mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 10:09:41 +00:00
Split k f16 (#97)
* init for splitk f16
* a working prototype
* debug
* perf debug
* update example
* instances for mk kn
* add instances for all layers
* clean
* clean
* add tuning
* format
* add mn_padding into irregular tile
* clean
Co-authored-by: Chao Liu <chao.liu2@amd.com>
[ROCm/composable_kernel commit: e221d11e51]
This commit is contained in:
zjing14
@@ -0,0 +1,743 @@
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#ifndef CK_GRIDWISE_GEMM_XDLOPS_V2R4R2_HPP
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#define CK_GRIDWISE_GEMM_XDLOPS_V2R4R2_HPP
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#include "common_header.hpp"
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#include "multi_index_transform_helper.hpp"
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#include "tensor_descriptor.hpp"
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#include "tensor_descriptor_helper.hpp"
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#include "blockwise_gemm_xdlops.hpp"
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#include "blockwise_tensor_slice_transfer_v4r1.hpp"
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#include "blockwise_tensor_slice_transfer_v6r1.hpp"
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#include "threadwise_tensor_slice_transfer.hpp"
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namespace ck {
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template <typename GridwiseGemm,
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typename FloatAB,
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typename FloatC,
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typename AGridDesc_B_K0_M_K1,
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typename BGridDesc_B_K0_N_K1,
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typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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typename CBlockClusterAdaptor,
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bool HasMainKBlockLoop>
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__global__ void
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#if CK_USE_LAUNCH_BOUNDS
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__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
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#endif
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kernel_gemm_xdlops_v2r4r2(const FloatAB* __restrict__ p_a_grid,
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const FloatAB* __restrict__ p_b_grid,
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FloatC* __restrict__ p_c_grid,
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const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
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const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
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const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
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c_grid_desc_mblock_mperblock_nblock_nperblock,
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const AElementwiseOperation a_element_op,
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const BElementwiseOperation b_element_op,
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const CElementwiseOperation c_element_op,
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const CBlockClusterAdaptor c_block_cluster_adaptor)
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{
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constexpr index_t shared_block_size =
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GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
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__shared__ FloatAB p_shared_block[shared_block_size];
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GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
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p_b_grid,
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p_c_grid,
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p_shared_block,
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a_b_k0_m_k1_grid_desc,
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b_b_k0_n_k1_grid_desc,
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c_grid_desc_mblock_mperblock_nblock_nperblock,
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a_element_op,
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b_element_op,
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c_element_op,
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c_block_cluster_adaptor);
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}
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template <index_t BlockSize,
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typename FloatAB,
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typename FloatAcc,
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typename FloatC,
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InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
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typename AGridDesc_B_K0_M_K1,
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typename BGridDesc_B_K0_N_K1,
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typename CMNGridDesc,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t K0PerBlock,
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index_t MPerXDL,
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index_t NPerXDL,
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index_t K1Value,
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index_t MRepeat,
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index_t NRepeat,
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typename ABlockTransferThreadClusterLengths_K0_M_K1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferSrcScalarPerVector,
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index_t ABlockTransferDstScalarPerVector_K1,
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bool AThreadTransferSrcResetCoordinateAfterRun,
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bool ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_K0_N_K1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferSrcScalarPerVector,
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index_t BBlockTransferDstScalarPerVector_K1,
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bool BThreadTransferSrcResetCoordinateAfterRun,
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bool BBlockLdsExtraN,
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index_t CShuffleMRepeatPerShuffle,
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index_t CShuffleNRepeatPerShuffle,
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index_t CBlockTransferScalarPerVector_NWaveNPerXDL,
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typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>
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struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
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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 I5 = Number<5>{};
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static constexpr auto I6 = Number<6>{};
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static constexpr auto I7 = Number<7>{};
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// K1 should be Number<...>
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static constexpr auto K1 = Number<K1Value>{};
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__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
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{
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constexpr auto max_lds_align = K1;
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// A matrix in LDS memory, dst of blockwise copy
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constexpr auto a_k0_m_k1_block_desc = [&]() {
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if constexpr(ABlockLdsExtraM)
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{
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return make_naive_tensor_descriptor(
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make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
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make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
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}
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else
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{
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return make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
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}
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}();
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// B matrix in LDS memory, dst of blockwise copy
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constexpr auto b_k0_n_k1_block_desc = [&]() {
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if constexpr(BBlockLdsExtraN)
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{
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return make_naive_tensor_descriptor(
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make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
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make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
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}
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else
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{
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return make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
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}
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}();
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// LDS allocation for A and B: be careful of alignment
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constexpr auto a_block_space_size =
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math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
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constexpr auto b_block_space_size =
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math::integer_least_multiple(b_k0_n_k1_block_desc.GetElementSpaceSize(), max_lds_align);
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constexpr auto c_block_size =
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GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock().GetElementSpaceSize();
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return math::max((a_block_space_size + b_block_space_size) * sizeof(FloatAB),
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c_block_size * sizeof(FloatC));
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}
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// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
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__host__ __device__ static constexpr bool
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CheckValidity(const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
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const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
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const CMNGridDesc& c_m_n_grid_desc,
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index_t M01,
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index_t N01)
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{
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static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
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"wrong! K1 need to be known at compile-time");
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static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
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(NPerBlock % (NRepeat * NPerXDL)) == 0,
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"Invalid tuning param!");
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const auto M = a_b_k0_m_k1_grid_desc.GetLength(I2);
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const auto N = b_b_k0_n_k1_grid_desc.GetLength(I2);
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const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
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const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);
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if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
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K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
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K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
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K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
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KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
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return false;
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if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
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return false;
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// check M01, N01
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constexpr auto M1 = Number<MPerBlock>{};
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constexpr auto N1 = Number<NPerBlock>{};
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const auto M0 = M / M1;
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const auto N0 = N / N1;
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if(!(M0 % M01 == 0 && N0 % N01 == 0))
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return false;
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// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
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return true;
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}
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__host__ __device__ static constexpr index_t
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CalculateGridSize(const CMNGridDesc& c_m_n_grid_desc, index_t KBatch)
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{
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const auto M = c_m_n_grid_desc.GetLength(I0);
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const auto N = c_m_n_grid_desc.GetLength(I1);
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const index_t grid_size = (M / MPerBlock) * (N / NPerBlock) * KBatch;
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return grid_size;
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}
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__host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
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{
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const bool has_main_k0_block_loop = K0 > K0PerBlock;
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return has_main_k0_block_loop;
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}
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__host__ __device__ static constexpr auto
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MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CMNGridDesc& c_m_n_grid_desc)
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{
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const auto M = c_m_n_grid_desc.GetLength(I0);
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const auto N = c_m_n_grid_desc.GetLength(I1);
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const auto MBlock = M / MPerBlock;
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const auto NBlock = N / NPerBlock;
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return transform_tensor_descriptor(
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c_m_n_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
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make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
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}
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// return block_id to C matrix tile idx (m0, n0) mapping
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__host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
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const CMNGridDesc& c_m_n_grid_desc, index_t M01, index_t N01, index_t KBatch)
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{
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const auto M = c_m_n_grid_desc.GetLength(I0);
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const auto N = c_m_n_grid_desc.GetLength(I1);
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constexpr auto M1 = Number<MPerBlock>{};
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constexpr auto N1 = Number<NPerBlock>{};
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const auto M0 = M / M1;
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const auto N0 = N / N1;
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const auto M00 = M0 / M01;
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const auto N00 = N0 / N01;
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const auto kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
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make_single_stage_tensor_adaptor(
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make_tuple(make_pass_through_transform(KBatch),
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make_unmerge_transform(make_tuple(M00, M01)),
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make_unmerge_transform(make_tuple(N00, N01))),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
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const auto c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor =
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make_single_stage_tensor_adaptor(
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make_tuple(make_merge_transform(make_tuple(KBatch, M00, N00, M01, N01))),
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make_tuple(Sequence<0, 1, 2, 3, 4>{}),
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make_tuple(Sequence<0>{}));
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const auto c_blockid_to_kbatch_m0_n0_block_cluster_adaptor =
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chain_tensor_adaptors(kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
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c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor);
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return c_blockid_to_kbatch_m0_n0_block_cluster_adaptor;
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}
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__host__ __device__ static constexpr auto
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GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
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{
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constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
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constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
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return make_naive_tensor_descriptor_packed(
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make_tuple(I1,
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Number<CShuffleMRepeatPerShuffle * MWaves * MPerXDL>{},
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I1,
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Number<CShuffleNRepeatPerShuffle * NWaves * NPerXDL>{}));
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}
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using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
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decltype(MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CMNGridDesc{}));
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using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));
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template <bool HasMainKBlockLoop>
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__device__ static void Run(const FloatAB* __restrict__ p_a_grid,
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const FloatAB* __restrict__ p_b_grid,
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FloatC* __restrict__ p_c_grid,
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FloatAB* __restrict__ p_shared_block,
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const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
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const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
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const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
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c_grid_desc_mblock_mperblock_nblock_nperblock,
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const AElementwiseOperation& a_element_op,
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const BElementwiseOperation& b_element_op,
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const CElementwiseOperation& c_element_op,
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const CBlockClusterAdaptor& c_block_cluster_adaptor)
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{
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const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
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p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
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const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
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p_b_grid, b_b_k0_n_k1_grid_desc.GetElementSpaceSize());
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auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
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p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
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const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
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// divide block work by [M, N]
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const auto block_work_idx =
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c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
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const index_t k_batch_id = block_work_idx[I0];
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// HACK: this force m/n_block_data_idx_on_grid into SGPR
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const index_t m_block_data_idx_on_grid =
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__builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
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const index_t n_block_data_idx_on_grid =
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__builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
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// lds max alignment
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constexpr auto max_lds_align = K1;
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// A matrix in LDS memory, dst of blockwise copy
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constexpr auto a_k0_m_k1_block_desc = [&]() {
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if constexpr(ABlockLdsExtraM)
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{
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return make_naive_tensor_descriptor(
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make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
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make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
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}
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else
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{
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return make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
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}
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}();
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constexpr auto a_b_k0_m_k1_block_desc = [&]() {
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if constexpr(ABlockLdsExtraM)
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{
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return make_naive_tensor_descriptor(
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make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
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make_tuple(Number<K0PerBlock>{} * Number<MPerBlock + 1>{} * K1,
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||||
Number<MPerBlock + 1>{} * K1,
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K1,
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I1));
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||||
}
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||||
else
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||||
{
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return make_naive_tensor_descriptor_aligned(
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make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
max_lds_align);
|
||||
}
|
||||
}();
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// B matrix in LDS memory, dst of blockwise copy
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constexpr auto b_k0_n_k1_block_desc = [&]() {
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if constexpr(BBlockLdsExtraN)
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||||
{
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return make_naive_tensor_descriptor(
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make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
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make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
constexpr auto b_b_k0_n_k1_block_desc = [&]() {
|
||||
if constexpr(BBlockLdsExtraN)
|
||||
{
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||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<K0PerBlock>{} * Number<NPerBlock + 1>{} * K1,
|
||||
Number<NPerBlock + 1>{} * K1,
|
||||
K1,
|
||||
I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
max_lds_align);
|
||||
}
|
||||
}();
|
||||
// A matrix blockwise copy
|
||||
auto a_blockwise_copy =
|
||||
BlockwiseTensorSliceTransfer_v4r1<BlockSize,
|
||||
AElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum_t::Set,
|
||||
Sequence<1, K0PerBlock, MPerBlock, K1>,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(a_b_k0_m_k1_grid_desc),
|
||||
decltype(a_b_k0_m_k1_block_desc),
|
||||
ABlockTransferSrcAccessOrder,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
ABlockTransferSrcVectorDim,
|
||||
3,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
1,
|
||||
1,
|
||||
AThreadTransferSrcResetCoordinateAfterRun,
|
||||
true>(
|
||||
a_b_k0_m_k1_grid_desc,
|
||||
make_multi_index(k_batch_id, 0, m_block_data_idx_on_grid, 0),
|
||||
a_element_op,
|
||||
a_b_k0_m_k1_block_desc,
|
||||
make_multi_index(0, 0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
// B matrix blockwise copy
|
||||
auto b_blockwise_copy =
|
||||
BlockwiseTensorSliceTransfer_v4r1<BlockSize,
|
||||
BElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum_t::Set,
|
||||
Sequence<1, K0PerBlock, NPerBlock, K1>,
|
||||
BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(b_b_k0_n_k1_grid_desc),
|
||||
decltype(b_b_k0_n_k1_block_desc),
|
||||
BBlockTransferSrcAccessOrder,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
BBlockTransferSrcVectorDim,
|
||||
3,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
1,
|
||||
1,
|
||||
BThreadTransferSrcResetCoordinateAfterRun,
|
||||
true>(
|
||||
b_b_k0_n_k1_grid_desc,
|
||||
make_multi_index(k_batch_id, 0, n_block_data_idx_on_grid, 0),
|
||||
b_element_op,
|
||||
b_b_k0_n_k1_block_desc,
|
||||
make_multi_index(0, 0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
// GEMM definition
|
||||
// c_mtx += transpose(a_mtx) * b_mtx
|
||||
// a_mtx[K0PerBlock, MPerBlock] is in LDS
|
||||
// b_mtx[K0PerBlock, NPerBlock] is in LDS
|
||||
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
|
||||
// register
|
||||
// sanity check
|
||||
|
||||
auto blockwise_gemm =
|
||||
BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
|
||||
FloatAB,
|
||||
FloatAcc,
|
||||
decltype(a_k0_m_k1_block_desc),
|
||||
decltype(b_k0_n_k1_block_desc),
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MRepeat,
|
||||
NRepeat,
|
||||
K1>{};
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_space_size =
|
||||
math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
FloatAB* p_a_block = p_shared_block;
|
||||
FloatAB* p_b_block = p_shared_block + a_block_space_size;
|
||||
|
||||
constexpr auto a_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
|
||||
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
|
||||
p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
|
||||
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
|
||||
p_b_block, b_k0_n_k1_block_desc.GetElementSpaceSize());
|
||||
|
||||
// preload data into LDS
|
||||
{
|
||||
a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
|
||||
b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
|
||||
|
||||
a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
|
||||
b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
|
||||
}
|
||||
|
||||
// Initialize C
|
||||
c_thread_buf.Clear();
|
||||
|
||||
// main body
|
||||
if constexpr(HasMainKBlockLoop)
|
||||
{
|
||||
index_t k0_block_data_begin = 0;
|
||||
|
||||
do
|
||||
{
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_b_k0_m_k1_grid_desc, a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_b_k0_n_k1_grid_desc, b_block_slice_copy_step);
|
||||
|
||||
a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
|
||||
|
||||
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
|
||||
b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
|
||||
|
||||
k0_block_data_begin += K0PerBlock;
|
||||
} while(k0_block_data_begin < (K0 - K0PerBlock));
|
||||
}
|
||||
|
||||
// tail
|
||||
{
|
||||
block_sync_lds();
|
||||
|
||||
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
|
||||
}
|
||||
|
||||
// output: register to global memory
|
||||
{
|
||||
constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
|
||||
constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
|
||||
|
||||
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc =
|
||||
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc =
|
||||
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto M0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I0);
|
||||
constexpr auto N0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I1);
|
||||
constexpr auto M1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I2);
|
||||
constexpr auto N1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I3);
|
||||
constexpr auto M2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I4);
|
||||
constexpr auto M3 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I5);
|
||||
constexpr auto M4 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I6);
|
||||
constexpr auto N2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I7);
|
||||
|
||||
constexpr auto c_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
|
||||
|
||||
auto c_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
|
||||
static_cast<FloatC*>(p_shared_block),
|
||||
c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
static_assert(M1 == MWaves, "");
|
||||
static_assert(N1 == NWaves, "");
|
||||
static_assert(M2 * M3 * M4 == MPerXDL, "");
|
||||
static_assert(N2 == NPerXDL, "");
|
||||
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
|
||||
c_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(
|
||||
make_freeze_transform(I0), // freeze mblock
|
||||
make_unmerge_transform(make_tuple(CShuffleMRepeatPerShuffle,
|
||||
M1,
|
||||
M2,
|
||||
M3,
|
||||
M4)), // M1 = MWave, M2 * M3 * M4 = MPerXDL
|
||||
make_freeze_transform(I0), // freeze nblock
|
||||
make_unmerge_transform(make_tuple(CShuffleNRepeatPerShuffle,
|
||||
N1,
|
||||
N2))), // M1 = MWave, M2 * M3 * M4 = MPerXDL
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(
|
||||
Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
|
||||
|
||||
// calculate origin of thread output tensor on global memory
|
||||
// blockwise GEMM c matrix starting index
|
||||
const auto c_thread_mtx_on_block =
|
||||
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
|
||||
|
||||
const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
|
||||
const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
|
||||
|
||||
const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto m_thread_data_on_block_idx =
|
||||
m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_block));
|
||||
|
||||
const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto n_thread_data_on_block_idx =
|
||||
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_block));
|
||||
|
||||
// VGPR to LDS
|
||||
auto c_thread_copy_vgpr_to_lds =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
|
||||
FloatC,
|
||||
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc),
|
||||
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
Sequence<CShuffleMRepeatPerShuffle,
|
||||
CShuffleNRepeatPerShuffle,
|
||||
I1,
|
||||
I1,
|
||||
M2,
|
||||
I1,
|
||||
M4,
|
||||
I1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
7,
|
||||
1,
|
||||
InMemoryDataOperationEnum_t::Set,
|
||||
1,
|
||||
true>{
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_multi_index(0,
|
||||
0,
|
||||
m_thread_data_on_block_idx[I1],
|
||||
n_thread_data_on_block_idx[I1],
|
||||
m_thread_data_on_block_idx[I2],
|
||||
m_thread_data_on_block_idx[I3],
|
||||
m_thread_data_on_block_idx[I4],
|
||||
n_thread_data_on_block_idx[I2]),
|
||||
ck::tensor_operation::element_wise::PassThrough{}};
|
||||
|
||||
auto c_block_copy_lds_to_global = BlockwiseTensorSliceTransfer_v6r1<
|
||||
BlockSize, // index_t BlockSize,
|
||||
CElementwiseOperation, // ElementwiseOperation,
|
||||
CGlobalMemoryDataOperation, // DstInMemOp,
|
||||
Sequence<1,
|
||||
CShuffleMRepeatPerShuffle * MWaves * MPerXDL,
|
||||
1,
|
||||
CShuffleNRepeatPerShuffle * NWaves * NPerXDL>, // BlockSliceLengths,
|
||||
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
|
||||
FloatC, // typename SrcData,
|
||||
FloatC, // typename DstData,
|
||||
decltype(c_block_desc_mblock_mperblock_nblock_nperblock),
|
||||
decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
|
||||
Sequence<0, 1, 2, 3>, // typename DimAccessOrder,
|
||||
3, // index_t VectorDim,
|
||||
CBlockTransferScalarPerVector_NWaveNPerXDL, // index_t ScalarPerVector,
|
||||
true, // bool ThreadTransferSrcResetCoordinateAfterRun,
|
||||
false> // bool ThreadTransferDstResetCoordinateAfterRun
|
||||
{c_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_multi_index(0, 0, 0, 0),
|
||||
c_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0),
|
||||
c_element_op};
|
||||
|
||||
constexpr auto mxdlperwave_forward_step =
|
||||
make_multi_index(0, CShuffleMRepeatPerShuffle * MWaves * MPerXDL, 0, 0);
|
||||
constexpr auto nxdlperwave_forward_step =
|
||||
make_multi_index(0, 0, 0, CShuffleNRepeatPerShuffle * NWaves * NPerXDL);
|
||||
constexpr auto nxdlperwave_backward_step =
|
||||
make_multi_index(0, 0, 0, -CShuffleNRepeatPerShuffle * NWaves * NPerXDL);
|
||||
|
||||
static_for<0, MRepeat, CShuffleMRepeatPerShuffle>{}([&](auto mxdlperwave_iter) {
|
||||
constexpr auto mxdlperwave = mxdlperwave_iter;
|
||||
|
||||
static_for<0, NRepeat, CShuffleNRepeatPerShuffle>{}([&](auto nxdlperwave_iter) {
|
||||
constexpr bool nxdlperwave_forward_sweep =
|
||||
(mxdlperwave % (2 * CShuffleMRepeatPerShuffle) == 0);
|
||||
|
||||
constexpr index_t nxdlperwave_value =
|
||||
nxdlperwave_forward_sweep
|
||||
? nxdlperwave_iter
|
||||
: (NRepeat - nxdlperwave_iter - CShuffleNRepeatPerShuffle);
|
||||
|
||||
constexpr auto nxdlperwave = Number<nxdlperwave_value>{};
|
||||
|
||||
// make sure it's safe to do ds_write
|
||||
block_sync_lds();
|
||||
|
||||
// VGPR to LDS
|
||||
c_thread_copy_vgpr_to_lds.Run(
|
||||
c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
|
||||
make_tuple(mxdlperwave, nxdlperwave, I0, I0, I0, I0, I0, I0),
|
||||
c_thread_buf,
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_block_buf);
|
||||
|
||||
// make sure it's safe to do ds_read
|
||||
block_sync_lds();
|
||||
|
||||
// LDS to global
|
||||
c_block_copy_lds_to_global.Run(c_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
c_block_buf,
|
||||
c_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
c_grid_buf);
|
||||
|
||||
// move on nxdlperwave dimension
|
||||
if constexpr(nxdlperwave_forward_sweep &&
|
||||
(nxdlperwave < NRepeat - CShuffleNRepeatPerShuffle))
|
||||
{
|
||||
c_block_copy_lds_to_global.MoveDstSliceWindow(
|
||||
c_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
nxdlperwave_forward_step);
|
||||
}
|
||||
else if constexpr((!nxdlperwave_forward_sweep) && (nxdlperwave > 0))
|
||||
{
|
||||
c_block_copy_lds_to_global.MoveDstSliceWindow(
|
||||
c_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
nxdlperwave_backward_step);
|
||||
}
|
||||
});
|
||||
|
||||
// move on mxdlperwave dimension
|
||||
if constexpr(mxdlperwave < MRepeat - CShuffleMRepeatPerShuffle)
|
||||
{
|
||||
c_block_copy_lds_to_global.MoveDstSliceWindow(
|
||||
c_grid_desc_mblock_mperblock_nblock_nperblock, mxdlperwave_forward_step);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
}; // namespace ck
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
@@ -31,7 +31,11 @@ set(DEVICE_GEMM_INSTANCE_SOURCE
|
||||
${PROJECT_SOURCE_DIR}/device_operation/src/device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instance.cpp;
|
||||
${PROJECT_SOURCE_DIR}/device_operation/src/device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instance.cpp;
|
||||
${PROJECT_SOURCE_DIR}/device_operation/src/device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instance.cpp;
|
||||
)
|
||||
${PROJECT_SOURCE_DIR}/device_operation/src/device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instance.cpp;
|
||||
${PROJECT_SOURCE_DIR}/device_operation/src/device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instance.cpp;
|
||||
${PROJECT_SOURCE_DIR}/device_operation/src/device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instance.cpp;
|
||||
${PROJECT_SOURCE_DIR}/device_operation/src/device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instance.cpp;
|
||||
)
|
||||
|
||||
# device_gemm_bias_2d_instance
|
||||
set(DEVICE_GEMM_BIAS_2D_INSTANCE_SOURCE
|
||||
|
||||
@@ -39,12 +39,12 @@ std::size_t GetFlops(ck::index_t N,
|
||||
std::accumulate(std::begin(output_spatial_lengths),
|
||||
std::end(output_spatial_lengths),
|
||||
static_cast<std::size_t>(1),
|
||||
std::multiplies<std::size_t>()) *
|
||||
std::multiplies<std::size_t>()) *
|
||||
C *
|
||||
std::accumulate(std::begin(filter_spatial_lengths),
|
||||
std::end(filter_spatial_lengths),
|
||||
static_cast<std::size_t>(1),
|
||||
std::multiplies<std::size_t>());
|
||||
std::multiplies<std::size_t>());
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
665
device_operation/include/device_gemm_xdl_splitk_c_shuffle.hpp
Normal file
665
device_operation/include/device_gemm_xdl_splitk_c_shuffle.hpp
Normal file
@@ -0,0 +1,665 @@
|
||||
#ifndef DEVICE_GEMM_XDL_SPLITK_C_SHUFFLE_HPP
|
||||
#define DEVICE_GEMM_XDL_SPLITK_C_SHUFFLE_HPP
|
||||
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include "device.hpp"
|
||||
#include "device_base.hpp"
|
||||
#include "device_gemm.hpp"
|
||||
#include "common_header.hpp"
|
||||
#include "tensor_layout.hpp"
|
||||
#include "tensor_descriptor.hpp"
|
||||
#include "tensor_descriptor_helper.hpp"
|
||||
#include "gridwise_gemm_xdlops_v2r4r2.hpp"
|
||||
#include "gemm_specialization.hpp"
|
||||
|
||||
#ifndef CK_RUN_KERNEL_AND_TIME
|
||||
#define CK_RUN_KERNEL_AND_TIME 1
|
||||
#endif
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename AccDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
GemmSpecialization_t GemmSpecialization,
|
||||
ck::index_t BlockSize,
|
||||
ck::index_t MPerBlock,
|
||||
ck::index_t NPerBlock,
|
||||
ck::index_t K0PerBlock,
|
||||
ck::index_t K1,
|
||||
ck::index_t MPerXDL,
|
||||
ck::index_t NPerXDL,
|
||||
ck::index_t MXdlPerWave,
|
||||
ck::index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
ck::index_t ABlockTransferSrcVectorDim,
|
||||
ck::index_t ABlockTransferSrcScalarPerVector,
|
||||
ck::index_t ABlockTransferDstScalarPerVector_K1,
|
||||
bool ABlockLdsAddExtraM,
|
||||
typename BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
ck::index_t BBlockTransferSrcVectorDim,
|
||||
ck::index_t BBlockTransferSrcScalarPerVector,
|
||||
ck::index_t BBlockTransferDstScalarPerVector_K1,
|
||||
bool BBlockLdsAddExtraN,
|
||||
index_t CShuffleMRepeatPerShuffle,
|
||||
index_t CShuffleNRepeatPerShuffle,
|
||||
typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
index_t CBlockTransferScalarPerVector_NWaveNPerXDL>
|
||||
struct DeviceGemmXdlSplitKCShuffle
|
||||
: public DeviceGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
|
||||
static constexpr auto K1Number = Number<K1>{};
|
||||
|
||||
static auto
|
||||
MakeAGridDescriptor_KBatch_K0_M_K1(index_t M, index_t K, index_t StrideA, int KBatch, int KPad)
|
||||
{
|
||||
assert(KPad % (K1 * KBatch) == 0);
|
||||
|
||||
const index_t K0 = KPad / (K1 * KBatch);
|
||||
|
||||
const auto a_grid_desc_m_k = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto a_grid_desc_m_kpad = transform_tensor_descriptor(
|
||||
a_grid_desc_m_k,
|
||||
make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
if constexpr(GemmSpecialization == GemmSpecialization_t::MNPadding)
|
||||
{
|
||||
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
|
||||
return transform_tensor_descriptor(
|
||||
a_grid_desc_m_kpad,
|
||||
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1Number)),
|
||||
make_right_pad_transform(M, PadM)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
a_grid_desc_m_kpad,
|
||||
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1Number)),
|
||||
make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
|
||||
}
|
||||
}
|
||||
|
||||
static auto
|
||||
MakeBGridDescriptor_KBatch_K0_N_K1(index_t K, index_t N, index_t StrideB, int KBatch, int KPad)
|
||||
{
|
||||
assert(KPad % (K1 * KBatch) == 0);
|
||||
|
||||
const index_t K0 = KPad / (K1 * KBatch);
|
||||
|
||||
const auto b_grid_desc_k_n = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto b_grid_desc_kpad_n = transform_tensor_descriptor(
|
||||
b_grid_desc_k_n,
|
||||
make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
if constexpr(GemmSpecialization == GemmSpecialization_t::MNPadding)
|
||||
{
|
||||
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
|
||||
return transform_tensor_descriptor(
|
||||
b_grid_desc_kpad_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1Number)),
|
||||
make_right_pad_transform(N, PadN)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
b_grid_desc_kpad_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1Number)),
|
||||
make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
|
||||
{
|
||||
const auto c_grid_desc_m_n = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
|
||||
}
|
||||
}();
|
||||
|
||||
if constexpr(GemmSpecialization == GemmSpecialization_t::MNPadding)
|
||||
{
|
||||
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
|
||||
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
}
|
||||
|
||||
static auto GetKPad(index_t K, index_t KBatch)
|
||||
{
|
||||
const index_t K0 = math::integer_divide_ceil(K, K1 * K0PerBlock * KBatch) * K0PerBlock;
|
||||
const index_t KPad = KBatch * K0 * K1;
|
||||
return KPad;
|
||||
}
|
||||
|
||||
using AGridDesc_K0_M_K1 = decltype(MakeAGridDescriptor_KBatch_K0_M_K1(1, 1, 1, 1, 1));
|
||||
using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_KBatch_K0_N_K1(1, 1, 1, 1, 1));
|
||||
using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
|
||||
BlockSize,
|
||||
ADataType, // TODO: distinguish A/B datatype
|
||||
AccDataType,
|
||||
CDataType,
|
||||
InMemoryDataOperationEnum_t::Set,
|
||||
AGridDesc_K0_M_K1,
|
||||
BGridDesc_K0_N_K1,
|
||||
CGridDesc_M_N,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
K0PerBlock,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
K1,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
false, // AThreadTransferSrcResetCoordinateAfterRun,
|
||||
ABlockLdsAddExtraM,
|
||||
BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
false, // BThreadTransferSrcResetCoordinateAfterRun,
|
||||
BBlockLdsAddExtraN,
|
||||
CShuffleMRepeatPerShuffle,
|
||||
CShuffleNRepeatPerShuffle,
|
||||
CBlockTransferScalarPerVector_NWaveNPerXDL,
|
||||
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemmAtomicAdd = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
|
||||
BlockSize,
|
||||
ADataType, // TODO: distinguish A/B datatype
|
||||
AccDataType,
|
||||
CDataType,
|
||||
InMemoryDataOperationEnum_t::AtomicAdd,
|
||||
AGridDesc_K0_M_K1,
|
||||
BGridDesc_K0_N_K1,
|
||||
CGridDesc_M_N,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
K0PerBlock,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
K1,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
false, // AThreadTransferSrcResetCoordinateAfterRun,
|
||||
ABlockLdsAddExtraM,
|
||||
BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
false, // BThreadTransferSrcResetCoordinateAfterRun,
|
||||
BBlockLdsAddExtraN,
|
||||
CShuffleMRepeatPerShuffle,
|
||||
CShuffleNRepeatPerShuffle,
|
||||
CBlockTransferScalarPerVector_NWaveNPerXDL,
|
||||
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
|
||||
|
||||
using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
|
||||
decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
|
||||
|
||||
using Block2CTileMap =
|
||||
decltype(GridwiseGemm::MakeCBlockClusterAdaptor(CGridDesc_M_N{}, 1, 1, 1));
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
Argument(const ADataType* p_a_grid,
|
||||
const BDataType* p_b_grid,
|
||||
CDataType* p_c_grid,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
index_t M01,
|
||||
index_t N01,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op,
|
||||
index_t k_batch)
|
||||
: p_a_grid_{p_a_grid},
|
||||
p_b_grid_{p_b_grid},
|
||||
p_c_grid_{p_c_grid},
|
||||
a_grid_desc_kbatch_k0_m_k1_{},
|
||||
b_grid_desc_kbatch_k0_n_k1_{},
|
||||
c_grid_desc_m_n_{},
|
||||
c_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
block_2_ctile_map_{},
|
||||
M01_{M01},
|
||||
N01_{N01},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
c_element_op_{c_element_op},
|
||||
k_batch_{k_batch}
|
||||
{
|
||||
int KPad = DeviceGemmXdlSplitKCShuffle::GetKPad(K, k_batch_);
|
||||
|
||||
a_grid_desc_kbatch_k0_m_k1_ =
|
||||
DeviceGemmXdlSplitKCShuffle::MakeAGridDescriptor_KBatch_K0_M_K1(
|
||||
M, K, StrideA, k_batch_, KPad);
|
||||
b_grid_desc_kbatch_k0_n_k1_ =
|
||||
DeviceGemmXdlSplitKCShuffle::MakeBGridDescriptor_KBatch_K0_N_K1(
|
||||
K, N, StrideB, k_batch_, KPad);
|
||||
c_grid_desc_m_n_ = DeviceGemmXdlSplitKCShuffle::MakeCGridDescriptor_M_N(M, N, StrideC);
|
||||
|
||||
if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_,
|
||||
b_grid_desc_kbatch_k0_n_k1_,
|
||||
c_grid_desc_m_n_,
|
||||
M01_,
|
||||
N01_))
|
||||
{
|
||||
c_grid_desc_mblock_mperblock_nblock_nperblock_ =
|
||||
GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n_);
|
||||
|
||||
block_2_ctile_map_ =
|
||||
GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
|
||||
}
|
||||
}
|
||||
|
||||
// private:
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
CDataType* p_c_grid_;
|
||||
AGridDesc_K0_M_K1 a_grid_desc_kbatch_k0_m_k1_;
|
||||
BGridDesc_K0_N_K1 b_grid_desc_kbatch_k0_n_k1_;
|
||||
CGridDesc_M_N c_grid_desc_m_n_;
|
||||
CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_;
|
||||
Block2CTileMap block_2_ctile_map_;
|
||||
index_t M01_;
|
||||
index_t N01_;
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CElementwiseOperation c_element_op_;
|
||||
index_t k_batch_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceGemmXdlSplitKCShuffle::Argument;
|
||||
|
||||
void ShowInfo(const Argument& arg)
|
||||
{
|
||||
std::cout << "arg.a_grid_desc_kbatch_k0_m_k1_{"
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0) << ", "
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1) << ", "
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I2) << ", "
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I3) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.b_grid_desc_kbatch_k0_n_k1_{"
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I0) << ", "
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I1) << ", "
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I2) << ", "
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I3) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
|
||||
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
|
||||
}
|
||||
float Run(const Argument& arg, int nrepeat = 1)
|
||||
{
|
||||
const auto kbatch = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0);
|
||||
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2 has invalid setting");
|
||||
}
|
||||
|
||||
const index_t grid_size = GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_, kbatch);
|
||||
|
||||
const auto K0 = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
|
||||
|
||||
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
const auto Run = [&](const auto& kernel) {
|
||||
if(nrepeat > 0)
|
||||
{
|
||||
ShowInfo(arg);
|
||||
ave_time =
|
||||
launch_and_time_kernel(kernel,
|
||||
nrepeat,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_k0_n_k1_,
|
||||
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
|
||||
if(kbatch > 1 || nrepeat <= 0)
|
||||
{
|
||||
hipGetErrorString(hipMemset(
|
||||
arg.p_c_grid_,
|
||||
0,
|
||||
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
|
||||
sizeof(CDataType)));
|
||||
|
||||
launch_kernel(kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_k0_n_k1_,
|
||||
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
};
|
||||
if(has_main_k0_block_loop)
|
||||
{
|
||||
if(kbatch == 1)
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::
|
||||
CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::Block2CTileMap>,
|
||||
true>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemmAtomicAdd,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::
|
||||
CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::Block2CTileMap>,
|
||||
true>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if(kbatch == 1)
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::
|
||||
CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::Block2CTileMap>,
|
||||
false>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemmAtomicAdd,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::
|
||||
CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<DeviceGemmXdlSplitKCShuffle::Block2CTileMap>,
|
||||
false>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const ADataType* p_a,
|
||||
const BDataType* p_b,
|
||||
CDataType* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op,
|
||||
index_t KBatch)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_c,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
1,
|
||||
1,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
KBatch};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
void* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op,
|
||||
ck::index_t KBatch = 1) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<CDataType*>(p_c),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
1,
|
||||
1,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
KBatch);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmXdlSplitKCShuffle"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< K0PerBlock
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
#endif
|
||||
@@ -20,7 +20,8 @@ using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
|
||||
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization_t::MNPadding;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
|
||||
using device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances =
|
||||
@@ -54,8 +55,8 @@ using device_gemm_xdl_f16_f16_f16_mk_nk_mn_irregular_tile_instances =
|
||||
//###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 144, 8, 8, 16, 16, 2, 9, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<8, 8, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, 7, 1>,
|
||||
DeviceGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 144, 4, 8, 16, 16, 2, 9, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, 7, 1>
|
||||
DeviceGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmMNPadding, 256, 128, 144, 8, 8, 16, 16, 2, 9, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<8, 8, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, 7, 1>,
|
||||
DeviceGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmMNPadding, 256, 128, 144, 4, 8, 16, 16, 2, 9, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
|
||||
@@ -0,0 +1,53 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_gemm_xdl_splitk_c_shuffle.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
|
||||
using device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#########################|AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#########################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
|
||||
//#########################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,53 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_gemm_xdl_splitk_c_shuffle.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
|
||||
using device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#########################|AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#########################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
|
||||
//#########################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,53 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_gemm_xdl_splitk_c_shuffle.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
|
||||
using device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#########################|AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#########################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
|
||||
//#########################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,71 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_gemm_xdl_splitk_c_shuffle.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
|
||||
using device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#########################|AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#########################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
|
||||
//#########################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
using device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_irregular_tile_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#########################|AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#########################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
|
||||
//#########################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGemmXdlSplitKCShuffle< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 144, 4, 8, 16, 16, 2, 9, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 4>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 2, 2, true, 1, 9, S<1, 2, 1, 72>, 2>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances{});
|
||||
|
||||
add_device_operation_instances(
|
||||
instances, device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_irregular_tile_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -44,6 +44,11 @@ void add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(std::vector<Devic
|
||||
void add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
|
||||
} // namespace device_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
@@ -68,7 +73,7 @@ void profile_gemm_impl(int do_verification,
|
||||
int StrideA,
|
||||
int StrideB,
|
||||
int StrideC,
|
||||
int KBatch = 1)
|
||||
int KBatch)
|
||||
{
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
@@ -181,7 +186,6 @@ void profile_gemm_impl(int do_verification,
|
||||
{
|
||||
if(KBatch > 1)
|
||||
{
|
||||
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
@@ -214,44 +218,76 @@ void profile_gemm_impl(int do_verification,
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
|
||||
if(KBatch > 1)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
|
||||
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
if(KBatch > 1)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
|
||||
if(KBatch > 1)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
|
||||
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
|
||||
if(KBatch > 1)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
|
||||
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -78,7 +78,8 @@ int profile_gemm(int argc, char* argv[])
|
||||
K,
|
||||
(StrideA < 0) ? K : StrideA,
|
||||
(StrideB < 0) ? N : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
KBatch);
|
||||
}
|
||||
else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
|
||||
{
|
||||
@@ -97,7 +98,8 @@ int profile_gemm(int argc, char* argv[])
|
||||
K,
|
||||
(StrideA < 0) ? K : StrideA,
|
||||
(StrideB < 0) ? K : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
KBatch);
|
||||
}
|
||||
else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_KN_MN)
|
||||
{
|
||||
@@ -116,7 +118,8 @@ int profile_gemm(int argc, char* argv[])
|
||||
K,
|
||||
(StrideA < 0) ? M : StrideA,
|
||||
(StrideB < 0) ? N : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
KBatch);
|
||||
}
|
||||
else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_NK_MN)
|
||||
{
|
||||
@@ -135,7 +138,8 @@ int profile_gemm(int argc, char* argv[])
|
||||
K,
|
||||
(StrideA < 0) ? M : StrideA,
|
||||
(StrideB < 0) ? K : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
KBatch);
|
||||
}
|
||||
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user