mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-21 13:29:20 +00:00
4fb078cc12
* Add int8 specialization for elementwise Add and Subtract.
* CGEMM examples bf16, fp32, int8
* Add convert reference output to CDataType.
* Skip BF16 data type during testing.
* Lower K value to get rid of accumulation error.
* Fix merge artifact.
* Fix changed function name: GetElementSpaceSize()
* Fix merge artifact.
Co-authored-by: Adam Osewski <aosewski@amd.com>
[ROCm/composable_kernel commit: fb0dc35861]
193 lines
8.5 KiB
C++
193 lines
8.5 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/stream_config.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F16 = ck::half_t;
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using F32 = float;
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using BF16 = ck::bhalf_t;
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using INT8 = std::int8_t;
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using INT32 = std::int32_t;
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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typename DeviceCGemmInstance,
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typename ReferenceCGemmInstance>
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int run_cgemm_xdl(ck::index_t M,
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ck::index_t N,
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ck::index_t K,
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ck::index_t StrideA,
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ck::index_t StrideB,
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ck::index_t StrideC,
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bool do_verification,
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int init_method,
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bool time_kernel)
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{
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1, stride}));
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}
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};
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Tensor<ADataType> a_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<ADataType> a_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<BDataType> b_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_m_n_real_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_imag_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k_real: " << a_m_k_real.mDesc << std::endl;
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std::cout << "a_m_k_imag: " << a_m_k_imag.mDesc << std::endl;
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std::cout << "b_k_n_real: " << b_k_n_real.mDesc << std::endl;
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std::cout << "b_k_n_imag: " << b_k_n_imag.mDesc << std::endl;
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std::cout << "c_m_n_real: " << c_m_n_real_device_result.mDesc << std::endl;
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std::cout << "c_m_n_imag: " << c_m_n_imag_device_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_m_k_real.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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break;
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default:
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a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
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b_k_n_real.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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b_k_n_imag.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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}
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auto cgemm = DeviceCGemmInstance{};
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DeviceMem a_m_k_real_device_buf(sizeof(ADataType) * a_m_k_real.mDesc.GetElementSpaceSize());
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DeviceMem a_m_k_imag_device_buf(sizeof(ADataType) * a_m_k_imag.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_real_device_buf(sizeof(BDataType) * b_k_n_real.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_imag_device_buf(sizeof(BDataType) * b_k_n_imag.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_real_device_buf(sizeof(CDataType) *
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c_m_n_real_device_result.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_imag_device_buf(sizeof(CDataType) *
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c_m_n_imag_device_result.mDesc.GetElementSpaceSize());
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DeviceMem workspace_device_buf(cgemm.GetWorkspaceSize(M, N, K, StrideA, StrideB, StrideC));
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a_m_k_real_device_buf.ToDevice(a_m_k_real.mData.data());
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a_m_k_imag_device_buf.ToDevice(a_m_k_imag.mData.data());
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b_k_n_real_device_buf.ToDevice(b_k_n_real.mData.data());
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b_k_n_imag_device_buf.ToDevice(b_k_n_imag.mData.data());
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auto a_element_op = AElementwiseOperation{};
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auto b_element_op = BElementwiseOperation{};
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auto c_element_op = CElementwiseOperation{};
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// do GEMM
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auto invoker = cgemm.MakeInvoker();
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auto argument =
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cgemm.MakeArgument(static_cast<ADataType*>(a_m_k_real_device_buf.GetDeviceBuffer()),
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static_cast<ADataType*>(a_m_k_imag_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_real_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_imag_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_real_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_imag_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(workspace_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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if(!cgemm.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_cgemm with the specified compilation parameters does "
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"not support this CGEMM problem");
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(8) * M * N * K;
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std::size_t num_btype =
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std::size_t(2) *
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(sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N);
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< cgemm.GetTypeString() << std::endl;
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c_m_n_real_device_buf.FromDevice(c_m_n_real_device_result.mData.data());
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c_m_n_imag_device_buf.FromDevice(c_m_n_imag_device_result.mData.data());
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if(do_verification)
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{
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Tensor<CDataType> c_m_n_real_host_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_imag_host_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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auto ref_cgemm = ReferenceCGemmInstance{};
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auto ref_invoker = ref_cgemm.MakeInvoker();
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auto ref_argument = ref_cgemm.MakeArgument(a_m_k_real,
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a_m_k_imag,
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b_k_n_real,
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b_k_n_imag,
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c_m_n_real_host_result,
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c_m_n_imag_host_result,
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a_element_op,
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b_element_op,
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c_element_op);
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ref_invoker.Run(ref_argument);
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bool result = true;
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result = ck::utils::check_err(c_m_n_real_device_result.mData,
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c_m_n_real_host_result.mData,
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"Verification error: incorrect results in real part!",
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1e-2f,
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1e-1f);
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result = result &&
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ck::utils::check_err(c_m_n_imag_device_result.mData,
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c_m_n_imag_host_result.mData,
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"Verification error: incorrect results in imaginary part!",
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1e-2f,
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1e-1f);
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return result ? 0 : 1;
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}
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return 0;
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}
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