Example added

example/19_cgemm/cgemm_xdl_bf16.cpp

@@ -11,10 +11,272 @@
 #include "host_tensor.hpp"
 #include "host_tensor_generator.hpp"
 #include "device_tensor.hpp"
-#include "device_gemm_xdl_cshuffle.hpp"
+#include "device_cgemm_4gemm_xdl_cshuffle.hpp"
 #include "element_wise_operation.hpp"
 #include "reference_cgemm.hpp"
 #include "gemm_specialization.hpp"
 
-// stub only
-int main() { return 0; }
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using BF16 = ck::bhalf_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   = BF16;
+using BDataType   = BF16;
+using CDataType   = BF16;
+using AccDataType = F32;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_CShuffle
+    <ALayout,                    // typename ALayout
+     BLayout,                    // typename BLayout
+     CLayout,                    // typename CLayout
+     ADataType,                  // typename ADataType
+     BDataType,                  // typename BDataType
+     CDataType,                  // typename CDataType
+     AccDataType,                // typename GemmAccDataType
+     CDataType,                  // typename CShuffleDataType
+     PassThrough,                // typename AElementwiseOperation
+     PassThrough,                // typename BElementwiseOperation
+     PassThrough,                // typename CElementwiseOperation
+     GemmDefault,                // GemmSpecialization GemmSpec
+     1,                          // index_t NumGemmKPrefetchStage
+     256,                        // index_t BlockSize
+     256,                        // index_t MPerBlock
+     128,                        // index_t NPerBlock
+     32,                         // index_t KPerBlock
+     8,                          // index_t AK1
+     8,                          // index_t BK1
+     32,                         // index_t MPerXDL
+     32,                         // index_t NPerXDL
+     4,                          // index_t MXdlPerWave
+     2,                          // index_t NXdlPerWave
+     S<4, 64, 1>,                // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
+     S<1, 0, 2>,                 // typename ABlockTransferThreadClusterArrangeOrder
+     S<1, 0, 2>,                 // typename ABlockTransferSrcAccessOrder
+     2,                          // index_t ABlockTransferSrcVectorDim
+     8,                          // index_t ABlockTransferSrcScalarPerVector
+     8,                          // index_t ABlockTransferDstScalarPerVector_AK1
+     1,                          // index_t ABlockLdsExtraM
+     S<4, 64, 1>,                // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
+     S<1, 0, 2>,                 // typename BBlockTransferThreadClusterArrangeOrder
+     S<1, 0, 2>,                 // typename BBlockTransferSrcAccessOrder
+     2,                          // index_t BBlockTransferSrcVectorDim
+     8,                          // index_t BBlockTransferSrcScalarPerVector
+     8,                          // index_t BBlockTransferDstScalarPerVector_BK1
+     1,                          // index_t BBlockLdsExtraN
+     1,                          // index_t CShuffleMXdlPerWavePerShuffle
+     1,                          // index_t CShuffleNXdlPerWavePerShuffle
+     S<1, 32, 1, 8>,             // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+     8>;                         // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
+// clang-format on
+
+using ReferenceCGemmInstance = ck::tensor_operation::host::
+    ReferenceCGemm<float, float, float, PassThrough, PassThrough, PassThrough>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    int nrepeat          = 5;
+
+    // CGEMM 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 == 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_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<ADataType> a_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<BDataType> b_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<CDataType> c_m_n_real_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<CDataType> c_m_n_imag_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<CDataType> aux(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+
+    std::cout << "a_m_k_real: " << a_m_k_real.mDesc << std::endl;
+    std::cout << "a_m_k_imag: " << a_m_k_imag.mDesc << std::endl;
+    std::cout << "b_k_n_real: " << b_k_n_real.mDesc << std::endl;
+    std::cout << "b_k_n_imag: " << b_k_n_imag.mDesc << std::endl;
+    std::cout << "c_m_n_real: " << c_m_n_real_device_result.mDesc << std::endl;
+    std::cout << "c_m_n_imag: " << c_m_n_imag_device_result.mDesc << std::endl;
+    std::cout << "aux: " << aux.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k_real.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    default:
+        a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n_real.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        b_k_n_imag.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+    }
+
+    DeviceMem a_m_k_real_device_buf(sizeof(ADataType) * a_m_k_real.mDesc.GetElementSpace());
+    DeviceMem a_m_k_imag_device_buf(sizeof(ADataType) * a_m_k_imag.mDesc.GetElementSpace());
+    DeviceMem b_k_n_real_device_buf(sizeof(BDataType) * b_k_n_real.mDesc.GetElementSpace());
+    DeviceMem b_k_n_imag_device_buf(sizeof(BDataType) * b_k_n_imag.mDesc.GetElementSpace());
+    DeviceMem c_m_n_real_device_buf(sizeof(CDataType) *
+                                    c_m_n_real_device_result.mDesc.GetElementSpace());
+    DeviceMem c_m_n_imag_device_buf(sizeof(CDataType) *
+                                    c_m_n_imag_device_result.mDesc.GetElementSpace());
+    DeviceMem aux_device_buf(sizeof(CDataType) * aux.mDesc.GetElementSpace());
+
+    a_m_k_real_device_buf.ToDevice(a_m_k_real.mData.data());
+    a_m_k_imag_device_buf.ToDevice(a_m_k_imag.mData.data());
+    b_k_n_real_device_buf.ToDevice(b_k_n_real.mData.data());
+    b_k_n_imag_device_buf.ToDevice(b_k_n_imag.mData.data());
+
+    auto a_element_op = PassThrough{};
+    auto b_element_op = PassThrough{};
+    auto c_element_op = PassThrough{};
+
+    // do GEMM
+    auto cgemm   = DeviceCGemmInstance{};
+    auto invoker = cgemm.MakeInvoker();
+    auto argument =
+        cgemm.MakeArgument(static_cast<ADataType*>(a_m_k_real_device_buf.GetDeviceBuffer()),
+                           static_cast<ADataType*>(a_m_k_imag_device_buf.GetDeviceBuffer()),
+                           static_cast<BDataType*>(b_k_n_real_device_buf.GetDeviceBuffer()),
+                           static_cast<BDataType*>(b_k_n_imag_device_buf.GetDeviceBuffer()),
+                           static_cast<CDataType*>(c_m_n_real_device_buf.GetDeviceBuffer()),
+                           static_cast<CDataType*>(c_m_n_imag_device_buf.GetDeviceBuffer()),
+                           static_cast<CDataType*>(aux_device_buf.GetDeviceBuffer()),
+                           M,
+                           N,
+                           K,
+                           StrideA,
+                           StrideB,
+                           StrideC,
+                           a_element_op,
+                           b_element_op,
+                           c_element_op);
+
+    if(!cgemm.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_cgemm with the specified compilation parameters does "
+            "not support this CGEMM problem");
+    }
+
+    float ave_time = invoker.Run(argument, nrepeat);
+
+    std::size_t flop      = std::size_t(8) * M * N * K;
+    std::size_t num_btype = std::size_t(2) * 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, "
+              << cgemm.GetTypeString() << std::endl;
+
+    c_m_n_real_device_buf.FromDevice(c_m_n_real_device_result.mData.data());
+    c_m_n_imag_device_buf.FromDevice(c_m_n_imag_device_result.mData.data());
+
+    if(do_verification)
+    {
+        Tensor<float> a_f32_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+        Tensor<float> a_f32_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+        Tensor<float> b_f32_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+        Tensor<float> b_f32_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+        Tensor<float> c_m_n_real_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+        Tensor<float> c_m_n_imag_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+        Tensor<float> c_m_n_real_device_f32_result(
+            f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+        Tensor<float> c_m_n_imag_device_f32_result(
+            f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+
+        bf16_to_f32_(a_m_k_real, a_f32_m_k_real);
+        bf16_to_f32_(a_m_k_imag, a_f32_m_k_imag);
+        bf16_to_f32_(b_k_n_real, b_f32_k_n_real);
+        bf16_to_f32_(b_k_n_imag, b_f32_k_n_imag);
+        bf16_to_f32_(c_m_n_real_device_result, c_m_n_real_device_f32_result);
+        bf16_to_f32_(c_m_n_imag_device_result, c_m_n_imag_device_f32_result);
+
+        auto ref_cgemm   = ReferenceCGemmInstance{};
+        auto ref_invoker = ref_cgemm.MakeInvoker();
+
+        auto ref_argument = ref_cgemm.MakeArgument(a_f32_m_k_real,
+                                                   a_f32_m_k_imag,
+                                                   b_f32_k_n_real,
+                                                   b_f32_k_n_imag,
+                                                   c_m_n_real_host_result,
+                                                   c_m_n_imag_host_result,
+                                                   a_element_op,
+                                                   b_element_op,
+                                                   c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+        ck::utils::check_err(c_m_n_real_device_f32_result.mData, c_m_n_real_host_result.mData);
+        ck::utils::check_err(c_m_n_imag_device_f32_result.mData, c_m_n_imag_host_result.mData);
+    }
+
+    return 0;
+}