mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 05:01:25 +00:00
refactored deviceBatchedGemm; removed GridwiseBatchedGemm; added fp32 and int8 to profiler (#120)
changed long_index_t to index_t when computing memory offset uncomment other ops in profiler added test for batched_gemm
This commit is contained in:
Jianfeng Yan
@@ -1,649 +0,0 @@
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#ifndef CK_GRIDWISE_BATCHED_GEMM_XDLOPS_V2R3_HPP
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#define CK_GRIDWISE_BATCHED_GEMM_XDLOPS_V2R3_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 "threadwise_tensor_slice_transfer.hpp"
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namespace ck {
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template <typename GridwiseBatchedGemm,
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typename FloatAB,
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typename FloatC,
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typename AGridDesc_G_K0_M_K1,
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typename BGridDesc_G_K0_N_K1,
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typename CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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typename Block2CTileMap,
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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_batched_gemm_xdlops_v2r3(
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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_G_K0_M_K1 a_grid_desc_g_k0_m_k1,
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const BGridDesc_G_K0_N_K1 b_grid_desc_g_k0_n_k1,
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const CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2,
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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 Block2CTileMap block_2_ctile_map)
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{
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__shared__ char p_shared[GridwiseBatchedGemm::GetSharedMemoryNumberOfByte()];
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GridwiseBatchedGemm::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,
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a_grid_desc_g_k0_m_k1,
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b_grid_desc_g_k0_n_k1,
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c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2,
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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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block_2_ctile_map);
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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_G_K0_M_K1,
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typename BGridDesc_G_K0_N_K1,
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typename CGridDesc_G_M_N,
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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 MXdlPerWave,
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index_t NXdlPerWave,
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typename ABlockTransferThreadClusterLengths_G_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_G_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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typename CThreadTransferSrcDstAccessOrder,
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index_t CThreadTransferSrcDstVectorDim,
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index_t CThreadTransferDstScalarPerVector>
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struct GridwiseBatchedGemm_gk0mk1_gk0nk1_gmn_xdlops_v2r3
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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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static constexpr auto I8 = Number<8>{};
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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 auto
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GetABlockDescriptor_BatchCount_K0PerBlock_MPerBlock_K1()
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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_block_desc_g_k0_m_k1 = [&]() {
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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(I1, 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(I1, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
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}
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}();
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return a_block_desc_g_k0_m_k1;
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}
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__host__ __device__ static constexpr auto
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GetBBlockDescriptor_BatchCount_K0PerBlock_NPerBlock_K1()
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{
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constexpr auto max_lds_align = K1;
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// B matrix in LDS memory, dst of blockwise copy
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constexpr auto b_block_desc_g_k0_n_k1 = [&]() {
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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(I1, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
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make_tuple(Number<K0PerBlock>{} * Number<NPerBlock + 1>{} * K1,
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Number<NPerBlock + 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(I1, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
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}
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}();
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return b_block_desc_g_k0_n_k1;
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}
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__host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
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{
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constexpr auto a_block_desc_g_k0_m_k1 =
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GetABlockDescriptor_BatchCount_K0PerBlock_MPerBlock_K1();
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constexpr auto K0 = a_block_desc_g_k0_m_k1.GetLength(I1);
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constexpr auto M = a_block_desc_g_k0_m_k1.GetLength(I2);
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constexpr auto a_block_desc_k0_m_k1 = transform_tensor_descriptor(
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a_block_desc_g_k0_m_k1,
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make_tuple(make_freeze_transform(I0),
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make_pass_through_transform(K0),
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make_pass_through_transform(M),
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make_pass_through_transform(K1)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
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return a_block_desc_k0_m_k1;
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}
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__host__ __device__ static constexpr auto GetBBlockDescriptor_K0PerBlock_NPerBlock_K1()
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{
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constexpr auto b_block_desc_g_k0_n_k1 =
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GetBBlockDescriptor_BatchCount_K0PerBlock_NPerBlock_K1();
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constexpr auto K0 = b_block_desc_g_k0_n_k1.GetLength(I1);
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constexpr auto N = b_block_desc_g_k0_n_k1.GetLength(I2);
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constexpr auto b_block_desc_k0_n_k1 = transform_tensor_descriptor(
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b_block_desc_g_k0_n_k1,
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make_tuple(make_freeze_transform(I0),
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make_pass_through_transform(K0),
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make_pass_through_transform(N),
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make_pass_through_transform(K1)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
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return b_block_desc_k0_n_k1;
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}
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__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
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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_desc_g_k0_m_k1 =
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GetABlockDescriptor_BatchCount_K0PerBlock_MPerBlock_K1();
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constexpr auto b_block_desc_g_k0_n_k1 =
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GetBBlockDescriptor_BatchCount_K0PerBlock_NPerBlock_K1();
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constexpr auto max_lds_align = K1;
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constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
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a_block_desc_g_k0_m_k1.GetElementSpaceSize(), max_lds_align);
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constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
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b_block_desc_g_k0_n_k1.GetElementSpaceSize(), max_lds_align);
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return (a_block_space_size_aligned + b_block_space_size_aligned) * sizeof(FloatAB);
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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_G_K0_M_K1& a_grid_desc_g_k0_m_k1,
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const BGridDesc_G_K0_N_K1& b_grid_desc_g_k0_n_k1,
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const CGridDesc_G_M_N& c_grid_desc_g_m_n,
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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 * MXdlPerWave) == 0) &&
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(NPerBlock % (NXdlPerWave * NPerXDL)) == 0,
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"Invalid tuning param!");
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// const auto G = a_grid_desc_g_k0_m_k1.GetLength(I0);
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const auto K0 = a_grid_desc_g_k0_m_k1.GetLength(I1);
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const auto M = a_grid_desc_g_k0_m_k1.GetLength(I2);
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const auto N = b_grid_desc_g_k0_n_k1.GetLength(I2);
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if(!(M == c_grid_desc_g_m_n.GetLength(I1) && N == c_grid_desc_g_m_n.GetLength(I2) &&
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K0 == b_grid_desc_g_k0_n_k1.GetLength(I1) &&
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K1 == a_grid_desc_g_k0_m_k1.GetLength(I3) &&
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K1 == b_grid_desc_g_k0_n_k1.GetLength(I3)))
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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 CGridDesc_G_M_N& c_grid_desc_g_m_n)
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{
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const auto G = c_grid_desc_g_m_n.GetLength(I0);
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const auto M = c_grid_desc_g_m_n.GetLength(I1);
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const auto N = c_grid_desc_g_m_n.GetLength(I2);
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const index_t grid_size = G * (M / MPerBlock) * (N / NPerBlock);
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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) > 1;
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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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MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_G_M_N& c_grid_desc_g_m_n)
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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_block_desc_k0_m_k1 = [&]() {
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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_block_desc_k0_n_k1 = [&]() {
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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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using BlockwiseGemm =
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BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
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FloatAB,
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FloatAcc,
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decltype(a_block_desc_k0_m_k1),
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decltype(b_block_desc_k0_n_k1),
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MPerXDL,
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NPerXDL,
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MXdlPerWave,
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NXdlPerWave,
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K1>;
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return BlockwiseGemm::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_g_m_n);
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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
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MakeDefaultBlock2CTileMap(const CGridDesc_G_M_N& c_grid_desc_g_m_n, index_t M01, index_t N01)
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{
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const auto G = c_grid_desc_g_m_n.GetLength(I0);
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const auto M = c_grid_desc_g_m_n.GetLength(I1);
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const auto N = c_grid_desc_g_m_n.GetLength(I2);
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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 g_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(G),
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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 cblockid_to_g_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(G, 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 cblockid_to_g_m0_n0_block_cluster_adaptor =
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chain_tensor_adaptors(g_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
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cblockid_to_g_m00_m01_n00_n01_block_cluster_adaptor);
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return cblockid_to_g_m0_n0_block_cluster_adaptor;
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}
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using CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2 =
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decltype(MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_G_M_N{}));
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using DefaultBlock2CTileMap = decltype(MakeDefaultBlock2CTileMap(CGridDesc_G_M_N{}, 1, 1));
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template <bool HasMainKBlockLoop, typename Block2CTileMap = DefaultBlock2CTileMap>
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__device__ static void
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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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void* __restrict__ p_shared,
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const AGridDesc_G_K0_M_K1& a_grid_desc_g_k0_m_k1,
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const BGridDesc_G_K0_N_K1& b_grid_desc_g_k0_n_k1,
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const CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2& c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
const AElementwiseOperation& a_element_op,
|
||||
const BElementwiseOperation& b_element_op,
|
||||
const CElementwiseOperation& c_element_op,
|
||||
const Block2CTileMap& block_2_ctile_map)
|
||||
{
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
|
||||
p_a_grid, a_grid_desc_g_k0_m_k1.GetElementSpaceSize());
|
||||
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
|
||||
p_b_grid, b_grid_desc_g_k0_n_k1.GetElementSpaceSize());
|
||||
auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
|
||||
p_c_grid, c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetElementSpaceSize());
|
||||
|
||||
const auto K0 = a_grid_desc_g_k0_m_k1.GetLength(I1);
|
||||
|
||||
// divide block work by [M, N]
|
||||
const auto block_work_idx =
|
||||
block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
// HACK: this force m/n_block_data_idx_on_grid into SGPR
|
||||
const index_t g_idx_on_grid = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);
|
||||
|
||||
const index_t m_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
|
||||
|
||||
const index_t n_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
|
||||
|
||||
// lds max alignment
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_g_k0_m_k1 =
|
||||
GetABlockDescriptor_BatchCount_K0PerBlock_MPerBlock_K1();
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_block_desc_g_k0_n_k1 =
|
||||
GetBBlockDescriptor_BatchCount_K0PerBlock_NPerBlock_K1();
|
||||
|
||||
// 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_G_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(a_grid_desc_g_k0_m_k1),
|
||||
decltype(a_block_desc_g_k0_m_k1),
|
||||
ABlockTransferSrcAccessOrder,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
ABlockTransferSrcVectorDim,
|
||||
3,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
1,
|
||||
1,
|
||||
AThreadTransferSrcResetCoordinateAfterRun,
|
||||
true>(
|
||||
a_grid_desc_g_k0_m_k1,
|
||||
make_multi_index(g_idx_on_grid, 0, m_block_data_idx_on_grid, 0),
|
||||
a_element_op,
|
||||
a_block_desc_g_k0_m_k1,
|
||||
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_G_K0_N_K1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(b_grid_desc_g_k0_n_k1),
|
||||
decltype(b_block_desc_g_k0_n_k1),
|
||||
BBlockTransferSrcAccessOrder,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
BBlockTransferSrcVectorDim,
|
||||
3,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
1,
|
||||
1,
|
||||
BThreadTransferSrcResetCoordinateAfterRun,
|
||||
true>(
|
||||
b_grid_desc_g_k0_n_k1,
|
||||
make_multi_index(g_idx_on_grid, 0, n_block_data_idx_on_grid, 0),
|
||||
b_element_op,
|
||||
b_block_desc_g_k0_n_k1,
|
||||
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
|
||||
|
||||
constexpr auto a_block_desc_k0_m_k1 = GetABlockDescriptor_K0PerBlock_MPerBlock_K1();
|
||||
constexpr auto b_block_desc_k0_n_k1 = GetBBlockDescriptor_K0PerBlock_NPerBlock_K1();
|
||||
|
||||
auto blockwise_gemm =
|
||||
BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
|
||||
FloatAB,
|
||||
FloatAcc,
|
||||
decltype(a_block_desc_k0_m_k1),
|
||||
decltype(b_block_desc_k0_n_k1),
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
K1>{};
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_g_k0_m_k1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
|
||||
static_cast<FloatAB*>(p_shared), a_block_desc_g_k0_m_k1.GetElementSpaceSize());
|
||||
|
||||
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
|
||||
static_cast<FloatAB*>(p_shared) + a_block_space_size_aligned,
|
||||
b_block_desc_g_k0_n_k1.GetElementSpaceSize());
|
||||
|
||||
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);
|
||||
|
||||
// preload data into LDS
|
||||
{
|
||||
a_blockwise_copy.RunRead(a_grid_desc_g_k0_m_k1, a_grid_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_g_k0_n_k1, b_grid_buf);
|
||||
|
||||
a_blockwise_copy.RunWrite(a_block_desc_g_k0_m_k1, a_block_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_g_k0_n_k1, 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_grid_desc_g_k0_m_k1, a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_g_k0_n_k1, b_block_slice_copy_step);
|
||||
|
||||
a_blockwise_copy.RunRead(a_grid_desc_g_k0_m_k1, a_grid_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
b_blockwise_copy.RunRead(b_grid_desc_g_k0_n_k1, b_grid_buf);
|
||||
|
||||
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
a_blockwise_copy.RunWrite(a_block_desc_g_k0_m_k1, a_block_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_g_k0_n_k1, 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 auto c_thread_desc_g_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
// constexpr auto G = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I0);
|
||||
constexpr auto M0 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I1);
|
||||
constexpr auto N0 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I2);
|
||||
constexpr auto M1 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I3);
|
||||
constexpr auto N1 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I4);
|
||||
constexpr auto M2 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I5);
|
||||
constexpr auto M3 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
|
||||
constexpr auto M4 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
|
||||
constexpr auto N2 = c_block_desc_g_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I8);
|
||||
|
||||
// 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_grid =
|
||||
m_block_data_idx_on_grid + c_thread_mtx_on_block[I0];
|
||||
|
||||
const index_t n_thread_data_on_grid =
|
||||
n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
|
||||
|
||||
const auto m_thread_data_on_grid_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_grid_idx =
|
||||
m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_grid));
|
||||
|
||||
const auto n_thread_data_on_grid_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_grid_idx =
|
||||
n_thread_data_on_grid_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_grid));
|
||||
|
||||
auto c_thread_copy = ThreadwiseTensorSliceTransfer_v1r3<
|
||||
FloatAcc,
|
||||
FloatC,
|
||||
decltype(c_thread_desc_g_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
decltype(c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
CElementwiseOperation,
|
||||
Sequence<I1, M0, N0, I1, I1, M2, I1, M4, I1>,
|
||||
CThreadTransferSrcDstAccessOrder,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector,
|
||||
CGlobalMemoryDataOperation,
|
||||
1,
|
||||
true>{c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_multi_index(g_idx_on_grid,
|
||||
m_thread_data_on_grid_idx[I0],
|
||||
n_thread_data_on_grid_idx[I0],
|
||||
m_thread_data_on_grid_idx[I1],
|
||||
n_thread_data_on_grid_idx[I1],
|
||||
m_thread_data_on_grid_idx[I2],
|
||||
m_thread_data_on_grid_idx[I3],
|
||||
m_thread_data_on_grid_idx[I4],
|
||||
n_thread_data_on_grid_idx[I2]),
|
||||
c_element_op};
|
||||
|
||||
c_thread_copy.Run(c_thread_desc_g_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0),
|
||||
c_thread_buf,
|
||||
c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_grid_buf);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
Reference in New Issue
Block a user