run_gemm_example.inc

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

bool run_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
{
#if defined(BUILD_INT4_EXAMPLE) && defined(CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4)
    static_assert(sizeof(ck::int4_t) == sizeof(int8_t));
#endif

    using namespace ck::literals;

    auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;

    auto f_host_tensor_descriptor =
        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
            {
                return HostTensorDescriptor({row, col}, {stride, 1_uz});
            }
            else
            {
                return HostTensorDescriptor({row, col}, {1_uz, stride});
            }
        };

    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
    Tensor<QuantDataType> quant_b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
    // assume scale tensor is [1, n]
    Tensor<ScaleDataType> scale_k_n(f_host_tensor_descriptor(K, N, 0, Row{}));

    switch(config.init_method)
    {
    case 0: break;
    case 1:
        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{-5.f, 5.f}(quant_b_k_n);
        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{-5.f, 5.f}(scale_k_n);
        break;
    case 2:
        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
        ck::utils::FillUniformDistribution<QuantDataType>{-1.f, 1.f}(quant_b_k_n);
        ck::utils::FillUniformDistribution<ScaleDataType>{-1.f, 1.f}(scale_k_n);
        break;
    case 3:
        ck::utils::FillUniformDistributionIntegerValue<ADataType>{1.f, 1.f}(a_m_k);
        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{-5.f, 5.f}(quant_b_k_n);
        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{-5.f, 5.f}(scale_k_n);
        break;
    case 4:
        ck::utils::FillUniformDistributionIntegerValue<ADataType>{1.f, 1.f}(a_m_k);
        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{1.f, 1.f}(quant_b_k_n);
        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{2.f, 2.f}(scale_k_n);
        break;
    case 5:
        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-2.f, 2.f}(a_m_k);
        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{-2.f, 2.f}(quant_b_k_n);
        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{-2.f, 2.f}(scale_k_n);
        break;
    default:
        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
        ck::utils::FillUniformDistribution<QuantDataType>{-1.f, 1.f}(quant_b_k_n);
        ck::utils::FillUniformDistribution<ScaleDataType>{-1.f, 1.f}(scale_k_n);
    }

    UnsignedWeightPreprocessor<QuantDataType> preprocessor;
    Tensor<BDataType> b_k_n = preprocessor(quant_b_k_n);

#if 0
    printf("Matrix A:\n");
    for (int im = 0; im < M; im++)
    {
        for (int ik = 0; ik < K; ik++)
        {
            if(ik % 16 == 0){
                printf("|");
            }
            
            printf(" %04x", *(reinterpret_cast<uint16_t*>(&a_m_k(im,ik))));
        }
        printf("\n");
    }
#endif
#if 0 
    printf("Matrix QuantB:\n");
    for (int in = 0; in < N; in++)
    {
        for (int ik = 0; ik < K; ik++)
        {
            if(ik % 16 == 0){
                printf("|");
            }
            
            printf(" %02x", *(reinterpret_cast<uint8_t*>(&quant_b_k_n(ik,in))));
        }
        printf("\n");
    }
#endif
#if 0
    printf("Matrix Scale:\n");
    for(int in = 0; in < N; in++)
    {
        for(int ik = 0; ik < 1; ik++)
        {
            if(ik % 16 == 0)
            {
                printf("|");
            }

            printf(" %04x", *(reinterpret_cast<uint16_t*>(&scale_k_n(ik, in))));
        }
        printf("\n");
    }
#endif
#if 0
    printf("Matrix B:\n");
    for (int in = 0; in < N; in++)
    {
        for (int ik = 0; ik < K; ik++)
        {
            if(ik % 16 == 0){
                printf("|");
            }
            
            printf(" %02x", b_k_n(ik,in));
        }
        printf("\n");
    }
#endif

    Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
    Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));

    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
    std::cout << "scale_k_n: " << scale_k_n.mDesc << std::endl;
    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;

#ifdef BUILD_INT4_EXAMPLE
    DeviceMem a_m_k_device_buf(sizeof(KernelADataType) * a_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b_k_n_device_buf(sizeof(KernelBDataType) * b_k_n.mDesc.GetElementSpaceSize());
    DeviceMem c_m_n_device_buf(sizeof(KernelCDataType) *
                               c_m_n_device_result.mDesc.GetElementSpaceSize());

    const Tensor<KernelADataType> a_m_k_converted(a_m_k);
    const Tensor<KernelBDataType> b_k_n_converted(b_k_n);

    a_m_k_device_buf.ToDevice(a_m_k_converted.mData.data());
    b_k_n_device_buf.ToDevice(b_k_n_converted.mData.data());
#else
    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
    DeviceMem scale_k_n_device_buf(sizeof(ScaleDataType) * scale_k_n.mDesc.GetElementSpaceSize());
    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());

    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
    scale_k_n_device_buf.ToDevice(scale_k_n.mData.data());
#endif

    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(
#ifdef BUILD_INT4_EXAMPLE
        static_cast<KernelADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
        static_cast<KernelBDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
        static_cast<KernelCDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
#else
        static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
        static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
        static_cast<ScaleDataType*>(scale_k_n_device_buf.GetDeviceBuffer()),
        static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
#endif
        M,
        N,
        K,
        StrideA,
        StrideB,
        StrideC,
        a_element_op,
        b_element_op,
        c_element_op);

    if(!gemm.IsSupportedArgument(argument))
    {
        std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;

        return true;
    }

    float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});

    std::size_t flop = 2_uz * 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(config.do_verification)
    {
        auto ref_gemm    = ReferenceGemmInstance{};
        auto ref_invoker = ref_gemm.MakeInvoker();

        auto ref_argument = ref_gemm.MakeArgument(a_m_k,
                                                  quant_b_k_n,
                                                  scale_k_n,
                                                  c_m_n_host_result,
                                                  a_element_op,
                                                  b_element_op,
                                                  c_element_op);

        ref_invoker.Run(ref_argument);

#ifdef BUILD_INT4_EXAMPLE
        Tensor<CDataType> c_m_n_device_result_converted(c_m_n_host_result.mDesc);

        c_m_n_device_buf.FromDevice(c_m_n_device_result_converted.mData.data());

        c_m_n_device_result = c_m_n_device_result_converted.CopyAsType<CDataType>();

        return ck::utils::check_err(c_m_n_device_result_converted, c_m_n_host_result);
#else
        c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());

        return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
#endif
    }

    return true;
}

bool run_gemm_example(int argc, char* argv[])
{
    ProblemSize problem_size;
    ExecutionConfig config;

    return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
}