mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
[Ck tile] support rmsnorm and related fusion (#1605)
* Add reduce2d new api
* Prevent user use cross warp reduction
* Fix bug of std caculation
* Add rmsnorm2d
* Add rmsnorm small example
* Remove static assert to prevent compile fail
* Add script to test performance and correctness
* Add missing cmake change
* refine naming
* refine example of rmsnorm
* Fix bug of rmsnorm
* Refine naming
* Fix cmake
* clang format
* Refine pipeline name
* Add add_rmsnorm2d_rdquant kernel
* Add reduce op
* host verification
* Fix bug of one pass pipeline
* Refine tile size
* Add two pass pipeline
* Rename two pass to three pass
* Fix bug of kSaveX == false
* Add instance library
* Add test script
* Fix bug of x verification
* Add save_x to trait
* Add README
* Move reduce2d into reduce folder
* Fix bug of welford when number of m warp > 1
* remove reduncant comment
* 1. move 06_rmsnorm2d to 10_rmsnorm2d
2. move 07_add_rmsnorm2d_rdquant to 11_add_rmsnorm2d_rdquant
* clang format and add missing header
* Add host validation of add + layernorm2d + rsquant
* Revert "Add host validation of add + layernorm2d + rsquant"
This reverts commit 936cb45797.
* Remove deprecated flag
This commit is contained in:
rocking
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/reduce/block/block_reduce2d_problem.hpp"
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#include "ck_tile/ops/reduce/block/block_reduce2d.hpp"
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namespace ck_tile {
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struct Rmsnorm2dFwdPipelineDefaultPolicy
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{
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template <typename Problem>
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CK_TILE_DEVICE static constexpr auto MakeXBlockTileDistribution()
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{
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using S = typename Problem::BlockShape;
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return make_static_tile_distribution(
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tile_distribution_encoding<
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sequence<>,
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tuple<sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M, S::Vector_M>,
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sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
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tuple<sequence<1, 2>, sequence<1, 2>>,
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tuple<sequence<1, 1>, sequence<2, 2>>,
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sequence<1, 1, 2, 2>,
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sequence<0, 3, 0, 3>>{});
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}
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template <typename Problem>
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CK_TILE_DEVICE static constexpr auto MakeGammaBlockTileDistribution()
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{
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using S = typename Problem::BlockShape;
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return make_static_tile_distribution(
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tile_distribution_encoding<
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sequence<S::WarpPerBlock_M, S::ThreadPerWarp_M>,
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tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
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tuple<sequence<0, 1>, sequence<0, 1>>,
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tuple<sequence<0, 1>, sequence<1, 2>>,
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sequence<1, 1>,
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sequence<0, 3>>{});
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}
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template <typename Problem>
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CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2d()
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{
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using P_ = BlockReduce2dProblem<typename Problem::XDataType,
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typename Problem::ComputeDataType,
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typename Problem::BlockShape>;
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return BlockReduce2d<P_>{};
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}
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template <typename Problem>
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CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dSync()
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{
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using P_ = BlockReduce2dProblem<typename Problem::XDataType,
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typename Problem::ComputeDataType,
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typename Problem::BlockShape>;
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return BlockReduce2dSync<P_>{};
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}
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template <typename Problem>
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CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dCrossWarpSync()
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{
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using P_ = BlockReduce2dProblem<typename Problem::XDataType,
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typename Problem::ComputeDataType,
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typename Problem::BlockShape>;
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return BlockReduce2dCrossWarpSync<P_>{};
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}
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template <typename Problem>
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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{
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if constexpr(Problem::kNeedCrossWarpSync)
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{
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using P_ = BlockReduce2dProblem<typename Problem::XDataType,
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typename Problem::ComputeDataType,
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typename Problem::BlockShape>;
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using block_reduce2d = BlockReduce2d<P_>;
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using x_block_tile =
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decltype(make_static_distributed_tensor<typename Problem::XDataType>(
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MakeXBlockTileDistribution<Problem>()));
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using y_block_tile = decltype(block_reduce2d::template MakeYBlockTile<x_block_tile>());
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return GetBlockReduce2dCrossWarpSync<Problem>().template GetSmemSize<y_block_tile>();
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}
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else
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{
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return 1; // zero size arrays are an extension
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}
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}
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};
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} // namespace ck_tile
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/rmsnorm2d/pipeline/rmsnorm2d_fwd_pipeline_default_policy.hpp"
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#include <string>
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#include <type_traits>
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namespace ck_tile {
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template <typename Problem_, typename Policy_ = Rmsnorm2dFwdPipelineDefaultPolicy>
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struct Rmsnorm2dFwdPipelineOnePass
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{
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using Problem = ck_tile::remove_cvref_t<Problem_>;
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using Policy = ck_tile::remove_cvref_t<Policy_>;
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using XDataType = ck_tile::remove_cvref_t<typename Problem::XDataType>;
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using GammaDataType = ck_tile::remove_cvref_t<typename Problem::GammaDataType>;
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using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
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using YDataType = ck_tile::remove_cvref_t<typename Problem::YDataType>;
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using InvRmsDataType = ck_tile::remove_cvref_t<typename Problem::InvRmsDataType>;
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static constexpr bool kHasGamma = !std::is_same_v<GammaDataType, ck_tile::null_type>;
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static constexpr bool kSaveInvRms = Problem::kSaveInvRms;
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static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync;
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static constexpr bool kPadM = false; // TODO - BlockRmsnorm2dFwdProblem::kPadM
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static constexpr bool kPadN = Problem::kPadN;
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static constexpr const char* name = []() {
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if constexpr(kNeedCrossWarpSync)
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return "bpr_op"; // block per row
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else
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return "wpr_op"; // warp per row
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}();
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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{
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return Policy::template GetSmemSize<Problem>();
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}
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template <typename XWindow, typename GammaWindow, typename YWindow, typename InvRmsWindow>
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CK_TILE_DEVICE auto operator()(const XWindow& x_window_,
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const GammaWindow& gamma_window_,
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YWindow& y_window,
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InvRmsWindow& inv_rms_window,
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ComputeDataType epsilon,
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ck_tile::index_t row_size,
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void* smem) const
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{
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const auto x_window =
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make_tile_window(x_window_, Policy::template MakeXBlockTileDistribution<Problem>());
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const auto gamma_window = make_tile_window(
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gamma_window_, Policy::template MakeGammaBlockTileDistribution<Problem>());
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auto reduce_square_sum_func = ReduceOp::SquareAdd{};
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auto reduce_sum_func = ReduceOp::Add{};
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auto block_reduce2d = Policy::template GetBlockReduce2d<Problem>();
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auto block_reduce2d_sync = Policy::template GetBlockReduce2dSync<Problem>();
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auto block_reduce2d_cross_warp_sync =
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Policy::template GetBlockReduce2dCrossWarpSync<Problem>();
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const auto x = load_tile(x_window);
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// load gamma (TODO: support no gamma?)
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const auto gamma = load_tile(gamma_window);
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// compute mean square each-thread->cross-lane->cross-warp
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auto square_sum = block_reduce2d(
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x, reduce_square_sum_func.GetIdentityValue<ComputeDataType>(), reduce_square_sum_func);
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block_reduce2d_sync(square_sum, reduce_sum_func);
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block_reduce2d_cross_warp_sync(square_sum, smem, reduce_sum_func);
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// compute inv-rms
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auto inv_rms = tile_elementwise_in(
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[&](const auto& v_) {
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return type_convert<ComputeDataType>(1.0f) / (sqrt(v_ / row_size + epsilon));
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},
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square_sum);
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if constexpr(kSaveInvRms)
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store_tile(inv_rms_window, cast_tile<InvRmsDataType>(inv_rms));
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// rmsnorm computation
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auto y = make_static_distributed_tensor<YDataType>(x.get_tile_distribution());
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sweep_tile(y, [&, inv_rms_ = inv_rms](auto idx) {
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constexpr auto i_idx = make_tuple(idx[number<0>{}]);
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constexpr auto j_idx = make_tuple(idx[number<1>{}]);
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const auto gamma_ = type_convert<ComputeDataType>(gamma[j_idx]);
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const auto x_ = type_convert<ComputeDataType>(x[idx]);
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auto y_ = x_ * inv_rms_[i_idx] * gamma_;
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y(idx) = type_convert<YDataType>(y_);
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});
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store_tile(y_window, y);
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}
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};
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} // namespace ck_tile
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core/utility/type_traits.hpp"
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namespace ck_tile {
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template <typename XDataType_,
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typename GammaDataType_,
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typename ComputeDataType_,
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typename YDataType_,
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typename InvRmsDataType_,
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typename BlockShape_,
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bool kPadN_,
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bool kSaveInvRms_,
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bool kTwoPass_>
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struct Rmsnorm2dFwdPipelineProblem
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{
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using XDataType = remove_cvref_t<XDataType_>;
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using GammaDataType = remove_cvref_t<GammaDataType_>;
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using ComputeDataType = remove_cvref_t<ComputeDataType_>;
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using YDataType = remove_cvref_t<YDataType_>;
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using InvRmsDataType = remove_cvref_t<InvRmsDataType_>;
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using BlockShape = remove_cvref_t<BlockShape_>;
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static constexpr bool kNeedCrossLaneSync = BlockShape::ThreadPerWarp_N > 1;
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static constexpr bool kNeedCrossWarpSync = BlockShape::WarpPerBlock_N > 1;
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static constexpr bool kPadN = kPadN_;
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static constexpr bool kSaveInvRms = kSaveInvRms_;
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static constexpr bool kTwoPass = kTwoPass_;
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};
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} // namespace ck_tile
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/rmsnorm2d/pipeline/rmsnorm2d_fwd_pipeline_default_policy.hpp"
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#include <string>
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#include <type_traits>
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namespace ck_tile {
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template <typename Problem_, typename Policy_ = Rmsnorm2dFwdPipelineDefaultPolicy>
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struct Rmsnorm2dFwdPipelineTwoPass
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{
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using Problem = ck_tile::remove_cvref_t<Problem_>;
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using Policy = ck_tile::remove_cvref_t<Policy_>;
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using XDataType = ck_tile::remove_cvref_t<typename Problem::XDataType>;
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using GammaDataType = ck_tile::remove_cvref_t<typename Problem::GammaDataType>;
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using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
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using YDataType = ck_tile::remove_cvref_t<typename Problem::YDataType>;
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using InvRmsDataType = ck_tile::remove_cvref_t<typename Problem::InvRmsDataType>;
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static constexpr bool kHasGamma = !std::is_same_v<GammaDataType, ck_tile::null_type>;
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static constexpr bool kSaveInvRms = Problem::kSaveInvRms;
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static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync;
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static constexpr bool kPadM = false; // TODO - BlockRmsnorm2dFwdProblem::kPadM
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static constexpr bool kPadN = Problem::kPadN;
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static constexpr const char* name = []() {
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if constexpr(kNeedCrossWarpSync)
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return "bpr_tp"; // block per row
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else
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return "wpr_tp"; // warp per row
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}();
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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{
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return Policy::template GetSmemSize<Problem>();
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}
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template <typename XWindow, typename GammaWindow, typename YWindow, typename InvRmsWindow>
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CK_TILE_DEVICE auto operator()(const XWindow& x_window_,
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const GammaWindow& gamma_window_,
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YWindow& y_window,
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InvRmsWindow& inv_rms_window,
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ComputeDataType epsilon,
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ck_tile::index_t row_size,
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void* smem) const
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{
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auto x_window =
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make_tile_window(x_window_, Policy::template MakeXBlockTileDistribution<Problem>());
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auto gamma_window = make_tile_window(
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gamma_window_, Policy::template MakeGammaBlockTileDistribution<Problem>());
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// Problem::BlockShape
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static constexpr index_t Block_N = Problem::BlockShape::Block_N;
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index_t num_n_tile_iteration =
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__builtin_amdgcn_readfirstlane(integer_divide_ceil(row_size, Block_N));
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auto reduce_square_sum_func = ReduceOp::SquareAdd{};
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auto reduce_sum_func = ReduceOp::Add{};
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auto block_reduce2d = Policy::template GetBlockReduce2d<Problem>();
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auto block_reduce2d_sync = Policy::template GetBlockReduce2dSync<Problem>();
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auto block_reduce2d_cross_warp_sync =
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Policy::template GetBlockReduce2dCrossWarpSync<Problem>();
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using XTensorType = decltype(load_tile(x_window));
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auto square_sum = block_reduce2d.template MakeYBlockTile<XTensorType>();
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set_tile(square_sum, reduce_square_sum_func.GetIdentityValue<ComputeDataType>());
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for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
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{
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const auto x = load_tile(x_window);
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block_reduce2d(x, square_sum, reduce_square_sum_func);
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move_tile_window(x_window, {0, Block_N});
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}
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block_reduce2d_sync(square_sum, reduce_sum_func);
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block_reduce2d_cross_warp_sync(square_sum, smem, reduce_sum_func);
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// compute inv-rms
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auto inv_rms = tile_elementwise_in(
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[&](const auto& v_) {
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return type_convert<ComputeDataType>(1.0f) / (sqrt(v_ / row_size + epsilon));
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},
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square_sum);
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if constexpr(kSaveInvRms)
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store_tile(inv_rms_window, cast_tile<InvRmsDataType>(inv_rms));
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// reverse read x to reuse cache
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ck_tile::index_t stride_to_right_most_window =
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row_size % Block_N == 0 ? row_size - Block_N : row_size - row_size % Block_N;
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move_tile_window(x_window, {0, -Block_N});
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move_tile_window(gamma_window, {stride_to_right_most_window});
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move_tile_window(y_window, {0, stride_to_right_most_window});
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// rmsnorm computation
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for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
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{
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const auto x = load_tile(x_window);
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// load gamma/beta (TODO: support no gamma/beta?)
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const auto gamma = load_tile(gamma_window);
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auto y = make_static_distributed_tensor<YDataType>(x.get_tile_distribution());
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sweep_tile(y, [&, inv_rms_ = inv_rms](auto idx) {
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constexpr auto i_idx = make_tuple(idx[number<0>{}]);
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constexpr auto j_idx = make_tuple(idx[number<1>{}]);
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const auto gamma_ = type_convert<ComputeDataType>(gamma[j_idx]);
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const auto x_ = type_convert<ComputeDataType>(x[idx]);
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auto y_ = x_ * inv_rms_[i_idx] * gamma_;
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y(idx) = type_convert<YDataType>(y_);
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});
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store_tile(y_window, y);
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move_tile_window(x_window, {0, -Block_N});
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move_tile_window(gamma_window, {-Block_N});
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move_tile_window(y_window, {0, -Block_N});
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}
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}
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};
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} // namespace ck_tile
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