profile_batched_gemm_impl.hpp

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include <memory>

#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"

#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"

#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"

namespace ck {
namespace profiler {

template <typename ADataType,
          typename BDataType,
          typename CDataType,
          typename ALayout,
          typename BLayout,
          typename CLayout>
bool profile_batched_gemm_impl(int do_verification,
                               int init_method,
                               bool do_log,
                               bool time_kernel,
                               int M,
                               int N,
                               int K,
                               int BatchStrideA,
                               int BatchStrideB,
                               int BatchStrideC,
                               int StrideA,
                               int StrideB,
                               int StrideC,
                               int BatchCount)
{
    bool pass = true;

    auto f_host_tensor_descriptor = [](std::size_t batch_count,
                                       std::size_t row,
                                       std::size_t col,
                                       std::size_t stride,
                                       std::size_t batch_stride,
                                       auto layout) {
        if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
        {
            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
                                        std::vector<std::size_t>({batch_stride, stride, 1}));
        }
        else
        {
            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
                                        std::vector<std::size_t>({batch_stride, 1, stride}));
        }
    };

    Tensor<ADataType> a_g_m_k(
        f_host_tensor_descriptor(BatchCount, M, K, StrideA, BatchStrideA, ALayout{}));
    Tensor<BDataType> b_g_k_n(
        f_host_tensor_descriptor(BatchCount, K, N, StrideB, BatchStrideB, BLayout{}));
    Tensor<CDataType> c_g_m_n_host_result(
        f_host_tensor_descriptor(BatchCount, M, N, StrideC, BatchStrideC, CLayout{}));
    Tensor<CDataType> c_g_m_n_device_result(
        f_host_tensor_descriptor(BatchCount, M, N, StrideC, BatchStrideC, CLayout{}));

    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;

    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});
    }

    using AElementOp = ck::tensor_operation::element_wise::PassThrough;
    using BElementOp = ck::tensor_operation::element_wise::PassThrough;
    using CElementOp = ck::tensor_operation::element_wise::PassThrough;

    const auto a_element_op = AElementOp{};
    const auto b_element_op = BElementOp{};
    const auto c_element_op = CElementOp{};

    if(do_verification)
    {
        using ReferenceBatchedGemmInstance =
            ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
                                                             BDataType,
                                                             CDataType,
                                                             AElementOp,
                                                             BElementOp,
                                                             CElementOp>;

        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);
    }

    DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpaceSize());
    DeviceMem c_device_buf(sizeof(CDataType) * c_g_m_n_device_result.mDesc.GetElementSpaceSize());

    a_device_buf.ToDevice(a_g_m_k.mData.data());
    b_device_buf.ToDevice(b_g_k_n.mData.data());
    c_device_buf.ToDevice(c_g_m_n_device_result.mData.data());

    using DeviceOp = ck::tensor_operation::device::DeviceBatchedGemm<ALayout,
                                                                     BLayout,
                                                                     CLayout,
                                                                     ADataType,
                                                                     BDataType,
                                                                     CDataType,
                                                                     AElementOp,
                                                                     BElementOp,
                                                                     CElementOp>;

    // get device op instances
    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
        DeviceOp>::GetInstances();

    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;

    std::string best_op_name;
    float best_ave_time   = 0;
    float best_tflops     = 0;
    float best_gb_per_sec = 0;

    // profile device op instances
    for(auto& op_ptr : op_ptrs)
    {
        auto argument_ptr =
            op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
                                        static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
                                        static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
                                        M,
                                        N,
                                        K,
                                        StrideA,
                                        StrideB,
                                        StrideC,
                                        BatchStrideA,
                                        BatchStrideB,
                                        BatchStrideC,
                                        BatchCount,
                                        ck::tensor_operation::element_wise::PassThrough{},
                                        ck::tensor_operation::element_wise::PassThrough{},
                                        ck::tensor_operation::element_wise::PassThrough{});

        auto invoker_ptr = op_ptr->MakeInvokerPointer();

        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
        {
            // re-init C to zero before profiling next kernel
            c_device_buf.SetZero();

            std::string op_name = op_ptr->GetTypeString();

            float ave_time =
                invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});

            std::size_t flop = std::size_t(2) * BatchCount * M * N * K;

            std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
                                     sizeof(CDataType) * M * N) *
                                    BatchCount;

            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, " << op_name << std::endl;

            if(tflops > best_tflops)
            {
                best_op_name    = op_name;
                best_tflops     = tflops;
                best_ave_time   = ave_time;
                best_gb_per_sec = gb_per_sec;
            }

            if(do_verification)
            {
                c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());

                pass = pass &
                       ck::utils::check_err(c_g_m_n_device_result.mData, c_g_m_n_host_result.mData);

                if(do_log)
                {
                    LogRangeAsType<float>(std::cout << "a : ", a_g_m_k.mData, ",") << std::endl;
                    LogRangeAsType<float>(std::cout << "b: ", b_g_k_n.mData, ",") << std::endl;
                    LogRangeAsType<float>(std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
                        << std::endl;
                    LogRangeAsType<float>(
                        std::cout << "c_device: ", c_g_m_n_device_result.mData, ",")
                        << std::endl;
                }
            }
        }
        else
        {
            std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
        }
    }

    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;

    return pass;
}

} // namespace profiler
} // namespace ck