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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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211
example/14_gemm_quantization/gemm_wmma_quantization_int8.cpp
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211
example/14_gemm_quantization/gemm_wmma_quantization_int8.cpp
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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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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <type_traits>
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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/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle_v3.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.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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#include "ck/library/utility/check_err.hpp"
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using I8 = int8_t;
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using I32 = int32_t;
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ActivationOp = PassThrough;
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using CDEElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>;
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using ADataType = I8;
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using BDataType = I8;
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using AccDataType = I32;
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using CShuffleDataType = I32;
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using DsDataType = ck::Tuple<>;
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using EDataType = I8;
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using ALayout = Col;
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using BLayout = Row;
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using DsLayout = ck::Tuple<>;
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using ELayout = Row;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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// clang-format off
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using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffleV3<
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ALayout,
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BLayout,
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DsLayout,
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ELayout,
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ADataType,
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BDataType,
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DsDataType,
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EDataType,
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AccDataType,
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CShuffleDataType,
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ActivationOp,
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ActivationOp,
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CDEElementOp,
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GemmDefault,
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256,
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128,
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128,
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64,
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8,
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8,
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16,
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16,
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4,
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2,
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S<4, 64, 1>,
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S<0, 2, 1>,
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S<0, 2, 1>,
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1,
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1,
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8,
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true,
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S<4, 64, 1>,
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S<0, 2, 1>,
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S<0, 2, 1>,
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1,
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1,
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8,
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true,
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1,
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1,
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S<1, 32, 1, 8>,
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S<1>,
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ck::BlockGemmPipelineScheduler::Intrawave,
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ck::BlockGemmPipelineVersion::v1,
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I8,
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I8>;
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::
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ReferenceGemm<ADataType, BDataType, EDataType, float, PassThrough, PassThrough, CDEElementOp>;
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int main(int /* argc */, char* /* argv */[])
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{
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bool do_verification = true;
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bool time_kernel = false;
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// GEMM shape
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ck::index_t M = 1024;
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ck::index_t N = 1024;
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ck::index_t K = 1024;
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ck::index_t StrideA = K;
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ck::index_t StrideB = N;
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ck::index_t StrideE = N;
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float requant_scale = 0.03;
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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(std::is_same<decltype(layout), ck::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_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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Tensor<EDataType> e_m_n_device_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_host_result.mDesc << std::endl;
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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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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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auto a_element_op = PassThrough{};
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auto b_element_op = PassThrough{};
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auto cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};
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// device GEMM
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auto gemm = DeviceGemmInstance{};
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auto invoker = gemm.MakeInvoker();
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auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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std::array<const void*, 0>{},
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static_cast<EDataType*>(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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1,
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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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if(!gemm.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_gemm with the specified compilation parameters does "
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"not support this GEMM problem");
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}
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float ave_time = invoker.Run(argument, 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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< gemm.GetTypeString() << std::endl;
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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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if(do_verification)
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{
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(
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a_m_k, b_k_n, e_m_n_host_result, a_element_op, b_element_op, cde_element_op);
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ref_invoker.Run(ref_argument);
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return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
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}
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return 0;
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}
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