mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 17:26:00 +00:00
394 lines
15 KiB
C++
394 lines
15 KiB
C++
#include <cstdint>
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <stdlib.h>
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#include <half.hpp>
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#include "config.hpp"
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#include "print.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "host_gemm.hpp"
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#include "device_tensor.hpp"
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#include "device_base.hpp"
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#include "device_batched_gemm_xdl.hpp"
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#include "profile_batched_gemm_impl.hpp"
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enum GemmMatrixLayout
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{
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MK_KN_MN, // 0
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MK_NK_MN, // 1
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KM_KN_MN, // 2
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KM_NK_MN, // 3
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MK_KN_NM, // 4
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MK_NK_NM, // 5
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KM_KN_NM, // 6
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KM_NK_NM, // 7
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};
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enum GemmDataType
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{
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F32_F32_F32, // 0
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F16_F16_F16, // 1
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BF16_BF16_BF16, // 2
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INT8_INT8_INT8, // 3
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};
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int profile_batched_gemm(int argc, char* argv[])
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{
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if(!(argc == 15))
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{
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printf("arg1: tensor operation (batched_gemm: Batched GEMM)\n");
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printf("arg2: data type (0: fp32; 1: fp16, 2: bf16, 3: int8)\n");
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printf("arg3: matrix layout (0: A[g, m, k] * B[g, k, n] = C[g, m, n];\n");
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printf(" 1: A[g, m, k] * B[g, n, k] = C[g, m, n];\n");
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printf(" 2: A[g, k, m] * B[g, k, n] = C[g, m, n];\n");
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printf(" 3: A[g, k, m] * B[g, n, k] = C[g, m, n])\n");
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printf("arg4: verification (0: no; 1: yes)\n");
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printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
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printf("arg8: print tensor value (0: no; 1: yes)\n");
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printf("arg7: run kernel # of times (>1)\n");
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printf("arg8 to 14: M, N, K, StrideA, StrideB, StrideC, BatchCount\n");
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exit(1);
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}
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const int data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
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const int layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
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const bool do_verification = std::stoi(argv[4]);
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const int init_method = std::stoi(argv[5]);
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const bool do_log = std::stoi(argv[6]);
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const int nrepeat = std::stoi(argv[7]);
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const int M = std::stoi(argv[8]);
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const int N = std::stoi(argv[9]);
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const int K = std::stoi(argv[10]);
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const int StrideA = std::stoi(argv[11]);
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const int StrideB = std::stoi(argv[12]);
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const int StrideC = std::stoi(argv[13]);
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const int BatchCount = std::stoi(argv[14]);
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if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC);
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC);
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}
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::bhalf_t,
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ck::bhalf_t,
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ck::bhalf_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::bhalf_t,
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ck::bhalf_t,
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ck::bhalf_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::bhalf_t,
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ck::bhalf_t,
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ck::bhalf_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC);
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}
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<ck::bhalf_t,
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ck::bhalf_t,
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ck::bhalf_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC);
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}
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else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<float,
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float,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<float,
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float,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<float,
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float,
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float,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC);
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}
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else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<float,
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float,
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float,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC);
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}
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else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<int8_t,
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int8_t,
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int8_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<int8_t,
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int8_t,
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int8_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? K : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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ck::profiler::profile_batched_gemm_impl<int8_t,
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int8_t,
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int8_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? N : StrideB,
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(StrideC < 0) ? N : StrideC);
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}
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else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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ck::profiler::profile_batched_gemm_impl<int8_t,
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int8_t,
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int8_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(
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do_verification,
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init_method,
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do_log,
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nrepeat,
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M,
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N,
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K,
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(StrideA < 0) ? M : StrideA,
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(StrideB < 0) ? K : StrideB,
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(StrideC < 0) ? N : StrideC,
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BatchCount);
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}
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else
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{
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throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
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}
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return 1;
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}
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