mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-13 09:45:56 +00:00
1f543bfa79
* Remove template from Reducton operation classes and add template to their operator() and GetIdentityValue() interfaces * Change to unary elementwise operators and the reduce_unary_operator (class for mapping) and dependent variations in all host layers * Remove the data type template parameter from reduce_binary_operator (class for mapping) and dependent variations in host layers * Add InMemoryDataOperatonSupportedOnDataType to check the matching between data type and InMemoryDataOperation * Use struct-scope operator template instantiation for binary and unary element-wise operations * Change a few more elementwise operations to use template for operator() * Tiny correction in Normalize operator * Add static_assert to check the data type appliability for some reduction accumulator and element-wise operatons * Correction in some examples with regard to using ReduceAccDataType * Use static_assert for UnaryDivide * Update to merged codes to use Element-wise operations and Reduction Accumulator operations correctly * Tiny fix with regard to SetWorkSpacePointer()
350 lines
15 KiB
C++
350 lines
15 KiB
C++
#pragma once
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#include "check_err.hpp"
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#include "config.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_conv.hpp"
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#include "tensor_layout.hpp"
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#include "device_tensor.hpp"
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#include "element_wise_operation.hpp"
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#include "reduction_operator.hpp"
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#include "device_gemm_reduce.hpp"
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#include "reference_gemm.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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using F32 = float;
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using F16 = ck::half_t;
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using DPtrsGlobal = ck::Tuple<F32*, F32*>;
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using Div = ck::tensor_operation::element_wise::UnaryDivide;
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using Identity = ck::tensor_operation::element_wise::PassThrough;
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using Square = ck::tensor_operation::element_wise::UnarySquare;
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using DInElementOps = ck::Tuple<Identity, Square>;
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using DOutElementOps = ck::Tuple<Div, Div>;
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using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePtr<
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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DInElementOps,
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DOutElementOps>;
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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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 DDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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bool profile_gemm_reduce_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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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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bool pass = true;
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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<DDataType> d0_m_host_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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Tensor<DDataType> d1_m_host_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<DDataType> d0_m_device_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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Tensor<DDataType> d1_m_device_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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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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std::cout << "d0_m: " << d0_m_host_result.mDesc << std::endl;
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std::cout << "d1_m: " << d1_m_host_result.mDesc << std::endl;
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std::size_t num_thread = 1;
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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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std::srand(0);
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
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break;
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default:
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std::srand(0);
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
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}
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::PassThrough;
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using D0ReduceOp = ck::reduce::Add;
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using D1ReduceOp = ck::reduce::Add;
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using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
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using UnaryIdenticElementOp = ck::tensor_operation::element_wise::PassThrough;
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using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
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using DxsInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
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using DxsOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
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const auto a_element_op = AElementOp{};
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const auto b_element_op = BElementOp{};
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const auto c_element_op = CElementOp{};
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const auto d0_reduce_op = D0ReduceOp{};
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const auto d1_reduce_op = D1ReduceOp{};
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auto dxs_in_element_op = DxsInElementOps{};
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auto dxs_out_element_op = DxsOutElementOps{N, N};
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if(do_verification)
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{
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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CDataType,
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DDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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using ReduceAccDataType = DDataType;
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(
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a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
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ref_invoker.Run(ref_argument);
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for(int m = 0; m < M; ++m)
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{
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auto d0_acc = d0_reduce_op.GetIdentityValue<ReduceAccDataType>();
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auto d1_acc = d1_reduce_op.GetIdentityValue<ReduceAccDataType>();
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for(int n = 0; n < N; ++n)
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{
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ReduceAccDataType c_val =
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ck::type_convert<ReduceAccDataType>(c_m_n_host_result(m, n));
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ReduceAccDataType d0_val;
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ReduceAccDataType d1_val;
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dxs_in_element_op(ck::Number<0>{})(d0_val, c_val);
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dxs_in_element_op(ck::Number<1>{})(d1_val, c_val);
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d0_reduce_op(d0_acc, d0_val);
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d1_reduce_op(d1_acc, d1_val);
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}
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dxs_out_element_op(ck::Number<0>{})(d0_acc, d0_acc);
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dxs_out_element_op(ck::Number<1>{})(d1_acc, d1_acc);
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d0_m_host_result(m) = ck::type_convert<DDataType>(d0_acc);
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d1_m_host_result(m) = ck::type_convert<DDataType>(d1_acc);
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}
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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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DeviceMem d0_device_buf(sizeof(DDataType) * d0_m_device_result.mDesc.GetElementSpace());
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DeviceMem d1_device_buf(sizeof(DDataType) * d1_m_device_result.mDesc.GetElementSpace());
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auto dxs_global = ck::make_tuple(static_cast<DDataType*>(d0_device_buf.GetDeviceBuffer()),
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static_cast<DDataType*>(d1_device_buf.GetDeviceBuffer()));
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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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// add device GEMM instances
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std::vector<ck::tensor_operation::device::device_gemm_instance::DeviceGemmReduceNoOpPtr>
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gemm_ptrs;
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if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value &&
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is_same<CDataType, half_t>::value)
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{
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if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
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gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances(
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gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances(
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gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances(
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gemm_ptrs);
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}
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}
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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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std::string best_gemm_name;
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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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&dxs_global,
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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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dxs_in_element_op,
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dxs_out_element_op);
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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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// init DO, D1 to 0
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d0_device_buf.SetZero();
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d1_device_buf.SetZero();
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float ave_time =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::string gemm_name = gemm_ptr->GetTypeString();
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(CDataType) * M * N + sizeof(CDataType) * 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, " << gemm_name << std::endl;
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if(tflops > best_tflops)
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{
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best_gemm_name = gemm_name;
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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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d0_device_buf.FromDevice(d0_m_device_result.mData.data());
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d1_device_buf.FromDevice(d1_m_device_result.mData.data());
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ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
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ck::utils::check_err(d0_m_device_result.mData, d0_m_host_result.mData);
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ck::utils::check_err(d1_m_device_result.mData, d1_m_host_result.mData);
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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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LogRangeAsType<float>(std::cout << "d0_host: ", d0_m_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "d0_device: ", d0_m_device_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "d1_host: ", d1_m_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "d1_device: ", d1_m_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 << "does not support this GEMM problem" << 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, " << best_gemm_name << std::endl;
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return pass;
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}
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} // namespace profiler
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} // namespace ck
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