flash-attention/csrc/composable_kernel/profiler/src/profile_batched_gemm.cpp

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

#include <cstdint>
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>

#include "profiler/profile_batched_gemm_impl.hpp"
#include "profiler_operation_registry.hpp"

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

enum struct GemmMatrixLayout
{
    MK_KN_MN, // 0
    MK_NK_MN, // 1
    KM_KN_MN, // 2
    KM_NK_MN, // 3
};

enum struct GemmDataType
{
    F32_F32_F32,    // 0
    F16_F16_F16,    // 1
    BF16_BF16_BF16, // 2
    INT8_INT8_INT8, // 3
};

#define OP_NAME "batched_gemm"
#define OP_DESC "Batched GEMM"

int profile_batched_gemm(int argc, char* argv[])
{
    if(argc != 18)
    {
        // clang-format off
        printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
        printf("arg2: data type (0: fp32; 1: fp16, 2: bf16, 3: int8)\n");
        printf("arg3: matrix layout (0: A[g, m, k] * B[g, k, n] = C[g, m, n];\n");
        printf("                     1: A[g, m, k] * B[g, n, k] = C[g, m, n];\n");
        printf("                     2: A[g, k, m] * B[g, k, n] = C[g, m, n];\n");
        printf("                     3: A[g, k, m] * B[g, n, k] = C[g, m, n])\n");
        printf("arg4: verification (0: no; 1: yes)\n");
        printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
        printf("arg6: print tensor value (0: no; 1: yes)\n");
        printf("arg7: time kernel (0=n0, 1=yes)\n");
        printf("arg8 to 17: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount\n");
        // clang-format on
        exit(1);
    }

    const auto data_type       = static_cast<GemmDataType>(std::stoi(argv[2]));
    const auto layout          = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
    const bool do_verification = std::stoi(argv[4]);
    const int init_method      = std::stoi(argv[5]);
    const bool do_log          = std::stoi(argv[6]);
    const bool time_kernel     = std::stoi(argv[7]);

    const int M = std::stoi(argv[8]);
    const int N = std::stoi(argv[9]);
    const int K = std::stoi(argv[10]);

    const int StrideA = std::stoi(argv[11]);
    const int StrideB = std::stoi(argv[12]);
    const int StrideC = std::stoi(argv[13]);

    const int BatchStrideA = std::stoi(argv[14]);
    const int BatchStrideB = std::stoi(argv[15]);
    const int BatchStrideC = std::stoi(argv[16]);

    const int BatchCount = std::stoi(argv[17]);

    using F32  = float;
    using F16  = ck::half_t;
    using BF16 = ck::bhalf_t;
    using INT8 = int8_t;

    using Row = ck::tensor_layout::gemm::RowMajor;
    using Col = ck::tensor_layout::gemm::ColumnMajor;

    auto profile =
        [&](auto a_type, auto b_type, auto c_type, auto a_layout, auto b_layout, auto c_layout) {
            using ADataType = decltype(a_type);
            using BDataType = decltype(b_type);
            using CDataType = decltype(c_type);

            using ALayout = decltype(a_layout);
            using BLayout = decltype(b_layout);
            using CLayout = decltype(c_layout);

            const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
            const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
            const int DefaultStrideC = ck::is_same_v<CLayout, Row> ? N : M;

            const int StrideA_ = (StrideA < 0) ? DefaultStrideA : StrideA;
            const int StrideB_ = (StrideB < 0) ? DefaultStrideB : StrideB;
            const int StrideC_ = (StrideC < 0) ? DefaultStrideC : StrideC;

            const int DefaultBatchStrideA = (ck::is_same_v<ALayout, Row> ? M : K) * StrideA_;
            const int DefaultBatchStrideB = (ck::is_same_v<BLayout, Row> ? K : N) * StrideB_;
            const int DefaultBatchStrideC = (ck::is_same_v<CLayout, Row> ? M : N) * StrideC_;

            const int BatchStrideA_ = (BatchStrideA < 0) ? DefaultBatchStrideA : BatchStrideA;
            const int BatchStrideB_ = (BatchStrideB < 0) ? DefaultBatchStrideB : BatchStrideB;
            const int BatchStrideC_ = (BatchStrideC < 0) ? DefaultBatchStrideC : BatchStrideC;

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

            bool pass = ck::profiler::profile_batched_gemm_impl<ADataType,
                                                                BDataType,
                                                                CDataType,
                                                                ALayout,
                                                                BLayout,
                                                                CLayout,
                                                                AElementOp,
                                                                BElementOp,
                                                                CElementOp,
                                                                DeviceOp>(do_verification,
                                                                          init_method,
                                                                          do_log,
                                                                          time_kernel,
                                                                          M,
                                                                          N,
                                                                          K,
                                                                          BatchStrideA_,
                                                                          BatchStrideB_,
                                                                          BatchStrideC_,
                                                                          StrideA_,
                                                                          StrideB_,
                                                                          StrideC_,
                                                                          BatchCount);

            return pass ? 0 : 1;
        };

    if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
    {
        return profile(F32{}, F32{}, F32{}, Row{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_NK_MN)
    {
        return profile(F32{}, F32{}, F32{}, Row{}, Col{}, Row{});
    }
    else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_KN_MN)
    {
        return profile(F32{}, F32{}, F32{}, Col{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_NK_MN)
    {
        return profile(F32{}, F32{}, F32{}, Col{}, Col{}, Row{});
    }
    else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
    {
        return profile(F16{}, F16{}, F16{}, Row{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
    {
        return profile(F16{}, F16{}, F16{}, Row{}, Col{}, Row{});
    }
    else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_KN_MN)
    {
        return profile(F16{}, F16{}, F16{}, Col{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_NK_MN)
    {
        return profile(F16{}, F16{}, F16{}, Col{}, Col{}, Row{});
    }
    else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
    {
        return profile(BF16{}, BF16{}, BF16{}, Row{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
    {
        return profile(BF16{}, BF16{}, BF16{}, Row{}, Col{}, Row{});
    }
    else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_KN_MN)
    {
        return profile(BF16{}, BF16{}, BF16{}, Col{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_NK_MN)
    {
        return profile(BF16{}, BF16{}, BF16{}, Col{}, Col{}, Row{});
    }
    else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
    {
        return profile(INT8{}, INT8{}, INT8{}, Row{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_NK_MN)
    {
        return profile(INT8{}, INT8{}, INT8{}, Row{}, Col{}, Row{});
    }
    else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::KM_KN_MN)
    {
        return profile(INT8{}, INT8{}, INT8{}, Col{}, Row{}, Row{});
    }
    else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::KM_NK_MN)
    {
        return profile(INT8{}, INT8{}, INT8{}, Col{}, Col{}, Row{});
    }
    else
    {
        std::cout << "this data_type & layout is not implemented" << std::endl;

        return 1;
    }
}

REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_batched_gemm);