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https://github.com/ROCm/composable_kernel.git
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[CK_TILE] Add FAv3 fwd pipeline (#2731)
* Add FAv3 fwd pipeline * Unpack v_pk_mul to hide v_mov * Avoid compiler moving l compute across phase * Sync sched_group_barrier() setting for masking cases
This commit is contained in:
Po Yen Chen
519
include/ck_tile/ops/fmha/kernel/fmha_fwd_v3_kernel.hpp
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519
include/ck_tile/ops/fmha/kernel/fmha_fwd_v3_kernel.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, 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/common.hpp"
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#include "ck_tile/ops/fmha/block/block_masking.hpp"
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#include <type_traits>
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#include <utility>
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namespace ck_tile {
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template <typename FmhaPipeline_, typename EpiloguePipeline_>
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struct FmhaFwdV3Kernel
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{
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using FmhaPipeline = ck_tile::remove_cvref_t<FmhaPipeline_>;
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using EpiloguePipeline = ck_tile::remove_cvref_t<EpiloguePipeline_>;
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static constexpr ck_tile::index_t kBlockSize = FmhaPipeline::kBlockSize;
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static constexpr ck_tile::index_t kBlockPerCu = FmhaPipeline::kBlockPerCu;
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static_assert(kBlockPerCu > 0);
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using QDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::QDataType>;
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using KDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::KDataType>;
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using VDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::VDataType>;
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using LSEDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::LSEDataType>;
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using ODataType = ck_tile::remove_cvref_t<typename FmhaPipeline::ODataType>;
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using SaccDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::SaccDataType>;
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static constexpr bool kIsGroupMode = FmhaPipeline::kIsGroupMode;
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static constexpr bool kPadSeqLenQ = FmhaPipeline::kPadSeqLenQ;
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static constexpr bool kPadSeqLenK = FmhaPipeline::kPadSeqLenK;
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static constexpr bool kPadHeadDimQ = FmhaPipeline::kPadHeadDimQ;
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static constexpr bool kPadHeadDimV = FmhaPipeline::kPadHeadDimV;
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static constexpr bool kStoreLSE = FmhaPipeline::kStoreLSE;
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using FmhaMask = ck_tile::remove_cvref_t<typename FmhaPipeline::FmhaMask>;
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static constexpr bool kHasMask = FmhaMask::IsMasking;
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template <ck_tile::index_t I> // to avoid duplicated base class prblem, introduce an template
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// arg
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struct FmhaFwdEmptyKargs
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{
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};
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// kargs use aggregate initializer, so no constructor will provided
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// use inheritance to minimize karg size
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// user need to use MakeKargs() function to create kargs.
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struct FmhaFwdCommonKargs
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{
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const void* q_ptr;
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const void* k_ptr;
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const void* v_ptr;
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void* o_ptr;
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ck_tile::index_t seqlen_q;
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ck_tile::index_t seqlen_k;
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ck_tile::index_t hdim_q;
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ck_tile::index_t hdim_v;
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ck_tile::index_t num_head_q;
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// for MQA/GQA, nhead could be different. This parameter is nhead_q / nhead_k
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// if this param is larger than 1, indicate MQA/GQA case
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ck_tile::index_t nhead_ratio_qk;
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float scale_s;
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ck_tile::index_t stride_q;
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ck_tile::index_t stride_k;
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ck_tile::index_t stride_v;
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ck_tile::index_t stride_o;
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ck_tile::index_t nhead_stride_q;
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ck_tile::index_t nhead_stride_k;
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ck_tile::index_t nhead_stride_v;
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ck_tile::index_t nhead_stride_o;
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};
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struct FmhaFwdMaskKargs
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{
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// ck_tile::index_t window_size_left, window_size_right;
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ck_tile::index_t window_size_left, window_size_right;
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ck_tile::GenericAttentionMaskEnum mask_type;
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};
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struct FmhaFwdCommonLSEKargs
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{
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void* lse_ptr = nullptr;
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ck_tile::index_t nhead_stride_lse = 0;
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ck_tile::index_t batch_stride_lse = 0;
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};
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struct FmhaFwdBatchModeKargs
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: FmhaFwdCommonKargs,
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std::conditional_t<kHasMask, FmhaFwdMaskKargs, FmhaFwdEmptyKargs<0>>,
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std::conditional_t<kStoreLSE, FmhaFwdCommonLSEKargs, FmhaFwdEmptyKargs<1>>
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{
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ck_tile::index_t batch_stride_q;
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ck_tile::index_t batch_stride_k;
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ck_tile::index_t batch_stride_v;
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ck_tile::index_t batch_stride_o;
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};
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struct FmhaFwdGroupModeKargs
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: FmhaFwdCommonKargs,
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std::conditional_t<kHasMask, FmhaFwdMaskKargs, FmhaFwdEmptyKargs<0>>,
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std::conditional_t<kStoreLSE, FmhaFwdCommonLSEKargs, FmhaFwdEmptyKargs<1>>
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{
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const int32_t* seqstart_q_ptr;
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const int32_t* seqstart_k_ptr;
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const int32_t* seqlen_k_ptr;
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};
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using Kargs = std::conditional_t<kIsGroupMode, FmhaFwdGroupModeKargs, FmhaFwdBatchModeKargs>;
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template <bool Cond = !kIsGroupMode>
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CK_TILE_HOST static constexpr std::enable_if_t<Cond, Kargs>
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MakeKargs(const void* q_ptr,
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const void* k_ptr,
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const void* v_ptr,
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void* lse_ptr,
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void* o_ptr,
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ck_tile::index_t seqlen_q,
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ck_tile::index_t seqlen_k,
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ck_tile::index_t hdim_q,
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ck_tile::index_t hdim_v,
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ck_tile::index_t num_head_q,
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ck_tile::index_t nhead_ratio_qk,
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float scale_s,
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ck_tile::index_t stride_q,
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ck_tile::index_t stride_k,
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ck_tile::index_t stride_v,
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ck_tile::index_t stride_o,
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ck_tile::index_t nhead_stride_q,
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ck_tile::index_t nhead_stride_k,
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ck_tile::index_t nhead_stride_v,
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ck_tile::index_t nhead_stride_lse,
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ck_tile::index_t nhead_stride_o,
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ck_tile::index_t batch_stride_q,
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ck_tile::index_t batch_stride_k,
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ck_tile::index_t batch_stride_v,
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ck_tile::index_t batch_stride_lse,
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ck_tile::index_t batch_stride_o,
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ck_tile::index_t window_size_left,
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ck_tile::index_t window_size_right,
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ck_tile::index_t mask_type)
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{
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Kargs kargs{{q_ptr,
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k_ptr,
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v_ptr,
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o_ptr,
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seqlen_q,
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seqlen_k,
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hdim_q,
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hdim_v,
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num_head_q,
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nhead_ratio_qk,
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static_cast<float>(scale_s * ck_tile::log2e_v<>),
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stride_q,
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stride_k,
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stride_v,
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stride_o,
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nhead_stride_q,
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nhead_stride_k,
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nhead_stride_v,
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nhead_stride_o}, // args for common karg
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{}, // placeholder for mask
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{}, // placeholder for lse
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batch_stride_q,
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batch_stride_k,
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batch_stride_v,
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batch_stride_o};
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if constexpr(kHasMask)
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{
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kargs.window_size_left = window_size_left;
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kargs.window_size_right = window_size_right;
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kargs.mask_type = static_cast<ck_tile::GenericAttentionMaskEnum>(mask_type);
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}
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if constexpr(kStoreLSE)
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{
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kargs.lse_ptr = lse_ptr;
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kargs.nhead_stride_lse = nhead_stride_lse;
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kargs.batch_stride_lse = batch_stride_lse;
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}
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return kargs;
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}
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template <bool Cond = kIsGroupMode>
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CK_TILE_HOST static constexpr std::enable_if_t<Cond, Kargs>
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MakeKargs(const void* q_ptr,
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const void* k_ptr,
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const void* v_ptr,
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void* lse_ptr,
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void* o_ptr,
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const void* seqstart_q_ptr,
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const void* seqstart_k_ptr,
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const void* seqlen_k_ptr,
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ck_tile::index_t hdim_q,
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ck_tile::index_t hdim_v,
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ck_tile::index_t num_head_q,
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ck_tile::index_t nhead_ratio_qk,
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float scale_s,
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ck_tile::index_t stride_q,
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ck_tile::index_t stride_k,
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ck_tile::index_t stride_v,
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ck_tile::index_t stride_o,
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ck_tile::index_t nhead_stride_q,
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ck_tile::index_t nhead_stride_k,
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ck_tile::index_t nhead_stride_v,
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ck_tile::index_t nhead_stride_lse,
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ck_tile::index_t nhead_stride_o,
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ck_tile::index_t window_size_left,
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ck_tile::index_t window_size_right,
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ck_tile::index_t mask_type)
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{
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Kargs kargs{{q_ptr,
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k_ptr,
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v_ptr,
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o_ptr,
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-1, // seqlen will be updated by another pointer
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-1, //
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hdim_q,
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hdim_v,
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num_head_q,
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nhead_ratio_qk,
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static_cast<float>(scale_s * ck_tile::log2e_v<>),
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stride_q,
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stride_k,
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stride_v,
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stride_o,
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nhead_stride_q,
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nhead_stride_k,
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nhead_stride_v,
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nhead_stride_o}, // args for common karg
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{}, // placeholder for mask
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{}, // placeholder for lse
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reinterpret_cast<const int32_t*>(seqstart_q_ptr),
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reinterpret_cast<const int32_t*>(seqstart_k_ptr),
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reinterpret_cast<const int32_t*>(seqlen_k_ptr)};
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if constexpr(kHasMask)
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{
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kargs.window_size_left = window_size_left;
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kargs.window_size_right = window_size_right;
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kargs.mask_type = static_cast<ck_tile::GenericAttentionMaskEnum>(mask_type);
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}
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if constexpr(kStoreLSE)
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{
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kargs.lse_ptr = lse_ptr;
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kargs.nhead_stride_lse = nhead_stride_lse;
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}
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return kargs;
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}
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CK_TILE_HOST static constexpr auto GridSize(ck_tile::index_t batch_size_,
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ck_tile::index_t nhead_,
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ck_tile::index_t seqlen_q_,
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ck_tile::index_t hdim_v_)
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{
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// TODO: this may need tuning
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return dim3(ck_tile::integer_divide_ceil(seqlen_q_, FmhaPipeline::kM0) *
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ck_tile::integer_divide_ceil(hdim_v_, FmhaPipeline::kN1),
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nhead_,
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batch_size_);
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}
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CK_TILE_DEVICE static constexpr auto GetTileIndex(const Kargs& kargs)
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{
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using namespace ck_tile;
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// const index_t num_tile_m0 = seqlen_q / kM0;
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const index_t num_tile_n1 = ck_tile::integer_divide_ceil(kargs.hdim_v, FmhaPipeline::kN1);
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const index_t i_block = blockIdx.x;
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const index_t i_nhead = blockIdx.y;
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const index_t i_batch = blockIdx.z;
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const auto f = [](index_t dividend, index_t divisor) {
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index_t quotient = dividend / divisor;
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index_t modulus = dividend - quotient * divisor;
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return ck_tile::make_tuple(quotient, modulus);
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};
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const auto [i_tile_m, i_tile_n] = f(i_block, num_tile_n1);
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if constexpr(kHasMask)
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{
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// assume that num_tile_n1 is always 1
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return ck_tile::make_tuple(gridDim.x - 1 - i_tile_m, i_tile_n, i_nhead, i_batch);
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}
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else
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{
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return ck_tile::make_tuple(i_tile_m, i_tile_n, i_nhead, i_batch);
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}
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}
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CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
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CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
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{
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return ck_tile::max(FmhaPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
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}
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CK_TILE_DEVICE void operator()(Kargs kargs) const
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{
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using namespace ck_tile;
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// allocate LDS
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__shared__ char smem_ptr[GetSmemSize()];
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// divide problem
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const auto [i_tile_m, i_tile_n, i_nhead, i_batch] = GetTileIndex(kargs);
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const index_t i_m0 = __builtin_amdgcn_readfirstlane(i_tile_m * FmhaPipeline::kM0);
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const index_t i_n1 = __builtin_amdgcn_readfirstlane(i_tile_n * FmhaPipeline::kN1);
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long_index_t batch_offset_q = 0;
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long_index_t batch_offset_k = 0;
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long_index_t batch_offset_v = 0;
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long_index_t batch_offset_lse = 0;
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long_index_t batch_offset_o = 0;
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if constexpr(kIsGroupMode)
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{
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// get starting offset for each batch
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const long_index_t query_start = kargs.seqstart_q_ptr[i_batch];
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const long_index_t key_start = kargs.seqstart_k_ptr[i_batch];
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batch_offset_q = query_start * kargs.stride_q;
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batch_offset_k = key_start * kargs.stride_k;
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batch_offset_v = key_start * kargs.stride_v;
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if constexpr(kStoreLSE)
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{
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batch_offset_lse = query_start;
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}
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batch_offset_o = query_start * kargs.stride_o;
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// get real # queries & # keys under group mode
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const auto adjusted_seqstart_q_ptr = kargs.seqstart_q_ptr + i_batch;
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kargs.seqlen_q = adjusted_seqstart_q_ptr[1] - adjusted_seqstart_q_ptr[0];
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// # of required blocks is different in each groups, terminate unnecessary blocks
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// earlier
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if(kargs.seqlen_q <= i_m0)
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{
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return;
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}
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if(kargs.seqlen_k_ptr != nullptr)
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{
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kargs.seqlen_k = kargs.seqlen_k_ptr[i_batch];
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}
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else
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{
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const auto adjusted_seqstart_k_ptr = kargs.seqstart_k_ptr + i_batch;
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kargs.seqlen_k = adjusted_seqstart_k_ptr[1] - adjusted_seqstart_k_ptr[0];
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}
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}
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else
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{
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batch_offset_q = static_cast<long_index_t>(i_batch) * kargs.batch_stride_q;
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batch_offset_k = static_cast<long_index_t>(i_batch) * kargs.batch_stride_k;
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batch_offset_v = static_cast<long_index_t>(i_batch) * kargs.batch_stride_v;
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if constexpr(kStoreLSE)
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{
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batch_offset_lse = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse;
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}
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batch_offset_o = static_cast<long_index_t>(i_batch) * kargs.batch_stride_o;
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}
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// for simplicity, batch stride we just modify the pointer
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const QDataType* q_ptr = reinterpret_cast<const QDataType*>(kargs.q_ptr) +
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static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_q +
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batch_offset_q;
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const KDataType* k_ptr =
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reinterpret_cast<const KDataType*>(kargs.k_ptr) +
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static_cast<long_index_t>(i_nhead / kargs.nhead_ratio_qk) * kargs.nhead_stride_k +
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batch_offset_k;
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const VDataType* v_ptr =
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reinterpret_cast<const VDataType*>(kargs.v_ptr) +
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static_cast<long_index_t>(i_nhead / kargs.nhead_ratio_qk) * kargs.nhead_stride_v +
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batch_offset_v;
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ODataType* o_ptr = reinterpret_cast<ODataType*>(kargs.o_ptr) +
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static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_o +
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batch_offset_o;
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// Q/K/V DRAM and DRAM window
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const auto q_dram = [&]() {
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const auto q_dram_naive = make_naive_tensor_view<address_space_enum::global>(
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q_ptr,
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make_tuple(kargs.seqlen_q, kargs.hdim_q),
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make_tuple(kargs.stride_q, 1),
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number<FmhaPipeline::kAlignmentQ>{},
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number<1>{});
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return pad_tensor_view(
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q_dram_naive,
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make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kSubQKHeaddim>{}),
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sequence<kPadSeqLenQ, kPadHeadDimQ>{});
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}();
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const auto k_dram = [&]() {
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const auto k_dram_naive = make_naive_tensor_view<address_space_enum::global>(
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k_ptr,
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make_tuple(kargs.seqlen_k, kargs.hdim_q),
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make_tuple(kargs.stride_k, 1),
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number<FmhaPipeline::kAlignmentK>{},
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number<1>{});
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return pad_tensor_view(
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k_dram_naive,
|
||||
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{}),
|
||||
sequence<kPadSeqLenK, kPadHeadDimQ>{});
|
||||
}();
|
||||
const auto v_dram = [&]() {
|
||||
const auto v_dram_naive = make_naive_tensor_view<address_space_enum::global>(
|
||||
v_ptr,
|
||||
make_tuple(kargs.seqlen_k, kargs.hdim_v),
|
||||
make_tuple(kargs.stride_v, 1),
|
||||
number<FmhaPipeline::kAlignmentV>{},
|
||||
number<1>{});
|
||||
|
||||
return pad_tensor_view(
|
||||
v_dram_naive,
|
||||
make_tuple(number<FmhaPipeline::kK1>{}, number<FmhaPipeline::kN1>{}),
|
||||
sequence<kPadSeqLenK, kPadHeadDimV>{});
|
||||
}();
|
||||
|
||||
auto q_dram_window = make_tile_window(
|
||||
q_dram,
|
||||
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kSubQKHeaddim>{}),
|
||||
{i_m0, 0});
|
||||
|
||||
auto k_dram_window = make_tile_window(
|
||||
k_dram, make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{}), {0, 0});
|
||||
|
||||
auto v_dram_window =
|
||||
make_tile_window(v_dram,
|
||||
make_tuple(number<FmhaPipeline::kK1>{}, number<FmhaPipeline::kN1>{}),
|
||||
{0, i_n1});
|
||||
|
||||
// lse
|
||||
auto lse_dram_window = [&, i_nhead_ = i_nhead]() {
|
||||
constexpr auto lse_dram_window_lengths = make_tuple(number<FmhaPipeline::kM0>{});
|
||||
if constexpr(kStoreLSE)
|
||||
{
|
||||
LSEDataType* lse_ptr =
|
||||
reinterpret_cast<LSEDataType*>(kargs.lse_ptr) +
|
||||
static_cast<long_index_t>(i_nhead_) * kargs.nhead_stride_lse + batch_offset_lse;
|
||||
|
||||
const auto lse_dram = [&]() {
|
||||
const auto lse_dram_naive = make_naive_tensor_view<address_space_enum::global>(
|
||||
lse_ptr,
|
||||
make_tuple(kargs.seqlen_q),
|
||||
make_tuple(1),
|
||||
number<1>{},
|
||||
number<1>{});
|
||||
|
||||
return pad_tensor_view(
|
||||
lse_dram_naive, lse_dram_window_lengths, sequence<kPadSeqLenQ>{});
|
||||
}();
|
||||
|
||||
return make_tile_window(lse_dram, lse_dram_window_lengths, {i_m0});
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_null_tile_window(lse_dram_window_lengths);
|
||||
}
|
||||
}();
|
||||
|
||||
FmhaMask mask = [&]() {
|
||||
if constexpr(kHasMask)
|
||||
return ck_tile::make_generic_attention_mask_from_lr_window<FmhaMask>(
|
||||
kargs.window_size_left,
|
||||
kargs.window_size_right,
|
||||
kargs.seqlen_q,
|
||||
kargs.seqlen_k,
|
||||
kargs.mask_type == GenericAttentionMaskEnum::MASK_FROM_TOP_LEFT);
|
||||
else
|
||||
return FmhaMask{kargs.seqlen_q, kargs.seqlen_k};
|
||||
}();
|
||||
|
||||
auto o_acc_tile = [&]() {
|
||||
return FmhaPipeline{}(q_dram_window,
|
||||
k_dram_window,
|
||||
v_dram_window,
|
||||
lse_dram_window,
|
||||
mask,
|
||||
kargs.scale_s,
|
||||
smem_ptr);
|
||||
}();
|
||||
|
||||
// O DRAM and O DRAM window
|
||||
auto o_dram = [&]() {
|
||||
const auto o_dram_naive = make_naive_tensor_view<address_space_enum::global>(
|
||||
o_ptr,
|
||||
make_tuple(kargs.seqlen_q, kargs.hdim_v),
|
||||
make_tuple(kargs.stride_o, 1),
|
||||
number<FmhaPipeline::kAlignmentO>{},
|
||||
number<1>{});
|
||||
|
||||
return pad_tensor_view(
|
||||
o_dram_naive,
|
||||
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{}),
|
||||
sequence<kPadSeqLenQ, kPadHeadDimV>{});
|
||||
}();
|
||||
|
||||
auto o_dram_window =
|
||||
make_tile_window(o_dram,
|
||||
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{}),
|
||||
{i_m0, i_n1});
|
||||
|
||||
EpiloguePipeline{}(o_dram_window, o_acc_tile);
|
||||
}
|
||||
};
|
||||
} // namespace ck_tile
|
||||
Reference in New Issue
Block a user