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[Navi3x-LWPCK-545] Block-wise GEMM + Real GEMM_WMMA_FP16 (#541)
* wmma_op + unit test * add arch limitation to wmma test * change arch limitation * Refactor + Add all type unit test(int4 compile failed) * Add f32_16x16x16_bf16 unit test * tempsave * tempsave * tempsave * runtime bug, cannot find symbol * workaround for incorrect HIP warpSize return value * debugging * tempsave * Correctness OK, waiting for optimization * Tidy up + format * temp save * temp save, reproduce the v_bfi_b32 issue * add inline asm for wmmaop test * tidy up * clean some debug purpose code * discard some codes * clang format * clang format * compiler issue fixed + increase tile size
This commit is contained in:
Haocong WANG
801
include/ck/tensor_operation/gpu/block/blockwise_gemm_wmma.hpp
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801
include/ck/tensor_operation/gpu/block/blockwise_gemm_wmma.hpp
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@@ -0,0 +1,801 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
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#include "ck/tensor_operation/gpu/warp/wmma_gemm.hpp"
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#include "ck/tensor_description/tensor_adaptor.hpp"
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#define CK_MNK_LOOP
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namespace ck {
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template <index_t BlockSize,
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typename FloatA,
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typename FloatB,
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typename FloatAcc,
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typename AK0MK1BlockDesc,
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typename BK0NK1BlockDesc,
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index_t MPerWMMA,
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index_t NPerWMMA,
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index_t MRepeat,
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index_t NRepeat,
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index_t KPack>
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/* A: K0PerBlock x MPerBlock x K1
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* B: K0PerBlock x NPerBlock x K1
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* C: MRepeat x MWave x MSubGroup x NRepeat x NWave x NThreadPerSubGroup x MAccVgprs
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* KPACK == WMMA_K = 16
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*/
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struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle
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{
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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static constexpr auto I4 = Number<4>{};
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static constexpr auto WmmaK = Number<16>{};
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using ThisThreadBlock = ThisThreadBlock<BlockSize>;
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// Hardcode of WaveSize, since current HIP Runtime(5.4.0-10984) could not return correct one.
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static constexpr index_t WaveSize = 32;
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static constexpr index_t MPerBlock = AK0MK1BlockDesc{}.GetLength(I1);
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static constexpr index_t NPerBlock = BK0NK1BlockDesc{}.GetLength(I1);
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static constexpr index_t KPerBlock =
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BK0NK1BlockDesc{}.GetLength(I0) * BK0NK1BlockDesc{}.GetLength(I2);
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static constexpr index_t A_K0 = AK0MK1BlockDesc{}.GetLength(I0);
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static constexpr index_t B_K0 = BK0NK1BlockDesc{}.GetLength(I0);
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static constexpr index_t A_K1 = AK0MK1BlockDesc{}.GetLength(I2);
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static constexpr index_t B_K1 = BK0NK1BlockDesc{}.GetLength(I2);
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static constexpr auto wmma_gemm =
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WmmaGemm<FloatA, FloatB, FloatAcc, MPerWMMA, NPerWMMA, KPack>{};
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static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerWMMA);
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static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerWMMA);
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StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
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FloatAcc,
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MRepeat * NRepeat,
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wmma_gemm.GetRegSizePerWmma(),
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true>
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c_thread_buf_;
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__host__ __device__ constexpr auto& GetCThreadBuffer() { return c_thread_buf_; }
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__device__ static auto GetWaveIdx()
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{
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const index_t thread_id = ThisThreadBlock::GetThreadId();
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constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
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make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
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make_tuple(Sequence<0, 1, 2>{}),
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make_tuple(Sequence<0>{}));
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return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
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}
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__device__ static auto CalculateAThreadOriginDataIndex()
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{
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const auto wave_idx = GetWaveIdx();
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const auto waveId_m = wave_idx[I0];
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const auto WMMA_a_idx = wmma_gemm.CalculateAThreadOriginDataIndex();
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// |KRepeat |MRepeat|MWave |MLane |KPack
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return make_tuple(0, 0, waveId_m, WMMA_a_idx, 0);
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}
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__device__ static auto CalculateBThreadOriginDataIndex()
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{
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const auto wave_idx = GetWaveIdx();
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const auto waveId_n = wave_idx[I1];
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const auto WMMA_b_idx = wmma_gemm.CalculateBThreadOriginDataIndex();
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// |KRepeat |NRepeat|Nwave |NLane |KPack
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return make_tuple(0, 0, waveId_n, WMMA_b_idx, 0);
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}
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template <index_t m0, index_t n0>
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__device__ static auto CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>)
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{
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const auto wave_idx = GetWaveIdx();
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const auto waveId_m = wave_idx[I0];
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const auto waveId_n = wave_idx[I1];
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const auto blk_idx = wmma_gemm.GetBeginOfThreadBlk();
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constexpr auto mrepeat_mwave_mperWMMA_to_m_adaptor = make_single_stage_tensor_adaptor(
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make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerWMMA))),
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make_tuple(Sequence<0>{}),
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make_tuple(Sequence<0, 1, 2>{}));
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constexpr auto nrepeat_nwave_nperWMMA_to_n_adaptor = make_single_stage_tensor_adaptor(
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make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerWMMA))),
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make_tuple(Sequence<0>{}),
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make_tuple(Sequence<0, 1, 2>{}));
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const index_t c_thread_m = mrepeat_mwave_mperWMMA_to_m_adaptor.CalculateBottomIndex(
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make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
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const index_t c_thread_n = nrepeat_nwave_nperWMMA_to_n_adaptor.CalculateBottomIndex(
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make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
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return make_tuple(c_thread_m, c_thread_n);
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}
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__host__ __device__ BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle()
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{
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static_assert(AK0MK1BlockDesc::IsKnownAtCompileTime() &&
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BK0NK1BlockDesc::IsKnownAtCompileTime(),
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"wrong! Desc should be known at compile-time");
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static_assert(ThisThreadBlock::GetNumOfThread() == MWaves * NWaves * WaveSize,
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"ThisThreadBlock::GetNumOfThread() != MWaves * NWaves * WaveSize\n");
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static_assert(MPerBlock % (MPerWMMA * MRepeat) == 0 &&
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NPerBlock % (NPerWMMA * NRepeat) == 0,
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"wrong!");
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}
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// Thread level, register decriptor. Vector-write
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__host__ __device__ static constexpr auto
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GetCThreadDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
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{
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constexpr auto c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens =
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wmma_gemm.GetCMSubGroupNThreadPerSubGroupMAccVgprsThreadBlkLengths();
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constexpr auto MSubGroup = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I0];
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constexpr auto NThreadPerSubGroup = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I1];
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constexpr auto MAccVgprs = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I2];
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return make_naive_tensor_descriptor_packed(
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// |MRepeat |MWave |MSubGroup |NRepeat |NWave
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// |NThreadPerSubGroup |MAccVgprs
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make_tuple(Number<MRepeat>{},
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I1,
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MSubGroup,
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Number<NRepeat>{},
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I1,
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NThreadPerSubGroup,
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MAccVgprs));
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}
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// Provide dimension size
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__host__ __device__ static constexpr auto
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GetCBlockDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
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{
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constexpr auto c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma =
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make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
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Number<MWaves>{},
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Number<MPerWMMA>{},
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Number<NRepeat>{},
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Number<NWaves>{},
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Number<NPerWMMA>{}));
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return wmma_gemm
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.MakeCDesc_MBlockxRepeat_MWave_MSubGroup_NBlockxRepeat_NWave_NThreadPerSubGroup_MAccVgprs(
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c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma);
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}
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__host__ __device__ static constexpr auto MakeABlockDescriptor_K0_M0_M1_M2_K1()
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{
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return transform_tensor_descriptor(
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AK0MK1BlockDesc{},
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make_tuple(make_pass_through_transform(Number<A_K0>{}),
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make_unmerge_transform(
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make_tuple(Number<MRepeat>{}, Number<MWaves>{}, Number<MPerWMMA>{})),
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make_pass_through_transform(Number<A_K1>{})),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}));
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}
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__host__ __device__ static constexpr auto MakeBBlockDescriptor_K0_N0_N1_N2_K1()
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{
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return transform_tensor_descriptor(
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BK0NK1BlockDesc{},
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make_tuple(make_pass_through_transform(Number<B_K0>{}),
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make_unmerge_transform(
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make_tuple(Number<NRepeat>{}, Number<NWaves>{}, Number<NPerWMMA>{})),
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make_pass_through_transform(Number<B_K1>{})),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}));
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}
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// M0_M1_M2 = MRepeat_MWave_MPerWmma, N0_N1_N2 = NRepeat_NWave_NPerWmma
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static constexpr auto a_block_desc_k0_m0_m1_m2_k1 = MakeABlockDescriptor_K0_M0_M1_M2_K1();
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static constexpr auto b_block_desc_k0_n0_n1_n2_k1 = MakeBBlockDescriptor_K0_N0_N1_N2_K1();
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template <typename ABlockBuffer, typename BBlockBuffer, typename CThreadBuffer>
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__device__ void Run(const ABlockBuffer& a_block_buf,
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const BBlockBuffer& b_block_buf,
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CThreadBuffer& c_thread_buf) const
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{
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auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatA>(
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a_thread_desc_.GetElementSpaceSize());
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auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatB>(
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b_thread_desc_.GetElementSpaceSize());
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static_for<0, KPerBlock / WmmaK, 1>{}([&](auto k) { // k=0,1,2 instead of k=0,kpack*1, ...
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static_for<0, MRepeat, 1>{}([&](auto m0) {
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// read A
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a_thread_copy_.Run(a_block_desc_k0_m0_m1_m2_k1,
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make_tuple(Number<k * WmmaK / A_K1>{}, m0, I0, I0, I0),
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a_block_buf,
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a_thread_desc_,
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make_tuple(I0, m0, I0, I0, I0),
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a_thread_buf);
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static_for<0, NRepeat, 1>{}([&](auto n0) {
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// read B
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b_thread_copy_.Run(b_block_desc_k0_n0_n1_n2_k1,
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make_tuple(Number<k * WmmaK / B_K1>{}, n0, I0, I0, I0),
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b_block_buf,
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b_thread_desc_,
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make_tuple(I0, n0, I0, I0, I0),
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b_thread_buf);
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vector_type<FloatA, WmmaK> a_thread_vec;
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vector_type<FloatB, WmmaK> b_thread_vec;
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static_for<0, WmmaK, 1>{}([&](auto i) {
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a_thread_vec.template AsType<FloatA>()(i) =
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a_thread_buf[Number<a_thread_desc_.CalculateOffset(
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make_tuple(i / A_K1, m0, 0, 0, i % A_K1))>{}];
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b_thread_vec.template AsType<FloatB>()(i) =
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b_thread_buf[Number<b_thread_desc_.CalculateOffset(
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make_tuple(i / B_K1, n0, 0, 0, i % B_K1))>{}];
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});
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using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
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using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
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constexpr index_t c_offset =
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c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
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wmma_gemm.template Run(
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a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
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b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
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c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
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});
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});
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});
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}
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protected:
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// A[K0, M0, M1, M2, K1]
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static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor_packed(
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make_tuple(Number<WmmaK / A_K1>{}, Number<MRepeat>{}, I1, I1, Number<A_K1>{}));
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// B[K0, N0, N1, N2, K1]
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static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor_packed(
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make_tuple(Number<WmmaK / B_K1>{}, Number<NRepeat>{}, I1, I1, Number<B_K1>{}));
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// C[M, N, NumRegWMMA]
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static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
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make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, wmma_gemm.GetRegSizePerWmma()));
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using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatA,
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FloatA,
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decltype(a_block_desc_k0_m0_m1_m2_k1),
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decltype(a_thread_desc_),
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Sequence<WmmaK / A_K1, 1, 1, 1, A_K1>,
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Sequence<0, 1, 2, 3, 4>,
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4,
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A_K1,
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A_K1>;
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using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatB,
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FloatB,
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decltype(b_block_desc_k0_n0_n1_n2_k1),
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decltype(b_thread_desc_),
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Sequence<WmmaK / B_K1, 1, 1, 1, B_K1>,
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Sequence<0, 1, 2, 3, 4>,
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4,
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B_K1,
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B_K1>;
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AThreadCopy a_thread_copy_{CalculateAThreadOriginDataIndex()};
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BThreadCopy b_thread_copy_{CalculateBThreadOriginDataIndex()};
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};
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// block wise level pipe designed for inline asm
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template <index_t BlockSize,
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typename FloatA,
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typename FloatB,
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typename FloatAcc,
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typename AK0MK1BlockDesc,
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typename BK0NK1BlockDesc,
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index_t MPerWMMA,
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index_t NPerWMMA,
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index_t MRepeat,
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index_t NRepeat,
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index_t KPack>
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/* A: K0PerBlock x MPerBlock x K1
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* B: K0PerBlock x NPerBlock x K1
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* C: MRepeat x MWave x MSubGroup x NRepeat x NWave x NThreadPerSubGroup x MAccVgprs
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* KPACK == WMMA_K = 16
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*/
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struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO
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{
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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static constexpr auto I4 = Number<4>{};
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static constexpr auto WmmaK = Number<16>{};
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using ThisThreadBlock = ThisThreadBlock<BlockSize>;
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// Hardcode of WaveSize, since current HIP Runtime(5.4.0-10984) could not return correct one.
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static constexpr index_t WaveSize = 32;
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static constexpr index_t MPerBlock = AK0MK1BlockDesc{}.GetLength(I1);
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static constexpr index_t NPerBlock = BK0NK1BlockDesc{}.GetLength(I1);
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static constexpr index_t KPerBlock =
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BK0NK1BlockDesc{}.GetLength(I0) * BK0NK1BlockDesc{}.GetLength(I2);
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static constexpr index_t A_K0 = AK0MK1BlockDesc{}.GetLength(I0);
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static constexpr index_t B_K0 = BK0NK1BlockDesc{}.GetLength(I0);
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static constexpr index_t A_K1 = AK0MK1BlockDesc{}.GetLength(I2);
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static constexpr index_t B_K1 = BK0NK1BlockDesc{}.GetLength(I2);
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static constexpr auto wmma_gemm =
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WmmaGemm<FloatA, FloatB, FloatAcc, MPerWMMA, NPerWMMA, KPack>{};
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static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerWMMA);
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static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerWMMA);
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StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
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FloatAcc,
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MRepeat * NRepeat,
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wmma_gemm.GetRegSizePerWmma(),
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true>
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c_thread_buf_;
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__host__ __device__ constexpr auto& GetCThreadBuffer() { return c_thread_buf_; }
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__device__ static auto GetWaveIdx()
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{
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const index_t thread_id = ThisThreadBlock::GetThreadId();
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constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
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make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
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make_tuple(Sequence<0, 1, 2>{}),
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make_tuple(Sequence<0>{}));
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return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
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}
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__device__ static auto CalculateAThreadOriginDataIndex()
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{
|
||||
const auto wave_idx = GetWaveIdx();
|
||||
|
||||
const auto waveId_m = wave_idx[I0];
|
||||
|
||||
const auto WMMA_a_idx = wmma_gemm.CalculateAThreadOriginDataIndex();
|
||||
// |KRepeat |MRepeat|MWave |MLane |KPack
|
||||
return make_tuple(0, 0, waveId_m, WMMA_a_idx, 0);
|
||||
}
|
||||
|
||||
__device__ static auto CalculateBThreadOriginDataIndex()
|
||||
{
|
||||
const auto wave_idx = GetWaveIdx();
|
||||
|
||||
const auto waveId_n = wave_idx[I1];
|
||||
|
||||
const auto WMMA_b_idx = wmma_gemm.CalculateBThreadOriginDataIndex();
|
||||
// |KRepeat |NRepeat|Nwave |NLane |KPack
|
||||
return make_tuple(0, 0, waveId_n, WMMA_b_idx, 0);
|
||||
}
|
||||
|
||||
template <index_t m0, index_t n0>
|
||||
__device__ static auto CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>)
|
||||
{
|
||||
const auto wave_idx = GetWaveIdx();
|
||||
|
||||
const auto waveId_m = wave_idx[I0];
|
||||
const auto waveId_n = wave_idx[I1];
|
||||
|
||||
const auto blk_idx = wmma_gemm.GetBeginOfThreadBlk();
|
||||
|
||||
constexpr auto mrepeat_mwave_mperWMMA_to_m_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerWMMA))),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1, 2>{}));
|
||||
|
||||
constexpr auto nrepeat_nwave_nperWMMA_to_n_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerWMMA))),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1, 2>{}));
|
||||
|
||||
const index_t c_thread_m = mrepeat_mwave_mperWMMA_to_m_adaptor.CalculateBottomIndex(
|
||||
make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
|
||||
const index_t c_thread_n = nrepeat_nwave_nperWMMA_to_n_adaptor.CalculateBottomIndex(
|
||||
make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
|
||||
|
||||
return make_tuple(c_thread_m, c_thread_n);
|
||||
}
|
||||
|
||||
__host__ __device__ BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO()
|
||||
{
|
||||
static_assert(AK0MK1BlockDesc::IsKnownAtCompileTime() &&
|
||||
BK0NK1BlockDesc::IsKnownAtCompileTime(),
|
||||
"wrong! Desc should be known at compile-time");
|
||||
|
||||
static_assert(ThisThreadBlock::GetNumOfThread() == MWaves * NWaves * WaveSize,
|
||||
"ThisThreadBlock::GetNumOfThread() != MWaves * NWaves * WaveSize\n");
|
||||
|
||||
static_assert(MPerBlock % (MPerWMMA * MRepeat) == 0 &&
|
||||
NPerBlock % (NPerWMMA * NRepeat) == 0,
|
||||
"wrong!");
|
||||
}
|
||||
// Thread level, register decriptor. Vector-write
|
||||
__host__ __device__ static constexpr auto
|
||||
GetCThreadDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
|
||||
{
|
||||
constexpr auto c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens =
|
||||
wmma_gemm.GetCMSubGroupNThreadPerSubGroupMAccVgprsThreadBlkLengths();
|
||||
|
||||
constexpr auto MSubGroup = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I0];
|
||||
constexpr auto NThreadPerSubGroup = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I1];
|
||||
constexpr auto MAccVgprs = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I2];
|
||||
|
||||
return make_naive_tensor_descriptor_packed(
|
||||
// |MRepeat |MWave |MSubGroup |NRepeat |NWave
|
||||
// |NThreadPerSubGroup |MAccVgprs
|
||||
make_tuple(Number<MRepeat>{},
|
||||
I1,
|
||||
MSubGroup,
|
||||
Number<NRepeat>{},
|
||||
I1,
|
||||
NThreadPerSubGroup,
|
||||
MAccVgprs));
|
||||
}
|
||||
|
||||
template <typename CGridDesc_M_N>
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeCGridDescriptor_MBlockxRepeat_MWave_MSubGroup_NBlockxRepeat_NWave_NThreadPerSubGroup_MAccVgprs(
|
||||
const CGridDesc_M_N& c_grid_desc_m_n)
|
||||
{
|
||||
const auto M = c_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = c_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
const auto c_grid_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma =
|
||||
transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(
|
||||
make_unmerge_transform(make_tuple(M / (MWaves * MPerWMMA), MWaves, MPerWMMA)),
|
||||
make_unmerge_transform(make_tuple(N / (NWaves * NPerWMMA), NWaves, NPerWMMA))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1, 2>{}, Sequence<3, 4, 5>{}));
|
||||
|
||||
return wmma_gemm
|
||||
.MakeCDesc_MBlockxRepeat_MWave_MSubGroup_NBlockxRepeat_NWave_NThreadPerSubGroup_MAccVgprs(
|
||||
c_grid_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma);
|
||||
}
|
||||
|
||||
// Provide dimension size
|
||||
__host__ __device__ static constexpr auto
|
||||
GetCBlockDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
|
||||
{
|
||||
constexpr auto c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
|
||||
Number<MWaves>{},
|
||||
Number<MPerWMMA>{},
|
||||
Number<NRepeat>{},
|
||||
Number<NWaves>{},
|
||||
Number<NPerWMMA>{}));
|
||||
|
||||
return wmma_gemm
|
||||
.MakeCDesc_MBlockxRepeat_MWave_MSubGroup_NBlockxRepeat_NWave_NThreadPerSubGroup_MAccVgprs(
|
||||
c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma);
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto MakeABlockDescriptor_K0_M0_M1_M2_K1()
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
AK0MK1BlockDesc{},
|
||||
make_tuple(make_pass_through_transform(Number<A_K0>{}),
|
||||
make_unmerge_transform(
|
||||
make_tuple(Number<MRepeat>{}, Number<MWaves>{}, Number<MPerWMMA>{})),
|
||||
make_pass_through_transform(Number<A_K1>{})),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto MakeBBlockDescriptor_K0_N0_N1_N2_K1()
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
BK0NK1BlockDesc{},
|
||||
make_tuple(make_pass_through_transform(Number<B_K0>{}),
|
||||
make_unmerge_transform(
|
||||
make_tuple(Number<NRepeat>{}, Number<NWaves>{}, Number<NPerWMMA>{})),
|
||||
make_pass_through_transform(Number<B_K1>{})),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}));
|
||||
}
|
||||
|
||||
// M0_M1_M2 = MRepeat_MWave_MPerWmma, N0_N1_N2 = NRepeat_NWave_NPerWmma
|
||||
static constexpr auto a_block_desc_k0_m0_m1_m2_k1 = MakeABlockDescriptor_K0_M0_M1_M2_K1();
|
||||
static constexpr auto b_block_desc_k0_n0_n1_n2_k1 = MakeBBlockDescriptor_K0_N0_N1_N2_K1();
|
||||
|
||||
template <typename ABlockBuffer, typename BBlockBuffer, typename CThreadBuffer>
|
||||
__device__ void Run(const ABlockBuffer& a_block_buf,
|
||||
const BBlockBuffer& b_block_buf,
|
||||
CThreadBuffer& c_thread_buf) const
|
||||
{
|
||||
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatA>(
|
||||
a_thread_desc_.GetElementSpaceSize());
|
||||
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatB>(
|
||||
b_thread_desc_.GetElementSpaceSize());
|
||||
|
||||
constexpr auto RepeatDiff = MRepeat - NRepeat;
|
||||
// Read all Mrepeat, Nrepeat
|
||||
static_for<0, NRepeat, 1>{}([&](auto iN) {
|
||||
b_thread_copy_.Run(b_block_desc_k0_n0_n1_n2_k1,
|
||||
make_tuple(I0, Number<iN>{}, I0, I0, I0),
|
||||
b_block_buf,
|
||||
b_thread_desc_,
|
||||
make_tuple(I0, Number<iN>{}, I0, I0, I0),
|
||||
b_thread_buf);
|
||||
});
|
||||
|
||||
static_for<0, MRepeat, 1>{}([&](auto iM) {
|
||||
a_thread_copy_.Run(a_block_desc_k0_m0_m1_m2_k1,
|
||||
make_tuple(I0, Number<iM>{}, I0, I0, I0),
|
||||
a_block_buf,
|
||||
a_thread_desc_,
|
||||
make_tuple(I0, Number<iM>{}, I0, I0, I0),
|
||||
a_thread_buf);
|
||||
});
|
||||
|
||||
// Stage 1: Cut to Repeat Retangle to Square, assume MRepeat > NRepeat
|
||||
static_for<0, RepeatDiff, 1>{}([&](auto iCut) {
|
||||
static_for<0, NRepeat, 1>{}([&](auto iN) {
|
||||
vector_type<FloatA, WmmaK> a_thread_vec;
|
||||
vector_type<FloatB, WmmaK> b_thread_vec;
|
||||
|
||||
static_for<0, WmmaK, 1>{}([&](auto iK) {
|
||||
a_thread_vec.template AsType<FloatA>()(iK) =
|
||||
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / A_K1, iCut, 0, 0, iK % A_K1))>{}];
|
||||
b_thread_vec.template AsType<FloatB>()(iK) =
|
||||
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / B_K1, iN, 0, 0, iK % B_K1))>{}];
|
||||
});
|
||||
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
|
||||
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
|
||||
|
||||
constexpr index_t c_offset =
|
||||
c_thread_desc_.CalculateOffset(make_tuple(iCut, iN, 0));
|
||||
// s_nop();
|
||||
wmma_gemm.template Run(
|
||||
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
|
||||
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
|
||||
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
|
||||
// s_nop();
|
||||
});
|
||||
if constexpr(KPerBlock > WmmaK)
|
||||
{
|
||||
// Read Consumed Next inner loop A
|
||||
a_thread_copy_.Run(a_block_desc_k0_m0_m1_m2_k1,
|
||||
make_tuple(Number<WmmaK / A_K1>{}, Number<iCut>{}, I0, I0, I0),
|
||||
a_block_buf,
|
||||
a_thread_desc_,
|
||||
make_tuple(I0, Number<iCut>{}, I0, I0, I0),
|
||||
a_thread_buf);
|
||||
}
|
||||
});
|
||||
|
||||
static_for<WmmaK, KPerBlock, WmmaK>{}([&](auto iWmmaK) {
|
||||
// Stage 2: Run FIFO fashion loopover in Square
|
||||
static_for<0, NRepeat, 1>{}([&](auto WmmaInnerloop) {
|
||||
// Row Repeatation
|
||||
static_for<WmmaInnerloop, NRepeat, 1>{}([&](auto iN) {
|
||||
vector_type<FloatA, WmmaK> a_thread_vec;
|
||||
vector_type<FloatB, WmmaK> b_thread_vec;
|
||||
|
||||
static_for<0, WmmaK, 1>{}([&](auto iK) {
|
||||
a_thread_vec.template AsType<FloatA>()(iK) =
|
||||
a_thread_buf[Number<a_thread_desc_.CalculateOffset(make_tuple(
|
||||
iK / A_K1, WmmaInnerloop + RepeatDiff, 0, 0, iK % A_K1))>{}];
|
||||
b_thread_vec.template AsType<FloatB>()(iK) =
|
||||
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / B_K1, iN, 0, 0, iK % B_K1))>{}];
|
||||
});
|
||||
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
|
||||
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
|
||||
|
||||
constexpr index_t c_offset = c_thread_desc_.CalculateOffset(
|
||||
make_tuple(WmmaInnerloop + RepeatDiff, iN, 0));
|
||||
// s_nop();
|
||||
wmma_gemm.template Run(
|
||||
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
|
||||
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
|
||||
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
|
||||
// s_nop();
|
||||
});
|
||||
|
||||
// Read Consumed Next inner loop A
|
||||
a_thread_copy_.Run(
|
||||
a_block_desc_k0_m0_m1_m2_k1,
|
||||
make_tuple(
|
||||
Number<iWmmaK / A_K1>{}, Number<WmmaInnerloop + RepeatDiff>{}, I0, I0, I0),
|
||||
a_block_buf,
|
||||
a_thread_desc_,
|
||||
make_tuple(I0, Number<WmmaInnerloop + RepeatDiff>{}, I0, I0, I0),
|
||||
a_thread_buf);
|
||||
|
||||
// Col Repeatation
|
||||
static_for<WmmaInnerloop + 1 + RepeatDiff, MRepeat, 1>{}([&](auto iM) {
|
||||
vector_type<FloatA, WmmaK> a_thread_vec;
|
||||
vector_type<FloatB, WmmaK> b_thread_vec;
|
||||
|
||||
static_for<0, WmmaK, 1>{}([&](auto iK) {
|
||||
a_thread_vec.template AsType<FloatA>()(iK) =
|
||||
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / A_K1, iM, 0, 0, iK % A_K1))>{}];
|
||||
b_thread_vec.template AsType<FloatB>()(iK) =
|
||||
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / B_K1, WmmaInnerloop, 0, 0, iK % B_K1))>{}];
|
||||
});
|
||||
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
|
||||
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
|
||||
|
||||
constexpr index_t c_offset =
|
||||
c_thread_desc_.CalculateOffset(make_tuple(iM, WmmaInnerloop, 0));
|
||||
// s_nop();
|
||||
wmma_gemm.template Run(
|
||||
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
|
||||
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
|
||||
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
|
||||
// s_nop();
|
||||
});
|
||||
// Read Consumed Next inner loop B
|
||||
b_thread_copy_.Run(
|
||||
b_block_desc_k0_n0_n1_n2_k1,
|
||||
make_tuple(Number<iWmmaK / B_K1>{}, Number<WmmaInnerloop>{}, I0, I0, I0),
|
||||
b_block_buf,
|
||||
b_thread_desc_,
|
||||
make_tuple(I0, Number<WmmaInnerloop>{}, I0, I0, I0),
|
||||
b_thread_buf);
|
||||
});
|
||||
|
||||
// Stage 1: Cut to Repeat Retangle to Square, assume MRepeat > NRepeat
|
||||
static_for<0, RepeatDiff, 1>{}([&](auto iCut) {
|
||||
static_for<0, NRepeat, 1>{}([&](auto iN) {
|
||||
vector_type<FloatA, WmmaK> a_thread_vec;
|
||||
vector_type<FloatB, WmmaK> b_thread_vec;
|
||||
|
||||
static_for<0, WmmaK, 1>{}([&](auto iK) {
|
||||
a_thread_vec.template AsType<FloatA>()(iK) =
|
||||
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / A_K1, iCut, 0, 0, iK % A_K1))>{}];
|
||||
b_thread_vec.template AsType<FloatB>()(iK) =
|
||||
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / B_K1, iN, 0, 0, iK % B_K1))>{}];
|
||||
});
|
||||
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
|
||||
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
|
||||
|
||||
constexpr index_t c_offset =
|
||||
c_thread_desc_.CalculateOffset(make_tuple(iCut, iN, 0));
|
||||
// s_nop();
|
||||
wmma_gemm.template Run(
|
||||
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
|
||||
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
|
||||
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
|
||||
// s_nop();
|
||||
});
|
||||
if constexpr(KPerBlock > WmmaK)
|
||||
{
|
||||
a_thread_copy_.Run(
|
||||
a_block_desc_k0_m0_m1_m2_k1,
|
||||
make_tuple(Number<(iWmmaK + WmmaK) / A_K1>{}, Number<iCut>{}, I0, I0, I0),
|
||||
a_block_buf,
|
||||
a_thread_desc_,
|
||||
make_tuple(I0, Number<iCut>{}, I0, I0, I0),
|
||||
a_thread_buf);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
// Stage 2: Run FIFO fashion loopover in Square
|
||||
static_for<0, NRepeat, 1>{}([&](auto WmmaInnerloop) {
|
||||
// Row Repeatation
|
||||
static_for<WmmaInnerloop, NRepeat, 1>{}([&](auto iN) {
|
||||
vector_type<FloatA, WmmaK> a_thread_vec;
|
||||
vector_type<FloatB, WmmaK> b_thread_vec;
|
||||
|
||||
static_for<0, WmmaK, 1>{}([&](auto iK) {
|
||||
a_thread_vec.template AsType<FloatA>()(iK) =
|
||||
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / A_K1, WmmaInnerloop + RepeatDiff, 0, 0, iK % A_K1))>{}];
|
||||
b_thread_vec.template AsType<FloatB>()(iK) =
|
||||
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / B_K1, iN, 0, 0, iK % B_K1))>{}];
|
||||
});
|
||||
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
|
||||
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
|
||||
|
||||
constexpr index_t c_offset =
|
||||
c_thread_desc_.CalculateOffset(make_tuple(WmmaInnerloop + RepeatDiff, iN, 0));
|
||||
// s_nop();
|
||||
wmma_gemm.template Run(
|
||||
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
|
||||
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
|
||||
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
|
||||
// s_nop();
|
||||
});
|
||||
|
||||
// Col Repeatation
|
||||
static_for<WmmaInnerloop + 1 + RepeatDiff, MRepeat, 1>{}([&](auto iM) {
|
||||
vector_type<FloatA, WmmaK> a_thread_vec;
|
||||
vector_type<FloatB, WmmaK> b_thread_vec;
|
||||
|
||||
static_for<0, WmmaK, 1>{}([&](auto iK) {
|
||||
a_thread_vec.template AsType<FloatA>()(iK) =
|
||||
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / A_K1, iM, 0, 0, iK % A_K1))>{}];
|
||||
b_thread_vec.template AsType<FloatB>()(iK) =
|
||||
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
|
||||
make_tuple(iK / B_K1, WmmaInnerloop, 0, 0, iK % B_K1))>{}];
|
||||
});
|
||||
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
|
||||
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
|
||||
|
||||
constexpr index_t c_offset =
|
||||
c_thread_desc_.CalculateOffset(make_tuple(iM, WmmaInnerloop, 0));
|
||||
// s_nop();
|
||||
wmma_gemm.template Run(
|
||||
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
|
||||
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
|
||||
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
|
||||
// s_nop();
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
protected:
|
||||
// A[M0, M1, M2, K0 = WmmaK]
|
||||
static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<WmmaK / A_K1>{}, Number<MRepeat>{}, I1, I1, Number<A_K1>{}));
|
||||
|
||||
// B[N0, N1, N2, K0 = WmmaK]
|
||||
static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<WmmaK / B_K1>{}, Number<NRepeat>{}, I1, I1, Number<B_K1>{}));
|
||||
|
||||
// C[M, N, NumRegWMMA]
|
||||
static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, wmma_gemm.GetRegSizePerWmma()));
|
||||
|
||||
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatA,
|
||||
FloatA,
|
||||
decltype(a_block_desc_k0_m0_m1_m2_k1),
|
||||
decltype(a_thread_desc_),
|
||||
Sequence<WmmaK / A_K1, 1, 1, 1, A_K1>,
|
||||
Sequence<0, 1, 2, 3, 4>,
|
||||
4,
|
||||
A_K1,
|
||||
A_K1>;
|
||||
|
||||
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatB,
|
||||
FloatB,
|
||||
decltype(b_block_desc_k0_n0_n1_n2_k1),
|
||||
decltype(b_thread_desc_),
|
||||
Sequence<WmmaK / B_K1, 1, 1, 1, B_K1>,
|
||||
Sequence<0, 1, 2, 3, 4>,
|
||||
4,
|
||||
B_K1,
|
||||
B_K1>;
|
||||
|
||||
AThreadCopy a_thread_copy_{CalculateAThreadOriginDataIndex()};
|
||||
BThreadCopy b_thread_copy_{CalculateBThreadOriginDataIndex()};
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user