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Introduce MX GEMM for FP8 data type (#2000)
This commit is contained in:
Andriy Roshchenko
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@@ -0,0 +1,877 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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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/host_utility/flush_cache.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_mx.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_xdl_cshuffle_v3_mx.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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namespace tensor_operation {
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namespace device {
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/**
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* \brief WIP: Implements XDL CShuffle V3 GEMM for microscale-compliant data types
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*
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* This class is a work-in-progress implementation of the XDL CShuffle V3 GEMM for
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* microscale-compliant data types.
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*
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* Assumptions:
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* - A and B data types are compliant with the OCP Microscaling Formats (MX) Specification
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* - Each scale applies to ScaleBlockSize elements in K direction
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* - A scale matrix is row-major
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* - B scale matrix is column-major
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* - Scale data types must have get_exponent_value() specialization, whereas lowest 8 bits of the
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* exponent will be interpreted as conventional biased Float32 exponent (E8M0)
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*
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* Tunable parameters.
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* The CK instance includes a series of tunable template parameters to control the parallel
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* granularity of the workload to achieve load balancing on different hardware platforms. These
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* parameters include Block Size, M/N/K Per Block, M/N per XDL, AK1, BK1, etc.
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* - Block Size determines the number of threads in the thread block.
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* - M/N/K Per Block determines the size of tile that each thread block is responsible for
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* calculating.
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* - M/N Per XDL refers to M/N size for Instinct accelerator Matrix Fused Multiply Add (MFMA)
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* instructions operating on a per-wavefront basis.
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* - A/B K1 is related to the data type. It can be any value ranging from 1 to K Per Block. To
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* achieve the optimal load/store performance, 128bit per load is suggested. In addition, the A/B
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* loading parameters must be changed accordingly to match the A/B K1 value; otherwise, it will
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* result in compilation errors.
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*
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* Conditions for achieving computational load balancing on different hardware platforms can vary.
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*
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* Serialized version of the algorithm:
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* \code
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* // E = A * B + C
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* // Loop over E[MPerBlock,NPerBlock] tiles
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* for(int mb = 0; mb < M; mb += MPerBlock){
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* for(int nb = 0; nb < N; nb += NPerBlock){
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* // initialize E[MPerBlock,NPerBlock] tile
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* for(int mt = mb; mt < mb + MPerBlock; mt++){
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* for(int nt = nb; nt < nb + NPerBlock; nt++){
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* E[mt,nt] = C[mt,nt];
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* }
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* }
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*
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* // multiply-accumulate per tile
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* for(int kb = 0; kb < K; kb += KPerBlock){
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* for(int m0 = mb; m0 < mb + MPerBlock; m0 += MWaves * MPerXDL){
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* for(int n0 = nb; n0 < nb + NPerBlock; n0 += NWaves * NPerXDL){
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* for(int mw = m0; mw < m0 + MWaves * MPerXDL; mw += MPerXDL){
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* for(int nw = n0; nw < n0 + NWaves * NPerXDL; nw += NPerXDL){
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* for(int k0 = kb; k0 < kb + KPerBlock; k0 += mfma.num_input_blks*KPack){
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* // MFMA accumulation for multirate instructions
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* for(int k_pack = k0; k_pack < k0 + mfma.num_input_blks*KPack; k_pack += KPack){
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* for(int k_mfma = k_pack; k_mfma < k_pack + KPack; k_mfma += mfma.k_per_blk){
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* // MFMA instruction
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* for(int m = mw; m < mw + MPerXDL; m++){
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* for(int n = nw; n < nw + NPerXDL; n++){
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* for(int k = k_mfma; k < k_mfma + mfma.k_per_blk; k++){
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* E[m,n] += A[m,k] * B[k,n];
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* }
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* }
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* }
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* }
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* }
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* }
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* }
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* }
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* }
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* }
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* }
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* }
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* }
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* \endcode
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*
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*/
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template <typename ALayout,
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typename BLayout,
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typename CLayout,
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typename ADataType,
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typename AScaleDataType,
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typename BDataType,
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typename BScaleDataType,
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typename CDataType,
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typename GemmAccDataType,
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typename CShuffleDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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GemmSpecialization GemmSpec,
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index_t ScaleBlockSize, // Scaling block size
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index_t BlockSize, // Thread block size
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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 MPerXDL,
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index_t NPerXDL,
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index_t MXdlPerWave,
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index_t NXdlPerWave,
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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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bool 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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bool BBlockLdsExtraN,
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index_t CShuffleMXdlPerWavePerShuffle,
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index_t CShuffleNXdlPerWavePerShuffle,
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typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
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BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
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BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
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typename ComputeTypeA =
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ADataType, // XXX: These should always be the same as ADataType and BDataType
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typename ComputeTypeB =
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BDataType // TODO: Hardcode them and remove from the list of template parameters
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>
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struct DeviceGemmMX_Xdl_CShuffleV3 : public DeviceGemmMX<ALayout,
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BLayout,
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CLayout,
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ADataType,
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AScaleDataType,
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BDataType,
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BScaleDataType,
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CDataType,
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ScaleBlockSize,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation>
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{
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// GridwiseGemm
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using GridwiseGemm = GridwiseGemmMX_xdl_cshuffle_v3<
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ALayout,
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BLayout,
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CLayout,
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ADataType,
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AScaleDataType,
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BDataType,
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BScaleDataType,
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GemmAccDataType,
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CShuffleDataType,
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CDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation,
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GemmSpec,
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ScaleBlockSize,
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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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MPerXDL,
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NPerXDL,
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MXdlPerWave,
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NXdlPerWave,
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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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CShuffleMXdlPerWavePerShuffle,
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CShuffleNXdlPerWavePerShuffle,
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CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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CShuffleBlockTransferScalarPerVector_NPerBlock,
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BlkGemmPipeSched,
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BlkGemmPipelineVer,
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ComputeTypeA,
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ComputeTypeB>;
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using Argument = typename GridwiseGemm::Argument;
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// Invoker
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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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if(stream_config.log_level_ > 0)
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{
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arg.Print();
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GridwiseGemm::BlockwiseGemmPipe::HotLoopInstList::Print();
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}
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if(!GridwiseGemm::CheckValidity(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.M, arg.N, arg.KBatch);
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float ave_time = 0;
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index_t k_grain = arg.KBatch * KPerBlock;
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index_t K_split = (arg.K + k_grain - 1) / k_grain * KPerBlock;
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const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
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const auto Run = [&](const auto& kernel) {
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if(stream_config.flush_cache)
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{
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Argument arg_ = arg;
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const auto a_grid_desc_ak0_m_ak1 = GridwiseGemm::MakeAGridDescriptor_AK0_M_AK1(
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arg_.M, arg_.MPadded, arg_.K, arg_.KPadded, arg_.StrideA, arg_.AK0);
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const auto b_grid_desc_bk0_n_bk1 = GridwiseGemm::MakeBGridDescriptor_BK0_N_BK1(
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arg_.K, arg_.KPadded, arg_.N, arg_.NPadded, arg_.StrideB, arg_.BK0);
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auto size_a_buffer =
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a_grid_desc_ak0_m_ak1.GetElementSpaceSize() * sizeof(ADataType);
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auto size_b_buffer =
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b_grid_desc_bk0_n_bk1.GetElementSpaceSize() * sizeof(BDataType);
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ck::utility::RotatingMemWrapper<Argument> rotating_mem(
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arg_, stream_config.rotating_count, size_a_buffer, size_b_buffer);
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rotating_mem.Print();
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auto run_flush_cache = [&]() {
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// flush icache
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ck::utility::flush_icache();
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// rotating mem
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rotating_mem.Next();
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// clear c mem
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if(arg_.KBatch > 1)
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hipGetErrorString(hipMemsetAsync(arg_.p_c_grid,
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0,
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arg_.M * arg_.N * sizeof(CDataType),
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stream_config.stream_id_));
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};
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ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
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stream_config,
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run_flush_cache,
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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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arg_);
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}
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else
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{
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if(arg.KBatch > 1)
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hipGetErrorString(hipMemsetAsync(arg.p_c_grid,
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0,
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arg.M * arg.N * sizeof(CDataType),
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stream_config.stream_id_));
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ave_time = launch_and_time_kernel(
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stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, arg);
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}
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};
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// TODO: Check if this is the right algorithm for minimum_occupancy
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constexpr index_t minimum_occupancy =
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BlkGemmPipeSched == BlockGemmPipelineScheduler::Intrawave
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? (BlkGemmPipelineVer == BlockGemmPipelineVersion::v3 &&
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MPerBlock * NPerBlock * KPerBlock * sizeof(ADataType) <= 128 * 128 * 64 * 2)
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? 2
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: 1
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: 2;
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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(arg.KBatch > 1)
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{
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const auto kernel =
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kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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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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const auto kernel =
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kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
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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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}
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// Tail number could be One to Seven
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else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
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{
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if(arg.KBatch > 1)
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{
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if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
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{
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const auto kernel =
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kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::One>;
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Run(kernel);
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}
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else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
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TailNumber::Full)
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{
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const auto kernel =
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kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::Full>;
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Run(kernel);
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}
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if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
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{
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if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
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{
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const auto kernel = kernel_gemm_xdl_cshuffle_v3<
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GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::Two>;
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Run(kernel);
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}
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}
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if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
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{
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if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
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TailNumber::Three)
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{
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const auto kernel = kernel_gemm_xdl_cshuffle_v3<
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GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::Three>;
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Run(kernel);
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}
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}
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if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
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{
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if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
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TailNumber::Four)
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{
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const auto kernel = kernel_gemm_xdl_cshuffle_v3<
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GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::Four>;
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Run(kernel);
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}
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}
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if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
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{
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if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
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TailNumber::Five)
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{
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const auto kernel = kernel_gemm_xdl_cshuffle_v3<
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GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::Five>;
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Run(kernel);
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}
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}
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if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
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{
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if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
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{
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const auto kernel = kernel_gemm_xdl_cshuffle_v3<
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GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::Six>;
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Run(kernel);
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}
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}
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if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
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{
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if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
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TailNumber::Seven)
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{
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const auto kernel = kernel_gemm_xdl_cshuffle_v3<
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GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy,
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TailNumber::Seven>;
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Run(kernel);
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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(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
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{
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const auto kernel =
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kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
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||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::One>;
|
||||
Run(kernel);
|
||||
}
|
||||
else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
|
||||
TailNumber::Full)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Full>;
|
||||
Run(kernel);
|
||||
}
|
||||
|
||||
if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Two>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
|
||||
TailNumber::Three)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Three>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
|
||||
TailNumber::Four)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Four>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
|
||||
TailNumber::Five)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Five>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Six>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
|
||||
TailNumber::Seven)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Seven>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// Tail number could be Odd or Even
|
||||
else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
|
||||
{
|
||||
if(arg.KBatch > 1)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdl_cshuffle_v3_2lds<
|
||||
GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::AtomicAdd,
|
||||
minimum_occupancy,
|
||||
TailNumber::Odd>;
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdl_cshuffle_v3_2lds<
|
||||
GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::AtomicAdd,
|
||||
minimum_occupancy,
|
||||
TailNumber::Even>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3_2lds<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Odd>;
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3_2lds<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Even>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if(arg.KBatch > 1)
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::AtomicAdd,
|
||||
minimum_occupancy,
|
||||
TailNumber::Odd>;
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::AtomicAdd,
|
||||
minimum_occupancy,
|
||||
TailNumber::Even>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Odd>;
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy,
|
||||
TailNumber::Even>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// Tail number always 1
|
||||
if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
|
||||
{
|
||||
if(arg.KBatch > 1)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
false,
|
||||
InMemoryDataOperationEnum::AtomicAdd,
|
||||
minimum_occupancy>;
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
|
||||
false,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
static_assert((is_same_v<ADataType, f8_t> || is_same_v<ADataType, bf8_t> ||
|
||||
is_same_v<ADataType, f6_t> || is_same_v<ADataType, bf6_t> ||
|
||||
is_same_v<ADataType, f4_t>)&&(is_same_v<BDataType, f8_t> ||
|
||||
is_same_v<BDataType, bf8_t> ||
|
||||
is_same_v<BDataType, f6_t> ||
|
||||
is_same_v<BDataType, bf6_t> ||
|
||||
is_same_v<BDataType, f4_t>),
|
||||
"Only microscaling formats are supported for ADataType and BDataType");
|
||||
|
||||
static_assert(ScaleBlockSize == 32, "Only ScaleBlockSize 32 is supported");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(!ck::is_xdl_supported())
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
if(!is_bf16_atomic_supported() && std::is_same_v<CDataType, ck::bhalf_t> && arg.KBatch > 1)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
if((arg.K % AK1 != 0 || arg.K % BK1 != 0) && !(GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding ||
|
||||
GemmSpec == GemmSpecialization::KPadding))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const ADataType* p_a,
|
||||
const AScaleDataType* p_a_scale,
|
||||
const BDataType* p_b,
|
||||
const BScaleDataType* p_b_scale,
|
||||
CDataType* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideScaleA,
|
||||
index_t StrideB,
|
||||
index_t StrideScaleB,
|
||||
index_t StrideC,
|
||||
index_t KBatch,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_a_scale,
|
||||
p_b,
|
||||
p_b_scale,
|
||||
p_c,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideScaleA,
|
||||
StrideB,
|
||||
StrideScaleB,
|
||||
StrideC,
|
||||
KBatch,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
|
||||
const void* p_a_scale,
|
||||
const void* p_b,
|
||||
const void* p_b_scale,
|
||||
void* p_c,
|
||||
ck::index_t M,
|
||||
ck::index_t N,
|
||||
ck::index_t K,
|
||||
ck::index_t StrideA,
|
||||
ck::index_t StrideScaleA,
|
||||
ck::index_t StrideB,
|
||||
ck::index_t StrideScaleB,
|
||||
ck::index_t StrideC,
|
||||
ck::index_t KBatch,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
|
||||
static_cast<const AScaleDataType*>(p_a_scale),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<const BScaleDataType*>(p_b_scale),
|
||||
static_cast<CDataType*>(p_c),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideScaleA,
|
||||
StrideB,
|
||||
StrideScaleB,
|
||||
StrideC,
|
||||
KBatch,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
std::map<BlockGemmPipelineScheduler, std::string> BlkGemmPipelineSchedulerToString{
|
||||
{BlockGemmPipelineScheduler::Intrawave, "Intrawave"},
|
||||
{BlockGemmPipelineScheduler::Interwave, "Interwave"}};
|
||||
|
||||
std::map<BlockGemmPipelineVersion, std::string> BlkGemmPipelineVersionToString{
|
||||
{BlockGemmPipelineVersion::v1, "v1"},
|
||||
{BlockGemmPipelineVersion::v2, "v2"},
|
||||
{BlockGemmPipelineVersion::v3, "v3"},
|
||||
{BlockGemmPipelineVersion::v4, "v4"},
|
||||
{BlockGemmPipelineVersion::v5, "v5"}};
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmMX_Xdl_CShuffleV3"
|
||||
<< "<"
|
||||
<< getGemmSpecializationString(GemmSpec) << ", "
|
||||
<< std::string(ALayout::name)[0]
|
||||
<< std::string(BLayout::name)[0]
|
||||
<< std::string(CLayout::name)[0]
|
||||
<< ">"
|
||||
<< " BlkSize: "
|
||||
<< BlockSize << ", "
|
||||
<< "BlkTile: "
|
||||
<< MPerBlock<<"x"<<NPerBlock<<"x"<<KPerBlock << ", "
|
||||
<< "WaveTile: "
|
||||
<< MPerXDL<<"x"<<NPerXDL << ", "
|
||||
<< "WaveMap: "
|
||||
<< MXdlPerWave<<"x" << NXdlPerWave<<", "
|
||||
<< "VmemReadVec: "
|
||||
<< ABlockTransferSrcScalarPerVector<<"x"<<BBlockTransferSrcScalarPerVector<<", "
|
||||
<< "BlkGemmPipelineScheduler: "
|
||||
<< BlkGemmPipelineSchedulerToString[BlkGemmPipeSched] << ", "
|
||||
<< "BlkGemmPipelineVersion: "
|
||||
<< BlkGemmPipelineVersionToString[BlkGemmPipelineVer] << ", "
|
||||
<< "BlkGemmPipelinePrefetchStages: "
|
||||
<< GridwiseGemm::BlockwiseGemmPipe::PrefetchStages << ", "
|
||||
<< "Kpack: "
|
||||
<< GridwiseGemm::BlockwiseGemmPipe::AMmaKStride << ", "
|
||||
<< "ScaleBlockSize: "
|
||||
<< ScaleBlockSize;
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
REGISTER_EXTRA_PRINTING_METHODS
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user