Files
composable_kernel/include/ck_tile/ops/epilogue/dynamic_quant_epilogue.hpp
ruanjm 04dd314883 [CK_TILE] Add Various Fusion Functions to RMSNorm (#1802)
* Add shortcut to RMSNorm

* Modify test for adding shortcut for RMSNorm

* Add fused parameter into tests

* 1. Add YDataType. 2. rmsnorm2d_fwd_traits_ from rmsnorm2d_fwd.hpp to rmsnorm2d_fwd_api.cpp and rmsnorm2d_fwd_instance_common.hpp

* 1. Supports various stride and percisions.

* Add support of Epilogue

* Add fuse and epilogue support to rmsnorm ref

* Modify rmsnorm example

* Refactor tests/examples

* Bug fix for newly added tests/examples

* Bug fix for new tests 2

* Modify smoke test scripts

remove dbg code

* Supports non-smooth dyanmic quant

* Update Rmsnorm2dFwd::GetName()

* rename xscale and prec_sx to smoothscale and prec_sm

Bug fix after rename

Remove files

* change example_rmsnorm2d_fwd.cpp

* update performance calculator

* Fix issue in two-pass when fuse add is enabled

* Remove comment of beta

---------

Co-authored-by: rocking <ChunYu.Lai@amd.com>
2025-01-15 10:23:48 +08:00

213 lines
8.4 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/reduce.hpp"
namespace ck_tile {
template <bool kPadM_,
bool kPadN_,
bool UseSmoothInputScale_,
bool UseRawStore_ = true,
bool UseMax3_ = false>
struct DynamicQuantEpilogueTraits
{
static constexpr bool kPadM = kPadM_;
static constexpr bool kPadN = kPadN_;
static constexpr bool UseSmoothInputScale = UseSmoothInputScale_;
static constexpr bool UseRawStore = UseRawStore_;
static constexpr bool UseMax3 = UseMax3_;
};
// this epilogue just store out a M*N matrix, row major
template <typename AccDataType_,
typename SmoothScaleDataType_,
typename YScaleDataType_,
typename ODataType_,
typename BlockShape_,
typename Traits_>
struct DynamicQuantEpilogueProblem
{
using AccDataType = remove_cvref_t<AccDataType_>;
using SmoothScaleDataType = remove_cvref_t<SmoothScaleDataType_>;
using YScaleDataType = remove_cvref_t<YScaleDataType_>;
using ODataType = remove_cvref_t<ODataType_>;
using BlockShape = remove_cvref_t<BlockShape_>; // can consum generic 2d shape
using Traits = remove_cvref_t<Traits_>;
};
// TODO: we should put descriptor creation function into policy
template <typename Problem_, typename Policy_ = void>
struct DynamicQuantEpilogue
{
using Problem = remove_cvref_t<Problem_>;
using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
using SmoothScaleDataType = remove_cvref_t<typename Problem::SmoothScaleDataType>;
using YScaleDataType = remove_cvref_t<typename Problem::YScaleDataType>;
using ODataType = remove_cvref_t<typename Problem::ODataType>;
using BlockShape = remove_cvref_t<typename Problem::BlockShape>;
static constexpr bool kPadM = Problem::Traits::kPadM;
static constexpr bool kPadN = Problem::Traits::kPadN;
static constexpr bool UseRawStore = Problem::Traits::UseRawStore;
static constexpr bool UseMax3 = Problem::Traits::UseMax3;
CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2d()
{
using P_ = BlockReduce2dProblem<AccDataType, AccDataType, BlockShape>;
return BlockReduce2d<P_>{};
}
CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dSync()
{
using P_ = BlockReduce2dProblem<AccDataType, AccDataType, BlockShape>;
return BlockReduce2dSync<P_>{};
}
CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dCrossWarpSync()
{
using P_ = BlockReduce2dProblem<AccDataType, AccDataType, BlockShape>;
return BlockReduce2dCrossWarpSync<P_>{};
}
CK_TILE_DEVICE static constexpr auto MakeSmoothInputScaleTileDistribution()
{
using S = BlockShape;
#if 0
// don't remove this
// Note that if we set encoding purposely like this, you will result in compile fail
// TODO: sm_scale create local-scratch to accept arbitrary acc input (with same length)
return make_static_tile_distribution(
tile_distribution_encoding<
sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M>,
tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
tuple<sequence<0, 1>, sequence<0, 1>>,
tuple<sequence<1, 1>, sequence<2, 2>>,
sequence<0, 1, 1>,
sequence<0, 0, 3>>{});
#else
return make_static_tile_distribution(
tile_distribution_encoding<
sequence<S::WarpPerBlock_M, S::ThreadPerWarp_M>,
tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
tuple<sequence<0, 1>, sequence<0, 1>>,
tuple<sequence<0, 1>, sequence<1, 2>>,
sequence<1, 1>,
sequence<0, 3>>{});
#endif
}
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
auto reduce_crosswarp_sync = GetBlockReduce2dCrossWarpSync();
return reduce_crosswarp_sync.GetSmemSize();
}
template <typename ODramWindowTmp, typename YScaleWindow, typename OAccTile>
CK_TILE_DEVICE auto Impl(ODramWindowTmp& o_dram_window_tmp,
YScaleWindow& y_scale_window,
const OAccTile& o_acc_tile,
void* smem)
{
auto reduce = GetBlockReduce2d();
auto reduce_sync = GetBlockReduce2dSync();
auto reduce_crosswarp_sync = GetBlockReduce2dCrossWarpSync();
auto o_acc_tmp = o_acc_tile;
const auto f_absmax = [](auto acc_, auto v_0_) { return max(acc_, abs(v_0_)); };
auto row_absmax = [&]() {
constexpr auto y_size_per_row =
OAccTile{}.get_tile_distribution().get_ys_to_d_descriptor().get_lengths().at(
number<1>{});
if constexpr(UseMax3 && std::is_same_v<AccDataType, float> && y_size_per_row % 2 == 0)
{
// fast max3+abs implementation
const auto f_max3 = [](auto acc_, auto v_0_, auto v_1_) {
float rtn;
asm volatile("v_max3_f32 %0, %1, abs(%2), abs(%3)"
: "=v"(rtn)
: "v"(acc_), "v"(v_0_), "v"(v_1_));
return rtn;
};
return reduce(o_acc_tmp, type_convert<AccDataType>(0), f_max3, sequence<1, 2>{});
}
else
{
return reduce(o_acc_tmp, type_convert<AccDataType>(0), f_absmax);
}
}();
reduce_sync(row_absmax, f_absmax);
reduce_crosswarp_sync(row_absmax, smem, f_absmax);
// here y_scale is Acc TYpe, need convert to YScale type later
auto y_scale = tile_elementwise_in(
[&](const auto& v_) {
return v_ / type_convert<AccDataType>(numeric<ODataType>::max());
},
row_absmax);
store_tile(y_scale_window, cast_tile<YScaleDataType>(y_scale));
sweep_tile(o_acc_tmp, [&](auto idx) {
constexpr auto row_id = make_tuple(idx[number<0>{}]);
o_acc_tmp(idx) = o_acc_tmp[idx] / y_scale(row_id);
});
// TODO: this is ugly
if constexpr(UseRawStore && (kPadM || kPadN))
{
store_tile_raw(o_dram_window_tmp, cast_tile<ODataType>(o_acc_tmp));
buffer_store_fence();
}
else
{
store_tile(o_dram_window_tmp, cast_tile<ODataType>(o_acc_tmp));
}
}
// TODO: this function assume store out vector size is the same as OAccTile last dimension size
// how do we fix this ?
// Smooth Dynamic Quant
template <typename ODramWindowTmp,
typename SmoothScaleWindow,
typename YScaleWindow,
typename OAccTile>
CK_TILE_DEVICE auto operator()(ODramWindowTmp& o_dram_window_tmp,
const SmoothScaleWindow& sm_scale_window_,
YScaleWindow& y_scale_window,
const OAccTile& o_acc_tile,
void* smem)
{
const auto sm_scale_window =
make_tile_window(sm_scale_window_, MakeSmoothInputScaleTileDistribution());
auto sm_scale = load_tile(sm_scale_window);
auto o_acc_tmp = o_acc_tile;
sweep_tile(o_acc_tmp, [&](auto idx) {
constexpr auto j_idx = make_tuple(idx[number<1>{}]);
const auto xs_ = type_convert<AccDataType>(sm_scale[j_idx]);
o_acc_tmp(idx) = o_acc_tmp(idx) * xs_;
});
Impl(o_dram_window_tmp, y_scale_window, o_acc_tmp, smem);
}
// Dynamic Quant
template <typename ODramWindowTmp, typename YScaleWindow, typename OAccTile>
CK_TILE_DEVICE auto operator()(ODramWindowTmp& o_dram_window_tmp,
YScaleWindow& y_scale_window,
const OAccTile& o_acc_tile,
void* smem)
{
Impl(o_dram_window_tmp, y_scale_window, o_acc_tile, smem);
}
};
} // namespace ck_tile