mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 01:10:17 +00:00
0a66c54e95
* fixed bfloat16 issues * refactor type_convert Co-authored-by: Chao Liu <chao.liu2@amd.com>
230 lines
8.6 KiB
C++
230 lines
8.6 KiB
C++
#pragma once
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#include "device_gemm_instance.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace device_gemm_instance {
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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template <>
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void add_device_gemm_instance<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>(std::vector<DeviceGemmPtr>&);
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} // namespace device_gemm_instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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namespace ck {
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namespace profiler {
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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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void profile_gemm(int do_verification,
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int init_method,
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bool do_log,
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int nrepeat,
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int M,
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int N,
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int K,
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int StrideA,
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int StrideB,
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int StrideC)
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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(is_same<decltype(layout), 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(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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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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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.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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}
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if(do_verification)
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{
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host_gemm_mk_kn_mn(a_m_k, b_k_n, c_m_n_host_result);
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
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DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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c_device_buf.ToDevice(c_m_n_device_result.mData.data());
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// add device GEMM instances
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std::vector<ck::tensor_operation::device::DeviceGemmPtr> gemm_ptrs;
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_instance<ADataType, BDataType, CDataType, ALayout, BLayout, CLayout>(
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gemm_ptrs);
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if(gemm_ptrs.size() <= 0)
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{
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throw std::runtime_error("wrong! no device GEMM instance found");
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}
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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// profile device GEMM instances
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for(auto& gemm_ptr : gemm_ptrs)
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{
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auto argument_ptr =
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gemm_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_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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auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
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if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K + sizeof(BDataType) * K * M + 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
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<< " GB/s" << std::endl;
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if(tflops > best_tflops)
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{
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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if(do_verification)
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{
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c_device_buf.FromDevice(c_m_n_device_result.mData.data());
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check_error(c_m_n_host_result, c_m_n_device_result);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c_host : ", c_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
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<< std::endl;
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}
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}
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}
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else
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{
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std::cout << "this device GEMM instance does not support this GEMM problem"
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<< std::endl;
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}
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}
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
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<< best_gb_per_sec << " GB/s" << std::endl;
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}
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} // namespace profiler
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} // namespace ck
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