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Normalization/split k (#615)
This commit is contained in:
rocking
@@ -3,8 +3,8 @@
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#pragma once
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#include "ck/tensor_operation/gpu/grid/gridwise_normalization_naive_variance.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_normalization_welford_variance.hpp"
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#include "ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_naive_variance.hpp"
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#include "ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_welford_variance.hpp"
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namespace ck {
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template <typename GridwiseReduction,
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@@ -0,0 +1,252 @@
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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 XDataType,
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typename ComputeDataType,
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typename MeanVarDataType,
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typename XGridDesc_M_K,
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typename MeanVarGridDesc_M_KBlock,
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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 XSrcVectorDim,
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index_t XSrcVectorSize>
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struct GridwiseNormalizationSplitK1st
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{
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static_assert((XSrcVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
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(XSrcVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 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 = (XSrcVectorDim == 0);
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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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 ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, XSrcVectorSize>;
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static constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
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make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{}));
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using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
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static constexpr auto thread_buffer_desc_m_1 =
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make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, I1));
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using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
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make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{})));
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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<ComputeDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
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using BlockwiseWelford = BlockwiseWelford<ComputeDataType,
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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 index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
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static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
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static constexpr index_t K_BlockTileStepSize = KThreadClusterSize * XSrcVectorSize;
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static constexpr auto ThreadBufferNumber = Number<KThreadSliceSize / XSrcVectorSize>{};
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__device__ static int
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GetKPerThread(int kRaw, int kGridSize, int block_k_cluster_id, int thread_k_cluster_id)
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{
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bool is_rightmost_block = block_k_cluster_id == kGridSize - 1;
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if(is_rightmost_block)
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{
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int left_kPerBlock = math::integer_divide_ceil(kRaw, kGridSize);
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int kPerBlock = kRaw % kGridSize == 0 ? left_kPerBlock : kRaw % left_kPerBlock;
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int kPerThread =
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kPerBlock < K_BlockTileSize ? 0 : KThreadSliceSize * (kPerBlock / K_BlockTileSize);
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int kPerBlockTail = kPerBlock - kPerThread * KThreadClusterSize;
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if(kPerBlockTail > 0)
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{
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static_for<0, ThreadBufferNumber, 1>{}([&](auto i) {
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int thread_max_len =
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(thread_k_cluster_id + 1) * XSrcVectorSize + K_BlockTileStepSize * i;
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int delta = thread_max_len - kPerBlockTail;
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delta = math::clamp(thread_max_len - kPerBlockTail, 0, XSrcVectorSize);
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kPerThread += XSrcVectorSize - delta;
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});
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}
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return kPerThread;
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}
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else
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{
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int kPerBlock = math::integer_divide_ceil(kRaw, kGridSize);
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return KThreadSliceSize * (kPerBlock / K_BlockTileSize);
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}
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}
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// Calculate mean and variance by welford along k dimension
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__device__ static void Run(const XGridDesc_M_K& x_grid_desc_m_k,
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const MeanVarGridDesc_M_KBlock& mean_var_grid_desc_m_kblock,
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index_t num_k_block_tile_iteration,
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const XDataType* const __restrict__ p_x_global,
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MeanVarDataType* const p_mean_global,
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MeanVarDataType* const p_variance_global,
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int32_t* const p_welford_count_global)
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{
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auto x_thread_buf = generate_tuple(
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[&](auto) {
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return StaticBuffer<AddressSpaceEnum::Vgpr,
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ComputeDataType,
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MThreadSliceSize * XSrcVectorSize,
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true>{};
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},
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Number<ThreadBufferNumber>{});
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StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
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mean_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
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var_thread_buf;
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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 k_grid_size = mean_var_grid_desc_m_kblock.GetLength(I1);
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const index_t block_m_cluster_id = block_global_id / k_grid_size;
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const index_t block_k_cluster_id = block_global_id % k_grid_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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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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ComputeDataType,
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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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XSrcVectorDim,
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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(
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block_m_cluster_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
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block_k_cluster_id * reduceSizePerBlock + thread_k_cluster_id * XSrcVectorSize));
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auto mean_var_count_store_index = make_multi_index(
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block_m_cluster_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
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block_k_cluster_id);
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auto threadwise_welford_mean_var_store =
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ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
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MeanVarDataType,
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decltype(thread_buffer_desc_m_1),
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MeanVarGridDesc_M_KBlock,
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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_grid_desc_m_kblock, mean_var_count_store_index, PassThroughOp{});
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constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
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const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_x_global, x_grid_desc_m_k.GetElementSpaceSize());
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auto mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_mean_global, mean_var_grid_desc_m_kblock.GetElementSpaceSize());
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auto var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_variance_global, mean_var_grid_desc_m_kblock.GetElementSpaceSize());
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auto threadwise_welford = ThreadwiseWelford();
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int kRaw = x_grid_desc_m_k.GetTransforms()[I2].GetUpperLengths()[I0];
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threadwise_welford.max_count_ =
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GetKPerThread(kRaw, k_grid_size, block_k_cluster_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<ComputeDataType>(0.0f);
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var_thread_buf(I) = type_convert<ComputeDataType>(0.0f);
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});
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for(index_t k = 0; k < num_k_block_tile_iteration; ++k)
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{
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static_for<0, ThreadBufferNumber, 1>{}([&](auto i) {
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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(i));
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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[i], mean_thread_buf, var_thread_buf);
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});
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}
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int welford_count = 0;
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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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int count = threadwise_welford.cur_count_;
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BlockwiseWelford::Run(mean_thread_buf(I), var_thread_buf(I), count);
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// The value of count is same for all I
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if constexpr(I == MThreadSliceSize - 1)
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welford_count = count;
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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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mean_thread_buf,
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mean_var_grid_desc_m_kblock,
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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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var_thread_buf,
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mean_var_grid_desc_m_kblock,
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var_global_val_buf);
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if(block_m_cluster_id == 0 && thread_m_cluster_id == 0)
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p_welford_count_global[block_k_cluster_id] = welford_count;
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}
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}
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};
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} // namespace ck
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@@ -0,0 +1,418 @@
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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 MeanVarDataType,
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typename XDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename YDataType,
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typename ComputeDataType,
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typename YElementwiseOperation,
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typename MeanVarGridDesc_M_KBlock,
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typename CountGridDesc_M_KBlock,
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typename XYGammaBetaGridDesc_M_K,
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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 XSrcVectorDim,
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index_t XSrcVectorSize,
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index_t GammaSrcVectorDim,
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index_t GammaSrcVectorSize,
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index_t BetaSrcVectorDim,
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index_t BetaSrcVectorSize,
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index_t YDstVectorDim,
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index_t YDstVectorSize>
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struct GridwiseNormalizationSplitK2nd
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{
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static_assert((XSrcVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
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(XSrcVectorDim == 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((YDstVectorDim == 0 && MThreadSliceSize % YDstVectorSize == 0) ||
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(YDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
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"Invalid thread slice sizes and/or vector sizes configuration, please check!");
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static_assert(XSrcVectorSize == YDstVectorSize);
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static_assert(XSrcVectorSize == GammaSrcVectorSize);
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static_assert(XSrcVectorSize == BetaSrcVectorSize);
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static constexpr bool reorder_thread_cluster = (XSrcVectorDim == 0);
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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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 ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, XSrcVectorSize>;
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static constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
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make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{}));
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using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
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static constexpr auto thread_buffer_desc_m_1 =
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make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, I1));
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using ThreadWelfordSrcDesc_M_1 = decltype(thread_buffer_desc_m_1);
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using ThreadWelfordDstDesc_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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ThreadwiseWelfordMerge<ComputeDataType, ThreadWelfordSrcDesc_M_1, ThreadWelfordDstDesc_M>;
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using BlockwiseWelford = BlockwiseWelford<ComputeDataType,
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BlockSize,
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ThreadClusterLengths_M_K,
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ThreadClusterArrangeOrder>;
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using PassThroughOp = tensor_operation::element_wise::PassThrough;
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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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static constexpr index_t K_BlockTileStepSize = KThreadClusterSize * XSrcVectorSize;
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static constexpr auto ThreadBufferNumber = Number<KThreadSliceSize / XSrcVectorSize>{};
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__device__ static void Run(const MeanVarGridDesc_M_KBlock& mean_var_grid_desc_m_kblock,
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const CountGridDesc_M_KBlock& count_grid_desc_m_kblock,
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const XYGammaBetaGridDesc_M_K& x_grid_desc_m_k,
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const XYGammaBetaGridDesc_M_K& gamma_grid_desc_m_k,
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const XYGammaBetaGridDesc_M_K& beta_grid_desc_m_k,
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const XYGammaBetaGridDesc_M_K& y_grid_desc_m_k,
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index_t num_k_mean_var_count_iteration,
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index_t num_k_block_tile_iteration,
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index_t k_grid_size,
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ComputeDataType epsilon,
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const MeanVarDataType* const p_mean_global,
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const MeanVarDataType* const p_variance_global,
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const int32_t* const p_welford_count_global,
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const XDataType* const __restrict__ p_x_global,
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const GammaDataType* const __restrict__ p_gamma_global,
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const BetaDataType* const __restrict__ p_beta_global,
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YDataType* const __restrict__ p_y_global,
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const YElementwiseOperation y_elementwise_op)
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{
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// Thread/Block id
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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 block_m_cluster_id = block_global_id / k_grid_size;
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const index_t block_k_cluster_id = block_global_id % k_grid_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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// Global Memory
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const auto mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_mean_global, mean_var_grid_desc_m_kblock.GetElementSpaceSize());
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const auto var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_variance_global, mean_var_grid_desc_m_kblock.GetElementSpaceSize());
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const auto welford_count_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_welford_count_global, count_grid_desc_m_kblock.GetElementSpaceSize());
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const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_x_global, x_grid_desc_m_k.GetElementSpaceSize());
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const auto gamma_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_gamma_global, gamma_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
const auto beta_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_beta_global, beta_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
|
||||
|
||||
// VGPR
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
|
||||
in_mean_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
|
||||
in_var_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
|
||||
in_welford_count_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
|
||||
mean_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
|
||||
var_thread_buf;
|
||||
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
|
||||
welford_count_thread_buf;
|
||||
|
||||
auto x_thread_buf = generate_tuple(
|
||||
[&](auto) {
|
||||
return StaticBuffer<AddressSpaceEnum::Vgpr,
|
||||
ComputeDataType,
|
||||
MThreadSliceSize * XSrcVectorSize,
|
||||
true>{};
|
||||
},
|
||||
Number<ThreadBufferNumber>{});
|
||||
|
||||
auto gamma_thread_buf = generate_tuple(
|
||||
[&](auto) {
|
||||
return StaticBuffer<AddressSpaceEnum::Vgpr,
|
||||
ComputeDataType,
|
||||
MThreadSliceSize * GammaSrcVectorSize,
|
||||
true>{};
|
||||
},
|
||||
Number<ThreadBufferNumber>{});
|
||||
|
||||
auto& beta_thread_buf = gamma_thread_buf;
|
||||
auto& y_thread_buf = x_thread_buf;
|
||||
|
||||
// IO
|
||||
auto threadwise_mean_var_load_m_kblock =
|
||||
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
|
||||
ComputeDataType,
|
||||
MeanVarGridDesc_M_KBlock,
|
||||
decltype(thread_buffer_desc_m_1),
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
true>(
|
||||
mean_var_grid_desc_m_kblock,
|
||||
make_multi_index(block_m_cluster_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
thread_k_cluster_id));
|
||||
|
||||
auto threadwise_count_load_m_kblock =
|
||||
ThreadwiseTensorSliceTransfer_v2<int32_t,
|
||||
int32_t,
|
||||
CountGridDesc_M_KBlock,
|
||||
decltype(thread_buffer_desc_m_1),
|
||||
ThreadBufferLengths_M_1,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
true>(
|
||||
count_grid_desc_m_kblock,
|
||||
make_multi_index(block_m_cluster_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
thread_k_cluster_id));
|
||||
|
||||
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
|
||||
ComputeDataType,
|
||||
XYGammaBetaGridDesc_M_K,
|
||||
decltype(thread_buffer_desc_m_k),
|
||||
ThreadBufferLengths_M_K,
|
||||
ThreadBufferDimAccessOrder,
|
||||
XSrcVectorDim,
|
||||
XSrcVectorSize,
|
||||
1,
|
||||
true>(
|
||||
x_grid_desc_m_k,
|
||||
make_multi_index(block_m_cluster_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
block_k_cluster_id * K_BlockTileSize * num_k_block_tile_iteration +
|
||||
thread_k_cluster_id * XSrcVectorSize));
|
||||
|
||||
auto threadwise_gamma_load =
|
||||
ThreadwiseTensorSliceTransfer_v2<GammaDataType,
|
||||
ComputeDataType,
|
||||
XYGammaBetaGridDesc_M_K,
|
||||
decltype(thread_buffer_desc_m_k),
|
||||
ThreadBufferLengths_M_K,
|
||||
ThreadBufferDimAccessOrder,
|
||||
GammaSrcVectorDim,
|
||||
GammaSrcVectorSize,
|
||||
1,
|
||||
true>(
|
||||
gamma_grid_desc_m_k,
|
||||
make_multi_index(block_m_cluster_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
block_k_cluster_id * K_BlockTileSize * num_k_block_tile_iteration +
|
||||
thread_k_cluster_id * GammaSrcVectorSize));
|
||||
|
||||
auto threadwise_beta_load =
|
||||
ThreadwiseTensorSliceTransfer_v2<BetaDataType,
|
||||
ComputeDataType,
|
||||
XYGammaBetaGridDesc_M_K,
|
||||
decltype(thread_buffer_desc_m_k),
|
||||
ThreadBufferLengths_M_K,
|
||||
ThreadBufferDimAccessOrder,
|
||||
BetaSrcVectorDim,
|
||||
BetaSrcVectorSize,
|
||||
1,
|
||||
true>(
|
||||
beta_grid_desc_m_k,
|
||||
make_multi_index(block_m_cluster_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
block_k_cluster_id * K_BlockTileSize * num_k_block_tile_iteration +
|
||||
thread_k_cluster_id * BetaSrcVectorSize));
|
||||
|
||||
auto threadwise_y_store =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
|
||||
YDataType,
|
||||
decltype(thread_buffer_desc_m_k),
|
||||
XYGammaBetaGridDesc_M_K,
|
||||
YElementwiseOperation,
|
||||
ThreadBufferLengths_M_K,
|
||||
ThreadBufferDimAccessOrder,
|
||||
YDstVectorDim,
|
||||
YDstVectorSize,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>(
|
||||
y_grid_desc_m_k,
|
||||
make_multi_index(block_m_cluster_id * M_BlockTileSize +
|
||||
thread_m_cluster_id * MThreadSliceSize,
|
||||
block_k_cluster_id * K_BlockTileSize * num_k_block_tile_iteration +
|
||||
thread_k_cluster_id * YDstVectorSize),
|
||||
y_elementwise_op);
|
||||
|
||||
// step1: Merge mean and variance
|
||||
constexpr auto mean_var_count_thread_copy_step_I0_k =
|
||||
make_multi_index(I0, KThreadClusterSize);
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
mean_thread_buf(I) = type_convert<ComputeDataType>(0.0f);
|
||||
var_thread_buf(I) = type_convert<ComputeDataType>(0.0f);
|
||||
welford_count_thread_buf(I) = 0;
|
||||
});
|
||||
|
||||
for(index_t k = 0; k < num_k_mean_var_count_iteration; ++k)
|
||||
{
|
||||
threadwise_mean_var_load_m_kblock.Run(mean_var_grid_desc_m_kblock,
|
||||
mean_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
in_mean_thread_buf);
|
||||
|
||||
threadwise_mean_var_load_m_kblock.Run(mean_var_grid_desc_m_kblock,
|
||||
var_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
in_var_thread_buf);
|
||||
|
||||
threadwise_count_load_m_kblock.Run(count_grid_desc_m_kblock,
|
||||
welford_count_global_val_buf,
|
||||
thread_buffer_desc_m_1,
|
||||
make_tuple(I0, I0),
|
||||
in_welford_count_thread_buf);
|
||||
|
||||
ThreadwiseWelford::Run(in_mean_thread_buf,
|
||||
in_var_thread_buf,
|
||||
in_welford_count_thread_buf,
|
||||
mean_thread_buf,
|
||||
var_thread_buf,
|
||||
welford_count_thread_buf);
|
||||
|
||||
threadwise_mean_var_load_m_kblock.MoveSrcSliceWindow(
|
||||
mean_var_grid_desc_m_kblock, mean_var_count_thread_copy_step_I0_k);
|
||||
threadwise_count_load_m_kblock.MoveSrcSliceWindow(count_grid_desc_m_kblock,
|
||||
mean_var_count_thread_copy_step_I0_k);
|
||||
}
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
|
||||
if constexpr(I > 0)
|
||||
block_sync_lds();
|
||||
|
||||
BlockwiseWelford::Run(
|
||||
mean_thread_buf(I), var_thread_buf(I), welford_count_thread_buf(I));
|
||||
});
|
||||
|
||||
// step2: normalization
|
||||
constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
|
||||
|
||||
for(index_t k = 0; k < num_k_block_tile_iteration; ++k)
|
||||
{
|
||||
static_for<0, ThreadBufferNumber, 1>{}([&](auto i) {
|
||||
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(i));
|
||||
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_fwd_step_m_k);
|
||||
});
|
||||
|
||||
static_for<0, ThreadBufferNumber, 1>{}([&](auto i) {
|
||||
threadwise_gamma_load.Run(gamma_grid_desc_m_k,
|
||||
gamma_global_val_buf,
|
||||
thread_buffer_desc_m_k,
|
||||
make_tuple(I0, I0),
|
||||
gamma_thread_buf(i));
|
||||
|
||||
threadwise_gamma_load.MoveSrcSliceWindow(gamma_grid_desc_m_k,
|
||||
thread_copy_fwd_step_m_k);
|
||||
});
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
|
||||
auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
|
||||
static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
|
||||
static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
|
||||
constexpr auto offset_m_k =
|
||||
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK1));
|
||||
|
||||
// normalize
|
||||
y_thread_buf(iK0)(Number<offset_m_k>{}) =
|
||||
(x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
|
||||
divisor;
|
||||
|
||||
// gamma
|
||||
y_thread_buf(iK0)(Number<offset_m_k>{}) =
|
||||
y_thread_buf(iK0)(Number<offset_m_k>{}) *
|
||||
gamma_thread_buf(iK0)(Number<offset_m_k>{});
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
static_for<0, ThreadBufferNumber, 1>{}([&](auto i) {
|
||||
threadwise_beta_load.Run(beta_grid_desc_m_k,
|
||||
beta_global_val_buf,
|
||||
thread_buffer_desc_m_k,
|
||||
make_tuple(I0, I0),
|
||||
beta_thread_buf(i));
|
||||
threadwise_beta_load.MoveSrcSliceWindow(beta_grid_desc_m_k,
|
||||
thread_copy_fwd_step_m_k);
|
||||
});
|
||||
|
||||
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
|
||||
static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
|
||||
static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
|
||||
constexpr auto offset_m_k =
|
||||
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK1));
|
||||
|
||||
// beta
|
||||
y_thread_buf(iK0)(Number<offset_m_k>{}) =
|
||||
y_thread_buf(iK0)(Number<offset_m_k>{}) +
|
||||
beta_thread_buf(iK0)(Number<offset_m_k>{});
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
static_for<0, ThreadBufferNumber, 1>{}([&](auto i) {
|
||||
threadwise_y_store.Run(thread_buffer_desc_m_k,
|
||||
make_tuple(I0, I0),
|
||||
y_thread_buf(i),
|
||||
y_grid_desc_m_k,
|
||||
y_global_val_buf);
|
||||
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, thread_copy_fwd_step_m_k);
|
||||
});
|
||||
} // end for (normalization)
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user