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|
| #include <iomanip> |
| #include <vector> |
| #include <iostream> |
|
|
| #include "ck/ck.hpp" |
| #include "ck/tensor_operation/gpu/device/tensor_layout.hpp" |
| #include "ck/tensor_operation/gpu/device/device_gemm.hpp" |
| #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" |
|
|
| #include "ck/library/tensor_operation_instance/gpu/gemm.hpp" |
|
|
| using F16 = ck::half_t; |
| using F32 = float; |
|
|
| using Row = ck::tensor_layout::gemm::RowMajor; |
| using Col = ck::tensor_layout::gemm::ColumnMajor; |
|
|
| using PassThrough = ck::tensor_operation::element_wise::PassThrough; |
|
|
| using AElementOp = PassThrough; |
| using BElementOp = PassThrough; |
| using CElementOp = PassThrough; |
|
|
| using ADataType = F16; |
| using BDataType = F16; |
| using CDataType = F16; |
|
|
| using ALayout = Row; |
| using BLayout = Col; |
| using CLayout = Row; |
|
|
| struct SimpleDeviceMem |
| { |
| SimpleDeviceMem() = delete; |
|
|
| SimpleDeviceMem(std::size_t mem_size) : p_mem_{} |
| { |
| (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size); |
| } |
|
|
| void* GetDeviceBuffer() { return p_mem_; } |
|
|
| ~SimpleDeviceMem() { (void)hipFree(p_mem_); } |
|
|
| void* p_mem_; |
| }; |
|
|
| int main(int argc, char* argv[]) |
| { |
| |
| ck::index_t M = 3840; |
| ck::index_t N = 4096; |
| ck::index_t K = 4096; |
|
|
| ck::index_t StrideA = 4096; |
| ck::index_t StrideB = 4096; |
| ck::index_t StrideC = 4096; |
|
|
| if(argc == 1) |
| { |
| |
| } |
| else if(argc == 7) |
| { |
| M = std::stoi(argv[1]); |
| N = std::stoi(argv[2]); |
| K = std::stoi(argv[3]); |
|
|
| StrideA = std::stoi(argv[4]); |
| StrideB = std::stoi(argv[5]); |
| StrideC = std::stoi(argv[6]); |
| } |
| else |
| { |
| printf("arg1 to 6: M, N, K, StrideA, StrideB, StrideC\n"); |
| exit(0); |
| } |
|
|
| auto f_matrix_space_size = |
| [](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) { |
| using Layout = decltype(layout); |
|
|
| if constexpr(std::is_same<Layout, Row>::value) |
| { |
| return (nRow - 1) * stride + nCol; |
| } |
| else |
| { |
| return (nCol - 1) * stride + nRow; |
| } |
| }; |
|
|
| SimpleDeviceMem a_device_buf(sizeof(ADataType) * f_matrix_space_size(M, K, StrideA, ALayout{})); |
| SimpleDeviceMem b_device_buf(sizeof(BDataType) * f_matrix_space_size(K, N, StrideB, BLayout{})); |
| SimpleDeviceMem c_device_buf(sizeof(CDataType) * f_matrix_space_size(M, N, StrideC, CLayout{})); |
|
|
| using DeviceOp = |
| ck::tensor_operation::device::DeviceGemm<ALayout, |
| BLayout, |
| CLayout, |
| ADataType, |
| BDataType, |
| CDataType, |
| ck::tensor_operation::element_wise::PassThrough, |
| ck::tensor_operation::element_wise::PassThrough, |
| ck::tensor_operation::element_wise::PassThrough>; |
|
|
| |
| const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory< |
| DeviceOp>::GetInstances(); |
|
|
| std::cout << "found " << op_ptrs.size() << " instances" << std::endl; |
|
|
| const auto a_element_op = AElementOp{}; |
| const auto b_element_op = BElementOp{}; |
| const auto c_element_op = CElementOp{}; |
|
|
| std::string best_op_name; |
| bool found = false; |
| int best_op_id = -1; |
| float best_ave_time = 0; |
| float best_tflops = 0; |
| float best_gb_per_sec = 0; |
|
|
| |
| std::cout << "Run all instances and do timing" << std::endl; |
|
|
| for(int i = 0; i < op_ptrs.size(); ++i) |
| { |
| auto& op_ptr = op_ptrs[i]; |
|
|
| auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(), |
| b_device_buf.GetDeviceBuffer(), |
| c_device_buf.GetDeviceBuffer(), |
| M, |
| N, |
| K, |
| StrideA, |
| StrideB, |
| StrideC, |
| a_element_op, |
| b_element_op, |
| c_element_op); |
|
|
| auto invoker_ptr = op_ptr->MakeInvokerPointer(); |
|
|
| std::string op_name = op_ptr->GetTypeString(); |
|
|
| if(op_ptr->IsSupportedArgument(argument_ptr.get())) |
| { |
| float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true}); |
|
|
| std::size_t flop = std::size_t(2) * M * N * K; |
|
|
| std::size_t num_btype = |
| sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N; |
|
|
| float tflops = static_cast<float>(flop) / 1.E9 / ave_time; |
|
|
| float gb_per_sec = num_btype / 1.E6 / ave_time; |
|
|
| std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, " |
| << gb_per_sec << " GB/s, " << op_name << std::endl; |
|
|
| if(tflops > best_tflops) |
| { |
| found = true; |
| best_op_id = i; |
| best_op_name = op_name; |
| best_tflops = tflops; |
| best_ave_time = ave_time; |
| best_gb_per_sec = gb_per_sec; |
| } |
| } |
| else |
| { |
| std::cout << op_name << " 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; |
|
|
| |
| if(found) |
| { |
| auto& op_ptr = op_ptrs[best_op_id]; |
|
|
| std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString() |
| << std::endl; |
|
|
| auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(), |
| b_device_buf.GetDeviceBuffer(), |
| c_device_buf.GetDeviceBuffer(), |
| M, |
| N, |
| K, |
| StrideA, |
| StrideB, |
| StrideC, |
| a_element_op, |
| b_element_op, |
| c_element_op); |
|
|
| auto invoker_ptr = op_ptr->MakeInvokerPointer(); |
|
|
| if(op_ptr->IsSupportedArgument(argument_ptr.get())) |
| { |
| invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false}); |
| } |
|
|
| std::cout << "Done" << std::endl; |
| } |
|
|
| return 0; |
| } |
|
|