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[CK_TILE] Add fmha fwd N-Warp S-Shuffle pipeline (fmha fwd splitkv pipeline variant) (#1705)

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* Add check for zero values

* Add static assertions

* Remove invalid option '-e' in smoke_test.sh

* Use correct path of smoke_test.sh

* Avoid zero-sized shared memory array

* Add warning comment

* Replace expr by integer_divide_ceil() call

* Use more readable constant names

* Write down assumption as static assertion

* Add more diagnostic error messages

* Fix wrong BlockWarps when using default pipeline policy

* Add more static assertions for A LDS desc

* Allow using vector size < 8 for data type fp16/bf16

* Align vector size between DRAM dist & LDS desc

* Remove no-longer used func decl

* Fix wrong displayed piepline name

* Undo policy template changes for tile_example_gemm_basic

* Add missing space and make error message stands out

* Unify print precision

* Add missing include directive <iomanip>

* Replace constant 64 by get_warp_size() call

* Replace constant 128 by named variable: BankLength

* Add kAMBlock/kBNBlock attributes

* Allow usig different A/B warp dist for multiple blocks

* Add helper function to get warp dist encodings

* Add 4x64x4 fp16 warp gemm attribute impl

* Complete the A/B warp dist encoding logic

* Fix wrong thread mapping for C matrix

* Use smaller vector size for small tile

* Add static assert to block unsupported warp gemm impl

* Extract common code out as helper method

* Add 4x64x16 fp16 warp gemm type alias

* Add comment to warning developers

* Undo WarpGemmAtrributeMfma<> changes

* Use more clear static assertion error message

* Add trivial wrapper to get warp dstr encodings

* Only transpose warp gemm result if it's square

* Fix compilation error

* Support multi-block warp gemm (on N direction)

* Remove duplicated code

* Fix output encoding of warp gemm

* Fix wrong shape of WarpGemmAtrributeMfmaIterateK<>

* Remove unused code

* Fix wrong shape of WarpGemmAttributeMfmaImplF16F16F32M4N64K4

* Add type config for bf16_t

* Add 4x64x16 bf16 warp gemm

* Update WarpGemmAtrributeMfmaIterateKAndTransposedCDistribution

* Add 64x4x4 fp16/bf16 warp gemm impl

* Add 64x4x16 fp16/bf16 warp gemm

* Add static assertion for better error diagnostic

* Get Q dram dstr directly form block gemm

* Add missing header: fused_moe.hpp

* Allow specifying different warp-gemm for gemm0 & gemm1

* Store P matrix into LDS before gemm1

* Fix inconsistant kernel name

* Remove constraint on gemm0 & gemm1 block warps

* Remove unsupported vector size from checking list

* Allow using 4x64x16 warp gemm for gemm0

* Finish policy customization

* Finish pipeline modification
F#

* Use block warps in codegen

* Fix wrong rank of m_lds_window origin

* Use better distributed tensor

* Make P-store earlier

* Remove duplicated experssions

* Remove unnecessary tile window

* Create new files for new splitkv pipeline

* Separate old/new pipeline codegen logic

* Sync changes form develop

* Undo gemm kernel/pipeline changes

* Undo gemm example changes

* Remove blank lines

* Fix typo

* Use new warp gemm interface

* Fix link error

* Fix wrong pipeline tag

* Fix more link error

* Avoid unnecessary padding

* Always use vector load for K

* Padding on fastest dimension when necessary

* Force padding Q on hdim_q

* Set high dimension padding flag to false

* Re-format headers

* Use warps=<1, 4, 1> for both gemm0 & gemm1

* Fix complilation errors

* Remove m/l shuffle logics

* Ignore duplicate data when write lse_acc

* Use gemm0 block warps as lds tile width

* Remove hard-coded numbers

* Fix wrong distribution width

* Remove unnecessary code

* Add s_barrier before writing to LDS

* Store Q into LDS before gemm0

* Fix wrong Q tile size

* Use simple Q lds descriptor for debuging

* Use more realistic Q lds descriptor

* Add comment & use better variable name

* Make Q lds space not overlapped with others

* Remove unnecessary block_tile_reduce_sync() call

* Move Q load statements

* Move block_sync_lds() right before use

* Re-order instructions

* Remove necessary lambda expression

* Use 8 threads on kMaxSplits direction while doing reduction

* Tiny correction for using 8 threads on kMaxSplits direction for combine kernel

* Padding num_split direction of o_acc tile window to 4x

* Update splitkv combine pipeline design

* Add kN1 back to splitkv combine pipeline problem

* Fix compilation errors

* Add missing template parameter

* Fix wrong splitkv combine kernel name

* Fix wrong origin

* Fix wrong LDS descriptor shape

* Fix sync & reduction logics

* Remove unnecessary static assertions

* Extract tile size computation logics

* Make sure we can reuse padding flags in combine kernels

* Rename variables

* Use OaccDataType in BlockFmhaSplitKVCombinePipelineTileSizes<>

* Remove unnecessary static assertion

* Fix function name typo

* Add constraint on kN1 template parameter

* Hide K tile loading latency in earlier iteration

* Fix wrong splitkv kernel name

* Use s_shuffling to replace p_shuffling which removes the needs of cross-warp reduction

* Rename pipeline

* Fix wrong pipeline name attribute

* Add GetAlignmentQ() for NWarpSShuffle pipeline

* Separate Q tile into dram tile & register tile concepts

* Remove non-squre warp gemm transpose c type alias

* Fallback tile size changes for fmha fwd splitkv

* Remove redundant change

* Refine naming for the S tile

* Use better naming of the S tile dstr (read from lds)

* Share Q lds with K lds

* Tiny change

* Fix with using static_for for passing CI checking

---------

Co-authored-by: Qianfeng Zhang <Qianfeng.Zhang@amd.com>
This commit is contained in:
Po Yen Chen
2024-12-20 14:41:01 +08:00
committed by GitHub
parent 2944c50894
commit 37cdbf4f0e
23 changed files with 1990 additions and 275 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
include/ck_tile/ops/fmha/kernel/fmha_fwd_kernel.hpp
View File

@@ -71,7 +71,8 @@ struct FmhaFwdKernel
using bfs = typename FmhaPipeline::BlockFmhaShape;
using g0br = typename bfs::Gemm0BlockWarps;
using g1br = typename bfs::Gemm1BlockWarps;
using gwt = typename bfs::Gemm0WarpTile;
using g0wt = typename bfs::Gemm0WarpTile;
using g1wt = typename bfs::Gemm1WarpTile;
#define _SS_ std::string
#define _TS_ std::to_string
auto pn = [&] () {
@@ -88,7 +89,8 @@ struct FmhaFwdKernel
_TS_(bfs::kN1) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kQKHeaddim) + "_" +
"r" + _TS_(g0br::at(ck_tile::number<0>{})) + "x" + _TS_(g0br::at(ck_tile::number<1>{})) + "x" + _TS_(g0br::at(ck_tile::number<2>{})) + "_" +
"r" + _TS_(g1br::at(ck_tile::number<0>{})) + "x" + _TS_(g1br::at(ck_tile::number<1>{})) + "x" + _TS_(g1br::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>{})) + "_" +
"w" + _TS_(g0wt::at(ck_tile::number<0>{})) + "x" + _TS_(g0wt::at(ck_tile::number<1>{})) + "x" + _TS_(g0wt::at(ck_tile::number<2>{})) + "_" +
"w" + _TS_(g1wt::at(ck_tile::number<0>{})) + "x" + _TS_(g1wt::at(ck_tile::number<1>{})) + "x" + _TS_(g1wt::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)) +

56
include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_combine_kernel.hpp
View File

@@ -8,9 +8,11 @@ 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_>;
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
using FmhaPipeline = remove_cvref_t<FmhaPipeline_>;
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
static constexpr index_t kNumWarps = FmhaPipeline::kNumWarps;
static constexpr index_t kBlockSize = FmhaPipeline::kBlockSize;
static constexpr index_t kBlockPerCu = FmhaPipeline::kBlockPerCu;
static_assert(kBlockPerCu > 0);
@@ -50,8 +52,7 @@ struct FmhaFwdSplitKVCombineKernel
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) + "_" +
"b" + _TS_(FmhaPipeline::kN1) + "_" +
(kBlockPerCuInput == -1 ? "" : ("o" + _TS_(kBlockPerCu) + "_")) +
_SS_(FmhaPipeline::name) +
(pn.empty() ? "" : "_" + pn) +
@@ -339,37 +340,56 @@ struct FmhaFwdSplitKVCombineKernel
number<FmhaPipeline::kAlignmentOacc>{},
number<1>{});
// read 4 * (kM0, kN1) o_acc tiles simultaneously by 4 warps
const 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>{});
make_tuple(
number<kNumWarps>{}, number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{}),
sequence<true, kPadSeqLenQ, kPadHeadDimV>{});
const index_t padded_num_splits =
o_acc_dram_view.get_tensor_descriptor().get_lengths()[number<0>{}];
const index_t padded_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(
const index_t num_m_tiles = integer_divide_floor(padded_seqlen_q, FmhaPipeline::kM0);
// transform tensor view by following steps, given shape: (padded_num_splits,
// padded_seqlen_q, padded_hdim_v)
// 1. unmerge to (padded_num_splits, num_m_tiles, kM0, padded_hdim_v)
// 2. transpose to (num_m_tiles, padded_num_splits, kM0, padded_hdim_v)
// 3. merge to (num_m_tiles * padded_num_splits * kM0, padded_hdim_v)
auto transposed = transform_tensor_view(
o_acc_dram_view,
make_tuple(make_merge_transform(make_tuple(kargs.num_splits, padded_seqlen_q)),
make_tuple(make_pass_through_transform(padded_num_splits),
make_unmerge_transform(make_tuple(num_m_tiles, FmhaPipeline::kM0)),
make_pass_through_transform(padded_hdim_v)),
make_tuple(sequence<0, 1>{}, sequence<2>{}),
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
make_tuple(sequence<1>{}, sequence<0, 2>{}, sequence<3>{}));
return transform_tensor_view(
transposed,
make_tuple(make_merge_transform(
make_tuple(num_m_tiles, padded_num_splits, FmhaPipeline::kM0)),
make_pass_through_transform(padded_hdim_v)),
make_tuple(sequence<0, 1, 2>{}, sequence<3>{}),
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>{});
}(),
make_tuple(number<FmhaPipeline::kMaxSplits>{}, number<FmhaPipeline::kM0>{}),
{0, i_m0});
const index_t padded_num_splits =
integer_divide_ceil(kargs.num_splits, kNumWarps) * kNumWarps;
auto o_acc_dram_window = make_tile_window(
o_acc_dram,
[&]() {
return make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{});
}(),
{i_m0, i_n1});
make_tuple(number<kNumWarps * FmhaPipeline::kM0>{}, number<FmhaPipeline::kN1>{}),
{i_tile_m * padded_num_splits * FmhaPipeline::kM0, i_n1});
// LSE DRAM window
auto lse_dram_window = [&, i_nhead_ = i_nhead]() {
@@ -410,7 +430,6 @@ struct FmhaFwdSplitKVCombineKernel
identity{}, // lse_element_func
composes(saturates<fp8_t>{}, scales{kargs.scale_o}), // o_acc_element_func
kargs.num_splits,
kargs.seqlen_q,
smem_ptr);
}
else
@@ -419,7 +438,6 @@ struct FmhaFwdSplitKVCombineKernel
o_acc_dram_window,
lse_dram_window,
kargs.num_splits,
kargs.seqlen_q,
smem_ptr);
}
}();

9
include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_kernel.hpp
View File

@@ -45,6 +45,7 @@ struct FmhaFwdSplitKVKernel
static constexpr bool kPadHeadDimQ = FmhaPipeline::kPadHeadDimQ;
static constexpr bool kPadHeadDimV = FmhaPipeline::kPadHeadDimV;
static constexpr auto BiasEnum = FmhaPipeline::BiasEnum;
static constexpr bool kStoreLSE = FmhaPipeline::kStoreLSE;
static constexpr bool kDoFp8StaticQuant = FmhaPipeline::Problem::kDoFp8StaticQuant;
static constexpr bool kIsPagedKV = FmhaPipeline::Problem::kIsPagedKV;
@@ -67,7 +68,8 @@ struct FmhaFwdSplitKVKernel
using bfs = typename FmhaPipeline::BlockFmhaShape;
using g0br = typename bfs::Gemm0BlockWarps;
using g1br = typename bfs::Gemm1BlockWarps;
using gwt = typename bfs::Gemm0WarpTile;
using g0wt = typename bfs::Gemm0WarpTile;
using g1wt = typename bfs::Gemm1WarpTile;
#define _SS_ std::string
#define _TS_ std::to_string
auto pn = [&] () {
@@ -84,11 +86,12 @@ struct FmhaFwdSplitKVKernel
_TS_(bfs::kN1) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kQKHeaddim) + "_" +
"r" + _TS_(g0br::at(ck_tile::number<0>{})) + "x" + _TS_(g0br::at(ck_tile::number<1>{})) + "x" + _TS_(g0br::at(ck_tile::number<2>{})) + "_" +
"r" + _TS_(g1br::at(ck_tile::number<0>{})) + "x" + _TS_(g1br::at(ck_tile::number<1>{})) + "x" + _TS_(g1br::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>{})) + "_" +
"w" + _TS_(g0wt::at(ck_tile::number<0>{})) + "x" + _TS_(g0wt::at(ck_tile::number<1>{})) + "x" + _TS_(g0wt::at(ck_tile::number<2>{})) + "_" +
"w" + _TS_(g1wt::at(ck_tile::number<0>{})) + "x" + _TS_(g1wt::at(ck_tile::number<1>{})) + "x" + _TS_(g1wt::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) : "") + (kDoFp8StaticQuant ? "_squant" : "") + (kIsPagedKV ? "_pagedkv" : "" );
(kHasMask ? "_" + _SS_(FmhaMask::name) : "") + (kStoreLSE ? "_lse" : "" ) + (kDoFp8StaticQuant ? "_squant" : "") + (kIsPagedKV ? "_pagedkv" : "" );
#undef _SS_
#undef _TS_
// clang-format on

89
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_combine_pipeline.hpp
View File

@@ -53,6 +53,7 @@ struct BlockFmhaFwdSplitKVCombinePipeline
using OaccDataType = remove_cvref_t<typename Problem::OaccDataType>;
using ODataType = remove_cvref_t<typename Problem::ODataType>;
static constexpr index_t kNumWarps = Problem::kNumWarps;
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t kHeadDimV = Problem::kHeadDimV;
@@ -117,7 +118,6 @@ struct BlockFmhaFwdSplitKVCombinePipeline
const LSEElementFunction& lse_element_func,
const OaccElementFunction& o_acc_element_func,
index_t num_splits,
index_t seqlen_q,
void* smem_ptr) const
{
// lse_acc tile in LDS
@@ -143,11 +143,12 @@ struct BlockFmhaFwdSplitKVCombinePipeline
// copy lse_acc tile (shape=[kMaxSplits, kM0]) to LDS (shape=[kMaxSplits, kM0]).
auto lse_acc_tile = load_tile(lse_acc_dram_window);
store_tile(lse_acc_lds_write_window, lse_acc_tile);
block_sync_lds();
auto lse_accum = make_static_distributed_tensor<LSEDataType>(
Policy::template MakeLSEaccRegTileDistribution<Problem>());
__builtin_amdgcn_sched_barrier(0);
block_sync_lds();
// copy LDS (shape=[kM0, kMaxSplits]) to lse_accum (shape=[kM0, kMaxSplits])
// and fill up -INF values outside the [kM0, num_splits] region.
{
@@ -264,45 +265,93 @@ struct BlockFmhaFwdSplitKVCombinePipeline
}
});
}
block_sync_lds();
if constexpr(kStoreLSE)
{
store_tile(lse_dram_window_tmp, tile_elementwise_in(lse_element_func, lse_logsum));
}
auto o_acc_dist = Policy::template MakeOaccDramTileDistribution<Problem>();
auto o_acc_dram_window =
auto o_acc_4_dist = Policy::template MakeOacc4DramTileDistribution<Problem>();
auto o_acc_4_dram_window =
make_tile_window(o_acc_dram_block_window_tmp.get_bottom_tensor_view(),
o_acc_dram_block_window_tmp.get_window_lengths(),
o_acc_dram_block_window_tmp.get_window_origin(),
o_acc_dist);
o_acc_4_dist);
// shape=[4 * KM0, kN1]
auto o_acc_4 = make_static_distributed_tensor<OaccDataType>(o_acc_4_dist);
clear_tile(o_acc_4);
const index_t padded_num_splits = integer_divide_ceil(num_splits, kNumWarps) * kNumWarps;
__builtin_amdgcn_sched_barrier(0);
block_sync_lds();
// each warp handles a [KM0, kN1] tile
for(index_t split_start = 0; split_start < padded_num_splits; split_start += kNumWarps)
{
auto o_tile = load_tile(o_acc_4_dram_window);
const index_t i_split = split_start + get_warp_id();
const index_t row_start = kM0 * get_warp_id();
{
constexpr auto spans = decltype(o_acc_4)::get_distributed_spans();
sweep_tile_span(spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
const auto x_indices = get_x_indices_from_distributed_indices(
o_acc_4.get_tile_distribution(), i_j_idx);
const auto row = x_indices.at(number<0>{});
const LSEDataType lse_scale = lse_acc_lds(row - row_start, i_split);
o_acc_4(i_j_idx) += lse_scale * o_tile(i_j_idx);
});
});
}
move_tile_window(o_acc_4_dram_window, {kNumWarps * kM0, 0});
}
// 4 o_acc tiles in LDS. shape=[4 * kM0, kN1]
OaccDataType* o_acc_4_lds_ptr = static_cast<OaccDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeLSEacc<Problem>()));
{
auto o_acc_4_lds_window = [&]() {
auto desc = Policy::template MakeOacc4LdsBlockDescriptor<Problem>();
auto view = make_tensor_view<address_space_enum::lds>(o_acc_4_lds_ptr, desc);
return make_tile_window(view, desc.get_lengths(), {0, 0});
}();
store_tile(o_acc_4_lds_window, o_acc_4);
}
auto o_acc_dist = Policy::template MakeOaccDramTileDistribution<Problem>();
auto o_acc_4_lds_window = [&]() {
auto desc = Policy::template MakeOacc4LdsBlockDescriptor<Problem>();
auto view = make_tensor_view<address_space_enum::lds>(o_acc_4_lds_ptr, desc);
return make_tile_window(view, desc.get_lengths(), {0, 0}, o_acc_dist);
}();
auto o_acc = make_static_distributed_tensor<OaccDataType>(o_acc_dist);
clear_tile(o_acc);
const index_t padded_seqlen_q = integer_divide_ceil(seqlen_q, kM0) * kM0;
__builtin_amdgcn_sched_barrier(0);
block_sync_lds();
static_for<0, kNumWarps, 1>{}([&](auto) {
auto o_acc_in = load_tile(o_acc_4_lds_window);
for(index_t i_split = 0; i_split < num_splits; ++i_split)
{
auto o_tile = load_tile(o_acc_dram_window);
{
constexpr auto spans = decltype(o_acc)::get_distributed_spans();
sweep_tile_span(spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
const auto x_indices = get_x_indices_from_distributed_indices(
o_acc.get_tile_distribution(), i_j_idx);
const auto row = x_indices.at(number<0>{});
const LSEDataType lse_scale = lse_acc_lds(row, i_split);
o_acc(i_j_idx) += lse_scale * o_tile(i_j_idx);
o_acc(i_j_idx) += o_acc_in(i_j_idx);
});
});
}
move_tile_window(o_acc_dram_window, {padded_seqlen_q, 0});
}
move_tile_window(o_acc_4_lds_window, {kM0, 0});
});
o_acc = tile_elementwise_in(o_acc_element_func, o_acc);
@@ -316,7 +365,6 @@ struct BlockFmhaFwdSplitKVCombinePipeline
const OaccDramBlockWindow& o_acc_dram_block_window,
LSEDramBlockWindow& lse_dram_block_window,
index_t num_splits,
index_t seqlen_q,
void* smem_ptr) const
{
return operator()(lse_acc_dram_block_window,
@@ -325,7 +373,6 @@ struct BlockFmhaFwdSplitKVCombinePipeline
identity{},
identity{},
num_splits,
seqlen_q,
smem_ptr);
}
};

173
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_combine_pipeline_default_policy.hpp
View File

@@ -10,23 +10,38 @@ namespace ck_tile {
struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
{
template <index_t BlockSize, index_t M, index_t N, typename DataType>
template <index_t NumWarps, index_t M, index_t N, typename DataType>
CK_TILE_HOST_DEVICE static constexpr auto GetMaxNumWarpsForTile()
{
static_assert(NumWarps == 1 || NumWarps == 2 || NumWarps == 4);
constexpr index_t ElemPerThread = (M * N) / (NumWarps * get_warp_size());
if constexpr(0 < ElemPerThread)
{
return NumWarps;
}
else
{ // try dividing tile by smaller # of warps
return GetMaxNumWarpsForTile<NumWarps / 2, M, N, DataType>();
}
}
template <index_t NumWarps, index_t M, index_t N, typename DataType>
CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeForTile()
{
constexpr index_t PixelsPerThread = (M * N) / BlockSize;
static_assert(0 < PixelsPerThread);
constexpr index_t MaxNumWarps = GetMaxNumWarpsForTile<NumWarps, M, N, DataType>();
constexpr index_t ElemPerThread = (M * N) / (MaxNumWarps * get_warp_size());
constexpr index_t MaxNPerThread = 16 / sizeof(DataType);
constexpr index_t NPerThread = min(MaxNPerThread, PixelsPerThread);
return NPerThread;
return min(MaxNPerThread, ElemPerThread);
}
// alignment for dram lse tile (shape=[kMaxSplits, kM0])
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentLSE()
{
return GetVectorSizeForTile<Problem::kBlockSize,
return GetVectorSizeForTile<Problem::kNumWarps,
Problem::kMaxSplits,
Problem::kM0,
typename Problem::LSEDataType>();
@@ -56,40 +71,54 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeLSEacc()
{
return sizeof(typename Problem::LSEDataType) *
MakeLSEaccLdsBlockDescriptor<Problem>().get_element_space_size();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeOacc4()
{
return sizeof(typename Problem::OaccDataType) *
MakeOacc4LdsBlockDescriptor<Problem>().get_element_space_size();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
return GetSmemSizeLSEacc<Problem>() + GetSmemSizeOacc4<Problem>();
}
// shape=[kMaxSplits, kM0]
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccDramTileDistribution()
{
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNumWarps = Problem::kNumWarps;
constexpr index_t kNPerBlock = Problem::kM0;
constexpr index_t kMPerBlock = Problem::kMaxSplits;
constexpr index_t kNPerBlock = Problem::kM0;
constexpr index_t MaxNumWarps =
GetMaxNumWarpsForTile<Problem::kNumWarps, kNPerBlock, kMPerBlock, LSEDataType>();
constexpr index_t Replicate = Problem::kNumWarps / MaxNumWarps;
constexpr index_t NPerThread =
GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
GetVectorSizeForTile<MaxNumWarps, kMPerBlock, kNPerBlock, LSEDataType>();
constexpr index_t NThreads = kNPerBlock / NPerThread;
constexpr index_t MThreadsPerWarp = get_warp_size() / NThreads;
constexpr index_t MPerThread = kMPerBlock / (kNumWarps * MThreadsPerWarp);
constexpr index_t MPerThread = kMPerBlock / (MaxNumWarps * MThreadsPerWarp);
static_assert(MPerThread * MaxNumWarps * MThreadsPerWarp == kMPerBlock);
static_assert(NThreads * NPerThread == kNPerBlock);
static_assert(MPerThread * kNumWarps * MThreadsPerWarp == kMPerBlock);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<MPerThread, kNumWarps, MThreadsPerWarp>,
tile_distribution_encoding<sequence<Replicate>,
tuple<sequence<MPerThread, MaxNumWarps, MThreadsPerWarp>,
sequence<NThreads, NPerThread>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
tuple<sequence<0, 1>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
@@ -100,17 +129,15 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
{
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kMaxSplits;
constexpr index_t kNPerBlock = Problem::kM0;
constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t kNPerBlock = Problem::kMaxSplits;
constexpr index_t NPack =
GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
GetVectorSizeForTile<Problem::kNumWarps, kMPerBlock, kNPerBlock, LSEDataType>();
constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}),
make_tuple(number<(kMPerBlock + 1) * NPack>{}, number<NPack>{}, number<1>{}),
number<8>{},
number<NPack>{},
number<1>{});
constexpr auto lse_acc_lds_block_desc = transform_tensor_descriptor(
@@ -129,17 +156,15 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
{
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kMaxSplits;
constexpr index_t kNPerBlock = Problem::kM0;
constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t kNPerBlock = Problem::kMaxSplits;
constexpr index_t NPack =
GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
GetVectorSizeForTile<Problem::kNumWarps, kMPerBlock, kNPerBlock, LSEDataType>();
constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}),
make_tuple(number<(kMPerBlock + 1) * NPack>{}, number<NPack>{}, number<1>{}),
number<8>{},
number<NPack>{},
number<1>{});
constexpr auto lse_acc_t_lds_block_desc = transform_tensor_descriptor(
@@ -152,33 +177,86 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
return lse_acc_t_lds_block_desc;
}
// 3d + padding, shape=[4 * kM0, kN1]
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOacc4LdsBlockDescriptor()
{
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
constexpr index_t kMPerBlock = 4 * Problem::kM0;
constexpr index_t kNPerBlock = Problem::kN1;
constexpr index_t NPack =
GetVectorSizeForTile<Problem::kNumWarps, kMPerBlock, kNPerBlock, LSEDataType>();
constexpr auto o_acc_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}),
make_tuple(number<(kMPerBlock + 1) * NPack>{}, number<NPack>{}, number<1>{}),
number<8>{},
number<1>{});
constexpr auto o_acc_t_lds_block_desc = transform_tensor_descriptor(
o_acc_lds_block_desc_0,
make_tuple(make_pass_through_transform(kMPerBlock),
make_merge_transform(make_tuple(kNPerBlock / NPack, NPack))),
make_tuple(sequence<1>{}, sequence<0, 2>{}),
make_tuple(sequence<1>{}, sequence<0>{}));
return o_acc_t_lds_block_desc;
}
// shape=[kM0, kMaxSplits]
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccRegTileDistribution()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::kMaxSplits;
constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t kNPerBlock = Problem::kMaxSplits;
constexpr index_t NThreads = 4;
constexpr index_t NPerThread = kNPerBlock / NThreads;
constexpr index_t MaxNThreads = 8;
constexpr index_t NThreads = min(kNPerBlock, MaxNThreads);
constexpr index_t NPerThread = kNPerBlock / NThreads;
constexpr index_t MThreads = kBlockSize / NThreads;
constexpr index_t MPerThread = kMPerBlock / MThreads;
constexpr index_t MWarps = kBlockSize / get_warp_size();
constexpr index_t MPerThread = 1;
constexpr index_t MThreads = kMPerBlock / MPerThread;
constexpr index_t MThreadPerWarp = get_warp_size() / NThreads;
constexpr index_t MaxNumWarps = (MThreads * NThreads) / get_warp_size();
constexpr index_t Replicate = Problem::kNumWarps / MaxNumWarps;
static_assert(MaxNumWarps * MThreadPerWarp * MPerThread == kMPerBlock);
static_assert(NThreads * NPerThread == kNPerBlock);
static_assert(MWarps * MThreadPerWarp * MPerThread == kMPerBlock);
return make_static_tile_distribution(
tile_distribution_encoding<
sequence<1>,
tuple<sequence<MWarps, MThreadPerWarp, MPerThread>, sequence<NThreads, NPerThread>>,
tuple<sequence<1>, sequence<2, 1>>,
tuple<sequence<0>, sequence<0, 1>>,
sequence<1, 2>,
sequence<2, 1>>{});
tile_distribution_encoding<sequence<Replicate>,
tuple<sequence<MaxNumWarps, MThreadPerWarp, MPerThread>,
sequence<NThreads, NPerThread>>,
tuple<sequence<0, 1>, sequence<2, 1>>,
tuple<sequence<0, 0>, sequence<0, 1>>,
sequence<1, 2>,
sequence<2, 1>>{});
}
// similar to MakeOaccDramTileDistribution(), but duplicate same 1-warp encoding 4 times on M
// direction
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOacc4DramTileDistribution()
{
constexpr index_t kMPerBlock = Problem::kM0; // real kMPerBlock we want is (4 * kM0)
constexpr index_t kNPerBlock = Problem::kN1;
static_assert(get_warp_size() <= kMPerBlock * kNPerBlock);
constexpr index_t M1 = 1; // compose encoding base on 1 warp
constexpr index_t M2 = min(kMPerBlock / M1, get_warp_size());
constexpr index_t N0 = get_warp_size() / M2;
constexpr index_t N1 = kNPerBlock / N0;
constexpr index_t M0 = kMPerBlock / (M2 * M1);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<4, M0, M1, M2>, sequence<N0, N1>>,
tuple<sequence<1, 1>, sequence<1, 2>>,
tuple<sequence<0, 2>, sequence<3, 0>>,
sequence<1, 2>,
sequence<1, 1>>{});
}
template <typename Problem>
@@ -187,6 +265,7 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t kNPerBlock = Problem::kN1;
static_assert(kBlockSize <= kMPerBlock * kNPerBlock);
constexpr index_t M1 = kBlockSize / get_warp_size();
constexpr index_t M2 = min(kMPerBlock / M1, get_warp_size());

794
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_nwarp_sshuffle_qr_ks_vs.hpp Normal file
View File

@@ -0,0 +1,794 @@
// 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/fmha/block/block_attention_bias_enum.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_nwarp_sshuffle_qr_ks_vs_default_policy.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
namespace ck_tile {
// This pipeline is qkv all located in LDS
template <typename Problem_,
typename Policy_ = BlockFmhaFwdSplitKVPipelineNWarpSShuffleQRKSVSDefaultPolicy>
struct BlockFmhaFwdSplitKVPipelineNWarpSShuffleQRKSVS
{
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using QDataType = remove_cvref_t<typename Problem::QDataType>;
using KDataType = remove_cvref_t<typename Problem::KDataType>;
using VDataType = remove_cvref_t<typename Problem::VDataType>;
using SaccDataType = remove_cvref_t<typename Problem::SaccDataType>;
using SMPLComputeDataType = remove_cvref_t<typename Problem::SMPLComputeDataType>;
using BiasDataType = remove_cvref_t<typename Problem::BiasDataType>;
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
using PDataType = remove_cvref_t<typename Problem::PDataType>;
using OaccDataType = remove_cvref_t<typename Problem::OaccDataType>;
using ODataType = remove_cvref_t<typename Problem::ODataType>;
using FmhaMask = remove_cvref_t<typename Problem::FmhaMask>;
using BlockFmhaShape = remove_cvref_t<typename Problem::BlockFmhaShape>;
using VLayout = remove_cvref_t<typename BlockFmhaShape::VLayout>;
static constexpr bool kQLoadOnce = true; // if q_tile load whole block length (hdim) at once
static_assert(kQLoadOnce == Policy::QLoadOnce);
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t kM0 = BlockFmhaShape::kM0;
static constexpr index_t kN0 = BlockFmhaShape::kN0;
static constexpr index_t kK0 = BlockFmhaShape::kK0;
static constexpr index_t kN1 = BlockFmhaShape::kN1;
static constexpr index_t kK1 = BlockFmhaShape::kK1;
static constexpr index_t kQKHeaddim = BlockFmhaShape::kQKHeaddim;
static constexpr index_t kSubQKHeaddim = BlockFmhaShape::kSubQKHeaddim;
static constexpr bool kIsGroupMode = Problem::kIsGroupMode;
static constexpr bool kPadSeqLenQ = Problem::kPadSeqLenQ;
static constexpr bool kPadSeqLenK = Problem::kPadSeqLenK;
static constexpr bool kPadHeadDimQ = Problem::kPadHeadDimQ;
static constexpr bool kPadHeadDimV = Problem::kPadHeadDimV;
static constexpr auto BiasEnum = Problem::BiasEnum;
static constexpr bool kStoreLSE = Problem::kStoreLSE;
static constexpr bool kIsPagedKV = Problem::kIsPagedKV;
static constexpr bool kHasUnevenSplits = Problem::kHasUnevenSplits;
// last dimension vector length used to create tensor view(and decide buffer_load vector length)
// ... together with tensor distribution. tensor dist should able to overwrite this
static constexpr index_t kAlignmentQ =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentQ<Problem>();
static constexpr index_t kAlignmentK =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentK<Problem>();
static constexpr index_t kAlignmentV = []() {
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
return kPadHeadDimV ? 1 : Policy::template GetAlignmentV<Problem>();
else
return kPadSeqLenK ? 1 : Policy::template GetAlignmentV<Problem>();
}();
static constexpr index_t kAlignmentOacc =
kPadHeadDimV ? 1 : Policy::template GetAlignmentOacc<Problem>();
static constexpr index_t kAlignmentBias =
kPadSeqLenK ? 1 : Policy::template GetAlignmentBias<Problem>();
static constexpr index_t kBlockPerCu = []() {
if constexpr(Problem::kBlockPerCu != -1)
return Problem::kBlockPerCu;
else
{
if constexpr(kQKHeaddim <= 32)
{
return 2;
}
else if constexpr(kQKHeaddim <= 64)
{
return 3;
}
else if constexpr(kQKHeaddim <= 128)
{
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
return 1;
else
return 2;
}
else if constexpr(kQKHeaddim <= 256)
{
return 1;
}
}
}();
static constexpr const char* name = "qr_nwarp_sshuffle";
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
}
template <typename QDramBlockWindowTmp,
typename KDramBlockWindowLengths,
typename KPageBlockNavigator,
typename VDramBlockWindowLengths,
typename VPageBlockNavigator,
typename BiasDramBlockWindowTmp,
typename LSEaccDramBlockWindowTmp,
typename QElementFunction,
typename KElementFunction,
typename VElementFunction,
typename BiasElementFunction,
typename LSEaccElementFunction,
typename SAccElementFunction,
typename PComputeElementFunction,
typename OAccElementFunction,
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp, // M0*K0 tile
const QElementFunction& q_element_func,
const KDramBlockWindowLengths& k_dram_block_window_lengths, // N0*K0 tile
const KPageBlockNavigator& k_page_block_navigator,
const KElementFunction& k_element_func,
const VDramBlockWindowLengths& v_dram_block_window_lengths, // N1*K1 tile
const VPageBlockNavigator& v_page_block_navigator,
const VElementFunction& v_element_func,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp, // M0*N0 tile
const BiasElementFunction& bias_element_func,
LSEaccDramBlockWindowTmp& lse_acc_dram_window_tmp, // M0*1 tile
const LSEaccElementFunction& lse_acc_element_func,
const SAccElementFunction& s_acc_element_func,
const PComputeElementFunction& p_compute_element_func,
const OAccElementFunction& o_acc_element_func,
index_t num_splits,
index_t i_split,
FmhaMask mask,
PositionEncoding position_encoding,
float scale_s,
index_t kv_l2p_offset, // logical-to-physical offset of seqlen_k coordinate
void* smem_ptr) const
{
static_assert(
std::is_same_v<QDataType, remove_cvref_t<typename QDramBlockWindowTmp::DataType>> &&
std::is_same_v<KDataType, remove_cvref_t<typename KPageBlockNavigator::DataType>> &&
std::is_same_v<VDataType, remove_cvref_t<typename VPageBlockNavigator::DataType>>,
"wrong!");
static_assert(kM0 == QDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kSubQKHeaddim ==
QDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
kN0 == KDramBlockWindowLengths{}[number<0>{}] &&
kK0 == KDramBlockWindowLengths{}[number<1>{}] &&
kN1 == VDramBlockWindowLengths{}[number<0>{}] &&
kK1 == VDramBlockWindowLengths{}[number<1>{}] &&
kM0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<1>{}],
"wrong!");
// Q tile in LDS
QDataType* q_lds_ptr =
static_cast<QDataType*>(static_cast<void*>(static_cast<char*>(smem_ptr)));
auto q_lds = make_tensor_view<address_space_enum::lds>(
q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
// K tile in LDS
KDataType* k_lds_ptr =
static_cast<KDataType*>(static_cast<void*>(static_cast<char*>(smem_ptr)));
auto k_lds = make_tensor_view<address_space_enum::lds>(
k_lds_ptr, Policy::template MakeKLdsBlockDescriptor<Problem>());
auto k_lds_window =
make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
// V tile in LDS
auto v_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<VDataType*>(static_cast<char*>(smem_ptr) +
max(Policy::template GetSmemSizeQ<Problem>(),
Policy::template GetSmemSizeK<Problem>())),
Policy::template MakeVLdsBlockDescriptor<Problem>());
auto v_lds_window = make_tile_window(
v_lds, Policy::template MakeVLdsBlockDescriptor<Problem>().get_lengths(), {0, 0});
// S tile in LDS
auto s_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<SaccDataType*>(reinterpret_cast<char*>(smem_ptr) +
max(Policy::template GetSmemSizeQ<Problem>(),
Policy::template GetSmemSizeK<Problem>())),
Policy::template MakeSLdsBlockDescriptor<Problem>());
auto s_write_lds_window = make_tile_window(
s_lds, Policy::template MakeSLdsBlockDescriptor<Problem>().get_lengths(), {0, 0});
auto s_read_lds_window =
make_tile_window(s_lds,
Policy::template MakeSLdsBlockDescriptor<Problem>().get_lengths(),
{0, 0},
Policy::template MakeSRegTileDistribution<Problem>());
// Block GEMM
constexpr auto gemm_0 = Policy::template GetQKBlockGemm<Problem>();
constexpr auto gemm_1 = Policy::template GetKVBlockGemm<Problem>();
auto q_dram_window =
make_tile_window(q_dram_block_window_tmp.get_bottom_tensor_view(),
q_dram_block_window_tmp.get_window_lengths(),
q_dram_block_window_tmp.get_window_origin(),
Policy::template MakeQDramTileDistribution<Problem>());
// load Q here, will store Q into LDS to maximize throughput
auto origin_q = load_tile(q_dram_window);
using SaccBlockTileType = decltype(gemm_0.MakeCBlockTile());
auto s_acc = SaccBlockTileType{};
// reduction function for softmax
const auto f_max = [](auto e0, auto e1) { return max(e0, e1); };
const auto f_sum = [](auto e0, auto e1) { return e0 + e1; };
using OaccBlockTileType = decltype(gemm_1.MakeCBlockTile());
auto o_acc = OaccBlockTileType{};
// infer Sacc, S, P, M, L, Oacc type
using SBlockTileType = decltype(cast_tile<SMPLComputeDataType>(o_acc));
using MLBlockTileType = decltype(block_tile_reduce<SMPLComputeDataType>(
SBlockTileType{}, sequence<1>{}, f_max, SMPLComputeDataType{0}));
// init M, L
auto m = MLBlockTileType{};
auto l = MLBlockTileType{};
clear_tile(o_acc);
set_tile(m, -numeric<SMPLComputeDataType>::infinity());
clear_tile(l);
const auto q_origin = q_dram_window.get_window_origin();
const auto [logical_seqlen_k_start, logical_seqlen_k_end] = mask.GetTileRangeAlongX(
q_origin.at(number<0>{}), number<kM0>{}, number<kN0>{}, num_splits, i_split);
// check early exit if no work to do
if constexpr(FmhaMask::IsMasking || kPadSeqLenK || kHasUnevenSplits)
{
const index_t logical_num_total_loop =
integer_divide_ceil(logical_seqlen_k_end - logical_seqlen_k_start, kN0);
if(logical_num_total_loop <= 0)
{
if constexpr(kStoreLSE)
{
auto lse_acc =
make_static_distributed_tensor<LSEDataType>(m.get_tile_distribution());
set_tile(lse_acc, -numeric<SMPLComputeDataType>::infinity());
if(get_thread_local_1d_id() < kM0)
{
store_tile(lse_acc_dram_window_tmp,
tile_elementwise_in(lse_acc_element_func, lse_acc));
}
}
// Note: here occ are all cleard, return it
// Note: q loaded but no fence, ignore it.
return o_acc;
}
}
const index_t physical_seqlen_k_start = logical_seqlen_k_start + kv_l2p_offset;
const index_t physical_seqlen_k_end = logical_seqlen_k_end + kv_l2p_offset;
// make sure the first tile is completely located in page-block (page-block size should be
// divisible by kN0)
// relationship between each *_start variables: aligned_physical_seqlen_k_start <=
// physical_seqlen_k_start, logical_seqlen_k_start <= physical_seqlen_k_start
const index_t aligned_physical_seqlen_k_start =
[&, physical_seqlen_k_start_ = physical_seqlen_k_start] {
if constexpr(kIsPagedKV)
{
return kN0 * integer_divide_floor(physical_seqlen_k_start_, kN0);
}
else
{
return physical_seqlen_k_start_;
}
}();
const index_t num_total_loop =
integer_divide_ceil(physical_seqlen_k_end - aligned_physical_seqlen_k_start, kN0);
auto [i_page_block_k, k_dram_block_window] = k_page_block_navigator.make_tile_window(
k_dram_block_window_lengths, {aligned_physical_seqlen_k_start, 0});
const auto bias_origin = bias_dram_block_window_tmp.get_window_origin();
auto bias_dram_window =
make_tile_window(bias_dram_block_window_tmp.get_bottom_tensor_view(),
bias_dram_block_window_tmp.get_window_lengths(),
{bias_origin.at(number<0>{}),
logical_seqlen_k_start - (physical_seqlen_k_start -
aligned_physical_seqlen_k_start)}, // M/N
Policy::template MakeBiasDramTileDistribution<decltype(gemm_0)>());
auto [i_page_block_v, v_dram_window] = v_page_block_navigator.make_tile_window(
v_dram_block_window_lengths,
{0, aligned_physical_seqlen_k_start}, // TODO: hdim split?
Policy::template MakeVDramTileDistribution<Problem>());
// store Q into LDS
__builtin_amdgcn_sched_barrier(0);
auto q_lds_window_for_store = make_tile_window(
q_lds, Policy::template MakeQLdsBlockDescriptor<Problem>().get_lengths(), {0, 0});
store_tile(q_lds_window_for_store, origin_q);
__builtin_amdgcn_sched_barrier(0);
// load Q from LDS
__builtin_amdgcn_sched_barrier(0);
auto q_lds_window_for_load = make_tile_window(
q_lds,
Policy::template MakeQLdsBlockDescriptor<Problem>().get_lengths(),
{0, 0},
Policy::template MakeQRegTileDistribution<Problem, decltype(gemm_0)>());
block_sync_lds();
auto q = load_tile(q_lds_window_for_load);
__builtin_amdgcn_sched_barrier(0);
auto q_tile = tile_elementwise_in(q_element_func, q);
// prefetch K tile
index_t i_total_loops = 0;
constexpr index_t k0_loops = kQKHeaddim / kK0;
constexpr index_t k1_loops = kN0 / kK1;
static_assert(2 <= k0_loops);
static_assert(1 <= k1_loops);
auto k_dram_window = make_tile_window(
k_dram_block_window,
Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window for
// load the first tile of the first iteration and store to LDS
auto k_block_tile = load_tile(k_dram_window);
// moving k_dram_window is an in-page-block operation, so there is
// no need to invoke k_page_block_navigator.move_tile_window() here.
move_tile_window(k_dram_window, {0, kK0});
store_tile(k_lds_window, tile_elementwise_in(k_element_func, k_block_tile));
do
{
// STAGE 1, QK gemm
clear_tile(s_acc); // initialize C
// load the second tile of the first iteration
k_block_tile = load_tile(k_dram_window);
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
__builtin_amdgcn_sched_barrier(
0); // prevent from messing up the order of global loads
}
const auto bias_tile = load_tile(bias_dram_window); // load bias tile
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
__builtin_amdgcn_sched_barrier(
0); // prevent from messing up the order of global loads
}
if constexpr(k0_loops > 2)
{
static_for<0, k0_loops - 2, 1>{}([&](auto i_k0) {
block_sync_lds();
gemm_0(s_acc,
get_slice_tile(q_tile,
sequence<0, i_k0 * kK0>{},
sequence<kM0, (i_k0 + 1) * kK0>{}),
k_lds_window);
block_sync_lds();
move_tile_window(k_dram_window, {0, kK0});
store_tile(
k_lds_window,
tile_elementwise_in(k_element_func, k_block_tile)); // LDS write i + 1
k_block_tile = load_tile(k_dram_window); // global read i + 2
});
}
const auto v_prefetch = load_tile(v_dram_window); // prefetch load v tile
{ // tail
block_sync_lds();
gemm_0(s_acc,
get_slice_tile(q_tile,
sequence<0, (k0_loops - 2) * kK0>{},
sequence<kM0, (k0_loops - 1) * kK0>{}),
k_lds_window);
block_sync_lds();
store_tile(k_lds_window, tile_elementwise_in(k_element_func, k_block_tile));
block_sync_lds();
gemm_0(s_acc,
get_slice_tile(q_tile,
sequence<0, (k0_loops - 1) * kK0>{},
sequence<kM0, k0_loops * kK0>{}),
k_lds_window);
}
// STAGE 2, scale_s, add bias, mask, softmax
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
s_acc = tile_elementwise_in(s_acc_element_func, s_acc);
tile_elementwise_inout([&scale_s](auto& x) { x = x * scale_s; }, s_acc);
tile_elementwise_inout(
[&](auto& x, const auto& y) {
#if !CK_TILE_FMHA_FWD_FAST_EXP2
x += type_convert<SaccDataType>(bias_element_func(y));
#else
x += log2e_v<SaccDataType> *
type_convert<SaccDataType>(bias_element_func(y));
#endif
},
s_acc,
bias_tile);
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
const auto k_origin = k_page_block_navigator.to_global_window_origin(
i_page_block_k, k_dram_block_window.get_window_origin());
constexpr auto s_spans = decltype(s_acc)::get_distributed_spans();
s_acc = tile_elementwise_in(s_acc_element_func, s_acc);
sweep_tile_span(s_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(s_spans[number<1>{}], [&](auto idx1) {
const auto tile_idx = get_x_indices_from_distributed_indices(
s_acc.get_tile_distribution(), make_tuple(idx0, idx1));
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
constexpr auto i_j_idx = make_tuple(idx0, idx1);
s_acc(i_j_idx) *= scale_s;
// position_encoding accept only logical coordinates, do conversion here
position_encoding.update(s_acc(i_j_idx), row, col - kv_l2p_offset);
});
});
}
else
{
s_acc = tile_elementwise_in(s_acc_element_func, s_acc);
#if !CK_TILE_FMHA_FWD_FAST_EXP2
tile_elementwise_inout([&scale_s](auto& x) { x = x * scale_s; }, s_acc);
#endif
}
move_tile_window(bias_dram_window, {0, kN0});
/// TODO: only check in first/last iteration without increasing code size
if constexpr(kHasUnevenSplits)
{
const auto k_origin = k_page_block_navigator.to_global_window_origin(
i_page_block_k, k_dram_block_window.get_window_origin());
set_tile_if(
s_acc,
-numeric<SMPLComputeDataType>::infinity(),
[&,
physical_seqlen_k_start_ = physical_seqlen_k_start,
physical_seqlen_k_end_ = physical_seqlen_k_end](auto tile_idx) {
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
if constexpr(kIsPagedKV)
{
return col < physical_seqlen_k_start_ || physical_seqlen_k_end_ <= col;
}
else
{
return physical_seqlen_k_end_ <= col;
}
});
}
if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
{
const auto k_origin = k_page_block_navigator.to_global_window_origin(
i_page_block_k, k_dram_block_window.get_window_origin());
// mask accept only logical coordinates, do conversion here
bool need_perpixel_check = mask.IsEdgeTile(q_origin.at(number<0>{}),
k_origin.at(number<0>{}) - kv_l2p_offset,
number<kM0>{},
number<kN0>{});
if(need_perpixel_check)
{
set_tile_if(
s_acc, -numeric<SMPLComputeDataType>::infinity(), [&](auto tile_idx) {
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
return mask.IsOutOfBound(row, col - kv_l2p_offset);
});
}
}
__builtin_amdgcn_sched_barrier(0);
// load the first tile for next iteration
if(i_total_loops < num_total_loop - 1)
{
// move K tile windows
i_page_block_k = k_page_block_navigator.move_tile_window(
i_page_block_k, k_dram_block_window, {kN0, 0});
k_dram_window = make_tile_window(
k_dram_block_window,
Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window
// laod the first tile of the first iteration and store to LDS
k_block_tile = load_tile(k_dram_window);
}
__builtin_amdgcn_sched_barrier(0);
const auto s = cast_tile<SMPLComputeDataType>(s_acc); // S{j}
// shuffle through LDS so that the tile layout is consistent with required by Gemm1
store_tile(s_write_lds_window, s);
block_sync_lds();
auto s_new = load_tile(s_read_lds_window);
auto m_local = block_tile_reduce<SMPLComputeDataType>(
s_new,
sequence<1>{},
f_max,
-numeric<SMPLComputeDataType>::infinity()); // m_local = rowmax(S{j})
block_tile_reduce_sync(m_local, f_max, bool_constant<false>{});
const auto m_old = m; // m{j-1}
tile_elementwise_inout(
[](auto& e0, auto e1, auto e2) { e0 = max(e1, e2); }, m, m_old, m_local); // m{j}
auto p_compute = make_static_distributed_tensor<SMPLComputeDataType>(
s_new.get_tile_distribution()); // Pcompute{j}
static const auto get_validated_m = [](SMPLComputeDataType raw_m) {
/// NOTICE: bias might be materialized mask including -inf values, need
/// consideration
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
FmhaMask::IsMasking)
{
return raw_m == -numeric<SMPLComputeDataType>::infinity()
? type_convert<SMPLComputeDataType>(0.f)
: raw_m;
}
else
{
return raw_m;
}
};
constexpr auto p_spans = decltype(p_compute)::get_distributed_spans();
sweep_tile_span(p_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
auto row_max = scale_s * get_validated_m(m[i_idx]);
#endif
sweep_tile_span(p_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
#if CK_TILE_FMHA_FWD_FAST_EXP2
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
p_compute(i_j_idx) = exp2(s_new[i_j_idx] - get_validated_m(m[i_idx]));
}
else
{
p_compute(i_j_idx) = exp2(scale_s * s_new[i_j_idx] - row_max);
}
#else
p_compute(i_j_idx) = exp(s_new[i_j_idx] - get_validated_m(m[i_idx]));
#endif
});
});
auto rowsum_p = block_tile_reduce<SMPLComputeDataType>(
p_compute, sequence<1>{}, f_sum, SMPLComputeDataType{0}); // rowsum(Pcompute{j})
block_tile_reduce_sync(rowsum_p, f_sum, bool_constant<false>{});
const auto p =
cast_tile<PDataType>(tile_elementwise_in(p_compute_element_func, p_compute));
// l{j}, Oacc{j}
constexpr auto o_spans = decltype(o_acc)::get_distributed_spans();
sweep_tile_span(o_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
const auto tmp = [&]() {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
return exp2(m_old[i_idx] - get_validated_m(m[i_idx]));
}
else
{
auto row_max = scale_s * get_validated_m(m[i_idx]);
return exp2(scale_s * m_old[i_idx] - row_max);
}
}();
#else
const auto tmp = exp(m_old[i_idx] - get_validated_m(m[i_idx]));
#endif
l(i_idx) = tmp * l[i_idx] + rowsum_p[i_idx];
sweep_tile_span(o_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
// FIXME: this use different equation from FA v2 paper,
// but produce correc result.
// Is the equation wrong?
o_acc(i_j_idx) *= tmp;
});
});
block_sync_lds();
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
auto v_shuffle_tmp = make_static_distributed_tensor<VDataType>(
Policy::template MakeShuffledVRegBlockDescriptor<Problem>());
shuffle_tile(v_shuffle_tmp, v_prefetch);
store_tile(
v_lds_window,
tile_elementwise_in(v_element_func, v_shuffle_tmp)); // store the prefetch
}
else
{
store_tile(v_lds_window,
tile_elementwise_in(v_element_func, v_prefetch)); // store the prefetch
}
i_page_block_v =
v_page_block_navigator.move_tile_window(i_page_block_v, v_dram_window, {0, kK1});
// STAGE 3, KV gemm
if constexpr(k1_loops > 1)
{
static_for<0, k1_loops - 1, 1>{}([&,
&i_page_block_v_ = i_page_block_v,
&v_dram_window_ = v_dram_window](auto i_k1) {
const auto v = load_tile(v_dram_window_); // load next v
block_sync_lds();
gemm_1(o_acc,
get_slice_tile(
p, sequence<0, i_k1 * kK1>{}, sequence<kM0, (i_k1 + 1) * kK1>{}),
v_lds_window);
block_sync_lds();
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
auto v_shuffle_tmp = make_static_distributed_tensor<VDataType>(
Policy::template MakeShuffledVRegBlockDescriptor<Problem>());
shuffle_tile(v_shuffle_tmp, v);
store_tile(v_lds_window,
tile_elementwise_in(v_element_func,
v_shuffle_tmp)); // store the prefetch
}
else
{
store_tile(v_lds_window,
tile_elementwise_in(v_element_func, v)); // store next v
}
i_page_block_v_ = v_page_block_navigator.move_tile_window(
i_page_block_v_, v_dram_window_, {0, kK1});
});
}
// tail
{
block_sync_lds();
gemm_1(o_acc,
get_slice_tile(
p, sequence<0, (k1_loops - 1) * kK1>{}, sequence<kM0, k1_loops * kK1>{}),
v_lds_window);
block_sync_lds();
}
__builtin_amdgcn_sched_barrier(0);
// load the first tile for next iteration
if(i_total_loops < num_total_loop - 1)
{
// store the first tile for next iteration to LDS
// moving k_dram_window is an in-page-block operation, so there is
// no need to invoke k_page_block_navigator.move_tile_window() here.
move_tile_window(k_dram_window, {0, kK0});
store_tile(k_lds_window, tile_elementwise_in(k_element_func, k_block_tile));
}
} while(++i_total_loops < num_total_loop);
if constexpr(kStoreLSE)
{
// store lse acc
auto lse_acc = make_static_distributed_tensor<LSEDataType>(m.get_tile_distribution());
constexpr auto lse_acc_spans = decltype(lse_acc)::get_distributed_spans();
sweep_tile_span(lse_acc_spans[number<0>{}], [&, m_ = m, l_ = l](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
lse_acc(i_idx) = m_[i_idx] / C_LOG2E + log(l_[i_idx]);
}
else
{
lse_acc(i_idx) = m_[i_idx] * scale_s / C_LOG2E + log(l_[i_idx]);
}
#else
lse_acc(i_idx) = m_[i_idx] + log(l_[i_idx]);
#endif
});
if(get_thread_local_1d_id() < kM0)
{
store_tile(lse_acc_dram_window_tmp,
tile_elementwise_in(lse_acc_element_func, lse_acc));
}
}
// finally, O
constexpr auto o_spans = decltype(o_acc)::get_distributed_spans();
sweep_tile_span(o_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
const auto tmp = [&]() {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
FmhaMask::IsMasking)
{
return l[i_idx] == 0.f ? 0.f : 1 / l[i_idx];
}
else
return 1 / l[i_idx];
}();
sweep_tile_span(o_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
o_acc(i_j_idx) *= tmp;
});
});
o_acc = tile_elementwise_in(o_acc_element_func, o_acc);
return o_acc;
}
template <typename QDramBlockWindowTmp,
typename KDramBlockWindowLengths,
typename KPageBlockNavigator,
typename VDramBlockWindowLengths,
typename VPageBlockNavigator,
typename BiasDramBlockWindowTmp,
typename LSEaccDramBlockWindowTmp,
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp, // M0*K0 tile
const KDramBlockWindowLengths& k_dram_block_window_lengths, // N0*K0 tile
const KPageBlockNavigator& k_page_block_navigator,
const VDramBlockWindowLengths& v_dram_block_window_lengths, // N1*K1 tile
const VPageBlockNavigator& v_page_block_navigator,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp, // M0*N0 tile
LSEaccDramBlockWindowTmp& lse_acc_dram_block_window_tmp, // M0*1 tile
index_t num_splits,
index_t i_split,
FmhaMask mask,
PositionEncoding position_encoding,
float scale_s,
index_t kv_l2p_offset, // logical-to-physical offset of seqlen_k coordinate
void* smem_ptr) const
{
return operator()(q_dram_block_window_tmp,
identity{},
k_dram_block_window_lengths,
k_page_block_navigator,
identity{},
v_dram_block_window_lengths,
v_page_block_navigator,
identity{},
bias_dram_block_window_tmp,
identity{},
lse_acc_dram_block_window_tmp,
identity{},
identity{},
identity{},
identity{},
num_splits,
i_split,
mask,
position_encoding,
scale_s,
kv_l2p_offset,
smem_ptr);
}
};
} // namespace ck_tile

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include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_nwarp_sshuffle_qr_ks_vs_default_policy.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/fmha/pipeline/block_fmha_pipeline_qx_ks_vs_custom_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1_custom_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1.hpp"
namespace ck_tile {
// This pipeline is qkv all located in LDS
struct BlockFmhaFwdSplitKVPipelineNWarpSShuffleQRKSVSDefaultPolicy
: BlockFmhaPipelineQXKSVSCustomPolicy</* QLoadOnce = */ true,
/* AsyncCopyK = */ false,
/* AsyncCopyV = */ false,
/* NumPrefetchK = */ 1,
/* NumPrefetchV = */ 1>
{
using BasePolicy = BlockFmhaPipelineQXKSVSCustomPolicy</* QLoadOnce = */ true,
/* AsyncCopyK = */ false,
/* AsyncCopyV = */ false,
/* NumPrefetchK = */ 1,
/* NumPrefetchV = */ 1>;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentQ()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kSubQKHeaddim;
constexpr index_t MaxVectorSize = 16 / sizeof(typename Problem::QDataType);
// this should align with MakeQDramTileDistribution()
constexpr index_t ElemPerThread = (kMPerBlock * kKPerBlock) / kBlockSize;
static_assert(0 < ElemPerThread);
return min(ElemPerThread, MaxVectorSize);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentOacc()
{
using OaccDataType = remove_cvref_t<typename Problem::OaccDataType>;
return static_cast<index_t>(16 / sizeof(OaccDataType));
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQDramTileDistribution()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kSubQKHeaddim;
constexpr index_t MaxVectorSize = 16 / sizeof(typename Problem::QDataType);
constexpr index_t ElemPerThread = (kMPerBlock * kKPerBlock) / kBlockSize;
static_assert(0 < ElemPerThread);
constexpr index_t kMaxVecLoad = min(ElemPerThread, MaxVectorSize);
constexpr index_t KPerThread = kMaxVecLoad;
constexpr index_t KThreads = kKPerBlock / KPerThread;
constexpr index_t MThreadPerWarp = get_warp_size() / KThreads;
constexpr index_t NumWarps = kBlockSize / get_warp_size();
constexpr index_t MPerThread = kMPerBlock / (MThreadPerWarp * NumWarps);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<MPerThread, NumWarps, MThreadPerWarp>,
sequence<KThreads, KPerThread>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
template <typename Problem, typename BlockGemm>
CK_TILE_HOST_DEVICE static constexpr auto MakeQRegTileDistribution()
{
return BasePolicy::template MakeQDramTileDistribution<Problem, BlockGemm>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackQ()
{
// TODO: this is for 3d layout
using QDataType = remove_cvref_t<typename Problem::QDataType>;
return static_cast<index_t>(16 / sizeof(QDataType));
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQLdsBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kSubQKHeaddim;
constexpr index_t ElemPerThread = (kMPerBlock * kKPerBlock) / kBlockSize;
static_assert(0 < ElemPerThread);
constexpr index_t kKPack = min(ElemPerThread, GetSmemKPackQ<Problem>());
constexpr auto q_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kKPerBlock / kKPack>{}, number<kMPerBlock>{}, number<kKPack>{}),
make_tuple(number<(kMPerBlock + 1) * kKPack>{}, number<kKPack>{}, number<1>{}),
number<kKPack>{},
number<1>{});
constexpr auto q_lds_block_desc = transform_tensor_descriptor(
q_lds_block_desc_0,
make_tuple(
make_pass_through_transform(number<kMPerBlock>{}),
make_merge_transform(make_tuple(number<kKPerBlock / kKPack>{}, number<kKPack>{}))),
make_tuple(sequence<1>{}, sequence<0, 2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return q_lds_block_desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemNPackS()
{
using SDataType = remove_cvref_t<typename Problem::SaccDataType>;
return static_cast<index_t>(16 / sizeof(SDataType));
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeSLdsBlockDescriptor()
{
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kNPack = GetSmemNPackS<Problem>();
constexpr auto s_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kNPerBlock / kNPack>{}, number<kMPerBlock>{}, number<kNPack>{}),
make_tuple(number<(kMPerBlock + 1) * kNPack>{}, number<kNPack>{}, number<1>{}),
number<kNPack>{},
number<1>{});
constexpr auto s_lds_block_desc = transform_tensor_descriptor(
s_lds_block_desc_0,
make_tuple(
make_pass_through_transform(number<kMPerBlock>{}),
make_merge_transform(make_tuple(number<kNPerBlock / kNPack>{}, number<kNPack>{}))),
make_tuple(sequence<1>{}, sequence<0, 2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return s_lds_block_desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeSRegTileDistribution()
{
using BlockGemm = remove_cvref_t<decltype(GetKVBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WG = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = config.template at<1>();
constexpr index_t NWarp = config.template at<2>();
static_assert(MWarp == 1, "Check failed!");
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK1;
constexpr index_t kTileK = Problem::BlockFmhaShape::kN0;
// K2 is equal to Impl::kABKPerLane * kKIterPerWarpGemm
constexpr index_t K3 = WG::kK / WG::WarpGemmAttribute::Impl::kABKLane;
constexpr index_t K2 = WG::WarpGemmAttribute::Impl::kABKLane;
constexpr index_t K1 = kKPerBlock / (K2 * K3);
constexpr index_t K0 = kTileK / kKPerBlock;
constexpr index_t M2 = WG::WarpGemmAttribute::Impl::kAMLane;
constexpr index_t M1 = MWarp;
constexpr index_t M0 = kMPerBlock / (M2 * M1);
constexpr auto s2_block_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1, K2, K3>>,
tuple<sequence<1, 0>, sequence<2, 1>>,
tuple<sequence<1, 0>, sequence<2, 2>>,
sequence<1, 2, 2, 2>,
sequence<0, 0, 1, 3>>{};
constexpr auto s2_block_dstr = make_static_tile_distribution(s2_block_dstr_encoding);
return s2_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeQ()
{
return MakeQLdsBlockDescriptor<Problem>().get_element_space_size() *
sizeof(typename Problem::QDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeK()
{
return MakeKLdsBlockDescriptor<Problem>().get_element_space_size() *
sizeof(typename Problem::KDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeV()
{
return MakeVLdsBlockDescriptor<Problem>().get_element_space_size() *
sizeof(typename Problem::VDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeS()
{
return MakeSLdsBlockDescriptor<Problem>().get_element_space_size() *
sizeof(typename Problem::SaccDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
return max(GetSmemSizeQ<Problem>(), GetSmemSizeK<Problem>()) +
max(GetSmemSizeV<Problem>(), GetSmemSizeS<Problem>());
}
};
} // namespace ck_tile

36
include/ck_tile/ops/fmha/pipeline/block_fmha_pipeline_problem.hpp
View File

@@ -106,28 +106,43 @@ struct BlockFmhaFwdSplitKVPipelineProblem
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
};
// extract tile size attributes to remove dependency on traits
template <typename OaccDataType_, ck_tile::index_t kN1_>
struct BlockFmhaSplitKVCombinePipelineTileSizes
{
static constexpr index_t MaxVectorSize = 16 / sizeof(OaccDataType_);
static constexpr index_t kN1 = kN1_;
static constexpr index_t NThreads = kN1 / MaxVectorSize;
static constexpr index_t kM0 = get_warp_size() / NThreads; // MThreadPerWarp
};
template <typename LSEDataType_,
typename OaccDataType_,
typename ODataType_,
index_t HeadDimV_,
index_t kM0_,
index_t kN1_,
bool kIsGroupMode_,
ck_tile::index_t kN1_,
typename Traits_>
struct BlockFmhaSplitKVCombinePipelineProblem
: BlockFmhaSplitKVCombinePipelineTileSizes<OaccDataType_, kN1_>
{
using BaseType = BlockFmhaSplitKVCombinePipelineTileSizes<OaccDataType_, kN1_>;
using LSEDataType = remove_cvref_t<LSEDataType_>;
using OaccDataType = remove_cvref_t<OaccDataType_>;
using ODataType = remove_cvref_t<ODataType_>;
using Traits = remove_cvref_t<Traits_>;
static constexpr index_t kNumWarps = kM0_ / (get_warp_size() / 4);
static constexpr index_t kBlockSize = kNumWarps * get_warp_size();
static constexpr bool kIsGroupMode = kIsGroupMode_;
static_assert(std::is_same_v<LSEDataType, OaccDataType>);
static constexpr index_t kHeadDimV = HeadDimV_;
static constexpr index_t kM0 = kM0_;
static constexpr index_t kN1 = kN1_;
static constexpr bool kIsGroupMode = kIsGroupMode_;
using BaseType::kM0;
using BaseType::kN1;
static_assert(kN1 <= kHeadDimV && kHeadDimV % kN1 == 0);
// attributes from traits
static constexpr bool kPadSeqLenQ = Traits::kPadSeqLenQ;
@@ -136,6 +151,13 @@ struct BlockFmhaSplitKVCombinePipelineProblem
static constexpr bool kDoFp8StaticQuant = Traits::kDoFp8StaticQuant;
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
static constexpr index_t kMaxSplits = Traits::kMaxSplits;
static_assert(8 <= kMaxSplits);
static constexpr index_t kNumWarps = 4; // always use 4 warps for each workgroup
static constexpr index_t kBlockSize = kNumWarps * get_warp_size();
static_assert(get_warp_size() <= (kM0 * kMaxSplits) &&
(kM0 * kMaxSplits) % get_warp_size() == 0);
};
template <typename QDataType_,

55
include/ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qx_ks_vs_custom_policy.hpp
View File

@@ -41,52 +41,21 @@ struct BlockFmhaPipelineQXCustomPolicy</* QLoadOnce = */ true>
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentQ()
{
constexpr index_t MaxVectorSize = 16 / sizeof(typename Problem::QDataType);
using BlockGemm = remove_cvref_t<decltype(GetQKBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WG = remove_cvref_t<decltype(config.template at<0>())>;
return WG::kK / WG::WarpGemmAttribute::Impl::kABKLane;
return min(MaxVectorSize, WG::kK / WG::WarpGemmAttribute::Impl::kABKLane);
}
template <typename Problem, typename BlockGemm>
CK_TILE_HOST_DEVICE static constexpr auto MakeQDramTileDistribution()
{
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WG = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = config.template at<1>();
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kSubQKHeaddim;
constexpr index_t K2 = WG::kK / WG::WarpGemmAttribute::Impl::kABKLane;
constexpr index_t K1 = WG::WarpGemmAttribute::Impl::kABKLane;
constexpr index_t K0 = kKPerBlock / (K1 * K2);
constexpr index_t M2 = WG::WarpGemmAttribute::Impl::kAMLane;
constexpr index_t M1 = MWarp;
constexpr index_t M0 = kMPerBlock / (M2 * M1);
if constexpr(1 < Problem::kNumGemm0Warps)
{
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1, K2>>,
tuple<sequence<1>, sequence<2, 1>>,
tuple<sequence<1>, sequence<1, 2>>,
sequence<1, 2, 2>,
sequence<0, 0, 2>>{});
}
else
{
static_assert(MWarp == 1);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1, K2>>,
tuple<sequence<2, 1>>,
tuple<sequence<1, 2>>,
sequence<1, 2, 2>,
sequence<0, 0, 2>>{});
}
return BlockGemm::template MakeABlockTileDistribution<
Problem::BlockFmhaShape::kM0,
Problem::BlockFmhaShape::kSubQKHeaddim>();
}
template <typename Problem>
@@ -105,7 +74,7 @@ struct BlockFmhaPipelineQXCustomPolicy</* QLoadOnce = */ true>
constexpr auto warp_gemm = []() {
constexpr index_t WarpGemmM = Problem::BlockFmhaShape::Gemm0WarpTile::at(number<0>{});
static_assert(WarpGemmM == 16 || WarpGemmM == 32);
static_assert(WarpGemmM == 4 || WarpGemmM == 16 || WarpGemmM == 32);
if constexpr(std::is_same_v<typename Problem::QDataType, half_t> &&
std::is_same_v<typename Problem::KDataType, half_t> &&
@@ -113,8 +82,10 @@ struct BlockFmhaPipelineQXCustomPolicy</* QLoadOnce = */ true>
{
if constexpr(WarpGemmM == 32)
return WarpGemmMfmaF16F16F32M32N32K16SwizzleBTransposedCDistribution{};
else // WarpGemmM == 16
else if constexpr(WarpGemmM == 16)
return WarpGemmMfmaF16F16F32M16N16K16TransposedCDistribution{};
else // WarpGemmM == 4
return WarpGemmMfmaF16F16F32M4N64K16{};
}
else if constexpr(std::is_same_v<typename Problem::QDataType, bf16_t> &&
std::is_same_v<typename Problem::KDataType, bf16_t> &&
@@ -122,8 +93,10 @@ struct BlockFmhaPipelineQXCustomPolicy</* QLoadOnce = */ true>
{
if constexpr(WarpGemmM == 32)
return WarpGemmMfmaBf16Bf16F32M32N32K16SwizzleBTransposedCDistribution{};
else // WarpGemmM == 16
else if constexpr(WarpGemmM == 16)
return WarpGemmMfmaBf16Bf16F32M16N16K16TransposedCDistribution{};
else // WarpGemmM == 4
return WarpGemmMfmaBf16Bf16F32M4N64K16{};
}
else if constexpr(std::is_same_v<typename Problem::QDataType, fp8_t> &&
std::is_same_v<typename Problem::KDataType, fp8_t> &&

2
include/ck_tile/ops/fmha/pipeline/tile_fmha_shape.hpp
View File

@@ -43,8 +43,6 @@ struct TileFmhaShape
static constexpr index_t NumWarps = max(NumGemm0Warps, NumGemm1Warps);
static_assert(std::is_same_v<Gemm0WarpTile, Gemm1WarpTile>);
static constexpr index_t kM0 = BlockTile::at(number<0>{}); // tile size along q seqlen
static constexpr index_t kN0 = BlockTile::at(number<1>{}); // tile size along k seqlen
static constexpr index_t kK0 = BlockTile::at(number<2>{}); // tile size along qk gemm unroll