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b661eab573
[CK TILE] Refactor MX FLATMM example Refactor the MX FLATMM example to support more pipelines across different architectures. This work facilitates the NPI team roadmap.
325 lines
14 KiB
C++
325 lines
14 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include <hip/hip_runtime.h>
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#include <cstring>
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#include <iostream>
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#include <ostream>
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#include <string>
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#include <tuple>
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#include <type_traits>
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#include "ck_tile/host.hpp"
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#include "mx_flatmm.hpp"
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template <typename Layout>
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static constexpr inline auto is_row_major(Layout layout_)
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{
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return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
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ck_tile::tensor_layout::gemm::RowMajor>>{};
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}
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template <typename MXFlatmmArchTraits,
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typename ADataType,
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typename BDataType,
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typename DsDatatype,
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typename AccDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename DsLayout,
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typename CLayout,
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typename ScaleA,
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typename ScaleB,
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bool UsePersistentKernel = false,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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float invoke_mx_flatmm(ck_tile::DeviceMem& a_dev_buf,
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ck_tile::DeviceMem& b_shuffle_dev_buf,
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ck_tile::DeviceMem& c_dev_buf,
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ck_tile::index_t M,
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ck_tile::index_t N,
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ck_tile::index_t K,
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ck_tile::index_t stride_A,
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ck_tile::index_t stride_B,
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ck_tile::index_t stride_C,
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ck_tile::index_t kbatch,
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ScaleA scale_a,
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ScaleB scale_b,
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int n_warmup,
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int n_repeat)
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{
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using FlatmmConfig = typename MXFlatmmArchTraits::Config;
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ck_tile::ScaleFlatmmHostArgs<ScaleA, ScaleB> args = {a_dev_buf.GetDeviceBuffer(),
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b_shuffle_dev_buf.GetDeviceBuffer(),
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{},
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c_dev_buf.GetDeviceBuffer(),
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kbatch,
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M,
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N,
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K,
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stride_A,
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stride_B,
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{},
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stride_C,
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scale_a,
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scale_b};
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using FlatmmShape = ck_tile::TileGemmShape<
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ck_tile::sequence<FlatmmConfig::M_Tile, FlatmmConfig::N_Tile, FlatmmConfig::K_Tile>,
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ck_tile::sequence<FlatmmConfig::M_Warp, FlatmmConfig::N_Warp, FlatmmConfig::K_Warp>,
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ck_tile::sequence<FlatmmConfig::M_Warp_Tile,
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FlatmmConfig::N_Warp_Tile,
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FlatmmConfig::K_Warp_Tile>>;
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using TilePartitioner =
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ck_tile::GemmSpatiallyLocalTilePartitioner<FlatmmShape,
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FlatmmConfig::TileParitionerGroupNum,
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FlatmmConfig::TileParitionerM01>;
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using Traits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
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FlatmmConfig::kPadN,
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FlatmmConfig::kPadK,
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ALayout,
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BLayout,
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CLayout,
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FlatmmConfig::NumWaveGroups>;
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using GemmPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, FlatmmShape, Traits>;
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using BaseFlatmmPipeline = ck_tile::BaseFlatmmPipelineAGmemBGmemCRegV1<GemmPipelineProblem>;
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const ck_tile::index_t k_grain = args.k_batch * FlatmmConfig::K_Tile;
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const ck_tile::index_t k_split = (K + k_grain - 1) / k_grain * FlatmmConfig::K_Tile;
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const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(k_split);
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const bool has_hot_loop = BaseFlatmmPipeline::BlockHasHotloop(num_loop);
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const ck_tile::TailNumber tail_num = BaseFlatmmPipeline::GetBlockLoopTailNum(num_loop);
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float ave_time = BaseFlatmmPipeline::template TailHandler<true>(
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[&](auto has_hot_loop_, auto tail_num_) {
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constexpr auto has_hot_loop_v = has_hot_loop_.value;
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constexpr auto tail_num_v = tail_num_.value;
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auto invoke_splitk_path = [&](auto split_k_) {
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return mx_flatmm_calc<MXFlatmmArchTraits,
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ADataType,
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BDataType,
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DsDatatype,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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DsLayout,
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CLayout,
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ScaleA,
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ScaleB,
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UsePersistentKernel,
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CDEElementWise,
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split_k_.value,
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has_hot_loop_v,
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tail_num_v>(
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args,
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ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
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};
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return (args.k_batch == 1) ? invoke_splitk_path(std::false_type{})
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: invoke_splitk_path(std::true_type{});
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},
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has_hot_loop,
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tail_num);
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constexpr int APackedSize = ck_tile::numeric_traits<ADataType>::PackedSize;
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constexpr int BPackedSize = ck_tile::numeric_traits<BDataType>::PackedSize;
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std::size_t flop = std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / 32;
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std::size_t num_byte = sizeof(ADataType) * M * K / APackedSize +
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sizeof(BDataType) * N * K / BPackedSize + sizeof(CDataType) * M * N +
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sizeof(ck_tile::e8m0_t) * M * K / 32 +
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sizeof(ck_tile::e8m0_t) * N * K / 32;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_byte / 1.E6 / ave_time;
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std::cout << "Run " << ck_tile::gemm_prec_str<ADataType, BDataType>() << " Flatmm kernel " //
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<< " M = " << M << " N = " << N << " K = " << K << " StrideA = " << stride_A
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<< " StrideB = " << stride_B << " StrideC = " << stride_C << " : " << ave_time
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<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
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return ave_time;
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}
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "32", "m dimension")
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.insert("n", "512", "n dimension")
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.insert("k", "256", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "C", "B tensor data layout - Row by default")
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.insert("c_layout", "R", "C tensor data layout - Row by default")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("mx_prec",
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"fp4xfp4",
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"data type for activation and weight, support: fp4xfp4, fp6xfp6, fp8xfp8, fp8xfp4 "
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"and fp4xfp8")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("split_k", "1", "splitK value")
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.insert("init", "0", "0:random, 1:constant(1)")
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.insert("persistent", "0", "0: no persistent, 1: persistent kernel")
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.insert("warp_tile", "0", "0: 16x16x128 on gfx950.");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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template <ck_tile::index_t NLane, typename dtype>
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auto preShuffleWeight(ck_tile::HostTensor<dtype>& src)
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{
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auto src_lengths = src.get_lengths();
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const int K = src_lengths[0];
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const int N = src_lengths[1];
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constexpr int packed_size = ck_tile::numeric_traits<dtype>::PackedSize;
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int KPack =
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std::is_same_v<dtype, ck_tile::pk_fp6x16_t> ? 32 : 16 * packed_size; // fp4/fp6:32 or fp8:16
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int KLane = ck_tile::get_warp_size() / NLane;
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int K0 = K / (KLane * KPack);
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ck_tile::HostTensor<dtype> shuffled(ck_tile::HostTensorDescriptor({N * K}, {1}));
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// K -> K0 KLane KPack
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// N -> N0 NLane
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// N, K -> N0 K0 KLane NLane KPack
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for(int n = 0; n < N; ++n)
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{
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for(int k = 0; k < K; k += packed_size)
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{
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int n0 = n / NLane;
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int n1 = n % NLane;
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int k0 = k / (KLane * KPack);
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int tempk = k % (KLane * KPack);
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int k1 = tempk / KPack;
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int k2 = tempk % KPack;
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int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
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k1 * KPack * NLane + n1 * KPack + k2;
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shuffled(outputIndex) = src(k, n);
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}
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}
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return shuffled;
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}
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#include "run_mx_flatmm.inc"
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int run_mx_flatmm_example(const ck_tile::ArgParser& arg_parser)
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{
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using Row = ck_tile::tensor_layout::gemm::RowMajor;
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using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
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std::string mx_prec = arg_parser.get_str("mx_prec");
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std::string a_layout = arg_parser.get_str("a_layout");
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std::string b_layout = arg_parser.get_str("b_layout");
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int persistent_opt = arg_parser.get_int("persistent");
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std::cout << "Using default warptile of 16x16x128." << std::endl;
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if(a_layout == "R" && b_layout == "C")
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{
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if(mx_prec == "fp4" || mx_prec == "fp4xfp4")
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{
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if(persistent_opt == 0)
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return run_mx_flatmm_with_layouts<ck_tile::pk_fp4_t,
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ck_tile::pk_fp4_t,
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ck_tile::fp16_t,
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MXFlatmm_GFX950_FP4FP4_Traits,
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false>(arg_parser, Row{}, Col{}, Row{});
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else
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throw std::runtime_error("Only non-persistent kernels are supported currently!");
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}
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else if(mx_prec == "fp6" || mx_prec == "fp6xfp6")
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{
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if(persistent_opt == 0)
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return run_mx_flatmm_with_layouts<ck_tile::pk_fp6x16_t,
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ck_tile::pk_fp6x16_t,
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ck_tile::fp16_t,
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MXFlatmm_GFX950_FP6FP6_Traits,
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false>(arg_parser, Row{}, Col{}, Row{});
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else
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throw std::runtime_error("Only support non-persistent kernel now!");
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}
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else if(mx_prec == "fp8" || mx_prec == "fp8xfp8")
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{
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if(persistent_opt == 0)
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return run_mx_flatmm_with_layouts<ck_tile::fp8_t,
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ck_tile::fp8_t,
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ck_tile::fp16_t,
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MXFlatmm_GFX950_FP8FP8_Traits,
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false>(arg_parser, Row{}, Col{}, Row{});
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else
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throw std::runtime_error("Only support non-persistent kernel now!");
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}
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else if(mx_prec == "fp8xfp4")
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{
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if(persistent_opt == 0)
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return run_mx_flatmm_with_layouts<ck_tile::fp8_t,
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ck_tile::pk_fp4_t,
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ck_tile::fp16_t,
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MXFlatmm_GFX950_FP8FP4_Traits,
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false>(arg_parser, Row{}, Col{}, Row{});
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else
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throw std::runtime_error("Only support non-persistent kernel now!");
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}
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else if(mx_prec == "fp4xfp8")
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{
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if(persistent_opt == 0)
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return run_mx_flatmm_with_layouts<ck_tile::pk_fp4_t,
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ck_tile::fp8_t,
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ck_tile::fp16_t,
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MXFlatmm_GFX950_FP4FP8_Traits,
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false>(arg_parser, Row{}, Col{}, Row{});
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else
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throw std::runtime_error("Only support non-persistent kernel now!");
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}
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else
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{
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throw std::runtime_error("Unsupported data_type!");
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}
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}
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else
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{
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throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
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}
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}
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int main(int argc, char* argv[])
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return EXIT_FAILURE;
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try
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{
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int warp_tile = arg_parser.get_int("warp_tile");
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if(warp_tile == 0)
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{
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return run_mx_flatmm_example(arg_parser);
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}
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else if(warp_tile == 1)
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{
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throw std::runtime_error("Only support MFMA_16x16x128 now!");
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}
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else
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{
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throw std::runtime_error("Unsupported warp_tile!");
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}
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}
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catch(const std::runtime_error& e)
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{
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std::cerr << "Runtime error: " << e.what() << '\n';
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return EXIT_FAILURE;
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}
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}
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