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[rocm-libraries] ROCm/rocm-libraries#4280 (commit b7de1e1)
[CK_TILE] Add blockscale GEMM support for EightWarps on gfx950 (#4280) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Proposed changes gemm blockscale eightwarps support ## Checklist Please put an `x` into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask. - [ ] I have added tests relevant to the introduced functionality, and the unit tests are passing locally - [ ] I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more than 30 seconds to run. - [ ] I have added inline documentation which enables the maintainers with understanding the motivation - [ ] I have removed the stale documentation which is no longer relevant after this pull request - [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request - [x] I have run `clang-format` on all changed files - [x] Any dependent changes have been merged ## Discussion If this is a relatively large or complex change, feel free to start a discussion by explaining why you chose the solution you did and what alternatives you considered
This commit is contained in:
kensclin
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assistant-librarian[bot]
parent
731afe535a
commit
5b3e527c88
@@ -0,0 +1,453 @@
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/core/arch/arch.hpp"
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#include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1_default_policy.hpp"
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#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
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#include "ck_tile/ops/elementwise.hpp"
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#include "ck_tile/ops/gemm_quant/block/block_gemm_quant_common.hpp"
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namespace ck_tile {
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// A is block window on shared memory
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// AQ (scale tensor) is block distributed tensor.
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// BQ (scale tensor) is block distributed tensor.
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// Consecutive QuantGroupSize elements of A and B are quantized with a separate scale.
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// B is block window on shared memory
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// C is block distributed tensor
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template <typename Problem_,
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typename Policy_ = BlockGemmASmemBSmemCRegV1DefaultPolicy,
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index_t UnaryOpSize_ = 8>
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struct ABQuantBlockUniversalGemmAsBsCrAsync : public BlockGemmQuantBase
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{
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private:
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template <typename PipelineProblem_, typename GemmPolicy_>
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struct GemmTraits_
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{
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using Problem = remove_cvref_t<PipelineProblem_>;
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using Policy = remove_cvref_t<GemmPolicy_>;
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using ADataType = remove_cvref_t<typename Problem::ADataType>;
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using AQDataType = remove_cvref_t<typename Problem::AQDataType>;
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using BDataType = remove_cvref_t<typename Problem::BDataType>;
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using BQDataType = remove_cvref_t<typename Problem::BQDataType>;
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using BQLayout = remove_cvref_t<typename Problem::BQLayout>;
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using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
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using CDataType = remove_cvref_t<typename Problem::CDataType>;
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using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
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using AQuantGroupSize = remove_cvref_t<typename Problem::AQuantGroupSize>;
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using BQuantGroupSize = remove_cvref_t<typename Problem::BQuantGroupSize>;
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static constexpr index_t kBlockSize = Problem::kBlockSize;
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static constexpr auto Scheduler = Problem::Scheduler;
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// Threadblock GEMM tile size
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static constexpr index_t MPerBlock = BlockGemmShape::kM;
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static constexpr index_t NPerBlock = BlockGemmShape::kN;
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static constexpr index_t KPerBlock = BlockGemmShape::kK;
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static constexpr index_t NQPerBlock = NPerBlock / BQuantGroupSize::kN;
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static constexpr index_t KQPerBlock = KPerBlock / BQuantGroupSize::kK;
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static constexpr index_t AQPerBlock = KPerBlock / AQuantGroupSize::kK;
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static constexpr auto config = Policy::template GetWarpGemmMWarpNWarp<Problem>();
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using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
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// number of warps along M and N for threadblock's GEMM problem size
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static constexpr index_t MWarp = config.template at<1>();
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static constexpr index_t NWarp = config.template at<2>();
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static constexpr index_t KWarp = Problem::BlockGemmShape::BlockWarps::at(number<2>{});
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using I0 = number<0>;
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using I1 = number<1>;
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static_assert(MWarp == BlockGemmShape::BlockWarps::at(I0{}),
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"Error! WarpGemm's MWarp is not consistent with BlockGemmShape!");
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static_assert(NWarp == BlockGemmShape::BlockWarps::at(I1{}),
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"Error! WarpGemm's NWarp is not consistent with BlockGemmShape!");
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static_assert(WarpGemm::kM == BlockGemmShape::WarpTile::at(I0{}),
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"Error! WarpGemm's M is not consistent with BlockGemmShape!");
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static_assert(WarpGemm::kN == BlockGemmShape::WarpTile::at(I1{}),
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"Error! WarpGemm's N is not consistent with BlockGemmShape!");
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static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WarpGemm::kM);
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static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WarpGemm::kN);
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static constexpr index_t KIterPerWarp = KPerBlock / (KWarp * WarpGemm::kK);
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static constexpr bool PreshuffleQuant = Problem::Traits::PreshuffleQuant;
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static constexpr index_t QScalesPerBlockRow =
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integer_divide_ceil(KPerBlock / KWarp, BQuantGroupSize::kK);
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static constexpr index_t QScalesPerWarpGemmRow =
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integer_divide_ceil(WarpGemm::kK, BQuantGroupSize::kK);
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static constexpr index_t KIterPerQScale = KIterPerWarp / QScalesPerBlockRow;
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static_assert(BQuantGroupSize::kK % WarpGemm::kK == 0,
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"Error! WarpGemm::kK should be a multiple of QuantGroupSize");
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static_assert(QScalesPerWarpGemmRow == 1,
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"Error! QuantGroupSize shouldn't be smaller than WarpGemm::kK");
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static_assert(KIterPerWarp % QScalesPerBlockRow == 0,
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"Error! KItersPerWarp should be a multiple of QscalesPerBlockRow");
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static_assert(KPerBlock / KWarp / BQuantGroupSize::kK > 0,
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"Error! Each row of blockgemm should have a separate scale");
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static_assert(MIterPerWarp * MWarp * WarpGemm::kM == MPerBlock,
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"Error! Warps should cover all Block tile!");
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static_assert(NIterPerWarp * NWarp * WarpGemm::kN == NPerBlock,
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"Error! Warps should cover all Block tile!");
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// Currently tested combinations (A, B, BQ)
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// 1. fp8, fp8, fp32 -> f32
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// 2. bf8, bf8, fp32 -> f32
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// 3. i4, fp8, (fp8/fp32) -> f32
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// 4. i4, bf8, (fp8/fp32) -> f32
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static_assert(
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(std::is_same_v<ADataType, fp8_t> || std::is_same_v<ADataType, bf8_t> ||
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std::is_same_v<ADataType, ck_tile::pk_int4_t>) &&
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(std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t> ||
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std::is_same_v<BDataType, ck_tile::pk_int4_t>) &&
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(std::is_same_v<AQDataType, float> || std::is_same_v<AQDataType, ck_tile::fp8_t> ||
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std::is_same_v<AQDataType, ck_tile::bf8_t>) &&
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(std::is_same_v<BQDataType, float> || std::is_same_v<BQDataType, ck_tile::fp8_t> ||
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std::is_same_v<BQDataType, ck_tile::bf8_t>) &&
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(std::is_same_v<ComputeDataType, fp8_t> || std::is_same_v<ComputeDataType, bf8_t>) &&
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std::is_same_v<CDataType, fp32_t>);
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static constexpr index_t InterWaveSchedulingMacClusters = 1;
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static constexpr index_t KPack = WarpGemm::kKPerThread;
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static constexpr index_t KPerThread = KIterPerWarp * WarpGemm::kKPerThread;
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static constexpr bool TransposeC = Problem::TransposeC;
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};
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public:
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using Traits = GemmTraits_<Problem_, Policy_>;
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using ADataType = remove_cvref_t<typename Traits::ADataType>;
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using AQDataType = remove_cvref_t<typename Traits::AQDataType>;
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using BDataType = remove_cvref_t<typename Traits::BDataType>;
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using BQDataType = remove_cvref_t<typename Traits::BQDataType>;
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using ComputeDataType = remove_cvref_t<typename Traits::ComputeDataType>;
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using CDataType = remove_cvref_t<typename Traits::CDataType>;
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// BDataType gets converted from PkInt4 during loading
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using OverrideBDataType =
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std::conditional_t<std::is_same_v<BDataType, pk_int4_t>, ADataType, BDataType>;
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using Base = BlockGemmQuantBase;
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using WarpGemm = remove_cvref_t<typename Traits::WarpGemm>;
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static constexpr index_t KIterPerWarp = Traits::KIterPerWarp;
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static constexpr index_t MIterPerWarp = Traits::MIterPerWarp;
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static constexpr index_t NIterPerWarp = Traits::NIterPerWarp;
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static constexpr index_t MWarp = Traits::MWarp;
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static constexpr index_t NWarp = Traits::NWarp;
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static constexpr index_t KWarp = Traits::KWarp;
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static constexpr auto Scheduler = Traits::Scheduler;
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using AWarpDstr = typename WarpGemm::AWarpDstr;
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using BWarpDstr = typename WarpGemm::BWarpDstr;
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using CWarpDstr = typename WarpGemm::CWarpDstr;
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using AWarpTensor = typename WarpGemm::AWarpTensor;
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using BWarpTensor = typename WarpGemm::BWarpTensor;
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using CWarpTensor = typename WarpGemm::CWarpTensor;
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static constexpr bool PreshuffleQuant = Traits::PreshuffleQuant;
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static_assert(std::is_same_v<typename WarpGemm::CDataType, float>);
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static constexpr auto a_warp_y_lengths =
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to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
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static constexpr auto b_warp_y_lengths =
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to_sequence(BWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
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static constexpr auto c_warp_y_lengths =
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to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
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static constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
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static constexpr auto b_warp_y_index_zeros = uniform_sequence_gen_t<BWarpDstr::NDimY, 0>{};
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static constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
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static constexpr index_t APackedSize =
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ck_tile::numeric_traits<remove_cvref_t<ADataType>>::PackedSize;
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static constexpr index_t BPackedSize =
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ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
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using I0 = number<0>;
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using I1 = number<1>;
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CK_TILE_DEVICE static constexpr auto MakeABlockDistributionEncode()
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{
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constexpr index_t KPerThread = Traits::KPerThread;
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constexpr index_t NumMacClusters = Traits::InterWaveSchedulingMacClusters;
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constexpr index_t KPerInnerLoop =
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ck_tile::max(KPerThread / NumMacClusters, WarpGemm::kKPerThread);
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constexpr index_t KIterInterwave = KPerInnerLoop / WarpGemm::kKPerThread;
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using KIterSeq = std::conditional_t<Scheduler == GemmPipelineScheduler::Interwave,
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sequence<KWarp, KIterInterwave>,
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sequence<KWarp, KIterPerWarp>>;
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constexpr auto a_block_outer_dstr_encoding =
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tile_distribution_encoding<sequence<2, NWarp / 2>,
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tuple<sequence<MIterPerWarp, MWarp>, KIterSeq>,
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tuple<sequence<0, 2, 1, 0>>,
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tuple<sequence<0, 0, 1, 1>>,
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sequence<1, 2>,
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sequence<0, 1>>{};
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constexpr auto a_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
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a_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
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return a_block_dstr_encode;
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}
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CK_TILE_DEVICE static constexpr auto MakeBBlockDistributionEncode()
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{
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constexpr index_t KPerThread = Traits::KPerThread;
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constexpr index_t NumMacClusters = Traits::InterWaveSchedulingMacClusters;
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constexpr index_t KPerInnerLoop =
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ck_tile::max(KPerThread / NumMacClusters, WarpGemm::kKPerThread);
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constexpr index_t KIterInterwave = KPerInnerLoop / WarpGemm::kKPerThread;
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using KIterSeq = std::conditional_t<Scheduler == GemmPipelineScheduler::Interwave,
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sequence<KWarp, KIterInterwave>,
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sequence<KWarp, KIterPerWarp>>;
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constexpr auto b_block_outer_dstr_encoding =
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tile_distribution_encoding<sequence<MWarp>,
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tuple<sequence<2, NIterPerWarp, NWarp / 2>, KIterSeq>,
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tuple<sequence<2, 1, 0, 1>>,
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tuple<sequence<0, 0, 0, 2>>,
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sequence</*1, 2*/>,
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sequence</*0, 1*/>>{};
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constexpr auto b_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
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b_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
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return b_block_dstr_encode;
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}
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CK_TILE_DEVICE static constexpr auto MakeCBlockDistributionEncode()
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{
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constexpr auto c_block_outer_dstr_encoding = tile_distribution_encoding<
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sequence<KWarp>,
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tuple<sequence<MIterPerWarp, MWarp>, sequence<2, NIterPerWarp, NWarp / 2>>,
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tuple<sequence<2, 0, 1, 2>>,
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tuple<sequence<0, 0, 1, 2>>,
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sequence<1, 2>,
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sequence<0, 1>>{};
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constexpr auto c_block_dstr_encoding = detail::make_embed_tile_distribution_encoding(
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c_block_outer_dstr_encoding, typename WarpGemm::CWarpDstrEncoding{});
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return c_block_dstr_encoding;
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}
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CK_TILE_DEVICE static constexpr auto MakeCBlockTile()
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{
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return make_static_distributed_tensor<CDataType>(
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make_static_tile_distribution(MakeCBlockDistributionEncode()));
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}
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using ALdsTile = decltype(make_static_distributed_tensor<ComputeDataType>(
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make_static_tile_distribution(MakeABlockDistributionEncode())));
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using BLdsTile = statically_indexed_array<
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statically_indexed_array<decltype(make_static_distributed_tensor<ComputeDataType>(
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make_static_tile_distribution(
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MakeBBlockDistributionEncode()))),
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KIterPerWarp>,
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NIterPerWarp>;
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private:
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template <GemmPipelineScheduler Scheduler, typename GemmTraits>
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struct BlockGemmImpl
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{
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};
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template <typename GemmTraits>
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struct BlockGemmImpl<GemmPipelineScheduler::Intrawave, GemmTraits>
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{
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template <typename ASmemBlockWindow,
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typename BSmemBlockWindow,
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bool ALoadTranspose = false,
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bool BLoadTranspose = false>
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CK_TILE_DEVICE void LocalPrefetch(const ASmemBlockWindow& /*a_block_window*/,
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const BSmemBlockWindow& /*b_block_window*/,
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bool_constant<ALoadTranspose> = {},
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bool_constant<BLoadTranspose> = {})
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{
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static_assert(false, "Not implemented yet!");
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}
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// C += A * B
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template <typename CBlockTensor, typename AQBlockTensor, typename BQBlockTensor>
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CK_TILE_DEVICE void operator()(CBlockTensor& c_block_tensor,
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const ALdsTile& a_warp_tile_,
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const BLdsTile& b_warp_tile_,
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AQBlockTensor& aq_block_tensor,
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BQBlockTensor& bq_block_tensor)
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{
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static_assert(std::is_same_v<CDataType, typename CBlockTensor::DataType>,
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"The CDataType as defined in traits should be the same as corresponding "
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"C block tensor data type!");
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constexpr auto warp_size = get_warp_size();
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auto q_block_tensor = aq_block_tensor;
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if constexpr(Traits::NQPerBlock / NWarp == 1)
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{
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constexpr auto aq_spans = AQBlockTensor::get_distributed_spans();
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sweep_tile_span(aq_spans[I0{}], [&](auto im) {
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sweep_tile_span(aq_spans[I1{}], [&](auto ik) {
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q_block_tensor(make_tuple(im, ik)) *=
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bq_block_tensor(make_tuple(tile_distributed_index<0>{}, ik));
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});
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});
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}
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// hot loop:
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static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
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static_for_product<number<NIterPerWarp>, number<MIterPerWarp>>{}([&](auto nIter,
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auto mIter) {
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CWarpTensor c_warp_tensor;
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static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
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static_assert(Traits::KIterPerQScale == 1);
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constexpr auto kIter =
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number<kQScale * Traits::KIterPerQScale + kIterInQScale>{};
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AWarpTensor a_warp_tensor;
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a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
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merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
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merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
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BWarpTensor b_warp_tensor;
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b_warp_tensor.get_thread_buffer() =
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b_warp_tile_[nIter][kIter].get_thread_buffer();
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if constexpr(kIterInQScale == 0)
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{
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c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
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}
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else
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{
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WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
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}
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});
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if constexpr(Traits::NQPerBlock / NWarp == 1)
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{
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constexpr auto cw_spans = CWarpTensor::get_distributed_spans();
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static_assert(cw_spans[I0{}].impl_.size() == 0);
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sweep_tile_span(cw_spans[I1{}], [&](auto in) {
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constexpr auto block_idx_m = tile_distributed_index<mIter>{};
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constexpr auto block_idx_n = detail::make_tile_distributed_index(
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merge_sequences(sequence<nIter>{}, in.impl_));
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constexpr auto block_idx_kq = tile_distributed_index<kQScale>{};
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constexpr auto empty_idx = tile_distributed_index<>{};
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c_block_tensor(make_tuple(block_idx_m, block_idx_n)) +=
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c_warp_tensor(make_tuple(empty_idx, in)) *
|
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q_block_tensor(make_tuple(block_idx_m, block_idx_kq));
|
||||
});
|
||||
}
|
||||
else
|
||||
{
|
||||
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
// a_scale
|
||||
AQPickerCommon<AQBlockTensor, Traits, mIter, kQScale> aq_picker(
|
||||
aq_block_tensor);
|
||||
|
||||
if constexpr(PreshuffleQuant)
|
||||
{
|
||||
constexpr index_t reg_offset = nIter;
|
||||
auto pull_from_lane =
|
||||
(__lane_id() & (WarpGemm::kN - 1)) * Traits::KQPerBlock + kQScale;
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
// cross lane ops
|
||||
uint32_t scale_reg_dword;
|
||||
|
||||
if constexpr(std::is_same_v<BQDataType, float>)
|
||||
{
|
||||
scale_reg_dword = ck_tile::bit_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
scale_reg_dword = static_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
|
||||
// cross lane ops to get the value of scale_reg.
|
||||
int gathered_scale_reg = __builtin_amdgcn_ds_bpermute(
|
||||
pull_from_lane << 2, __builtin_bit_cast(int, scale_reg_dword));
|
||||
|
||||
float b_scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(
|
||||
gathered_scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
float a_scale_reg_f = aq_picker.template pick<c_row>();
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * a_scale_reg_f *
|
||||
b_scale_reg_f);
|
||||
});
|
||||
}
|
||||
else
|
||||
{
|
||||
// Multiply bquant with accumulated C
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >=
|
||||
(NWarp * WarpGemm::kN))
|
||||
return (nIter * NWarp * WarpGemm::kN) /
|
||||
GemmTraits::BQuantGroupSize::kN *
|
||||
Traits::KQPerBlock +
|
||||
kQScale;
|
||||
else
|
||||
{
|
||||
return nIter * Traits::KQPerBlock + kQScale;
|
||||
}
|
||||
}();
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float b_scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
float a_scale_reg_f = aq_picker.template pick<c_row>();
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * a_scale_reg_f *
|
||||
b_scale_reg_f);
|
||||
});
|
||||
}
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
public:
|
||||
template <typename... Args>
|
||||
CK_TILE_DEVICE void LocalPrefetch(Args&&... args)
|
||||
{
|
||||
block_gemm_impl_.LocalPrefetch(std::forward<Args>(args)...);
|
||||
}
|
||||
|
||||
// C += A * B
|
||||
template <typename CBlockTensor, typename... Rest>
|
||||
CK_TILE_DEVICE void operator()(CBlockTensor& c_block_tensor, Rest&&... rest)
|
||||
{
|
||||
block_gemm_impl_(c_block_tensor, std::forward<Rest>(rest)...);
|
||||
}
|
||||
|
||||
private:
|
||||
BlockGemmImpl<Scheduler, Traits> block_gemm_impl_{};
|
||||
};
|
||||
|
||||
} // namespace ck_tile
|
||||
Reference in New Issue
Block a user