mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 13:11:25 +00:00
Fused elementwise normalization (#492)
* add fused addition lyernorm * add fused addition lyernorm * changed CMakelist * removed annotates * modified descriptor of C * fixed bug in gridwise add layernorm * format the files * modified name from add&layernorm into elementwise&layernorm * created fused elementwise layernorm branch * change input into tuple type * add sweep once to reduce load & read of C from global memory * modified Argument api * modified way to malloc c in global memory * changed gamma and beta to m_k_desc * fixed bug when sweep once and move CDataType when define device level struct * add src dim for gamma and beta * implement optimization for coalesced * delete a annotation line * fixed some bug to meet the requirements of ck * add bandwidth computing in example, and fixed the time unit * move device_elementwise_layernorm_impl.hpp into device/impl * fixed bug in device_elementwise_layernorm_impl.hpp * changed name from layernorm into normalization * clang-format the changed files * changed the names * moved immidiate results into lds, it become faster in non-sweeponce cases * changed naming of C into X to make the defination more clear * changed naming in example * add tests for elementwise normalization * move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization * move test_elementwise_layernorm_fp16 into new folder * move elementwise_normalization_instances into a new folder * add more tests in test_elementwise_layernorm_fp16.cpp * added some corner cases in test * fixed method to compute lds size for matrix X * changed name of 44_elementwise_normalization into 45_elementwise_normalization * modified some comments * modified some other confused comments * reduce redundant tests in test_elementwise_layernorm_fp16.cpp
This commit is contained in:
guangzlu
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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/tensor_operation/gpu/element/binary_element_wise_operation.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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// X = Elementwise(input1, input2, input3, ...)
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// Y = Normalization(X, beta, gamma)
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template <typename InDataTypePointerTuple,
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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 AccDataType,
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typename XElementwiseOperation,
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typename YElementwiseOperation,
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typename InGrid2dDescTuple,
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typename GridDesc_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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bool SweepOnce>
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struct GridwiseElementwiseLayernormWelfordVariance_mk_to_mk
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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 constexpr index_t NumInput = InDataTypePointerTuple::Size();
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static constexpr bool reorder_thread_cluster = (XSrcVectorDim == 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<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<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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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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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 XThreadBufferNumber = Number<KThreadSliceSize / XSrcVectorSize>{};
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static constexpr auto GammaThreadBufferNumber = Number<KThreadSliceSize / GammaSrcVectorSize>{};
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static constexpr auto BetaThreadBufferNumber = Number<KThreadSliceSize / BetaSrcVectorSize>{};
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static constexpr auto YThreadBufferNumber = Number<KThreadSliceSize / YDstVectorSize>{};
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__device__ static int GetKPerThread(const GridDesc_M_K& x_grid_desc_m_k,
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int thread_k_cluster_id)
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{
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int kPerBlock = x_grid_desc_m_k.GetTransforms()[I2].GetUpperLengths()[I0];
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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, XThreadBufferNumber, 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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__device__ static void Run(const InGrid2dDescTuple in_grid_2d_desc_tuple,
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const GridDesc_M_K& x_grid_desc_m_k,
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const GridDesc_M_K& gamma_grid_desc_m_k,
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const GridDesc_M_K& beta_grid_desc_m_k,
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const GridDesc_M_K& y_grid_desc_m_k,
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index_t num_k_block_tile_iteration,
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AccDataType epsilon,
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const InDataTypePointerTuple p_in_global_tuple,
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XDataType* const __restrict__ p_x_lds,
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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 XElementwiseOperation x_elementwise_op,
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const YElementwiseOperation y_elementwise_op)
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{
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if constexpr(SweepOnce)
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{
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num_k_block_tile_iteration = 1;
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}
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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 grid_size = get_grid_size();
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auto in_global_buf_tuple = generate_tuple(
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[&](auto I) {
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static_assert(in_grid_2d_desc_tuple[I].GetNumOfDimension() ==
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2); // matrix dimension
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return make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_in_global_tuple[I], in_grid_2d_desc_tuple[I].GetElementSpaceSize());
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},
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Number<NumInput>{});
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auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
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auto x_lds_val_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
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p_x_lds, x_grid_desc_m_k.GetElementSpaceSize() / grid_size);
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auto in_thread_buf_tuple = generate_tuple(
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[&](auto) {
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return generate_tuple(
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[&](auto) {
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return StaticBuffer<AddressSpaceEnum::Vgpr,
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AccDataType,
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MThreadSliceSize * XSrcVectorSize,
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true>{};
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},
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Number<NumInput>{});
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},
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Number<XThreadBufferNumber>{});
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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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AccDataType,
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MThreadSliceSize * XSrcVectorSize,
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true>{};
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},
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Number<XThreadBufferNumber>{});
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auto gamma_thread_buf = generate_tuple(
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[&](auto) {
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return StaticBuffer<AddressSpaceEnum::Vgpr,
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AccDataType,
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MThreadSliceSize * GammaSrcVectorSize,
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true>{};
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},
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Number<GammaThreadBufferNumber>{});
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auto beta_thread_buf = generate_tuple(
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[&](auto) {
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return StaticBuffer<AddressSpaceEnum::Vgpr,
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AccDataType,
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MThreadSliceSize * BetaSrcVectorSize,
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true>{};
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},
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Number<BetaThreadBufferNumber>{});
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auto y_thread_buf = generate_tuple(
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[&](auto) {
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return StaticBuffer<AddressSpaceEnum::Vgpr,
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AccDataType,
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MThreadSliceSize * YDstVectorSize,
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true>{};
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},
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Number<YThreadBufferNumber>{});
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
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StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> var_thread_buf;
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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, XSrcVectorSize>;
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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<XSrcVectorSize>{}));
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auto in_global_load_tuple = generate_tuple(
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[&](auto I) {
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using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
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using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
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return ThreadwiseTensorSliceTransfer_v2<DataType,
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AccDataType,
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decltype(in_grid_2d_desc_tuple[I]),
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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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false>{
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in_grid_2d_desc_tuple[I],
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make_multi_index(block_global_id * M_BlockTileSize +
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thread_m_cluster_id * MThreadSliceSize,
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thread_k_cluster_id * XSrcVectorSize)};
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},
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Number<NumInput>{});
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auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
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AccDataType,
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GridDesc_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(thread_m_cluster_id * MThreadSliceSize,
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thread_k_cluster_id * XSrcVectorSize));
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auto threadwise_gamma_load =
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ThreadwiseTensorSliceTransfer_v2<GammaDataType,
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AccDataType,
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GridDesc_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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GammaSrcVectorDim,
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GammaSrcVectorSize,
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1,
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true>(
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gamma_grid_desc_m_k,
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make_multi_index(block_global_id * M_BlockTileSize +
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thread_m_cluster_id * MThreadSliceSize,
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thread_k_cluster_id * GammaSrcVectorSize));
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auto threadwise_beta_load =
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ThreadwiseTensorSliceTransfer_v2<BetaDataType,
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AccDataType,
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GridDesc_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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BetaSrcVectorDim,
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BetaSrcVectorSize,
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1,
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true>(
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beta_grid_desc_m_k,
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make_multi_index(block_global_id * M_BlockTileSize +
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thread_m_cluster_id * MThreadSliceSize,
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thread_k_cluster_id * BetaSrcVectorSize));
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using PassThrough = tensor_operation::element_wise::PassThrough;
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PassThrough pass_through_op;
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auto threadwise_x_store =
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ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
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XDataType,
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decltype(thread_buffer_desc_m_k),
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GridDesc_M_K,
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YElementwiseOperation,
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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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InMemoryDataOperationEnum::Set,
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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(thread_m_cluster_id * MThreadSliceSize,
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thread_k_cluster_id * XSrcVectorSize),
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pass_through_op);
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auto threadwise_y_store =
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ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
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YDataType,
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decltype(thread_buffer_desc_m_k),
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GridDesc_M_K,
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YElementwiseOperation,
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ThreadBufferLengths_M_K,
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ThreadBufferDimAccessOrder,
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YDstVectorDim,
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YDstVectorSize,
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InMemoryDataOperationEnum::Set,
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1,
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true>(
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y_grid_desc_m_k,
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make_multi_index(block_global_id * M_BlockTileSize +
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thread_m_cluster_id * MThreadSliceSize,
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thread_k_cluster_id * YDstVectorSize),
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y_elementwise_op);
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// Copy x from Cache
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// one pass: fwd, second pass: bwd
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constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
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constexpr auto thread_copy_bwd_step_m_k =
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make_multi_index(0, SweepOnce ? 0 : -K_BlockTileSize);
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const auto gamma_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_gamma_global, gamma_grid_desc_m_k.GetElementSpaceSize());
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const auto beta_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_beta_global, beta_grid_desc_m_k.GetElementSpaceSize());
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auto threadwise_welford = ThreadwiseWelford();
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threadwise_welford.max_count_ = GetKPerThread(x_grid_desc_m_k, thread_k_cluster_id);
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static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
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mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
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var_thread_buf(I) = type_convert<AccDataType>(0.0f);
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});
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for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
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{
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static_for<0, XThreadBufferNumber, 1>{}([&](auto iK0) {
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static_for<0, NumInput, 1>{}([&](auto I) { // input load loop
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in_global_load_tuple(I).Run(in_grid_2d_desc_tuple[I],
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in_global_buf_tuple[I],
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thread_buffer_desc_m_k,
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make_tuple(I0, I0),
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in_thread_buf_tuple(iK0)(I));
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in_global_load_tuple(I).MoveSrcSliceWindow(in_grid_2d_desc_tuple[I],
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thread_copy_fwd_step_m_k);
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});
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static_for<0, MThreadSliceSize, 1>{}([&](auto iM) { // input add loop
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static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
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constexpr auto offset_m_k =
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thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK1));
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// get reference to in data
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const auto in_data_refs = generate_tie(
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// return type should be lvalue
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[&](auto I) -> const auto& {
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return in_thread_buf_tuple(iK0)(I)(Number<offset_m_k>{});
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},
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Number<NumInput>{});
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// get reference to dst data
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auto out_data_refs = generate_tie(
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// return type should be lvalue
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[&](auto) -> auto& { return x_thread_buf(iK0)(Number<offset_m_k>{}); },
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I1);
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unpack2(x_elementwise_op, out_data_refs, in_data_refs);
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});
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});
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threadwise_welford.Run(x_thread_buf[iK0], mean_thread_buf, var_thread_buf);
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if constexpr(!SweepOnce)
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{
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threadwise_x_store.Run(thread_buffer_desc_m_k,
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make_tuple(I0, I0),
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x_thread_buf(iK0),
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x_grid_desc_m_k,
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x_lds_val_buf);
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threadwise_x_store.MoveDstSliceWindow(x_grid_desc_m_k,
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thread_copy_fwd_step_m_k);
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}
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});
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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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||||
|
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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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});
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auto thread_copy_tail_m_k =
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(num_k_block_tile_iteration - 1) * XThreadBufferNumber * thread_copy_fwd_step_m_k;
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||||
|
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if constexpr(!SweepOnce)
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threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_tail_m_k);
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threadwise_gamma_load.MoveSrcSliceWindow(gamma_grid_desc_m_k, thread_copy_tail_m_k);
|
||||
threadwise_beta_load.MoveSrcSliceWindow(beta_grid_desc_m_k, thread_copy_tail_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)
|
||||
{
|
||||
if constexpr(!SweepOnce)
|
||||
{
|
||||
static_for<0, XThreadBufferNumber, 1>{}([&](auto i) {
|
||||
threadwise_x_load.Run(x_grid_desc_m_k,
|
||||
x_lds_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, GammaThreadBufferNumber, 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 / __builtin_amdgcn_sqrtf(var_thread_buf(iM) + epsilon);
|
||||
static_for<0, XThreadBufferNumber, 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, BetaThreadBufferNumber, 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, XThreadBufferNumber, 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, YThreadBufferNumber, 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);
|
||||
});
|
||||
|
||||
if constexpr(!SweepOnce)
|
||||
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, 2 * thread_copy_bwd_step_m_k);
|
||||
threadwise_gamma_load.MoveSrcSliceWindow(gamma_grid_desc_m_k,
|
||||
2 * thread_copy_bwd_step_m_k);
|
||||
threadwise_beta_load.MoveSrcSliceWindow(beta_grid_desc_m_k,
|
||||
2 * thread_copy_bwd_step_m_k);
|
||||
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, 2 * thread_copy_bwd_step_m_k);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user