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Wmma support for gemm_bias_add_reduce (#3316)
* Add tests for gemm_bias_add_reduce * Initial working implementation * Generalize implementation of reduce epilogue * Add tests for all layouts * Add instances * Fix test archs * Fix xdl bug * Remove library/profiler duplications * Fix num_byted error profiler * Fix typos * Fix copyright
This commit is contained in:
Enrico Degregori
@@ -0,0 +1,682 @@
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_reduce.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_wmma_cshuffle_v3.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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template <typename GridwiseGemm,
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typename ReduceTrait,
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bool HasMainKBlockLoop,
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InMemoryDataOperationEnum EGlobalMemoryDataOperation,
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index_t MinimumOccupancy = 1,
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TailNumber TailNum = TailNumber::Full>
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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, MinimumOccupancy)
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#endif
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kernel_gemm_bias_add_reduce_wmma_cshuffle_v3(
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typename GridwiseGemm::Argument karg,
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typename ReduceTrait::ReducePtrsGlobal_ p_reduces_grid,
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const typename ReduceTrait::ReduceInElementwiseOperations_ reduce_in_element_ops,
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const typename ReduceTrait::ReduceAccElementwiseOperations_ reduce_out_element_ops,
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const typename ReduceTrait::D0ElementwiseOperation_ d0_element_op)
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{
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#if(defined(__gfx11__) || defined(__gfx12__))
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#if defined(__gfx11__)
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// gfx11 does not support *_atomic_pk_add_f16/bf16 instructions
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using e_data_type = remove_cvref_t<remove_pointer_t<decltype(karg.p_e_grid)>>;
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if constexpr(!(EGlobalMemoryDataOperation == InMemoryDataOperationEnum::AtomicAdd &&
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(std::is_same_v<e_data_type, ck::half_t> ||
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std::is_same_v<e_data_type, ck::bhalf_t>)))
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{
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#endif
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using EpilogueType = typename GridwiseGemm::template EpilogueReduceCShuffle<ReduceTrait>;
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constexpr index_t LDS_size =
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GridwiseGemm::template GetSharedMemoryNumberOfByte<EpilogueType>();
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__shared__ char p_shared[LDS_size];
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auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);
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auto epilogue_args = EpilogueType(
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p_reduces_grid, reduce_in_element_ops, reduce_out_element_ops, karg.M, d0_element_op);
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GridwiseGemm::template Run<HasMainKBlockLoop, EGlobalMemoryDataOperation, TailNum>(
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p_shared, splitk_batch_offset, karg, epilogue_args);
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#if defined(__gfx11__)
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}
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#endif
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#else
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ignore = karg;
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ignore = p_reduces_grid;
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ignore = reduce_in_element_ops;
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ignore = reduce_out_element_ops;
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ignore = d0_element_op;
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#endif
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}
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} // namespace ck
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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 ALayout,
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typename BLayout,
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typename ELayout,
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typename ADataType,
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typename BDataType,
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typename EDataType,
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typename BiasDataType,
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typename D0DataType,
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typename AccDataType,
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typename CShuffleDataType,
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typename ReduceAccDataType, // Reduce
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typename ReducePtrsGlobal, // Reduce
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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typename D0ElementwiseOperation,
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typename ReduceOperations, // Reduce
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typename ReduceInElementwiseOperations, // Reduce
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typename ReduceAccElementwiseOperations, // Reduce
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typename ReduceGlobalMemoryDataOperation, // Reduce
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GemmSpecialization GemmSpec,
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index_t BlockSize,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t KPerBlock,
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index_t AK1,
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index_t BK1,
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index_t MPerWmma,
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index_t NPerWmma,
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index_t MRepeat,
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index_t NRepeat,
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typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferSrcScalarPerVector,
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index_t ABlockTransferDstScalarPerVector_AK1,
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index_t ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferSrcScalarPerVector,
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index_t BBlockTransferDstScalarPerVector_BK1,
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index_t BBlockLdsExtraN,
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index_t CShuffleMRepeatPerShuffle,
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index_t CShuffleNRepeatPerShuffle,
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typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CShuffleBlockTransferScalarPerVector,
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typename CReduceThreadClusterLengths_MPerBlock_NPerBlock, // Reduce
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index_t CReduceThreadLds2VGprCopySrcDstScalarPerVector_NPerBlock, // Reduce
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index_t CReduceThreadVgpr2GlobalCopySrcDstScalarPerVector_MPerBlock, // Reduce
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BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
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BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
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typename ComputeTypeA = EDataType,
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typename ComputeTypeB = ComputeTypeA,
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bool PermuteA = false,
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bool PermuteB = false>
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struct DeviceGemmBiasAddReduce_Wmma_CShuffleV3
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: public DeviceGemmReduce<1, ReduceOperations::Size()>
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{
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using CDEShuffleBlockTransferScalarPerVectors = Sequence<CShuffleBlockTransferScalarPerVector,
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CShuffleBlockTransferScalarPerVector,
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CShuffleBlockTransferScalarPerVector>;
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using GridwiseGemm = GridwiseGemm_wmma_cshuffle_v3<
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ALayout,
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BLayout,
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Tuple<ELayout, ELayout>,
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ELayout,
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Tuple<ADataType>,
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Tuple<BDataType>,
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AccDataType,
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CShuffleDataType,
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Tuple<BiasDataType, D0DataType>,
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EDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation,
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GemmSpec,
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BlockSize,
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MPerBlock,
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NPerBlock,
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KPerBlock,
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AK1,
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BK1,
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MPerWmma,
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NPerWmma,
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MRepeat,
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NRepeat,
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ABlockTransferThreadClusterLengths_AK0_M_AK1,
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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_AK1,
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false,
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ABlockLdsExtraM,
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BBlockTransferThreadClusterLengths_BK0_N_BK1,
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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_BK1,
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false,
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BBlockLdsExtraN,
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CShuffleMRepeatPerShuffle,
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CShuffleNRepeatPerShuffle,
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CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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CDEShuffleBlockTransferScalarPerVectors,
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BlkGemmPipeSched,
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BlkGemmPipelineVer,
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ComputeTypeA,
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ComputeTypeB,
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PermuteA,
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PermuteB>;
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using ReduceTrait = ReduceTrait_<ReduceAccDataType,
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ReducePtrsGlobal,
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D0ElementwiseOperation,
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ReduceOperations,
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ReduceInElementwiseOperations,
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ReduceAccElementwiseOperations,
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ReduceGlobalMemoryDataOperation,
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CReduceThreadClusterLengths_MPerBlock_NPerBlock,
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CReduceThreadLds2VGprCopySrcDstScalarPerVector_NPerBlock,
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CReduceThreadVgpr2GlobalCopySrcDstScalarPerVector_MPerBlock>;
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// Argument
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struct Argument : public BaseArgument
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{
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Argument(const ADataType* p_a_grid,
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const BDataType* p_b_grid,
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EDataType* p_e_grid,
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const BiasDataType* p_bias_grid,
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const D0DataType* p_d0_grid,
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ReducePtrsGlobal p_reduces_grid,
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index_t MRaw,
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index_t NRaw,
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index_t KRaw,
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index_t StrideA,
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index_t StrideB,
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index_t StrideC,
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index_t StrideC1,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CElementwiseOperation c_element_op,
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D0ElementwiseOperation d0_element_op,
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ReduceInElementwiseOperations reduce_in_element_ops,
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ReduceAccElementwiseOperations reduce_out_element_ops)
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: p_a_grid_{p_a_grid},
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p_b_grid_{p_b_grid},
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p_e_grid_{p_e_grid},
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p_bias_grid_{p_bias_grid},
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p_d0_grid_{p_d0_grid},
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p_reduces_grid_{p_reduces_grid},
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MRaw_{MRaw},
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NRaw_{NRaw},
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KRaw_{KRaw},
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StrideA_{StrideA},
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StrideB_{StrideB},
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StrideC_{StrideC},
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StrideC1_{StrideC1},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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c_element_op_{c_element_op},
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d0_element_op_{d0_element_op},
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reduce_in_element_ops_{reduce_in_element_ops},
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reduce_out_element_ops_{reduce_out_element_ops}
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{
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}
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// private:
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const ADataType* p_a_grid_;
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const BDataType* p_b_grid_;
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EDataType* p_e_grid_;
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const BiasDataType* p_bias_grid_;
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const D0DataType* p_d0_grid_;
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ReducePtrsGlobal p_reduces_grid_;
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index_t MRaw_;
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index_t NRaw_;
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index_t KRaw_;
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index_t StrideA_;
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index_t StrideB_;
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index_t StrideC_;
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index_t StrideC1_;
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AElementwiseOperation a_element_op_;
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BElementwiseOperation b_element_op_;
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CElementwiseOperation c_element_op_;
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D0ElementwiseOperation d0_element_op_;
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ReduceInElementwiseOperations reduce_in_element_ops_;
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ReduceAccElementwiseOperations reduce_out_element_ops_;
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};
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struct Invoker : public BaseInvoker
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{
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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typename GridwiseGemm::Argument gemm_arg{
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std::array<const void*, 1>{arg.p_a_grid_},
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std::array<const void*, 1>{arg.p_b_grid_},
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std::array<const void*, 2>{arg.p_bias_grid_, arg.p_d0_grid_},
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static_cast<EDataType*>(arg.p_e_grid_),
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arg.MRaw_,
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arg.NRaw_,
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arg.KRaw_,
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std::array<index_t, 1>{arg.StrideA_}, // StrideAs
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std::array<index_t, 1>{arg.StrideB_}, // StrideBs
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std::array<index_t, 2>{0, arg.StrideC1_}, // StrideDs
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arg.StrideC_, // StrideE
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1, // kbatch
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arg.a_element_op_,
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arg.b_element_op_,
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arg.c_element_op_};
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if(stream_config.log_level_ > 0)
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{
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gemm_arg.Print();
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GridwiseGemm::BlockwiseGemmPipe::HotLoopInstList::Print();
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}
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if(!GridwiseGemm::CheckValidity(gemm_arg))
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{
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throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
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}
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index_t gdx, gdy, gdz;
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std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(arg.MRaw_, arg.NRaw_, 1);
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float ave_time = 0;
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index_t K_split = (arg.KRaw_ + KPerBlock - 1) / KPerBlock * KPerBlock;
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const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
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TailNumber TailNum = GridwiseGemm::CalculateKBlockLoopTailNum(arg.KRaw_);
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const auto Run = [&](const auto& kernel) {
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// Note: cache flushing not supported
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ave_time += launch_and_time_kernel(stream_config,
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kernel,
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dim3(gdx, gdy, gdz),
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dim3(BlockSize),
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0,
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gemm_arg,
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arg.p_reduces_grid_,
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arg.reduce_in_element_ops_,
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arg.reduce_out_element_ops_,
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arg.d0_element_op_);
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};
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constexpr index_t minimum_occupancy = []() {
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if constexpr(BlkGemmPipeSched == BlockGemmPipelineScheduler::Interwave)
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{
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return 2;
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}
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else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
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{
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return (MPerBlock * NPerBlock / BlockSize <= 128) ? 2 : 1;
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}
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else
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{
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return 1;
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}
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}();
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if(has_main_k_block_loop)
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{
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// Tail number always full
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if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1 ||
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BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
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{
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if(TailNum == TailNumber::Full)
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{
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const auto kernel = kernel_gemm_bias_add_reduce_wmma_cshuffle_v3<
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GridwiseGemm,
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ReduceTrait,
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true,
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InMemoryDataOperationEnum::Set,
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minimum_occupancy>;
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Run(kernel);
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}
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else
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{
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throw std::runtime_error("wrong! Invalid pipeline setting");
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}
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}
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}
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else
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{
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if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
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{
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if(TailNum == TailNumber::Full)
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{
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const auto kernel = kernel_gemm_bias_add_reduce_wmma_cshuffle_v3<
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GridwiseGemm,
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ReduceTrait,
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false,
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InMemoryDataOperationEnum::Set,
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minimum_occupancy>;
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Run(kernel);
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}
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else
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{
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throw std::runtime_error("wrong! Invalid pipeline v1 setting");
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}
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}
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else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
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{
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if(TailNum == TailNumber::Even)
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{
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const auto kernel = kernel_gemm_bias_add_reduce_wmma_cshuffle_v3<
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GridwiseGemm,
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ReduceTrait,
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false,
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InMemoryDataOperationEnum::Set,
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minimum_occupancy,
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TailNumber::Even>;
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Run(kernel);
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}
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else if(TailNum == TailNumber::Odd)
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{
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const auto kernel = kernel_gemm_bias_add_reduce_wmma_cshuffle_v3<
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GridwiseGemm,
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ReduceTrait,
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false,
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InMemoryDataOperationEnum::Set,
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minimum_occupancy,
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TailNumber::Odd>;
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Run(kernel);
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}
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else
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{
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throw std::runtime_error("wrong! Invalid pipeline v3 setting");
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}
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}
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}
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return ave_time;
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}
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// polymorphic
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float Run(const BaseArgument* p_arg,
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const StreamConfig& stream_config = StreamConfig{}) override
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{
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return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
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}
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};
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static constexpr bool IsValidCompilationParameter()
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{
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// TODO: properly implement this check
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return true;
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}
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static bool IsSupportedArgument(const Argument& arg)
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{
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if(!ck::is_gfx11_supported() && !ck::is_gfx12_supported())
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{
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if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
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{
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std::cout << "Device implementation supports only gfx11 and gfx12! " << __FILE__
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<< ":" << __LINE__ << ", in function: " << __func__ << std::endl;
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}
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return false;
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}
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if constexpr(std::is_same_v<ComputeTypeA, f8_t> || std::is_same_v<ComputeTypeA, bf8_t> ||
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std::is_same_v<ComputeTypeB, f8_t> || std::is_same_v<ComputeTypeB, bf8_t>)
|
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{
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if(ck::is_gfx11_supported())
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{
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if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
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{
|
||||
std::cout << "FP8 and BF8 not supported on gfx11! " << __FILE__ << ":"
|
||||
<< __LINE__ << ", in function: " << __func__ << std::endl;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if((arg.KRaw_ % AK1 != 0 || arg.KRaw_ % BK1 != 0) &&
|
||||
!(GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding ||
|
||||
GemmSpec == GemmSpecialization::KPadding))
|
||||
{
|
||||
if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
|
||||
{
|
||||
std::cout << "Without padding, K must be divisible by AK1 and BK1! " << __FILE__
|
||||
<< ":" << __LINE__ << ", in function: " << __func__ << std::endl;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
typename GridwiseGemm::Argument gemm_arg{
|
||||
std::array<const void*, 1>{arg.p_a_grid_},
|
||||
std::array<const void*, 1>{arg.p_b_grid_},
|
||||
std::array<const void*, 2>{arg.p_bias_grid_, arg.p_d0_grid_},
|
||||
static_cast<EDataType*>(arg.p_e_grid_),
|
||||
arg.MRaw_,
|
||||
arg.NRaw_,
|
||||
arg.KRaw_,
|
||||
std::array<index_t, 1>{arg.StrideA_}, // StrideAs
|
||||
std::array<index_t, 1>{arg.StrideB_}, // StrideBs
|
||||
std::array<index_t, 2>{0, arg.StrideC1_}, // StrideDs
|
||||
arg.StrideC_, // StrideE
|
||||
1, // kbatch
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_};
|
||||
|
||||
return GridwiseGemm::CheckValidity(gemm_arg);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static constexpr int NumReduce = ReduceOperations::Size();
|
||||
static auto MakeArgument(const void* p_a,
|
||||
const void* p_b,
|
||||
const void* p_bias,
|
||||
std::array<const void*, 1> p_ds,
|
||||
void* p_c,
|
||||
std::array<void*, NumReduce> p_reduces,
|
||||
ck::index_t M,
|
||||
ck::index_t N,
|
||||
ck::index_t K,
|
||||
ck::index_t StrideA,
|
||||
ck::index_t StrideB,
|
||||
ck::index_t StrideC,
|
||||
std::array<ck::index_t, 1> StrideDs,
|
||||
std::array<void*, 3> gemm_element_ops,
|
||||
std::array<void*, 1> d_element_ops,
|
||||
std::array<void*, NumReduce> reduce_in_element_op,
|
||||
std::array<void*, NumReduce> reduce_out_element_op)
|
||||
{
|
||||
ReducePtrsGlobal reduce_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto tmp = ReducePtrsGlobal{}[I];
|
||||
using T = remove_pointer_t<decltype(tmp)>;
|
||||
return static_cast<T*>(p_reduces[I]);
|
||||
},
|
||||
Number<NumReduce>{});
|
||||
|
||||
ReduceInElementwiseOperations reduce_in_element_ops = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto tmp = ReduceInElementwiseOperations{}[I];
|
||||
using T = remove_pointer_t<decltype(tmp)>;
|
||||
return *(static_cast<T*>(reduce_in_element_op[I]));
|
||||
},
|
||||
Number<NumReduce>{});
|
||||
ReduceAccElementwiseOperations reduce_out_element_ops = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto tmp = ReduceAccElementwiseOperations{}[I];
|
||||
using T = remove_pointer_t<decltype(tmp)>;
|
||||
return *(static_cast<T*>(reduce_out_element_op[I]));
|
||||
},
|
||||
Number<NumReduce>{});
|
||||
|
||||
AElementwiseOperation a_element_op =
|
||||
*(static_cast<AElementwiseOperation*>(gemm_element_ops[0]));
|
||||
BElementwiseOperation b_element_op =
|
||||
*(static_cast<BElementwiseOperation*>(gemm_element_ops[1]));
|
||||
CElementwiseOperation c_element_op =
|
||||
*(static_cast<CElementwiseOperation*>(gemm_element_ops[2]));
|
||||
D0ElementwiseOperation d_element_op =
|
||||
*(static_cast<D0ElementwiseOperation*>(d_element_ops[0]));
|
||||
|
||||
return Argument{static_cast<const ADataType*>(p_a),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<EDataType*>(p_c),
|
||||
static_cast<const BiasDataType*>(p_bias),
|
||||
static_cast<const D0DataType*>(p_ds[0]),
|
||||
reduce_tuple,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
StrideDs[0],
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
d_element_op,
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
const void* p_bias,
|
||||
std::array<const void*, 1> p_ds,
|
||||
void* p_c,
|
||||
std::array<void*, NumReduce> p_reduces,
|
||||
ck::index_t M,
|
||||
ck::index_t N,
|
||||
ck::index_t K,
|
||||
ck::index_t StrideA,
|
||||
ck::index_t StrideB,
|
||||
ck::index_t StrideC,
|
||||
std::array<ck::index_t, 1> StrideDs,
|
||||
std::array<void*, 3> gemm_element_ops,
|
||||
std::array<void*, 1> d_element_ops,
|
||||
std::array<void*, NumReduce> reduce_in_element_op,
|
||||
std::array<void*, NumReduce> reduce_out_element_op,
|
||||
index_t /* KBatch */ = 1) override
|
||||
{
|
||||
ReducePtrsGlobal reduce_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto tmp = ReducePtrsGlobal{}[I];
|
||||
using T = remove_pointer_t<decltype(tmp)>;
|
||||
return static_cast<T*>(p_reduces[I]);
|
||||
},
|
||||
Number<NumReduce>{});
|
||||
|
||||
ReduceInElementwiseOperations reduce_in_element_ops = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto tmp = ReduceInElementwiseOperations{}[I];
|
||||
using T = remove_pointer_t<decltype(tmp)>;
|
||||
return *(static_cast<T*>(reduce_in_element_op[I]));
|
||||
},
|
||||
Number<NumReduce>{});
|
||||
ReduceAccElementwiseOperations reduce_out_element_ops = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto tmp = ReduceAccElementwiseOperations{}[I];
|
||||
using T = remove_pointer_t<decltype(tmp)>;
|
||||
return *(static_cast<T*>(reduce_out_element_op[I]));
|
||||
},
|
||||
Number<NumReduce>{});
|
||||
|
||||
AElementwiseOperation a_element_op =
|
||||
*(static_cast<AElementwiseOperation*>(gemm_element_ops[0]));
|
||||
BElementwiseOperation b_element_op =
|
||||
*(static_cast<BElementwiseOperation*>(gemm_element_ops[1]));
|
||||
CElementwiseOperation c_element_op =
|
||||
*(static_cast<CElementwiseOperation*>(gemm_element_ops[2]));
|
||||
D0ElementwiseOperation d_element_op =
|
||||
*(static_cast<D0ElementwiseOperation*>(d_element_ops[0]));
|
||||
|
||||
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<EDataType*>(p_c),
|
||||
static_cast<const BiasDataType*>(p_bias),
|
||||
static_cast<const D0DataType*>(p_ds[0]),
|
||||
reduce_tuple,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
StrideDs[0],
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
d_element_op,
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmBiasAddReduce_Wmma_CShuffleV3"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1 << ", "
|
||||
<< MPerWmma << ", "
|
||||
<< NPerWmma << ", "
|
||||
<< MRepeat << ", "
|
||||
<< NRepeat << ", "
|
||||
<< ABlockTransferSrcScalarPerVector << ", "
|
||||
<< BBlockTransferSrcScalarPerVector << ", "
|
||||
<< CShuffleMRepeatPerShuffle << ", "
|
||||
<< CShuffleNRepeatPerShuffle
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -49,8 +49,11 @@ __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
|
||||
|
||||
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);
|
||||
|
||||
auto epilogue_args =
|
||||
EpilogueType(p_reduces_grid, reduce_in_element_ops, reduce_out_element_ops, karg.M);
|
||||
auto epilogue_args = EpilogueType(p_reduces_grid,
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops,
|
||||
karg.M,
|
||||
tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
GridwiseGemm::template Run<HasMainKBlockLoop, EGlobalMemoryDataOperation, TailNum>(
|
||||
p_shared, splitk_batch_offset, karg, epilogue_args);
|
||||
@@ -188,6 +191,7 @@ struct DeviceGemmReduce_Wmma_CShuffleV3 : public DeviceGemmReduce<0, ReduceOpera
|
||||
|
||||
using ReduceTrait = ReduceTrait_<ReduceAccDataType,
|
||||
ReducePtrsGlobal,
|
||||
tensor_operation::element_wise::PassThrough,
|
||||
ReduceOperations,
|
||||
ReduceInElementwiseOperations,
|
||||
ReduceAccElementwiseOperations,
|
||||
|
||||
Reference in New Issue
Block a user