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#pragma once |
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#include "ck/ck.hpp" |
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#include "ck/tensor_operation/gpu/device/tensor_layout.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/library/utility/literals.hpp" |
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" |
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namespace ck { |
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namespace gemm_util { |
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struct GemmParams |
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{ |
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ck::index_t M = 1024; |
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ck::index_t N = 1024; |
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ck::index_t K = 1024; |
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ck::index_t StrideA = 1024; |
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ck::index_t StrideB = 1024; |
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ck::index_t StrideC = 1024; |
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}; |
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template <typename GemmInstance, |
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typename ADataType, |
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typename BDataType, |
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typename CDataType, |
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typename AElementwiseOperation, |
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typename BElementwiseOperation, |
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typename CElementwiseOperation> |
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void RunHostGEMM(const Tensor<ADataType>& A, |
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const Tensor<BDataType>& B, |
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Tensor<CDataType>& C, |
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AElementwiseOperation a_element_op, |
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BElementwiseOperation b_element_op, |
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CElementwiseOperation c_element_op) |
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{ |
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auto ref_gemm = GemmInstance{}; |
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auto ref_invoker = ref_gemm.MakeInvoker(); |
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auto ref_argument = ref_gemm.MakeArgument(A, B, C, a_element_op, b_element_op, c_element_op); |
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ref_invoker.Run(ref_argument); |
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} |
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template <typename DeviceGemmPtr_, |
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typename ADataType, |
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typename BDataType, |
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typename CDataType, |
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typename AElementwiseOperation, |
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typename BElementwiseOperation, |
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typename CElementwiseOperation> |
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bool RunDeviceGEMM(DeviceGemmPtr_& gemmPtr, |
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const ck::gemm_util::GemmParams& params, |
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const Tensor<ADataType>& A, |
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const Tensor<BDataType>& B, |
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Tensor<CDataType>& C, |
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AElementwiseOperation a_element_op, |
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BElementwiseOperation b_element_op, |
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CElementwiseOperation c_element_op, |
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bool time_kernel) |
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{ |
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DeviceMem a_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpaceSize()); |
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DeviceMem b_k_n_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpaceSize()); |
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DeviceMem c_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpaceSize()); |
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auto invoker_ptr = gemmPtr->MakeInvokerPointer(); |
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auto argument_ptr = |
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gemmPtr->MakeArgumentPointer(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()), |
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static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()), |
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static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()), |
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params.M, |
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params.N, |
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params.K, |
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params.StrideA, |
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params.StrideB, |
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params.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(gemmPtr->IsSupportedArgument(argument_ptr.get())) |
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{ |
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a_m_k_device_buf.ToDevice(A.mData.data()); |
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b_k_n_device_buf.ToDevice(B.mData.data()); |
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel}); |
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std::size_t flop = std::size_t(2) * params.M * params.N * params.K; |
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std::size_t num_btype = sizeof(ADataType) * params.M * params.K + |
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sizeof(BDataType) * params.K * params.N + |
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sizeof(CDataType) * params.M * params.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 |
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<< " GB/s, " << std::endl; |
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c_m_n_device_buf.FromDevice(C.mData.data()); |
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return true; |
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} |
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else |
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{ |
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std::cout << "device_gemm with the specified compilation parameters does " |
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"not support this GEMM problem" |
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<< std::endl; |
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return false; |
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} |
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} |
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template <typename AccDataType> |
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struct TestGemm |
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{ |
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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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auto PrepareGemmTensor(const ck::gemm_util::GemmParams& params) |
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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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using namespace ck::literals; |
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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({row, col}, {stride, 1_uz}); |
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} |
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else |
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{ |
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return HostTensorDescriptor({row, col}, {1_uz, stride}); |
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} |
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}; |
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Tensor<ADataType> a_m_k( |
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f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{})); |
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Tensor<BDataType> b_k_n( |
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f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{})); |
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Tensor<CDataType> c_m_n_host_result( |
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f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{})); |
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Tensor<CDataType> c_m_n_device_result( |
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f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{})); |
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auto f_generate_tensor_value = [](auto& tensor, auto type) { |
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using dataType = decltype(type); |
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tensor.GenerateTensorValue(GeneratorTensor_2<dataType>{-5, 5}); |
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}; |
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f_generate_tensor_value(a_m_k, ADataType{}); |
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f_generate_tensor_value(b_k_n, BDataType{}); |
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl; |
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl; |
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std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl; |
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return std::make_tuple(a_m_k, b_k_n, c_m_n_host_result, c_m_n_device_result); |
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} |
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template <template <class...> class DeviceGemmPtr_, |
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typename ALayout, |
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typename BLayout, |
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typename CLayout, |
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typename ADataType, |
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typename BDataType, |
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typename CDataType, |
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typename AElementwiseOperation, |
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typename BElementwiseOperation, |
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typename CElementwiseOperation> |
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auto operator()(DeviceGemmPtr_<ALayout, |
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BLayout, |
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CLayout, |
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ADataType, |
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BDataType, |
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CDataType, |
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AElementwiseOperation, |
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BElementwiseOperation, |
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CElementwiseOperation>* gemmPtr, |
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const GemmParams& params = GemmParams{}, |
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bool do_verification = true, |
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bool time_kernel = false) |
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{ |
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std::cout << "ALayout = " << ALayout{}.name << ", BLayout = " << BLayout{}.name |
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<< ", CLayout = " << CLayout{}.name << std::endl; |
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std::cout << gemmPtr->GetTypeString() << std::endl; |
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auto host_tensors = |
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PrepareGemmTensor<ADataType, BDataType, CDataType, ALayout, BLayout, CLayout>(params); |
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const Tensor<ADataType>& a = std::get<0>(host_tensors); |
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const Tensor<BDataType>& b = std::get<1>(host_tensors); |
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Tensor<CDataType>& c_host = std::get<2>(host_tensors); |
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Tensor<CDataType>& c_device = std::get<3>(host_tensors); |
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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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using ReferenceGemmInstance = |
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ck::tensor_operation::host::ReferenceGemm<ADataType, |
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BDataType, |
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CDataType, |
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AccDataType, |
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AElementwiseOperation, |
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BElementwiseOperation, |
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CElementwiseOperation>; |
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if(do_verification) |
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{ |
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ck::gemm_util::RunHostGEMM<ReferenceGemmInstance>( |
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a, b, c_host, a_element_op, b_element_op, c_element_op); |
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} |
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bool is_supported = ck::gemm_util::RunDeviceGEMM( |
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gemmPtr, params, a, b, c_device, a_element_op, b_element_op, c_element_op, time_kernel); |
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if(is_supported && do_verification) |
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{ |
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bool res = false; |
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if(std::is_same<CDataType, float>::value) |
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{ |
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res = ck::utils::check_err(c_device, c_host); |
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; |
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} |
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else if(std::is_same<CDataType, ck::half_t>::value) |
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{ |
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res = ck::utils::check_err(c_device, c_host); |
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; |
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} |
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else if(std::is_same<CDataType, ck::bhalf_t>::value) |
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{ |
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res = ck::utils::check_err(c_device, c_host); |
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; |
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} |
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else if(std::is_same<CDataType, int8_t>::value) |
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{ |
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res = ck::utils::check_err(c_device, c_host); |
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; |
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} |
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else if(std::is_same<CDataType, double>::value) |
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{ |
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res = ck::utils::check_err(c_device, c_host); |
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; |
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} |
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return res; |
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} |
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else |
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{ |
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return true; |
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} |
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} |
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}; |
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} |
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} |
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