mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-16 10:59:55 +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
[ROCm/composable_kernel commit: cb87b049de]
This commit is contained in:
Jianfeng Yan
@@ -10,12 +10,68 @@
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#include "tensor_layout.hpp"
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#include "tensor_descriptor.hpp"
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#include "tensor_descriptor_helper.hpp"
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#include "gridwise_batched_gemm_xdlops_v2r3.hpp"
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#include "gridwise_gemm_xdlops_v2r3.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename GridwiseGemm,
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typename FloatAB,
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typename FloatC,
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typename AGridDesc_K0_M_K1,
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typename BGridDesc_K0_N_K1,
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typename CGridDesc_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 ComputeBasePrtOfBatch,
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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 index_t num_batches,
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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_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_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 ComputeBasePrtOfBatch compute_base_ptr_of_batch_,
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const Block2CTileMap block_2_ctile_map)
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{
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const index_t num_blocks_per_batch =
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__builtin_amdgcn_readfirstlane(get_grid_size() / num_batches);
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const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
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const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
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static_cast<long_index_t>(compute_base_ptr_of_batch_.GetABasePtr(g_idx)));
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const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
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static_cast<long_index_t>(compute_base_ptr_of_batch_.GetBBasePtr(g_idx)));
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const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
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static_cast<long_index_t>(compute_base_ptr_of_batch_.GetCBasePtr(g_idx)));
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__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
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GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
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p_b_grid + b_batch_offset,
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p_c_grid + c_batch_offset,
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p_shared,
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a_grid_desc_k0_m_k1,
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b_grid_desc_k0_n_k1,
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c_grid_desc_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 <typename ADataType,
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typename BDataType,
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typename CDataType,
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@@ -35,14 +91,14 @@ template <typename ADataType,
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ck::index_t NPerXDL,
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ck::index_t MXdlPerWave,
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ck::index_t NXdlPerWave,
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typename ABlockTransferThreadClusterLengths_G_K0_M_K1,
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typename ABlockTransferThreadClusterLengths_K0_M_K1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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ck::index_t ABlockTransferSrcVectorDim,
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ck::index_t ABlockTransferSrcScalarPerVector,
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ck::index_t ABlockTransferDstScalarPerVector_K1,
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bool ABlockLdsAddExtraM,
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typename BBlockTransferThreadClusterLengths_G_K0_N_K1,
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typename BBlockTransferThreadClusterLengths_K0_N_K1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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ck::index_t BBlockTransferSrcVectorDim,
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@@ -57,149 +113,215 @@ struct DeviceBatchedGemmXdl
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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 K1Number = Number<K1>{};
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static auto
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MakeAGridDescriptor_G_K0_M_K1(index_t BatchCount, index_t M, index_t K, index_t StrideA)
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static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
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{
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assert(K % K1 == 0);
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const index_t K0 = K / K1;
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const auto a_grid_desc_g_m_k = [&]() {
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const auto a_grid_desc_m_k = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(BatchCount, M, K),
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make_tuple(M * StrideA, StrideA, I1));
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return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(BatchCount, M, K),
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make_tuple(K * StrideA, I1, StrideA));
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return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, M));
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}
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}();
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const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
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const auto a_grid_desc_g_k0_mp_k1 =
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transform_tensor_descriptor(a_grid_desc_g_m_k,
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make_tuple(make_pass_through_transform(BatchCount),
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make_unmerge_transform(make_tuple(K0, K1Number)),
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const auto a_grid_desc_k0_mp_k1 =
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transform_tensor_descriptor(a_grid_desc_m_k,
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make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
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make_right_pad_transform(M, PadM)),
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make_tuple(Sequence<0>{}, Sequence<2>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2>{}));
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make_tuple(Sequence<1>{}, Sequence<0>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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return a_grid_desc_g_k0_mp_k1;
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return a_grid_desc_k0_mp_k1;
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}
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static auto
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MakeBGridDescriptor_G_K0_N_K1(index_t BatchCount, index_t K, index_t N, index_t StrideB)
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static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
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{
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assert(K % K1 == 0);
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const index_t K0 = K / K1;
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const auto b_grid_desc_g_k_n = [&]() {
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const auto b_grid_desc_k_n = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(BatchCount, K, N),
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make_tuple(K * StrideB, StrideB, I1));
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return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(BatchCount, K, N),
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make_tuple(N * StrideB, I1, StrideB));
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return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, K));
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}
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}();
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const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
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const auto b_grid_desc_g_k0_np_k1 =
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transform_tensor_descriptor(b_grid_desc_g_k_n,
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make_tuple(make_pass_through_transform(BatchCount),
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make_unmerge_transform(make_tuple(K0, K1Number)),
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const auto b_grid_desc_k0_np_k1 =
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transform_tensor_descriptor(b_grid_desc_k_n,
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make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
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make_right_pad_transform(N, PadN)),
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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>{}));
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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return b_grid_desc_g_k0_np_k1;
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return b_grid_desc_k0_np_k1;
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}
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static auto MakeCGridDescriptor_G_M_N(index_t BatchCount, index_t M, index_t N, index_t StrideC)
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static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
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{
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const auto c_grid_desc_g_m_n = [&]() {
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const auto c_grid_desc_m_n = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(BatchCount, M, N),
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make_tuple(M * StrideC, StrideC, I1));
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return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(BatchCount, M, N),
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make_tuple(N * StrideC, I1, StrideC));
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return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, M));
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}
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}();
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const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
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const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
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const auto c_grid_desc_g_mp_np =
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transform_tensor_descriptor(c_grid_desc_g_m_n,
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make_tuple(make_pass_through_transform(BatchCount),
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make_right_pad_transform(M, PadM),
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make_right_pad_transform(N, PadN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
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const auto c_grid_desc_mp_np = transform_tensor_descriptor(
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c_grid_desc_m_n,
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make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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return c_grid_desc_g_mp_np;
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return c_grid_desc_mp_np;
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}
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using AGridDesc_G_K0_M_K1 = decltype(MakeAGridDescriptor_G_K0_M_K1(1, 1, 1, 1));
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using BGridDesc_G_K0_N_K1 = decltype(MakeBGridDescriptor_G_K0_N_K1(1, 1, 1, 1));
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using CGridDesc_G_M_N = decltype(MakeCGridDescriptor_G_M_N(1, 1, 1, 1));
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using AGridDesc_K0_M_K1 = decltype(MakeAGridDescriptor_K0_M_K1(1, 1, 1));
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using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1));
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using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
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// GridwiseBatchedGemm
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using GridwiseBatchedGemm = GridwiseBatchedGemm_gk0mk1_gk0nk1_gmn_xdlops_v2r3<
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BlockSize,
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ADataType, // TODO: distinguish A/B datatype
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AccDataType,
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CDataType,
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InMemoryDataOperationEnum_t::Set,
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AGridDesc_G_K0_M_K1,
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BGridDesc_G_K0_N_K1,
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CGridDesc_G_M_N,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation,
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MPerBlock,
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NPerBlock,
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K0PerBlock,
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MPerXDL,
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NPerXDL,
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K1,
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MXdlPerWave,
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NXdlPerWave,
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ABlockTransferThreadClusterLengths_G_K0_M_K1,
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ABlockTransferThreadClusterArrangeOrder,
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ABlockTransferSrcAccessOrder,
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ABlockTransferSrcVectorDim,
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ABlockTransferSrcScalarPerVector,
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ABlockTransferDstScalarPerVector_K1,
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false, // AThreadTransferSrcResetCoordinateAfterRun,
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ABlockLdsAddExtraM,
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BBlockTransferThreadClusterLengths_G_K0_N_K1,
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BBlockTransferThreadClusterArrangeOrder,
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BBlockTransferSrcAccessOrder,
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BBlockTransferSrcVectorDim,
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BBlockTransferSrcScalarPerVector,
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BBlockTransferDstScalarPerVector_K1,
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false, // BThreadTransferSrcResetCoordinateAfterRun,
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BBlockLdsAddExtraN,
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Sequence<0, 1, 3, 5, 6, 7, 2, 4, 8>, // CThreadTransferSrcDstAccessOrder,
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CThreadTransferSrcDstVectorDim,
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CThreadTransferDstScalarPerVector>;
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struct Block2CTileMapMaker
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{
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Block2CTileMapMaker(index_t num_batches) : num_batches_(num_batches) {}
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__host__ __device__ constexpr auto
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MakeBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n, index_t M01, index_t N01)
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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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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_insert_transform(num_batches_),
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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<>{}, Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
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const auto globalblockid_to_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(num_batches_, 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 globalblockid_to_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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globalblockid_to_m00_m01_n00_n01_block_cluster_adaptor);
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return globalblockid_to_m0_n0_block_cluster_adaptor;
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}
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private:
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index_t num_batches_;
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};
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struct ComputeBasePtrOfStridedBatch
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{
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ComputeBasePtrOfStridedBatch(index_t BatchStrideA,
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index_t BatchStrideB,
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index_t BatchStrideC)
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: BatchStrideA_(BatchStrideA), BatchStrideB_(BatchStrideB), BatchStrideC_(BatchStrideC)
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{
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}
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__host__ __device__ constexpr long_index_t GetABasePtr(index_t g_idx) const
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{
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return g_idx * static_cast<long_index_t>(BatchStrideA_);
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}
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__host__ __device__ constexpr long_index_t GetBBasePtr(index_t g_idx) const
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{
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return g_idx * static_cast<long_index_t>(BatchStrideB_);
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}
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__host__ __device__ constexpr long_index_t GetCBasePtr(index_t g_idx) const
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{
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return g_idx * static_cast<long_index_t>(BatchStrideC_);
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}
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||||
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private:
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index_t BatchStrideA_;
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index_t BatchStrideB_;
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||||
index_t BatchStrideC_;
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};
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// GridwiseGemm
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using GridwiseGemm =
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GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
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ADataType, // TODO: distinguish A/B datatype
|
||||
AccDataType,
|
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CDataType,
|
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InMemoryDataOperationEnum_t::Set,
|
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AGridDesc_K0_M_K1,
|
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BGridDesc_K0_N_K1,
|
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CGridDesc_M_N,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
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MPerBlock,
|
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NPerBlock,
|
||||
K0PerBlock,
|
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MPerXDL,
|
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NPerXDL,
|
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K1,
|
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MXdlPerWave,
|
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NXdlPerWave,
|
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ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
false, // AThreadTransferSrcResetCoordinateAfterRun,
|
||||
ABlockLdsAddExtraM,
|
||||
BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
false, // BThreadTransferSrcResetCoordinateAfterRun,
|
||||
BBlockLdsAddExtraN,
|
||||
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector>;
|
||||
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||||
using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 =
|
||||
decltype(GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_M_N{}));
|
||||
using Block2CTileMap =
|
||||
decltype(Block2CTileMapMaker{1}.MakeBlock2CTileMap(CGridDesc_M_N{}, 1, 1));
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
@@ -222,10 +344,16 @@ struct DeviceBatchedGemmXdl
|
||||
: p_a_grid_{p_a_grid},
|
||||
p_b_grid_{p_b_grid},
|
||||
p_c_grid_{p_c_grid},
|
||||
a_grid_desc_g_k0_m_k1_{},
|
||||
b_grid_desc_g_k0_n_k1_{},
|
||||
c_grid_desc_g_m_n_{},
|
||||
c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2_{},
|
||||
BatchCount_(BatchCount),
|
||||
a_grid_desc_k0_m_k1_{
|
||||
DeviceBatchedGemmXdl::MakeAGridDescriptor_K0_M_K1(M, K, StrideA)},
|
||||
b_grid_desc_k0_n_k1_{
|
||||
DeviceBatchedGemmXdl::MakeBGridDescriptor_K0_N_K1(K, N, StrideB)},
|
||||
c_grid_desc_m_n_{DeviceBatchedGemmXdl::MakeCGridDescriptor_M_N(M, N, StrideC)},
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_{},
|
||||
compute_base_ptr_of_batch_{a_grid_desc_k0_m_k1_.GetElementSpaceSize(),
|
||||
b_grid_desc_k0_n_k1_.GetElementSpaceSize(),
|
||||
c_grid_desc_m_n_.GetElementSpaceSize()},
|
||||
block_2_ctile_map_{},
|
||||
M01_{M01},
|
||||
N01_{N01},
|
||||
@@ -233,22 +361,14 @@ struct DeviceBatchedGemmXdl
|
||||
b_element_op_{b_element_op},
|
||||
c_element_op_{c_element_op}
|
||||
{
|
||||
a_grid_desc_g_k0_m_k1_ =
|
||||
DeviceBatchedGemmXdl::MakeAGridDescriptor_G_K0_M_K1(BatchCount, M, K, StrideA);
|
||||
b_grid_desc_g_k0_n_k1_ =
|
||||
DeviceBatchedGemmXdl::MakeBGridDescriptor_G_K0_N_K1(BatchCount, K, N, StrideB);
|
||||
c_grid_desc_g_m_n_ =
|
||||
DeviceBatchedGemmXdl::MakeCGridDescriptor_G_M_N(BatchCount, M, N, StrideC);
|
||||
|
||||
if(GridwiseBatchedGemm::CheckValidity(
|
||||
a_grid_desc_g_k0_m_k1_, b_grid_desc_g_k0_n_k1_, c_grid_desc_g_m_n_, M01_, N01_))
|
||||
if(GridwiseGemm::CheckValidity(
|
||||
a_grid_desc_k0_m_k1_, b_grid_desc_k0_n_k1_, c_grid_desc_m_n_, M01_, N01_))
|
||||
{
|
||||
c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2_ =
|
||||
GridwiseBatchedGemm::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
|
||||
c_grid_desc_g_m_n_);
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ =
|
||||
GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_);
|
||||
|
||||
block_2_ctile_map_ =
|
||||
GridwiseBatchedGemm::MakeDefaultBlock2CTileMap(c_grid_desc_g_m_n_, M01, N01);
|
||||
Block2CTileMapMaker{BatchCount}.MakeBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -256,12 +376,13 @@ struct DeviceBatchedGemmXdl
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
CDataType* p_c_grid_;
|
||||
AGridDesc_G_K0_M_K1 a_grid_desc_g_k0_m_k1_;
|
||||
BGridDesc_G_K0_N_K1 b_grid_desc_g_k0_n_k1_;
|
||||
CGridDesc_G_M_N c_grid_desc_g_m_n_;
|
||||
typename GridwiseBatchedGemm::CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2
|
||||
c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2_;
|
||||
typename GridwiseBatchedGemm::DefaultBlock2CTileMap block_2_ctile_map_;
|
||||
index_t BatchCount_;
|
||||
AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
|
||||
BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
|
||||
CGridDesc_M_N c_grid_desc_m_n_;
|
||||
CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_;
|
||||
ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch_;
|
||||
Block2CTileMap block_2_ctile_map_;
|
||||
index_t M01_;
|
||||
index_t N01_;
|
||||
AElementwiseOperation a_element_op_;
|
||||
@@ -277,57 +398,51 @@ struct DeviceBatchedGemmXdl
|
||||
float Run(const Argument& arg, int nrepeat = 1)
|
||||
{
|
||||
{
|
||||
std::cout << "arg.a_grid_desc_g_k0_m_k1_{"
|
||||
<< arg.a_grid_desc_g_k0_m_k1_.GetLength(I0) << ", "
|
||||
<< arg.a_grid_desc_g_k0_m_k1_.GetLength(I1) << ", "
|
||||
<< arg.a_grid_desc_g_k0_m_k1_.GetLength(I2) << ", "
|
||||
<< arg.a_grid_desc_g_k0_m_k1_.GetLength(I3) << "}" << std::endl;
|
||||
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
|
||||
<< ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
|
||||
<< arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.b_grid_desc_g_k0_n_k1_{"
|
||||
<< arg.b_grid_desc_g_k0_n_k1_.GetLength(I0) << ", "
|
||||
<< arg.b_grid_desc_g_k0_n_k1_.GetLength(I1) << ", "
|
||||
<< arg.b_grid_desc_g_k0_n_k1_.GetLength(I2) << ", "
|
||||
<< arg.b_grid_desc_g_k0_n_k1_.GetLength(I3) << "}" << std::endl;
|
||||
std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
|
||||
<< ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
|
||||
<< arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.c_grid_desc_g_m_n_{" << arg.c_grid_desc_g_m_n_.GetLength(I0)
|
||||
<< ", " << arg.c_grid_desc_g_m_n_.GetLength(I1) << ", "
|
||||
<< arg.c_grid_desc_g_m_n_.GetLength(I2) << "}" << std::endl;
|
||||
std::cout << "arg.c_grid_desc_m_n_{" << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
|
||||
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
|
||||
}
|
||||
|
||||
if(!GridwiseBatchedGemm::CheckValidity(arg.a_grid_desc_g_k0_m_k1_,
|
||||
arg.b_grid_desc_g_k0_n_k1_,
|
||||
arg.c_grid_desc_g_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_))
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! GridwiseBatchedGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
|
||||
}
|
||||
|
||||
const index_t grid_size =
|
||||
GridwiseBatchedGemm::CalculateGridSize(arg.c_grid_desc_g_m_n_);
|
||||
GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_) * arg.BatchCount_;
|
||||
|
||||
const auto K0 = arg.a_grid_desc_g_k0_m_k1_.GetLength(I1);
|
||||
const auto K0 = arg.a_grid_desc_k0_m_k1_.GetLength(I0);
|
||||
|
||||
const bool has_main_k0_block_loop =
|
||||
GridwiseBatchedGemm::CalculateHasMainK0BlockLoop(K0);
|
||||
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
if(has_main_k0_block_loop)
|
||||
{
|
||||
const auto kernel = kernel_batched_gemm_xdlops_v2r3<
|
||||
GridwiseBatchedGemm,
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::AGridDesc_G_K0_M_K1>,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::BGridDesc_G_K0_N_K1>,
|
||||
remove_reference_t<
|
||||
typename GridwiseBatchedGemm::CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<typename GridwiseBatchedGemm::DefaultBlock2CTileMap>,
|
||||
ComputeBasePtrOfStridedBatch,
|
||||
remove_reference_t<Block2CTileMap>,
|
||||
true>;
|
||||
|
||||
ave_time = launch_and_time_kernel(kernel,
|
||||
@@ -338,28 +453,30 @@ struct DeviceBatchedGemmXdl
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_g_k0_m_k1_,
|
||||
arg.b_grid_desc_g_k0_n_k1_,
|
||||
arg.c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
arg.BatchCount_,
|
||||
arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.compute_base_ptr_of_batch_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_batched_gemm_xdlops_v2r3<
|
||||
GridwiseBatchedGemm,
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::AGridDesc_G_K0_M_K1>,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::BGridDesc_G_K0_N_K1>,
|
||||
remove_reference_t<
|
||||
typename GridwiseBatchedGemm::CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceBatchedGemmXdl::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<typename GridwiseBatchedGemm::DefaultBlock2CTileMap>,
|
||||
ComputeBasePtrOfStridedBatch,
|
||||
remove_reference_t<Block2CTileMap>,
|
||||
false>;
|
||||
|
||||
ave_time = launch_and_time_kernel(kernel,
|
||||
@@ -370,12 +487,14 @@ struct DeviceBatchedGemmXdl
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_g_k0_m_k1_,
|
||||
arg.b_grid_desc_g_k0_n_k1_,
|
||||
arg.c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
arg.BatchCount_,
|
||||
arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.compute_base_ptr_of_batch_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
|
||||
@@ -397,11 +516,11 @@ struct DeviceBatchedGemmXdl
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
return GridwiseBatchedGemm::CheckValidity(arg.a_grid_desc_g_k0_m_k1_,
|
||||
arg.b_grid_desc_g_k0_n_k1_,
|
||||
arg.c_grid_desc_g_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_);
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
|
||||
@@ -1,649 +0,0 @@
|
||||
#ifndef CK_GRIDWISE_BATCHED_GEMM_XDLOPS_V2R3_HPP
|
||||
#define CK_GRIDWISE_BATCHED_GEMM_XDLOPS_V2R3_HPP
|
||||
|
||||
#include "common_header.hpp"
|
||||
#include "multi_index_transform_helper.hpp"
|
||||
#include "tensor_descriptor.hpp"
|
||||
#include "tensor_descriptor_helper.hpp"
|
||||
#include "blockwise_gemm_xdlops.hpp"
|
||||
#include "blockwise_tensor_slice_transfer_v4r1.hpp"
|
||||
#include "threadwise_tensor_slice_transfer.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <typename GridwiseBatchedGemm,
|
||||
typename FloatAB,
|
||||
typename FloatC,
|
||||
typename AGridDesc_G_K0_M_K1,
|
||||
typename BGridDesc_G_K0_N_K1,
|
||||
typename CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
typename Block2CTileMap,
|
||||
bool HasMainKBlockLoop>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_batched_gemm_xdlops_v2r3(
|
||||
const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatC* __restrict__ p_c_grid,
|
||||
const AGridDesc_G_K0_M_K1 a_grid_desc_g_k0_m_k1,
|
||||
const BGridDesc_G_K0_N_K1 b_grid_desc_g_k0_n_k1,
|
||||
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)
|
||||
{
|
||||
__shared__ char p_shared[GridwiseBatchedGemm::GetSharedMemoryNumberOfByte()];
|
||||
|
||||
GridwiseBatchedGemm::template Run<HasMainKBlockLoop>(p_a_grid,
|
||||
p_b_grid,
|
||||
p_c_grid,
|
||||
p_shared,
|
||||
a_grid_desc_g_k0_m_k1,
|
||||
b_grid_desc_g_k0_n_k1,
|
||||
c_grid_desc_g_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
block_2_ctile_map);
|
||||
}
|
||||
|
||||
template <index_t BlockSize,
|
||||
typename FloatAB,
|
||||
typename FloatAcc,
|
||||
typename FloatC,
|
||||
InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
|
||||
typename AGridDesc_G_K0_M_K1,
|
||||
typename BGridDesc_G_K0_N_K1,
|
||||
typename CGridDesc_G_M_N,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
index_t MPerBlock,
|
||||
index_t NPerBlock,
|
||||
index_t K0PerBlock,
|
||||
index_t MPerXDL,
|
||||
index_t NPerXDL,
|
||||
index_t K1Value,
|
||||
index_t MXdlPerWave,
|
||||
index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_G_K0_M_K1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
index_t ABlockTransferSrcVectorDim,
|
||||
index_t ABlockTransferSrcScalarPerVector,
|
||||
index_t ABlockTransferDstScalarPerVector_K1,
|
||||
bool AThreadTransferSrcResetCoordinateAfterRun,
|
||||
bool ABlockLdsExtraM,
|
||||
typename BBlockTransferThreadClusterLengths_G_K0_N_K1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
index_t BBlockTransferSrcVectorDim,
|
||||
index_t BBlockTransferSrcScalarPerVector,
|
||||
index_t BBlockTransferDstScalarPerVector_K1,
|
||||
bool BThreadTransferSrcResetCoordinateAfterRun,
|
||||
bool BBlockLdsExtraN,
|
||||
typename CThreadTransferSrcDstAccessOrder,
|
||||
index_t CThreadTransferSrcDstVectorDim,
|
||||
index_t CThreadTransferDstScalarPerVector>
|
||||
struct GridwiseBatchedGemm_gk0mk1_gk0nk1_gmn_xdlops_v2r3
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
static constexpr auto I4 = Number<4>{};
|
||||
static constexpr auto I5 = Number<5>{};
|
||||
static constexpr auto I6 = Number<6>{};
|
||||
static constexpr auto I7 = Number<7>{};
|
||||
static constexpr auto I8 = Number<8>{};
|
||||
|
||||
// K1 should be Number<...>
|
||||
static constexpr auto K1 = Number<K1Value>{};
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
GetABlockDescriptor_BatchCount_K0PerBlock_MPerBlock_K1()
|
||||
{
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_g_k0_m_k1 = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(I1, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<K0PerBlock>{} * Number<MPerBlock + 1>{} * K1,
|
||||
Number<MPerBlock + 1>{} * K1,
|
||||
K1,
|
||||
I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(I1, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
return a_block_desc_g_k0_m_k1;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
GetBBlockDescriptor_BatchCount_K0PerBlock_NPerBlock_K1()
|
||||
{
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_block_desc_g_k0_n_k1 = [&]() {
|
||||
if constexpr(BBlockLdsExtraN)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(I1, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<K0PerBlock>{} * Number<NPerBlock + 1>{} * K1,
|
||||
Number<NPerBlock + 1>{} * K1,
|
||||
K1,
|
||||
I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(I1, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
return b_block_desc_g_k0_n_k1;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
|
||||
{
|
||||
constexpr auto a_block_desc_g_k0_m_k1 =
|
||||
GetABlockDescriptor_BatchCount_K0PerBlock_MPerBlock_K1();
|
||||
|
||||
constexpr auto K0 = a_block_desc_g_k0_m_k1.GetLength(I1);
|
||||
constexpr auto M = a_block_desc_g_k0_m_k1.GetLength(I2);
|
||||
|
||||
constexpr auto a_block_desc_k0_m_k1 = transform_tensor_descriptor(
|
||||
a_block_desc_g_k0_m_k1,
|
||||
make_tuple(make_freeze_transform(I0),
|
||||
make_pass_through_transform(K0),
|
||||
make_pass_through_transform(M),
|
||||
make_pass_through_transform(K1)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
|
||||
|
||||
return a_block_desc_k0_m_k1;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetBBlockDescriptor_K0PerBlock_NPerBlock_K1()
|
||||
{
|
||||
constexpr auto b_block_desc_g_k0_n_k1 =
|
||||
GetBBlockDescriptor_BatchCount_K0PerBlock_NPerBlock_K1();
|
||||
|
||||
constexpr auto K0 = b_block_desc_g_k0_n_k1.GetLength(I1);
|
||||
constexpr auto N = b_block_desc_g_k0_n_k1.GetLength(I2);
|
||||
|
||||
constexpr auto b_block_desc_k0_n_k1 = transform_tensor_descriptor(
|
||||
b_block_desc_g_k0_n_k1,
|
||||
make_tuple(make_freeze_transform(I0),
|
||||
make_pass_through_transform(K0),
|
||||
make_pass_through_transform(N),
|
||||
make_pass_through_transform(K1)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
|
||||
|
||||
return b_block_desc_k0_n_k1;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
|
||||
{
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_desc_g_k0_m_k1 =
|
||||
GetABlockDescriptor_BatchCount_K0PerBlock_MPerBlock_K1();
|
||||
|
||||
constexpr auto b_block_desc_g_k0_n_k1 =
|
||||
GetBBlockDescriptor_BatchCount_K0PerBlock_NPerBlock_K1();
|
||||
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_g_k0_m_k1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
|
||||
b_block_desc_g_k0_n_k1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
return (a_block_space_size_aligned + b_block_space_size_aligned) * sizeof(FloatAB);
|
||||
}
|
||||
|
||||
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
|
||||
__host__ __device__ static constexpr bool
|
||||
CheckValidity(const AGridDesc_G_K0_M_K1& a_grid_desc_g_k0_m_k1,
|
||||
const BGridDesc_G_K0_N_K1& b_grid_desc_g_k0_n_k1,
|
||||
const CGridDesc_G_M_N& c_grid_desc_g_m_n,
|
||||
index_t M01,
|
||||
index_t N01)
|
||||
{
|
||||
static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
|
||||
"wrong! K1 need to be known at compile-time");
|
||||
|
||||
static_assert((MPerBlock % (MPerXDL * MXdlPerWave) == 0) &&
|
||||
(NPerBlock % (NXdlPerWave * NPerXDL)) == 0,
|
||||
"Invalid tuning param!");
|
||||
|
||||
// const auto G = a_grid_desc_g_k0_m_k1.GetLength(I0);
|
||||
const auto K0 = a_grid_desc_g_k0_m_k1.GetLength(I1);
|
||||
const auto M = a_grid_desc_g_k0_m_k1.GetLength(I2);
|
||||
const auto N = b_grid_desc_g_k0_n_k1.GetLength(I2);
|
||||
|
||||
if(!(M == c_grid_desc_g_m_n.GetLength(I1) && N == c_grid_desc_g_m_n.GetLength(I2) &&
|
||||
K0 == b_grid_desc_g_k0_n_k1.GetLength(I1) &&
|
||||
K1 == a_grid_desc_g_k0_m_k1.GetLength(I3) &&
|
||||
K1 == b_grid_desc_g_k0_n_k1.GetLength(I3)))
|
||||
return false;
|
||||
|
||||
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
|
||||
return false;
|
||||
|
||||
// check M01, N01
|
||||
constexpr auto M1 = Number<MPerBlock>{};
|
||||
constexpr auto N1 = Number<NPerBlock>{};
|
||||
|
||||
const auto M0 = M / M1;
|
||||
const auto N0 = N / N1;
|
||||
|
||||
if(!(M0 % M01 == 0 && N0 % N01 == 0))
|
||||
return false;
|
||||
|
||||
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
|
||||
return true;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t
|
||||
CalculateGridSize(const CGridDesc_G_M_N& c_grid_desc_g_m_n)
|
||||
{
|
||||
const auto G = c_grid_desc_g_m_n.GetLength(I0);
|
||||
const auto M = c_grid_desc_g_m_n.GetLength(I1);
|
||||
const auto N = c_grid_desc_g_m_n.GetLength(I2);
|
||||
|
||||
const index_t grid_size = G * (M / MPerBlock) * (N / NPerBlock);
|
||||
|
||||
return grid_size;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
|
||||
{
|
||||
const bool has_main_k0_block_loop = (K0 / K0PerBlock) > 1;
|
||||
|
||||
return has_main_k0_block_loop;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_G_M_N& c_grid_desc_g_m_n)
|
||||
{
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_k0_m_k1 = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_block_desc_k0_n_k1 = [&]() {
|
||||
if constexpr(BBlockLdsExtraN)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
using BlockwiseGemm =
|
||||
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>;
|
||||
|
||||
return BlockwiseGemm::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_g_m_n);
|
||||
}
|
||||
|
||||
// return block_id to C matrix tile idx (m0, n0) mapping
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeDefaultBlock2CTileMap(const CGridDesc_G_M_N& c_grid_desc_g_m_n, index_t M01, index_t N01)
|
||||
{
|
||||
const auto G = c_grid_desc_g_m_n.GetLength(I0);
|
||||
const auto M = c_grid_desc_g_m_n.GetLength(I1);
|
||||
const auto N = c_grid_desc_g_m_n.GetLength(I2);
|
||||
|
||||
constexpr auto M1 = Number<MPerBlock>{};
|
||||
constexpr auto N1 = Number<NPerBlock>{};
|
||||
|
||||
const auto M0 = M / M1;
|
||||
const auto N0 = N / N1;
|
||||
|
||||
const auto M00 = M0 / M01;
|
||||
const auto N00 = N0 / N01;
|
||||
|
||||
const auto g_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_pass_through_transform(G),
|
||||
make_unmerge_transform(make_tuple(M00, M01)),
|
||||
make_unmerge_transform(make_tuple(N00, N01))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
|
||||
|
||||
const auto cblockid_to_g_m00_m01_n00_n01_block_cluster_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(G, M00, N00, M01, N01))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto cblockid_to_g_m0_n0_block_cluster_adaptor =
|
||||
chain_tensor_adaptors(g_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
|
||||
cblockid_to_g_m00_m01_n00_n01_block_cluster_adaptor);
|
||||
|
||||
return cblockid_to_g_m0_n0_block_cluster_adaptor;
|
||||
}
|
||||
|
||||
using CGridDesc_G_M0_N0_M1_N1_M2_M3_M4_N2 =
|
||||
decltype(MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_G_M_N{}));
|
||||
using DefaultBlock2CTileMap = decltype(MakeDefaultBlock2CTileMap(CGridDesc_G_M_N{}, 1, 1));
|
||||
|
||||
template <bool HasMainKBlockLoop, typename Block2CTileMap = DefaultBlock2CTileMap>
|
||||
__device__ static void
|
||||
Run(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatC* __restrict__ p_c_grid,
|
||||
void* __restrict__ p_shared,
|
||||
const AGridDesc_G_K0_M_K1& a_grid_desc_g_k0_m_k1,
|
||||
const BGridDesc_G_K0_N_K1& b_grid_desc_g_k0_n_k1,
|
||||
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
|
||||
@@ -317,7 +317,7 @@ float bf16_to_f32_(ck::bhalf_t src_val);
|
||||
void bf16_to_f32_(const Tensor<ck::bhalf_t>& src, Tensor<float>& dst);
|
||||
|
||||
template <typename T>
|
||||
void check_error(const Tensor<T>& ref, const Tensor<T>& result)
|
||||
float check_error(const Tensor<T>& ref, const Tensor<T>& result)
|
||||
{
|
||||
float error = 0;
|
||||
float max_diff = -1;
|
||||
@@ -354,6 +354,7 @@ void check_error(const Tensor<T>& ref, const Tensor<T>& result)
|
||||
|
||||
std::cout << "error: " << error << std::endl;
|
||||
std::cout << "max_diff: " << max_diff << ", " << ref_value << ", " << result_value << std::endl;
|
||||
return max_diff;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
|
||||
@@ -93,8 +93,8 @@ struct GeneratorTensor_2<int8_t>
|
||||
template <typename T>
|
||||
struct GeneratorTensor_3
|
||||
{
|
||||
T min_value = 0;
|
||||
T max_value = 1;
|
||||
float min_value = 0;
|
||||
float max_value = 1;
|
||||
|
||||
template <typename... Is>
|
||||
T operator()(Is...)
|
||||
@@ -122,22 +122,6 @@ struct GeneratorTensor_3<ck::bhalf_t>
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct GeneratorTensor_3<int8_t>
|
||||
{
|
||||
float min_value = 0;
|
||||
float max_value = 1;
|
||||
|
||||
template <typename... Is>
|
||||
int8_t operator()(Is...)
|
||||
{
|
||||
int8_t min_tmp = static_cast<int8_t>(min_value);
|
||||
int8_t max_tmp = static_cast<int8_t>(max_value);
|
||||
|
||||
return (std::rand() % (max_tmp - min_tmp)) + min_tmp;
|
||||
}
|
||||
};
|
||||
|
||||
struct GeneratorTensor_Checkboard
|
||||
{
|
||||
template <typename... Ts>
|
||||
|
||||
@@ -4,6 +4,14 @@ set(DEVICE_BATCHED_GEMM_INSTANCE_SOURCE
|
||||
device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instance.cpp;
|
||||
device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instance.cpp;
|
||||
)
|
||||
|
||||
add_library(device_batched_gemm_instance SHARED ${DEVICE_BATCHED_GEMM_INSTANCE_SOURCE})
|
||||
|
||||
@@ -21,23 +21,22 @@ using S = ck::Sequence<Is...>;
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[k, m] * b[k, n] = c[m, n]
|
||||
using device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instances =
|
||||
std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
using device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
|
||||
@@ -21,23 +21,22 @@ using S = ck::Sequence<Is...>;
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[k, m] * b[n, k] = c[m, n]
|
||||
using device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances =
|
||||
std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
using device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
|
||||
@@ -21,27 +21,31 @@ using S = ck::Sequence<Is...>;
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
|
||||
using device_batched_gemm_xdl_f16_f16_f16_gmk_gkn_gmn_instances =
|
||||
std::tuple<
|
||||
// clang-format off
|
||||
//####################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//####################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//####################| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//####################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 32, 32, 4, 8, 16, 16, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 64, 32, 32, 4, 8, 32, 32, 1, 1, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 64, 16, 16, 4, 8, 16, 16, 1, 1, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, true, 8, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
using device_batched_gemm_xdl_f16_f16_f16_gmk_gkn_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//#################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//#################| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//#################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 32, 256, 4, 8, 32, 32, 1, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 32, 64, 4, 8, 32, 32, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 64, 32, 32, 4, 8, 32, 32, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 16, 256, 4, 8, 16, 16, 1, 8, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 16, 128, 4, 8, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 16, 64, 4, 8, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 16, 32, 4, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 64, 16, 16, 4, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
|
||||
@@ -27,19 +27,19 @@ using device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances = std::tuple<
|
||||
//#################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//#################| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//#################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, true, 8, 1>
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
|
||||
@@ -0,0 +1,51 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[k, m] * b[k, n] = c[m, n]
|
||||
using device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,51 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[k, m] * b[n, k] = c[m, n]
|
||||
using device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,51 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
|
||||
using device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,56 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
|
||||
using device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,66 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using AData = int8_t;
|
||||
using BData = int8_t;
|
||||
using CData = int8_t;
|
||||
using AccData = int32_t;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
|
||||
using device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 16, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 16, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 16, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 64, 64, 64, 4, 16, 32, 32, 2, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 16, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 16, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 16, 32, 32, 2, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 16, 32, 32, 1, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 64, 64, 32, 4, 16, 32, 32, 2, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 64, 32, 64, 4, 16, 32, 32, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 16, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 16, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 16, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 16, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 16, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 16, 16, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,66 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using AData = int8_t;
|
||||
using BData = int8_t;
|
||||
using CData = int8_t;
|
||||
using AccData = int32_t;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
|
||||
using device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 16, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 16, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 16, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 64, 4, 16, 32, 32, 2, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 16, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 16, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 16, 32, 32, 2, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 16, 32, 32, 1, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 32, 4, 16, 32, 32, 2, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 64, 32, 64, 4, 16, 32, 32, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 16, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 16, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 16, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 16, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Col, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 16, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,66 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using AData = int8_t;
|
||||
using BData = int8_t;
|
||||
using CData = int8_t;
|
||||
using AccData = int32_t;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
|
||||
using device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 16, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 16, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 16, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 64, 64, 64, 4, 16, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 16, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 16, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 16, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 16, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 64, 64, 32, 4, 16, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 64, 32, 64, 4, 16, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 16, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 16, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 16, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 16, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 16, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Row, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 16, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 16, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,58 @@
|
||||
#include <stdlib.h>
|
||||
#include "config.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "device_operation_instance.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using AData = int8_t;
|
||||
using BData = int8_t;
|
||||
using CData = int8_t;
|
||||
using AccData = int32_t;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
|
||||
using device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##########| | | | | | | | Operation| Operation| Operation| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 256, 4, 16, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 128, 4, 16, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 128, 4, 16, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 64, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 64, 128, 4, 16, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 64, 4, 16, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 128, 64, 4, 16, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 256, 64, 128, 4, 16, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 128, 32, 4, 16, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 128, 32, 128, 4, 16, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 64, 32, 4, 16, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>,
|
||||
DeviceBatchedGemmXdl< AData, BData, CData, AccData, Row, Col, Row, PassThrough, PassThrough, PassThrough, 64, 32, 64, 4, 16, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instances(
|
||||
std::vector<DeviceGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instances{});
|
||||
}
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -15,6 +15,18 @@ void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gkn_gmn_instances(std::vector<D
|
||||
void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instances(
|
||||
std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances(
|
||||
std::vector<DeviceGemmNoOpPtr>&);
|
||||
void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instances(
|
||||
std::vector<DeviceGemmNoOpPtr>&);
|
||||
|
||||
} // namespace device_batched_gemm_instance
|
||||
} // namespace device
|
||||
@@ -156,6 +168,70 @@ void profile_batched_gemm_impl(int do_verification,
|
||||
add_device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
else if constexpr(is_same<ADataType, float>::value && is_same<BDataType, float>::value &&
|
||||
is_same<CDataType, float>::value)
|
||||
{
|
||||
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
else if constexpr(is_same<ADataType, int8_t>::value && is_same<BDataType, int8_t>::value &&
|
||||
is_same<CDataType, int8_t>::value)
|
||||
{
|
||||
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
|
||||
if(gemm_ptrs.size() <= 0)
|
||||
{
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
#include <cstdint>
|
||||
#include <iostream>
|
||||
#include <numeric>
|
||||
#include <initializer_list>
|
||||
@@ -29,8 +30,9 @@ enum GemmMatrixLayout
|
||||
|
||||
enum GemmDataType
|
||||
{
|
||||
F32_F32_F32, // 0
|
||||
F16_F16_F16, // 1
|
||||
F32_F32_F32, // 0
|
||||
F16_F16_F16, // 1
|
||||
Int8_Int8_Int8, // 2
|
||||
};
|
||||
|
||||
int profile_batched_gemm(int argc, char* argv[])
|
||||
@@ -38,7 +40,7 @@ int profile_batched_gemm(int argc, char* argv[])
|
||||
if(!(argc == 15))
|
||||
{
|
||||
printf("arg1: tensor operation (batched_gemm: Batched GEMM)\n");
|
||||
printf("arg2: data type (0: fp32; 1: fp16)\n");
|
||||
printf("arg2: data type (0: fp32; 1: fp16, 2: int8)\n");
|
||||
printf("arg3: matrix layout (0: A[g, m, k] * B[g, k, n] = C[g, m, n];\n");
|
||||
printf(" 1: A[g, m, k] * B[g, n, k] = C[g, m, n];\n");
|
||||
printf(" 2: A[g, k, m] * B[g, k, n] = C[g, m, n];\n");
|
||||
@@ -146,6 +148,163 @@ int profile_batched_gemm(int argc, char* argv[])
|
||||
(StrideB < 0) ? K : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
}
|
||||
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<float,
|
||||
float,
|
||||
float,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? K : StrideA,
|
||||
(StrideB < 0) ? N : StrideB,
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
BatchCount);
|
||||
}
|
||||
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_NK_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<float,
|
||||
float,
|
||||
float,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? K : StrideA,
|
||||
(StrideB < 0) ? K : StrideB,
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
BatchCount);
|
||||
}
|
||||
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_KN_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<float,
|
||||
float,
|
||||
float,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? M : StrideA,
|
||||
(StrideB < 0) ? N : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
}
|
||||
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_NK_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<float,
|
||||
float,
|
||||
float,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? M : StrideA,
|
||||
(StrideB < 0) ? K : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
}
|
||||
else if(data_type == GemmDataType::Int8_Int8_Int8 && layout == GemmMatrixLayout::MK_KN_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<int8_t,
|
||||
int8_t,
|
||||
int8_t,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? K : StrideA,
|
||||
(StrideB < 0) ? N : StrideB,
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
BatchCount);
|
||||
}
|
||||
else if(data_type == GemmDataType::Int8_Int8_Int8 && layout == GemmMatrixLayout::MK_NK_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<int8_t,
|
||||
int8_t,
|
||||
int8_t,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? K : StrideA,
|
||||
(StrideB < 0) ? K : StrideB,
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
BatchCount);
|
||||
}
|
||||
else if(data_type == GemmDataType::Int8_Int8_Int8 && layout == GemmMatrixLayout::KM_KN_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<int8_t,
|
||||
int8_t,
|
||||
int8_t,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::RowMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? M : StrideA,
|
||||
(StrideB < 0) ? N : StrideB,
|
||||
(StrideC < 0) ? N : StrideC);
|
||||
}
|
||||
else if(data_type == GemmDataType::Int8_Int8_Int8 && layout == GemmMatrixLayout::KM_NK_MN)
|
||||
{
|
||||
ck::profiler::profile_batched_gemm_impl<int8_t,
|
||||
int8_t,
|
||||
int8_t,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::ColumnMajor,
|
||||
ck::tensor_layout::gemm::RowMajor>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
nrepeat,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? M : StrideA,
|
||||
(StrideB < 0) ? K : StrideB,
|
||||
(StrideC < 0) ? N : StrideC,
|
||||
BatchCount);
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
|
||||
|
||||
@@ -39,3 +39,4 @@ add_subdirectory(gemm_split_k)
|
||||
add_subdirectory(conv2d_fwd)
|
||||
add_subdirectory(convnd_fwd)
|
||||
add_subdirectory(conv2d_bwd_data)
|
||||
add_subdirectory(batched_gemm)
|
||||
|
||||
4
test/batched_gemm/CMakeLists.txt
Normal file
4
test/batched_gemm/CMakeLists.txt
Normal file
@@ -0,0 +1,4 @@
|
||||
add_test_executable(test_batched_gemm_fp16 batched_gemm_fp16.cpp)
|
||||
target_link_libraries(test_batched_gemm_fp16 PRIVATE host_tensor)
|
||||
target_link_libraries(test_batched_gemm_fp16 PRIVATE device_batched_gemm_instance)
|
||||
|
||||
137
test/batched_gemm/batched_gemm_fp16.cpp
Normal file
137
test/batched_gemm/batched_gemm_fp16.cpp
Normal file
@@ -0,0 +1,137 @@
|
||||
#include <half.hpp>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
|
||||
#include "batched_gemm_util.hpp"
|
||||
#include "reference_batched_gemm.hpp"
|
||||
#include "config.hpp"
|
||||
#include "device.hpp"
|
||||
#include "host_tensor.hpp"
|
||||
#include "host_tensor_generator.hpp"
|
||||
#include "device_tensor.hpp"
|
||||
#include "device_batched_gemm_xdl.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
#include "test_util.hpp"
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using DeviceBatchedGemmPtr =
|
||||
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough>;
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_batched_gemm_instance {
|
||||
void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(
|
||||
std::vector<DeviceBatchedGemmPtr>& instances);
|
||||
}
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
|
||||
namespace {
|
||||
using ADataType = ck::half_t;
|
||||
using BDataType = ck::half_t;
|
||||
using CDataType = ck::half_t;
|
||||
using AccDataType = float;
|
||||
|
||||
using ALayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
|
||||
using CLayout = ck::tensor_layout::gemm::RowMajor;
|
||||
|
||||
auto PrepareGemmTensor(const std::size_t batch_count,
|
||||
const ck::batched_gemm_util::GemmParams& params)
|
||||
{
|
||||
auto f_host_tensor_descriptor =
|
||||
[batch_count](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
|
||||
std::vector<std::size_t>({row * stride, stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
|
||||
std::vector<std::size_t>({col * stride, 1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_g_m_k(
|
||||
f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_g_k_n(
|
||||
f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
|
||||
Tensor<CDataType> c_g_m_n_host_result(
|
||||
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
|
||||
Tensor<CDataType> c_g_m_n_device_result(
|
||||
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
|
||||
|
||||
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
|
||||
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
|
||||
return std::make_tuple(a_g_m_k, b_g_k_n, c_g_m_n_host_result, c_g_m_n_device_result);
|
||||
}
|
||||
|
||||
bool TestBatchedGemm(const std::size_t batch_count, DeviceBatchedGemmPtr& gemmPtr)
|
||||
{
|
||||
// Arrange
|
||||
ck::batched_gemm_util::GemmParams params;
|
||||
params.M = 1024;
|
||||
params.N = 1024;
|
||||
params.K = 1024;
|
||||
params.StrideA = 1024;
|
||||
params.StrideB = 1024;
|
||||
params.StrideC = 1024;
|
||||
|
||||
auto host_tensors = PrepareGemmTensor(batch_count, params);
|
||||
const Tensor<ADataType>& a = std::get<0>(host_tensors);
|
||||
const Tensor<BDataType>& b = std::get<1>(host_tensors);
|
||||
Tensor<CDataType>& c_host = std::get<2>(host_tensors);
|
||||
Tensor<CDataType>& c_device = std::get<3>(host_tensors);
|
||||
|
||||
auto a_element_op = PassThrough{};
|
||||
auto b_element_op = PassThrough{};
|
||||
auto c_element_op = PassThrough{};
|
||||
|
||||
using ReferenceBatchedGemmInstance =
|
||||
ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>;
|
||||
ck::batched_gemm_util::RunHostBatchedGemm<ReferenceBatchedGemmInstance>(
|
||||
a, b, c_host, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
// Act
|
||||
ck::batched_gemm_util::RunDeviceBatchedGemm(
|
||||
gemmPtr, params, a, b, c_device, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
// Assert
|
||||
// bool res = test_util::check_err(
|
||||
// c_device.mData, c_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
|
||||
bool res = check_error(c_device, c_host) < 0.007815f;
|
||||
|
||||
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
|
||||
|
||||
return res;
|
||||
}
|
||||
} // namespace
|
||||
|
||||
int main()
|
||||
{
|
||||
std::vector<DeviceBatchedGemmPtr> batched_gemm_ptrs;
|
||||
ck::tensor_operation::device::device_batched_gemm_instance::
|
||||
add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(batched_gemm_ptrs);
|
||||
|
||||
bool res = true;
|
||||
|
||||
const std::size_t batch_count = 4;
|
||||
for(auto& gemmPtr : batched_gemm_ptrs)
|
||||
{
|
||||
res &= TestBatchedGemm(batch_count, gemmPtr);
|
||||
}
|
||||
|
||||
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
|
||||
}
|
||||
106
test/batched_gemm/batched_gemm_util.hpp
Normal file
106
test/batched_gemm/batched_gemm_util.hpp
Normal file
@@ -0,0 +1,106 @@
|
||||
#ifndef BATCHED_GEMM_UTILS_HPP
|
||||
#define BATCHED_GEMM_UTILS_HPP
|
||||
|
||||
#include "config.hpp"
|
||||
#include "device.hpp"
|
||||
#include "host_tensor.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace batched_gemm_util {
|
||||
|
||||
struct GemmParams
|
||||
{
|
||||
GemmParams()
|
||||
: M(1024), N(1024), K(1024), StrideA(1024), StrideB(1024), StrideC(1024), alpha(1), beta(0)
|
||||
{
|
||||
}
|
||||
|
||||
ck::index_t M;
|
||||
ck::index_t N;
|
||||
ck::index_t K;
|
||||
|
||||
ck::index_t StrideA;
|
||||
ck::index_t StrideB;
|
||||
ck::index_t StrideC;
|
||||
|
||||
float alpha;
|
||||
float beta;
|
||||
};
|
||||
|
||||
template <typename BatchedGemmInstance,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation>
|
||||
void RunHostBatchedGemm(const Tensor<ADataType>& A,
|
||||
const Tensor<BDataType>& B,
|
||||
Tensor<CDataType>& C,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
{
|
||||
auto ref_batched_gemm = BatchedGemmInstance{};
|
||||
auto ref_invoker = ref_batched_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_batched_gemm.MakeArgument(A, B, C, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
}
|
||||
|
||||
template <typename DeviceGemmPtr,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation>
|
||||
void RunDeviceBatchedGemm(DeviceGemmPtr& batched_gemm_ptr,
|
||||
const ck::batched_gemm_util::GemmParams& params,
|
||||
const Tensor<ADataType>& A,
|
||||
const Tensor<BDataType>& B,
|
||||
Tensor<CDataType>& C,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
{
|
||||
DeviceMem a_g_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpace());
|
||||
DeviceMem b_g_k_n_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpace());
|
||||
DeviceMem c_g_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpace());
|
||||
|
||||
a_g_m_k_device_buf.ToDevice(A.mData.data());
|
||||
b_g_k_n_device_buf.ToDevice(B.mData.data());
|
||||
|
||||
const auto batch_count = A.mDesc.GetLengths()[0];
|
||||
auto invoker_ptr = batched_gemm_ptr->MakeInvokerPointer();
|
||||
auto argument_ptr = batched_gemm_ptr->MakeArgumentPointer(
|
||||
static_cast<ADataType*>(a_g_m_k_device_buf.GetDeviceBuffer()),
|
||||
static_cast<BDataType*>(b_g_k_n_device_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c_g_m_n_device_buf.GetDeviceBuffer()),
|
||||
params.M,
|
||||
params.N,
|
||||
params.K,
|
||||
params.StrideA,
|
||||
params.StrideB,
|
||||
params.StrideC,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
batch_count);
|
||||
|
||||
if(!batched_gemm_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
invoker_ptr->Run(argument_ptr.get());
|
||||
c_g_m_n_device_buf.FromDevice(C.mData.data());
|
||||
}
|
||||
|
||||
} // namespace batched_gemm_util
|
||||
} // namespace ck
|
||||
#endif
|
||||
Reference in New Issue
Block a user