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https://github.com/ROCm/composable_kernel.git
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Batchnorm splitk single kernel (#771)
* Use dim 0 as faster dim for writing mean/var/count workspace in batchnorm multiblock method [performance]
* Add CountDataType as template parameter in blockwise_welford
* Add utility/get_shift.hpp
* Add BatchNorm multiblock single-kernel implementation
* Add smem inline assembly based implementation of gms_init/gms_barrier/gms_reset for gfx90a
* Renaming in device_batchnorm_forward_impl.hpp
* Tiny fix in the batchnorm_fwd profiler
* Revert "Add smem inline assembly based implementation of gms_init/gms_barrier/gms_reset for gfx90a"
This reverts commit d16d00919c.
* Use the old two-kernel batchnorm multiblock method for gfx1030
* Use the old two-kernel batchnorm multiblock method for gfx908
* use the single-kernel batchnorm multiblock method only for gfx90a
* Remove get_wave_id() from utility/get_id.hpp since it is not used
* Set true for testing running mean/variance and saving mean/invvariance in the examples
* Fix to copy-right words
* Remove un-needed including in utility/get_id.hpp
* Add comments to workgroup_synchronization.hpp
* Remove un-used codes in gridwise_multiblock_batchnorm_forward.hpp
* Renaming in the kernels
* Remove un-used kernel file
This commit is contained in:
Qianfeng
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/math_v2.hpp"
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#include "ck/tensor_operation/gpu/block/blockwise_welford.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_welford.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/utility/workgroup_synchronization.hpp"
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namespace ck {
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template <typename GridwiseMultiblockBatchNormForward_,
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typename XDataType,
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typename YDataType,
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typename AccDataType,
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typename ScaleDataType,
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typename BiasDataType,
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typename MeanVarDataType,
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typename YElementwiseOp,
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typename XYGridDesc_M_K,
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typename MeanVarCountGridDesc_M_G,
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typename MeanVarCountGridDesc_M_K,
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typename ScaleBiasGridDesc_M,
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typename MeanVarGridDesc_M,
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typename GetReduceCountPerThreadFunctor>
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__global__ void kernel_multiblock_batchnorm_forward(
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const XYGridDesc_M_K x_grid_desc_m_k,
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const XYGridDesc_M_K y_grid_desc_m_k,
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const MeanVarCountGridDesc_M_G mean_var_count_grid_desc_m_g,
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const MeanVarCountGridDesc_M_K mean_var_count_grid_desc_m_k,
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const ScaleBiasGridDesc_M scale_grid_desc_m,
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const ScaleBiasGridDesc_M bias_grid_desc_m,
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const MeanVarGridDesc_M mean_var_grid_desc_m,
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const GetReduceCountPerThreadFunctor get_reduce_count_per_thread,
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index_t num_k_block_tile_iteration,
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AccDataType epsilon,
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const XDataType* const __restrict__ p_x,
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MeanVarDataType* const __restrict__ p_welford_mean,
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MeanVarDataType* const __restrict__ p_welford_variance,
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int32_t* const __restrict__ p_welford_count,
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int32_t* const __restrict__ p_control,
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const ScaleDataType* const __restrict__ p_scale,
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const BiasDataType* const __restrict__ p_bias,
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const YElementwiseOp y_elementwise_op,
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YDataType* const __restrict__ p_y,
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bool updateMovingAverage,
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AccDataType averageFactor,
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MeanVarDataType* const __restrict__ resultRunningMean,
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MeanVarDataType* const __restrict__ resultRunningVariance,
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bool saveMeanInvVariance,
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MeanVarDataType* const __restrict__ resultSaveMean,
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MeanVarDataType* const __restrict__ resultSaveInvVariance)
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{
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GridwiseMultiblockBatchNormForward_::Run(x_grid_desc_m_k,
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y_grid_desc_m_k,
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mean_var_count_grid_desc_m_g,
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mean_var_count_grid_desc_m_k,
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scale_grid_desc_m,
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bias_grid_desc_m,
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mean_var_grid_desc_m,
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get_reduce_count_per_thread,
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num_k_block_tile_iteration,
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epsilon,
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p_x,
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p_welford_mean,
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p_welford_variance,
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p_welford_count,
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p_control,
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p_scale,
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p_bias,
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y_elementwise_op,
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p_y,
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updateMovingAverage,
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averageFactor,
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resultRunningMean,
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resultRunningVariance,
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saveMeanInvVariance,
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resultSaveMean,
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resultSaveInvVariance);
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};
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template <typename XDataType,
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typename YDataType,
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typename AccDataType,
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typename ScaleDataType,
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typename BiasDataType,
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typename MeanVarDataType,
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typename YElementwiseOp,
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typename XYGridDesc_M_K,
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typename MeanVarCountGridDesc_M_G,
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typename MeanVarCountGridDesc_M_K,
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typename ScaleBiasGridDesc_M,
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typename MeanVarGridDesc_M,
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typename GetReduceCountPerThreadFunctor,
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index_t BlockSize,
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index_t MThreadClusterSize,
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index_t KThreadClusterSize,
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index_t MThreadSliceSize,
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index_t KThreadSliceSize,
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index_t XSrcYDstVectorDim,
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index_t XSrcVectorSize,
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index_t YDstVectorSize,
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index_t ScaleSrcVectorSize,
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index_t BiasSrcVectorSize,
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index_t MeanVarSrcDstVectorSize>
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struct GridwiseMultiblockBatchNormForward
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{
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static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
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(XSrcYDstVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),
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"Invalid thread slice sizes and/or vector sizes configuration, please check!");
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static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % YDstVectorSize == 0) ||
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(XSrcYDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
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"Invalid thread slice sizes and/or vector sizes configuration, please check!");
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static constexpr bool reorder_thread_cluster = (XSrcYDstVectorDim == 0);
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using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
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using ThreadBufferDimAccessOrder =
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typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
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using ThreadClusterArrangeOrder =
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typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
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static constexpr auto thread_cluster_desc =
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make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
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using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
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make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
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using ThreadReduceDstDesc_M =
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decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
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using ThreadReduceSrcDesc_M_1 = decltype(
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make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, Number<1>{})));
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using ThreadwiseWelford1 =
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ThreadwiseWelford<AccDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
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using ThreadwiseWelford2 =
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ThreadwiseWelfordMerge<AccDataType, ThreadReduceSrcDesc_M_1, ThreadReduceDstDesc_M>;
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using BlockwiseWelford1 = BlockwiseWelford<AccDataType,
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BlockSize,
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ThreadClusterLengths_M_K,
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ThreadClusterArrangeOrder,
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false>;
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using BlockwiseWelford2 = BlockwiseWelford<AccDataType,
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BlockSize,
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ThreadClusterLengths_M_K,
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ThreadClusterArrangeOrder,
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true>;
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using PassThroughOp = tensor_operation::element_wise::PassThrough;
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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 index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
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static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
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__device__ static void Run(const XYGridDesc_M_K& x_grid_desc_m_k,
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const XYGridDesc_M_K& y_grid_desc_m_k,
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const MeanVarCountGridDesc_M_G& mean_var_count_grid_desc_m_g,
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const MeanVarCountGridDesc_M_K& mean_var_count_grid_desc_m_k,
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const ScaleBiasGridDesc_M& scale_grid_desc_m,
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const ScaleBiasGridDesc_M& bias_grid_desc_m,
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const MeanVarGridDesc_M& mean_var_grid_desc_m,
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const GetReduceCountPerThreadFunctor& get_reduce_count_per_thread,
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index_t num_k_block_tile_iteration,
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AccDataType epsilon,
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const XDataType* const __restrict__ p_x,
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MeanVarDataType* const __restrict__ p_welford_mean,
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MeanVarDataType* const __restrict__ p_welford_variance,
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int32_t* const __restrict__ p_welford_count,
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int32_t* const __restrict__ p_control,
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const ScaleDataType* const __restrict__ p_scale,
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const BiasDataType* const __restrict__ p_bias,
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const YElementwiseOp y_elementwise_op,
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YDataType* const __restrict__ p_y,
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bool updateMovingAverage,
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AccDataType averageFactor,
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MeanVarDataType* const __restrict__ resultRunningMean,
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MeanVarDataType* const __restrict__ resultRunningVariance,
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bool saveMeanInvVariance,
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MeanVarDataType* const __restrict__ resultSaveMean,
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MeanVarDataType* const __restrict__ resultSaveInvVariance)
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{
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using ck::math::sqrt;
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const index_t blkgroup_size = mean_var_count_grid_desc_m_g.GetLength(I1);
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const index_t thread_local_id = get_thread_local_1d_id();
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const index_t block_global_id = get_block_1d_id();
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const index_t blkgroup_id = block_global_id / blkgroup_size;
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const index_t block_local_id = block_global_id % blkgroup_size;
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if(block_local_id == 0)
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gms_init(BlockSize / warpSize * blkgroup_size, &p_control[blkgroup_id * 2]);
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const auto thread_cluster_idx =
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thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
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const auto thread_m_cluster_id = thread_cluster_idx[I0];
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const auto thread_k_cluster_id = thread_cluster_idx[I1];
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using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
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using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
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using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
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constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
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make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
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constexpr auto thread_buffer_desc_m =
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make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
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constexpr auto thread_buffer_desc_m_1 = make_naive_tensor_descriptor_packed(
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make_tuple(Number<MThreadSliceSize>{}, Number<1>{}));
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
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x_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> var_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true> count_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
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tmp_mean_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
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tmp_var_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true> tmp_count_thread_buf;
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const index_t reduceSizePerBlock = K_BlockTileSize * num_k_block_tile_iteration;
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auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
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AccDataType,
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XYGridDesc_M_K,
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decltype(thread_buffer_desc_m_k),
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ThreadBufferLengths_M_K,
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ThreadBufferDimAccessOrder,
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XSrcYDstVectorDim,
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XSrcVectorSize,
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1,
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true>(
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x_grid_desc_m_k,
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make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
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block_local_id * reduceSizePerBlock +
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thread_k_cluster_id * KThreadSliceSize));
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constexpr auto xy_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileSize);
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const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_x, x_grid_desc_m_k.GetElementSpaceSize());
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// Step 1: each workgroup does local welford reduction
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auto threadwise_welford_1 = ThreadwiseWelford1();
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threadwise_welford_1.max_count_ =
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get_reduce_count_per_thread(block_local_id, thread_k_cluster_id);
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static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
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mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
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var_thread_buf(I) = type_convert<AccDataType>(0.0f);
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});
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for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
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{
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threadwise_x_load.Run(x_grid_desc_m_k,
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x_global_val_buf,
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thread_buffer_desc_m_k,
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make_tuple(I0, I0),
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x_thread_buf);
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threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, xy_copy_fwd_step_m_k);
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threadwise_welford_1.Run(x_thread_buf, mean_thread_buf, var_thread_buf);
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}
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static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
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if constexpr(I > 0)
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block_sync_lds();
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count_thread_buf(I) = threadwise_welford_1.cur_count_;
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BlockwiseWelford1::Run(mean_thread_buf(I), var_thread_buf(I), count_thread_buf(I));
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});
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// Step 2: each workgroup writes its local welford result to workspace memory
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auto mean_global_val_buf =
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make_dynamic_buffer<AddressSpaceEnum::Global, AmdBufferCoherenceEnum::GLC>(
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p_welford_mean, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
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auto var_global_val_buf =
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make_dynamic_buffer<AddressSpaceEnum::Global, AmdBufferCoherenceEnum::GLC>(
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p_welford_variance, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
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auto count_global_val_buf =
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make_dynamic_buffer<AddressSpaceEnum::Global, AmdBufferCoherenceEnum::GLC>(
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p_welford_count, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
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auto threadwise_mean_var_store_m_g =
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ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
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MeanVarDataType,
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decltype(thread_buffer_desc_m_1),
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MeanVarCountGridDesc_M_G,
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PassThroughOp,
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ThreadBufferLengths_M_1,
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Sequence<0, 1>,
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0,
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1,
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InMemoryDataOperationEnum::Set,
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1,
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true>(
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mean_var_count_grid_desc_m_g,
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make_multi_index(blkgroup_id * M_BlockTileSize +
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thread_m_cluster_id * MThreadSliceSize,
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block_local_id),
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PassThroughOp{});
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auto threadwise_count_store_m_g =
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ThreadwiseTensorSliceTransfer_v1r3<int32_t,
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int32_t,
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decltype(thread_buffer_desc_m_1),
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MeanVarCountGridDesc_M_G,
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PassThroughOp,
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ThreadBufferLengths_M_1,
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Sequence<0, 1>,
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0,
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1,
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InMemoryDataOperationEnum::Set,
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1,
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true>(
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mean_var_count_grid_desc_m_g,
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make_multi_index(blkgroup_id * M_BlockTileSize +
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thread_m_cluster_id * MThreadSliceSize,
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block_local_id),
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PassThroughOp{});
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if(thread_k_cluster_id == 0)
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{
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threadwise_mean_var_store_m_g.Run(thread_buffer_desc_m_1,
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make_tuple(I0, I0),
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mean_thread_buf,
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mean_var_count_grid_desc_m_g,
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mean_global_val_buf);
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threadwise_mean_var_store_m_g.Run(thread_buffer_desc_m_1,
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make_tuple(I0, I0),
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var_thread_buf,
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mean_var_count_grid_desc_m_g,
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var_global_val_buf);
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threadwise_count_store_m_g.Run(thread_buffer_desc_m_1,
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make_tuple(I0, I0),
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count_thread_buf,
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mean_var_count_grid_desc_m_g,
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count_global_val_buf);
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};
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gms_barrier(&p_control[blkgroup_id * 2]);
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if(block_local_id == 0)
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gms_reset(&p_control[blkgroup_id * 2]);
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// Step 3: each workgroup reads welford results from workspace memory and does final welford
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// reduction
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auto threadwise_mean_var_load_m_k =
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ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
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AccDataType,
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MeanVarCountGridDesc_M_K,
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decltype(thread_buffer_desc_m_1),
|
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ThreadBufferLengths_M_1,
|
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Sequence<0, 1>,
|
||||
0,
|
||||
1,
|
||||
1,
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||||
true>(
|
||||
mean_var_count_grid_desc_m_k,
|
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make_multi_index(blkgroup_id * M_BlockTileSize +
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thread_m_cluster_id * MThreadSliceSize,
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thread_k_cluster_id * 1));
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auto threadwise_count_load_m_k =
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ThreadwiseTensorSliceTransfer_v2<int32_t,
|
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int32_t,
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MeanVarCountGridDesc_M_K,
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decltype(thread_buffer_desc_m_1),
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
true>(
|
||||
mean_var_count_grid_desc_m_k,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
thread_k_cluster_id * 1));
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
|
||||
var_thread_buf(I) = type_convert<AccDataType>(0.0f);
|
||||
count_thread_buf(I) = 0;
|
||||
});
|
||||
|
||||
constexpr auto mean_var_count_read_fwd_step_m_k = make_multi_index(0, KThreadClusterSize);
|
||||
|
||||
int32_t reducedSize = 0;
|
||||
while(reducedSize < blkgroup_size)
|
||||
{
|
||||
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
|
||||
mean_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
tmp_mean_thread_buf);
|
||||
|
||||
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
|
||||
var_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
tmp_var_thread_buf);
|
||||
|
||||
threadwise_count_load_m_k.Run(mean_var_count_grid_desc_m_k,
|
||||
count_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
tmp_count_thread_buf);
|
||||
|
||||
ThreadwiseWelford2::Run(tmp_mean_thread_buf,
|
||||
tmp_var_thread_buf,
|
||||
tmp_count_thread_buf,
|
||||
mean_thread_buf,
|
||||
var_thread_buf,
|
||||
count_thread_buf);
|
||||
|
||||
reducedSize += KThreadClusterSize;
|
||||
|
||||
threadwise_mean_var_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
|
||||
mean_var_count_read_fwd_step_m_k);
|
||||
threadwise_count_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
|
||||
mean_var_count_read_fwd_step_m_k);
|
||||
};
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
if constexpr(I > 0)
|
||||
block_sync_lds();
|
||||
|
||||
BlockwiseWelford2::Run(mean_thread_buf(I), var_thread_buf(I), count_thread_buf(I));
|
||||
});
|
||||
|
||||
// Step 4: do normalization using the mean/variance
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> scale_thread_buf;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> bias_thread_buf;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
|
||||
y_thread_buf;
|
||||
|
||||
auto threadwise_y_store =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
|
||||
YDataType,
|
||||
decltype(thread_buffer_desc_m_k),
|
||||
XYGridDesc_M_K,
|
||||
YElementwiseOp,
|
||||
ThreadBufferLengths_M_K,
|
||||
ThreadBufferDimAccessOrder,
|
||||
XSrcYDstVectorDim,
|
||||
YDstVectorSize,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>(
|
||||
y_grid_desc_m_k,
|
||||
make_multi_index(
|
||||
blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
|
||||
block_local_id * reduceSizePerBlock + thread_k_cluster_id * KThreadSliceSize),
|
||||
y_elementwise_op);
|
||||
|
||||
auto threadwise_scale_load =
|
||||
ThreadwiseTensorSliceTransfer_v2<ScaleDataType,
|
||||
AccDataType,
|
||||
ScaleBiasGridDesc_M,
|
||||
decltype(thread_buffer_desc_m),
|
||||
ThreadBufferLengths_M,
|
||||
Sequence<0>,
|
||||
0,
|
||||
ScaleSrcVectorSize,
|
||||
1,
|
||||
true>(
|
||||
scale_grid_desc_m,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize));
|
||||
|
||||
auto threadwise_bias_load = ThreadwiseTensorSliceTransfer_v2<BiasDataType,
|
||||
AccDataType,
|
||||
ScaleBiasGridDesc_M,
|
||||
decltype(thread_buffer_desc_m),
|
||||
ThreadBufferLengths_M,
|
||||
Sequence<0>,
|
||||
0,
|
||||
BiasSrcVectorSize,
|
||||
1,
|
||||
true>(
|
||||
bias_grid_desc_m,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize));
|
||||
|
||||
const auto scale_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_scale, scale_grid_desc_m.GetElementSpaceSize());
|
||||
|
||||
const auto bias_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_bias, bias_grid_desc_m.GetElementSpaceSize());
|
||||
|
||||
auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_y, y_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
threadwise_scale_load.Run(scale_grid_desc_m,
|
||||
scale_global_val_buf,
|
||||
thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
scale_thread_buf);
|
||||
|
||||
threadwise_bias_load.Run(bias_grid_desc_m,
|
||||
bias_global_val_buf,
|
||||
thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
bias_thread_buf);
|
||||
|
||||
threadwise_x_load.SetSrcSliceOrigin(
|
||||
x_grid_desc_m_k,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
|
||||
block_local_id * reduceSizePerBlock +
|
||||
thread_k_cluster_id * KThreadSliceSize));
|
||||
|
||||
for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
|
||||
{
|
||||
threadwise_x_load.Run(x_grid_desc_m_k,
|
||||
x_global_val_buf,
|
||||
thread_buffer_desc_m_k,
|
||||
make_tuple(I0, I0),
|
||||
x_thread_buf);
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
|
||||
AccDataType multiplier =
|
||||
scale_thread_buf[Number<iM>{}] / sqrt(var_thread_buf[iM] + epsilon);
|
||||
|
||||
AccDataType fused_mean_bias =
|
||||
bias_thread_buf[Number<iM>{}] - mean_thread_buf[iM] * multiplier;
|
||||
|
||||
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
|
||||
constexpr auto offset =
|
||||
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
|
||||
|
||||
// normalize
|
||||
y_thread_buf(Number<offset>{}) =
|
||||
x_thread_buf[Number<offset>{}] * multiplier + fused_mean_bias;
|
||||
});
|
||||
});
|
||||
|
||||
threadwise_y_store.Run(thread_buffer_desc_m_k,
|
||||
make_tuple(I0, I0),
|
||||
y_thread_buf,
|
||||
y_grid_desc_m_k,
|
||||
y_global_val_buf);
|
||||
|
||||
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, xy_copy_fwd_step_m_k);
|
||||
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, xy_copy_fwd_step_m_k);
|
||||
}
|
||||
|
||||
// Step 5: update the moving average of mean and variance (optional)
|
||||
|
||||
if(updateMovingAverage && block_local_id == 0 && thread_k_cluster_id == 0)
|
||||
{
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
|
||||
running_mean_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
|
||||
running_var_thread_buf;
|
||||
|
||||
auto running_mean_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
resultRunningMean, mean_var_grid_desc_m.GetElementSpaceSize());
|
||||
|
||||
auto running_var_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
resultRunningVariance, mean_var_grid_desc_m.GetElementSpaceSize());
|
||||
|
||||
auto threadwise_mean_var_load =
|
||||
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
|
||||
AccDataType,
|
||||
MeanVarGridDesc_M,
|
||||
decltype(thread_buffer_desc_m),
|
||||
ThreadBufferLengths_M,
|
||||
Sequence<0>,
|
||||
0,
|
||||
MeanVarSrcDstVectorSize,
|
||||
1,
|
||||
true>(
|
||||
mean_var_grid_desc_m,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize));
|
||||
|
||||
threadwise_mean_var_load.Run(mean_var_grid_desc_m,
|
||||
running_mean_global_buf,
|
||||
thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
running_mean_thread_buf);
|
||||
|
||||
threadwise_mean_var_load.Run(mean_var_grid_desc_m,
|
||||
running_var_global_buf,
|
||||
thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
running_var_thread_buf);
|
||||
|
||||
AccDataType oneMinusAverageFactor = type_convert<AccDataType>(1.0) - averageFactor;
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
running_mean_thread_buf(I) = running_mean_thread_buf[I] * oneMinusAverageFactor +
|
||||
mean_thread_buf[I] * averageFactor;
|
||||
running_var_thread_buf(I) = running_var_thread_buf[I] * oneMinusAverageFactor +
|
||||
var_thread_buf[I] * averageFactor;
|
||||
});
|
||||
|
||||
auto threadwise_mean_var_store =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
|
||||
MeanVarDataType,
|
||||
decltype(thread_buffer_desc_m),
|
||||
MeanVarGridDesc_M,
|
||||
PassThroughOp,
|
||||
ThreadBufferLengths_M,
|
||||
Sequence<0>,
|
||||
0,
|
||||
MeanVarSrcDstVectorSize,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>(
|
||||
mean_var_grid_desc_m,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize),
|
||||
PassThroughOp{});
|
||||
|
||||
threadwise_mean_var_store.Run(thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
running_mean_thread_buf,
|
||||
mean_var_grid_desc_m,
|
||||
running_mean_global_buf);
|
||||
|
||||
threadwise_mean_var_store.Run(thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
running_var_thread_buf,
|
||||
mean_var_grid_desc_m,
|
||||
running_var_global_buf);
|
||||
};
|
||||
|
||||
// Step 6: save mean and inv-variance (optional)
|
||||
|
||||
if(saveMeanInvVariance && block_local_id == 0 && thread_k_cluster_id == 0)
|
||||
{
|
||||
auto result_mean_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
resultSaveMean, mean_var_grid_desc_m.GetElementSpaceSize());
|
||||
|
||||
auto result_inv_var_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
resultSaveInvVariance, mean_var_grid_desc_m.GetElementSpaceSize());
|
||||
|
||||
// calculate inv-variance as 1/sqrt(epsilon+variance), stored in place of variance
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
var_thread_buf(I) =
|
||||
type_convert<AccDataType>(1.0f) / sqrt(epsilon + var_thread_buf[I]);
|
||||
});
|
||||
|
||||
auto threadwise_mean_inv_var_store =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
|
||||
MeanVarDataType,
|
||||
decltype(thread_buffer_desc_m),
|
||||
MeanVarGridDesc_M,
|
||||
PassThroughOp,
|
||||
ThreadBufferLengths_M,
|
||||
Sequence<0>,
|
||||
0,
|
||||
MeanVarSrcDstVectorSize,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>(
|
||||
mean_var_grid_desc_m,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize),
|
||||
PassThroughOp{});
|
||||
|
||||
threadwise_mean_inv_var_store.Run(thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
mean_thread_buf,
|
||||
mean_var_grid_desc_m,
|
||||
result_mean_global_buf);
|
||||
|
||||
threadwise_mean_inv_var_store.Run(thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
var_thread_buf,
|
||||
mean_var_grid_desc_m,
|
||||
result_inv_var_global_buf);
|
||||
};
|
||||
}
|
||||
}; // namespace ck
|
||||
|
||||
} // namespace ck
|
||||
@@ -161,7 +161,7 @@ struct GridwiseMultiblockWelfordFirstHalf
|
||||
PassThroughOp,
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
@@ -180,7 +180,7 @@ struct GridwiseMultiblockWelfordFirstHalf
|
||||
PassThroughOp,
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
|
||||
@@ -33,7 +33,6 @@ __global__ void kernel_welford_second_half_batchnorm_forward_final(
|
||||
const MeanVarGridDesc_M mean_var_grid_desc_m,
|
||||
index_t blkgroup_size,
|
||||
index_t num_xy_k_block_tile_iteration,
|
||||
index_t num_mean_var_count_k_block_tile_iteration,
|
||||
AccDataType epsilon,
|
||||
const MeanVarDataType* const __restrict__ p_in_welford_mean,
|
||||
const MeanVarDataType* const __restrict__ p_in_welford_variance,
|
||||
@@ -59,7 +58,6 @@ __global__ void kernel_welford_second_half_batchnorm_forward_final(
|
||||
mean_var_grid_desc_m,
|
||||
blkgroup_size,
|
||||
num_xy_k_block_tile_iteration,
|
||||
num_mean_var_count_k_block_tile_iteration,
|
||||
epsilon,
|
||||
p_in_welford_mean,
|
||||
p_in_welford_variance,
|
||||
@@ -152,7 +150,6 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
|
||||
const MeanVarGridDesc_M& mean_var_grid_desc_m,
|
||||
index_t blkgroup_size,
|
||||
index_t num_xy_k_block_tile_iteration,
|
||||
index_t num_mean_var_count_k_block_tile_iteration,
|
||||
AccDataType epsilon,
|
||||
const MeanVarDataType* const __restrict__ p_in_welford_mean,
|
||||
const MeanVarDataType* const __restrict__ p_in_welford_variance,
|
||||
@@ -223,7 +220,7 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
|
||||
decltype(thread_buffer_desc_m_1),
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
true>(
|
||||
@@ -239,7 +236,7 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
|
||||
decltype(thread_buffer_desc_m_1),
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
true>(
|
||||
@@ -257,9 +254,6 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
|
||||
const auto welford_count_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_in_welford_count, mean_var_count_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
constexpr auto mean_var_count_thread_copy_step_m_k =
|
||||
make_multi_index(0, KThreadClusterSize * 1);
|
||||
|
||||
// Step 1: do final welford reduction to get mean and variance
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
@@ -268,8 +262,11 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
|
||||
welford_count_thread_buf(I) = 0;
|
||||
});
|
||||
|
||||
for(index_t reducedTiles = 0; reducedTiles < num_mean_var_count_k_block_tile_iteration;
|
||||
++reducedTiles)
|
||||
constexpr auto mean_var_count_thread_copy_step_m_k =
|
||||
make_multi_index(0, KThreadClusterSize);
|
||||
|
||||
int32_t reducedSize = 0;
|
||||
while(reducedSize < blkgroup_size)
|
||||
{
|
||||
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
|
||||
welford_mean_global_val_buf,
|
||||
@@ -296,6 +293,8 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
|
||||
welford_var_thread_buf,
|
||||
welford_count_thread_buf);
|
||||
|
||||
reducedSize += KThreadClusterSize;
|
||||
|
||||
threadwise_mean_var_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
|
||||
mean_var_count_thread_copy_step_m_k);
|
||||
threadwise_count_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
|
||||
Reference in New Issue
Block a user