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Added wmma support for gemm quantization: (#2841)
- profiler for gemm quantization for DL/XDL - tests for gemm quantization for DL/XDL - implementation for gemm quantization for WMMA - profiler/tests for gemm qunatization for WMMA Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
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Wojciech Laskowski
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GitHub
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231
profiler/include/profiler/profile_gemm_quantization_impl.hpp
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231
profiler/include/profiler/profile_gemm_quantization_impl.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 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/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.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/quantization/gemm_quantization.hpp"
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#include "ck/library/utility/check_err.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/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 AccDataType,
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typename EDataType,
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typename ALayout,
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typename BLayout,
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typename ELayout>
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bool profile_gemm_quantization_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 StrideE,
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float requant_scale = 0.03f)
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{
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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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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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<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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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 << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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}
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using MulClamp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<PassThrough>;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using ActivationOp = PassThrough;
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using CDEElementOp = MulClamp;
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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 cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};
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using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleD<
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ALayout,
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BLayout,
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ck::Tuple<>,
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ELayout,
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ADataType,
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BDataType,
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ck::Tuple<>,
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EDataType,
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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::Activation_Mul_Clamp<PassThrough>>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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// run reference
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if(do_verification)
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{
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Tensor<AccDataType> c_m_n({M, N});
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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AccDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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PassThrough>;
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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 =
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ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
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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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for(int n = 0; n < N; ++n)
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{
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cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n));
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}
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}
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
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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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std::string best_op_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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bool pass = true;
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// profile device operation instances
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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std::array<const void*, 0>{},
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e_device_buf.GetDeviceBuffer(),
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M,
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N,
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K,
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StrideA,
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StrideB,
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std::array<ck::index_t, 0>{},
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StrideE,
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a_element_op,
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b_element_op,
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cde_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// re-init E to zero before profiling a kernel
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e_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::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * 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: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_op_name = op_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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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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pass = pass && ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
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if(do_log)
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{
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LogRangeAsType<float>(
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std::cout << "e_m_n_device_result: ", e_m_n_device_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "e_m_n_host_result: ", e_m_n_host_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 << op_name << " does not support this 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_op_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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