mirror of
https://github.com/ROCm/composable_kernel.git
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Batchnorm-forward implemented using welford method to calculate variance (#403)
* Update to the batchnorm-forward API and base class * Fix leeked header including in gridwise_set_buffer_value.hpp * Add kernels and device file for batchnorm-forward welford supporting both blockwise and multi-block reduction * Update to the batchnorm-forward example to use the new batchnorm-forward device interface * Change the batchnorm-forward reference to use sequential welford method * Change to assign the workspace into four buffers in the host layer * Use GetReduceCountPerThread functor to replace the initial count for Blockwise and Multiblock welford * Tiny correction and remove un-used file under example/34_batchnorm * Renaming in the kernel arguments * Explicitly use ck::math::sqrt in batchnorm-forward kernels * Add some comments to some kernels * Tiny fix * Generalize the data types in reference_batchnorm_forward_nhwc_c * Use ck::ignore to mark un-used parameters * Move GetReduceCountPerThread functor codes from kernel to device * Remove some un-used codes in device_batchnorm_forward_impl.hpp * Tiny fix in batchnorm_forward example * Move GetReduceCountPerThread() to welford_helper.hpp * Use seperate data type for Scale and Bias * Renaming in device Op * Tiny fix in forward example * Updata to batchnorm-infer (type spliting, renaming) * Add time and bandwidth measurement to the batchnorm-forward example * Add support of elementwise operation for batchnorm forward output * Reduce object copying by passing object as reference type * Tiny change for performance * Updates for performance again * Some Renamings * Add GetActualVariance template parameter for ThreadwiseWelfordMerge * Tiny update in reference batchnorm forward nhwc/c * Move batchnorm multiblock kernel files to grid/batchnorm_multiblock sub-directory * Fuse mean and bias in the normalization calculation Co-authored-by: root <root@dc-smc-18.amd.com> Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
This commit is contained in:
Qianfeng
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/math.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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namespace ck {
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template <typename GridwiseMultiblockWelfordFirstHalf_,
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typename XDataType,
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typename MeanVarDataType,
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typename XGridDesc_M_K,
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typename MeanVarCountGridDesc_M_G,
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typename GetReduceCountPerThreadFunctor>
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__global__ void kernel_multiblock_welford_first_half(
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const XGridDesc_M_K x_grid_desc_m_k,
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const MeanVarCountGridDesc_M_G mean_var_count_grid_desc_m_g,
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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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const XDataType* const __restrict__ p_x,
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MeanVarDataType* const p_welford_mean,
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MeanVarDataType* const p_welford_variance,
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int32_t* const p_welford_count)
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{
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GridwiseMultiblockWelfordFirstHalf_::Run(x_grid_desc_m_k,
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mean_var_count_grid_desc_m_g,
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get_reduce_count_per_thread,
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num_k_block_tile_iteration,
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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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};
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template <typename XDataType,
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typename AccDataType,
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typename MeanVarDataType,
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typename XGridDesc_M_K,
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typename MeanVarCountGridDesc_M_G,
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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 XSrcCountSrcVectorDim,
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index_t XSrcCountSrcVectorSize>
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struct GridwiseMultiblockWelfordFirstHalf
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{
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static_assert((XSrcCountSrcVectorDim == 0 && MThreadSliceSize % XSrcCountSrcVectorSize == 0) ||
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(XSrcCountSrcVectorDim == 1 &&
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KThreadSliceSize % XSrcCountSrcVectorSize == 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 = (XSrcCountSrcVectorDim == 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 ThreadwiseWelford =
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ThreadwiseWelford<AccDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
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using BlockwiseWelford = 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 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 XGridDesc_M_K& x_grid_desc_m_k,
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const MeanVarCountGridDesc_M_G& mean_var_count_grid_desc_m_g,
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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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const XDataType* const __restrict__ p_x,
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MeanVarDataType* const p_welford_mean,
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MeanVarDataType* const p_welford_variance,
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int32_t* const p_welford_count)
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{
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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>
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welford_mean_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
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welford_var_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
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welford_count_thread_buf;
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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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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_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_1 = make_naive_tensor_descriptor_packed(
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make_tuple(Number<MThreadSliceSize>{}, Number<1>{}));
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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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XGridDesc_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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XSrcCountSrcVectorDim,
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XSrcCountSrcVectorSize,
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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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auto threadwise_welford_mean_var_store =
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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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1,
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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_welford_count_store =
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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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1,
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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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constexpr auto thread_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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auto welford_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_welford_mean, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
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auto welford_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_welford_variance, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
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auto welford_count_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_welford_count, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
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auto threadwise_welford = ThreadwiseWelford();
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threadwise_welford.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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welford_mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
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welford_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, thread_copy_fwd_step_m_k);
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threadwise_welford.Run(x_thread_buf, welford_mean_thread_buf, welford_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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welford_count_thread_buf(I) = threadwise_welford.cur_count_;
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BlockwiseWelford::Run(
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welford_mean_thread_buf(I), welford_var_thread_buf(I), welford_count_thread_buf(I));
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});
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if(thread_k_cluster_id == 0)
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{
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threadwise_welford_mean_var_store.Run(thread_buffer_desc_m_1,
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make_tuple(I0, I0),
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welford_mean_thread_buf,
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mean_var_count_grid_desc_m_g,
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welford_mean_global_val_buf);
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threadwise_welford_mean_var_store.Run(thread_buffer_desc_m_1,
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make_tuple(I0, I0),
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welford_var_thread_buf,
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mean_var_count_grid_desc_m_g,
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welford_var_global_val_buf);
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threadwise_welford_count_store.Run(thread_buffer_desc_m_1,
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make_tuple(I0, I0),
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welford_count_thread_buf,
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mean_var_count_grid_desc_m_g,
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welford_count_global_val_buf);
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};
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}
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};
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} // namespace ck
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@@ -0,0 +1,570 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#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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namespace ck {
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template <typename GridwiseWelfordSecondHalfBatchNormForwardFinal_,
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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_K,
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typename ScaleBiasGridDesc_M,
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typename MeanVarGridDesc_M>
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__global__ void kernel_welford_second_half_batchnorm_forward_final(
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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_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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index_t blkgroup_size,
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index_t num_xy_k_block_tile_iteration,
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index_t num_mean_var_count_k_block_tile_iteration,
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AccDataType epsilon,
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const MeanVarDataType* const __restrict__ p_in_welford_mean,
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const MeanVarDataType* const __restrict__ p_in_welford_variance,
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const int32_t* const __restrict__ p_in_welford_count,
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const XDataType* const __restrict__ p_x,
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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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GridwiseWelfordSecondHalfBatchNormForwardFinal_::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_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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blkgroup_size,
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num_xy_k_block_tile_iteration,
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num_mean_var_count_k_block_tile_iteration,
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epsilon,
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p_in_welford_mean,
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p_in_welford_variance,
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p_in_welford_count,
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p_x,
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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_K,
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typename ScaleBiasGridDesc_M,
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typename MeanVarGridDesc_M,
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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 GridwiseWelfordSecondHalfBatchNormForwardFinal
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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_1 = decltype(
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make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, Number<1>{})));
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using ThreadReduceDstDesc_M =
|
||||
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
|
||||
|
||||
using ThreadwiseWelford =
|
||||
ThreadwiseWelfordMerge<AccDataType, ThreadReduceSrcDesc_M_1, ThreadReduceDstDesc_M>;
|
||||
|
||||
using BlockwiseWelford = BlockwiseWelford<AccDataType,
|
||||
BlockSize,
|
||||
ThreadClusterLengths_M_K,
|
||||
ThreadClusterArrangeOrder>;
|
||||
|
||||
using PassThroughOp = tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
|
||||
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
|
||||
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
|
||||
|
||||
__device__ static void Run(const XYGridDesc_M_K& x_grid_desc_m_k,
|
||||
const XYGridDesc_M_K& y_grid_desc_m_k,
|
||||
const MeanVarCountGridDesc_M_K& mean_var_count_grid_desc_m_k,
|
||||
const ScaleBiasGridDesc_M& scale_grid_desc_m,
|
||||
const ScaleBiasGridDesc_M& bias_grid_desc_m,
|
||||
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,
|
||||
const int32_t* const __restrict__ p_in_welford_count,
|
||||
const XDataType* const __restrict__ p_x,
|
||||
const ScaleDataType* const __restrict__ p_scale,
|
||||
const BiasDataType* const __restrict__ p_bias,
|
||||
const YElementwiseOp y_elementwise_op,
|
||||
YDataType* const __restrict__ p_y,
|
||||
bool updateMovingAverage,
|
||||
AccDataType averageFactor,
|
||||
MeanVarDataType* const __restrict__ resultRunningMean,
|
||||
MeanVarDataType* const __restrict__ resultRunningVariance,
|
||||
bool saveMeanInvVariance,
|
||||
MeanVarDataType* const __restrict__ resultSaveMean,
|
||||
MeanVarDataType* const __restrict__ resultSaveInvVariance)
|
||||
|
||||
{
|
||||
using ck::math::sqrt;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * 1, true>
|
||||
in_welford_mean_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * 1, true>
|
||||
in_welford_var_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize * 1, true>
|
||||
in_welford_count_thread_buf;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
|
||||
welford_mean_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
|
||||
welford_var_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
|
||||
welford_count_thread_buf;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
|
||||
x_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
|
||||
y_thread_buf;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> scale_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> bias_thread_buf;
|
||||
|
||||
const index_t thread_local_id = get_thread_local_1d_id();
|
||||
const index_t block_global_id = get_block_1d_id();
|
||||
const index_t blkgroup_id = block_global_id / blkgroup_size;
|
||||
const index_t block_local_id = block_global_id % blkgroup_size;
|
||||
|
||||
const auto thread_cluster_idx =
|
||||
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
|
||||
|
||||
const auto thread_m_cluster_id = thread_cluster_idx[I0];
|
||||
const auto thread_k_cluster_id = thread_cluster_idx[I1];
|
||||
|
||||
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
|
||||
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
|
||||
using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
|
||||
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
|
||||
constexpr auto thread_buffer_desc_m =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
|
||||
constexpr auto thread_buffer_desc_m_1 = make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<MThreadSliceSize>{}, Number<1>{}));
|
||||
|
||||
auto threadwise_mean_var_load_m_k =
|
||||
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
|
||||
AccDataType,
|
||||
MeanVarCountGridDesc_M_K,
|
||||
decltype(thread_buffer_desc_m_1),
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
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));
|
||||
|
||||
auto threadwise_count_load_m_k =
|
||||
ThreadwiseTensorSliceTransfer_v2<int32_t,
|
||||
int32_t,
|
||||
MeanVarCountGridDesc_M_K,
|
||||
decltype(thread_buffer_desc_m_1),
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
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));
|
||||
|
||||
const auto welford_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_in_welford_mean, mean_var_count_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
const auto welford_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_in_welford_variance, mean_var_count_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
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) {
|
||||
welford_mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
|
||||
welford_var_thread_buf(I) = type_convert<AccDataType>(0.0f);
|
||||
welford_count_thread_buf(I) = 0;
|
||||
});
|
||||
|
||||
for(index_t reducedTiles = 0; reducedTiles < num_mean_var_count_k_block_tile_iteration;
|
||||
++reducedTiles)
|
||||
{
|
||||
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
|
||||
welford_mean_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
in_welford_mean_thread_buf);
|
||||
|
||||
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
|
||||
welford_var_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
in_welford_var_thread_buf);
|
||||
|
||||
threadwise_count_load_m_k.Run(mean_var_count_grid_desc_m_k,
|
||||
welford_count_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
in_welford_count_thread_buf);
|
||||
|
||||
ThreadwiseWelford::Run(in_welford_mean_thread_buf,
|
||||
in_welford_var_thread_buf,
|
||||
in_welford_count_thread_buf,
|
||||
welford_mean_thread_buf,
|
||||
welford_var_thread_buf,
|
||||
welford_count_thread_buf);
|
||||
|
||||
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,
|
||||
mean_var_count_thread_copy_step_m_k);
|
||||
}
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
if constexpr(I > 0)
|
||||
block_sync_lds();
|
||||
|
||||
BlockwiseWelford::Run(
|
||||
welford_mean_thread_buf(I), welford_var_thread_buf(I), welford_count_thread_buf(I));
|
||||
});
|
||||
|
||||
// Step 2: do normalization and output y
|
||||
|
||||
const index_t workSizePerBlock = K_BlockTileSize * num_xy_k_block_tile_iteration;
|
||||
|
||||
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
|
||||
AccDataType,
|
||||
XYGridDesc_M_K,
|
||||
decltype(thread_buffer_desc_m_k),
|
||||
ThreadBufferLengths_M_K,
|
||||
ThreadBufferDimAccessOrder,
|
||||
XSrcYDstVectorDim,
|
||||
XSrcVectorSize,
|
||||
1,
|
||||
true>(
|
||||
x_grid_desc_m_k,
|
||||
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
|
||||
workSizePerBlock * block_local_id +
|
||||
thread_k_cluster_id * KThreadSliceSize));
|
||||
|
||||
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,
|
||||
workSizePerBlock * block_local_id + 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 x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_x, x_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
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);
|
||||
|
||||
constexpr auto xy_thread_copy_step_m_k = make_multi_index(0, K_BlockTileSize);
|
||||
|
||||
for(index_t workTiles = 0; workTiles < num_xy_k_block_tile_iteration; ++workTiles)
|
||||
{
|
||||
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[iM] / sqrt(welford_var_thread_buf[iM] + epsilon);
|
||||
|
||||
AccDataType fused_mean_bias =
|
||||
bias_thread_buf[iM] - welford_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));
|
||||
|
||||
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_thread_copy_step_m_k);
|
||||
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, xy_thread_copy_step_m_k);
|
||||
}
|
||||
|
||||
// Step 3: 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_m =
|
||||
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_m.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_m.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 +
|
||||
welford_mean_thread_buf[I] * averageFactor;
|
||||
running_var_thread_buf(I) = running_var_thread_buf[I] * oneMinusAverageFactor +
|
||||
welford_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 4: 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());
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
welford_var_thread_buf(I) =
|
||||
type_convert<AccDataType>(1.0f) / sqrt(epsilon + welford_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),
|
||||
welford_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),
|
||||
welford_var_thread_buf,
|
||||
mean_var_grid_desc_m,
|
||||
result_inv_var_global_buf);
|
||||
};
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,482 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/utility/data_type.hpp"
|
||||
#include "ck/utility/math_v2.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/blockwise_welford.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_welford.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <typename GridwiseBatchrNormForwardWithBlockwiseWelford_,
|
||||
typename XDataType,
|
||||
typename YDataType,
|
||||
typename AccDataType,
|
||||
typename ScaleDataType,
|
||||
typename BiasDataType,
|
||||
typename MeanVarDataType,
|
||||
typename YElementwiseOp,
|
||||
typename XYGridDesc_M_K,
|
||||
typename ScaleBiasGridDesc_M,
|
||||
typename MeanVarGridDesc_M,
|
||||
typename GetReduceCountPerThreadFunctor>
|
||||
__global__ void kernel_batchnorm_forward_with_blockwise_welford(
|
||||
const XYGridDesc_M_K x_grid_desc_m_k,
|
||||
const XYGridDesc_M_K y_grid_desc_m_k,
|
||||
const ScaleBiasGridDesc_M scale_grid_desc_m,
|
||||
const ScaleBiasGridDesc_M bias_grid_desc_m,
|
||||
const MeanVarGridDesc_M mean_var_grid_desc_m,
|
||||
const GetReduceCountPerThreadFunctor get_reduce_count_per_thread,
|
||||
index_t num_k_block_tile_iteration,
|
||||
AccDataType epsilon,
|
||||
const XDataType* const __restrict__ p_x,
|
||||
const ScaleDataType* const __restrict__ p_scale,
|
||||
const BiasDataType* const __restrict__ p_bias,
|
||||
const YElementwiseOp y_elementwise_op,
|
||||
YDataType* const __restrict__ p_y,
|
||||
bool updateMovingAverage,
|
||||
AccDataType averageFactor,
|
||||
MeanVarDataType* const __restrict__ resultRunningMean,
|
||||
MeanVarDataType* const __restrict__ resultRunningVariance,
|
||||
bool saveMeanInvVariance,
|
||||
MeanVarDataType* const __restrict__ resultSaveMean,
|
||||
MeanVarDataType* const __restrict__ resultSaveInvVariance)
|
||||
{
|
||||
GridwiseBatchrNormForwardWithBlockwiseWelford_::Run(x_grid_desc_m_k,
|
||||
y_grid_desc_m_k,
|
||||
scale_grid_desc_m,
|
||||
bias_grid_desc_m,
|
||||
mean_var_grid_desc_m,
|
||||
get_reduce_count_per_thread,
|
||||
num_k_block_tile_iteration,
|
||||
epsilon,
|
||||
p_x,
|
||||
p_scale,
|
||||
p_bias,
|
||||
y_elementwise_op,
|
||||
p_y,
|
||||
updateMovingAverage,
|
||||
averageFactor,
|
||||
resultRunningMean,
|
||||
resultRunningVariance,
|
||||
saveMeanInvVariance,
|
||||
resultSaveMean,
|
||||
resultSaveInvVariance);
|
||||
};
|
||||
|
||||
template <typename XDataType,
|
||||
typename YDataType,
|
||||
typename AccDataType,
|
||||
typename ScaleDataType,
|
||||
typename BiasDataType,
|
||||
typename MeanVarDataType,
|
||||
typename YElementwiseOp,
|
||||
typename XYGridDesc_M_K,
|
||||
typename ScaleBiasGridDesc_M,
|
||||
typename MeanVarGridDesc_M,
|
||||
typename GetReduceCountPerThreadFunctor,
|
||||
index_t BlockSize,
|
||||
index_t MThreadClusterSize,
|
||||
index_t KThreadClusterSize,
|
||||
index_t MThreadSliceSize,
|
||||
index_t KThreadSliceSize,
|
||||
index_t XSrcYDstVectorDim,
|
||||
index_t XSrcVectorSize,
|
||||
index_t YDstVectorSize,
|
||||
index_t ScaleSrcVectorSize,
|
||||
index_t BiasSrcVectorSize,
|
||||
index_t MeanVarSrcDstVectorSize>
|
||||
struct GridwiseBatchNormForwardWithBlockwiseWelford
|
||||
{
|
||||
static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
|
||||
(XSrcYDstVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),
|
||||
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
|
||||
|
||||
static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % YDstVectorSize == 0) ||
|
||||
(XSrcYDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
|
||||
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
|
||||
|
||||
static constexpr bool reorder_thread_cluster = (XSrcYDstVectorDim == 0);
|
||||
|
||||
using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
|
||||
|
||||
using ThreadBufferDimAccessOrder =
|
||||
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
|
||||
|
||||
using ThreadClusterArrangeOrder =
|
||||
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
|
||||
|
||||
static constexpr auto thread_cluster_desc =
|
||||
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
|
||||
|
||||
using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
|
||||
using ThreadReduceDstDesc_M =
|
||||
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
|
||||
|
||||
using ThreadwiseWelford =
|
||||
ThreadwiseWelford<AccDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
|
||||
|
||||
using BlockwiseWelford = BlockwiseWelford<AccDataType,
|
||||
BlockSize,
|
||||
ThreadClusterLengths_M_K,
|
||||
ThreadClusterArrangeOrder>;
|
||||
|
||||
using PassThroughOp = tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
|
||||
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
|
||||
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
|
||||
|
||||
__device__ static void Run(const XYGridDesc_M_K& x_grid_desc_m_k,
|
||||
const XYGridDesc_M_K& y_grid_desc_m_k,
|
||||
const ScaleBiasGridDesc_M& scale_grid_desc_m,
|
||||
const ScaleBiasGridDesc_M& bias_grid_desc_m,
|
||||
const MeanVarGridDesc_M& mean_var_grid_desc_m,
|
||||
const GetReduceCountPerThreadFunctor& get_reduce_count_per_thread,
|
||||
index_t num_k_block_tile_iteration,
|
||||
AccDataType epsilon,
|
||||
const XDataType* const __restrict__ p_x,
|
||||
const ScaleDataType* const __restrict__ p_scale,
|
||||
const BiasDataType* const __restrict__ p_bias,
|
||||
const YElementwiseOp y_elementwise_op,
|
||||
YDataType* const __restrict__ p_y,
|
||||
bool updateMovingAverage,
|
||||
AccDataType averageFactor,
|
||||
MeanVarDataType* const __restrict__ resultRunningMean,
|
||||
MeanVarDataType* const __restrict__ resultRunningVariance,
|
||||
bool saveMeanInvVariance,
|
||||
MeanVarDataType* const __restrict__ resultSaveMean,
|
||||
MeanVarDataType* const __restrict__ resultSaveInvVariance)
|
||||
{
|
||||
using ck::math::sqrt;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
|
||||
x_thread_buf;
|
||||
|
||||
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;
|
||||
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> var_thread_buf;
|
||||
|
||||
const index_t thread_local_id = get_thread_local_1d_id();
|
||||
const index_t block_global_id = get_block_1d_id();
|
||||
|
||||
const auto thread_cluster_idx =
|
||||
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
|
||||
|
||||
const auto thread_m_cluster_id = thread_cluster_idx[I0];
|
||||
const auto thread_k_cluster_id = thread_cluster_idx[I1];
|
||||
|
||||
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
|
||||
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
|
||||
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
|
||||
constexpr auto thread_buffer_desc_m =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
|
||||
|
||||
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
|
||||
AccDataType,
|
||||
XYGridDesc_M_K,
|
||||
decltype(thread_buffer_desc_m_k),
|
||||
ThreadBufferLengths_M_K,
|
||||
ThreadBufferDimAccessOrder,
|
||||
XSrcYDstVectorDim,
|
||||
XSrcVectorSize,
|
||||
1,
|
||||
true>(
|
||||
x_grid_desc_m_k,
|
||||
make_multi_index(block_global_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
thread_k_cluster_id * KThreadSliceSize));
|
||||
|
||||
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(block_global_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
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(block_global_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(block_global_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize));
|
||||
|
||||
constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileSize);
|
||||
constexpr auto thread_copy_bwd_step_m_k = make_multi_index(0, -K_BlockTileSize);
|
||||
|
||||
const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_x, x_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
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());
|
||||
|
||||
// Step 1: do welford reduction to get mean and variance
|
||||
|
||||
auto threadwise_welford = ThreadwiseWelford();
|
||||
threadwise_welford.max_count_ = get_reduce_count_per_thread(thread_k_cluster_id);
|
||||
|
||||
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);
|
||||
});
|
||||
|
||||
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);
|
||||
|
||||
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_fwd_step_m_k);
|
||||
threadwise_welford.Run(x_thread_buf, mean_thread_buf, var_thread_buf);
|
||||
}
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
if constexpr(I > 0)
|
||||
block_sync_lds();
|
||||
|
||||
int count = threadwise_welford.cur_count_;
|
||||
BlockwiseWelford::Run(mean_thread_buf(I), var_thread_buf(I), count);
|
||||
});
|
||||
|
||||
// Step 2: do normalization and output y
|
||||
|
||||
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);
|
||||
|
||||
auto thread_copy_tail_m_k = (num_k_block_tile_iteration - 1) * thread_copy_fwd_step_m_k;
|
||||
|
||||
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_bwd_step_m_k);
|
||||
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, thread_copy_tail_m_k);
|
||||
|
||||
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, thread_copy_bwd_step_m_k);
|
||||
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, thread_copy_bwd_step_m_k);
|
||||
}
|
||||
|
||||
// Step 3: update the moving average of mean and variance (optional)
|
||||
|
||||
if(updateMovingAverage && 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(block_global_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(block_global_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 4: save mean and inv-variance (optional)
|
||||
|
||||
if(saveMeanInvVariance && 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());
|
||||
|
||||
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(block_global_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
|
||||
@@ -3,6 +3,7 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
Reference in New Issue
Block a user