32, 1, 8>,              8>,
+        DeviceGemmXdlSplitKCShuffle<     Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              2,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>
+        // clang-format on
+        >;
 
 void add_device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_kn_mn_instances(
     std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
 {
-    add_device_operation_instances(instances,
-                                   device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_kn_mn_instances{});
+    add_device_operation_instances(
+        instances, device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_kn_mn_instances{});
 }
 
 } // namespace device_gemm_instance
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
-

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_nk_mn_instance.cpp

@@ -23,12 +23,13 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[m, k] * b[n, k] = c[m, n]
-using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_nk_mn_instances = std::tuple<
-    // clang-format off
-        //#####################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
-        //#####################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
-        //#####################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
-        //#####################|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_nk_mn_instances =
+    std::tuple<
+        // clang-format off
+        //#########################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+        //#########################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+        //#########################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+        //#########################|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
         DeviceGemmXdlSplitKCShuffle<     Row,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
         DeviceGemmXdlSplitKCShuffle<     Row,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>,
         DeviceGemmXdlSplitKCShuffle<     Row,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
@@ -42,18 +43,17 @@ using device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_nk_mn_instances = std::tup
         DeviceGemmXdlSplitKCShuffle<     Row,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,   128,    32,   128,    32,   8,   8,   32,   32,    1,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 8>,              8>,
         DeviceGemmXdlSplitKCShuffle<     Row,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,    64,    64,    32,    32,   8,   8,   32,   32,    2,    1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 4>,              8>,
         DeviceGemmXdlSplitKCShuffle<     Row,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough,    GemmDefault,        1,    64,    32,    64,    32,   8,   8,   32,   32,    1,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 16, 1, 4>,              8>
-    // clang-format on
-    >;
+        // clang-format on
+        >;
 
 void add_device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_nk_mn_instances(
     std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
 {
-    add_device_operation_instances(instances,
-                                   device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_nk_mn_instances{});
+    add_device_operation_instances(
+        instances, device_gemm_xdl_splitk_c_shuffle_f16_f16_f16_mk_nk_mn_instances{});
 }
 
 } // namespace device_gemm_instance
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
-