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Add Grouped Conv Backward Weight on Navi21 for ResNet50. (#505)
* Add DeviceOp and examples * Format DeviceOp template arguments * Remove bf16 example * Format * Format * Update MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N * Refactor argument preparation * Update conv_bwd_weight_dl to grouped_conv_bwd_weight_dl * Rename device op file * Update include directive in the example file * Update descriptor preparation for grouped op * Update the argument * Update batch handling * Add gridwise gemm supporting batched input * Update blockwise indexing, working version * Update copyright year * Update check if argument is supported * Refactor and make consistent with xdl examples * Update check if argument is supported * Add changelog entry * Added comments on Dl op split_k>1 support --------- Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com> Co-authored-by: zjing14 <zhangjing14@gmail.com>
This commit is contained in:
Rostyslav Geyyer
@@ -1,5 +1,5 @@
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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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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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@@ -574,4 +574,546 @@ struct GridwiseGemmDl_km_kn_mn_v1r3
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}
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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 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 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 GridwiseGemmDl_bkm_bkn_mn_v1r3
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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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// 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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// 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_b_k0_m_k1 = 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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// 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_b_k0_n_k1 = make_naive_tensor_descriptor_aligned(
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make_tuple(Number<1>{}, 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 = math::integer_least_multiple(
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a_block_desc_b_k0_m_k1.GetElementSpaceSize(), max_lds_align);
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constexpr auto b_block_aligned_space_size = math::integer_least_multiple(
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b_block_desc_b_k0_n_k1.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_B_K0_M_K1& a_grid_desc_b_k0_m_k1,
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const BGridDesc_B_K0_N_K1& b_grid_desc_b_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_b_k0_m_k1.GetLength(I2);
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const auto N = b_grid_desc_b_k0_n_k1.GetLength(I2);
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const auto K0 = a_grid_desc_b_k0_m_k1.GetLength(I1);
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const auto KBatch = a_grid_desc_b_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_b_k0_n_k1.GetLength(I1) &&
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K1 == a_grid_desc_b_k0_m_k1.GetLength(I3) &&
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K1 == b_grid_desc_b_k0_n_k1.GetLength(I3)) &&
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KBatch == b_grid_desc_b_k0_n_k1.GetLength(I0) &&
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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_B_K0_M0_M1_K1(const AGridDesc_B_K0_M_K1& a_grid_desc_b_k0_m_k1)
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{
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const auto KBatch = a_grid_desc_b_k0_m_k1.GetLength(I0);
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const auto K0 = a_grid_desc_b_k0_m_k1.GetLength(I1);
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const auto M = a_grid_desc_b_k0_m_k1.GetLength(I2);
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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_b_k0_m0_m1_k1 = transform_tensor_descriptor(
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a_grid_desc_b_k0_m_k1,
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make_tuple(make_pass_through_transform(KBatch),
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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>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}));
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return a_grid_desc_b_k0_m0_m1_k1;
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}
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__host__ __device__ static constexpr auto
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MakeBGridDescriptor_B_K0_N0_N1_K1(const BGridDesc_B_K0_N_K1& b_grid_desc_b_k0_n_k1)
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{
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const auto KBatch = b_grid_desc_b_k0_n_k1.GetLength(I0);
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const auto K0 = b_grid_desc_b_k0_n_k1.GetLength(I1);
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const auto N = b_grid_desc_b_k0_n_k1.GetLength(I2);
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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_b_k0_n0_n1_k1 = transform_tensor_descriptor(
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b_grid_desc_b_k0_n_k1,
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make_tuple(make_pass_through_transform(KBatch),
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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>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}));
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return b_grid_desc_b_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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// 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 CGridDesc_M_N& c_m_n_grid_desc, index_t M01, index_t N01, index_t KBatch)
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{
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return BlockToCTileMap_KSplit_M00_N00_M01_N01<MPerBlock, NPerBlock, CGridDesc_M_N>(
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c_m_n_grid_desc, M01, N01, KBatch);
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}
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using AGridDesc_B_K0_M0_M1_K1 =
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decltype(MakeAGridDescriptor_B_K0_M0_M1_K1(AGridDesc_B_K0_M_K1{}));
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using BGridDesc_B_K0_N0_N1_K1 =
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decltype(MakeBGridDescriptor_B_K0_N0_N1_K1(BGridDesc_B_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 CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CGridDesc_M_N{}, 1, 1, 1));
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template <bool HasMainKBlockLoop, 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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FloatC* __restrict__ p_c_grid,
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FloatAB* __restrict__ p_shared_block,
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const AGridDesc_B_K0_M0_M1_K1& a_grid_desc_b_k0_m0_m1_k1,
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const BGridDesc_B_K0_N0_N1_K1& b_grid_desc_b_k0_n0_n1_k1,
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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 CBlockClusterAdaptor& c_block_cluster_adaptor,
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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_b_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_b_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 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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if(!c_block_cluster_adaptor.ValidCTileIndex(
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make_tuple(block_work_idx[I1], block_work_idx[I2]),
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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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// 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 = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);
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const index_t n_block_data_idx_on_grid = __builtin_amdgcn_readfirstlane(block_work_idx[I2]);
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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_b_k0_m0_m1_k1 = make_naive_tensor_descriptor_aligned(
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make_tuple(I1, 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_b_k0_n0_n1_k1 = make_naive_tensor_descriptor_aligned(
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make_tuple(I1, 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, 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<1, 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_b_k0_m0_m1_k1)>,
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decltype(a_block_desc_b_k0_m0_m1_k1),
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ABlockTransferSrcAccessOrder,
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Sequence<0, 1, 2, 3, 4>,
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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, 4>, // DstVectorTensorContiguousDimOrder
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false,
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true>(a_grid_desc_b_k0_m0_m1_k1,
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make_multi_index(k_batch_id, 0, m_block_data_idx_on_grid, 0, 0),
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a_block_desc_b_k0_m0_m1_k1,
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make_multi_index(0, 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<1, 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_b_k0_n0_n1_k1)>,
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decltype(b_block_desc_b_k0_n0_n1_k1),
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BBlockTransferSrcAccessOrder,
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Sequence<0, 1, 2, 3, 4>,
|
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BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1, // SrcVectorTensorLengths
|
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BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1, // DstVectorTensorLengths
|
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BBlockTransferSrcVectorTensorContiguousDimOrder, // SrcVectorTensorContiguousDimOrder
|
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Sequence<0, 1, 2, 3, 4>, // DstVectorTensorContiguousDimOrder
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false,
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true>(b_grid_desc_b_k0_n0_n1_k1,
|
||||
make_multi_index(k_batch_id, 0, n_block_data_idx_on_grid, 0, 0),
|
||||
b_block_desc_b_k0_n0_n1_k1,
|
||||
make_multi_index(0, 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(0, K0PerBlock, 0, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(0, 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_b_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_b_k0_n0_n1_k1, b_global_buf);
|
||||
|
||||
a_blockwise_copy.RunWrite(a_block_desc_b_k0_m0_m1_k1, a_block_even_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_b_k0_n0_n1_k1, b_block_even_buf);
|
||||
}
|
||||
|
||||
if constexpr(HasMainKBlockLoop)
|
||||
{
|
||||
const auto K0 = a_grid_desc_b_k0_m0_m1_k1.GetLength(I1);
|
||||
|
||||
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_b_k0_m0_m1_k1,
|
||||
a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_b_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_b_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_b_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_b_k0_m0_m1_k1, a_block_odd_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_b_k0_n0_n1_k1, b_block_odd_buf);
|
||||
|
||||
// odd iteration
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_b_k0_m0_m1_k1,
|
||||
a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_b_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_b_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_b_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_b_k0_m0_m1_k1, a_block_even_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_b_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_b_k0_m0_m1_k1, a_block_slice_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_b_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_b_k0_m0_m1_k1, a_global_buf);
|
||||
b_blockwise_copy.RunRead(b_grid_desc_b_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_b_k0_m0_m1_k1, a_block_odd_buf);
|
||||
b_blockwise_copy.RunWrite(b_block_desc_b_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());
|
||||
|
||||
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(m_block_data_idx_on_grid,
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I0],
|
||||
c_m10_m11_n10_n11_thread_origin_idx_on_block[I1],
|
||||
n_block_data_idx_on_grid,
|
||||
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