bf16A_Int8B with fastgelu/bias (#1264)

* changed the copy function to v7r2

* adding multi_abd

* in-progress

* add post-load oob check

* debugging

* adjust instances

* add run_lds

* add elemntwise_op

* replace multi_abd_device with v3

* clean up

* clean

* clean

* Added LDSType

* profiling

* adjust oobcheck

* add missing file

* refactor

* clean

* add examples

client_example/30_gemm_multi_abd/CMakeLists.txt → client_example/30_gemm_bf16Aint8B/CMakeLists.txt

@@ -10,4 +10,7 @@ if(GPU_TARGETS MATCHES "gfx9" AND ((DTYPES MATCHES "int8" AND DTYPES MATCHES "bf
 
 	add_executable(client_gemm_bf16_i8_bf16 gemm_xdl_bf16_i8.cpp)
 	target_link_libraries(client_gemm_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
+
+	add_executable(client_gemm_multiply_bf16_i8_bf16 gemm_xdl_multiply_bf16_i8.cpp)
+	target_link_libraries(client_gemm_multiply_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
 endif()

client_example/30_gemm_multi_abd/gemm_bias_fastgelu_xdl_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_bias_fastgelu_xdl_bf16_i8.cpp

@@ -38,19 +38,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<D0Layout>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = AddFastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/30_gemm_multi_abd/gemm_bias_xdl_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_bias_xdl_bf16_i8.cpp

@@ -36,7 +36,7 @@ using D0DataType       = BF16;
 using DsDataType       = ck::Tuple<D0DataType>;
 using EDataType        = BF16;
 
-using A0Layout = Col;
+using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
 using B0Layout = Row;
 using B1Layout = B0Layout;
@@ -45,12 +45,12 @@ using D0Layout = Row;
 using DsLayout = ck::Tuple<D0Layout>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using Add         = ck::tensor_operation::element_wise::Add;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = Add;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/30_gemm_multi_abd/gemm_xdl_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_xdl_bf16_i8.cpp

@@ -37,19 +37,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using Add         = ck::tensor_operation::element_wise::Add;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = PassThrough;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
@@ -74,12 +74,12 @@ struct SimpleDeviceMem
 int main(int argc, char* argv[])
 {
     // GEMM shape
-    ck::index_t M = 64;
-    ck::index_t N = 1024;
-    ck::index_t K = 512;
+    ck::index_t M = 4096;
+    ck::index_t N = 768;
+    ck::index_t K = 6144;
 
     ck::index_t StrideA = K;
-    ck::index_t StrideB = N;
+    ck::index_t StrideB = K;
     ck::index_t StrideE = N;
 
     if(argc == 1)

client_example/30_gemm_multi_abd/gemm_xdl_gelu_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_xdl_gelu_bf16_i8.cpp

@@ -37,19 +37,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using FastGelu    = ck::tensor_operation::element_wise::FastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = FastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/30_gemm_bf16Aint8B/gemm_xdl_multiply_bf16_i8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <iomanip>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/device_gemm_multiple_abd.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/gemm_multi_abd.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using BF16 = ck::bhalf_t;
+using I8   = int8_t;
+using F32  = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using A0DataType       = BF16;
+using AsDataType       = ck::Tuple<A0DataType>;
+using B0DataType       = I8;
+using B1DataType       = BF16;
+using BsDataType       = ck::Tuple<B0DataType>;
+using AccDataType      = F32;
+using CShuffleDataType = BF16;
+using DsDataType       = ck::Tuple<B1DataType>;
+using EDataType        = BF16;
+
+using A0Layout = Row;
+using AsLayout = ck::Tuple<A0Layout>;
+using B0Layout = Row;
+using B1Layout = B0Layout;
+using BsLayout = ck::Tuple<B0Layout>;
+using D0Layout = Row;
+using DsLayout = ck::Tuple<B1Layout>;
+using ELayout  = Row;
+
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = Multiply;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+// clang-format on
+int main(int argc, char* argv[])
+{
+    // GEMM shape
+    ck::index_t M = 4096;
+    ck::index_t N = 768;
+    ck::index_t K = 6144;
+
+    ck::index_t StrideA = K;
+    ck::index_t StrideB = K;
+    ck::index_t StrideE = N;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 7)
+    {
+        M = std::stoi(argv[1]);
+        N = std::stoi(argv[2]);
+        K = std::stoi(argv[3]);
+
+        StrideA = std::stoi(argv[4]);
+        StrideB = std::stoi(argv[5]);
+        StrideE = std::stoi(argv[6]);
+    }
+    else
+    {
+        printf("arg1 to 7: M, N, K, StrideA, StrideB, StrideE\n");
+        exit(0);
+    }
+
+    auto f_matrix_space_size =
+        [](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
+            using Layout = decltype(layout);
+
+            if constexpr(std::is_same<Layout, Row>::value)
+            {
+                return (nRow - 1) * stride + nCol;
+            }
+            else
+            {
+                return (nCol - 1) * stride + nRow;
+            }
+        };
+
+    SimpleDeviceMem a0_device_buf(sizeof(A0DataType) *
+                                  f_matrix_space_size(M, K, StrideA, A0Layout{}));
+    SimpleDeviceMem b0_device_buf(sizeof(B0DataType) *
+                                  f_matrix_space_size(K, N, StrideB, B0Layout{}));
+    SimpleDeviceMem b1_device_buf(sizeof(B1DataType) * f_matrix_space_size(K, N, 0, B1Layout{}));
+    SimpleDeviceMem e_device_buf(sizeof(EDataType) * f_matrix_space_size(M, N, StrideE, ELayout{}));
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    constexpr ck::index_t NumATensor = 1;
+    constexpr ck::index_t NumBTensor = 1;
+    constexpr ck::index_t NumDTensor = 1;
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleABD<AsLayout,
+                                                                         BsLayout,
+                                                                         DsLayout,
+                                                                         Row,
+                                                                         AsDataType,
+                                                                         BsDataType,
+                                                                         DsDataType,
+                                                                         BF16,
+                                                                         AElementOp,
+                                                                         BElementOp,
+                                                                         CDEElementOp>;
+
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            std::array<const void*, NumATensor>{a0_device_buf.GetDeviceBuffer()},
+            std::array<const void*, NumBTensor>{b0_device_buf.GetDeviceBuffer()},
+            std::array<const void*, NumDTensor>{b1_device_buf.GetDeviceBuffer()},
+            e_device_buf.GetDeviceBuffer(),
+            M,
+            N,
+            K,
+            std::array<ck::index_t, NumATensor>{StrideA},
+            std::array<ck::index_t, NumBTensor>{StrideB},
+            std::array<ck::index_t, NumDTensor>{0},
+            StrideE,
+            a_element_op,
+            b_element_op,
+            cde_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop = std::size_t(2) * M * N * K;
+
+            std::size_t num_btype =
+                sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * M * N;
+
+            float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
+              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    return 0;
+}

client_example/31_grouped_gemm_multi_abd/grouped_gemm_bias_fastgelu_xdl_bf16_i8.cpp → client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_bias_fastgelu_xdl_bf16_i8.cpp

@@ -38,19 +38,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<D0Layout>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = AddFastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/31_grouped_gemm_multi_abd/grouped_gemm_fastgelu_xdl_bf16_i8.cpp → client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_fastgelu_xdl_bf16_i8.cpp

@@ -36,7 +36,7 @@ using D0DataType       = BF16;
 using DsDataType       = ck::Tuple<>;
 using EDataType        = BF16;
 
-using A0Layout = Col;
+using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
 using B0Layout = Row;
 using B1Layout = B0Layout;
@@ -45,12 +45,12 @@ using D0Layout = Row;
 using DsLayout = ck::Tuple<>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using FastGelu    = ck::tensor_operation::element_wise::FastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = FastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_xdl_fixed_nk_bias_bf16_i8.cpp

@@ -52,12 +52,12 @@ using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using DsLayout = ck::Tuple<Row>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = AddFastGelu;
 
 static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

example/60_gemm_multi_ABD/CMakeLists.txt

 add_example_executable(example_gemm_multi_ABD_xdl_fp16 gemm_multi_ABD_xdl_fp16.cpp)
-add_example_executable(example_gemm_multi_ABD_xdl_bf16_i8 gemm_multi_ABD_xdl_bf16_i8.cpp)
\ No newline at end of file
+add_example_executable(example_gemm_multi_ABD_xdl_bias_fastgelu_bf16_i8 gemm_multi_ABD_xdl_bias_fastgelu_bf16_i8.cpp)
+add_example_executable(example_gemm_multi_ABD_xdl_multiply_bias_fastgelu_bf16_i8 gemm_multi_ABD_xdl_multiply_bias_fastgelu_bf16_i8.cpp)
+add_example_executable(example_gemm_multi_ABD_xdl_fastgelu_bf16_i8 gemm_multi_ABD_xdl_fastgelu_bf16_i8.cpp)

example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_bf16_i8.cpp → example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_bias_fastgelu_bf16_i8.cpp

@@ -18,9 +18,12 @@
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 #include "ck/library/utility/check_err.hpp"
 
+#include "ck/utility/blkgemmpipe_scheduler.hpp"
+
 template <ck::index_t... Is>
 using S = ck::Sequence<Is...>;
 
+using F16  = ck::half_t;
 using BF16 = ck::bhalf_t;
 using I8   = int8_t;
 using F32  = float;
@@ -41,22 +44,22 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<D0Layout>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = AddFastGelu;
 
-static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
 
 using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleABD_Xdl_CShuffle
     // clang-format off
@@ -64,9 +67,9 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleABD_Xdl
 ///######|         |         |         |        |       Type|       Type|        Type|         DataType|       Type|      Type| 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|
 ///######|         |         |         |        |           |           |            |                 |           |          |            |            |             |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-         < AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,        1,   128,    16,   128,    32,   8,   8,   16,   16,    1,    4,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              1,              1,         1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              1,              1,         1,           1,           1,               S<1, 16, 1, 8>,               1>;
-
+         < AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,        1,   256,   128,   128,    64,   8,   4,  32,   32,    2,    2,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<16, 16, 1>,    S<0, 2, 1>,     S<0, 2, 1>,             1,               8,              4,          0,          1,           1,               S<1, 32, 1, 8>,               8,  ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v4>;
 // clang-format on
+
 int main(int argc, char* argv[])
 {
     bool do_verification = true;
@@ -74,13 +77,13 @@ int main(int argc, char* argv[])
     bool time_kernel     = false;
 
     // GEMM shape
-    ck::index_t M = 64;
-    ck::index_t N = 1024;
-    ck::index_t K = 512;
+    ck::index_t M = 4096;
+    ck::index_t N = 768;
+    ck::index_t K = 6144;
 
     ck::index_t StrideA = K;
-    ck::index_t StrideB = K;
-    ck::index_t StrideD = N;
+    ck::index_t StrideB = N;
+    ck::index_t StrideD = 0;
     ck::index_t StrideE = N;
 
     if(argc == 1)

example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_fastgelu_bf16_i8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_abd_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+#include "ck/utility/blkgemmpipe_scheduler.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16  = ck::half_t;
+using BF16 = ck::bhalf_t;
+using I8   = int8_t;
+using F32  = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using A0DataType       = BF16;
+using AsDataType       = ck::Tuple<A0DataType>;
+using B0DataType       = I8;
+using B1DataType       = BF16;
+using BsDataType       = ck::Tuple<B0DataType, B1DataType>;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using D0DataType       = BF16;
+using DsDataType       = ck::Tuple<>;
+using EDataType        = BF16;
+
+using A0Layout = Row;
+using AsLayout = ck::Tuple<A0Layout>;
+using B0Layout = Row;
+using B1Layout = B0Layout;
+using BsLayout = ck::Tuple<B0Layout, B1Layout>;
+using D0Layout = Row;
+using DsLayout = ck::Tuple<>;
+using ELayout  = Row;
+
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using FastGelu    = ck::tensor_operation::element_wise::FastGelu;
+
+using AElementOp   = PassThrough;
+using BElementOp   = Multiply;
+using CDEElementOp = FastGelu;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
+
+using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleABD_Xdl_CShuffle
+    // clang-format off
+///######|  ALayout|  BLayout| DsLayout| ELayout|      AData|      BData|     AccData|         CShuffle|     DsData|     EData|           A|           B|          CDE|           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|         DataType|       Type|      Type| 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|
+///######|         |         |         |        |           |           |            |                 |           |          |            |            |             |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+         < AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,        1,   256,   128,   128,    64,   8,   4,  32,   32,    2,    2,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<16, 16, 1>,    S<0, 2, 1>,     S<0, 2, 1>,             1,               8,              4,          0,          1,           1,               S<1, 32, 1, 8>,               8,  ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v4>;
+// clang-format on
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // GEMM shape
+    ck::index_t M = 4096;
+    ck::index_t N = 768;
+    ck::index_t K = 6144;
+
+    ck::index_t StrideA = K;
+    ck::index_t StrideB = N;
+    ck::index_t StrideD = 0;
+    ck::index_t StrideE = N;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 11)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideD = std::stoi(argv[9]);
+        StrideE = std::stoi(argv[10]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
+    Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
+    Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, 0, B1Layout{}));
+    Tensor<D0DataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+
+    std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
+    std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
+    std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
+    std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-5, 5});
+        b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_2<B1DataType>{0, 5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
+        break;
+    default:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
+        b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});
+    }
+
+    DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b0_device_buf(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem b1_device_buf(sizeof(B1DataType) * b1_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem d_device_buf(sizeof(D0DataType) * d_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a0_device_buf.ToDevice(a0_m_k.mData.data());
+    b0_device_buf.ToDevice(b0_k_n.mData.data());
+    b1_device_buf.ToDevice(b1_k_n.mData.data());
+    d_device_buf.ToDevice(d_m_n.mData.data());
+    e_device_buf.ToDevice(e_m_n_device_result.mData.data());
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    constexpr ck::index_t NumATensor = 1;
+    constexpr ck::index_t NumBTensor = 2;
+    constexpr ck::index_t NumDTensor = 0;
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+    auto invoker   = device_op.MakeInvoker();
+    auto argument =
+        device_op.MakeArgument(std::array<const void*, NumATensor>{a0_device_buf.GetDeviceBuffer()},
+                               std::array<const void*, NumBTensor>{b0_device_buf.GetDeviceBuffer(),
+                                                                   b1_device_buf.GetDeviceBuffer()},
+                               std::array<const void*, NumDTensor>{},
+                               e_device_buf.GetDeviceBuffer(),
+                               M,
+                               N,
+                               K,
+                               std::array<ck::index_t, NumATensor>{StrideA},
+                               std::array<ck::index_t, NumBTensor>{StrideB, 0},
+                               std::array<ck::index_t, NumDTensor>{},
+                               StrideE,
+                               a_element_op,
+                               b_element_op,
+                               cde_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * M * N;
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+    if(do_verification)
+    {
+        Tensor<CShuffleDataType> c_m_n({M, N});
+
+        Tensor<A0DataType> a_m_k({M, K});
+
+        Tensor<B1DataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
+
+        for(int n = 0; n < N; ++n)
+        {
+            for(int k = 0; k < K; ++k)
+            {
+                b_element_op(b_k_n(k, n), b0_k_n(k, n), b1_k_n(k, n));
+            }
+        }
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
+                                                                                B1DataType,
+                                                                                CShuffleDataType,
+                                                                                AccDataType,
+                                                                                PassThrough,
+                                                                                PassThrough,
+                                                                                PassThrough>;
+        auto ref_gemm               = ReferenceGemmInstance{};
+        auto ref_invoker            = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(
+            a0_m_k, b_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n));
+            }
+        }
+
+        e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+        return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
+    }
+
+    return 0;
+}

example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_multiply_bias_fastgelu_bf16_i8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_abd_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+#include "ck/utility/blkgemmpipe_scheduler.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16  = ck::half_t;
+using BF16 = ck::bhalf_t;
+using I8   = int8_t;
+using F32  = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using A0DataType       = BF16;
+using AsDataType       = ck::Tuple<A0DataType>;
+using B0DataType       = I8;
+using B1DataType       = BF16;
+using BsDataType       = ck::Tuple<B0DataType>;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using D0DataType       = BF16;
+using DsDataType       = ck::Tuple<B1DataType, D0DataType>;
+using EDataType        = BF16;
+
+using A0Layout = Row;
+using AsLayout = ck::Tuple<A0Layout>;
+using B0Layout = Row;
+using B1Layout = B0Layout;
+using BsLayout = ck::Tuple<B0Layout>;
+using D0Layout = Row;
+using DsLayout = ck::Tuple<B1Layout, D0Layout>;
+using ELayout  = Row;
+
+using PassThrough         = ck::tensor_operation::element_wise::PassThrough;
+using MultiplyAddFastGelu = ck::tensor_operation::element_wise::MultiplyAddFastGelu;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = MultiplyAddFastGelu;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
+
+using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleABD_Xdl_CShuffle
+    // clang-format off
+///######|  ALayout|  BLayout| DsLayout| ELayout|      AData|      BData|     AccData|         CShuffle|     DsData|     EData|           A|           B|          CDE|           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|         DataType|       Type|      Type| 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|
+///######|         |         |         |        |           |           |            |                 |           |          |            |            |             |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+         < AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,        1,   256,   128,   128,    64,   8,   4,  32,   32,    2,    2,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<16, 16, 1>,    S<0, 2, 1>,     S<0, 2, 1>,             1,               8,              4,          0,          1,           1,               S<1, 32, 1, 8>,               8,  ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v4>;
+// clang-format on
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // GEMM shape
+    ck::index_t M = 4096;
+    ck::index_t N = 768;
+    ck::index_t K = 6144;
+
+    ck::index_t StrideA = K;
+    ck::index_t StrideB = N;
+    ck::index_t StrideD = 0;
+    ck::index_t StrideE = N;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 11)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideD = std::stoi(argv[9]);
+        StrideE = std::stoi(argv[10]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
+    Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
+    Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, 0, B1Layout{}));
+    Tensor<D0DataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+
+    std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
+    std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
+    std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
+    std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-5, 5});
+        b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_2<B1DataType>{0, 5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
+        break;
+    default:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
+        b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});
+    }
+
+    DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b0_device_buf(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem b1_device_buf(sizeof(B1DataType) * b1_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem d_device_buf(sizeof(D0DataType) * d_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a0_device_buf.ToDevice(a0_m_k.mData.data());
+    b0_device_buf.ToDevice(b0_k_n.mData.data());
+    b1_device_buf.ToDevice(b1_k_n.mData.data());
+    d_device_buf.ToDevice(d_m_n.mData.data());
+    e_device_buf.ToDevice(e_m_n_device_result.mData.data());
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    constexpr ck::index_t NumATensor = 1;
+    constexpr ck::index_t NumBTensor = 1;
+    constexpr ck::index_t NumDTensor = 2;
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+    auto invoker   = device_op.MakeInvoker();
+    auto argument =
+        device_op.MakeArgument(std::array<const void*, NumATensor>{a0_device_buf.GetDeviceBuffer()},
+                               std::array<const void*, NumBTensor>{b0_device_buf.GetDeviceBuffer()},
+                               std::array<const void*, NumDTensor>{b1_device_buf.GetDeviceBuffer(),
+                                                                   d_device_buf.GetDeviceBuffer()},
+                               e_device_buf.GetDeviceBuffer(),
+                               M,
+                               N,
+                               K,
+                               std::array<ck::index_t, NumATensor>{StrideA},
+                               std::array<ck::index_t, NumBTensor>{StrideB},
+                               std::array<ck::index_t, NumDTensor>{0, StrideD},
+                               StrideE,
+                               a_element_op,
+                               b_element_op,
+                               cde_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * M * N;
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+    if(do_verification)
+    {
+        Tensor<CShuffleDataType> c_m_n({M, N});
+
+        Tensor<A0DataType> a_m_k({M, K});
+
+        Tensor<B1DataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
+
+#if 0
+        for(int n = 0; n < N; ++n)
+        {
+            for(int k = 0; k < K; ++k)
+            {
+                b_element_op(b_k_n(k, n), b0_k_n(k, n), b1_k_n(k, n));
+            }
+        }
+#endif
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
+                                                                                B0DataType,
+                                                                                CShuffleDataType,
+                                                                                AccDataType,
+                                                                                PassThrough,
+                                                                                PassThrough,
+                                                                                PassThrough>;
+        auto ref_gemm               = ReferenceGemmInstance{};
+        auto ref_invoker            = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(
+            a0_m_k, b0_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), b1_k_n(0, n), d_m_n(m, n));
+            }
+        }
+
+        e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+        return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
+    }
+
+    return 0;
+}

include/ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7r2.hpp

@@ -41,7 +41,8 @@ template <typename ThreadGroup,
           index_t SrcScalarPerVector,
           index_t DstScalarPerVector,
           typename ThreadTransferSrcResetCoordinateAfterRunFlags,
-          typename ThreadTransferDstResetCoordinateAfterRunFlags>
+          typename ThreadTransferDstResetCoordinateAfterRunFlags,
+          index_t NumThreadScratch = 1>
 struct ThreadGroupTensorSliceTransfer_v7r2
 {
     static constexpr index_t nDim =
@@ -100,7 +101,7 @@ struct ThreadGroupTensorSliceTransfer_v7r2
            ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
         {
             const auto thread_cluster_idx = thread_cluster_desc_.CalculateBottomIndex(
-                make_multi_index(get_thread_local_1d_id()));
+                make_multi_index(ThreadGroup::GetThreadId()));
 
             const auto thread_data_idx_begin = thread_cluster_idx * thread_slice_lengths;
 
@@ -117,29 +118,33 @@ struct ThreadGroupTensorSliceTransfer_v7r2
         }
     }
 
-    template <typename SrcBuffers>
-    __device__ void RunRead(const SrcDescs& src_descs, const SrcBuffers& src_bufs)
+    template <typename SrcBuffers, index_t ThreadScratchId = 0>
+    __device__ void RunRead(const SrcDescs& src_descs,
+                            const SrcBuffers& src_bufs,
+                            Number<ThreadScratchId> thread_scratch_id = Number<ThreadScratchId>{})
     {
         if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
            ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
         {
-            threadwise_transfer_.RunRead(src_descs, src_bufs);
+            threadwise_transfer_.RunRead(src_descs, src_bufs, thread_scratch_id);
         }
     }
 
     template <typename T>
     using is_tuple = decltype(std::declval<T&>().IsTuple());
 
-    template <typename DstBuffers>
-    __device__ void RunWrite(const DstDescs& dst_descs, DstBuffers dst_bufs)
+    template <typename DstBuffers, index_t ThreadScratchId = 0>
+    __device__ void RunWrite(const DstDescs& dst_descs,
+                             DstBuffers dst_bufs,
+                             Number<ThreadScratchId> thread_scratch_id = Number<ThreadScratchId>{})
     {
         if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
            ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
         {
             if constexpr(is_detected<is_tuple, decltype(dst_bufs)>::value)
-                threadwise_transfer_.RunWrite(dst_descs, dst_bufs);
+                threadwise_transfer_.RunWrite(dst_descs, dst_bufs, thread_scratch_id);
             else
-                threadwise_transfer_.RunWrite(dst_descs, tie(dst_bufs));
+                threadwise_transfer_.RunWrite(dst_descs, tie(dst_bufs), thread_scratch_id);
         }
     }
 
@@ -206,7 +211,8 @@ struct ThreadGroupTensorSliceTransfer_v7r2
                                            SrcScalarPerVector,
                                            DstScalarPerVector,
                                            ThreadTransferSrcResetCoordinateAfterRunFlags,
-                                           ThreadTransferDstResetCoordinateAfterRunFlags>;
+                                           ThreadTransferDstResetCoordinateAfterRunFlags,
+                                           NumThreadScratch>;
 
     ThreadwiseTransfer threadwise_transfer_;
 };

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

@@ -10,110 +10,30 @@
 #include "ck/tensor_description/tensor_descriptor.hpp"
 #include "ck/tensor_description/tensor_descriptor_helper.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "ck/tensor_operation/gpu/device/device_gemm_multiple_abd.hpp"
+#include "ck/tensor_operation/gpu/device/device_gemm_v2.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
-#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3_multi_abd.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
-
-namespace ck {
-
-template <typename GridwiseGemm,
-          typename AsPointer,
-          typename BsPointer,
-          typename DsPointer,
-          typename EDataType,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CDEElementwiseOperation,
-          typename AsGridDesc_AK0_M_AK1,
-          typename BsGridDesc_BK0_N_BK1,
-          typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename Block2ETileMap,
-          bool HasMainKBlockLoop>
-__global__ void
-#if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
-#endif
-        kernel_gemm_multiple_abd_xdl_cshuffle(
-            AsPointer p_as_grid,
-            BsPointer p_bs_grid,
-            DsPointer p_ds_grid,
-            EDataType* __restrict__ p_e_grid,
-            const AElementwiseOperation a_element_op,
-            const BElementwiseOperation b_element_op,
-            const CDEElementwiseOperation cde_element_op,
-            const AsGridDesc_AK0_M_AK1 as_grid_desc_ak0_m_ak1,
-            const BsGridDesc_BK0_N_BK1 bs_grid_desc_bk0_n_bk1,
-            const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
-                ds_grid_desc_mblock_mperblock_nblock_nperblock,
-            const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
-                e_grid_desc_mblock_mperblock_nblock_nperblock,
-            const Block2ETileMap block_2_etile_map)
-{
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
-    defined(__gfx94__))
-    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
-
-    GridwiseGemm::template Run<HasMainKBlockLoop>(p_as_grid,
-                                                  p_bs_grid,
-                                                  p_ds_grid,
-                                                  p_e_grid,
-                                                  p_shared,
-                                                  a_element_op,
-                                                  b_element_op,
-                                                  cde_element_op,
-                                                  as_grid_desc_ak0_m_ak1,
-                                                  bs_grid_desc_bk0_n_bk1,
-                                                  ds_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                  e_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                  block_2_etile_map);
-#else
-    ignore = p_as_grid;
-    ignore = p_bs_grid;
-    ignore = p_ds_grid;
-    ignore = p_e_grid;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = cde_element_op;
-    ignore = as_grid_desc_ak0_m_ak1;
-    ignore = bs_grid_desc_bk0_n_bk1;
-    ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = block_2_etile_map;
-#endif
-}
-
-} // namespace ck
+#include "ck/tensor_operation/gpu/device/device_gemm_multiple_abd.hpp"
 
 namespace ck {
 namespace tensor_operation {
 namespace device {
 
-// GEMM:
-//   input : A[M, K]
-//   input : B[N, K]
-//   input : D0[M, N], D1[M, N], ...
-//   output : E[M, N]
-//   C = a_op(A) * b_op(B)
-//   E = cde_op(C, D0, D1, ...)
-// Assume:
-//   D0, D1, ... and E have the same layout
 template <typename AsLayout,
           typename BsLayout,
           typename DsLayout,
-          typename ELayout,
+          typename CLayout,
           typename AsDataType,
           typename BsDataType,
-          typename AccDataType,
+          typename GemmAccDataType,
           typename CShuffleDataType,
           typename DsDataType,
-          typename EDataType,
+          typename CDataType,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
-          typename CDEElementwiseOperation,
+          typename CElementwiseOperation,
           GemmSpecialization GemmSpec,
           index_t NumGemmKPrefetchStage,
           index_t BlockSize,
@@ -132,59 +52,56 @@ template <typename AsLayout,
           index_t ABlockTransferSrcVectorDim,
           index_t ABlockTransferSrcScalarPerVector,
           index_t ABlockTransferDstScalarPerVector_AK1,
-          index_t ABlockLdsExtraM,
+          bool ABlockLdsExtraM,
           typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
           typename BBlockTransferThreadClusterArrangeOrder,
           typename BBlockTransferSrcAccessOrder,
           index_t BBlockTransferSrcVectorDim,
           index_t BBlockTransferSrcScalarPerVector,
           index_t BBlockTransferDstScalarPerVector_BK1,
-          index_t BBlockLdsExtraN,
+          bool BBlockLdsExtraN,
           index_t CShuffleMXdlPerWavePerShuffle,
           index_t CShuffleNXdlPerWavePerShuffle,
-          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
-          index_t CDEBlockTransferScalarPerVector_NPerBlock,
-          LoopScheduler LoopSched     = make_default_loop_scheduler(),
-          PipelineVersion PipelineVer = PipelineVersion::v1>
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
+          BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
+          typename ComputeTypeA                       = CDataType,
+          typename ComputeTypeB                       = ComputeTypeA>
 struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayout,
                                                                          BsLayout,
                                                                          DsLayout,
-                                                                         ELayout,
+                                                                         CLayout,
                                                                          AsDataType,
                                                                          BsDataType,
                                                                          DsDataType,
-                                                                         EDataType,
+                                                                         CDataType,
                                                                          AElementwiseOperation,
                                                                          BElementwiseOperation,
-                                                                         CDEElementwiseOperation>
+                                                                         CElementwiseOperation>
 {
-    using DeviceOp = DeviceGemmMultipleABD_Xdl_CShuffle;
-
     static constexpr index_t NumATensor = AsDataType::Size();
     static constexpr index_t NumBTensor = BsDataType::Size();
     static constexpr index_t NumDTensor = DsDataType::Size();
 
-    static constexpr auto I0 = Number<0>{};
-    static constexpr auto I1 = Number<1>{};
-    static constexpr auto I2 = Number<2>{};
-    static constexpr auto I3 = Number<3>{};
-
-    using ComputeDataType = EDataType;
+    using ALayout = remove_cvref_t<tuple_element_t<0, AsLayout>>;
+    using BLayout = remove_cvref_t<tuple_element_t<0, BsLayout>>;
 
     // GridwiseGemm
-    using GridwiseGemm = GridwiseGemmMultipleABD_xdl_cshuffle<
+    using GridwiseGemm = GridwiseGemm_xdl_cshuffle_v3<
+        ALayout,
+        BLayout,
+        CLayout,
         AsDataType,
         BsDataType,
-        ComputeDataType,
-        AccDataType,
+        GemmAccDataType,
         CShuffleDataType,
         DsDataType,
-        EDataType,
+        CDataType,
         AElementwiseOperation,
         BElementwiseOperation,
-        CDEElementwiseOperation,
-        InMemoryDataOperationEnum::Set,
-        NumGemmKPrefetchStage,
+        CElementwiseOperation,
+        GemmSpec,
         BlockSize,
         MPerBlock,
         NPerBlock,
@@ -213,360 +130,476 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
         BBlockLdsExtraN,
         CShuffleMXdlPerWavePerShuffle,
         CShuffleNXdlPerWavePerShuffle,
-        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
-        CDEBlockTransferScalarPerVector_NPerBlock,
-        LoopSched,
-        PipelineVer>;
-
-    // desc for problem definition
-    using AsGridDesc_M_K =
-        remove_cvref_t<decltype(GridwiseGemm::template MakeAsGridDescriptor_M_K<AsLayout, GemmSpec>(
-            {}, {}, {}))>;
-    using BsGridDesc_N_K =
-        remove_cvref_t<decltype(GridwiseGemm::template MakeBsGridDescriptor_N_K<BsLayout, GemmSpec>(
-            {}, {}, {}))>;
-    using DsGridDesc_M_N =
-        remove_cvref_t<decltype(GridwiseGemm::template MakeDsGridDescriptor_M_N<DsLayout, GemmSpec>(
-            {}, {}, {}))>;
-    using EGridDesc_M_N =
-        decltype(GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(1, 1, 1));
-
-    // desc for blockwise copy
-    using AsGridDesc_AK0_M_AK1 =
-        remove_cvref_t<decltype(GridwiseGemm::MakeDefaultAsGridDescriptor_AK0_M_AK1(
-            AsGridDesc_M_K{}))>;
-    using BsGridDesc_BK0_N_BK1 =
-        remove_cvref_t<decltype(GridwiseGemm::MakeDefaultBsGridDescriptor_BK0_N_BK1(
-            BsGridDesc_N_K{}))>;
-    using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<
-        decltype(GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
-            DsGridDesc_M_N{}))>;
-    using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
-        remove_cvref_t<decltype(GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
-            EGridDesc_M_N{}))>;
-
-    // block-to-e-tile map
-    using Block2ETileMap =
-        remove_cvref_t<decltype(GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
-
-    // Argument
-    struct Argument : public BaseArgument
-    {
-        Argument(std::array<const void*, NumATensor> p_as_grid,
-                 std::array<const void*, NumBTensor> p_bs_grid,
-                 std::array<const void*, NumDTensor> p_ds_grid,
-                 void* p_e_grid,
-                 index_t MRaw,
-                 index_t NRaw,
-                 index_t KRaw,
-                 std::array<index_t, NumATensor> StrideAs,
-                 std::array<index_t, NumBTensor> StrideBs,
-                 std::array<index_t, NumDTensor> StrideDs,
-                 index_t StrideE,
-                 AElementwiseOperation a_element_op,
-                 BElementwiseOperation b_element_op,
-                 CDEElementwiseOperation cde_element_op)
-            : p_as_grid_{},
-              p_bs_grid_{},
-              p_ds_grid_{},
-              p_e_grid_{static_cast<EDataType*>(p_e_grid)},
-              as_grid_desc_m_k_{},
-              bs_grid_desc_n_k_{},
-              ds_grid_desc_m_n_{},
-              e_grid_desc_m_n_{GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
-                  MRaw, NRaw, StrideE)},
-              as_grid_desc_ak0_m_ak1_{},
-              bs_grid_desc_bk0_n_bk1_{},
-              ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
-              e_grid_desc_mblock_mperblock_nblock_nperblock_{},
-              block_2_etile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
-              a_element_op_{a_element_op},
-              b_element_op_{b_element_op},
-              cde_element_op_{cde_element_op},
-              MRaw_{MRaw},
-              NRaw_{NRaw},
-              KRaw_{KRaw}
-        {
-            // populate pointer, desc for As
-            static_for<0, NumATensor, 1>{}([&](auto i) {
-                using ALayout   = remove_cvref_t<tuple_element_t<i.value, AsLayout>>;
-                using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
-
-                // A pointer
-                p_as_grid_(i) = static_cast<const ADataType*>(p_as_grid[i]);
-
-                // A desc
-                as_grid_desc_m_k_(i) =
-                    GridwiseGemm::template MakeAGridDescriptor_M_K<ALayout, GemmSpec>(
-                        MRaw, KRaw, StrideAs[i]);
-            });
-
-            // populate pointer, desc for Bs
-            static_for<0, NumBTensor, 1>{}([&](auto i) {
-                using BLayout   = remove_cvref_t<tuple_element_t<i.value, BsLayout>>;
-                using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
-
-                // B pointer
-                p_bs_grid_(i) = static_cast<const BDataType*>(p_bs_grid[i]);
-
-                // B desc
-                bs_grid_desc_n_k_(i) =
-                    GridwiseGemm::template MakeBGridDescriptor_N_K<BLayout, GemmSpec>(
-                        NRaw, KRaw, StrideBs[i]);
-            });
-
-            // populate pointer, desc for Ds
-            static_for<0, NumDTensor, 1>{}([&](auto i) {
-                using DLayout   = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
-                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
-
-                // D pointer
-                p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
-
-                // D desc
-                ds_grid_desc_m_n_(i) =
-                    GridwiseGemm::template MakeEGridDescriptor_M_N<DLayout, GemmSpec>(
-                        MRaw, NRaw, StrideDs[i]);
-            });
-
-            // populate desc for Ds/E
-            if(GridwiseGemm::CheckValidity(as_grid_desc_m_k_,
-                                           bs_grid_desc_n_k_,
-                                           ds_grid_desc_m_n_,
-                                           e_grid_desc_m_n_,
-                                           block_2_etile_map_))
-            {
-                as_grid_desc_ak0_m_ak1_ =
-                    GridwiseGemm::MakeDefaultAsGridDescriptor_AK0_M_AK1(as_grid_desc_m_k_);
-
-                bs_grid_desc_bk0_n_bk1_ =
-                    GridwiseGemm::MakeDefaultBsGridDescriptor_BK0_N_BK1(bs_grid_desc_n_k_);
-
-                ds_grid_desc_mblock_mperblock_nblock_nperblock_ =
-                    GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
-                        ds_grid_desc_m_n_);
-
-                e_grid_desc_mblock_mperblock_nblock_nperblock_ =
-                    GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
-                        e_grid_desc_m_n_);
-            }
-        }
+        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        BlkGemmPipeSched,
+        BlkGemmPipelineVer,
+        ComputeTypeA,
+        ComputeTypeB>;
 
-        //  private:
-        // pointers
-        typename GridwiseGemm::AsGridPointer p_as_grid_;
-        typename GridwiseGemm::BsGridPointer p_bs_grid_;
-        typename GridwiseGemm::DsGridPointer p_ds_grid_;
-        EDataType* p_e_grid_;
-
-        // tensor descriptors for problem definiton
-        AsGridDesc_M_K as_grid_desc_m_k_;
-        BsGridDesc_N_K bs_grid_desc_n_k_;
-        DsGridDesc_M_N ds_grid_desc_m_n_;
-        EGridDesc_M_N e_grid_desc_m_n_;
-
-        // tensor descriptors for block/thread-wise copy
-        AsGridDesc_AK0_M_AK1 as_grid_desc_ak0_m_ak1_;
-        BsGridDesc_BK0_N_BK1 bs_grid_desc_bk0_n_bk1_;
-        DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
-            ds_grid_desc_mblock_mperblock_nblock_nperblock_;
-        EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
-
-        // block-to-e-tile map
-        Block2ETileMap block_2_etile_map_;
-
-        // element-wise op
-        AElementwiseOperation a_element_op_;
-        BElementwiseOperation b_element_op_;
-        CDEElementwiseOperation cde_element_op_;
-
-        // for checking vector load/store
-        index_t MRaw_;
-        index_t NRaw_;
-        index_t KRaw_;
-    };
+    using Argument = typename GridwiseGemm::Argument;
 
     // Invoker
     struct Invoker : public BaseInvoker
     {
-        using Argument = DeviceOp::Argument;
-
         float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
         {
-            if(!GridwiseGemm::CheckValidity(arg.as_grid_desc_m_k_,
-                                            arg.bs_grid_desc_n_k_,
-                                            arg.ds_grid_desc_m_n_,
-                                            arg.e_grid_desc_m_n_,
-                                            arg.block_2_etile_map_))
+            if(stream_config.log_level_ > 0)
+            {
+                arg.Print();
+            }
+
+            if(!GridwiseGemm::CheckValidity(arg))
             {
                 throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
             }
 
-            const index_t grid_size =
-                arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
-
-            auto launch_kernel = [&](auto has_main_k_block_loop) {
-                constexpr bool has_main_loop = has_main_k_block_loop.value;
-
-                const auto kernel = kernel_gemm_multiple_abd_xdl_cshuffle<
-                    GridwiseGemm,
-                    typename GridwiseGemm::AsGridPointer,
-                    typename GridwiseGemm::BsGridPointer,
-                    typename GridwiseGemm::DsGridPointer,
-                    EDataType,
-                    AElementwiseOperation,
-                    BElementwiseOperation,
-                    CDEElementwiseOperation,
-                    DeviceOp::AsGridDesc_AK0_M_AK1,
-                    DeviceOp::BsGridDesc_BK0_N_BK1,
-                    DeviceOp::DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
-                    DeviceOp::EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
-                    DeviceOp::Block2ETileMap,
-                    has_main_loop>;
-
-                return launch_and_time_kernel(stream_config,
-                                              kernel,
-                                              dim3(grid_size),
-                                              dim3(BlockSize),
-                                              0,
-                                              arg.p_as_grid_,
-                                              arg.p_bs_grid_,
-                                              arg.p_ds_grid_,
-                                              arg.p_e_grid_,
-                                              arg.a_element_op_,
-                                              arg.b_element_op_,
-                                              arg.cde_element_op_,
-                                              arg.as_grid_desc_ak0_m_ak1_,
-                                              arg.bs_grid_desc_bk0_n_bk1_,
-                                              arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
-                                              arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
-                                              arg.block_2_etile_map_);
-            };
+            index_t gdx, gdy, gdz;
+            std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.KBatch);
 
-            const auto K = arg.as_grid_desc_m_k_[I0].GetLength(I1);
+            float ave_time = 0;
 
-            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
-            {
-                return launch_kernel(integral_constant<bool, true>{});
-            }
-            else
-            {
-                return launch_kernel(integral_constant<bool, false>{});
-            }
-        }
+            index_t k_grain = arg.KBatch * KPerBlock;
+            index_t K_split = (arg.K + k_grain - 1) / k_grain * KPerBlock;
 
-        // polymorphic
-        float Run(const BaseArgument* p_arg,
-                  const StreamConfig& stream_config = StreamConfig{}) override
-        {
-            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
-        }
-    };
+            const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
 
-    static bool IsSupportedArgument(const Argument& arg)
-    {
-        if(!ck::is_xdl_supported())
-        {
-            return false;
-        }
+            const auto Run = [&](const auto& kernel) {
+                if(arg.KBatch > 1)
+                    hipGetErrorString(hipMemsetAsync(arg.p_c_grid,
+                                                     0,
+                                                     arg.M * arg.N * sizeof(CDataType),
+                                                     stream_config.stream_id_));
 
-        // check vector load/store
-        {
-            using Row = ck::tensor_layout::gemm::RowMajor;
-            using Col = ck::tensor_layout::gemm::ColumnMajor;
+                ave_time = launch_and_time_kernel(
+                    stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, arg);
+            };
 
-            bool all_valid = true;
+            constexpr index_t minimum_occupancy =
+                BlkGemmPipeSched == BlockGemmPipelineScheduler::Intrawave ? 1 : 2;
 
-            static_for<0, NumATensor, 1>{}([&](auto i) {
-                using ALayout = remove_cvref_t<tuple_element_t<i.value, AsLayout>>;
-                // check vector load of A
-                if constexpr(is_same_v<ALayout, Row> && ABlockTransferSrcVectorDim == 2)
+            if(has_main_k_block_loop)
+            {
+                // Tail number always full
+                if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1 ||
+                             BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
                 {
-                    if(arg.KRaw_ % ABlockTransferSrcScalarPerVector != 0)
+#if 0
+                    if(arg.KBatch > 1)
                     {
-                        all_valid = false;
+                        const auto kernel =
+                            kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                        true,
+                                                        InMemoryDataOperationEnum::AtomicAdd,
+                                                        minimum_occupancy>;
+                        Run(kernel);
                     }
-                }
-                else if constexpr(is_same_v<ALayout, Col> && ABlockTransferSrcVectorDim == 1)
-                {
-                    // FIXME: not rigorous
-                    if(arg.MRaw_ % ABlockTransferSrcScalarPerVector != 0)
+                    else
+#endif
                     {
-                        all_valid = false;
+                        const auto kernel =
+                            kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                        true,
+                                                        InMemoryDataOperationEnum::Set,
+                                                        minimum_occupancy>;
+                        Run(kernel);
                     }
                 }
-                else
+                // Tail number could be One to Seven
+                else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
                 {
-                    if(ABlockTransferSrcScalarPerVector != 1)
+#if 0
+                    if(arg.KBatch > 1)
                     {
-                        all_valid = false;
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::AtomicAdd,
+                                                            minimum_occupancy,
+                                                            TailNumber::One>;
+                            Run(kernel);
+                        }
+                        else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                                TailNumber::Full)
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::AtomicAdd,
+                                                            minimum_occupancy,
+                                                            TailNumber::Full>;
+                            Run(kernel);
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
+                            {
+                                const auto kernel = kernel_gemm_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Two>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Three)
+                            {
+                                const auto kernel = kernel_gemm_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Three>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Four)
+                            {
+                                const auto kernel = kernel_gemm_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Four>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Five)
+                            {
+                                const auto kernel = kernel_gemm_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Five>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
+                            {
+                                const auto kernel = kernel_gemm_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Six>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Seven)
+                            {
+                                const auto kernel = kernel_gemm_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Seven>;
+                                Run(kernel);
+                            }
+                        }
                     }
-                }
-            });
-
-            static_for<0, NumBTensor, 1>{}([&](auto i) {
-                using BLayout = remove_cvref_t<tuple_element_t<i.value, BsLayout>>;
-                // check vector laod of B
-                if constexpr(is_same_v<BLayout, Col> && BBlockTransferSrcVectorDim == 2)
-                {
-                    if(arg.KRaw_ % BBlockTransferSrcScalarPerVector != 0)
+                    else
+#endif
                     {
-                        all_valid = false;
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::Set,
+                                                            minimum_occupancy,
+                                                            TailNumber::One>;
+                            Run(kernel);
+                        }
+                        else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                                TailNumber::Full)
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::Set,
+                                                            minimum_occupancy,
+                                                            TailNumber::Full>;
+                            Run(kernel);
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
+                            {
+                                const auto kernel =
+                                    kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                                true,
+                                                                InMemoryDataOperationEnum::Set,
+                                                                minimum_occupancy,
+                                                                TailNumber::Two>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Three)
+                            {
+                                const auto kernel =
+                                    kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                                true,
+                                                                InMemoryDataOperationEnum::Set,
+                                                                minimum_occupancy,
+                                                                TailNumber::Three>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Four)
+                            {
+                                const auto kernel =
+                                    kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                                true,
+                                                                InMemoryDataOperationEnum::Set,
+                                                                minimum_occupancy,
+                                                                TailNumber::Four>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Five)
+                            {
+                                const auto kernel =
+                                    kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                                true,
+                                                                InMemoryDataOperationEnum::Set,
+                                                                minimum_occupancy,
+                                                                TailNumber::Five>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
+                            {
+                                const auto kernel =
+                                    kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                                true,
+                                                                InMemoryDataOperationEnum::Set,
+                                                                minimum_occupancy,
+                                                                TailNumber::Six>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Seven)
+                            {
+                                const auto kernel =
+                                    kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                                true,
+                                                                InMemoryDataOperationEnum::Set,
+                                                                minimum_occupancy,
+                                                                TailNumber::Seven>;
+                                Run(kernel);
+                            }
+                        }
                     }
                 }
-                else if constexpr(is_same_v<BLayout, Row> && BBlockTransferSrcVectorDim == 1)
+                // Tail number could be Odd or Even
+                else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
                 {
-                    // FIXME: not rigorous
-                    if(arg.NRaw_ % BBlockTransferSrcScalarPerVector != 0)
+#if 0
+                    if(arg.KBatch > 1)
                     {
-                        all_valid = false;
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_gemm_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_gemm_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                    else
+#endif
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3_2lds<GridwiseGemm,
+                                                                 true,
+                                                                 InMemoryDataOperationEnum::Set,
+                                                                 minimum_occupancy,
+                                                                 TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3_2lds<GridwiseGemm,
+                                                                 true,
+                                                                 InMemoryDataOperationEnum::Set,
+                                                                 minimum_occupancy,
+                                                                 TailNumber::Even>;
+                            Run(kernel);
+                        }
                     }
                 }
                 else
                 {
-                    if(BBlockTransferSrcScalarPerVector != 1)
+#if 0
+                    if(arg.KBatch > 1)
                     {
-                        all_valid = false;
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::AtomicAdd,
+                                                            minimum_occupancy,
+                                                            TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::AtomicAdd,
+                                                            minimum_occupancy,
+                                                            TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                    else
+#endif
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::Set,
+                                                            minimum_occupancy,
+                                                            TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel =
+                                kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                            true,
+                                                            InMemoryDataOperationEnum::Set,
+                                                            minimum_occupancy,
+                                                            TailNumber::Even>;
+                            Run(kernel);
+                        }
                     }
-                }
-            });
-
-            // check vector load of Ds
-            // only support RowMajor for now
-            static_for<0, NumDTensor, 1>{}([&](auto i) {
-                using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
-
-                if constexpr(!is_same_v<DLayout, Row>)
-                {
-                    all_valid = false;
-                }
-            });
-
-            // check vector store of E
-            // only support RowMajor for now
-            if constexpr(is_same_v<ELayout, Row>)
-            {
-                if(arg.NRaw_ % CDEBlockTransferScalarPerVector_NPerBlock != 0)
-                {
-                    all_valid = false;
                 }
             }
             else
             {
-                all_valid = false;
+                // Tail number always 1
+                if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
+                {
+#if 0
+                    if(arg.KBatch > 1)
+                    {
+                        const auto kernel =
+                            kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                        false,
+                                                        InMemoryDataOperationEnum::AtomicAdd,
+                                                        minimum_occupancy>;
+                        Run(kernel);
+                    }
+                    else
+#endif
+                    {
+                        const auto kernel =
+                            kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
+                                                        false,
+                                                        InMemoryDataOperationEnum::Set,
+                                                        minimum_occupancy>;
+                        Run(kernel);
+                    }
+                }
             }
 
-            if(!all_valid)
-            {
-                return false;
-            }
+            return ave_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if(!ck::is_xdl_supported())
+        {
+            return false;
         }
 
-        return GridwiseGemm::CheckValidity(arg.as_grid_desc_m_k_,
-                                           arg.bs_grid_desc_n_k_,
-                                           arg.ds_grid_desc_m_n_,
-                                           arg.e_grid_desc_m_n_,
-                                           arg.block_2_etile_map_);
+        if((arg.K % AK1 != 0 || arg.K % BK1 != 0) && !(GemmSpec == GemmSpecialization::MKPadding ||
+                                                       GemmSpec == GemmSpecialization::NKPadding ||
+                                                       GemmSpec == GemmSpecialization::MNKPadding ||
+                                                       GemmSpec == GemmSpecialization::KPadding))
+        {
+            return false;
+        }
+
+        return GridwiseGemm::CheckValidity(arg);
     }
 
     // polymorphic
@@ -588,8 +621,27 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
                              index_t StrideE,
                              AElementwiseOperation a_element_op,
                              BElementwiseOperation b_element_op,
-                             CDEElementwiseOperation cde_element_op)
+                             CElementwiseOperation c_element_op)
     {
+
+        static_for<0, NumATensor, 1>{}([&](auto i) {
+            using ALayout_ = remove_cvref_t<tuple_element_t<i.value, AsLayout>>;
+
+            static_assert(is_same<ALayout_, ALayout>::value, "");
+        });
+
+        static_for<0, NumBTensor, 1>{}([&](auto i) {
+            using BLayout_ = remove_cvref_t<tuple_element_t<i.value, BsLayout>>;
+
+            static_assert(is_same<BLayout_, BLayout>::value, "");
+        });
+
+        static_for<0, NumDTensor, 1>{}([&](auto i) {
+            using DLayout_ = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
+
+            static_assert(is_same<DLayout_, CLayout>::value, "");
+        });
+
         return Argument{p_as,
                         p_bs,
                         p_ds,
@@ -601,29 +653,29 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
                         StrideBs,
                         StrideDs,
                         StrideE,
+                        1,
                         a_element_op,
                         b_element_op,
-                        cde_element_op};
+                        c_element_op};
     }
 
     static auto MakeInvoker() { return Invoker{}; }
 
     // polymorphic
-    std::unique_ptr<BaseArgument>
-    MakeArgumentPointer(std::array<const void*, NumATensor> p_as,
-                        std::array<const void*, NumBTensor> p_bs,
-                        std::array<const void*, NumDTensor> p_ds,
-                        void* p_e,
-                        index_t MRaw,
-                        index_t NRaw,
-                        index_t KRaw,
-                        std::array<ck::index_t, NumATensor> StrideAs,
-                        std::array<ck::index_t, NumBTensor> StrideBs,
-                        std::array<ck::index_t, NumDTensor> StrideDs,
-                        index_t StrideE,
-                        AElementwiseOperation a_element_op,
-                        BElementwiseOperation b_element_op,
-                        CDEElementwiseOperation cde_element_op) override
+    std::unique_ptr<BaseArgument> MakeArgumentPointer(std::array<const void*, NumATensor> p_as,
+                                                      std::array<const void*, NumBTensor> p_bs,
+                                                      std::array<const void*, NumDTensor> p_ds,
+                                                      void* p_e,
+                                                      index_t MRaw,
+                                                      index_t NRaw,
+                                                      index_t KRaw,
+                                                      std::array<ck::index_t, NumATensor> StrideAs,
+                                                      std::array<ck::index_t, NumBTensor> StrideBs,
+                                                      std::array<ck::index_t, NumDTensor> StrideDs,
+                                                      index_t StrideE,
+                                                      AElementwiseOperation a_element_op,
+                                                      BElementwiseOperation b_element_op,
+                                                      CElementwiseOperation c_element_op) override
     {
         return std::make_unique<Argument>(p_as,
                                           p_bs,
@@ -636,9 +688,10 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
                                           StrideBs,
                                           StrideDs,
                                           StrideE,
+                                          1,
                                           a_element_op,
                                           b_element_op,
-                                          cde_element_op);
+                                          c_element_op);
     }
 
     // polymorphic
@@ -652,35 +705,41 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
     {
         auto str = std::stringstream();
 
-        std::map<LoopScheduler, std::string> LoopSchedToString{
-            {LoopScheduler::Default, "Default"}, {LoopScheduler::Interwave, "Interwave"}};
+        std::map<BlockGemmPipelineScheduler, std::string> BlkGemmPipelineSchedulerToString{
+            {BlockGemmPipelineScheduler::Intrawave, "Intrawave"},
+            {BlockGemmPipelineScheduler::Interwave, "Interwave"}};
 
-        std::map<PipelineVersion, std::string> PipelineVersionToString{{PipelineVersion::v1, "v1"},
-                                                                       {PipelineVersion::v2, "v2"}};
+        std::map<BlockGemmPipelineVersion, std::string> BlkGemmPipelineVersionToString{
+            {BlockGemmPipelineVersion::v1, "v1"},
+            {BlockGemmPipelineVersion::v2, "v2"},
+            {BlockGemmPipelineVersion::v3, "v3"},
+            {BlockGemmPipelineVersion::v4, "v4"},
+            {BlockGemmPipelineVersion::v5, "v5"}};
 
         // clang-format off
-        str << "DeviceGemmMultipleABD_Xdl_CShuffle"
+        str << "DeviceGemmXdlUniversal"
             << "<"
-            << BlockSize << ", "
-            << MPerBlock << ", "
-            << NPerBlock << ", "
-            << KPerBlock << ", "
-            << AK1 << ", "
-            << BK1 << ", "
-            << MPerXDL << ", "
-            << NPerXDL << ", "
-            << MXdlPerWave << ", "
-            << NXdlPerWave << ", "
-            << ABlockTransferSrcScalarPerVector << ", "
-            << BBlockTransferSrcScalarPerVector << ", "
-            << CShuffleMXdlPerWavePerShuffle << ", "
-            << CShuffleNXdlPerWavePerShuffle << ", "
-            << getGemmSpecializationString(GemmSpec)
+            << getGemmSpecializationString(GemmSpec) << ", "
+            << std::string(ALayout::name)[0]
+            << std::string(BLayout::name)[0]
+            << std::string(CLayout::name)[0]
             << ">"
-            << " LoopScheduler: "
-            << LoopSchedToString[LoopSched] << ", "
-            << "PipelineVersion: "
-            << PipelineVersionToString[PipelineVer];
+            << " BlkSize: "
+            << BlockSize << ", "
+            << "BlkTile: "
+            << MPerBlock<<"x"<<NPerBlock<<"x"<<KPerBlock << ", "
+            << "WaveTile: "
+            << MPerXDL<<"x"<<NPerXDL << ", "
+            << "WaveMap: "
+            << MXdlPerWave<<"x" << NXdlPerWave<<", "
+            << "VmemReadVec: "
+            << ABlockTransferSrcScalarPerVector<<"x"<<BBlockTransferSrcScalarPerVector<<", "
+            << "BlkGemmPipelineScheduler: "
+            << BlkGemmPipelineSchedulerToString[BlkGemmPipeSched] << ", "
+            << "BlkGemmPipelineVersion: "
+            << BlkGemmPipelineVersionToString[BlkGemmPipelineVer] << ", "
+            << "BlkGemmPipelinePrefetchStages: "
+            << GridwiseGemm::BlockwiseGemmPipe::PrefetchStages;
         // clang-format on
 
         return str.str();

include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp

@@ -92,15 +92,6 @@ struct Add
     };
 };
 
-struct Scales
-{
-    template <typename Y, typename X0, typename X1>
-    __host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const
-    {
-        y = ck::type_convert<Y>(ck::type_convert<float>(x0) * ck::type_convert<float>(x1));
-    }
-};
-
 struct Max
 {
     template <typename Y, typename X0, typename X1>
@@ -188,6 +179,16 @@ struct Multiply
         y                  = ck::type_convert<bhalf_t>(y_tmp);
     }
 
+    template <>
+    __host__ __device__ constexpr void
+    operator()<bhalf_t>(bhalf_t& y, const int8_t& x0, const bhalf_t& x1) const
+    {
+        const float x1_tmp = ck::type_convert<float>(x0);
+        const float x2_tmp = ck::type_convert<float>(x1);
+        const float y_tmp  = x1_tmp * x2_tmp;
+        y                  = ck::type_convert<bhalf_t>(y_tmp);
+    }
+
     template <>
     __host__ __device__ constexpr void
     operator()<bhalf_t>(bhalf_t& y, const float& x0, const bhalf_t& x1) const
@@ -521,6 +522,71 @@ struct AddFastGelu
     }
 };
 
+// E = MultiplyFastGelu(C + D)
+struct MultiplyFastGelu
+{
+    template <typename E, typename C, typename D>
+    __host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
+
+    template <>
+    __host__ __device__ constexpr void
+    operator()<float, float, float>(float& e, const float& c, const float& d) const
+    {
+        const float x = c * d;
+
+        FastGelu{}.template operator()<float, float>(e, x);
+    }
+
+    template <>
+    __host__ __device__ constexpr void
+    operator()<half_t, half_t, half_t>(half_t& e, const half_t& c, const half_t& d) const
+    {
+        const half_t x = c * d;
+
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<half_t, half_t>(e, x);
+    }
+
+    template <>
+    __host__ __device__ constexpr void
+    operator()<half_t, float, half_t>(half_t& e, const float& c, const half_t& d) const
+    {
+        const float x0_f = c * d;
+
+        float x1_f = 0;
+
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<float, float>(x1_f,
+                                                                                         x0_f);
+
+        e = type_convert<half_t>(x1_f);
+    }
+
+    template <>
+    __host__ __device__ constexpr void
+    operator()<bhalf_t, bhalf_t, bhalf_t>(bhalf_t& e, const bhalf_t& c, const bhalf_t& d) const
+    {
+        const float x0_f = type_convert<float>(c) * type_convert<float>(d);
+
+        float x1_f = 0;
+
+        FastGelu{}.template operator()<float, float>(x1_f, x0_f);
+
+        e = type_convert<bhalf_t>(x1_f);
+    }
+
+    template <>
+    __host__ __device__ constexpr void
+    operator()<bhalf_t, float, bhalf_t>(bhalf_t& e, const float& c, const bhalf_t& d) const
+    {
+        const float x0_f = c * type_convert<float>(d);
+
+        float x1_f = 0;
+
+        FastGelu{}.template operator()<float, float>(x1_f, x0_f);
+
+        e = type_convert<bhalf_t>(x1_f);
+    }
+};
+
 // E = Silu(C + D)
 struct AddSilu
 {

include/ck/tensor_operation/gpu/element/element_wise_operation.hpp

@@ -221,6 +221,15 @@ struct MultiplyAdd
         e              = y;
     }
     template <>
+    __host__ __device__ void operator()<bhalf_t, float, bhalf_t, bhalf_t>(bhalf_t& e,
+                                                                          const float& c,
+                                                                          const bhalf_t& d0,
+                                                                          const bhalf_t& d1) const
+    {
+        const bhalf_t y = type_convert<bhalf_t>(c) * d0 + d1;
+        e               = y;
+    }
+    template <>
     __host__ __device__ void operator()<float, float, half_t, half_t>(float& e,
                                                                       const float& c,
                                                                       const half_t& d0,
@@ -240,6 +249,26 @@ struct MultiplyAdd
     }
 };
 
+struct MultiplyAddFastGelu
+{
+    template <typename E, typename C, typename D0, typename D1>
+    __host__ __device__ constexpr void
+    operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
+
+    template <>
+    __host__ __device__ constexpr void operator()<ck::bhalf_t, float, ck::bhalf_t, ck::bhalf_t>(
+        ck::bhalf_t& e, const float& c, const ck::bhalf_t& d0, const ck::bhalf_t& d1) const
+    {
+        const float x0_f = c * ck::type_convert<float>(d0) + ck::type_convert<float>(d1);
+
+        float x1_f = 0;
+
+        FastGelu{}.template operator()<float, float>(x1_f, x0_f);
+
+        e = ck::type_convert<ck::bhalf_t>(x1_f);
+    }
+};
+
 // E = FastGelu(C + D0 + D1)
 struct AddAddFastGelu
 {

include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp

@@ -504,6 +504,16 @@ struct FastGelu
         y = type_convert<half_t>(y_f);
     }
 
+    template <>
+    __host__ void operator()<bhalf_t, float>(bhalf_t& y, const float& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, x);
+
+        y = type_convert<bhalf_t>(y_f);
+    }
+
     template <>
     __device__ void operator()<bhalf_t, float>(bhalf_t& y, const float& x) const
     {

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

@@ -594,11 +594,6 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
             generate_tuple([&](auto) { return make_multi_index(0, m_block_data_idx_on_grid, 0); },
                            Number<NumATensor>{});
 
-#if 0
-        static_assert(ABlockTransferSrcScalarPerVector == ABlockTransferDstScalarPerVector_AK1,
-                      "Src and Dst ScalarPerVector must be the same");
-#endif
-
         auto a_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
             ThisThreadBlock,
             AsDataType,
@@ -616,7 +611,7 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
             2,
             ABlockTransferSrcScalarPerVector,
             ABlockTransferDstScalarPerVector_AK1,
-            uniform_sequence_gen_t<NumATensor, false>,
+            uniform_sequence_gen_t<NumATensor, AThreadTransferSrcResetCoordinateAfterRun>,
             Sequence<true>>{as_grid_desc_ak0_m_ak1,
                             idx_as_block_begin,
                             tie(a_block_desc_ak0_m_ak1),
@@ -627,11 +622,6 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
             generate_tuple([&](auto) { return make_multi_index(0, n_block_data_idx_on_grid, 0); },
                            Number<NumBTensor>{});
 
-#if 0
-        static_assert(BBlockTransferSrcScalarPerVector == BBlockTransferDstScalarPerVector_BK1,
-                      "Src and Dst ScalarPerVector must be the same");
-#endif
-
         auto b_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
             ThisThreadBlock,
             BsDataType,
@@ -649,7 +639,7 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
             2,
             BBlockTransferSrcScalarPerVector,
             BBlockTransferDstScalarPerVector_BK1,
-            uniform_sequence_gen_t<NumBTensor, false>,
+            uniform_sequence_gen_t<NumBTensor, BThreadTransferSrcResetCoordinateAfterRun>,
             Sequence<true>>{bs_grid_desc_bk0_n_bk1,
                             idx_bs_block_begin,
                             tie(b_block_desc_bk0_n_bk1),