Merge branch 'develop' into jd/dev_pkg

example/15_grouped_gemm/CMakeLists.txt

+add_example_executable(example_grouped_gemm_xdl_fp16 grouped_gemm_xdl_fp16.cpp)

example/15_grouped_gemm/README.md

+# Instructions for ```example_grouped_gemm_xdl```
+
+## Run ```example_grouped_gemm_xdl```
+```bash
+#arg1: verification (0=no, 1=yes)
+#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
+#arg3: run kernel # of times (>1)
+./bin/example_grouped_gemm_xdl_fp16 0 1 5
+```
+
+Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
+```
+gemm[0] a_m_k: dim 2, lengths {256, 64}, strides {64, 1} b_k_n: dim 2, lengths {64, 128}, strides {1, 64} c_m_n: dim 2, lengths {256, 128}, strides {128, 1}
+gemm[1] a_m_k: dim 2, lengths {512, 128}, strides {128, 1} b_k_n: dim 2, lengths {128, 256}, strides {1, 128} c_m_n: dim 2, lengths {512, 256}, strides {256, 1}
+gemm[2] a_m_k: dim 2, lengths {768, 192}, strides {192, 1} b_k_n: dim 2, lengths {192, 384}, strides {1, 192} c_m_n: dim 2, lengths {768, 384}, strides {384, 1}
+gemm[3] a_m_k: dim 2, lengths {1024, 256}, strides {256, 1} b_k_n: dim 2, lengths {256, 512}, strides {1, 256} c_m_n: dim 2, lengths {1024, 512}, strides {512, 1}
+group: 0 arg.a_grid_desc_k0_m_k1_{8, 256, 8}, arg.b_grid_desc_k0_n_k1_{8, 128, 8}, arg.c_grid_desc_m_n_{ 256, 128}
+group: 1 arg.a_grid_desc_k0_m_k1_{16, 512, 8}, arg.b_grid_desc_k0_n_k1_{16, 256, 8}, arg.c_grid_desc_m_n_{ 512, 256}
+group: 2 arg.a_grid_desc_k0_m_k1_{24, 768, 8}, arg.b_grid_desc_k0_n_k1_{24, 384, 8}, arg.c_grid_desc_m_n_{ 768, 384}
+group: 3 arg.a_grid_desc_k0_m_k1_{32, 1024, 8}, arg.b_grid_desc_k0_n_k1_{32, 512, 8}, arg.c_grid_desc_m_n_{ 1024, 512}
+launch_and_time_kernel: grid_dim {30, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 5 times...
+Perf: 0.037887 ms, 11.0706 TFlops, 90.8132 GB/s, DeviceGroupedGemmXdl<256, 256, 128, 4, 8, 32, 32, 4, 2>
+```

example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "check_err.hpp"
+#include "config.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "host_gemm.hpp"
+#include "device_tensor.hpp"
+#include "device_grouped_gemm_xdl.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType   = ck::half_t;
+using BDataType   = ck::half_t;
+using CDataType   = ck::half_t;
+using AccDataType = float;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+// static constexpr auto GemmMNPadding =
+// ck::tensor_operation::device::GemmSpecialization::MNPadding;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdl
+//######| AData| BData| CData| AccData| ALayout| BLayout| CLayout|           A|           B|           C|          GEMM| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|      Num|
+//######|  Type|  Type|  Type|    Type|        |        |        | Elementwise| Elementwise| Elementwise|Spacialization|  Size| Block| Block| Block|   |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| SrcDstVectorDim|       DstScalar| Prefetch|
+//######|      |      |      |        |        |        |        |   Operation|   Operation|   Operation|              |      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|         |
+//######|      |      |      |        |        |        |        |            |            |            |              |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |         |
+        <   F16,   F16,   F16,     F32,     Row,     Col,     Row, PassThrough, PassThrough, PassThrough,   GemmDefault,   256,   256,   128,     4,  8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,               7,               1,        1>;
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::
+    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    int nrepeat          = 5;
+
+    if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: run kernel # of times (>1)\n");
+        exit(0);
+    }
+
+    int group_count = 4;
+
+    // GEMM shape
+    std::vector<ck::tensor_operation::device::GemmShape> gemm_shapes;
+    std::vector<const void*> p_a, p_b;
+    std::vector<void*> p_c;
+
+    gemm_shapes.reserve(group_count);
+
+    for(int i = 0; i < group_count; i++)
+    {
+        int M = 256 + 256 * i;
+        int N = 128 + 128 * i;
+        int K = 64 + 64 * i;
+
+        gemm_shapes.push_back({M, N, K, K, K, N});
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    std::vector<Tensor<ADataType>> a_tensors;
+    ;
+    std::vector<Tensor<BDataType>> b_tensors;
+    std::vector<Tensor<CDataType>> c_host_tensors;
+    std::vector<Tensor<CDataType>> c_device_tensors;
+
+    a_tensors.reserve(group_count);
+    b_tensors.reserve(group_count);
+    c_host_tensors.reserve(group_count);
+    c_device_tensors.reserve(group_count);
+
+    using DeviceMemPtr = std::unique_ptr<DeviceMem>;
+
+    std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, c_tensors_device;
+
+    a_tensors_device.reserve(group_count);
+    b_tensors_device.reserve(group_count);
+    c_tensors_device.reserve(group_count);
+
+    std::size_t flop = 0, num_btype = 0;
+
+    for(int i = 0; i < gemm_shapes.size(); i++)
+    {
+        a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
+            gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{})));
+        b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
+            gemm_shapes[i].K, gemm_shapes[i].N, gemm_shapes[i].StrideB, BLayout{})));
+        c_host_tensors.push_back(Tensor<CDataType>(f_host_tensor_descriptor(
+            gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{})));
+        c_device_tensors.push_back(Tensor<CDataType>(f_host_tensor_descriptor(
+            gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{})));
+
+        std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
+                  << " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << c_device_tensors[i].mDesc
+                  << std::endl;
+
+        flop += std::size_t(2) * gemm_shapes[i].M * gemm_shapes[i].K * gemm_shapes[i].N;
+        num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
+                     sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
+                     sizeof(CDataType) * c_device_tensors[i].mDesc.GetElementSize();
+
+        switch(init_method)
+        {
+        case 0: break;
+        case 1:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+            break;
+        case 2:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+            break;
+        default:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        }
+    }
+
+    for(int i = 0; i < gemm_shapes.size(); i++)
+    {
+        a_tensors_device.emplace_back(
+            std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace()));
+        b_tensors_device.emplace_back(
+            std::make_unique<DeviceMem>(sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpace()));
+        c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
+            sizeof(CDataType) * c_device_tensors[i].mDesc.GetElementSpace()));
+
+        a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
+        b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
+
+        p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
+        p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
+        p_c.push_back(c_tensors_device[i]->GetDeviceBuffer());
+    }
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm    = DeviceGemmInstance{};
+    auto invoker = gemm.MakeInvoker();
+    auto argument =
+        gemm.MakeArgument(p_a, p_b, p_c, gemm_shapes, a_element_op, b_element_op, c_element_op);
+
+    if(!gemm.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, nrepeat);
+
+    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, "
+              << gemm.GetTypeString() << std::endl;
+
+    if(do_verification)
+    {
+        for(int i = 0; i < gemm_shapes.size(); i++)
+        {
+            c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
+            auto ref_gemm    = ReferenceGemmInstance{};
+            auto ref_invoker = ref_gemm.MakeInvoker();
+
+            auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
+                                                      b_tensors[i],
+                                                      c_host_tensors[i],
+                                                      a_element_op,
+                                                      b_element_op,
+                                                      c_element_op);
+
+            ref_invoker.Run(ref_argument);
+            ck::utils::check_err(c_device_tensors[i].mData, c_host_tensors[i].mData);
+        }
+    }
+
+    return 0;
+}

example/16_gemm_reduce/CMakeLists.txt

+add_example_executable(example_gemm_reduce_xdl_fp16 gemm_reduce_xdl_fp16.cpp)

example/16_gemm_reduce/gemm_reduce_xdl_fp16.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_reduce_xdl_cshuffle.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+#include "element_wise_reduce_operation.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using ADataType = F16;
+using BDataType = F16;
+using CDataType = F16;
+using DDataType = F32;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+using D0ReduceOp = ck::tensor_operation::element_wise::ReduceSum;
+using D1ReduceOp = ck::tensor_operation::element_wise::ReduceSquareSum;
+
+static constexpr auto GemmSpecialization =
+    ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_Xdl_CShuffle
+//######| ALayout| BLayout| CLayout|AData| BData| CData|  GemmAcc| CShuffle| ReduceAcc| DData|           A|           B|           C|         D0|         D1|               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|              CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
+//######|        |        |        | Type|  Type|  Type| DataType| DataType|  DataType|  Type| Elementwise| Elementwise| Elementwise|     Reduce|     Reduce|     Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar|    ExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar|    ExtraN| MXdlPerWave| NXdlPerWave|            _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths|     SrcDstScalarPerVector|        SrcDstScalarPerVector|
+//######|        |        |        |     |      |      |         |         |          |      |   Operation|   Operation|   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_NPerBlock|      _NPerBlock| _MPerBlock_NPerBlock|                _NPerBlock|                   _MPerBlock|
+//######|        |        |        |     |      |      |         |         |          |      |            |            |            |           |           |                   |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |                     |                          |                             |
+        <     Row,     Col,     Row,  F16,   F16,   F16,      F32,      F32,       F32,   F32,  AElementOp,  BElementOp,  CElementOp, D0ReduceOp, D1ReduceOp, GemmSpecialization,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               8,             S<64, 4>,                         4,                            1>;
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::
+    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 1;
+    int init_method      = 1;
+    int nrepeat          = 5;
+
+    // GEMM shape
+    ck::index_t M = 3840;
+    ck::index_t N = 4096;
+    ck::index_t K = 4096;
+
+    ck::index_t StrideA = 4096;
+    ck::index_t StrideB = 4096;
+    ck::index_t StrideC = 4096;
+
+    if(argc == 1)
+    {
+        // do nothing
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+    }
+    else if(argc == 10)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = 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]);
+        StrideC = std::stoi(argv[9]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+
+    Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<DDataType> d0_m_host_result(
+        HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
+    Tensor<DDataType> d1_m_host_result(
+        HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
+
+    Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<DDataType> d0_m_device_result(
+        HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
+    Tensor<DDataType> d1_m_device_result(
+        HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
+
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+    std::cout << "d0_m: " << d0_m_host_result.mDesc << std::endl;
+    std::cout << "d1_m: " << d1_m_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        break;
+    }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
+    DeviceMem d0_device_buf(sizeof(DDataType) * d0_m_device_result.mDesc.GetElementSpace());
+    DeviceMem d1_device_buf(sizeof(DDataType) * d1_m_device_result.mDesc.GetElementSpace());
+
+    a_device_buf.ToDevice(a_m_k.mData.data());
+    b_device_buf.ToDevice(b_k_n.mData.data());
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+    auto d0_reduce_op = D0ReduceOp{};
+    auto d1_reduce_op = D1ReduceOp{};
+
+    // do GEMM
+    auto gemm     = DeviceGemmReduceInstance{};
+    auto invoker  = gemm.MakeInvoker();
+    auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
+                                      static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
+                                      static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
+                                      static_cast<DDataType*>(d0_device_buf.GetDeviceBuffer()),
+                                      static_cast<DDataType*>(d1_device_buf.GetDeviceBuffer()),
+                                      M,
+                                      N,
+                                      K,
+                                      StrideA,
+                                      StrideB,
+                                      StrideC,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op,
+                                      d0_reduce_op,
+                                      d1_reduce_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    // warm up
+    invoker.Run(argument);
+
+    // timing
+    float total_time = 0;
+
+    for(int i = 0; i < nrepeat; ++i)
+    {
+        // init DO, D1 to 0
+        d0_device_buf.SetZero();
+        d1_device_buf.SetZero();
+
+        KernelTimer timer;
+
+        timer.Start();
+
+        invoker.Run(argument);
+
+        timer.End();
+
+        total_time += timer.GetElapsedTime();
+    }
+
+    float ave_time = total_time / nrepeat;
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * 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, "
+              << gemm.GetTypeString() << std::endl;
+
+    if(do_verification)
+    {
+        c_device_buf.FromDevice(c_m_n_device_result.mData.data());
+        d0_device_buf.FromDevice(d0_m_device_result.mData.data());
+        d1_device_buf.FromDevice(d1_m_device_result.mData.data());
+
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(
+            a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            float d0_acc = d0_reduce_op.GetReduceZeroValue();
+            float d1_acc = d1_reduce_op.GetReduceZeroValue();
+
+            for(int n = 0; n < N; ++n)
+            {
+                d0_reduce_op.Reduce(d0_acc, c_m_n_host_result(m, n));
+                d1_reduce_op.Reduce(d1_acc, c_m_n_host_result(m, n));
+            }
+
+            d0_m_host_result(m) = d0_acc;
+            d1_m_host_result(m) = d1_acc;
+        }
+
+        check_error(c_m_n_host_result, c_m_n_device_result);
+        check_error(d0_m_host_result, d0_m_device_result);
+        check_error(d1_m_host_result, d1_m_device_result);
+    }
+
+    return 0;
+}

example/17_convnd_bwd_data_xdl/CMakeLists.txt

+add_example_executable(example_convnd_bwd_data_xdl convnd_bwd_data_xdl.cpp)

example/17_convnd_bwd_data_xdl/README.md

+# Instructions for ```example_convnd_bwd_data_xdl```
+
+## Run ```example_example_convnd_bwd_data_xdl```
+```bash
+#arg1: verification (0=no, 1=yes)
+#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
+#arg3: run kernel # of times (>1)
+#arg4: num_dim_spatial(1|2|3)
+#arg5 to ...: N, K, C, [Z,] [Y,] X, [Di,] [Hi,] Wi, S[z,] [Sy,] Sx, [Dz,] [Dy,] Dx, [LeftPz,] [LeftPy,] LeftPx, [RightPy,] [RightPy,] RightPx
+./bin/example_convnd_bwd_data_xdl 0 1 5 
+```
+
+Result
+```
+in_n_c_hi_wi: dim 4, lengths {128, 128, 71, 71}, strides {645248, 1, 9088, 128}
+wei_k_c_y_x: dim 4, lengths {256, 128, 3, 3}, strides {1152, 1, 384, 128}
+out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
+arg.a_grid_desc_k0_m_k1_container_{128, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{128, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{64, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{64, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+arg.a_grid_desc_k0_m_k1_container_{32, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{32, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+Perf: 1.40031 ms, 69.8734 TFlops, 179.037 GB/s
+```

example/17_convnd_bwd_data_xdl/convnd_bwd_data_xdl.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "config.hpp"
+#include "conv_fwd_util.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "tensor_layout.hpp"
+#include "element_wise_operation.hpp"
+#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
+#include "reference_conv_bwd_data.hpp"
+
+using InDataType  = ck::half_t;
+using WeiDataType = ck::half_t;
+using OutDataType = ck::half_t;
+using AccDataType = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
+using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
+using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
+static constexpr auto ConvBwdDefault =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Default;
+
+using DeviceConvBwdDataBasePtr =
+    ck::tensor_operation::device::DeviceConvBwdDataPtr<InElementOp, WeiElementOp, OutElementOp>;
+
+template <ck::index_t NumDimSpatial>
+using DeviceConvNDBwdDataInstance = ck::tensor_operation::device::
+    DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<
+        InDataType,     // InDataType
+        WeiDataType,    // WeiDataType
+        OutDataType,    // OutDataType
+        AccDataType,    // AccDataType
+        InElementOp,    // InElementwiseOperation
+        WeiElementOp,   // WeiElementwiseOperation
+        OutElementOp,   // OutElementwiseOperation
+        ConvBwdDefault, // ConvolutionBackwardDataSpecialization
+        NumDimSpatial,  // NumDimSpatial
+        256,            // BlockSize
+        128,            // MPerBlock
+        128,            // NPerBlock
+        4,              // K0PerBlock
+        8,              // K1
+        32,             // MPerXdl
+        32,             // NPerXdl
+        2,              // MXdlPerWave
+        2,              // NXdlPerWave
+        S<4, 64, 1>,    // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<1, 0, 2>,     // ABlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,     // ABlockTransferSrcAccessOrder
+        2,              // ABlockTransferSrcVectorDim
+        8,              // ABlockTransferSrcScalarPerVector
+        8,              // ABlockTransferDstScalarPerVector_K1
+        true,           // ABlockLdsAddExtraM
+        S<4, 64, 1>,    // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<2, 0, 1>,     // BBlockTransferThreadClusterArrangeOrder
+        S<0, 2, 1>,     // BBlockTransferSrcAccessOrder
+        1,              // BBlockTransferSrcVectorDim
+        2,              // BBlockTransferSrcScalarPerVector
+        8,              // BBlockTransferDstScalarPerVector_K1
+        true,           // BBlockLdsAddExtraN
+        7,
+        1>; // GemmCThreadTransferDstScalarPerVector
+
+template <ck::index_t NumDimSpatial>
+using ReferenceConvBwdDataInstance =
+    ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
+                                                     WeiDataType,
+                                                     OutDataType,
+                                                     AccDataType,
+                                                     InElementOp,
+                                                     WeiElementOp,
+                                                     OutElementOp,
+                                                     NumDimSpatial>;
+
+void print_use_msg()
+{
+    std::cout << "arg1: verification (0=no, 1=yes)\n"
+              << "arg2: initialization (0=no init, 1=random value, 2= init to 1 )\n"
+              << "arg3: run kernel # of times (>1)\n"
+              << "arg4: N spatial dimensions (default 2)\n"
+              << "Following arguments (depending on number of spatial dims):\n"
+              << " N, K, C, \n"
+              << " <filter spatial dimensions>, (ie Y, X for 2D)\n"
+              << " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
+              << " <strides>, (ie Sy, Sx for 2D)\n"
+              << " <dilations>, (ie Dy, Dx for 2D)\n"
+              << " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
+              << " <right padding>, (ie RightPy, RightPx for 2D)\n"
+              << std::endl;
+}
+ck::utils::conv::ConvParams parse_conv_params(int num_dim_spatial, char* argv[])
+{
+    // (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
+    ck::utils::conv::ConvParams params;
+    int arg_idx = 5;
+
+    params.num_dim_spatial = num_dim_spatial;
+    params.N               = std::stoi(argv[arg_idx++]);
+    params.K               = std::stoi(argv[arg_idx++]);
+    params.C               = std::stoi(argv[arg_idx++]);
+
+    params.filter_spatial_lengths.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_spatial_lengths.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_strides.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_dilations.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_left_pads.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_left_pads[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_right_pads.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_right_pads[i] = std::stoi(argv[arg_idx++]);
+    }
+
+    return params;
+}
+
+DeviceConvBwdDataBasePtr get_conv_instance(int num_dim_spatial)
+{
+    switch(num_dim_spatial)
+    {
+    case 3: {
+        return std::make_unique<DeviceConvNDBwdDataInstance<3>>();
+    }
+    case 2: {
+        return std::make_unique<DeviceConvNDBwdDataInstance<2>>();
+    }
+    case 1: {
+        return std::make_unique<DeviceConvNDBwdDataInstance<1>>();
+    }
+    default: {
+        throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+    }
+    }
+}
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    int nrepeat          = 5;
+    int num_dim_spatial  = 2;
+
+    ck::utils::conv::ConvParams params;
+    params.C = 128;
+
+    if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+    }
+    else if(argc > 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+        num_dim_spatial = std::stoi(argv[4]);
+        // check args number
+        int conv_args     = 3 + num_dim_spatial * 6;
+        int cmdline_nargs = conv_args + 5;
+        if(cmdline_nargs != argc)
+        {
+            print_use_msg();
+            exit(1);
+        }
+
+        params = parse_conv_params(num_dim_spatial, argv);
+    }
+    else if(argc != 1)
+    {
+        print_use_msg();
+        exit(1);
+    }
+
+    std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N),
+                                        static_cast<std::size_t>(params.C)};
+    input_dims.insert(std::end(input_dims),
+                      std::begin(params.input_spatial_lengths),
+                      std::end(params.input_spatial_lengths));
+
+    std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K),
+                                         static_cast<std::size_t>(params.C)};
+    filter_dims.insert(std::end(filter_dims),
+                       std::begin(params.filter_spatial_lengths),
+                       std::end(params.filter_spatial_lengths));
+
+    const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
+    std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N),
+                                         static_cast<std::size_t>(params.K)};
+    output_dims.insert(std::end(output_dims),
+                       std::begin(output_spatial_lengths),
+                       std::end(output_spatial_lengths));
+
+    Tensor<InDataType> in_n_c_hi_wi_host_result(
+        ck::utils::conv::get_input_host_tensor_descriptor(input_dims, num_dim_spatial));
+    Tensor<InDataType> in_n_c_hi_wi_device_result(
+        ck::utils::conv::get_input_host_tensor_descriptor(input_dims, num_dim_spatial));
+    Tensor<WeiDataType> wei_k_c_y_x(
+        ck::utils::conv::get_filters_host_tensor_descriptor(filter_dims, num_dim_spatial));
+    Tensor<OutDataType> out_n_k_ho_wo(
+        ck::utils::conv::get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
+
+    std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi_host_result.mDesc << std::endl;
+    std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
+    std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_3<OutDataType>{-0.2, 0.2});
+        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.2, 0.2});
+        break;
+    default:
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
+        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
+    }
+
+    DeviceMem in_device_buf(sizeof(InDataType) *
+                            in_n_c_hi_wi_device_result.mDesc.GetElementSpace());
+    DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
+    DeviceMem out_device_buf(sizeof(OutDataType) * out_n_k_ho_wo.mDesc.GetElementSpace());
+
+    out_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
+    wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
+    // reset input to zero
+    in_device_buf.SetZero();
+
+    // do GEMM
+    auto conv    = get_conv_instance(num_dim_spatial);
+    auto invoker = conv->MakeInvokerPointer();
+    auto argument =
+        conv->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
+                                  static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
+                                  static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
+                                  params.N,
+                                  params.K,
+                                  params.C,
+                                  params.input_spatial_lengths,
+                                  params.filter_spatial_lengths,
+                                  output_spatial_lengths,
+                                  params.conv_filter_strides,
+                                  params.conv_filter_dilations,
+                                  params.input_left_pads,
+                                  params.input_right_pads,
+                                  InElementOp{},
+                                  WeiElementOp{},
+                                  OutElementOp{});
+
+    if(!conv->IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "wrong! device_conv with the specified compilation parameters does "
+            "not support this Conv problem");
+    }
+
+    float ave_time = invoker->Run(argument.get(), nrepeat);
+
+    std::size_t flop = ck::utils::conv::get_flops(
+        params.N, params.C, params.K, params.filter_spatial_lengths, output_spatial_lengths);
+    std::size_t num_btype = ck::utils::conv::get_btype<InDataType, WeiDataType, OutDataType>(
+        params.N,
+        params.C,
+        params.K,
+        params.input_spatial_lengths,
+        params.filter_spatial_lengths,
+        output_spatial_lengths);
+
+    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;
+
+    if(do_verification)
+    {
+        auto verify_f = [&](const auto& ref_conv) {
+            auto ref_invoker = ref_conv.MakeInvoker();
+
+            auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi_host_result,
+                                                      wei_k_c_y_x,
+                                                      out_n_k_ho_wo,
+                                                      params.conv_filter_strides,
+                                                      params.conv_filter_dilations,
+                                                      params.input_left_pads,
+                                                      params.input_right_pads,
+                                                      InElementOp{},
+                                                      WeiElementOp{},
+                                                      OutElementOp{});
+
+            ref_invoker.Run(ref_argument);
+
+            in_device_buf.FromDevice(in_n_c_hi_wi_device_result.mData.data());
+
+            check_error(in_n_c_hi_wi_host_result, in_n_c_hi_wi_device_result);
+        };
+
+        switch(num_dim_spatial)
+        {
+        case 3: {
+            auto ref_conv = ReferenceConvBwdDataInstance<3>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 2: {
+            auto ref_conv = ReferenceConvBwdDataInstance<2>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 1: {
+            auto ref_conv = ReferenceConvBwdDataInstance<1>();
+            verify_f(ref_conv);
+            break;
+        }
+        default: {
+            throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+        }
+        }
+    }
+}

example/18_batched_gemm_reduce/CMakeLists.txt

+add_example_executable(example_batched_gemm_reduce_xdl_fp16 batched_gemm_reduce_xdl_fp16.cpp)
+

example/18_batched_gemm_reduce/batched_gemm_reduce_xdl_fp16.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "device_batched_gemm_reduce_xdl_cshuffle.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_batched_gemm.hpp"
+#include "gemm_specialization.hpp"
+#include "element_wise_reduce_operation.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using ADataType = F16;
+using BDataType = F16;
+using CDataType = F16;
+using DDataType = F32;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+using D0ReduceOp = ck::tensor_operation::element_wise::ReduceSum;
+using D1ReduceOp = ck::tensor_operation::element_wise::ReduceSquareSum;
+
+static constexpr auto GemmSpecialization =
+    ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceBatchedGemmReduceInstance = ck::tensor_operation::device::DeviceBatchedGemmReduce_Xdl_CShuffle
+//######| ALayout| BLayout| CLayout|AData| BData| CData|  GemmAcc| CShuffle| ReduceAcc| DData|           A|           B|           C|         D0|         D1|               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|              CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
+//######|        |        |        | Type|  Type|  Type| DataType| DataType|  DataType|  Type| Elementwise| Elementwise| Elementwise|     Reduce|     Reduce|     Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar|    ExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar|    ExtraN| MXdlPerWave| NXdlPerWave|            _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths|     SrcDstScalarPerVector|        SrcDstScalarPerVector|
+//######|        |        |        |     |      |      |         |         |          |      |   Operation|   Operation|   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_NPerBlock|      _NPerBlock| _MPerBlock_NPerBlock|                _NPerBlock|                   _MPerBlock|
+//######|        |        |        |     |      |      |         |         |          |      |            |            |            |           |           |                   |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |                     |                          |                             |
+        <     Row,     Col,     Row,  F16,   F16,   F16,      F32,      F32,       F32,   F32,  AElementOp,  BElementOp,  CElementOp, D0ReduceOp, D1ReduceOp, GemmSpecialization,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               8,             S<64, 4>,                         4,                            1>;
+// clang-format on
+
+using ReferenceBatchedGemmInstance = ck::tensor_operation::host::
+    ReferenceBatchedGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 1;
+    int init_method      = 1;
+    int nrepeat          = 5;
+
+    // GEMM shape
+    ck::index_t M = 3840;
+    ck::index_t N = 4096;
+    ck::index_t K = 4096;
+
+    ck::index_t StrideA = 4096;
+    ck::index_t StrideB = 4096;
+    ck::index_t StrideC = 4096;
+
+    ck::index_t BatchCount = 4;
+
+    if(argc == 1)
+    {
+        // do nothing
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+    }
+    else if(argc == 11)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = 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]);
+        StrideC = std::stoi(argv[9]);
+
+        BatchCount = std::stoi(argv[9]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, BatchCount\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor = [](std::size_t batch_count,
+                                       std::size_t row,
+                                       std::size_t col,
+                                       std::size_t stride,
+                                       auto layout) {
+        if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+        {
+            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
+                                        std::vector<std::size_t>({row * stride, stride, 1}));
+        }
+        else
+        {
+            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
+                                        std::vector<std::size_t>({col * stride, 1, stride}));
+        }
+    };
+
+    Tensor<ADataType> a_g_m_k(f_host_tensor_descriptor(BatchCount, M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_g_k_n(f_host_tensor_descriptor(BatchCount, K, N, StrideB, BLayout{}));
+
+    Tensor<CDataType> c_g_m_n_host_result(
+        f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
+    Tensor<DDataType> d0_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
+        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
+    Tensor<DDataType> d1_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
+        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
+
+    Tensor<CDataType> c_g_m_n_device_result(
+        f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
+    Tensor<DDataType> d0_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
+        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
+    Tensor<DDataType> d1_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
+        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
+
+    std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
+    std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
+    std::cout << "c_g_m_n: " << c_g_m_n_host_result.mDesc << std::endl;
+    std::cout << "d0_g_m: " << d0_g_m_host_result.mDesc << std::endl;
+    std::cout << "d1_g_m: " << d1_g_m_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    default:
+        a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        break;
+    }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpace());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpace());
+    DeviceMem c_device_buf(sizeof(CDataType) * c_g_m_n_device_result.mDesc.GetElementSpace());
+    DeviceMem d0_device_buf(sizeof(DDataType) * d0_g_m_device_result.mDesc.GetElementSpace());
+    DeviceMem d1_device_buf(sizeof(DDataType) * d1_g_m_device_result.mDesc.GetElementSpace());
+
+    a_device_buf.ToDevice(a_g_m_k.mData.data());
+    b_device_buf.ToDevice(b_g_k_n.mData.data());
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+    auto d0_reduce_op = D0ReduceOp{};
+    auto d1_reduce_op = D1ReduceOp{};
+
+    // do GEMM
+    auto batched_gemm = DeviceBatchedGemmReduceInstance{};
+    auto invoker      = batched_gemm.MakeInvoker();
+    auto argument =
+        batched_gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
+                                  static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
+                                  static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
+                                  static_cast<DDataType*>(d0_device_buf.GetDeviceBuffer()),
+                                  static_cast<DDataType*>(d1_device_buf.GetDeviceBuffer()),
+                                  M,
+                                  N,
+                                  K,
+                                  StrideA,
+                                  StrideB,
+                                  StrideC,
+                                  a_element_op,
+                                  b_element_op,
+                                  c_element_op,
+                                  d0_reduce_op,
+                                  d1_reduce_op,
+                                  BatchCount);
+
+    if(!batched_gemm.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    // warm up
+    invoker.Run(argument);
+
+    // timing
+    float total_time = 0;
+
+    for(int i = 0; i < nrepeat; ++i)
+    {
+        // init DO, D1 to 0
+        d0_device_buf.SetZero();
+        d1_device_buf.SetZero();
+
+        KernelTimer timer;
+
+        timer.Start();
+
+        invoker.Run(argument);
+
+        timer.End();
+
+        total_time += timer.GetElapsedTime();
+    }
+
+    float ave_time = total_time / nrepeat;
+
+    std::size_t flop      = std::size_t(2) * BatchCount * M * N * K;
+    std::size_t num_btype = sizeof(ADataType) * BatchCount * M * K +
+                            sizeof(BDataType) * BatchCount * K * N +
+                            sizeof(CDataType) * BatchCount * 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, "
+              << batched_gemm.GetTypeString() << std::endl;
+
+    if(do_verification)
+    {
+        c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
+        d0_device_buf.FromDevice(d0_g_m_device_result.mData.data());
+        d1_device_buf.FromDevice(d1_g_m_device_result.mData.data());
+
+        auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
+        auto ref_invoker      = ref_batched_gemm.MakeInvoker();
+
+        auto ref_argument = ref_batched_gemm.MakeArgument(
+            a_g_m_k, b_g_k_n, c_g_m_n_host_result, a_element_op, b_element_op, c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+        for(int batch = 0; batch < BatchCount; ++batch)
+        {
+            for(int m = 0; m < M; ++m)
+            {
+                float d0_acc = d0_reduce_op.GetReduceZeroValue();
+                float d1_acc = d1_reduce_op.GetReduceZeroValue();
+
+                for(int n = 0; n < N; ++n)
+                {
+                    d0_reduce_op.Reduce(d0_acc, c_g_m_n_host_result(batch, m, n));
+                    d1_reduce_op.Reduce(d1_acc, c_g_m_n_host_result(batch, m, n));
+                }
+
+                d0_g_m_host_result(batch, m) = d0_acc;
+                d1_g_m_host_result(batch, m) = d1_acc;
+            }
+        }
+
+        check_error(c_g_m_n_host_result, c_g_m_n_device_result);
+        check_error(d0_g_m_host_result, d0_g_m_device_result);
+        check_error(d1_g_m_host_result, d1_g_m_device_result);
+    }
+
+    return 0;
+}

example/CMakeLists.txt

@@ -13,6 +13,7 @@ include_directories(BEFORE
     ${PROJECT_SOURCE_DIR}/library/include/ck/library/host_tensor
     ${PROJECT_SOURCE_DIR}/library/include/ck/library/reference_tensor_operation/cpu
     ${PROJECT_SOURCE_DIR}/library/include/ck/library/reference_tensor_operation/gpu
+    ${PROJECT_SOURCE_DIR}/library/include/ck/library/utility
     ${PROJECT_SOURCE_DIR}/external/include/half
 )
 
@@ -30,12 +31,15 @@ add_subdirectory(01_gemm)
 add_subdirectory(02_gemm_alpha_beta)
 add_subdirectory(03_gemm_bias_relu)
 add_subdirectory(04_gemm_bias_relu_add)
-add_subdirectory(05_conv2d_fwd)
 add_subdirectory(06_conv2d_fwd_bias_relu)
 add_subdirectory(07_conv2d_fwd_bias_relu_add)
-add_subdirectory(08_conv3d_fwd)
 add_subdirectory(09_convnd_fwd)
 add_subdirectory(10_conv2d_bwd_data)
-add_subdirectory(11_conv2d_bwd_wgt)
+add_subdirectory(11_conv2d_bwd_weight)
 add_subdirectory(12_reduce)
 add_subdirectory(13_pool2d_fwd)
+add_subdirectory(14_gemm_xdl_requant_relu_requant)
+add_subdirectory(17_convnd_bwd_data_xdl)
+add_subdirectory(15_grouped_gemm)
+add_subdirectory(16_gemm_reduce)
+add_subdirectory(18_batched_gemm_reduce)

include/ck/config.hpp

@@ -6,15 +6,9 @@
 #include "hip/hip_fp16.h"
 #endif
 
-// "Constant" address space for kernel parameter
-#define CONSTANT __attribute__((address_space(4)))
-
-// GPU target
-// should enable one and only one GPU target
-#if !(defined(CK_AMD_GPU_GFX803) || defined(CK_AMD_GPU_GFX900) || defined(CK_AMD_GPU_GFX906) || \
-      defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90A) || defined(CK_AMD_GPU_GFX1030))
-#error Need to define (only) one GPU target
-#endif
+// constant address space for kernel parameter
+// https://llvm.org/docs/AMDGPUUsage.html#address-spaces
+#define CK_CONSTANT_ADDRESS_SPACE __attribute__((address_space(4)))
 
 // launch bounds
 #define CK_USE_LAUNCH_BOUNDS 1
@@ -24,149 +18,134 @@
 #define CK_MIN_BLOCK_PER_CU 2
 #endif
 
-// GPU-specific parameters
-#if defined(CK_AMD_GPU_GFX803) || defined(CK_AMD_GPU_GFX900) || defined(CK_AMD_GPU_GFX906) || \
-    defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90A)
-// buffer resourse
+// check GPU target
+#ifdef __HIP_DEVICE_COMPILE__
+#if !(defined(__gfx803__) || defined(__gfx900__) || defined(__gfx906__) || defined(__gfx908__) || \
+      defined(__gfx90a__) || defined(__gfx1030__))
+#error Not supported target
+#endif
+#endif
+
+// buffer resourse, wave size
+#ifndef __HIP_DEVICE_COMPILE__ // for host code
+#define CK_BUFFER_RESOURCE_3RD_DWORD -1
+#define CK_GPU_WAVE_SIZE -1
+#elif defined(__gfx803__) || defined(__gfx900__) || defined(__gfx906__) || defined(__gfx908__) || \
+    defined(__gfx90a__) // for GPU code
 #define CK_BUFFER_RESOURCE_3RD_DWORD 0x00020000
-// wave size
 #define CK_GPU_WAVE_SIZE 64
-#elif defined(CK_AMD_GPU_GFX1030)
+#elif defined(__gfx1030__) // for GPU code
 #define CK_BUFFER_RESOURCE_3RD_DWORD 0x31014000
 #define CK_GPU_WAVE_SIZE 32
 #endif
 
 // FMA instruction
-#if defined(CK_AMD_GPU_GFX803) || defined(CK_AMD_GPU_GFX900)
+#ifndef __HIP_DEVICE_COMPILE__                   // for host code, define nothing
+#elif defined(__gfx803__) || defined(__gfx900__) // for GPU code
 #define CK_USE_AMD_V_MAC_F32
-#elif defined(CK_AMD_GPU_GFX906) || defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90a) || \
-    defined(CK_AMD_GPU_GFX1030)
+#elif defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx1030__) // for GPU code
 #define CK_USE_AMD_V_FMAC_F32
 #define CK_USE_AMD_V_DOT2_F32_F16
 #define CK_USE_AMD_V_DOT4_I32_I8
 #endif
 
-// multi index
-#define CK_USE_DYNAMICALLY_INDEXED_MULTI_INDEX 0
-
-// AMD inline asm
-#ifndef CK_USE_AMD_INLINE_ASM
-#define CK_USE_AMD_INLINE_ASM 1
+// MFMA instruction
+#ifndef __HIP_DEVICE_COMPILE__ // for host code
+#define CK_USE_AMD_MFMA
+#elif defined(__gfx908__) || defined(__gfx90a__) // for GPU code
+#define CK_USE_AMD_MFMA
 #endif
 
-// AMD inner product (DLOP)
-#ifndef CK_USE_AMD_INNER_PRODUCT_INLINE_ASM
-#define CK_USE_AMD_INNER_PRODUCT_INLINE_ASM 1
+#if defined(__gfx90a__)
+#define CK_USE_AMD_MFMA_BF16_1K_OP
 #endif
 
-// AMD buffer_load
-#ifndef CK_USE_AMD_BUFFER_LOAD
+// buffer load
 #define CK_USE_AMD_BUFFER_LOAD 1
-#endif
 
-// AMD buffer_store
-#ifndef CK_USE_AMD_BUFFER_STORE
+// buffer store
 #define CK_USE_AMD_BUFFER_STORE 1
-#endif
 
-// AMD buffer_atomic_add
-#ifndef CK_USE_AMD_BUFFER_ATOMIC_ADD
-#define CK_USE_AMD_BUFFER_ATOMIC_ADD 1
-#endif
+// buffer atomic add: integer
+#define CK_USE_AMD_BUFFER_ATOMIC_ADD_INTEGER 1
 
-// AMD XDLOPS
-#ifndef CK_USE_AMD_XDLOPS
-#define CK_USE_AMD_XDLOPS 0
+// buffer atomic add: floating point
+#ifndef __HIP_DEVICE_COMPILE__ // for host code
+#define CK_USE_AMD_BUFFER_ATOMIC_ADD_FLOAT 1
+#elif defined(__gfx908__) || defined(__gfx90a__) // for GPU code
+#define CK_USE_AMD_BUFFER_ATOMIC_ADD_FLOAT 1
+#else // for GPU code
+#define CK_USE_AMD_BUFFER_ATOMIC_ADD_FLOAT 0
 #endif
 
+// inline asm
+#define CK_USE_AMD_INLINE_ASM 1
+
+// inner product (DLOP)
+#define CK_USE_AMD_INNER_PRODUCT_INLINE_ASM 1
+
 // block synchronization only s_wait lgkmcnt(0), not vmcnt(0)
-#ifndef CK_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM
-#define CK_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM 1
-#endif
+#define CK_EXPERIMENTAL_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM 1
 
-// experimental implementation for buffer load/store/atomic
-#ifndef CK_EXPERIMENTAL_USE_BUFFER_LOAD_OOB_CHECK_OFFSET_TRICK
-#define CK_EXPERIMENTAL_USE_BUFFER_LOAD_OOB_CHECK_OFFSET_TRICK 0
-#endif
+// experimental feature: multi index implemented as array
+#define CK_EXPERIMENTAL_USE_DYNAMICALLY_INDEXED_MULTI_INDEX 0
 
-#ifndef CK_EXPERIMENTAL_USE_BUFFER_STORE_OOB_CHECK_OFFSET_TRICK
-#define CK_EXPERIMENTAL_USE_BUFFER_STORE_OOB_CHECK_OFFSET_TRICK 1
-#endif
+// experimental feature: static tensor descriptor
+#define CK_EXPERIMENTAL_STATIC_TENSOR_DESCRIPTOR 0
 
-#ifndef CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_ADD_OOB_CHECK_OFFSET_TRICK
+// experimental feature: buffer load/store/atomic-add OOB trick
+#define CK_EXPERIMENTAL_USE_BUFFER_LOAD_OOB_CHECK_OFFSET_TRICK 0
+#define CK_EXPERIMENTAL_USE_BUFFER_STORE_OOB_CHECK_OFFSET_TRICK 1
 #define CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_ADD_OOB_CHECK_OFFSET_TRICK 1
-#endif
 
-// experimental implementation for in-regsiter sub-dword transpose
-#ifndef CK_EXPERIMENTAL_USE_IN_REGISTER_SUB_DWORD_TRANSPOSE
+// experimental feature: in-regsiter sub-dword transpose
 #define CK_EXPERIMENTAL_USE_IN_REGISTER_SUB_DWORD_TRANSPOSE 1
-#endif
-
-#define CK_EXPERIMENTAL_STATIC_TENSOR_DESCRIPTOR 0
 
-// merge transformation use magic number division
-#ifndef CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION
+// experimental feature: merge transformation use magic number division
 #define CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION 1
-#endif
 
-// use __builtin_memcpy instead of pointer cast to access a vector from pointer of scalar
-#ifndef CK_EXPERIMENTAL_USE_MEMCPY_FOR_VECTOR_ACCESS
+// experimental feature: use __builtin_memcpy instead of pointer cast to access a vector from
+// pointer of scalar
 #define CK_EXPERIMENTAL_USE_MEMCPY_FOR_VECTOR_ACCESS 0
-#endif
 
-// use __builtin_memcpy instead of union to do bit_cast
-#ifndef CK_EXPERIMENTAL_USE_MEMCPY_FOR_BIT_CAST
+// experimental feature: use __builtin_memcpy instead of union to do bit_cast
 #define CK_EXPERIMENTAL_USE_MEMCPY_FOR_BIT_CAST 1
-#endif
 
 // hack: have underlying assumption that need to be satsified, otherwise it's a bug
 // hack for forcing register to keep idx_diff_low_const in SGPR. idx_diff_low_const must be
 // thread-invariant, otherwise it's a bug
 // TODO: separate index calculation into "compile-time", "global", "block", "wave", "thread"
-#ifndef CK_HACK_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
 #define CK_HACK_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE 0
-#endif
 
-// workaround for compiler crash when compiling recursive lambda
-#ifndef CK_WORKAROUND_SWDEV_275126
+// workaround: compiler crash when compiling recursive lambda
 #define CK_WORKAROUND_SWDEV_275126 1
-#endif
 
-// workaround for compiler crash when using buffer load/store for i8
-#ifndef CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
+// workaround: compiler crash when using buffer load/store for i8
 #define CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE 1
-#endif
 
-// workaround for compiler gnerating inefficient ds_write instructions
-#ifndef CK_WORKAROUND_SWDEV_XXXXXX_INT8_DS_WRITE_ISSUE
+// workaround: compiler gnerating inefficient ds_write instructions
 #define CK_WORKAROUND_SWDEV_XXXXXX_INT8_DS_WRITE_ISSUE 1
-#endif
-
-// workaround for register spill due to compiler issue, when casting type between fp32 and fp16
-#ifndef CK_WORKAROUND_SWDEV_XXXXXX_THREAD_WISE_COPY_V1R4_TYPE_CONVERT_ISSUE
-#define CK_WORKAROUND_SWDEV_XXXXXX_THREAD_WISE_COPY_V1R4_TYPE_CONVERT_ISSUE 1
-#endif
 
-#ifndef CK_WORKAROUND_SWDEV_XXXXXX_THREAD_WISE_COPY_V1R5_TYPE_CONVERT_ISSUE
-#define CK_WORKAROUND_SWDEV_XXXXXX_THREAD_WISE_COPY_V1R5_TYPE_CONVERT_ISSUE 1
-#endif
-
-// workaround for verifaction failure, due to compiler regression, for conv bwd-data fp16 using some
+// workaround: verifaction failure, due to compiler regression, for conv bwd-data fp16 using some
 // tuning parameter
-#ifndef CK_WORKAROUND_SWDEV_325164
 #define CK_WORKAROUND_SWDEV_325164 1
-#endif
+
+// workaround for verification failure ConvNd forward
+// https://github.com/ROCmSoftwarePlatform/composable_kernel/issues/135
+#define CK_WORKAROUND_GITHUB_135 1
 
 namespace ck {
 
-enum InMemoryDataOperationEnum_t
+enum struct InMemoryDataOperationEnum
 {
     Set,
     AtomicAdd,
     Add
 };
 
-enum ActivTypeEnum_t
+// TODO: no longer needed, remove this
+enum struct ActivTypeEnum
 {
     None,
     LeakyRelu,

include/ck/problem_transform/transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk.hpp

@@ -7,9 +7,9 @@
 
 namespace ck {
 
-// Number of GEMMs = YTilda * XTilda
+// Number of GEMMs = YTilde * XTilde
 // GemmM = C
-// GemmN = N * HTildaSlice * WTildaSlice
+// GemmN = N * HTildeSlice * WTildeSlice
 // GemmK = K * YDotSlice * XDotSlice
 template <typename... Wei,
           typename... In,
@@ -18,8 +18,8 @@ template <typename... Wei,
           typename ConvDilations,
           typename InLeftPads,
           typename InRightPads,
-          index_t IYTildaValue,
-          index_t IXTildaValue,
+          index_t IYTildeValue,
+          index_t IXTildeValue,
           index_t GemmK1Value>
 __host__ __device__ constexpr auto
 transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
@@ -30,8 +30,8 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
     const ConvDilations& conv_dilations,
     const InLeftPads& in_left_pads,
     const InRightPads& in_right_pads,
-    Number<IYTildaValue>,
-    Number<IXTildaValue>,
+    Number<IYTildeValue>,
+    Number<IXTildeValue>,
     Number<GemmK1Value>)
 {
     constexpr auto I0 = Number<0>{};
@@ -40,8 +40,8 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
     constexpr auto I3 = Number<3>{};
 
     constexpr auto GemmK1  = Number<GemmK1Value>{};
-    constexpr auto IYTilda = Number<IYTildaValue>{};
-    constexpr auto IXTilda = Number<IXTildaValue>{};
+    constexpr auto IYTilde = Number<IYTildeValue>{};
+    constexpr auto IXTilde = Number<IXTildeValue>{};
 
     const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
     const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
@@ -71,55 +71,55 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
     const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
     const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
 
-    const auto YTilda = ConvStrideH / GcdStrideDilationH;
-    const auto XTilda = ConvStrideW / GcdStrideDilationW;
+    const auto YTilde = ConvStrideH / GcdStrideDilationH;
+    const auto XTilde = ConvStrideW / GcdStrideDilationW;
 
-    const auto YDot = math::integer_divide_ceil(Y, YTilda);
-    const auto XDot = math::integer_divide_ceil(X, XTilda);
+    const auto YDot = math::integer_divide_ceil(Y, YTilde);
+    const auto XDot = math::integer_divide_ceil(X, XTilde);
 
-    const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
-    const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+    const auto HTilde = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
+    const auto WTilde = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
 
-    // only work on HTilda and WTilda that contribute to non-padding area of input tensor
-    const auto IHTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
-    const auto IWTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
+    // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+    const auto IHTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+    const auto IWTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
 
-    const auto IHTildaSliceEnd =
-        math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
-    const auto IWTildaSliceEnd =
-        math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+    const auto IHTildeSliceEnd =
+        math::min(HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+    const auto IWTildeSliceEnd =
+        math::min(WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
 
-    const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
-    const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
+    const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+    const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
 
     // GemmK is different for each GEMM
-    const auto YDotSlice = math::integer_divide_ceil(Y - IYTilda, YTilda);
-    const auto XDotSlice = math::integer_divide_ceil(X - IXTilda, XTilda);
+    const auto YDotSlice = math::integer_divide_ceil(Y - IYTilde, YTilde);
+    const auto XDotSlice = math::integer_divide_ceil(X - IXTilde, XTilde);
 
     const auto K1 = GemmK1;
     const auto K0 = K / K1;
 
     // weight tensor
-    const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
         wei_k_y_x_c_grid_desc,
         make_tuple(make_pass_through_transform(K),
-                   make_embed_transform(make_tuple(YDot, YTilda),
+                   make_embed_transform(make_tuple(YDot, YTilde),
                                         make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, XTilda),
+                   make_embed_transform(make_tuple(XDot, XTilde),
                                         make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
     const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
-        transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
+        transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
                                     make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
                                                make_slice_transform(YDot, I0, YDotSlice),
                                                make_slice_transform(XDot, I0, XDotSlice),
-                                               make_freeze_transform(IYTilda),
-                                               make_freeze_transform(IXTilda),
+                                               make_freeze_transform(IYTilde),
+                                               make_freeze_transform(IXTilde),
                                                make_pass_through_transform(C)),
                                     make_tuple(Sequence<0>{},
                                                Sequence<1>{},
@@ -163,25 +163,25 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
+    const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
         out_n_hop_wop_k_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YDot, HTilda),
+                   make_embed_transform(make_tuple(YDot, HTilde),
                                         make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, WTilda),
+                   make_embed_transform(make_tuple(XDot, WTilde),
                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(K)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
+    const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
         transform_tensor_descriptor(
-            out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
+            out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
             make_tuple(make_pass_through_transform(N),
                        make_slice_transform(YDot, I0, YDotSlice),
-                       make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
+                       make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
                        make_slice_transform(XDot, I0, XDotSlice),
-                       make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                       make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                        make_unmerge_transform(make_tuple(K0, K1))),
             make_tuple(Sequence<0>{},
                        Sequence<1>{},
@@ -198,17 +198,17 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
 
 #if 1
     const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
 #else
     const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
@@ -224,24 +224,24 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
         in_n_hip_wip_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YTilda, HTilda),
+                   make_embed_transform(make_tuple(YTilde, HTilde),
                                         make_tuple(ConvDilationH, ConvStrideH)),
-                   make_embed_transform(make_tuple(XTilda, WTilda),
+                   make_embed_transform(make_tuple(XTilde, WTilde),
                                         make_tuple(ConvDilationW, ConvStrideW)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
-        in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
+    const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+        in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_freeze_transform(IYTilda),
-                   make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
-                   make_freeze_transform(IXTilda),
-                   make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                   make_freeze_transform(IYTilde),
+                   make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                   make_freeze_transform(IXTilde),
+                   make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{},
                    Sequence<1>{},
@@ -257,9 +257,9 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
                    Sequence<3>{}));
 
     const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
-        in_n_htildaslice_wtildaslice_c_grid_desc,
+        in_n_htildeslice_wtildeslice_c_grid_desc,
         make_tuple(make_pass_through_transform(C),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice))),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice))),
         make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}));
 

include/ck/problem_transform/transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk.hpp

@@ -10,8 +10,8 @@ namespace ck {
 // A: out
 // B: wei
 // C: in
-// Number of GEMMs = YTilda * XTilda
-// GemmM = N * HTildaSlice * WTildaSlice
+// Number of GEMMs = YTilde * XTilde
+// GemmM = N * HTildeSlice * WTildeSlice
 // GemmN = C
 // GemmK = K * YDotSlice * XDotSlice
 template <typename... Wei,
@@ -21,8 +21,8 @@ template <typename... Wei,
           typename ConvDilations,
           typename InLeftPads,
           typename InRightPads,
-          typename IYTilda,
-          typename IXTilda,
+          typename IYTilde,
+          typename IXTilde,
           index_t GemmK1Value>
 __host__ __device__ constexpr auto
 transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
@@ -33,8 +33,8 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
     const ConvDilations& conv_dilations,
     const InLeftPads& in_left_pads,
     const InRightPads& in_right_pads,
-    IYTilda i_ytilda,
-    IXTilda i_xtilda,
+    IYTilde i_ytilde,
+    IXTilde i_xtilde,
     Number<GemmK1Value>)
 {
     constexpr auto I0 = Number<0>{};
@@ -72,32 +72,32 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
     const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
     const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
 
-    const auto YTilda = ConvStrideH / GcdStrideDilationH;
-    const auto XTilda = ConvStrideW / GcdStrideDilationW;
+    const auto YTilde = ConvStrideH / GcdStrideDilationH;
+    const auto XTilde = ConvStrideW / GcdStrideDilationW;
 
-    const auto YDot = math::integer_divide_ceil(Y, YTilda);
-    const auto XDot = math::integer_divide_ceil(X, XTilda);
+    const auto YDot = math::integer_divide_ceil(Y, YTilde);
+    const auto XDot = math::integer_divide_ceil(X, XTilde);
 
-    const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
-    const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+    const auto HTilde = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
+    const auto WTilde = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
 
-    // only work on HTilda and WTilda that contribute to non-padding area of input tensor
-    const auto IHTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
-    const auto IWTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
+    // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+    const auto IHTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+    const auto IWTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
 
-    const auto IHTildaSliceEnd =
-        math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
-    const auto IWTildaSliceEnd =
-        math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+    const auto IHTildeSliceEnd =
+        math::min(HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+    const auto IWTildeSliceEnd =
+        math::min(WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
 
-    const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
-    const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
+    const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+    const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
 
     // GemmK is different for each GEMM
-    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
-    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
+    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
 
     const auto K1 = GemmK1;
     const auto K0 = K / K1;
@@ -113,25 +113,25 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
+    const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
         out_n_hop_wop_k_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YDot, HTilda),
+                   make_embed_transform(make_tuple(YDot, HTilde),
                                         make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, WTilda),
+                   make_embed_transform(make_tuple(XDot, WTilde),
                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(K)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
+    const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
         transform_tensor_descriptor(
-            out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
+            out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
             make_tuple(make_pass_through_transform(N),
                        make_slice_transform(YDot, I0, YDotSlice),
-                       make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
+                       make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
                        make_slice_transform(XDot, I0, XDotSlice),
-                       make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                       make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                        make_unmerge_transform(make_tuple(K0, K1))),
             make_tuple(Sequence<0>{},
                        Sequence<1>{},
@@ -148,41 +148,41 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
 
 #if 1
     const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
 #else
     const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
 #endif
 
     // B: weight tensor
-    const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
         wei_k_y_x_c_grid_desc,
         make_tuple(make_pass_through_transform(K),
-                   make_embed_transform(make_tuple(YDot, YTilda),
+                   make_embed_transform(make_tuple(YDot, YTilde),
                                         make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, XTilda),
+                   make_embed_transform(make_tuple(XDot, XTilde),
                                         make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
     const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
-        transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
+        transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
                                     make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
                                                make_slice_transform(YDot, I0, YDotSlice),
                                                make_slice_transform(XDot, I0, XDotSlice),
-                                               make_freeze_transform(i_ytilda),
-                                               make_freeze_transform(i_xtilda),
+                                               make_freeze_transform(i_ytilde),
+                                               make_freeze_transform(i_xtilde),
                                                make_pass_through_transform(C)),
                                     make_tuple(Sequence<0>{},
                                                Sequence<1>{},
@@ -225,24 +225,24 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
         in_n_hip_wip_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YTilda, HTilda),
+                   make_embed_transform(make_tuple(YTilde, HTilde),
                                         make_tuple(ConvDilationH, ConvStrideH)),
-                   make_embed_transform(make_tuple(XTilda, WTilda),
+                   make_embed_transform(make_tuple(XTilde, WTilde),
                                         make_tuple(ConvDilationW, ConvStrideW)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
-        in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
+    const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+        in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_freeze_transform(i_ytilda),
-                   make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
-                   make_freeze_transform(i_xtilda),
-                   make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                   make_freeze_transform(i_ytilde),
+                   make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                   make_freeze_transform(i_xtilde),
+                   make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{},
                    Sequence<1>{},
@@ -258,8 +258,8 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
                    Sequence<3>{}));
 
     const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
-        in_n_htildaslice_wtildaslice_c_grid_desc,
-        make_tuple(make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+        in_n_htildeslice_wtildeslice_c_grid_desc,
+        make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}));

include/ck/tensor/static_tensor.hpp

@@ -4,7 +4,7 @@
 namespace ck {
 
 // StaticTensor for Scalar
-template <AddressSpaceEnum_t AddressSpace,
+template <AddressSpaceEnum AddressSpace,
           typename T,
           typename TensorDesc,
           bool InvalidElementUseNumericalZeroValue,
@@ -80,7 +80,7 @@ struct StaticTensor
 };
 
 // StaticTensor for vector
-template <AddressSpaceEnum_t AddressSpace,
+template <AddressSpaceEnum AddressSpace,
           typename S,
           index_t ScalarPerVector,
           typename TensorDesc,
@@ -245,7 +245,7 @@ struct StaticTensorTupleOfVectorBuffer
     S ignored_element_scalar_;
 };
 
-template <AddressSpaceEnum_t AddressSpace,
+template <AddressSpaceEnum AddressSpace,
           typename T,
           typename TensorDesc,
           typename enable_if<TensorDesc::IsKnownAtCompileTime(), bool>::type = false>
@@ -255,7 +255,7 @@ __host__ __device__ constexpr auto make_static_tensor(TensorDesc)
 }
 
 template <
-    AddressSpaceEnum_t AddressSpace,
+    AddressSpaceEnum AddressSpace,
     typename T,
     typename TensorDesc,
     typename X,

include/ck/tensor_operation/gpu/block/blockwise_gemm_dlops_v2r2.hpp

@@ -207,9 +207,9 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
                           CM0M1N0N1ThreadDesc{}.GetLength(I2) == N0,
                       "wrong");
 
-        auto a_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatA>(
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatA>(
             a_k_m0_m1_thread_desc_.GetElementSpaceSize());
-        auto b_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatB>(
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatB>(
             b_k_n0_n1_thread_desc_.GetElementSpaceSize());
 
         constexpr auto threadwise_gemm =

include/ck/tensor_operation/gpu/block/blockwise_gemm_dlops_v2r3.hpp

@@ -220,9 +220,9 @@ struct BlockwiseGemmDlops_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_B
                           CThreadDesc_BM0_BM11_BN0_BN11{}.GetLength(I2) == BN0,
                       "wrong");
 
-        auto a_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatA>(
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatA>(
             a_thread_desc_bk0_bm0_bm1_bk1_.GetElementSpaceSize());
-        auto b_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatB>(
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatB>(
             b_thread_desc_bk0_bn0_bn1_bk1_.GetElementSpaceSize());
 
         constexpr auto threadwise_contraction =

include/ck/tensor_operation/gpu/block/blockwise_gemm_dlops_v3.hpp

@@ -119,7 +119,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
         constexpr auto a_block_mtx = ABlockDesc_E1_K1_E2{};
 
         // thread A buffer for GEMM
-        StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatA, a_thread_mtx_.GetElementSpaceSize(), true>
+        StaticBuffer<AddressSpaceEnum::Vgpr, FloatA, a_thread_mtx_.GetElementSpaceSize(), true>
             a_thread_buf;
 
         constexpr auto threadwise_gemm = ThreadwiseGemmDlops_km_kn_mn_v3<FloatA,

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

@@ -42,7 +42,7 @@ struct BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1
     static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
     static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
 
-    StaticBufferTupleOfVector<AddressSpaceEnum_t::Vgpr,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
                               FloatAcc,
                               MRepeat * NRepeat,
                               xdlops_gemm.GetRegSizePerXdlops(),
@@ -250,9 +250,9 @@ struct BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1
                         const BBlockBuffer& b_block_buf,
                         CThreadBuffer& c_thread_buf) const
     {
-        auto a_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAB>(
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
             a_thread_desc_.GetElementSpaceSize());
-        auto b_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAB>(
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
             b_thread_desc_.GetElementSpaceSize());
 
         static_for<0, MRepeat, 1>{}([&](auto m0) {

include/ck/tensor_operation/gpu/block/blockwise_tensor_slice_transfer_v4r1.hpp

@@ -16,7 +16,7 @@ namespace ck {
 template <index_t BlockSize,
           typename SrcElementwiseOperation,
           typename DstElementwiseOperation,
-          InMemoryDataOperationEnum_t DstInMemOp,
+          InMemoryDataOperationEnum DstInMemOp,
           typename BlockSliceLengths,
           typename ThreadClusterLengths,
           typename ThreadClusterArrangeOrder,