mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-11 17:00:18 +00:00
86185bd7ce
* Use the unified naming for math functions on host and HIP kernel * Corresponding change/simplification in reduction host/profiler/examples due to unified math functions renaming * Renaming GetReductionZeroVal() to GetIdentityValue() * Tiny renaming in profile_reduce_impl.hpp * More renaming in profile_reduce_impl.hpp * Replace zeroVal by identiyVal * Remove ck_ prefix in the naming of ck::math provided functions
360 lines
16 KiB
C++
360 lines
16 KiB
C++
#pragma once
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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_batched_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 Identity = ck::tensor_operation::element_wise::UnaryIdentic<F32, F32, false>;
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using Square = ck::tensor_operation::element_wise::UnarySquare<F32, F32, false>;
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using DInElementOps = ck::Tuple<Identity, Square>;
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using DOutElementOps = ck::Tuple<Identity, Identity>;
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using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePtr<
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DPtrsGlobal,
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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_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gmk_gkn_gmn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gmk_gnk_gmn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gkm_gkn_gmn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gkm_gnk_gmn_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_batched_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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int BatchCount)
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{
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bool pass = true;
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auto f_host_tensor_descriptor = [](std::size_t batch_count,
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std::size_t row,
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std::size_t col,
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std::size_t stride,
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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>({batch_count, row, col}),
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std::vector<std::size_t>({row * stride, 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>({batch_count, row, col}),
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std::vector<std::size_t>({col * stride, 1, stride}));
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}
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};
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Tensor<ADataType> a_g_m_k(f_host_tensor_descriptor(BatchCount, M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_g_k_n(f_host_tensor_descriptor(BatchCount, K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_g_m_n_host_result(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
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Tensor<DDataType> d0_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
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{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
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Tensor<DDataType> d1_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
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{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
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Tensor<CDataType> c_g_m_n_device_result(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
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Tensor<DDataType> d0_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
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{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
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Tensor<DDataType> d1_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
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{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
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std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
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std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
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std::cout << "c_g_m_n: " << c_g_m_n_host_result.mDesc << std::endl;
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std::cout << "d0_g_m: " << d0_g_m_host_result.mDesc << std::endl;
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std::cout << "d1_g_m: " << d1_g_m_host_result.mDesc << std::endl;
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std::size_t num_thread = std::thread::hardware_concurrency();
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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_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
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b_g_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_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
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b_g_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<float>;
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using D1ReduceOp = ck::reduce::Add<float>;
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using UnaryIdenticElementOp =
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ck::tensor_operation::element_wise::UnaryIdentic<float, float, false>;
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using UnarySquareElementOp =
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ck::tensor_operation::element_wise::UnarySquare<float, float, false>;
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using DxsInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
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using DxsOutElementOps = ck::Tuple<UnaryIdenticElementOp, UnaryIdenticElementOp>;
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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 dxs_in_element_op = DxsInElementOps{};
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const auto dxs_out_element_op = DxsOutElementOps{};
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const auto d0_reduce_op = D0ReduceOp{};
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const auto d1_reduce_op = D1ReduceOp{};
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if(do_verification)
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{
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using ReferenceBatchedGemmInstance =
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ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
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BDataType,
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CDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
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auto ref_invoker = ref_batched_gemm.MakeInvoker();
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auto ref_argument = ref_batched_gemm.MakeArgument(
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a_g_m_k, b_g_k_n, c_g_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 batch = 0; batch < BatchCount; ++batch)
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{
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for(int m = 0; m < M; ++m)
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{
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float d0_acc = d0_reduce_op.GetIdentityValue();
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float d1_acc = d1_reduce_op.GetIdentityValue();
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for(int n = 0; n < N; ++n)
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{
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float d0_val = ck::type_convert<float>(c_g_m_n_host_result(batch, m, n));
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float d1_val;
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UnarySquareElementOp{}(d1_val, d0_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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d0_g_m_host_result(batch, m) = ck::type_convert<DDataType>(d0_acc);
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d1_g_m_host_result(batch, m) = ck::type_convert<DDataType>(d1_acc);
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}
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}
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpace());
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DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpace());
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DeviceMem c_device_buf(sizeof(CDataType) * c_g_m_n_device_result.mDesc.GetElementSpace());
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DeviceMem d0_device_buf(sizeof(DDataType) * d0_g_m_device_result.mDesc.GetElementSpace());
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DeviceMem d1_device_buf(sizeof(DDataType) * d1_g_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_g_m_k.mData.data());
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b_device_buf.ToDevice(b_g_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_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gmk_gkn_gmn_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_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gmk_gnk_gmn_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_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gkm_gkn_gmn_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_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gkm_gnk_gmn_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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BatchCount);
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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) * BatchCount * M * N * K;
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std::size_t num_btype = sizeof(ADataType) * BatchCount * M * K +
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sizeof(BDataType) * BatchCount * K * N +
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sizeof(CDataType) * BatchCount * 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, " << 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_g_m_n_device_result.mData.data());
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d0_device_buf.FromDevice(d0_g_m_device_result.mData.data());
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d1_device_buf.FromDevice(d1_g_m_device_result.mData.data());
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float c_error = check_error(c_g_m_n_host_result, c_g_m_n_device_result);
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float d0_error = check_error(d0_g_m_host_result, d0_g_m_device_result);
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float d1_error = check_error(d1_g_m_host_result, d1_g_m_device_result);
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pass = pass && (c_error < 1E-6);
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pass = pass && (d0_error < 1E-6);
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pass = pass && (d1_error < 1E-6);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a_g_m_k.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_g_k_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_device: ", c_g_m_n_device_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "d0_host: ", d0_g_m_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "d0_device: ", d0_g_m_device_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "d1_host: ", d1_g_m_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "d1_device: ", d1_g_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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