mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 14:59:17 +00:00
Grouped GEMM Multiple D tile loop. (#1247)
* Overload output stream operator for LoopScheduler and PiplineVersion * Add Run overload accepting grid descriptors MK. * Add __device__ keyword for CalculateGridSize * Create device op GroupedGemmMultipleD * Add GroupedGemm MultipleD Tile Loop implementation. * Add an example for GroupedGemm MultipleD tile loop. * Device Op GroupedGEMMTileLoop. * Bunch of small changes in exmaple. * CkProfiler * Remove unused tparam. * Fix include statement. * Fix output stream overloads. * Do not make descriptors and check validity untill we find group. * Fix gemm desc initialization. * Revert device op * Fix compilation for DTYPES=FP16 * Validate tensor transfers paramters. * Validate on host only NK dims if M is not known. * Fix bug. * A convenient debug func for selecting threads. * Fix has main k block loop bug. * Make sure that b2c has up to date tile offset. * Output stream operator for Sequence type. * Cmake file formatting.
This commit is contained in:
Adam Osewski
@@ -26,6 +26,9 @@ add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int8)
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add_example_executable(example_grouped_gemm_xdl_fixed_nk_fp16_fp8 grouped_gemm_xdl_fixed_nk_fp16_fp8.cpp)
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add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_fixed_nk_fp16_fp8)
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add_example_executable(example_grouped_gemm_multiple_d_xdl_fp16 grouped_gemm_multiple_d_xdl_fp16.cpp)
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add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_multiple_d_xdl_fp16)
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if(USE_BITINT_EXTENSION_INT4)
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add_example_executable(example_grouped_gemm_xdl_int4 grouped_gemm_xdl_int4.cpp)
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add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int4)
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403
example/15_grouped_gemm/grouped_gemm_multiple_d_xdl_fp16.cpp
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403
example/15_grouped_gemm/grouped_gemm_multiple_d_xdl_fp16.cpp
Normal file
@@ -0,0 +1,403 @@
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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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#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 "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_grouped_gemm_multiple_d_xdl_cshuffle_tile_loop.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_gemm_tile_loop.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include <ck/utility/data_type.hpp>
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#include <ck/utility/tuple.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_multiple_d.hpp"
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F16 = ck::half_t;
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using F32 = float;
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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 AddAdd = ck::tensor_operation::element_wise::AddAdd;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F32;
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using DDataType = F16;
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using DsDataType = ck::Tuple<DDataType, DDataType>;
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using EDataType = F16;
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using ALayout = Row;
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using BLayout = Col;
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using DLayout = Row;
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using DsLayout = ck::Tuple<DLayout, DLayout>;
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using ELayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = AddAdd;
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static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
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static constexpr int NumDs = 2;
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using DeviceGemmInstance =
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ck::tensor_operation::device::DeviceGroupedGemmMultipleDXdlCShuffleTileLoop
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// clang-format off
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//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//######| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>;
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// clang-format on
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struct ProblemSize final
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{
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std::vector<ck::index_t> Ms;
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std::vector<ck::index_t> Ns;
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std::vector<ck::index_t> Ks;
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std::vector<ck::index_t> stride_As;
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std::vector<ck::index_t> stride_Bs;
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std::vector<std::vector<ck::index_t>> stride_Ds;
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std::vector<ck::index_t> stride_Cs;
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ck::index_t group_count;
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};
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struct ExecutionConfig final
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = true;
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};
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bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
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{
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auto group_count = problem_size.group_count;
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using KernelArguments = ck::tensor_operation::device::GroupedGemmTileLoopKernelArguments<NumDs>;
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// GEMM shape
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std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
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std::vector<KernelArguments> ggemm_kargs;
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std::vector<void*> p_Cs;
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std::vector<const void*> p_As;
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std::vector<const void*> p_Bs;
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std::vector<std::array<const void*, NumDs>> p_Ds = {};
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gemm_descs.reserve(group_count);
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ggemm_kargs.reserve(group_count);
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p_As.reserve(group_count);
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p_Bs.reserve(group_count);
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p_Ds.reserve(group_count);
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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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std::vector<Tensor<ADataType>> a_tensors;
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std::vector<Tensor<BDataType>> b_tensors;
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std::vector<std::array<Tensor<DDataType>, NumDs>> d_tensors;
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std::vector<Tensor<EDataType>> c_host_tensors;
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std::vector<Tensor<EDataType>> c_device_result_tensors;
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a_tensors.reserve(group_count);
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b_tensors.reserve(group_count);
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d_tensors.reserve(group_count);
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c_host_tensors.reserve(group_count);
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c_device_result_tensors.reserve(group_count);
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using DeviceMemPtr = std::unique_ptr<DeviceMem>;
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std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, c_tensors_device;
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std::vector<std::vector<DeviceMemPtr>> d_tensors_device;
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a_tensors_device.reserve(group_count);
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b_tensors_device.reserve(group_count);
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d_tensors_device.reserve(group_count);
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c_tensors_device.reserve(group_count);
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std::size_t flop = 0, num_btype = 0;
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for(int i = 0; i < group_count; i++)
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{
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a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], ALayout{})));
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b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
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problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], BLayout{})));
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auto d0_tensor = Tensor<DDataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], DLayout{}));
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auto d1_tensor = Tensor<DDataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], DLayout{}));
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std::array<Tensor<DDataType>, NumDs> d_tens = {d0_tensor, d1_tensor};
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d_tensors.push_back(d_tens);
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c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
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c_device_result_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
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std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
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<< " b_k_n: " << b_tensors[i].mDesc
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<< " c_m_n: " << c_device_result_tensors[i].mDesc << std::endl;
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flop += std::size_t(2) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
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num_btype += sizeof(ADataType) * a_tensors[i].GetElementSize() +
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sizeof(BDataType) * b_tensors[i].GetElementSize() +
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sizeof(DDataType) * d_tensors[i][0].GetElementSize() * NumDs +
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sizeof(EDataType) * c_device_result_tensors[i].GetElementSize();
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switch(config.init_method)
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{
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case 0: break;
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case 1:
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a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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for(int j = 0; j < NumDs; ++j)
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{
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d_tensors[i][j].GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
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}
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break;
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case 2:
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a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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for(int j = 0; j < NumDs; ++j)
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{
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d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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}
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break;
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default:
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a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
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b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
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for(int j = 0; j < NumDs; ++j)
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{
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d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
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}
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}
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}
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for(int i = 0; i < group_count; i++)
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{
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a_tensors_device.emplace_back(
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std::make_unique<DeviceMem>(a_tensors[i].GetElementSpaceSize() * sizeof(ADataType)));
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b_tensors_device.emplace_back(
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std::make_unique<DeviceMem>(b_tensors[i].GetElementSpaceSize() * sizeof(BDataType)));
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c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
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c_device_result_tensors[i].GetElementSpaceSize() * sizeof(EDataType)));
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for(int j = 0; j < NumDs; ++j)
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{
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d_tensors_device[i].emplace_back(std::make_unique<DeviceMem>(
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d_tensors[i][j].GetElementSpaceSize() * sizeof(DDataType)));
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}
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a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
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b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
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for(int j = 0; j < NumDs; ++j)
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{
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d_tensors_device[i][j]->ToDevice(d_tensors[i][j].mData.data());
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}
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c_tensors_device[i]->SetZero();
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p_As.push_back(a_tensors_device[i]->GetDeviceBuffer());
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p_Bs.push_back(b_tensors_device[i]->GetDeviceBuffer());
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p_Ds.push_back(
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{d_tensors_device[i][0]->GetDeviceBuffer(), d_tensors_device[i][1]->GetDeviceBuffer()});
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p_Cs.push_back(c_tensors_device[i]->GetDeviceBuffer());
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// The device op does not have to know M problem size at lunch time.
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gemm_descs.push_back({0,
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problem_size.Ns[i],
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problem_size.Ks[i],
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problem_size.stride_As[i],
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problem_size.stride_Bs[i],
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problem_size.stride_Cs[i],
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{problem_size.stride_Cs[i], problem_size.stride_Cs[i]}});
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ggemm_kargs.push_back(
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{a_tensors_device[i]->GetDeviceBuffer(),
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b_tensors_device[i]->GetDeviceBuffer(),
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{d_tensors_device[i][0]->GetDeviceBuffer(), d_tensors_device[i][1]->GetDeviceBuffer()},
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c_tensors_device[i]->GetDeviceBuffer(),
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problem_size.Ms[i],
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problem_size.Ns[i],
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problem_size.Ks[i],
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problem_size.stride_As[i],
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problem_size.stride_Bs[i],
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{problem_size.stride_Cs[i], problem_size.stride_Cs[i]},
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problem_size.stride_Cs[i]});
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}
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto cde_element_op = CDEElementOp{};
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auto gemm = DeviceGemmInstance{};
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auto invoker = gemm.MakeInvoker();
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// do GEMM
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auto argument = gemm.MakeArgument(
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p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, 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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DeviceMem gemm_arg_dev_mem(gemm.GetDeviceKernelArgSize(&argument));
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hip_check_error(hipMemcpy(gemm_arg_dev_mem.GetDeviceBuffer(),
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ggemm_kargs.data(),
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gemm.GetDeviceKernelArgSize(&argument),
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hipMemcpyHostToDevice));
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gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
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invoker.Run(argument, StreamConfig{nullptr, false, 1});
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bool pass = true;
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if(config.do_verification)
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{
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using ReferenceGemmInstance =
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ck::tensor_operation::host::ReferenceGemmMultipleD<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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AElementOp,
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BElementOp,
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CDEElementOp>;
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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{
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auto karg = ggemm_kargs[i];
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auto dev_res_tensor =
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Tensor<float>(f_host_tensor_descriptor(karg.M, karg.N, karg.StrideE, ELayout{}));
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c_tensors_device[i]->FromDevice(c_device_result_tensors[i].mData.data());
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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_tensors[i],
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b_tensors[i],
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d_tensors[i],
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c_host_tensors[i],
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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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ref_invoker.Run(ref_argument);
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pass &= ck::utils::check_err(c_device_result_tensors[i], c_host_tensors[i]);
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}
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std::cout << "Verification: " << (pass ? "SUCCESS" : "FAILURE") << "!" << std::endl;
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}
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if(config.time_kernel)
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{
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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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.GetTypeString() << std::endl;
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}
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return pass;
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}
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std::vector<int> argToIntArray(char* input)
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{
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std::vector<int> out;
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std::istringstream in(input);
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std::string item;
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while(std::getline(in, item, ','))
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{
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out.push_back(std::stoi(item));
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}
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return out;
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}
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int main(int argc, char* argv[])
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{
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ProblemSize problem_size;
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ExecutionConfig config;
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||||
|
||||
if(argc < 10)
|
||||
{
|
||||
std::vector<ck::index_t> Ms{64, 127, 255, 129, 260, 190, 77};
|
||||
problem_size.group_count = Ms.size();
|
||||
|
||||
for(int i = 0; i < problem_size.group_count; i++)
|
||||
{
|
||||
problem_size.Ms.push_back(Ms[i]);
|
||||
problem_size.Ns.push_back(252);
|
||||
problem_size.Ks.push_back(4608);
|
||||
|
||||
problem_size.stride_As.push_back(problem_size.Ks[i]);
|
||||
problem_size.stride_Bs.push_back(problem_size.Ks[i]);
|
||||
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
|
||||
|
||||
problem_size.stride_Ds.push_back({});
|
||||
for(int j = 0; j < NumDs; ++j)
|
||||
{
|
||||
problem_size.stride_Ds[i].push_back(problem_size.Ns[i]);
|
||||
}
|
||||
}
|
||||
|
||||
std::cout
|
||||
<< "Usage:\n"
|
||||
<< "arg1: verification (0=no, 1=yes)\n"
|
||||
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
|
||||
<< "arg3: time kernel (0=n0, 1=yes)\n"
|
||||
<< "arg4 to 9: Ms, Ns, Ks, StrideAs, StrideBs, StrideCs (e.g., 256,256 128,128 64,64 "
|
||||
"64,64 64,64 128,128)\n"
|
||||
<< "... setting default values." << std::endl;
|
||||
}
|
||||
else
|
||||
{
|
||||
config.do_verification = std::stoi(argv[1]);
|
||||
config.init_method = std::stoi(argv[2]);
|
||||
config.time_kernel = std::stoi(argv[3]);
|
||||
|
||||
problem_size.Ms = argToIntArray(argv[4]);
|
||||
problem_size.Ns = argToIntArray(argv[5]);
|
||||
problem_size.Ks = argToIntArray(argv[6]);
|
||||
|
||||
problem_size.stride_As = argToIntArray(argv[7]);
|
||||
problem_size.stride_Bs = argToIntArray(argv[8]);
|
||||
problem_size.stride_Cs = argToIntArray(argv[9]);
|
||||
|
||||
for(int j = 0; j < NumDs; ++j)
|
||||
{
|
||||
problem_size.stride_Ds.push_back(problem_size.stride_Cs);
|
||||
}
|
||||
|
||||
problem_size.group_count = problem_size.Ms.size();
|
||||
}
|
||||
|
||||
return !run_grouped_gemm(problem_size, config);
|
||||
}
|
||||
Reference in New Issue
Block a user