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Add multiple d gridwise gemm on Navi21 for ResNet50 (#517)
* start add example * add multiple d fp16 example * device transfer elementwiseop to gridwise * gridwise add multiple d * change example for multiple d * fix spill registers * fix for passthrough element op * fix int8 overflow * change example file name * add instance for dl multiple d * example add DsDataType * remove grouped_convolution_forward_dl.hpp * add head file(was deleted before) * fix not support device issue * format * remove passthrough check Co-authored-by: letaoqin <letaoqin@amd.com>
This commit is contained in:
ltqin
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/multi_index_transform_helper.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
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#include "ck/tensor_operation/gpu/block/blockwise_gemm_dl_v2r3.hpp"
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#include "ck/tensor_operation/gpu/block/blockwise_tensor_slice_transfer_v5r1.hpp"
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#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_set.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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namespace ck {
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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 DsDataType,
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typename FloatC,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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InMemoryDataOperationEnum CGlobalMemoryDataOperation,
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typename AGridDesc_K0_M_K1,
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typename BGridDesc_K0_N_K1,
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typename CGridDesc_M_N,
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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 K1Value,
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index_t M1PerThreadM111,
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index_t N1PerThreadN111,
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index_t KPerThread,
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typename M11N11ThreadClusterM110Xs,
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typename M11N11ThreadClusterN110Xs,
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typename ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
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typename ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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typename ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
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typename ABlockTransferSrcVectorTensorContiguousDimOrder,
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typename ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
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typename BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
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typename BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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typename BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
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typename BBlockTransferSrcVectorTensorContiguousDimOrder,
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typename BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
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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 GridwiseGemmDlMultipleD_km_kn_mn
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{
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static constexpr index_t NumDTensor = DsDataType::Size();
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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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// K1 should be Number<...>
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static constexpr auto K1 = Number<K1Value>{};
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// ck::Tuple<const D0DataType*, const D1DataType*, ...>
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static constexpr auto MakeDsGridPointer()
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{
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return generate_tuple(
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[&](auto i) {
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using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
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return static_cast<const DDataType*>(nullptr);
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},
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Number<NumDTensor>{});
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}
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__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
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{
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// TODO: change this. I think it needs multi-dimensional alignment
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constexpr auto max_lds_align = K1;
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// TODO: check alignment
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// A matrix in LDS memory, dst of blockwise copy
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constexpr auto a_block_desc_k_m = make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
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// TODO: check alignment
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// B matrix in LDS memory, dst of blockwise copy
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constexpr auto b_block_desc_k_n = make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
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// TODO: check alignment
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// LDS allocation for A and B: be careful of alignment
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constexpr auto a_block_aligned_space_size =
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math::integer_least_multiple(a_block_desc_k_m.GetElementSpaceSize(), max_lds_align);
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constexpr auto b_block_aligned_space_size =
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math::integer_least_multiple(b_block_desc_k_n.GetElementSpaceSize(), max_lds_align);
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return 2 * (a_block_aligned_space_size + b_block_aligned_space_size) * sizeof(FloatAB);
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}
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__host__ __device__ static constexpr bool
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CheckValidity(const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
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const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1,
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const CGridDesc_M_N& c_grid_desc_m_n)
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{
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const auto M = a_grid_desc_k0_m_k1.GetLength(I1);
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const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
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const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
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// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
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return (M == c_grid_desc_m_n.GetLength(I0) && N == c_grid_desc_m_n.GetLength(I1) &&
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K0 == b_grid_desc_k0_n_k1.GetLength(I0) &&
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K1 == a_grid_desc_k0_m_k1.GetLength(I2) &&
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K1 == b_grid_desc_k0_n_k1.GetLength(I2)) &&
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(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0);
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}
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__host__ __device__ static constexpr index_t CalculateGridSize(index_t M, index_t N)
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{
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const index_t grid_size = (M / MPerBlock) * (N / NPerBlock);
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return grid_size;
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}
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__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K0)
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{
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const bool has_main_k_block_loop = (K0 + K0PerBlock) / (2 * K0PerBlock) > 1;
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return has_main_k_block_loop;
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}
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__host__ __device__ static constexpr bool CalculateHasDoubleTailKBlockLoop(index_t K0)
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{
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const bool has_double_tail_k_block_loop = (K0 / K0PerBlock) % 2 == 0;
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return has_double_tail_k_block_loop;
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}
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__host__ __device__ static constexpr auto
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MakeAGridDescriptor_K0_M0_M1_K1(const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1)
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{
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const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
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const auto M = a_grid_desc_k0_m_k1.GetLength(I1);
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const auto M1 = Number<MPerBlock>{};
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const auto M0 = M / M1;
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const auto a_grid_desc_k0_m0_m1_k1 =
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transform_tensor_descriptor(a_grid_desc_k0_m_k1,
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make_tuple(make_pass_through_transform(K0),
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make_unmerge_transform(make_tuple(M0, M1)),
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make_pass_through_transform(K1)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
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return a_grid_desc_k0_m0_m1_k1;
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}
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__host__ __device__ static constexpr auto
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MakeBGridDescriptor_K0_N0_N1_K1(const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1)
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{
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const auto K0 = b_grid_desc_k0_n_k1.GetLength(I0);
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const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
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const auto N1 = Number<NPerBlock>{};
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const auto N0 = N / N1;
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const auto b_grid_desc_k0_n0_n1_k1 =
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transform_tensor_descriptor(b_grid_desc_k0_n_k1,
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make_tuple(make_pass_through_transform(K0),
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make_unmerge_transform(make_tuple(N0, N1)),
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make_pass_through_transform(K1)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
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return b_grid_desc_k0_n0_n1_k1;
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}
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__host__ __device__ static constexpr auto
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MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(const CGridDesc_M_N& c_grid_desc_m_n)
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{
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const auto M = c_grid_desc_m_n.GetLength(I0);
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const auto N = c_grid_desc_m_n.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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constexpr auto M11 =
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Number<container_reduce(M11N11ThreadClusterM110Xs{}, math::multiplies{}, I1) *
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M1PerThreadM111>{};
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constexpr auto N11 =
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Number<container_reduce(M11N11ThreadClusterN110Xs{}, math::multiplies{}, I1) *
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N1PerThreadN111>{};
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constexpr auto M10 = M1 / M11;
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constexpr auto N10 = N1 / N11;
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const auto c_grid_desc_m0_m10_m11_n0_n10_n11 = transform_tensor_descriptor(
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c_grid_desc_m_n,
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make_tuple(make_unmerge_transform(make_tuple(M0, M10, M11)),
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make_unmerge_transform(make_tuple(N0, N10, N11))),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1, 2>{}, Sequence<3, 4, 5>{}));
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return c_grid_desc_m0_m10_m11_n0_n10_n11;
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}
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// Ds desc for source in blockwise copy
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template <typename DsGridDesc_M_N>
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__host__ __device__ static constexpr auto
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MakeDsGridDescriptor_M0_M10_M11_N0_N10_N11(const DsGridDesc_M_N& ds_grid_desc_m_n)
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{
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return generate_tuple(
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[&](auto i) { return MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(ds_grid_desc_m_n[i]); },
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Number<NumDTensor>{});
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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_M_N& c_grid_desc_m_n)
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{
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return BlockToCTileMap_M00_N00_M01_N01<MPerBlock, NPerBlock, CGridDesc_M_N>(
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c_grid_desc_m_n);
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}
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using AGridDesc_K0_M0_M1_K1 = decltype(MakeAGridDescriptor_K0_M0_M1_K1(AGridDesc_K0_M_K1{}));
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using BGridDesc_K0_N0_N1_K1 = decltype(MakeBGridDescriptor_K0_N0_N1_K1(BGridDesc_K0_N_K1{}));
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using CGridDesc_M0_M10_M11_N0_N10_N11 =
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decltype(MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(CGridDesc_M_N{}));
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using Block2CTileMap = decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}));
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using DsGridPointer = decltype(MakeDsGridPointer());
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template <typename DsGridDesc_M0_M10_M11_N0_N10_N11,
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bool HasMainKBlockLoop,
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bool HasDoubleTailKBlockLoop>
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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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DsGridPointer p_ds_grid,
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FloatC* __restrict__ p_c_grid,
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FloatAB* __restrict__ p_shared_block,
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const AElementwiseOperation&,
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const BElementwiseOperation&,
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const CDEElementwiseOperation& cde_element_op,
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const AGridDesc_K0_M0_M1_K1& a_grid_desc_k0_m0_m1_k1,
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const BGridDesc_K0_N0_N1_K1& b_grid_desc_k0_n0_n1_k1,
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const DsGridDesc_M0_M10_M11_N0_N10_N11& ds_grid_desc_m0_m10_m11_n0_n10_n11,
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const CGridDesc_M0_M10_M11_N0_N10_N11& c_grid_desc_m0_m10_m11_n0_n10_n11,
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const Block2CTileMap& block_2_ctile_map,
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integral_constant<bool, HasMainKBlockLoop>,
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integral_constant<bool, HasDoubleTailKBlockLoop>)
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{
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const auto a_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_a_grid, a_grid_desc_k0_m0_m1_k1.GetElementSpaceSize());
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const auto b_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_b_grid, b_grid_desc_k0_n0_n1_k1.GetElementSpaceSize());
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auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_c_grid, c_grid_desc_m0_m10_m11_n0_n10_n11.GetElementSpaceSize());
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// divide block work by [M, N]
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const auto c_m0_n0_block_cluster_idx =
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block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
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// HACK: this force index data into SGPR
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const index_t im0 = __builtin_amdgcn_readfirstlane(c_m0_n0_block_cluster_idx[I0]);
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const index_t in0 = __builtin_amdgcn_readfirstlane(c_m0_n0_block_cluster_idx[I1]);
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if(!block_2_ctile_map.ValidCTileIndex(
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make_tuple(im0, in0),
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make_tuple(c_grid_desc_m0_m10_m11_n0_n10_n11.GetLength(I0),
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c_grid_desc_m0_m10_m11_n0_n10_n11.GetLength(I3))))
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{
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return;
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}
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// TODO: change this. I think it needs multi-dimensional alignment
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constexpr auto max_lds_align = K1;
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// TODO: check alignment
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// A matrix in LDS memory, dst of blockwise copy
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// be careful of LDS alignment
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constexpr auto a_block_desc_k0_m0_m1_k1 = make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, I1, Number<MPerBlock>{}, K1), max_lds_align);
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// TODO: check alignment
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// B matrix in LDS memory, dst of blockwise copy
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// be careful of LDS alignment
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constexpr auto b_block_desc_k0_n0_n1_k1 = make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, I1, Number<NPerBlock>{}, K1), max_lds_align);
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// TODO: check alignment
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// A matrix in LDS memory, for blockwise GEMM
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constexpr auto a_k0_m_k1_block_desc = make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
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// TODO: check alignment
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// B matrix in LDS memory, for blockwise GEMM
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constexpr auto b_k0_n_k1_block_desc = make_naive_tensor_descriptor_aligned(
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make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
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static_assert(a_block_desc_k0_m0_m1_k1.GetElementSpaceSize() ==
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a_k0_m_k1_block_desc.GetElementSpaceSize() &&
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b_block_desc_k0_n0_n1_k1.GetElementSpaceSize() ==
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b_k0_n_k1_block_desc.GetElementSpaceSize() &&
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"wrong!");
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// A matrix blockwise copy
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auto a_blockwise_copy = BlockwiseTensorSliceTransfer_v5r1<
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BlockSize,
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InMemoryDataOperationEnum::Set,
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Sequence<K0PerBlock, 1, MPerBlock, K1.value>,
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ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
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ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
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ABlockTransferThreadClusterArrangeOrder,
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FloatAB,
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FloatAB,
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remove_reference_t<decltype(a_grid_desc_k0_m0_m1_k1)>,
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decltype(a_block_desc_k0_m0_m1_k1),
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ABlockTransferSrcAccessOrder,
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Sequence<0, 1, 2, 3>,
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ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1, // SrcVectorTensorLengths
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ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1, // DstVectorTensorLengths
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ABlockTransferSrcVectorTensorContiguousDimOrder, // SrcVectorTensorContiguousDimOrder
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Sequence<0, 1, 2, 3>, // DstVectorTensorContiguousDimOrder
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false,
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true>(a_grid_desc_k0_m0_m1_k1,
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make_multi_index(0, im0, 0, 0),
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a_block_desc_k0_m0_m1_k1,
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make_multi_index(0, 0, 0, 0));
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// B matrix blockwise copy
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auto b_blockwise_copy = BlockwiseTensorSliceTransfer_v5r1<
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BlockSize,
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InMemoryDataOperationEnum::Set,
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Sequence<K0PerBlock, 1, NPerBlock, K1.value>,
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BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
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BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
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BBlockTransferThreadClusterArrangeOrder,
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FloatAB,
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FloatAB,
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remove_reference_t<decltype(b_grid_desc_k0_n0_n1_k1)>,
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decltype(b_block_desc_k0_n0_n1_k1),
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BBlockTransferSrcAccessOrder,
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Sequence<0, 1, 2, 3>,
|
||||
BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1, // SrcVectorTensorLengths
|
||||
BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1, // DstVectorTensorLengths
|
||||
BBlockTransferSrcVectorTensorContiguousDimOrder, // SrcVectorTensorContiguousDimOrder
|
||||
Sequence<0, 1, 2, 3>, // DstVectorTensorContiguousDimOrder
|
||||
false,
|
||||
true>(b_grid_desc_k0_n0_n1_k1,
|
||||
make_multi_index(0, in0, 0, 0),
|
||||
b_block_desc_k0_n0_n1_k1,
|
||||
make_multi_index(0, 0, 0, 0));
|
||||
|
||||
// GEMM definition
|
||||
// c_mtx += transpose(a_mtx) * b_mtx
|
||||
// a_mtx[K0PerBlock, MPerBlock] is in LDS
|
||||
// b_mtx[KPerBlocl, NPerBlock] is in LDS
|
||||
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
|
||||
// register
|
||||
const auto blockwise_gemm =
|
||||
BlockwiseGemmDl_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_BM0_2_BN0_2<
|
||||
BlockSize,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
FloatAcc,
|
||||
decltype(a_k0_m_k1_block_desc),
|
||||
decltype(b_k0_n_k1_block_desc),
|
||||
M1PerThreadM111,
|
||||
N1PerThreadN111,
|
||||
KPerThread,
|
||||
M11N11ThreadClusterM110Xs,
|
||||
M11N11ThreadClusterN110Xs,
|
||||
M1PerThreadM111,
|
||||
N1PerThreadN111>{};
|
||||
|
||||
constexpr auto c_m10_m11_n10_n11_thread_tensor_lengths =
|
||||
decltype(blockwise_gemm)::GetCThreadTensorLengths_BM0_BM1_BN0_BN1();
|
||||
|
||||
constexpr auto c_thread_desc_m10_m11_n10_n11 = make_naive_tensor_descriptor_packed(
|
||||
sequence_to_tuple_of_number(c_m10_m11_n10_n11_thread_tensor_lengths));
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_aligned_space_size = math::integer_least_multiple(
|
||||
a_block_desc_k0_m0_m1_k1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
constexpr auto b_block_aligned_space_size = math::integer_least_multiple(
|
||||
b_block_desc_k0_n0_n1_k1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
FloatAB* p_a_block_double = p_shared_block;
|
||||
FloatAB* p_b_block_double = p_shared_block + 2 * a_block_aligned_space_size;
|
||||
|
||||
// register allocation for output
|
||||
auto c_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAcc>(
|
||||
c_thread_desc_m10_m11_n10_n11.GetElementSpaceSize());
|
||||
|
||||
// Initialize C
|
||||
c_thread_buf.Clear();
|
||||
|
||||
constexpr auto a_block_slice_copy_step = make_multi_index(K0PerBlock, 0, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(K0PerBlock, 0, 0, 0);
|
||||
|
||||
auto a_block_even_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
p_a_block_double, a_block_desc_k0_m0_m1_k1.GetElementSpaceSize());
|
||||
auto b_block_even_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
p_b_block_double, b_block_desc_k0_n0_n1_k1.GetElementSpaceSize());
|
||||
|
||||
auto a_block_odd_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
p_a_block_double + a_block_aligned_space_size,
|
||||
a_block_desc_k0_m0_m1_k1.GetElementSpaceSize());
|
||||
auto b_block_odd_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
p_b_block_double + b_block_aligned_space_size,
|
||||
b_block_desc_k0_n0_n1_k1.GetElementSpaceSize());
|
||||
|
||||
// LDS double buffer: preload data into LDS
|
||||
{
|
||||
a_blockwise_copy.RunRead(a_grid_desc_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_k0_n0_n1_k1, b_global_buf);
|
||||
|
||||
a_blockwise_copy.RunWrite(a_block_desc_k0_m0_m1_k1, a_block_even_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_k0_n0_n1_k1, b_block_even_buf);
|
||||
}
|
||||
|
||||
if constexpr(HasMainKBlockLoop)
|
||||
{
|
||||
const auto K0 = a_grid_desc_k0_m0_m1_k1.GetLength(I0);
|
||||
|
||||
index_t k_block_data_begin = 0;
|
||||
|
||||
// LDS double buffer: main body
|
||||
// use Do-While loop instead of For loop to simplify control flow
|
||||
do
|
||||
{
|
||||
// even iteration
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_m0_m1_k1,
|
||||
a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_n0_n1_k1,
|
||||
b_block_slice_copy_step);
|
||||
|
||||
// LDS doubel buffer: load next data from device mem
|
||||
a_blockwise_copy.RunRead(a_grid_desc_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_k0_n0_n1_k1, b_global_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
// LDS double buffer: GEMM on current data
|
||||
blockwise_gemm.Run(c_thread_desc_m10_m11_n10_n11,
|
||||
a_block_even_buf,
|
||||
b_block_even_buf,
|
||||
c_thread_buf);
|
||||
|
||||
// LDS double buffer: store next data to LDS
|
||||
a_blockwise_copy.RunWrite(a_block_desc_k0_m0_m1_k1, a_block_odd_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_k0_n0_n1_k1, b_block_odd_buf);
|
||||
|
||||
// odd iteration
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_m0_m1_k1,
|
||||
a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_n0_n1_k1,
|
||||
b_block_slice_copy_step);
|
||||
|
||||
// LDS doubel buffer: load next data from device mem
|
||||
a_blockwise_copy.RunRead(a_grid_desc_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_k0_n0_n1_k1, b_global_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
// LDS double buffer: GEMM on current data
|
||||
blockwise_gemm.Run(
|
||||
c_thread_desc_m10_m11_n10_n11, a_block_odd_buf, b_block_odd_buf, c_thread_buf);
|
||||
|
||||
// LDS double buffer: store next data to LDS
|
||||
a_blockwise_copy.RunWrite(a_block_desc_k0_m0_m1_k1, a_block_even_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_k0_n0_n1_k1, b_block_even_buf);
|
||||
|
||||
k_block_data_begin += 2 * K0PerBlock;
|
||||
} while(k_block_data_begin < K0 - 2 * K0PerBlock);
|
||||
}
|
||||
|
||||
// LDS double buffer: tail
|
||||
if constexpr(HasDoubleTailKBlockLoop) // if has 2 iteration left
|
||||
{
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_m0_m1_k1, a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_n0_n1_k1, b_block_slice_copy_step);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
// LDS double buffer: load last data from device mem
|
||||
a_blockwise_copy.RunRead(a_grid_desc_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_k0_n0_n1_k1, b_global_buf);
|
||||
|
||||
// LDS double buffer: GEMM on 2nd-last data
|
||||
blockwise_gemm.Run(
|
||||
c_thread_desc_m10_m11_n10_n11, a_block_even_buf, b_block_even_buf, c_thread_buf);
|
||||
|
||||
// LDS double buffer: store last data to LDS
|
||||
a_blockwise_copy.RunWrite(a_block_desc_k0_m0_m1_k1, a_block_odd_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_k0_n0_n1_k1, b_block_odd_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
// LDS double buffer: GEMM on last data
|
||||
blockwise_gemm.Run(
|
||||
c_thread_desc_m10_m11_n10_n11, a_block_odd_buf, b_block_odd_buf, c_thread_buf);
|
||||
}
|
||||
else // if has 1 iteration left
|
||||
{
|
||||
__syncthreads();
|
||||
|
||||
// LDS double buffer: GEMM on last data
|
||||
blockwise_gemm.Run(
|
||||
c_thread_desc_m10_m11_n10_n11, a_block_even_buf, b_block_even_buf, c_thread_buf);
|
||||
}
|
||||
|
||||
// output: register to global memory
|
||||
{
|
||||
constexpr auto c_thread_desc_m0_m10_m11_n0_n10_n11 =
|
||||
make_naive_tensor_descriptor_packed(
|
||||
make_tuple(I1,
|
||||
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I0]>{},
|
||||
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I1]>{},
|
||||
I1,
|
||||
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I2]>{},
|
||||
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I3]>{}));
|
||||
|
||||
const auto c_m10_m11_n10_n11_thread_origin_idx_on_block =
|
||||
blockwise_gemm.CalculateCThreadOriginOnBlock_BM0_BM1_BN0_BN1(
|
||||
get_thread_local_1d_id());
|
||||
|
||||
const auto ds_grid_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_ds_grid[i], ds_grid_desc_m0_m10_m11_n0_n10_n11[i].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
auto ds_thread_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
|
||||
|
||||
return StaticBuffer<AddressSpaceEnum::Vgpr,
|
||||
DDataType,
|
||||
c_m10_m11_n10_n11_thread_tensor_lengths[I3],
|
||||
true>{};
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
auto ds_threadwise_copy = generate_tuple(
|
||||
[&](auto i) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
|
||||
|
||||
return ThreadwiseTensorSliceTransfer_v2<
|
||||
DDataType,
|
||||
DDataType,
|
||||
decltype(ds_grid_desc_m0_m10_m11_n0_n10_n11[i]),
|
||||
decltype(c_thread_desc_m0_m10_m11_n0_n10_n11),
|
||||
Sequence<I1,
|
||||
I1,
|
||||
I1,
|
||||
I1,
|
||||
I1,
|
||||
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I3]>{}>,
|
||||
CThreadTransferSrcDstAccessOrder,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector,
|
||||
1,
|
||||
false>(ds_grid_desc_m0_m10_m11_n0_n10_n11[i],
|
||||
make_multi_index(im0,
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I0],
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I1],
|
||||
in0,
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I2],
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I3]));
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
static_for<0, c_m10_m11_n10_n11_thread_tensor_lengths[I0], 1>{}([&](auto m10) {
|
||||
static_for<0, c_m10_m11_n10_n11_thread_tensor_lengths[I1], 1>{}([&](auto m11) {
|
||||
static_for<0, c_m10_m11_n10_n11_thread_tensor_lengths[I2], 1>{}([&](auto n10) {
|
||||
// load d matrix data
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
ds_threadwise_copy(i).Run(ds_grid_desc_m0_m10_m11_n0_n10_n11[i],
|
||||
ds_grid_buf[i],
|
||||
c_thread_desc_m0_m10_m11_n0_n10_n11,
|
||||
make_tuple(I0, I0, I0, I0, I0, I0),
|
||||
ds_thread_buf(i));
|
||||
});
|
||||
// cal element op
|
||||
static_for<0, c_m10_m11_n10_n11_thread_tensor_lengths[I3], 1>{}(
|
||||
[&](auto i) {
|
||||
// get reference to src data
|
||||
const auto src_data_refs = generate_tie(
|
||||
// return type should be lvalue
|
||||
[&](auto iSrc) -> const auto& {
|
||||
return ds_thread_buf[iSrc][i];
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
// get reference to dst data
|
||||
constexpr index_t c_offset =
|
||||
c_thread_desc_m0_m10_m11_n0_n10_n11.CalculateOffset(
|
||||
make_tuple(0, m10, m11, 0, n10, i));
|
||||
auto dst_data_refs = generate_tie(
|
||||
// return type should be lvalue
|
||||
[&](auto) -> auto& { return c_thread_buf(Number<c_offset>{}); },
|
||||
Number<2>{});
|
||||
|
||||
unpack2(cde_element_op, dst_data_refs, src_data_refs);
|
||||
});
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
ds_threadwise_copy(i).MoveSrcSliceWindow(
|
||||
ds_grid_desc_m0_m10_m11_n0_n10_n11[i],
|
||||
make_multi_index(0, 0, 0, 0, 1, 0));
|
||||
});
|
||||
});
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
ds_threadwise_copy(i).MoveSrcSliceWindow(
|
||||
ds_grid_desc_m0_m10_m11_n0_n10_n11[i],
|
||||
make_multi_index(
|
||||
0, 0, 1, 0, -c_m10_m11_n10_n11_thread_tensor_lengths[I2], 0));
|
||||
});
|
||||
});
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
ds_threadwise_copy(i).MoveSrcSliceWindow(
|
||||
ds_grid_desc_m0_m10_m11_n0_n10_n11[i],
|
||||
make_multi_index(
|
||||
0, 1, -c_m10_m11_n10_n11_thread_tensor_lengths[I1], 0, 0, 0));
|
||||
});
|
||||
});
|
||||
|
||||
ThreadwiseTensorSliceTransfer_v1r3<
|
||||
FloatAcc,
|
||||
FloatC,
|
||||
decltype(c_thread_desc_m0_m10_m11_n0_n10_n11),
|
||||
decltype(c_grid_desc_m0_m10_m11_n0_n10_n11),
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
Sequence<1,
|
||||
c_m10_m11_n10_n11_thread_tensor_lengths[I0],
|
||||
c_m10_m11_n10_n11_thread_tensor_lengths[I1],
|
||||
1,
|
||||
c_m10_m11_n10_n11_thread_tensor_lengths[I2],
|
||||
c_m10_m11_n10_n11_thread_tensor_lengths[I3]>,
|
||||
CThreadTransferSrcDstAccessOrder,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector,
|
||||
CGlobalMemoryDataOperation,
|
||||
1,
|
||||
true>{c_grid_desc_m0_m10_m11_n0_n10_n11,
|
||||
make_multi_index(im0,
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I0],
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I1],
|
||||
in0,
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I2],
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I3]),
|
||||
ck::tensor_operation::element_wise::PassThrough{}}
|
||||
.Run(c_thread_desc_m0_m10_m11_n0_n10_n11,
|
||||
make_tuple(I0, I0, I0, I0, I0, I0),
|
||||
c_thread_buf,
|
||||
c_grid_desc_m0_m10_m11_n0_n10_n11,
|
||||
c_grid_buf);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user