mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-13 09:45:56 +00:00
Add support for mixed precision bf16&int8 grouped gemm (#1166)
* add support for mixed precision bf16&int8 grouped gemm * fix gfx versions and add bf16 kbatch condition * added reviewers comments
This commit is contained in:
jakpiase
370
profiler/include/profiler/profile_grouped_gemm_fixed_nk_impl.hpp
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370
profiler/include/profiler/profile_grouped_gemm_fixed_nk_impl.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, 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/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.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_fixed_nk.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/host_tensor_generator.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_fixed_nk_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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int kbatch = 1,
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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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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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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 DEBUG_LOG
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std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i
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<< "]:" << 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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#endif // DEBUG_LOG
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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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a_m_k[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
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b_k_n[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
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break;
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default:
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a_m_k[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
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b_k_n[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
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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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std::vector<ck::tensor_operation::device::GroupedGemmKernelArgument<1>>
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grouped_gemm_kernel_args_;
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grouped_gemm_kernel_args_.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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grouped_gemm_kernel_args_.push_back({a_device_buf[i]->GetDeviceBuffer(),
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b_device_buf[i]->GetDeviceBuffer(),
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{},
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c_device_buf[i]->GetDeviceBuffer(),
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Ms[i],
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Ns[i],
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Ks[i],
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StrideAs[i],
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StrideBs[i],
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{},
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StrideCs[i]});
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}
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmFixedNK<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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const 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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DeviceMem gemm_desc_workspace(gemm_ptr->GetWorkSpaceSize(argument_ptr.get()));
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DeviceMem grouped_gemm_kernel_args_dev(
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gemm_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
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hipGetErrorString(hipMemcpy(grouped_gemm_kernel_args_dev.GetDeviceBuffer(),
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grouped_gemm_kernel_args_.data(),
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gemm_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
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hipMemcpyHostToDevice));
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gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
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gemm_ptr->SetDeviceKernelArgs(argument_ptr.get(),
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grouped_gemm_kernel_args_dev.GetDeviceBuffer());
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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(kbatch > 0)
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{
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kbatch_list = {kbatch};
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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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gemm_ptr->SetKBatch(argument_ptr.get(), kbatch_curr);
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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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if(std::is_same_v<CDataType, ck::half_t> && kbatch_curr > 1)
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{
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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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0.06);
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}
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else
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{
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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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}
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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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float ave_time = invoker_ptr->Run(
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argument_ptr.get(), StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter});
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if(time_kernel)
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{
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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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