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[CK_TILE] fmha forward split-kv + combine kernels (#1338)

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* FA fwd dropout

* FA bwd

* epilogue reuse

* CMakeLists update

* [CK_TILE] support alibi (#1269)

* add alibi support

* fix code

* update code based on comment

* Support more hdim

* fix fp8 bias

* support seqlen_k=0 case

* remove unused printf

* fix format

---------

Co-authored-by: rocking <ChunYu.Lai@amd.com>

* now fwd/bwd can build

* bwd alibi

* add bwd validation stream_config

* update generated filenames

* update bwd kernel launch

* CK_TILE_HOST_DEVICE in philox

* Transpose -> transpose

* format

* format

* format

* Generate the instance for FA required

* format

* fix error in WarpGemm

* Add num_splits option and dummy split-kv api method

* Generate fmha_fwd_splitkv()

* Add SplitKV kernel codegen logics

* Add SplitKV combine kernel codegen logics

* Fix mismatched return type

* Clean-up code

* Replace sentinel value before storing

* Fix wrong layout of LSE/LSEacc/Oacc

* Format codes

* Fix o_acc memory error

* Fix wrong kBlockSize used in policy

* Reduce # of combine kernels

* Fix split-kv combine kernel name

* Fix wrong LDS indexing logics

* Fix wrong loop counter step logic

* Undo vector size changes

* Remove no-longer used field

* Remove in-consistent comment

* Remove debug statements in example

* Remove more debug statements

* Add constness to local variables

* Clearn up generate.py

* Fix unstable clang-format comment

* Remove unused include directive

* Use shorter template parameter name

* Enable non-split-kv blobs

* Update license date

* Print num_splits conditionally

* Undo disabling data types

* Remove unnessary tile size for fp8

* Fix wrong pipeline args for fp8

* Fix example output format

* Remove more debug code in combine pipeline

* Add stride kernel arguments for LSE/O acc workspace

* Re-order split-kv pipeline call operator arguments

* Pass LSE/O strides in kernel argument

* Re-order pipeline call operator arguments

* Use tensor_descriptor to locate LSEacc elements

* Support providing invalid element for tensor view

* Set invalid element value for LSEacc tensor view

* Remove hand-written store_tile() code

* Remove necessary value-overwrite logic

* Add transposed lds descriptor

* Support load_tile() for tile_window_with_static_lengths<>

* Undo removing necessary value-overwrite logic

* Use read descriptor to locate lds elements

* Simplify pipeline source code

* Add constraint to kMaxSplits

* Default use kMaxSplits=64 in generate.py

* Revert "Add constraint to kMaxSplits"

This reverts commit 0a2132d758.

* Revert "Default use kMaxSplits=64 in generate.py"

This reverts commit c7d9c80b77.

* Decide alignment by the padding parameter

* Remove no-longer used utility functions

* Remove not-working code

* Add comment & remove no-longer used code

* Fix computation errors

* Add heuristic to override num_splits option

* Add constraint to kMaxSplits

* Fix compilation error

* Clean up pipeline code

* Wrap pointer access as lambda function

* Rename confusing methods

* Use kLogMasSplits as template parameter

* Finish splitkv combine kernel codegen

* Update kMaxSplits limit

* Use smaller kM0 for splitkv combine kernel

* Ignore droupout flag in splitkv pipeline

* Unify flag usage

* Add back flag kStoreLSE

* Merge lambda calls in pipeline

* Fix compilation errors

* Avoid all empty splits

* Always check for empty loop in splitkv pipelines

* Re-order parameters

* Remove redundant p_drop option check

* Add traits/problem for fwd splitkv kernel

* Conditionally enable uneven split boundary checks

* Add comment for the splitkv traits field

* Change even split criteria

* Re-order statements

* Refine occupancy value for hdim=128&256

* Refine occupancy value for hdim=32&64

* Remove redundant kernel argument

* Separate fmha bwd codegen logics

* Separate fmha fwd codegen logics

* Remove redundant direction parameter in fwd&bwd codegen logics

* Support generate multiple APIs for an example

* Let 'api' an alias of 'direction' option

* Remove choices for the 'direction' option

* Use dictionary to config all the functions

* Move fmha splitkv codegen logics to other file

* Add fwd_splitkv api for tile_example_fmha_fwd

---------

Co-authored-by: danyao12 <danyao12>
Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: rocking <ChunYu.Lai@amd.com>
Co-authored-by: Jing Zhang <jizhan@amd.com>
This commit is contained in:
Po Yen Chen
2024-06-26 17:41:15 +08:00
committed by GitHub
parent 3e9711f0cb
commit 0cb2e06ddc
25 changed files with 5861 additions and 1201 deletions
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455
include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_combine_kernel.hpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
namespace ck_tile {
template <typename TilePartitioner_, typename FmhaPipeline_, typename EpiloguePipeline_>
struct FmhaFwdSplitKVCombineKernel
{
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
using FmhaPipeline = remove_cvref_t<FmhaPipeline_>;
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
static constexpr index_t kBlockSize = FmhaPipeline::kBlockSize;
static constexpr index_t kBlockPerCu = FmhaPipeline::kBlockPerCu;
static_assert(kBlockPerCu > 0);
static constexpr index_t kBlockPerCuInput = FmhaPipeline::Problem::kBlockPerCu;
using LSEDataType = remove_cvref_t<typename FmhaPipeline::LSEDataType>;
using OaccDataType = remove_cvref_t<typename FmhaPipeline::OaccDataType>;
using ODataType = remove_cvref_t<typename FmhaPipeline::ODataType>;
static constexpr bool kIsGroupMode = FmhaPipeline::kIsGroupMode;
static constexpr bool kPadSeqLenQ = FmhaPipeline::kPadSeqLenQ;
static constexpr bool kPadHeadDimV = FmhaPipeline::kPadHeadDimV;
static constexpr bool kStoreLSE = FmhaPipeline::kStoreLSE;
static constexpr bool kDoFp8StaticQuant = FmhaPipeline::Problem::kDoFp8StaticQuant;
// clang-format off
template <typename T> struct t2s;
template <> struct t2s<float> { static constexpr const char * name = "fp32"; };
template <> struct t2s<ck_tile::fp16_t> { static constexpr const char * name = "fp16"; };
template <> struct t2s<ck_tile::bf16_t> { static constexpr const char * name = "bf16"; };
template <> struct t2s<ck_tile::fp8_t> { static constexpr const char * name = "fp8"; };
template <> struct t2s<ck_tile::bf8_t> { static constexpr const char * name = "bf8"; };
// clang-format on
__host__ static std::string GetName()
{
// sync with generate.py
// clang-format off
#define _SS_ std::string
#define _TS_ std::to_string
auto pn = [&] () {
std::string n;
if (kPadSeqLenQ) n += "s";
if (kPadHeadDimV) n += "dv";
return n.empty() ? n : std::string("p") + n; }();
return
_SS_("fmha_fwd_splitkv_combine_d") + _TS_(FmhaPipeline::kHeadDimV) + "_" + _SS_(t2s<ODataType>::name) +
"_" + (kIsGroupMode ? "group" : "batch") + "_"
"b" + _TS_(FmhaPipeline::kM0) + "x" +
_TS_(FmhaPipeline::kN1) + "_" +
(kBlockPerCuInput == -1 ? "" : ("o" + _TS_(kBlockPerCu) + "_")) +
_SS_(FmhaPipeline::name) +
(pn.empty() ? "" : "_" + pn) +
(kStoreLSE ? "_lse" : "" ) +
(kDoFp8StaticQuant ? "_squant" : "" );
#undef _SS_
#undef _TS_
// clang-format on
}
template <ck_tile::index_t I> // to avoid duplicated base class prblem, introduce an template
// arg
struct EmptyKargs
{
};
// kargs use aggregate initializer, so no constructor will provided
// use inheritance to minimize karg size
// user need to use MakeKargs() function to create kargs.
struct CommonKargs
{
const void* lse_acc_ptr;
const void* o_acc_ptr;
void* o_ptr;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t seqlen_q;
ck_tile::index_t hdim_v;
ck_tile::index_t num_splits;
ck_tile::index_t row_stride_o_acc;
ck_tile::index_t row_stride_o;
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_lse_acc;
ck_tile::index_t batch_stride_o_acc;
ck_tile::index_t split_stride_lse_acc;
ck_tile::index_t split_stride_o_acc;
};
struct CommonLSEKargs
{
void* lse_ptr = nullptr;
ck_tile::index_t nhead_stride_lse = 0;
ck_tile::index_t batch_stride_lse = 0;
};
struct Fp8StaticQuantKargs
{
float scale_o;
};
struct BatchModeKargs
: CommonKargs,
std::conditional_t<kStoreLSE, CommonLSEKargs, EmptyKargs<0>>,
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<1>>
{
ck_tile::index_t batch_stride_o;
};
struct GroupModeKargs
: CommonKargs,
std::conditional_t<kStoreLSE, CommonLSEKargs, EmptyKargs<0>>,
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<3>>
{
const int32_t* seqstart_q_ptr;
};
using Kargs = std::conditional_t<kIsGroupMode, GroupModeKargs, BatchModeKargs>;
template <bool Cond = !kIsGroupMode>
__host__ static constexpr std::enable_if_t<Cond, Kargs>
MakeKargs(const void* lse_acc_ptr,
const void* o_acc_ptr,
void* lse_ptr,
void* o_ptr,
ck_tile::index_t batch,
ck_tile::index_t max_seqlen_q,
ck_tile::index_t seqlen_q,
ck_tile::index_t hdim_v,
ck_tile::index_t num_splits,
float scale_o,
ck_tile::index_t row_stride_o_acc,
ck_tile::index_t row_stride_o,
ck_tile::index_t nhead_stride_lse_acc,
ck_tile::index_t nhead_stride_o_acc,
ck_tile::index_t nhead_stride_lse,
ck_tile::index_t nhead_stride_o,
ck_tile::index_t batch_stride_lse_acc,
ck_tile::index_t batch_stride_o_acc,
ck_tile::index_t batch_stride_lse,
ck_tile::index_t batch_stride_o,
ck_tile::index_t split_stride_lse_acc,
ck_tile::index_t split_stride_o_acc)
{
Kargs kargs{{lse_acc_ptr,
o_acc_ptr,
o_ptr,
batch,
max_seqlen_q,
seqlen_q,
hdim_v,
num_splits,
row_stride_o_acc,
row_stride_o,
nhead_stride_lse_acc,
nhead_stride_o_acc,
nhead_stride_o,
batch_stride_lse_acc,
batch_stride_o_acc,
split_stride_lse_acc,
split_stride_o_acc}, // args for common karg
{}, // placeholder for lse
{}, // placeholder for fp8_static_quant args
batch_stride_o};
if constexpr(kStoreLSE)
{
kargs.lse_ptr = lse_ptr;
kargs.nhead_stride_lse = nhead_stride_lse;
kargs.batch_stride_lse = batch_stride_lse;
}
if constexpr(kDoFp8StaticQuant)
{
kargs.scale_o = scale_o;
}
return kargs;
}
template <bool Cond = kIsGroupMode>
__host__ static constexpr std::enable_if_t<Cond, Kargs>
MakeKargs(const void* lse_acc_ptr,
const void* o_acc_ptr,
void* lse_ptr,
void* o_ptr,
ck_tile::index_t batch,
ck_tile::index_t max_seqlen_q,
const void* seqstart_q_ptr,
ck_tile::index_t hdim_v,
ck_tile::index_t num_splits,
float scale_o,
ck_tile::index_t row_stride_o_acc,
ck_tile::index_t row_stride_o,
ck_tile::index_t nhead_stride_lse_acc,
ck_tile::index_t nhead_stride_o_acc,
ck_tile::index_t nhead_stride_lse,
ck_tile::index_t nhead_stride_o,
ck_tile::index_t batch_stride_lse_acc,
ck_tile::index_t batch_stride_o_acc,
ck_tile::index_t batch_stride_lse,
ck_tile::index_t split_stride_lse_acc,
ck_tile::index_t split_stride_o_acc)
{
Kargs kargs{{lse_acc_ptr,
o_acc_ptr,
o_ptr,
batch,
max_seqlen_q,
-1, // seqlen will be updated by another pointer
hdim_v,
num_splits,
row_stride_o_acc,
row_stride_o,
nhead_stride_lse_acc,
nhead_stride_o_acc,
nhead_stride_o,
batch_stride_lse_acc,
batch_stride_o_acc,
split_stride_lse_acc,
split_stride_o_acc}, // args for common karg
{}, // placeholder for lse
{}, // placeholder for fp8_static_quant args
reinterpret_cast<const int32_t*>(seqstart_q_ptr)};
if constexpr(kStoreLSE)
{
kargs.lse_ptr = lse_ptr;
kargs.nhead_stride_lse = nhead_stride_lse;
kargs.batch_stride_lse = batch_stride_lse;
}
if constexpr(kDoFp8StaticQuant)
{
kargs.scale_o = scale_o;
}
return kargs;
}
__host__ static constexpr auto GridSize(ck_tile::index_t batch_size_,
ck_tile::index_t nhead_,
ck_tile::index_t seqlen_q_,
ck_tile::index_t hdim_v_)
{
return TilePartitioner::GridSize(batch_size_, nhead_, seqlen_q_, hdim_v_);
}
__host__ static constexpr auto BlockSize() { return dim3(kBlockSize); }
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
return ck_tile::max(FmhaPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
}
CK_TILE_DEVICE void operator()(Kargs kargs) const
{
// allocate LDS
__shared__ char smem_ptr[GetSmemSize()];
// divide problem
const auto [i_tile_m, i_tile_n, i_nhead, i_batch] =
TilePartitioner{}(kargs.seqlen_q, kargs.hdim_v);
const index_t i_m0 = __builtin_amdgcn_readfirstlane(i_tile_m * FmhaPipeline::kM0);
const index_t i_n1 = __builtin_amdgcn_readfirstlane(i_tile_n * FmhaPipeline::kN1);
const long_index_t batch_offset_lse_acc =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse_acc;
const long_index_t batch_offset_o_acc =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_o_acc;
long_index_t batch_offset_lse = 0;
long_index_t batch_offset_o = 0;
if constexpr(kStoreLSE)
{
batch_offset_lse = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse;
}
if constexpr(kIsGroupMode)
{
// get starting offset for each batch
const long_index_t query_start = kargs.seqstart_q_ptr[i_batch];
batch_offset_o = query_start * kargs.row_stride_o;
// get real # queries & # keys under group mode
const auto adjusted_seqstart_q_ptr = kargs.seqstart_q_ptr + i_batch;
kargs.seqlen_q = adjusted_seqstart_q_ptr[1] - adjusted_seqstart_q_ptr[0];
// # of required blocks is different in each groups, terminate unnecessary blocks
// earlier
if(kargs.seqlen_q <= i_m0)
{
return;
}
}
else
{
batch_offset_o = static_cast<long_index_t>(i_batch) * kargs.batch_stride_o;
}
// for simplicity, batch stride we just modify the pointer
const LSEDataType* lse_acc_ptr =
reinterpret_cast<const LSEDataType*>(kargs.lse_acc_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_lse_acc + batch_offset_lse_acc;
const OaccDataType* o_acc_ptr =
reinterpret_cast<const OaccDataType*>(kargs.o_acc_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_o_acc + batch_offset_o_acc;
ODataType* o_ptr = reinterpret_cast<ODataType*>(kargs.o_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_o +
batch_offset_o;
// LSEacc/Oacc DRAM and DRAM windows
const auto lse_acc_dram = [&]() {
const auto lse_acc_dram_naive = make_naive_tensor_view<address_space_enum::global>(
lse_acc_ptr,
make_tuple(kargs.num_splits, kargs.seqlen_q),
make_tuple(kargs.split_stride_lse_acc, 1),
number<FmhaPipeline::kAlignmentLSEacc>{},
number<1>{});
return pad_tensor_view(
lse_acc_dram_naive,
make_tuple(number<FmhaPipeline::kMaxSplits>{}, number<FmhaPipeline::kM0>{}),
sequence<true, kPadSeqLenQ>{});
}();
auto o_acc_dram = [&]() {
const auto o_acc_dram_naive = make_naive_tensor_view<address_space_enum::global>(
o_acc_ptr,
make_tuple(kargs.num_splits, kargs.max_seqlen_q, kargs.hdim_v),
make_tuple(kargs.split_stride_o_acc, kargs.row_stride_o_acc, 1),
number<FmhaPipeline::kAlignmentOacc>{},
number<1>{});
auto o_acc_dram_view = pad_tensor_view(
o_acc_dram_naive,
make_tuple(number<1>{}, number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{}),
sequence<false, kPadSeqLenQ, kPadHeadDimV>{});
const index_t padded_max_seqlen_q =
o_acc_dram_view.get_tensor_descriptor().get_lengths()[number<1>{}];
const index_t padded_hdim_v =
o_acc_dram_view.get_tensor_descriptor().get_lengths()[number<2>{}];
return transform_tensor_view(
o_acc_dram_view,
make_tuple(make_merge_transform(make_tuple(kargs.num_splits, padded_max_seqlen_q)),
make_pass_through_transform(padded_hdim_v)),
make_tuple(sequence<0, 1>{}, sequence<2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
}();
auto lse_acc_dram_window = make_tile_window(
lse_acc_dram,
[&]() {
return make_tuple(number<FmhaPipeline::kMaxSplits>{}, number<FmhaPipeline::kM0>{});
}(),
{0, i_m0});
auto o_acc_dram_window = make_tile_window(
o_acc_dram,
[&]() {
return make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{});
}(),
{i_m0, i_n1});
// LSE DRAM window
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<FmhaPipeline::kAlignmentLSE>{},
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);
}
}();
auto o_acc_tile = [&]() {
if constexpr(kDoFp8StaticQuant)
{
return FmhaPipeline{}(
lse_acc_dram_window,
o_acc_dram_window,
lse_dram_window,
identity{}, // lse_element_func
composes(saturates<fp8_t>{}, scales{kargs.scale_o}), // o_acc_element_func
kargs.num_splits,
kargs.max_seqlen_q,
smem_ptr);
}
else
{
return FmhaPipeline{}(lse_acc_dram_window,
o_acc_dram_window,
lse_dram_window,
kargs.num_splits,
kargs.max_seqlen_q,
smem_ptr);
}
}();
// O DRAM and 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.row_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

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include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_combine_tile_partitioner.hpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
template <index_t kM0_, index_t kN1_>
struct FmhaFwdSplitKVCombineTilePartitioner
{
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN1 = kN1_;
CK_TILE_HOST static constexpr auto GridSize(ck_tile::index_t batch_size_,
ck_tile::index_t nhead_,
ck_tile::index_t seqlen_q_,
ck_tile::index_t hdim_v_)
{
// TODO: this may need tuning
return dim3(ck_tile::integer_divide_ceil(seqlen_q_, kM0) *
ck_tile::integer_divide_ceil(hdim_v_, kN1),
nhead_,
batch_size_);
}
CK_TILE_DEVICE auto operator()(ck_tile::index_t /*seqlen_q*/, ck_tile::index_t hdim_v)
{
// const index_t num_tile_m0 = seqlen_q / kM0;
const index_t num_tile_n1 = ck_tile::integer_divide_ceil(hdim_v, kN1);
const index_t i_block = blockIdx.x;
const index_t i_nhead = blockIdx.y;
const index_t i_batch = blockIdx.z;
const auto f = [](index_t dividend, index_t divisor) {
index_t quotient = dividend / divisor;
index_t modulus = dividend - quotient * divisor;
return ck_tile::make_tuple(quotient, modulus);
};
const auto [i_tile_m, i_tile_n] = f(i_block, num_tile_n1);
return ck_tile::make_tuple(i_tile_m, i_tile_n, i_nhead, i_batch);
}
};
} // namespace ck_tile

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include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_kernel.hpp Normal file
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// 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/ops/common.hpp"
#include "ck_tile/ops/fmha/block/block_attention_bias_enum.hpp"
#include <string>
#include <type_traits>
// S[seqlen_q, seqlen_k] = Q[seqlen_q, hdim_q] @ K[seqlen_k, hdim_q]
// S'[seqlen_q, seqlen_k] = S[seqlen_q, seqlen_k] * Scale[1]
// S''[seqlen_q, seqlen_k] = S'[seqlen_q, seqlen_k] + Bias[seqlen_q, seqlen_k]
// P[seqlen_q, seqlen_k] = Softmax(S''[seqlen_q, seqlen_k])
// O[seqlen_q, hdim_v] = P[seqlen_q, seqlen_k] @ V^T[hdim_v, seqlen_k]
namespace ck_tile {
template <typename TilePartitioner_, typename FmhaPipeline_, typename EpiloguePipeline_>
struct FmhaFwdSplitKVKernel
{
using TilePartitioner = ck_tile::remove_cvref_t<TilePartitioner_>;
using FmhaPipeline = ck_tile::remove_cvref_t<FmhaPipeline_>;
using EpiloguePipeline = ck_tile::remove_cvref_t<EpiloguePipeline_>;
static constexpr ck_tile::index_t kBlockSize = FmhaPipeline::kBlockSize;
static constexpr ck_tile::index_t kBlockPerCu = FmhaPipeline::kBlockPerCu;
static_assert(kBlockPerCu > 0);
static constexpr ck_tile::index_t kBlockPerCuInput = FmhaPipeline::Problem::kBlockPerCu;
using QDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::QDataType>;
using KDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::KDataType>;
using VDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::VDataType>;
using BiasDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::BiasDataType>;
using RandValOutputDataType =
ck_tile::remove_cvref_t<typename FmhaPipeline::RandValOutputDataType>;
using LSEDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::LSEDataType>;
using SaccDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::SaccDataType>;
using OaccDataType = remove_cvref_t<typename FmhaPipeline::OaccDataType>;
using VLayout = ck_tile::remove_cvref_t<typename FmhaPipeline::VLayout>;
static constexpr bool kIsGroupMode = FmhaPipeline::kIsGroupMode;
static constexpr bool kPadSeqLenQ = FmhaPipeline::kPadSeqLenQ;
static constexpr bool kPadSeqLenK = FmhaPipeline::kPadSeqLenK;
static constexpr bool kPadHeadDimQ = FmhaPipeline::kPadHeadDimQ;
static constexpr bool kPadHeadDimV = FmhaPipeline::kPadHeadDimV;
static constexpr auto BiasEnum = FmhaPipeline::BiasEnum;
static constexpr bool kHasDropout = FmhaPipeline::kHasDropout;
static constexpr bool kDoFp8StaticQuant = FmhaPipeline::Problem::kDoFp8StaticQuant;
using FmhaMask = ck_tile::remove_cvref_t<typename FmhaPipeline::FmhaMask>;
static constexpr bool kHasMask = FmhaMask::IsMasking;
// clang-format off
template <typename T> struct t2s;
template <> struct t2s<float> { static constexpr const char * name = "fp32"; };
template <> struct t2s<ck_tile::fp16_t> { static constexpr const char * name = "fp16"; };
template <> struct t2s<ck_tile::bf16_t> { static constexpr const char * name = "bf16"; };
template <> struct t2s<ck_tile::fp8_t> { static constexpr const char * name = "fp8"; };
template <> struct t2s<ck_tile::bf8_t> { static constexpr const char * name = "bf8"; };
// clang-format on
__host__ static std::string GetName()
{
// sync with generate.py
// clang-format off
using bfs = typename FmhaPipeline::BlockFmhaShape;
using gbr = typename bfs::Gemm0BlockWarps;
using gwt = typename bfs::Gemm0WarpTile;
#define _SS_ std::string
#define _TS_ std::to_string
auto pn = [&] () {
std::string n;
if (kPadSeqLenQ) n += "s";
if (kPadSeqLenK) n += "sk";
if (kPadHeadDimQ) n += "d";
if (kPadHeadDimV) n += "dv";
return n.empty() ? n : std::string("p") + n; }();
return
_SS_("fmha_fwd_splitkv_d") + _TS_(bfs::kK0BlockLength) + "_" + _SS_(t2s<QDataType>::name) +
"_" + (kIsGroupMode ? "group" : "batch") + "_"
"b" + _TS_(bfs::kM0) + "x" + _TS_(bfs::kN0) + "x" + _TS_(bfs::kK0) + "x" +
_TS_(bfs::kN1) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kK0BlockLength) + "_" +
"r" + _TS_(gbr::at(ck_tile::number<0>{})) + "x" + _TS_(gbr::at(ck_tile::number<1>{})) + "x" + _TS_(gbr::at(ck_tile::number<2>{})) + "_" +
"w" + _TS_(gwt::at(ck_tile::number<0>{})) + "x" + _TS_(gwt::at(ck_tile::number<1>{})) + "x" + _TS_(gwt::at(ck_tile::number<2>{})) + "_" +
(kBlockPerCuInput == -1 ? "" : ("o" + _TS_(kBlockPerCu) + "_")) + _SS_(FmhaPipeline::name) + "_" +
"v" + (std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor> ? "r" : "c") + (pn.empty() ? "" : "_" + pn) +
(BiasEnum == BlockAttentionBiasEnum::NO_BIAS ? _SS_("") : (_SS_("_") + BlockAttentionBiasEnumToStr<BiasEnum>::name)) +
(kHasMask ? "_" + _SS_(FmhaMask::name) : "") + (kHasDropout ? "_dropout" : "" ) + (kDoFp8StaticQuant ? "_squant" : "" );
#undef _SS_
#undef _TS_
// clang-format on
}
template <ck_tile::index_t I> // to avoid duplicated base class prblem, introduce an template
// arg
struct EmptyKargs
{
};
// kargs use aggregate initializer, so no constructor will provided
// use inheritance to minimize karg size
// user need to use MakeKargs() function to create kargs.
struct CommonKargs
{
const void* q_ptr;
const void* k_ptr;
const void* v_ptr;
void* lse_acc_ptr;
void* o_acc_ptr;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t num_head_q;
// for MQA/GQA, nhead could be different. This parameter is nhead_q / nhead_k
// if this param is larger than 1, indicate MQA/GQA case
ck_tile::index_t nhead_ratio_qk;
ck_tile::index_t num_splits;
float scale_s;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_o_acc;
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_lse_acc;
ck_tile::index_t nhead_stride_o_acc;
ck_tile::index_t batch_stride_lse_acc;
ck_tile::index_t batch_stride_o_acc;
ck_tile::index_t split_stride_lse_acc;
ck_tile::index_t split_stride_o_acc;
};
struct CommonBiasKargs
{
const void* bias_ptr = nullptr;
ck_tile::index_t stride_bias = 0;
ck_tile::index_t nhead_stride_bias = 0;
};
struct BatchModeBiasKargs : CommonBiasKargs
{
ck_tile::index_t batch_stride_bias = 0;
};
struct AlibiKargs
{
// alibi is batch*nhead*1, no matter in batch/group mode, they are the same
const void* alibi_slope_ptr;
ck_tile::index_t alibi_slope_stride; // stride in batch, or 0 for all batch share same slope
};
struct MaskKargs
{
// ck_tile::index_t window_size_left, window_size_right;
ck_tile::index_t window_size_left, window_size_right;
ck_tile::GenericAttentionMaskEnum mask_type;
};
struct Fp8StaticQuantKargs
{
float scale_p;
};
struct CommonDropoutKargs
{
void init_dropout(const float p_drop,
const std::tuple<uint64_t, uint64_t>& drop_seed_offset)
{
float p_undrop = 1.0 - p_drop;
p_undrop_in_uint8_t =
uint8_t(std::floor(p_undrop * std::numeric_limits<uint8_t>::max()));
rp_undrop = 1.0 / p_undrop;
drop_seed = std::get<0>(drop_seed_offset);
drop_offset = std::get<1>(drop_seed_offset);
}
float rp_undrop = 1;
uint8_t p_undrop_in_uint8_t = std::numeric_limits<uint8_t>::max();
bool is_store_randval = false;
uint64_t drop_seed = 1;
uint64_t drop_offset = 0;
void* rand_val_ptr = nullptr;
ck_tile::index_t stride_randval = 0;
ck_tile::index_t nhead_stride_randval = 0;
};
struct BatchModeDropoutKargs : CommonDropoutKargs
{
ck_tile::index_t batch_stride_randval = 0;
};
struct BatchModeKargs
: CommonKargs,
std::conditional_t<BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS,
BatchModeBiasKargs,
std::conditional_t<BiasEnum == BlockAttentionBiasEnum::ALIBI,
AlibiKargs,
EmptyKargs<0>>>,
std::conditional_t<kHasMask, MaskKargs, EmptyKargs<1>>,
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<2>>,
std::conditional_t<kHasDropout, BatchModeDropoutKargs, EmptyKargs<3>>
{
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
};
struct GroupModeKargs
: CommonKargs,
std::conditional_t<BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS,
CommonBiasKargs,
std::conditional_t<BiasEnum == BlockAttentionBiasEnum::ALIBI,
AlibiKargs,
EmptyKargs<0>>>,
std::conditional_t<kHasMask, MaskKargs, EmptyKargs<1>>,
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<2>>,
std::conditional_t<kHasDropout, CommonDropoutKargs, EmptyKargs<3>>
{
const int32_t* seqstart_q_ptr;
const int32_t* seqstart_k_ptr;
const int32_t* seqlen_k_ptr;
};
using Kargs = std::conditional_t<kIsGroupMode, GroupModeKargs, BatchModeKargs>;
template <bool Cond = !kIsGroupMode>
__host__ static constexpr std::enable_if_t<Cond, Kargs>
MakeKargs(const void* q_ptr,
const void* k_ptr,
const void* v_ptr,
const void* bias_ptr,
void* rand_val_ptr,
void* lse_acc_ptr,
void* o_acc_ptr,
ck_tile::index_t batch,
ck_tile::index_t max_seqlen_q,
ck_tile::index_t seqlen_q,
ck_tile::index_t seqlen_k,
ck_tile::index_t hdim_q,
ck_tile::index_t hdim_v,
ck_tile::index_t num_head_q,
ck_tile::index_t nhead_ratio_qk,
ck_tile::index_t num_splits,
float scale_s,
float scale_p,
ck_tile::index_t stride_q,
ck_tile::index_t stride_k,
ck_tile::index_t stride_v,
ck_tile::index_t stride_bias,
ck_tile::index_t stride_randval,
ck_tile::index_t stride_o_acc,
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_acc,
ck_tile::index_t nhead_stride_o_acc,
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_acc,
ck_tile::index_t batch_stride_o_acc,
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,
float p_drop,
bool s_randval,
const std::tuple<uint64_t, uint64_t>& drop_seed_offset)
{
Kargs kargs{{q_ptr,
k_ptr,
v_ptr,
lse_acc_ptr,
o_acc_ptr,
batch,
max_seqlen_q,
seqlen_q,
seqlen_k,
hdim_q,
hdim_v,
num_head_q,
nhead_ratio_qk,
num_splits,
#if CK_TILE_FMHA_FWD_FAST_EXP2
static_cast<float>(scale_s * ck_tile::log2e_v<>),
#else
scale_s,
#endif
stride_q,
stride_k,
stride_v,
stride_o_acc,
nhead_stride_q,
nhead_stride_k,
nhead_stride_v,
nhead_stride_lse_acc,
nhead_stride_o_acc,
batch_stride_lse_acc,
batch_stride_o_acc,
split_stride_lse_acc,
split_stride_o_acc}, // args for common karg
{}, // placeholder for bias
{}, // placeholder for mask
{}, // placeholder for fp8_static_quant args
{}, // placeholder for dropout
batch_stride_q,
batch_stride_k,
batch_stride_v};
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
kargs.bias_ptr = bias_ptr;
kargs.stride_bias = stride_bias;
kargs.nhead_stride_bias = nhead_stride_bias;
kargs.batch_stride_bias = batch_stride_bias;
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
kargs.alibi_slope_ptr = bias_ptr;
kargs.alibi_slope_stride = stride_bias;
}
if constexpr(kHasMask)
{
kargs.window_size_left = window_size_left;
kargs.window_size_right = window_size_right;
kargs.mask_type = static_cast<ck_tile::GenericAttentionMaskEnum>(mask_type);
}
if constexpr(kDoFp8StaticQuant)
{
kargs.scale_p = scale_p;
}
if constexpr(kHasDropout)
{
kargs.init_dropout(p_drop, drop_seed_offset);
kargs.rand_val_ptr = rand_val_ptr;
kargs.stride_randval = stride_randval;
kargs.nhead_stride_randval = nhead_stride_randval;
kargs.batch_stride_randval = batch_stride_randval;
kargs.is_store_randval = s_randval;
}
return kargs;
}
template <bool Cond = kIsGroupMode>
__host__ static constexpr std::enable_if_t<Cond, Kargs>
MakeKargs(const void* q_ptr,
const void* k_ptr,
const void* v_ptr,
const void* bias_ptr,
void* rand_val_ptr,
void* lse_acc_ptr,
void* o_acc_ptr,
ck_tile::index_t batch,
ck_tile::index_t max_seqlen_q,
const void* seqstart_q_ptr,
const void* seqstart_k_ptr,
const void* seqlen_k_ptr,
ck_tile::index_t hdim_q,
ck_tile::index_t hdim_v,
ck_tile::index_t num_head_q,
ck_tile::index_t nhead_ratio_qk,
ck_tile::index_t num_splits,
float scale_s,
float scale_p,
ck_tile::index_t stride_q,
ck_tile::index_t stride_k,
ck_tile::index_t stride_v,
ck_tile::index_t stride_bias,
ck_tile::index_t stride_randval,
ck_tile::index_t stride_o_acc,
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_acc,
ck_tile::index_t nhead_stride_o_acc,
ck_tile::index_t batch_stride_lse_acc,
ck_tile::index_t batch_stride_o_acc,
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,
float p_drop,
bool s_randval,
const std::tuple<uint64_t, uint64_t>& drop_seed_offset)
{
Kargs kargs{{q_ptr,
k_ptr,
v_ptr,
lse_acc_ptr,
o_acc_ptr,
batch,
max_seqlen_q,
-1, // seqlen will be updated by another pointer
-1, //
hdim_q,
hdim_v,
num_head_q,
nhead_ratio_qk,
num_splits,
#if CK_TILE_FMHA_FWD_FAST_EXP2
static_cast<float>(scale_s * ck_tile::log2e_v<>),
#else
scale_s,
#endif
stride_q,
stride_k,
stride_v,
stride_o_acc,
nhead_stride_q,
nhead_stride_k,
nhead_stride_v,
nhead_stride_lse_acc,
nhead_stride_o_acc,
batch_stride_lse_acc,
batch_stride_o_acc,
split_stride_lse_acc,
split_stride_o_acc}, // args for common karg
{}, // placeholder for bias
{}, // placeholder for mask
{}, // placeholder for fp8_static_quant args
{}, // placeholder for dropout
reinterpret_cast<const int32_t*>(seqstart_q_ptr),
reinterpret_cast<const int32_t*>(seqstart_k_ptr),
reinterpret_cast<const int32_t*>(seqlen_k_ptr)};
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
kargs.bias_ptr = bias_ptr;
kargs.stride_bias = stride_bias;
kargs.nhead_stride_bias = nhead_stride_bias;
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
kargs.alibi_slope_ptr = bias_ptr;
kargs.alibi_slope_stride = stride_bias;
}
if constexpr(kHasMask)
{
kargs.window_size_left = window_size_left;
kargs.window_size_right = window_size_right;
kargs.mask_type = static_cast<ck_tile::GenericAttentionMaskEnum>(mask_type);
}
if constexpr(kDoFp8StaticQuant)
{
kargs.scale_p = scale_p;
}
if constexpr(kHasDropout)
{
kargs.init_dropout(p_drop, drop_seed_offset);
kargs.rand_val_ptr = rand_val_ptr;
kargs.stride_randval = stride_randval;
kargs.nhead_stride_randval = nhead_stride_randval;
kargs.is_store_randval = s_randval;
}
return kargs;
}
__host__ static constexpr auto GridSize(ck_tile::index_t batch_size,
ck_tile::index_t nhead,
ck_tile::index_t seqlen_q,
ck_tile::index_t hdim_v,
ck_tile::index_t num_splits)
{
return TilePartitioner::GridSize(batch_size, nhead, seqlen_q, hdim_v, num_splits);
}
__host__ static constexpr auto BlockSize() { return dim3(kBlockSize); }
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
return ck_tile::max(FmhaPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
}
CK_TILE_DEVICE void operator()(Kargs kargs) const
{
// allocate LDS
__shared__ char smem_ptr[GetSmemSize()];
// divide problem
const auto [i_tile_m, i_tile_n, i_split, i_nhead, i_batch] =
TilePartitioner{}(kargs.seqlen_q, kargs.hdim_v, kargs.num_splits);
const index_t i_m0 = __builtin_amdgcn_readfirstlane(i_tile_m * FmhaPipeline::kM0);
const index_t i_n1 = __builtin_amdgcn_readfirstlane(i_tile_n * FmhaPipeline::kN1);
long_index_t batch_offset_q = 0;
long_index_t batch_offset_k = 0;
long_index_t batch_offset_v = 0;
long_index_t batch_offset_bias = 0;
long_index_t batch_offset_randval = 0;
const long_index_t batch_offset_lse_acc =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse_acc;
const long_index_t batch_offset_o_acc =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_o_acc;
if constexpr(kIsGroupMode)
{
// get starting offset for each batch
const long_index_t query_start = kargs.seqstart_q_ptr[i_batch];
const long_index_t key_start = kargs.seqstart_k_ptr[i_batch];
batch_offset_q = query_start * kargs.stride_q;
batch_offset_k = key_start * kargs.stride_k;
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
batch_offset_v = key_start * kargs.stride_v;
}
else
{
batch_offset_v = key_start;
}
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
batch_offset_bias = query_start * kargs.stride_bias + key_start;
}
if constexpr(kHasDropout)
{
batch_offset_randval = query_start * kargs.stride_randval;
}
// get real # queries & # keys under group mode
const auto adjusted_seqstart_q_ptr = kargs.seqstart_q_ptr + i_batch;
kargs.seqlen_q = adjusted_seqstart_q_ptr[1] - adjusted_seqstart_q_ptr[0];
// # of required blocks is different in each groups, terminate unnecessary blocks
// earlier
if(kargs.seqlen_q <= i_m0)
{
return;
}
if(kargs.seqlen_k_ptr != nullptr)
{
kargs.seqlen_k = kargs.seqlen_k_ptr[i_batch];
}
else
{
const auto adjusted_seqstart_k_ptr = kargs.seqstart_k_ptr + i_batch;
kargs.seqlen_k = adjusted_seqstart_k_ptr[1] - adjusted_seqstart_k_ptr[0];
}
}
else
{
batch_offset_q = static_cast<long_index_t>(i_batch) * kargs.batch_stride_q;
batch_offset_k = static_cast<long_index_t>(i_batch) * kargs.batch_stride_k;
batch_offset_v = static_cast<long_index_t>(i_batch) * kargs.batch_stride_v;
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
batch_offset_bias = static_cast<long_index_t>(i_batch) * kargs.batch_stride_bias;
}
if constexpr(kHasDropout)
{
batch_offset_randval =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_randval;
}
}
// for simplicity, batch stride we just modify the pointer
const QDataType* q_ptr = reinterpret_cast<const QDataType*>(kargs.q_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_q +
batch_offset_q;
const KDataType* k_ptr =
reinterpret_cast<const KDataType*>(kargs.k_ptr) +
static_cast<long_index_t>(i_nhead / kargs.nhead_ratio_qk) * kargs.nhead_stride_k +
batch_offset_k;
const VDataType* v_ptr =
reinterpret_cast<const VDataType*>(kargs.v_ptr) +
static_cast<long_index_t>(i_nhead / kargs.nhead_ratio_qk) * kargs.nhead_stride_v +
batch_offset_v;
OaccDataType* o_acc_ptr = reinterpret_cast<OaccDataType*>(kargs.o_acc_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_o_acc +
batch_offset_o_acc + i_split * kargs.split_stride_o_acc;
// Q/K/V DRAM and DRAM window
const auto q_dram = [&]() {
const auto q_dram_naive = make_naive_tensor_view<address_space_enum::global>(
q_ptr,
make_tuple(kargs.seqlen_q, kargs.hdim_q),
make_tuple(kargs.stride_q, 1),
number<FmhaPipeline::kAlignmentQ>{},
number<1>{});
if constexpr(FmhaPipeline::kQLoadOnce)
{
return pad_tensor_view(
q_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0BlockLength>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}
else
{
return pad_tensor_view(
q_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}
}();
const auto k_dram = [&]() {
const auto k_dram_naive = make_naive_tensor_view<address_space_enum::global>(
k_ptr,
make_tuple(kargs.seqlen_k, kargs.hdim_q),
make_tuple(kargs.stride_k, 1),
number<FmhaPipeline::kAlignmentK>{},
number<1>{});
return pad_tensor_view(
k_dram_naive,
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{}),
sequence<kPadSeqLenK, kPadHeadDimQ>{});
}();
const auto v_dram = [&]() {
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
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>{});
const auto v_dram_transposed =
transform_tensor_view(v_dram_naive,
make_tuple(make_pass_through_transform(kargs.hdim_v),
make_pass_through_transform(kargs.seqlen_k)),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return pad_tensor_view(
v_dram_transposed,
make_tuple(number<FmhaPipeline::kN1>{}, number<FmhaPipeline::kK1>{}),
sequence<kPadHeadDimV, kPadSeqLenK>{});
}
else
{
const auto v_dram_naive = make_naive_tensor_view<address_space_enum::global>(
v_ptr,
make_tuple(kargs.hdim_v, kargs.seqlen_k),
make_tuple(kargs.stride_v, 1),
number<FmhaPipeline::kAlignmentV>{},
number<1>{});
return pad_tensor_view(
v_dram_naive,
make_tuple(number<FmhaPipeline::kN1>{}, number<FmhaPipeline::kK1>{}),
sequence<kPadHeadDimV, kPadSeqLenK>{});
}
}();
auto q_dram_window = make_tile_window(
q_dram,
[&]() {
if constexpr(FmhaPipeline::kQLoadOnce)
return make_tuple(number<FmhaPipeline::kM0>{},
number<FmhaPipeline::kK0BlockLength>{});
else
return make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0>{});
}(),
{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::kN1>{}, number<FmhaPipeline::kK1>{}),
{i_n1, 0});
/// FIXME: Before C++20, capturing structured binding variables are not supported. Remove
/// following copy capture of the 'i_nhead' if in C++20
const auto bias_dram_window = [&, i_nhead_ = i_nhead]() {
constexpr auto bias_dram_window_lengths =
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN0>{});
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
const BiasDataType* bias_ptr =
reinterpret_cast<const BiasDataType*>(kargs.bias_ptr) +
static_cast<long_index_t>(i_nhead_) * kargs.nhead_stride_bias +
batch_offset_bias;
const auto bias_dram = [&]() {
const auto bias_dram_naive = make_naive_tensor_view<address_space_enum::global>(
bias_ptr,
make_tuple(kargs.seqlen_q, kargs.seqlen_k),
make_tuple(kargs.stride_bias, 1),
number<FmhaPipeline::kAlignmentBias>{},
number<1>{});
return pad_tensor_view(bias_dram_naive,
bias_dram_window_lengths,
sequence<kPadSeqLenQ, kPadSeqLenK>{});
}();
return make_tile_window(bias_dram, bias_dram_window_lengths, {i_m0, 0});
}
else
{
return make_null_tile_window(bias_dram_window_lengths);
}
}();
// lse acc
auto lse_acc_dram_window = [&, i_nhead_ = i_nhead, i_split_ = i_split]() {
constexpr auto lse_acc_dram_window_lengths = make_tuple(number<FmhaPipeline::kM0>{});
LSEDataType* lse_acc_ptr =
reinterpret_cast<LSEDataType*>(kargs.lse_acc_ptr) +
static_cast<long_index_t>(i_nhead_) * kargs.nhead_stride_lse_acc +
batch_offset_lse_acc + i_split_ * kargs.split_stride_lse_acc;
const auto lse_acc_dram = [&]() {
const auto lse_acc_dram_naive =
make_naive_tensor_view<address_space_enum::global>(lse_acc_ptr,
make_tuple(kargs.seqlen_q),
make_tuple(1),
number<1>{},
number<1>{});
return pad_tensor_view(
lse_acc_dram_naive, lse_acc_dram_window_lengths, sequence<kPadSeqLenQ>{});
}();
return make_tile_window(lse_acc_dram, lse_acc_dram_window_lengths, {i_m0});
}();
// dropout
float rp_undrop = 1;
uint8_t p_undrop_in_uint8_t = std::numeric_limits<uint8_t>::max();
uint64_t drop_seed = 0;
uint64_t drop_offset = 0;
bool is_store_randval = false;
if constexpr(kHasDropout)
{
rp_undrop = kargs.rp_undrop;
p_undrop_in_uint8_t = kargs.p_undrop_in_uint8_t;
drop_seed = kargs.drop_seed;
drop_offset = kargs.drop_offset;
is_store_randval = kargs.is_store_randval;
}
BlockDropout dropout(i_batch,
i_nhead,
kargs.num_head_q,
drop_seed,
drop_offset,
rp_undrop,
p_undrop_in_uint8_t,
is_store_randval);
auto randval_dram_window = [&, i_nhead_ = i_nhead]() {
constexpr auto randval_dram_window_lengths =
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN0>{});
if constexpr(kHasDropout)
{
RandValOutputDataType* rand_val_ptr =
reinterpret_cast<RandValOutputDataType*>(kargs.rand_val_ptr) +
static_cast<long_index_t>(i_nhead_) * kargs.nhead_stride_randval +
batch_offset_randval;
const auto randval_dram = [&]() {
const auto randval_dram_naive =
make_naive_tensor_view<address_space_enum::global>(
rand_val_ptr,
make_tuple(kargs.seqlen_q, kargs.seqlen_k),
make_tuple(kargs.stride_randval, 1),
number<1>{},
number<1>{});
return pad_tensor_view(randval_dram_naive,
randval_dram_window_lengths,
sequence<kPadSeqLenQ, kPadSeqLenK>{});
}();
return make_tile_window(randval_dram, randval_dram_window_lengths, {i_m0, 0});
}
else
{
return make_null_tile_window(randval_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};
}();
// WA i_batch capture structure binding before c++20
auto position_encoding = [&, i_batch_ = i_batch, i_nhead_ = i_nhead]() {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
// data loading, shared by entire wg
// TODO: how to use s_read?
SaccDataType slope =
*(reinterpret_cast<const SaccDataType*>(kargs.alibi_slope_ptr) +
i_batch_ * kargs.alibi_slope_stride + i_nhead_);
#if CK_TILE_FMHA_FWD_FAST_EXP2
slope *= ck_tile::log2e_v<>;
#endif
if constexpr(kHasMask)
{
return make_alibi_from_lr_mask<SaccDataType, true>(slope,
kargs.window_size_left,
kargs.window_size_right,
kargs.seqlen_q,
kargs.seqlen_k,
kargs.mask_type);
}
else
{
return Alibi<SaccDataType, true>{
slope, kargs.seqlen_q, kargs.seqlen_k, AlibiMode::FROM_BOTTOM_RIGHT};
}
}
else
{
return EmptyPositionEncoding<SaccDataType>{};
}
}();
auto o_acc_tile = [&, i_split_ = i_split]() {
if constexpr(kDoFp8StaticQuant)
{
return FmhaPipeline{}(q_dram_window,
identity{}, // q_element_func
k_dram_window,
identity{}, // k_element_func
v_dram_window,
identity{}, // v_element_func
bias_dram_window,
identity{}, // bias_element_func
randval_dram_window,
lse_acc_dram_window,
identity{}, // lse_element_func
identity{}, // s_acc_element_func
scales{kargs.scale_p}, // p_compute_element_func
identity{}, // o_acc_element_func
kargs.num_splits,
i_split_,
mask,
position_encoding,
kargs.scale_s,
smem_ptr,
dropout);
}
else
{
return FmhaPipeline{}(q_dram_window,
k_dram_window,
v_dram_window,
bias_dram_window,
randval_dram_window,
lse_acc_dram_window,
kargs.num_splits,
i_split_,
mask,
position_encoding,
kargs.scale_s,
smem_ptr,
dropout);
}
}();
// Oacc DRAM and Oacc DRAM window
auto o_acc_dram = [&]() {
const auto o_acc_dram_naive = make_naive_tensor_view<address_space_enum::global>(
o_acc_ptr,
make_tuple(kargs.seqlen_q, kargs.hdim_v),
make_tuple(kargs.hdim_v, 1),
number<FmhaPipeline::kAlignmentO>{},
number<1>{});
return pad_tensor_view(
o_acc_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{}),
sequence<kPadSeqLenQ, kPadHeadDimV>{});
}();
auto o_acc_dram_window =
make_tile_window(o_acc_dram,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{}),
{i_m0, i_n1});
EpiloguePipeline{}(o_acc_dram_window, o_acc_tile);
}
};
} // namespace ck_tile

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include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_tile_partitioner.hpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
template <typename BlockFmhaShape_>
struct FmhaFwdSplitKVTilePartitioner
{
using BlockFmhaShape = ck_tile::remove_cvref_t<BlockFmhaShape_>;
static constexpr ck_tile::index_t kM0 = BlockFmhaShape::kM0;
static constexpr ck_tile::index_t kN0 = BlockFmhaShape::kN0;
static constexpr ck_tile::index_t kK0 = BlockFmhaShape::kK0;
static constexpr ck_tile::index_t kN1 = BlockFmhaShape::kN1;
static constexpr ck_tile::index_t kK1 = BlockFmhaShape::kK1;
__host__ static constexpr auto GridSize(ck_tile::index_t batch_size,
ck_tile::index_t nhead,
ck_tile::index_t seqlen_q,
ck_tile::index_t hdim_v,
ck_tile::index_t num_splits)
{
// TODO: this may need tuning
return dim3(ck_tile::integer_divide_ceil(seqlen_q, kM0) *
ck_tile::integer_divide_ceil(hdim_v, kN1),
nhead * num_splits,
batch_size);
}
CK_TILE_DEVICE auto
operator()(ck_tile::index_t /*seqlen_q*/, ck_tile::index_t hdim_v, ck_tile::index_t num_splits)
{
const index_t num_tile_n1 = ck_tile::integer_divide_ceil(hdim_v, kN1);
const auto f = [](index_t dividend, index_t divisor) {
index_t quotient = dividend / divisor;
index_t modulus = dividend - quotient * divisor;
return ck_tile::make_tuple(quotient, modulus);
};
const auto [i_tile_m, i_tile_n] = f(blockIdx.x, num_tile_n1);
const auto [i_nhead, i_split] = f(blockIdx.y, num_splits);
const index_t i_batch = blockIdx.z;
return ck_tile::make_tuple(i_tile_m, i_tile_n, i_split, i_nhead, i_batch);
}
};
} // namespace ck_tile