Part of gemm + softmax, Add gemm + reduceMax

example/19_gemm_softmax/CMakeLists.txt

+add_example_executable(example_gemm_softmax_xdl_fp16 gemm_softmax_xdl_fp16.cpp)

example/19_gemm_softmax/gemm_softmax_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 "host_reduce_util.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_xdl.hpp"
+#include "device_gemm_xdl_c_shuffle.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+
+#include "device_reduce_blockwise.hpp"
+#include "reduction_enums.hpp"
+#include "reduction_operator_mapping.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   = F16;
+using BDataType   = F16;
+using CDataType   = F16;
+using AccDataType = F32;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle<
+    ADataType,              // ADataType
+    BDataType,              // BDataType
+    CDataType,              // CDataType
+    AccDataType,            // AccDataType
+    CDataType,              // CShuffleDataType
+    ALayout,                // ALayout
+    BLayout,                // BLayout
+    CLayout,                // CLayout
+    PassThrough,            // AElementwiseOperation
+    PassThrough,            // BElementwiseOperation
+    PassThrough,            // CElementwiseOperation
+    256,                    // BlockSize
+    256,                    // MPerBlock
+    128,                    // NPerBlock
+    32,                     // KPerBlock
+    8,                      // AK1
+    8,                      // BK1
+    32,                     // MPerXDL
+    32,                     // NPerXDL
+    4,                      // 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<1, 0, 2>,             // BBlockTransferThreadClusterArrangeOrder
+    S<1, 0, 2>,             // BBlockTransferSrcAccessOrder
+    2,                      // BBlockTransferSrcVectorDim
+    8,                      // BBlockTransferSrcScalarPerVector
+    8,                      // BBlockTransferDstScalarPerVector_K1
+    true,                   // BBlockLdsAddExtraN
+    1,                      // CShuffleMXdlPerWavePerShuffle
+    1,                      // CShuffleNXdlPerWavePerShuffle
+    S<1, 1, 32, 1, 1, 8>,   // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+    8>;                     // CBlockTransferScalarPerVector_NWaveNPerXdl
+// clang-format on
+
+constexpr int Rank                        = 2;
+constexpr int NumReduceDim                = 1;
+constexpr ck::ReduceTensorOp_t ReduceOpId = ck::ReduceTensorOp_t::MAX;
+constexpr ck::NanPropagation_t NanOpt     = ck::NanPropagation_t::PROPAGATE_NAN;
+constexpr bool PropagateNan = (NanOpt == ck::NanPropagation_t::NOT_PROPAGATE_NAN) ? false : true;
+// constexpr ck::ReduceTensorIndices_t IndicesOpt = ck::ReduceTensorIndices_t::NO_INDICES;
+using ReduceOperation = typename ck::reduce_binary_operator<CDataType, ReduceOpId>::opType;
+using InElementwiseOperation =
+    typename ck::reduce_unary_operator<CDataType, ReduceOpId, true, true>::InElementwiseOperation;
+using AccElementwiseOperation =
+    typename ck::reduce_unary_operator<CDataType, ReduceOpId, true, true>::AccElementwiseOperation;
+
+using DeviceReduceInstance =
+    ck::tensor_operation::device::DeviceReduceBlockWise<CDataType,
+                                                        CDataType,
+                                                        CDataType,
+                                                        Rank,
+                                                        NumReduceDim,
+                                                        ReduceOperation,
+                                                        InElementwiseOperation,
+                                                        AccElementwiseOperation,
+                                                        PropagateNan,
+                                                        false,
+                                                        256,
+                                                        4,
+                                                        64,
+                                                        1,
+                                                        1,
+                                                        0,
+                                                        1,
+                                                        1>;
+
+using ReferenceGemmInstance = ck::tensor_operation::host::
+    ReferenceGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    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;
+
+    const std::vector<int> reduceDims{0};
+    const std::vector<int> reduceInvariantDims{1};
+
+    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(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<int> c_m_n_max(std::vector<std::size_t>({static_cast<std::size_t>(N)}),
+                          std::vector<std::size_t>({1}));
+
+    const auto i_inLengths  = ck::to_int_vector(c_m_n.mDesc.GetLengths());
+    const auto i_inStrides  = ck::to_int_vector(c_m_n.mDesc.GetStrides());
+    const auto i_outLengths = ck::to_int_vector(c_m_n_max.mDesc.GetLengths());
+    const auto i_outStrides = ck::to_int_vector(c_m_n_max.mDesc.GetStrides());
+
+    size_t reduce_total_length = c_m_n.mDesc.GetElementSize() / c_m_n_max.mDesc.GetElementSize();
+
+    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.mDesc << std::endl;
+    std::cout << "c_m_n_max: " << c_m_n_max.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});
+    }
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_max_device_buf(sizeof(CDataType) * c_m_n_max.mDesc.GetElementSpace());
+    DeviceMem c_m_n_max_indices_dev(0);
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+
+    // do GEMM
+    auto gemm         = DeviceGemmInstance{};
+    auto gemm_invoker = gemm.MakeInvoker();
+    auto gemm_argument =
+        gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                          static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
+                          static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                          M,
+                          N,
+                          K,
+                          StrideA,
+                          StrideB,
+                          StrideC,
+                          PassThrough{},
+                          PassThrough{},
+                          PassThrough{});
+
+    if(!gemm.IsSupportedArgument(gemm_argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    gemm_invoker.Run(gemm_argument, nrepeat);
+
+    // do reduce max
+    auto reduce        = DeviceReduceInstance{};
+    auto wsSizeInBytes = reduce.GetWorkspaceSizeInBytes(i_inLengths, reduceDims);
+    DeviceMem ws_dev(wsSizeInBytes);
+
+    auto argument_ptr =
+        reduce.MakeArgumentPointer(i_inLengths,
+                                   i_inStrides,
+                                   i_outLengths,
+                                   i_outStrides,
+                                   reduceDims,
+                                   1,
+                                   0,
+                                   c_m_n_device_buf.GetDeviceBuffer(),
+                                   c_m_n_max_device_buf.GetDeviceBuffer(),
+                                   c_m_n_max_indices_dev.GetDeviceBuffer(),
+                                   ws_dev.GetDeviceBuffer(),
+                                   InElementwiseOperation{static_cast<int>(reduce_total_length)},
+                                   AccElementwiseOperation{static_cast<int>(reduce_total_length)});
+
+    if(!reduce.IsSupportedArgument(argument_ptr.get()))
+    {
+        std::cout
+            << "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
+            << std::endl;
+    };
+
+    auto invoker_ptr = reduce.MakeInvokerPointer();
+    invoker_ptr->Run(argument_ptr.get(), nrepeat);
+
+    // TODO = do_verification
+    if(do_verification)
+        ;
+    return 0;
+}

example/CMakeLists.txt

@@ -43,3 +43,4 @@ add_subdirectory(17_convnd_bwd_data_xdl)
 add_subdirectory(15_grouped_gemm)
 add_subdirectory(16_gemm_reduce)
 add_subdirectory(18_batched_gemm_reduce)
+add_subdirectory(19_gemm_softmax)