mirror of
https://github.com/ROCm/composable_kernel.git
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380 lines
16 KiB
C++
380 lines
16 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iomanip>
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#include "ck/ck.hpp"
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#include "ck/utility/env.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_gemm_splitk.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/convolution_parameter.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/literals.hpp"
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#include "ck/library/utility/fill.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 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 AccDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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bool profile_grouped_gemm_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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const std::vector<int>& Ms,
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const std::vector<int>& Ns,
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const std::vector<int>& Ks,
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const std::vector<int>& StrideAs,
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const std::vector<int>& StrideBs,
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const std::vector<int>& StrideCs,
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const std::vector<int>& kbatches = {},
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int n_warmup = 1,
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int n_iter = 10)
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{
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bool pass = true;
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// TODO: Fixme - we do not pass compute data type here but need it
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// to compute error thresholds.
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using ComputeDataType = ADataType;
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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(is_same<decltype(layout), 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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std::size_t group_count = Ms.size();
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if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() &&
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group_count == StrideBs.size() && group_count == StrideCs.size()))
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{
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throw std::runtime_error("wrong! inconsistent M/N/Ks, StrideA/B/Cs size\n");
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}
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std::vector<Tensor<ADataType>> a_m_k;
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std::vector<Tensor<BDataType>> b_k_n;
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std::vector<Tensor<CDataType>> c_m_n_host_results;
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std::vector<Tensor<CDataType>> c_m_n_device_results;
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double max_abs_in_val = 0.f;
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for(std::size_t i = 0; i < group_count; i++)
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{
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a_m_k.push_back(
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Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{})));
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b_k_n.push_back(
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Tensor<BDataType>(f_host_tensor_descriptor(Ks[i], Ns[i], StrideBs[i], BLayout{})));
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c_m_n_device_results.push_back(
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Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
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c_m_n_host_results.push_back(
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Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
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if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
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{
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std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n["
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<< i << "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i
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<< "]:" << c_m_n_device_results[i].mDesc << std::endl;
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}
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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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ck::utils::FillUniformDistributionIntegerValue<ADataType>{-2.f, 2.f}(a_m_k[i]);
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ck::utils::FillUniformDistributionIntegerValue<BDataType>{-2.f, 2.f}(b_k_n[i]);
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max_abs_in_val = 2.f;
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break;
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default:
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ck::utils::FillUniformDistribution<ADataType>{-0.5f, 0.5f}(a_m_k[i]);
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ck::utils::FillUniformDistribution<BDataType>{-0.5f, 0.5f}(b_k_n[i]);
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max_abs_in_val = 0.5f;
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}
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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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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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using DeviceMemPtr = std::unique_ptr<DeviceMem>;
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std::vector<DeviceMemPtr> a_device_buf, b_device_buf, c_device_buf;
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a_device_buf.reserve(group_count);
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b_device_buf.reserve(group_count);
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c_device_buf.reserve(group_count);
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std::vector<const void*> p_a, p_b;
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std::vector<void*> p_c;
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p_a.reserve(group_count);
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p_b.reserve(group_count);
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p_c.reserve(group_count);
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std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
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gemm_descs.reserve(group_count);
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for(std::size_t i = 0; i < group_count; i++)
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{
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a_device_buf.emplace_back(
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std::make_unique<DeviceMem>(sizeof(ADataType) * a_m_k[i].mDesc.GetElementSpaceSize()));
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b_device_buf.emplace_back(
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std::make_unique<DeviceMem>(sizeof(BDataType) * b_k_n[i].mDesc.GetElementSpaceSize()));
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c_device_buf.emplace_back(std::make_unique<DeviceMem>(
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sizeof(CDataType) * c_m_n_device_results[i].mDesc.GetElementSpaceSize()));
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a_device_buf[i]->ToDevice(a_m_k[i].mData.data());
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b_device_buf[i]->ToDevice(b_k_n[i].mData.data());
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gemm_descs.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}});
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p_a.push_back(a_device_buf[i]->GetDeviceBuffer());
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p_b.push_back(b_device_buf[i]->GetDeviceBuffer());
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p_c.push_back(c_device_buf[i]->GetDeviceBuffer());
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}
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemm<ALayout,
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BLayout,
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ck::Tuple<>,
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CLayout,
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ADataType,
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BDataType,
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ck::Tuple<>,
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CDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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// If kbatch would be bigger than 1, then we will use SplitK version.
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using DeviceOpSplitK = ck::tensor_operation::device::DeviceGroupedGemmSplitK<ALayout,
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BLayout,
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ck::Tuple<>,
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CLayout,
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ADataType,
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BDataType,
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ck::Tuple<>,
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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 op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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if(op_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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float best_kbatch = 0;
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auto p_ds = std::vector<std::array<const void*, 0>>{};
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if(do_verification)
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{
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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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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AccDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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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(a_m_k[i],
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b_k_n[i],
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c_m_n_host_results[i],
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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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ref_invoker.Run(ref_argument);
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}
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}
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// profile device GEMM instances
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for(auto& gemm_ptr : op_ptrs)
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{
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auto argument_ptr =
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gemm_ptr->MakeArgumentPointer(p_a,
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p_b,
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p_ds,
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p_c,
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gemm_descs,
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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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auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
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std::size_t workspace_size = gemm_ptr->GetWorkSpaceSize(argument_ptr.get());
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std::size_t kargs_size = gemm_ptr->GetDeviceKernelArgSize(argument_ptr.get());
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DeviceMem gemm_workspace, gemm_kargs;
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// The following is necessary since TwoStage kernel is using additional memory both
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// for Workspace and kernel arguments.
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if(kargs_size > 0)
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{
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gemm_kargs.Realloc(kargs_size);
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gemm_ptr->SetDeviceKernelArgs(argument_ptr.get(), gemm_kargs.GetDeviceBuffer());
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}
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if(workspace_size > 0 && workspace_size != kargs_size)
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{
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gemm_workspace.Realloc(workspace_size);
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gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_workspace.GetDeviceBuffer());
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}
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std::string gemm_name = gemm_ptr->GetTypeString();
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std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 24, 32, 48, 64};
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// If the user will provide not empty kbatches list, then we test predefined set of kbatch
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// values.
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if(!kbatches.empty())
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{
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kbatch_list = kbatches;
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}
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for(std::size_t j = 0; j < kbatch_list.size(); j++)
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{
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auto kbatch_curr = kbatch_list[j];
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if(kbatch_curr > 1 && dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get()) != nullptr)
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{
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dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
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->SetKBatchSize(argument_ptr.get(), kbatch_curr);
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}
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if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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c_device_buf[i]->SetZero();
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invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr, false, 0, n_warmup, n_iter});
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if(do_verification)
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{
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bool instance_pass = true;
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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{
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c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
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auto atol = ck::utils::get_absolute_threshold<ComputeDataType, CDataType>(
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max_abs_in_val, gemm_descs[i].K_);
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auto rtol = ck::utils::get_relative_threshold<ComputeDataType, CDataType>(
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gemm_descs[i].K_);
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instance_pass =
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instance_pass && ck::utils::check_err(c_m_n_device_results[i],
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c_m_n_host_results[i],
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"Error: Incorrect results!",
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rtol,
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atol);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a_m_k[i].mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_k_n[i].mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_device: ", c_m_n_device_results[i].mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_host : ", c_m_n_host_results[i].mData, ",")
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<< std::endl;
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}
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}
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std::cout << "Instance: " << gemm_name << " verification "
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<< (instance_pass ? "SUCCEED" : "FAILED") << std::endl;
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pass = pass && instance_pass;
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}
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if(time_kernel)
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{
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float ave_time =
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invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter});
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std::size_t flop = 0, num_btype = 0;
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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{
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flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
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num_btype += sizeof(ADataType) * Ms[i] * Ks[i] +
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sizeof(BDataType) * Ks[i] * Ns[i] +
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sizeof(CDataType) * Ms[i] * Ns[i];
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}
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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: " << std::setw(10) << ave_time << " ms, " << tflops
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<< " TFlops, " << gb_per_sec << " GB/s, " << gemm_name << ", KBatch "
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<< kbatch_curr << 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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best_kbatch = kbatch_curr;
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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 << "Instance: " << gemm_name << ", does not support this GEMM problem"
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<< std::endl;
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}
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}
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}
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if(time_kernel)
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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 << ", KBatch = " << best_kbatch
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<< std::endl;
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}
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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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