Files
composable_kernel/example/ck_tile/01_fmha/fmha_fwd.hpp
Po Yen Chen c156989298 [CK_TILE] Add PagedAttention kernels (#1387)
* Use dictionary to config all the functions

* Add init codegen logic for fmha fwd appendkv

* Call HIP_CHECK_ERROR() macro to get real source info

* Setup meaningfull arguments

* Sync kernel name with the codegen

* Add knew/vnew tensors to the kernel argument

* Fix wrong K values after appending

* Fix vnew append errro

* Extract common logics

* Fix Vnew tile dstr for row major case

* Conditionally add fwd_splitkv API in fmha_fwd example

* Conditionally add call to fmha_fwd_splitkv()

* Remove "EXAMPLE_" prefix of cmake variables

* Regsiter API handlers automatically

* Early return if 0 < s_k_new is not supported

* Show message if we are ignoring option

* Unify CMakeLists.txt coding style

* Set num_splits=1 if split-kv is not supported

* Add length/stride getters for HostTensor

* Add RoPE example utilities

* Add reference_rotary_position_embedding() (not implemented)

* Finish reference_rotary_position_embedding() impl

* Fix typo of HostTensor<>::get_length()

* Fix compilation errors

* Fix wrong answer when interleaved=false

* Fix wrong answer when interleaved=true

* Append K/V in the host verification code

* Simplify K appending logics

* Simplify v_host_ref definition

* Reduce input/output dimensions

* Rename function: add "batched" prefix

* Apply RoPE on host side

* Rename RoPE utility function

* Fix wrong tensor size

* Avoid invoking deprecated method 'find_module'

* Pass RoPE kernel args

* Create Rotary Cos/Sin tile windows in kernel

* Add compute data type alias for RoPE

* Randomly generate seqlen_knew if needed

* Fix seqlen_knew enabling check logic

* Add minimum seqlen_k to generate compliance kvcache

* Fix compilation error in debug mode

* Fix wrong boundaries

* Fix wrong seqlen_k for kvcache

* Rename variables used in distributio encoding

* Fix rotary cos/sin tensor/tile size

* Add constraint to the rotary_dim option

* Remove unused inner namespace

* Add dram distribution for rotary_cos/rotary_sin (interleaved)

* Only apply interleaved RoPE on Knew for now

* Fix wrong thread starting offset

* Instantiate multiple kernels for RoPE approaches

* Clean-up pipeline

* Fix error in RoPE host reference

* Handle RoPE half-rotated logics

* Support 8x rotary_dim under half-rotated RoPE

* Add comment

* Apply elementwise function to the loaded tiles

* Unify parameter/variable naming style

* Remove constness from q_ptr

* Add code blocks for q_tile

* Apply RoPE to q_tile

* Remove debug print code in kernel

* Fix wrong knew/vnew appending positions

* Use better naming for tile indices

* Add make_tile_window() for adding distribution only

* Skip code if # of block is more than needed

* Move thread locating logics into policy

* Remove always true static_assert()

* Rename header

* Rename RotaryEmbeddingEnum

* Extract rotary embedding logic out

* Re-order parameters

* Align naming of some tile size constants

* Rename more tile size constants

* Fix wrong grid size

* Fix wrong shape of knew_host/vnew_host

* Fix wrong index into knew_host/vnew_host

* Fix wrong rotary_cos/rotary_sin memory size for Q

* Extract Q/Knew vector size to helper methods

* Use different rotary_cos/rotary_sin distr for Q/Knew

* Update host/device specifiers

* Fix wrong data type for Q rotary_cos/rotary_sin

* Remove RoPEComputeDataType type alias

* Shift rotary_cos/rotary_sin by cache_seqlen_k

* Add comment for why I just 't' for all padding flags

* Align commit message to the real comment

* Fix wrong pipeline

* Rename utility function

* Disable host verification if API not exist

* Fix wrong rope key for fp8 pipeline

* Allow only apply RoPE on Q (without append KV)

* Add append-kv smoke tests

* Remove debug statements

* Remove more debug statements

* Re-arrange the 'set +x' command

* Remove no-longer used method in pipeline

* Add missing init code

* Refine pipeline padding settings

* Enlarge rotary_dim limit (8 -> 16)

* Enlarge KPerThread for rotary_interleaved=false

* Update rotary_dim range in smoke_test_fwd.sh

* Add template argument 'kIsPagedKV' for splitkv kernels

* Launch splitkv kernel if given page_block_size

* Fix wrong kernel name

* Fix seqlen_k_min for pre-fill case (1 -> 0)

* Add copy_const<> type trait

* Add another make_tile_window()

* Introduce 'TileWindowNavigator' types

* Simplify TileWindowNavigator interfaces

* Fix tile window navigation bugs

* Disable calling fmha_fwd()

* Remove ununnecessary data members

* Simplify more make_tile_window() overloads

* Move V tile through TileWindowNavigator

* Fix uneven split checking logic

* Move code after decide seqlen_q/seqlen_k

* Make sure we always start reading complete tile

* Use 128 as minimus page_block_size

* Fix wrong origin for bias

* Add batch_stride_k/batch_stride_v in group mode

* Unify origin

* Add missing kernel arguments for group mode

* Add paged-kv codegen logic for appendkv kernels

* Add block_table kernel args for appendkv kernel

* Add tile navigators to the appendkv kernel

* Fix wrong tensor descriptor lengths

* Pass re-created tile window to pipeline

* Fix wrong strides for appendkv kernel

* Allow transit tile_window to another page-block

* Handle cross-page-block write

* Donot perform write again if already in last page-block

* Always add fmha_fwd() api

* Add missing group mode argument

* Remove debug macro usages

* Rename option s_k_new to s_knew

* Separate splitkv/non-splitkv args/traits

* Remove fmha_fwd_dispatch()

* Fix compilation errors

* Remove dropout code in splitkv kernel

* Allow problem types without define kHasDropout attr

* Use generic lambda to init traits objects

* Separate more non-splitkv & splitkv traits/args

* Display more info for specific kernels

* Show more detailed warning message

* Rename 'max_num_blocks' to 'max_num_page_blocks'

* Remove no-longer used pipeline files

* Wrap code by #if directives

* Move functors to the begining of validation code

* Use generic lambda to init all the api traits/args

* Fix wrong seqlen for kvcache

* Add missing comment

* Rename TileWindowNavigator to PageBlockNavigator

* Only expose necessary methods (not attributes)

* Re-order pipeline paremeters

* Refine smoke_test_fwd.sh

* Fix wrong arugment count

* Make tile window directly via PageBlockNavigator

* Remove unused template paremeter

* Remove group mode from appendkv kernel

* Fix skcheck logic

* Fix wrong syntax in skcheck expr

* Use meaningful options in smoke test

* Remove options

* Fix formatting

* Fix more format

* Re-organize bash functions

* Pass cache_batch_idx to kernels

* Support cache_batch_idx in example

* Fix compilation error

* Add more appendkv test

* Add more case for appendkv

* Fix unexisted attribute

* Remove 0 < seqlen_knew constraint

* Clarify the case in warning message

* Remove macro checking

* Force batch mode when invoking appendkv & splitkv apis

* Fix mode overriding logics

* Fix wrong parameter name

* Randomize seqlen_k if use kvcache

* Use randomized seqlen_k for kvcache

* Avoid using too small rotary_cos & rotary_sin

* Rename parameter

* Add seqlen_q & seqlen_k rules

* Add comment

* Add more comments

* Fix compilation errors

* Fix typo in comment

* Remove type argument

* Avoid seqlen_k=0 for kvcache

* Revert "Avoid seqlen_k=0 for kvcache"

This reverts commit 21c4df89e4.

* Fix wrong uneven split checking logics

* Only randomize kvcache seqlen_k if 1 < batch

* Return earlier if split is empty

* Revert "Only randomize kvcache seqlen_k if 1 < batch"

This reverts commit b9a4ab0d7e.

* Re-order seqlen_k_start adjustment logics

* Fix compilation errors

* Re-format script

* Find executable from folder automatically

* Fix kvcache seqlen_k generating logic

* Make comment more clear

* Fix wrong knew/vew appending logic on host

* Add s_barrier to sync threads

* Revert "Add s_barrier to sync threads"

This reverts commit d3f550f30c.

* Support only using 1 row of rotary_cos/rotary_sin

* Rotate Q in different way

* Unify tensor view creation logics

* Fix wrong argument

* Add mask to switch how we use the rotary_cos/sin

* Move attr from traits to problem

* Move has_mask to fmha_fwd_appendkv_args

* Support use uint32_t as SAD operand in Alibi<>

* Use sad_u32() in splitkv kernels

* Store tensor views in PageBlockNavigator

* Use stored tensor view to update tile windows

* Enlarge tensor view size

* Remove debug code

* Fix wrong tensor view size

* Wrap tensor view into PageBlockNavigator

* Add DataType member to PageBlockNavigator

* Remove unnecessary member functions

* Refind macro use

* Fix typo

* Add blank line between directives and actual code

* Re-format files

* Remove type in comment

---------

Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: rocking <ChunYu.Lai@amd.com>
2024-08-28 20:50:43 +08:00

775 lines
32 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/fmha.hpp"
#include "bias.hpp"
#include "mask.hpp"
#include "rotary.hpp"
#include <type_traits>
template <typename DataType>
struct FmhaFwdTypeConfig;
template <>
struct FmhaFwdTypeConfig<ck_tile::half_t>
{
using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t;
using VDataType = ck_tile::half_t;
using BiasDataType = ck_tile::half_t;
using RandValOutputDataType = uint8_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SaccDataType = float; // data type for first gemm accumulation
using SMPLComputeDataType = float; // data type for reduction, softmax
using PDataType = ck_tile::half_t; // data type for A matrix of second gemm
using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::half_t;
};
template <>
struct FmhaFwdTypeConfig<ck_tile::bf16_t>
{
using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t;
using VDataType = ck_tile::bf16_t;
using BiasDataType = ck_tile::bf16_t;
using RandValOutputDataType = uint8_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SaccDataType = float; // data type for first gemm accumulation
using SMPLComputeDataType = float; // data type for reduction, softmax
using PDataType = ck_tile::bf16_t; // data type for A matrix of second gemm
using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::bf16_t;
};
template <>
struct FmhaFwdTypeConfig<ck_tile::fp8_t>
{
using QDataType = ck_tile::fp8_t;
using KDataType = ck_tile::fp8_t;
using VDataType = ck_tile::fp8_t;
using BiasDataType = float;
using RandValOutputDataType = uint8_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SaccDataType = float; // data type for first gemm accumulation
using SMPLComputeDataType = float; // data type for reduction, softmax
using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm
using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::fp8_t;
};
template <>
struct FmhaFwdTypeConfig<ck_tile::bf8_t>
{
using QDataType = ck_tile::bf8_t;
using KDataType = ck_tile::bf8_t;
using VDataType = ck_tile::bf8_t;
using BiasDataType = ck_tile::bf8_t;
using RandValOutputDataType = uint8_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SaccDataType = float; // data type for first gemm accumulation
using SMPLComputeDataType = float; // data type for reduction, softmax
using PDataType = ck_tile::bf8_t; // data type for A matrix of second gemm
using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::bf8_t;
};
struct FmhaMasks
{
using NoMask = ck_tile::GenericAttentionMask<false>;
using GenericMask = ck_tile::GenericAttentionMask<true, true>;
using CausalMask = ck_tile::GenericAttentionMask<true, false>;
};
// runtime args, some will passed to karg, some will used to compute grids/blocks
struct fmha_fwd_args
{
const void* q_ptr;
const void* k_ptr;
const void* v_ptr;
const void* bias_ptr; // bias or alibi_slope pointer
void* rand_val_ptr;
void* lse_ptr;
void* o_ptr;
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void*
seqlen_k_ptr; // only used if both 'seqstart_q_ptr' & 'seqstart_k_ptr' are not nullptr
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
float scale_s;
float scale_p;
float scale_o;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
ck_tile::index_t stride_randval;
ck_tile::index_t stride_o;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_bias;
ck_tile::index_t nhead_stride_randval;
ck_tile::index_t nhead_stride_lse;
ck_tile::index_t nhead_stride_o;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_bias;
ck_tile::index_t batch_stride_randval;
ck_tile::index_t batch_stride_lse;
ck_tile::index_t batch_stride_o;
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t mask_type;
float p_drop;
bool s_randval;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
};
struct fmha_fwd_splitkv_args
{
const void* q_ptr;
const void* k_ptr;
const void* v_ptr;
const void* bias_ptr; // bias or alibi_slope pointer
void* lse_acc_ptr;
void* o_acc_ptr;
void* lse_ptr;
void* o_ptr;
void* block_table_ptr;
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
const void* cache_batch_idx;
// the real seqlen_q & seqlen_k are decided by following:
// batch mode: seqlen_q = kargs.seqlen_q
// seqlen_k = kargs.seqlen_k
// group mode: seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// kvcache mode (use same kernel as batch mode):
// seqlen_q = kargs.seqlen_q
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void* seqlen_k_ptr;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
ck_tile::index_t num_splits;
float scale_s;
float scale_p;
float scale_o;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
ck_tile::index_t stride_o_acc;
ck_tile::index_t stride_o;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_bias;
ck_tile::index_t nhead_stride_lse;
ck_tile::index_t nhead_stride_lse_acc;
ck_tile::index_t nhead_stride_o_acc;
ck_tile::index_t nhead_stride_o;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_bias;
ck_tile::index_t batch_stride_lse;
ck_tile::index_t batch_stride_lse_acc;
ck_tile::index_t batch_stride_o_acc;
ck_tile::index_t batch_stride_o;
ck_tile::index_t split_stride_lse_acc;
ck_tile::index_t split_stride_o_acc;
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t mask_type;
};
struct fmha_fwd_appendkv_args
{
void* q_ptr;
void* k_ptr;
const void* knew_ptr;
void* v_ptr;
const void* vnew_ptr;
const void* seqlen_k_ptr;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_knew;
ck_tile::index_t batch;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
const void* rotary_cos_ptr; // only used if 'rotary_dim' > 0
const void* rotary_sin_ptr; // only used if 'rotary_dim' > 0
ck_tile::index_t rotary_dim;
bool has_mask;
void* block_table_ptr;
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
const void* cache_batch_idx;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_knew;
ck_tile::index_t stride_v;
ck_tile::index_t stride_vnew;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_knew;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_vnew;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_knew;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_vnew;
};
template <typename FmhaKernel>
auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = [&] {
// create group mode kernel arguments
if constexpr(FmhaKernel::kIsGroupMode)
{
return FmhaKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
else
{ // create batch mode kernel arguments
return FmhaKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_lse,
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
}();
dim3 grids = FmhaKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v);
return ck_tile::make_tuple(kargs, grids);
}
template <typename Kernel>
auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = [&] {
// create group mode kernel arguments
if constexpr(Kernel::kIsGroupMode)
{
return Kernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_acc_ptr,
args.o_acc_ptr,
args.batch,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.num_splits,
args.scale_s,
args.scale_p,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_o_acc,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.split_stride_lse_acc,
args.split_stride_o_acc,
args.window_size_left,
args.window_size_right,
args.mask_type);
}
else
{ // create batch mode kernel arguments
return Kernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_acc_ptr,
args.o_acc_ptr,
args.batch,
args.seqlen_q,
args.seqlen_k,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.num_splits,
args.block_table_ptr,
args.batch_stride_block_table,
args.page_block_size,
args.cache_batch_idx,
args.scale_s,
args.scale_p,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_o_acc,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.split_stride_lse_acc,
args.split_stride_o_acc,
args.window_size_left,
args.window_size_right,
args.mask_type);
}
}();
dim3 grids =
Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.num_splits);
return ck_tile::make_tuple(kargs, grids);
}
template <typename Kernel>
auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_splitkv_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = [&] {
// create group mode kernel argumentszs
if constexpr(Kernel::kIsGroupMode)
{
return Kernel::MakeKargs(args.lse_acc_ptr,
args.o_acc_ptr,
args.lse_ptr,
args.o_ptr,
args.batch,
args.max_seqlen_q,
args.seqstart_q_ptr,
args.hdim_v,
args.num_splits,
args.scale_o,
args.stride_o_acc,
args.stride_o,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_o_acc,
args.split_stride_lse_acc,
args.split_stride_o_acc);
}
else
{ // create batch mode kernel arguments
return Kernel::MakeKargs(args.lse_acc_ptr,
args.o_acc_ptr,
args.lse_ptr,
args.o_ptr,
args.batch,
args.max_seqlen_q,
args.seqlen_q,
args.hdim_v,
args.num_splits,
args.scale_o,
args.stride_o_acc,
args.stride_o,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.batch_stride_lse,
args.batch_stride_o,
args.split_stride_lse_acc,
args.split_stride_o_acc);
}
}();
dim3 grids = Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v);
return ck_tile::make_tuple(kargs, grids);
}
template <typename Kernel>
auto fmha_fwd_appendkv_create_kargs_and_grids(fmha_fwd_appendkv_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = Kernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.knew_ptr,
args.v_ptr,
args.vnew_ptr,
args.seqlen_q,
args.seqlen_k_ptr,
args.seqlen_knew,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.rotary_cos_ptr,
args.rotary_sin_ptr,
args.rotary_dim,
args.has_mask,
args.block_table_ptr,
args.batch_stride_block_table,
args.page_block_size,
args.cache_batch_idx,
args.stride_q,
args.stride_k,
args.stride_knew,
args.stride_v,
args.stride_vnew,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_knew,
args.nhead_stride_v,
args.nhead_stride_vnew,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_knew,
args.batch_stride_v,
args.batch_stride_vnew);
dim3 grids = Kernel::GridSize(args.batch, args.nhead_q, args.seqlen_q, args.seqlen_knew);
return ck_tile::make_tuple(kargs, grids);
}
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN0_,
ck_tile::index_t kK0_,
ck_tile::index_t kN1_,
ck_tile::index_t kK1_,
ck_tile::index_t kK0BlockLength_,
bool kIsVLayoutRowMajor_,
ck_tile::BlockFmhaPipelineEnum FmhaPipelineEnum_,
typename FmhaMask_,
ck_tile::BlockAttentionBiasEnum BiasEnum_,
bool kStoreLse_,
bool kHasDropout_,
bool kDoFp8StaticQuant_,
bool kPadS_,
bool kPadSK_,
bool kPadD_,
bool kPadDv_>
struct fmha_fwd_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN0 = kN0_;
static constexpr ck_tile::index_t kK0 = kK0_;
static constexpr ck_tile::index_t kN1 = kN1_;
static constexpr ck_tile::index_t kK1 = kK1_;
static constexpr ck_tile::index_t kK0BlockLength = kK0BlockLength_;
static constexpr bool kIsVLayoutRowMajor = kIsVLayoutRowMajor_;
static constexpr auto FmhaPipelineEnum = FmhaPipelineEnum_;
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
static constexpr auto BiasEnum = BiasEnum_;
static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kHasDropout = kHasDropout_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSK = kPadSK_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
};
template <typename Traits_>
float fmha_fwd_(const ck_tile::stream_config&, fmha_fwd_args);
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN0_,
ck_tile::index_t kK0_,
ck_tile::index_t kN1_,
ck_tile::index_t kK1_,
ck_tile::index_t kK0BlockLength_,
bool kIsVLayoutRowMajor_,
ck_tile::BlockFmhaPipelineEnum FmhaPipelineEnum_,
typename FmhaMask_,
ck_tile::BlockAttentionBiasEnum BiasEnum_,
bool kStoreLse_,
bool kDoFp8StaticQuant_,
bool kIsPagedKV_,
bool kPadS_,
bool kPadSK_,
bool kPadD_,
bool kPadDv_>
struct fmha_fwd_splitkv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN0 = kN0_;
static constexpr ck_tile::index_t kK0 = kK0_;
static constexpr ck_tile::index_t kN1 = kN1_;
static constexpr ck_tile::index_t kK1 = kK1_;
static constexpr ck_tile::index_t kK0BlockLength = kK0BlockLength_;
static constexpr bool kIsVLayoutRowMajor = kIsVLayoutRowMajor_;
static constexpr auto FmhaPipelineEnum = FmhaPipelineEnum_;
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
static constexpr auto BiasEnum = BiasEnum_;
static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSK = kPadSK_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
static constexpr bool kIsPagedKV = kIsPagedKV_;
};
template <typename Traits_>
void fmha_fwd_splitkv_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);
template <typename Traits_>
std::string fmha_fwd_splitkv_get_name_();
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN1_,
bool kStoreLse_,
bool kDoFp8StaticQuant_,
bool kPadS_,
bool kPadDv_>
struct fmha_fwd_splitkv_combine_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN1 = kN1_;
static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadDv = kPadDv_;
};
template <typename Traits_>
void fmha_fwd_splitkv_combine_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);
template <typename Traits_>
std::string fmha_fwd_splitkv_combine_get_name_();
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
typename DataType_,
ck_tile::index_t kTileSizeS_,
ck_tile::index_t kTileSizeSk_,
ck_tile::index_t kTileSizeD_,
ck_tile::index_t kTileSizeDv_,
bool kIsVLayoutRowMajor_,
bool kPadS_,
bool kPadSk_,
bool kPadD_,
bool kPadDv_,
ck_tile::RotaryEmbeddingEnum RotaryEnum_,
bool kIsPagedKV_>
struct fmha_fwd_appendkv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr ck_tile::index_t kTileSizeS = kTileSizeS_;
static constexpr ck_tile::index_t kTileSizeSk = kTileSizeSk_;
static constexpr ck_tile::index_t kTileSizeD = kTileSizeD_;
static constexpr ck_tile::index_t kTileSizeDv = kTileSizeDv_;
static constexpr bool kIsVLayoutRowMajor = kIsVLayoutRowMajor_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSk = kPadSk_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
static constexpr auto RotaryEnum = RotaryEnum_;
static constexpr bool kIsPagedKV = kIsPagedKV_;
};
template <typename Traits_>
float fmha_fwd_appendkv_(const ck_tile::stream_config&, fmha_fwd_appendkv_args);
// This is the public API, will be generated by script
struct fmha_fwd_traits
{
int hdim_q;
int hdim_v;
std::string data_type;
bool is_group_mode;
bool is_v_rowmajor;
mask_enum mask_type;
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
bool has_lse;
bool has_dropout;
bool do_fp8_static_quant;
// TODO: padding check is inside this api
};
float fmha_fwd(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
struct fmha_fwd_splitkv_traits
{
int hdim_q;
int hdim_v;
std::string data_type;
bool is_group_mode;
bool is_v_rowmajor;
mask_enum mask_type;
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
bool has_lse;
bool do_fp8_static_quant;
// TODO: padding check is inside this api
};
float fmha_fwd_splitkv(fmha_fwd_splitkv_traits,
fmha_fwd_splitkv_args,
const ck_tile::stream_config&);
struct fmha_fwd_appendkv_traits
{
int hdim_q;
int hdim_v;
std::string data_type;
bool is_v_rowmajor;
rope_enum rope_type;
};
float fmha_fwd_appendkv(fmha_fwd_appendkv_traits,
fmha_fwd_appendkv_args,
const ck_tile::stream_config&);