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Unverified Commit f84e2020 authored by Rostyslav Geyyer's avatar Rostyslav Geyyer Committed by GitHub
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Merge branch 'develop' into lwpck-1815

parents 408534d4 25935b57
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Showing with 4338 additions and 2285 deletions
include/ck_tile/ops/fmha/kernel/fmha_bwd_kernel.hpp
View file @ f84e2020
......@@ -23,13 +23,9 @@
namespace ck_tile {
template <typename TilePartitioner_,
typename FmhaPipeline_,
typename KGradEpiloguePipeline_,
typename VGradEpiloguePipeline_>
template <typename FmhaPipeline_, typename KGradEpiloguePipeline_, typename VGradEpiloguePipeline_>
struct FmhaBwdDQDKDVKernel
{
using TilePartitioner = ck_tile::remove_cvref_t<TilePartitioner_>;
using FmhaPipeline = ck_tile::remove_cvref_t<FmhaPipeline_>;
using KGradEpiloguePipeline = ck_tile::remove_cvref_t<KGradEpiloguePipeline_>;
using VGradEpiloguePipeline = ck_tile::remove_cvref_t<VGradEpiloguePipeline_>;
......@@ -59,9 +55,12 @@ struct FmhaBwdDQDKDVKernel
static constexpr bool kPadHeadDimV = FmhaPipeline::kPadHeadDimV;
static constexpr auto BiasEnum = FmhaPipeline::BiasEnum;
static constexpr bool kHasBiasGrad = FmhaPipeline::kHasBiasGrad;
static constexpr bool kHasDropout = FmhaPipeline::kHasDropout;
using FmhaMask = ck_tile::remove_cvref_t<typename FmhaPipeline::FmhaMask>;
using FmhaDropout = ck_tile::remove_cvref_t<typename FmhaPipeline::FmhaDropout>;
static constexpr bool kHasMask = FmhaMask::IsMasking;
static constexpr bool kHasDropout = FmhaDropout::IsDropout;
static constexpr bool kIsStoreRandval = FmhaDropout::IsStoreRandval;
static constexpr bool kIsDeterministic = FmhaPipeline::kIsDeterministic;
// clang-format off
template <typename T> struct t2s;
......@@ -74,8 +73,11 @@ struct FmhaBwdDQDKDVKernel
// sync with generate.py
// clang-format off
using bfs = typename FmhaPipeline::BlockFmhaShape;
using gbr = typename bfs::Gemm0BlockWarps;
using gwt = typename bfs::Gemm0WarpTile;
using gbr0 = typename bfs::Gemm0BlockWarps;
using gbr1 = typename bfs::Gemm1BlockWarps;
using gbr4 = typename bfs::Gemm4BlockWarps;
using gwt0 = typename bfs::Gemm0WarpTile;
using gwt1 = typename bfs::Gemm1WarpTile;
#define _SS_ std::string
#define _TS_ std::to_string
auto pn = [&] () {
......@@ -88,13 +90,17 @@ struct FmhaBwdDQDKDVKernel
return
_SS_("fmha_bwd_d") + _TS_(bfs::kQKHeaddim) + "_" + _SS_(t2s<QDataType>::name) +
"_" + (kIsGroupMode ? "group" : "batch") + "_" +
"b" + _TS_(bfs::kM0) + "x" + _TS_(bfs::kN0) + "x" + _TS_(bfs::kK0) + "x" +
_TS_(bfs::kQKHeaddim) + "x" + _TS_(bfs::kVHeaddim) + "_" +
"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>{})) + "_" +
"b" + _TS_(bfs::kM0) + "x" + _TS_(bfs::kN0) + "x" + _TS_(bfs::kK0) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kK2) + "x" + _TS_(bfs::kK3) + "x" +
_TS_(bfs::kK4) + "x" + _TS_(bfs::kQKHeaddim) + "x" + _TS_(bfs::kVHeaddim) + "_" +
"r" + _TS_(gbr0::at(ck_tile::number<0>{})) + "x" + _TS_(gbr0::at(ck_tile::number<1>{})) + "x" + _TS_(gbr0::at(ck_tile::number<2>{})) + "_" +
"r" + _TS_(gbr1::at(ck_tile::number<0>{})) + "x" + _TS_(gbr1::at(ck_tile::number<1>{})) + "x" + _TS_(gbr1::at(ck_tile::number<2>{})) + "_" +
"r" + _TS_(gbr4::at(ck_tile::number<0>{})) + "x" + _TS_(gbr4::at(ck_tile::number<1>{})) + "x" + _TS_(gbr4::at(ck_tile::number<2>{})) + "_" +
"w" + _TS_(gwt0::at(ck_tile::number<0>{})) + "x" + _TS_(gwt0::at(ck_tile::number<1>{})) + "x" + _TS_(gwt0::at(ck_tile::number<2>{})) + "_" +
"w" + _TS_(gwt1::at(ck_tile::number<0>{})) + "x" + _TS_(gwt1::at(ck_tile::number<1>{})) + "x" + _TS_(gwt1::at(ck_tile::number<2>{})) + "_" +
("o" + _TS_(kBlockPerCu) + "_") + _SS_(FmhaPipeline::name) + (pn.empty() ? "" : "_" + pn) +
(BiasEnum == BlockAttentionBiasEnum::NO_BIAS ? _SS_("") : (_SS_("_") + BlockAttentionBiasEnumToStr<BiasEnum>::name)) +
(kHasBiasGrad ? "_dbias" : "") + (kHasMask ? "_" + _SS_(FmhaMask::name) : "") + (kHasDropout ? "_dropout" : "" );
(kHasBiasGrad ? "_dbias" : "") + (kHasMask ? "_" + _SS_(FmhaMask::name) : "") + (kHasDropout ? "_dropout" : "" ) +
(kIsStoreRandval ? "_storerandval" : "" ) + (kIsDeterministic ? "_deterministic" : "" );
#undef _SS_
#undef _TS_
// clang-format on
......@@ -117,7 +123,7 @@ struct FmhaBwdDQDKDVKernel
const void* lse_ptr;
const void* do_ptr;
const void* d_ptr;
void* dq_ptr;
void* dq_acc_ptr;
void* dk_ptr;
void* dv_ptr;
......@@ -131,14 +137,13 @@ struct FmhaBwdDQDKDVKernel
ck_tile::index_t num_head_q;
ck_tile::index_t nhead_ratio_qk;
float raw_scale;
#if CK_TILE_FMHA_FWD_FAST_EXP2
float scale;
#endif
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_do;
ck_tile::index_t stride_dq_acc;
ck_tile::index_t stride_dk;
ck_tile::index_t stride_dv;
......@@ -147,8 +152,9 @@ struct FmhaBwdDQDKDVKernel
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_do;
ck_tile::index_t nhead_stride_lsed;
ck_tile::index_t batch_stride_lsed;
ck_tile::index_t nhead_stride_dq_acc;
ck_tile::index_t nhead_stride_dk;
ck_tile::index_t nhead_stride_dv;
};
struct FmhaBwdCommonBiasKargs
......@@ -206,7 +212,6 @@ struct FmhaBwdDQDKDVKernel
float rp_undrop = 1;
float scale_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;
......@@ -218,6 +223,10 @@ struct FmhaBwdDQDKDVKernel
{
ck_tile::index_t batch_stride_randval = 0;
};
struct FmhaBwdDeterministicKargs
{
ck_tile::index_t split_stride_dq_acc = 0;
};
struct FmhaBwdBatchModeKargs
: FmhaBwdCommonKargs,
......@@ -228,12 +237,15 @@ struct FmhaBwdDQDKDVKernel
FmhaBwdEmptyKargs<0>>>,
std::conditional_t<kHasBiasGrad, FmhaBwdBatchModeBiasGradKargs, FmhaBwdEmptyKargs<1>>,
std::conditional_t<kHasMask, FmhaBwdMaskKargs, FmhaBwdEmptyKargs<2>>,
std::conditional_t<kHasDropout, FmhaBwdBatchModeDropoutKargs, FmhaBwdEmptyKargs<3>>
std::conditional_t<kHasDropout, FmhaBwdBatchModeDropoutKargs, FmhaBwdEmptyKargs<3>>,
std::conditional_t<kIsDeterministic, FmhaBwdDeterministicKargs, FmhaBwdEmptyKargs<4>>
{
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_do;
ck_tile::index_t batch_stride_lsed;
ck_tile::index_t batch_stride_dq_acc;
ck_tile::index_t batch_stride_dk;
ck_tile::index_t batch_stride_dv;
};
......@@ -247,7 +259,8 @@ struct FmhaBwdDQDKDVKernel
FmhaBwdEmptyKargs<0>>>,
std::conditional_t<kHasBiasGrad, FmhaBwdCommonBiasGradKargs, FmhaBwdEmptyKargs<1>>,
std::conditional_t<kHasMask, FmhaBwdMaskKargs, FmhaBwdEmptyKargs<2>>,
std::conditional_t<kHasDropout, FmhaBwdCommonDropoutKargs, FmhaBwdEmptyKargs<3>>
std::conditional_t<kHasDropout, FmhaBwdCommonDropoutKargs, FmhaBwdEmptyKargs<3>>,
std::conditional_t<kIsDeterministic, FmhaBwdDeterministicKargs, FmhaBwdEmptyKargs<4>>
{
const int32_t* seqstart_q_ptr;
const int32_t* seqstart_k_ptr;
......@@ -266,10 +279,10 @@ struct FmhaBwdDQDKDVKernel
const void* do_ptr,
const void* d_ptr,
void* rand_val_ptr,
void* dq_ptr,
void* dk_ptr,
void* dv_ptr,
void* dbias_ptr,
void* dq_acc_ptr,
ck_tile::index_t seqlen_q,
ck_tile::index_t seqlen_k,
ck_tile::index_t hdim_q,
......@@ -283,6 +296,7 @@ struct FmhaBwdDQDKDVKernel
ck_tile::index_t stride_bias,
ck_tile::index_t stride_randval,
ck_tile::index_t stride_do,
ck_tile::index_t stride_dq_acc,
ck_tile::index_t stride_dk,
ck_tile::index_t stride_dv,
ck_tile::index_t stride_dbias,
......@@ -293,6 +307,9 @@ struct FmhaBwdDQDKDVKernel
ck_tile::index_t nhead_stride_randval,
ck_tile::index_t nhead_stride_do,
ck_tile::index_t nhead_stride_lsed,
ck_tile::index_t nhead_stride_dq_acc,
ck_tile::index_t nhead_stride_dk,
ck_tile::index_t nhead_stride_dv,
ck_tile::index_t nhead_stride_dbias,
ck_tile::index_t batch_stride_q,
ck_tile::index_t batch_stride_k,
......@@ -301,14 +318,15 @@ struct FmhaBwdDQDKDVKernel
ck_tile::index_t batch_stride_randval,
ck_tile::index_t batch_stride_do,
ck_tile::index_t batch_stride_lsed,
ck_tile::index_t batch_stride_dq_acc,
ck_tile::index_t batch_stride_dk,
ck_tile::index_t batch_stride_dv,
ck_tile::index_t batch_stride_dbias,
ck_tile::index_t split_stride_dq_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,
......@@ -317,7 +335,7 @@ struct FmhaBwdDQDKDVKernel
lse_ptr,
do_ptr,
d_ptr,
dq_ptr,
dq_acc_ptr,
dk_ptr,
dv_ptr,
seqlen_q,
......@@ -327,13 +345,12 @@ struct FmhaBwdDQDKDVKernel
num_head_q,
nhead_ratio_qk,
scale,
#if CK_TILE_FMHA_FWD_FAST_EXP2
static_cast<float>(scale * ck_tile::log2e_v<>),
#endif
stride_q,
stride_k,
stride_v,
stride_do,
stride_dq_acc,
stride_dk,
stride_dv,
nhead_stride_q,
......@@ -341,15 +358,20 @@ struct FmhaBwdDQDKDVKernel
nhead_stride_v,
nhead_stride_do,
nhead_stride_lsed,
batch_stride_lsed}, // args for common karg
nhead_stride_dq_acc,
nhead_stride_dk,
nhead_stride_dv}, // args for common karg
{}, // placeholder for bias
{}, // placeholder for dbias
{}, // placeholder for mask
{}, // placeholder for dropout
{}, // placeholder for deterministic
batch_stride_q,
batch_stride_k,
batch_stride_v,
batch_stride_do,
batch_stride_lsed,
batch_stride_dq_acc,
batch_stride_dk,
batch_stride_dv};
......@@ -384,11 +406,18 @@ struct FmhaBwdDQDKDVKernel
if constexpr(kHasDropout)
{
kargs.init_dropout(p_drop, drop_seed_offset, scale);
if constexpr(kIsStoreRandval)
{
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;
}
}
if constexpr(kIsDeterministic)
{
kargs.split_stride_dq_acc = split_stride_dq_acc;
}
return kargs;
......@@ -404,10 +433,10 @@ struct FmhaBwdDQDKDVKernel
const void* do_ptr,
const void* d_ptr,
void* rand_val_ptr,
void* dq_ptr,
void* dk_ptr,
void* dv_ptr,
void* dbias_ptr,
void* dq_acc_ptr,
const void* seqstart_q_ptr,
const void* seqstart_k_ptr,
const void* seqlen_k_ptr,
......@@ -422,6 +451,7 @@ struct FmhaBwdDQDKDVKernel
ck_tile::index_t stride_bias,
ck_tile::index_t stride_randval,
ck_tile::index_t stride_do,
ck_tile::index_t stride_dq_acc,
ck_tile::index_t stride_dk,
ck_tile::index_t stride_dv,
ck_tile::index_t stride_dbias,
......@@ -432,13 +462,15 @@ struct FmhaBwdDQDKDVKernel
ck_tile::index_t nhead_stride_randval,
ck_tile::index_t nhead_stride_do,
ck_tile::index_t nhead_stride_lsed,
ck_tile::index_t nhead_stride_dq_acc,
ck_tile::index_t nhead_stride_dk,
ck_tile::index_t nhead_stride_dv,
ck_tile::index_t nhead_stride_dbias,
ck_tile::index_t batch_stride_lsed,
ck_tile::index_t split_stride_dq_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,
......@@ -447,7 +479,7 @@ struct FmhaBwdDQDKDVKernel
lse_ptr,
do_ptr,
d_ptr,
dq_ptr,
dq_acc_ptr,
dk_ptr,
dv_ptr,
-1, // seqlen will be updated by another pointer
......@@ -457,13 +489,12 @@ struct FmhaBwdDQDKDVKernel
num_head_q,
nhead_ratio_qk,
scale,
#if CK_TILE_FMHA_FWD_FAST_EXP2
static_cast<float>(scale * ck_tile::log2e_v<>),
#endif
stride_q,
stride_k,
stride_v,
stride_do,
stride_dq_acc,
stride_dk,
stride_dv,
nhead_stride_q,
......@@ -471,11 +502,14 @@ struct FmhaBwdDQDKDVKernel
nhead_stride_v,
nhead_stride_do,
nhead_stride_lsed,
batch_stride_lsed}, // args for common karg
nhead_stride_dq_acc,
nhead_stride_dk,
nhead_stride_dv}, // args for common karg
{}, // placeholder for bias
{}, // placeholder for dbias
{}, // placeholder for mask
{}, // placeholder for dropout
{}, // placeholder for deterministic
reinterpret_cast<const int32_t*>(seqstart_q_ptr),
reinterpret_cast<const int32_t*>(seqstart_k_ptr),
reinterpret_cast<const int32_t*>(seqlen_k_ptr)};
......@@ -506,10 +540,16 @@ struct FmhaBwdDQDKDVKernel
if constexpr(kHasDropout)
{
kargs.init_dropout(p_drop, drop_seed_offset, scale);
if constexpr(kIsStoreRandval)
{
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;
}
}
if constexpr(kIsDeterministic)
{
kargs.split_stride_dq_acc = split_stride_dq_acc;
}
return kargs;
......@@ -518,7 +558,17 @@ struct FmhaBwdDQDKDVKernel
CK_TILE_HOST static constexpr auto
GridSize(ck_tile::index_t batch_size_, ck_tile::index_t nhead_, ck_tile::index_t seqlen_k_)
{
return TilePartitioner::GridSize(batch_size_, nhead_, seqlen_k_);
return dim3(
ck_tile::integer_divide_ceil(seqlen_k_, FmhaPipeline::kN0), nhead_, batch_size_);
}
CK_TILE_DEVICE static constexpr auto GetTileIndex()
{
const index_t i_block = blockIdx.x;
const index_t i_nhead = blockIdx.y;
const index_t i_batch = blockIdx.z;
return ck_tile::make_tuple(i_block, i_nhead, i_batch);
}
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
......@@ -536,7 +586,7 @@ struct FmhaBwdDQDKDVKernel
__shared__ char smem_ptr[GetSmemSize()];
// divide problem
const auto [i_tile_n, i_nhead, i_batch] = TilePartitioner{}(kargs.seqlen_k);
const auto [i_tile_n, i_nhead, i_batch] = GetTileIndex();
const index_t i_n0 = __builtin_amdgcn_readfirstlane(i_tile_n * FmhaPipeline::kN0);
......@@ -547,6 +597,7 @@ struct FmhaBwdDQDKDVKernel
long_index_t batch_offset_randval = 0;
long_index_t batch_offset_do = 0;
long_index_t batch_offset_lsed = 0;
long_index_t batch_offset_dq_acc = 0;
long_index_t batch_offset_dk = 0;
long_index_t batch_offset_dv = 0;
long_index_t batch_offset_dbias = 0;
......@@ -561,7 +612,8 @@ struct FmhaBwdDQDKDVKernel
batch_offset_k = key_start * kargs.stride_k;
batch_offset_v = key_start * kargs.stride_v;
batch_offset_do = query_start * kargs.stride_do;
batch_offset_lsed = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lsed;
batch_offset_lsed = query_start;
batch_offset_dq_acc = query_start * kargs.stride_dq_acc;
batch_offset_dk = key_start * kargs.stride_dk;
batch_offset_dv = key_start * kargs.stride_dv;
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
......@@ -576,7 +628,7 @@ struct FmhaBwdDQDKDVKernel
{
batch_offset_dbias = key_start;
}
if constexpr(kHasDropout)
if constexpr(kIsStoreRandval)
{
batch_offset_randval = query_start * kargs.stride_randval;
}
......@@ -608,6 +660,7 @@ struct FmhaBwdDQDKDVKernel
batch_offset_v = static_cast<long_index_t>(i_batch) * kargs.batch_stride_v;
batch_offset_do = static_cast<long_index_t>(i_batch) * kargs.batch_stride_do;
batch_offset_lsed = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lsed;
batch_offset_dq_acc = static_cast<long_index_t>(i_batch) * kargs.batch_stride_dq_acc;
batch_offset_dk = static_cast<long_index_t>(i_batch) * kargs.batch_stride_dk;
batch_offset_dv = static_cast<long_index_t>(i_batch) * kargs.batch_stride_dv;
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
......@@ -618,7 +671,7 @@ struct FmhaBwdDQDKDVKernel
{
batch_offset_dbias = static_cast<long_index_t>(i_batch) * kargs.batch_stride_dbias;
}
if constexpr(kHasDropout)
if constexpr(kIsStoreRandval)
{
batch_offset_randval =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_randval;
......@@ -646,14 +699,11 @@ struct FmhaBwdDQDKDVKernel
const OGradDataType* do_ptr = reinterpret_cast<const OGradDataType*>(kargs.do_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_do +
batch_offset_do;
QGradDataType* dq_ptr = reinterpret_cast<QGradDataType*>(kargs.dq_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_q +
batch_offset_q;
KGradDataType* dk_ptr = reinterpret_cast<KGradDataType*>(kargs.dk_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_k +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_dk +
batch_offset_dk;
VGradDataType* dv_ptr = reinterpret_cast<VGradDataType*>(kargs.dv_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_v +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_dv +
batch_offset_dv;
// Q/K/V/LSE/D/dO/dQ/dK/dV DRAM and DRAM window
......@@ -663,45 +713,10 @@ struct FmhaBwdDQDKDVKernel
make_tuple(kargs.stride_q, 1),
number<FmhaPipeline::kAlignmentQ>{},
number<1>{});
const auto q_dram = [&]() {
if constexpr(FmhaPipeline::kQLoadOnce)
{
return pad_tensor_view(
const auto q_dram = pad_tensor_view(
q_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kQKHeaddim>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}
else
{
return pad_tensor_view(
q_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}
}();
const auto qt_dram_naive =
transform_tensor_view(q_dram_naive,
make_tuple(make_pass_through_transform(kargs.hdim_q),
make_pass_through_transform(kargs.seqlen_q)),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
const auto qt_dram = [&]() {
if constexpr(FmhaPipeline::kQTLoadOnce)
{
return pad_tensor_view(
qt_dram_naive,
make_tuple(number<FmhaPipeline::kQKHeaddim>{}, number<FmhaPipeline::kM0>{}),
sequence<kPadHeadDimQ, kPadSeqLenQ>{});
}
else
{
return pad_tensor_view(
qt_dram_naive,
make_tuple(number<FmhaPipeline::kQKHeaddim>{}, number<FmhaPipeline::kK3>{}),
sequence<kPadHeadDimQ, kPadSeqLenQ>{});
}
}();
const auto k_dram_naive = make_naive_tensor_view<address_space_enum::global>(
k_ptr,
......@@ -709,45 +724,10 @@ struct FmhaBwdDQDKDVKernel
make_tuple(kargs.stride_k, 1),
number<FmhaPipeline::kAlignmentK>{},
number<1>{});
const auto k_dram = [&]() {
if constexpr(FmhaPipeline::kKLoadOnce)
{
return pad_tensor_view(
const auto k_dram = pad_tensor_view(
k_dram_naive,
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kQKHeaddim>{}),
sequence<kPadSeqLenK, kPadHeadDimQ>{});
}
else
{
return pad_tensor_view(
k_dram_naive,
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{}),
sequence<kPadSeqLenK, kPadHeadDimQ>{});
}
}();
const auto kt_dram_naive =
transform_tensor_view(k_dram_naive,
make_tuple(make_pass_through_transform(kargs.hdim_q),
make_pass_through_transform(kargs.seqlen_k)),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
const auto kt_dram = [&]() {
if constexpr(FmhaPipeline::kKTLoadOnce)
{
return pad_tensor_view(
kt_dram_naive,
make_tuple(number<FmhaPipeline::kQKHeaddim>{}, number<FmhaPipeline::kN0>{}),
sequence<kPadHeadDimQ, kPadSeqLenK>{});
}
else
{
return pad_tensor_view(
kt_dram_naive,
make_tuple(number<FmhaPipeline::kQKHeaddim>{}, number<FmhaPipeline::kK4>{}),
sequence<kPadHeadDimQ, kPadSeqLenK>{});
}
}();
const auto v_dram = [&]() {
const auto v_dram_naive = make_naive_tensor_view<address_space_enum::global>(
......@@ -756,20 +736,10 @@ struct FmhaBwdDQDKDVKernel
make_tuple(kargs.stride_v, 1),
number<FmhaPipeline::kAlignmentV>{},
number<1>{});
if constexpr(FmhaPipeline::kVLoadOnce)
{
return pad_tensor_view(
v_dram_naive,
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kVHeaddim>{}),
sequence<kPadSeqLenK, kPadHeadDimV>{});
}
else
{
return pad_tensor_view(
v_dram_naive,
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK2>{}),
sequence<kPadSeqLenK, kPadHeadDimV>{});
}
}();
const auto lse_dram = [&]() {
......@@ -792,145 +762,89 @@ struct FmhaBwdDQDKDVKernel
make_tuple(kargs.stride_do, 1),
number<FmhaPipeline::kAlignmentOGrad>{},
number<1>{});
const auto do_dram = [&]() {
if constexpr(FmhaPipeline::kOGradLoadOnce)
{
return pad_tensor_view(
const auto do_dram = pad_tensor_view(
do_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kVHeaddim>{}),
sequence<kPadSeqLenQ, kPadHeadDimV>{});
}
else
{
return pad_tensor_view(
do_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK2>{}),
sequence<kPadSeqLenQ, kPadHeadDimV>{});
}
}();
const auto dot_dram_naive =
transform_tensor_view(do_dram_naive,
make_tuple(make_pass_through_transform(kargs.hdim_v),
make_pass_through_transform(kargs.seqlen_q)),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
const auto dot_dram = [&]() {
if constexpr(FmhaPipeline::kOGradTLoadOnce)
{
return pad_tensor_view(
dot_dram_naive,
make_tuple(number<FmhaPipeline::kVHeaddim>{}, number<FmhaPipeline::kM0>{}),
sequence<kPadHeadDimV, kPadSeqLenQ>{});
}
else
{
return pad_tensor_view(
dot_dram_naive,
make_tuple(number<FmhaPipeline::kVHeaddim>{}, number<FmhaPipeline::kK1>{}),
sequence<kPadHeadDimV, kPadSeqLenQ>{});
}
}();
auto dq_dram = [&]() {
const auto dq_dram_naive = make_naive_tensor_view<address_space_enum::global,
memory_operation_enum::atomic_add>(
dq_ptr,
make_tuple(kargs.seqlen_q, kargs.hdim_q),
make_tuple(kargs.stride_q, 1),
number<FmhaPipeline::kAlignmentQGrad>{},
number<1>{});
return pad_tensor_view(
dq_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kQKHeaddim>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}();
auto q_dram_window = make_tile_window(
q_dram,
[&]() {
if constexpr(FmhaPipeline::kQLoadOnce)
return make_tuple(number<FmhaPipeline::kM0>{},
number<FmhaPipeline::kQKHeaddim>{});
else
return make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0>{});
}(),
{0, 0});
auto qt_dram_window =
make_tile_window(qt_dram,
[&]() {
if constexpr(FmhaPipeline::kQTLoadOnce)
return make_tuple(number<FmhaPipeline::kQKHeaddim>{},
number<FmhaPipeline::kM0>{});
else
return make_tuple(number<FmhaPipeline::kQKHeaddim>{},
number<FmhaPipeline::kK3>{});
}(),
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kQKHeaddim>{}),
{0, 0});
auto k_dram_window = make_tile_window(
k_dram,
[&]() {
if constexpr(FmhaPipeline::kKLoadOnce)
return make_tuple(number<FmhaPipeline::kN0>{},
number<FmhaPipeline::kQKHeaddim>{});
else
return make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{});
}(),
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kQKHeaddim>{}),
{i_n0, 0});
auto kt_dram_window =
make_tile_window(kt_dram,
[&]() {
if constexpr(FmhaPipeline::kKTLoadOnce)
return make_tuple(number<FmhaPipeline::kQKHeaddim>{},
number<FmhaPipeline::kN0>{});
else
return make_tuple(number<FmhaPipeline::kQKHeaddim>{},
number<FmhaPipeline::kK4>{});
}(),
{0, i_n0});
auto v_dram_window = make_tile_window(
v_dram,
[&]() {
if constexpr(FmhaPipeline::kVLoadOnce)
return make_tuple(number<FmhaPipeline::kN0>{},
number<FmhaPipeline::kVHeaddim>{});
else
return make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK2>{});
}(),
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kVHeaddim>{}),
{i_n0, 0});
auto do_dram_window = make_tile_window(
do_dram,
[&]() {
if constexpr(FmhaPipeline::kOGradLoadOnce)
return make_tuple(number<FmhaPipeline::kM0>{},
number<FmhaPipeline::kVHeaddim>{});
else
return make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK2>{});
}(),
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kVHeaddim>{}),
{0, 0});
auto dot_dram_window =
make_tile_window(dot_dram,
[&]() {
if constexpr(FmhaPipeline::kOGradTLoadOnce)
return make_tuple(number<FmhaPipeline::kVHeaddim>{},
number<FmhaPipeline::kM0>{});
else
return make_tuple(number<FmhaPipeline::kVHeaddim>{},
number<FmhaPipeline::kK1>{});
}(),
auto dq_dram_window = [&, i_tile_n_ = i_tile_n, i_nhead_ = i_nhead]() {
if constexpr(kIsDeterministic)
{
AccDataType* dq_acc_ptr =
reinterpret_cast<AccDataType*>(kargs.dq_acc_ptr) +
static_cast<long_index_t>(i_nhead_) * kargs.nhead_stride_dq_acc +
static_cast<long_index_t>(i_tile_n_) * kargs.split_stride_dq_acc +
batch_offset_dq_acc;
auto dq_acc_dram = [&]() {
const auto dq_acc_dram_naive =
make_naive_tensor_view<address_space_enum::global>(
dq_acc_ptr,
make_tuple(kargs.seqlen_q, kargs.hdim_q),
make_tuple(kargs.stride_dq_acc, 1),
number<FmhaPipeline::kAlignmentQGrad>{},
number<1>{});
return pad_tensor_view(
dq_acc_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kQKHeaddim>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}();
return make_tile_window(
dq_acc_dram,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kQKHeaddim>{}),
{0, 0});
}
else
{
AccDataType* dq_acc_ptr =
reinterpret_cast<AccDataType*>(kargs.dq_acc_ptr) +
static_cast<long_index_t>(i_nhead_) * kargs.nhead_stride_dq_acc +
batch_offset_dq_acc;
auto dq_acc_dram = [&]() {
const auto dq_acc_dram_naive =
make_naive_tensor_view<address_space_enum::global,
memory_operation_enum::atomic_add>(
dq_acc_ptr,
make_tuple(kargs.seqlen_q, kargs.hdim_q),
make_tuple(kargs.stride_dq_acc, 1),
number<FmhaPipeline::kAlignmentQGrad>{},
number<1>{});
auto dq_dram_window = make_tile_window(
dq_dram,
return pad_tensor_view(
dq_acc_dram_naive,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kQKHeaddim>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}();
return make_tile_window(
dq_acc_dram,
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kQKHeaddim>{}),
{0, 0});
}
}();
auto lse_dram_window =
make_tile_window(lse_dram, make_tuple(number<FmhaPipeline::kM0>{}), {0});
......@@ -1008,9 +922,7 @@ struct FmhaBwdDQDKDVKernel
// TODO: how to use s_read?
AccDataType slope = *(reinterpret_cast<const AccDataType*>(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<AccDataType, false>(slope,
......@@ -1035,33 +947,32 @@ struct FmhaBwdDQDKDVKernel
// dropout
float rp_undrop = 1;
float scale_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;
scale_rp_undrop = kargs.scale_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,
auto dropout = [&, i_nhead_ = i_nhead, i_batch_ = i_batch]() {
if constexpr(kHasDropout)
{
return FmhaDropout{i_batch_,
i_nhead_,
kargs.num_head_q,
drop_seed,
drop_offset,
rp_undrop,
p_undrop_in_uint8_t,
is_store_randval);
kargs.drop_seed,
kargs.drop_offset,
kargs.rp_undrop,
kargs.p_undrop_in_uint8_t};
}
else
{
return FmhaDropout{};
};
}();
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)
if constexpr(kIsStoreRandval)
{
RandValOutputDataType* rand_val_ptr =
reinterpret_cast<RandValOutputDataType*>(kargs.rand_val_ptr) +
......@@ -1103,14 +1014,11 @@ struct FmhaBwdDQDKDVKernel
}();
auto [dk_acc_tile, dv_acc_tile] = FmhaPipeline{}(q_dram_window,
qt_dram_window,
k_dram_window,
kt_dram_window,
v_dram_window,
bias_dram_window,
randval_dram_window,
do_dram_window,
dot_dram_window,
lse_dram_window,
d_dram_window,
dq_dram_window,
......@@ -1118,9 +1026,7 @@ struct FmhaBwdDQDKDVKernel
mask,
position_encoding,
kargs.raw_scale,
#if CK_TILE_FMHA_FWD_FAST_EXP2
kargs.scale,
#endif
rp_undrop,
scale_rp_undrop,
smem_ptr,
......@@ -1169,10 +1075,9 @@ struct FmhaBwdDQDKDVKernel
}
};
template <typename TilePartitioner_, typename FmhaBwdOGradDotO_>
template <typename FmhaBwdOGradDotO_>
struct FmhaBwdOGradDotOKernel
{
using TilePartitioner = ck_tile::remove_cvref_t<TilePartitioner_>;
using FmhaBwdOGradDotO = ck_tile::remove_cvref_t<FmhaBwdOGradDotO_>;
static constexpr ck_tile::index_t kBlockSize = FmhaBwdOGradDotO::kBlockSize;
static constexpr ck_tile::index_t kBlockPerCu = FmhaBwdOGradDotO::kBlockPerCu;
......@@ -1234,13 +1139,13 @@ struct FmhaBwdOGradDotOKernel
ck_tile::index_t nhead_stride_do;
ck_tile::index_t nhead_stride_o;
ck_tile::index_t nhead_stride_d;
ck_tile::index_t batch_stride_d;
};
struct FmhaBwdOGradDotOBatchModeKargs : FmhaBwdOGradDotOCommonKargs
{
ck_tile::index_t batch_stride_do;
ck_tile::index_t batch_stride_o;
ck_tile::index_t batch_stride_d;
};
struct FmhaBwdOGradDotOGroupModeKargs : FmhaBwdOGradDotOCommonKargs
......@@ -1278,10 +1183,10 @@ struct FmhaBwdOGradDotOKernel
stride_o,
nhead_stride_do,
nhead_stride_o,
nhead_stride_d,
batch_stride_d},
nhead_stride_d},
batch_stride_do,
batch_stride_o};
batch_stride_o,
batch_stride_d};
return kargs;
}
......@@ -1298,8 +1203,7 @@ struct FmhaBwdOGradDotOKernel
ck_tile::index_t stride_o,
ck_tile::index_t nhead_stride_do,
ck_tile::index_t nhead_stride_o,
ck_tile::index_t nhead_stride_d,
ck_tile::index_t batch_stride_d)
ck_tile::index_t nhead_stride_d)
{
Kargs kargs{{o_ptr,
do_ptr,
......@@ -1311,8 +1215,7 @@ struct FmhaBwdOGradDotOKernel
stride_o,
nhead_stride_do,
nhead_stride_o,
nhead_stride_d,
batch_stride_d},
nhead_stride_d},
reinterpret_cast<const int32_t*>(seqstart_q_ptr)};
return kargs;
......@@ -1321,7 +1224,16 @@ struct FmhaBwdOGradDotOKernel
CK_TILE_HOST static constexpr auto
GridSize(ck_tile::index_t batch_size_, ck_tile::index_t nhead_, ck_tile::index_t seqlen_q_)
{
return TilePartitioner::GridSize(batch_size_, nhead_, seqlen_q_);
return dim3(ck_tile::integer_divide_ceil(seqlen_q_, kM0), nhead_, batch_size_);
}
CK_TILE_DEVICE static constexpr auto GetTileIndex()
{
const index_t i_block = blockIdx.x;
const index_t i_nhead = blockIdx.y;
const index_t i_batch = blockIdx.z;
return ck_tile::make_tuple(i_block, i_nhead, i_batch);
}
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
......@@ -1331,7 +1243,7 @@ struct FmhaBwdOGradDotOKernel
CK_TILE_DEVICE void operator()(Kargs kargs) const
{
// divide problem
const auto [i_tile_m, i_nhead, i_batch] = TilePartitioner{}(kargs.seqlen_q);
const auto [i_tile_m, i_nhead, i_batch] = GetTileIndex();
const index_t i_m0 = __builtin_amdgcn_readfirstlane(i_tile_m * kM0);
......@@ -1346,7 +1258,7 @@ struct FmhaBwdOGradDotOKernel
batch_offset_o = query_start * kargs.stride_o;
batch_offset_do = query_start * kargs.stride_do;
batch_offset_d = static_cast<long_index_t>(i_batch) * kargs.batch_stride_d;
batch_offset_d = query_start;
// get real # queries & # keys under group mode
const auto adjusted_seqstart_q_ptr = kargs.seqstart_q_ptr + i_batch;
......@@ -1418,4 +1330,315 @@ struct FmhaBwdOGradDotOKernel
}
};
template <typename FmhaBwdConvertQGrad_>
struct FmhaBwdConvertQGradKernel
{
using FmhaBwdConvertQGrad = ck_tile::remove_cvref_t<FmhaBwdConvertQGrad_>;
static constexpr ck_tile::index_t kBlockSize = FmhaBwdConvertQGrad::kBlockSize;
static constexpr ck_tile::index_t kBlockPerCu = FmhaBwdConvertQGrad::kBlockPerCu;
static constexpr ck_tile::index_t kM0 = FmhaBwdConvertQGrad::kM0;
static constexpr ck_tile::index_t kN0 = FmhaBwdConvertQGrad::kN0;
static constexpr ck_tile::index_t kQKHeaddim = FmhaBwdConvertQGrad::kQKHeaddim;
using AccDataType = ck_tile::remove_cvref_t<typename FmhaBwdConvertQGrad::AccDataType>;
using QGradDataType = ck_tile::remove_cvref_t<typename FmhaBwdConvertQGrad::QGradDataType>;
static constexpr bool kIsGroupMode = FmhaBwdConvertQGrad::kIsGroupMode;
static constexpr bool kPadSeqLenQ = FmhaBwdConvertQGrad::kPadSeqLenQ;
static constexpr bool kPadHeadDimQ = FmhaBwdConvertQGrad::kPadHeadDimQ;
static constexpr bool kIsDeterministic = FmhaBwdConvertQGrad::kIsDeterministic;
// clang-format off
template <typename T> struct t2s;
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"; };
// clang-format on
CK_TILE_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 (kPadHeadDimQ) n += "d";
return n.empty() ? n : std::string("p") + n; }();
return
_SS_("fmha_bwd_convert_dq_d") + _TS_(kQKHeaddim) + "_" + _SS_(t2s<QGradDataType>::name) +
"_" + (kIsGroupMode ? "group" : "batch") + (kIsDeterministic ? "_deterministic" : "") + "_" +
("o" + _TS_(kBlockPerCu)) + (pn.empty() ? "" : "_" + pn);
#undef _SS_
#undef _TS_
// clang-format on
}
// to avoid duplicated base class prblem, introduce an template arg
template <ck_tile::index_t I>
struct FmhaBwdConvertQGradEmptyKargs
{
};
// 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 FmhaBwdConvertQGradCommonKargs
{
const void* dq_acc_ptr;
void* dq_ptr;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t hdim_q;
ck_tile::index_t stride_dq;
ck_tile::index_t stride_dq_acc;
ck_tile::index_t nhead_stride_dq;
ck_tile::index_t nhead_stride_dq_acc;
};
struct FmhaBwdConvertQGradDeterministicKargs
{
ck_tile::index_t split_stride_dq_acc = 0;
};
struct FmhaBwdConvertQGradBatchModeKargs
: FmhaBwdConvertQGradCommonKargs,
std::conditional_t<kIsDeterministic,
FmhaBwdConvertQGradDeterministicKargs,
FmhaBwdConvertQGradEmptyKargs<0>>
{
ck_tile::index_t batch_stride_dq;
ck_tile::index_t batch_stride_dq_acc;
};
struct FmhaBwdConvertQGradGroupModeKargs
: FmhaBwdConvertQGradCommonKargs,
std::conditional_t<kIsDeterministic,
FmhaBwdConvertQGradDeterministicKargs,
FmhaBwdConvertQGradEmptyKargs<0>>
{
const int32_t* seqstart_q_ptr;
const int32_t* seqstart_k_ptr;
};
using Kargs = std::conditional_t<kIsGroupMode,
FmhaBwdConvertQGradGroupModeKargs,
FmhaBwdConvertQGradBatchModeKargs>;
template <bool Cond = !kIsGroupMode>
CK_TILE_HOST static constexpr std::enable_if_t<Cond, Kargs>
MakeKargs(const void* dq_acc_ptr,
void* dq_ptr,
ck_tile::index_t seqlen_q,
ck_tile::index_t seqlen_k,
ck_tile::index_t hdim_q,
ck_tile::index_t stride_dq,
ck_tile::index_t stride_dq_acc,
ck_tile::index_t nhead_stride_dq,
ck_tile::index_t nhead_stride_dq_acc,
ck_tile::index_t batch_stride_dq,
ck_tile::index_t batch_stride_dq_acc,
ck_tile::index_t split_stride_dq_acc)
{
Kargs kargs{{dq_acc_ptr,
dq_ptr,
seqlen_q,
seqlen_k,
hdim_q,
stride_dq,
stride_dq_acc,
nhead_stride_dq,
nhead_stride_dq_acc},
{},
batch_stride_dq,
batch_stride_dq_acc};
if constexpr(kIsDeterministic)
{
kargs.split_stride_dq_acc = split_stride_dq_acc;
}
return kargs;
}
template <bool Cond = kIsGroupMode>
CK_TILE_HOST static constexpr std::enable_if_t<Cond, Kargs>
MakeKargs(const void* dq_acc_ptr,
void* dq_ptr,
const void* seqstart_q_ptr,
const void* seqstart_k_ptr,
ck_tile::index_t hdim_q,
ck_tile::index_t stride_dq,
ck_tile::index_t stride_dq_acc,
ck_tile::index_t nhead_stride_dq,
ck_tile::index_t nhead_stride_dq_acc,
ck_tile::index_t split_stride_dq_acc)
{
Kargs kargs{{dq_acc_ptr,
dq_ptr,
-1, // seqlen will be updated by another pointer
-1, //
hdim_q,
stride_dq,
stride_dq_acc,
nhead_stride_dq,
nhead_stride_dq_acc},
{},
reinterpret_cast<const int32_t*>(seqstart_q_ptr),
reinterpret_cast<const int32_t*>(seqstart_k_ptr)};
if constexpr(kIsDeterministic)
{
kargs.split_stride_dq_acc = split_stride_dq_acc;
}
return kargs;
}
CK_TILE_HOST static constexpr auto
GridSize(ck_tile::index_t batch_size_, ck_tile::index_t nhead_, ck_tile::index_t seqlen_q_)
{
return dim3(ck_tile::integer_divide_ceil(seqlen_q_, kM0), nhead_, batch_size_);
}
CK_TILE_DEVICE static constexpr auto GetTileIndex()
{
const index_t i_block = blockIdx.x;
const index_t i_nhead = blockIdx.y;
const index_t i_batch = blockIdx.z;
return ck_tile::make_tuple(i_block, i_nhead, i_batch);
}
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize() { return 0; }
CK_TILE_DEVICE void operator()(Kargs kargs) const
{
// divide problem
const auto [i_tile_m, i_nhead, i_batch] = GetTileIndex();
const index_t i_m0 = __builtin_amdgcn_readfirstlane(i_tile_m * kM0);
long_index_t batch_offset_dq = 0;
long_index_t batch_offset_dq_acc = 0;
if constexpr(kIsGroupMode)
{
// get starting offset for each batch
const long_index_t query_start = kargs.seqstart_q_ptr[i_batch];
batch_offset_dq = query_start * kargs.stride_dq;
batch_offset_dq_acc = query_start * kargs.stride_dq_acc;
// 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];
if constexpr(kIsDeterministic)
{
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];
}
// # of required blocks is different in each groups, terminate unnecessary blocks
// earlier
if(kargs.seqlen_q <= i_m0)
{
return;
}
}
else
{
batch_offset_dq = static_cast<long_index_t>(i_batch) * kargs.batch_stride_dq;
batch_offset_dq_acc = static_cast<long_index_t>(i_batch) * kargs.batch_stride_dq_acc;
}
// for simplicity, batch stride we just modify the pointer
QGradDataType* dq_ptr = reinterpret_cast<QGradDataType*>(kargs.dq_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_dq +
batch_offset_dq;
// dQAcc/dQ DRAM and DRAM window
const auto dq_acc_dram = [&, i_nhead_ = i_nhead]() {
if constexpr(kIsDeterministic)
{
const AccDataType* dq_acc_ptr =
reinterpret_cast<const AccDataType*>(kargs.dq_acc_ptr) +
static_cast<long_index_t>(i_nhead_) * (kargs.nhead_stride_dq_acc) +
batch_offset_dq_acc;
const index_t nsplits = ck_tile::integer_divide_ceil(kargs.seqlen_k, kN0);
auto dq_acc_dram_naive = make_naive_tensor_view<address_space_enum::global>(
dq_acc_ptr,
make_tuple(nsplits, kargs.seqlen_q, kargs.hdim_q),
make_tuple(kargs.split_stride_dq_acc, kargs.stride_dq_acc, 1),
number<FmhaBwdConvertQGrad::kAlignmentQGradAcc>{},
number<1>{});
return pad_tensor_view(dq_acc_dram_naive,
make_tuple(number<1>{}, number<kM0>{}, number<kQKHeaddim>{}),
sequence<false, kPadSeqLenQ, kPadHeadDimQ>{});
}
else
{
const AccDataType* dq_acc_ptr =
reinterpret_cast<const AccDataType*>(kargs.dq_acc_ptr) +
static_cast<long_index_t>(i_nhead_) * (kargs.nhead_stride_dq_acc) +
batch_offset_dq_acc;
auto dq_acc_dram_naive = make_naive_tensor_view<address_space_enum::global>(
dq_acc_ptr,
make_tuple(kargs.seqlen_q, kargs.hdim_q),
make_tuple(kargs.stride_dq_acc, 1),
number<FmhaBwdConvertQGrad::kAlignmentQGradAcc>{},
number<1>{});
return pad_tensor_view(dq_acc_dram_naive,
make_tuple(number<kM0>{}, number<kQKHeaddim>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}
}();
auto dq_dram = [&]() {
auto dq_dram_naive = make_naive_tensor_view<address_space_enum::global>(
dq_ptr,
make_tuple(kargs.seqlen_q, kargs.hdim_q),
make_tuple(kargs.stride_dq, 1),
number<FmhaBwdConvertQGrad::kAlignmentQGrad>{},
number<1>{});
return pad_tensor_view(dq_dram_naive,
make_tuple(number<kM0>{}, number<kQKHeaddim>{}),
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}();
auto dq_acc_dram_window = [&]() {
if constexpr(kIsDeterministic)
{
return make_tile_window(
dq_acc_dram,
make_tuple(number<1>{}, number<kM0>{}, number<kQKHeaddim>{}),
{0, i_m0, 0});
}
else
{
return make_tile_window(
dq_acc_dram, make_tuple(number<kM0>{}, number<kQKHeaddim>{}), {i_m0, 0});
}
}();
auto dq_dram_window =
make_tile_window(dq_dram, make_tuple(number<kM0>{}, number<kQKHeaddim>{}), {i_m0, 0});
if constexpr(kIsDeterministic)
{
const index_t nsplits = ck_tile::integer_divide_ceil(kargs.seqlen_k, kN0);
FmhaBwdConvertQGrad{}(dq_acc_dram_window, dq_dram_window, nsplits);
}
else
{
FmhaBwdConvertQGrad{}(dq_acc_dram_window, dq_dram_window);
}
}
};
} // namespace ck_tile
include/ck_tile/ops/fmha/kernel/fmha_bwd_tile_partitioner.hpp deleted 100644 → 0
View file @ 408534d4
// 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 FmhaBwdTilePartitioner
{
using BlockFmhaShape = ck_tile::remove_cvref_t<BlockFmhaShape_>;
static constexpr ck_tile::index_t kN0 = BlockFmhaShape::kN0;
CK_TILE_HOST static constexpr auto
GridSize(ck_tile::index_t batch_size_, ck_tile::index_t nhead_, ck_tile::index_t seqlen_k_)
{
// TODO: this may need tuning
return dim3(ck_tile::integer_divide_ceil(seqlen_k_, kN0), nhead_, batch_size_);
}
CK_TILE_DEVICE auto operator()(ck_tile::index_t /*seqlen_k*/)
{
const index_t i_block = blockIdx.x;
const index_t i_nhead = blockIdx.y;
const index_t i_batch = blockIdx.z;
return ck_tile::make_tuple(i_block, i_nhead, i_batch);
}
};
template <ck_tile::index_t kBlockSize>
struct FmhaBwdOGradDotOTilePartitioner
{
CK_TILE_HOST static constexpr auto
GridSize(ck_tile::index_t batch_size_, ck_tile::index_t nhead_, ck_tile::index_t seqlen_q_)
{
// TODO: this may need tuning
return dim3(ck_tile::integer_divide_ceil(seqlen_q_, kBlockSize), nhead_, batch_size_);
}
CK_TILE_DEVICE auto operator()(ck_tile::index_t /*seqlen_q*/)
{
const index_t i_block = blockIdx.x;
const index_t i_nhead = blockIdx.y;
const index_t i_batch = blockIdx.z;
return ck_tile::make_tuple(i_block, i_nhead, i_batch);
}
};
} // namespace ck_tile
include/ck_tile/ops/fmha/kernel/fmha_fwd_kernel.hpp
View file @ f84e2020
......@@ -387,7 +387,6 @@ struct FmhaFwdKernel
ck_tile::index_t nhead_stride_randval,
ck_tile::index_t nhead_stride_lse,
ck_tile::index_t nhead_stride_o,
ck_tile::index_t batch_stride_lse,
ck_tile::index_t window_size_left,
ck_tile::index_t window_size_right,
ck_tile::index_t mask_type,
......@@ -448,7 +447,6 @@ struct FmhaFwdKernel
{
kargs.lse_ptr = lse_ptr;
kargs.nhead_stride_lse = nhead_stride_lse;
kargs.batch_stride_lse = batch_stride_lse;
}
if constexpr(kDoFp8StaticQuant)
{
......@@ -524,7 +522,7 @@ struct FmhaFwdKernel
}
if constexpr(kStoreLSE)
{
batch_offset_lse = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse;
batch_offset_lse = query_start;
}
if constexpr(kHasDropout)
{
......
include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_combine_kernel.hpp
View file @ f84e2020
......@@ -91,7 +91,6 @@ struct FmhaFwdSplitKVCombineKernel
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;
......@@ -116,6 +115,7 @@ struct FmhaFwdSplitKVCombineKernel
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<1>>
{
ck_tile::index_t batch_stride_o;
ck_tile::index_t batch_stride_lse_acc;
};
struct GroupModeKargs
......@@ -166,13 +166,13 @@ struct FmhaFwdSplitKVCombineKernel
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};
batch_stride_o,
batch_stride_lse_acc};
if constexpr(kStoreLSE)
{
......@@ -206,9 +206,7 @@ struct FmhaFwdSplitKVCombineKernel
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)
{
......@@ -225,7 +223,6 @@ struct FmhaFwdSplitKVCombineKernel
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
......@@ -237,7 +234,6 @@ struct FmhaFwdSplitKVCombineKernel
{
kargs.lse_ptr = lse_ptr;
kargs.nhead_stride_lse = nhead_stride_lse;
kargs.batch_stride_lse = batch_stride_lse;
}
if constexpr(kDoFp8StaticQuant)
{
......@@ -274,24 +270,25 @@ struct FmhaFwdSplitKVCombineKernel
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_acc = 0;
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;
batch_offset_lse_acc = query_start;
if constexpr(kStoreLSE)
{
batch_offset_lse = query_start;
}
// get real # queries & # keys under group mode
const auto adjusted_seqstart_q_ptr = kargs.seqstart_q_ptr + i_batch;
......@@ -307,6 +304,12 @@ struct FmhaFwdSplitKVCombineKernel
else
{
batch_offset_o = static_cast<long_index_t>(i_batch) * kargs.batch_stride_o;
batch_offset_lse_acc = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse_acc;
if constexpr(kStoreLSE)
{
batch_offset_lse = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse;
}
}
// for simplicity, batch stride we just modify the pointer
......
include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_kernel.hpp
View file @ f84e2020
......@@ -136,7 +136,6 @@ struct FmhaFwdSplitKVKernel
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;
......@@ -216,6 +215,7 @@ struct FmhaFwdSplitKVKernel
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_lse_acc;
};
struct GroupModeKargs
......@@ -313,7 +313,6 @@ struct FmhaFwdSplitKVKernel
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
......@@ -323,7 +322,8 @@ struct FmhaFwdSplitKVKernel
{}, // placeholder for dropout
batch_stride_q,
batch_stride_k,
batch_stride_v};
batch_stride_v,
batch_stride_lse_acc};
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
......@@ -394,7 +394,6 @@ struct FmhaFwdSplitKVKernel
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,
......@@ -433,7 +432,6 @@ struct FmhaFwdSplitKVKernel
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
......@@ -511,8 +509,7 @@ struct FmhaFwdSplitKVKernel
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;
long_index_t batch_offset_lse_acc = 0;
const long_index_t batch_offset_o_acc =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_o_acc;
......@@ -524,6 +521,7 @@ struct FmhaFwdSplitKVKernel
batch_offset_q = query_start * kargs.stride_q;
batch_offset_k = key_start * kargs.stride_k;
batch_offset_lse_acc = query_start;
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
batch_offset_v = key_start * kargs.stride_v;
......@@ -567,6 +565,7 @@ struct FmhaFwdSplitKVKernel
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;
batch_offset_lse_acc = static_cast<long_index_t>(i_batch) * kargs.batch_stride_lse_acc;
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
batch_offset_bias = static_cast<long_index_t>(i_batch) * kargs.batch_stride_bias;
......
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_convert_dq.hpp 0 → 100644
View file @ f84e2020
// 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_bwd_pipeline_default_policy.hpp"
namespace ck_tile {
template <typename Problem, typename Policy = BlockFmhaBwdPipelineDefaultPolicy>
struct BlockFmhaBwdConvertQGrad
{
using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
using QGradDataType = remove_cvref_t<typename Problem::QGradDataType>;
static constexpr index_t kM0 = Problem::kM0;
static constexpr index_t kN0 = Problem::kN0;
static constexpr index_t kBlockPerCu = Problem::kBlockPerCu;
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t kQKHeaddim = Problem::kQKHeaddim;
static constexpr bool kIsGroupMode = Problem::kIsGroupMode;
static constexpr bool kPadSeqLenQ = Problem::kPadSeqLenQ;
static constexpr bool kPadHeadDimQ = Problem::kPadHeadDimQ;
static constexpr bool kIsDeterministic = Problem::kIsDeterministic;
static constexpr index_t kAlignmentQGradAcc =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentPostQGradAcc<Problem>();
static constexpr index_t kAlignmentQGrad =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentPostQGrad<Problem>();
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize() { return 0; }
// Convert only
template <typename QGradAccDramBlockWindowTmp, typename QGradDramBlockWindowTmp>
CK_TILE_HOST_DEVICE void
operator()(const QGradAccDramBlockWindowTmp& dq_acc_dram_block_window_tmp,
QGradDramBlockWindowTmp& dq_dram_block_window_tmp) const
{
static_assert(
std::is_same_v<AccDataType,
remove_cvref_t<typename QGradAccDramBlockWindowTmp::DataType>> &&
std::is_same_v<QGradDataType,
remove_cvref_t<typename QGradDramBlockWindowTmp::DataType>>,
"wrong!");
static_assert(kM0 == QGradDramBlockWindowTmp{}.get_window_lengths()[number<0>{}], "wrong!");
auto dq_acc_dram_window =
make_tile_window(dq_acc_dram_block_window_tmp.get_bottom_tensor_view(),
dq_acc_dram_block_window_tmp.get_window_lengths(),
dq_acc_dram_block_window_tmp.get_window_origin(),
Policy::template MakePostQGradDramTileDistribution<Problem>());
auto dq_acc = load_tile(dq_acc_dram_window);
const auto dq = cast_tile<QGradDataType>(dq_acc);
store_tile(dq_dram_block_window_tmp, dq);
}
// Reduce + Convert
template <typename QGradAccDramBlockWindowTmp, typename QGradDramBlockWindowTmp>
CK_TILE_HOST_DEVICE void
operator()(const QGradAccDramBlockWindowTmp& dq_acc_dram_block_window_tmp,
QGradDramBlockWindowTmp& dq_dram_block_window_tmp,
index_t nsplits) const
{
static_assert(
std::is_same_v<AccDataType,
remove_cvref_t<typename QGradAccDramBlockWindowTmp::DataType>> &&
std::is_same_v<QGradDataType,
remove_cvref_t<typename QGradDramBlockWindowTmp::DataType>>,
"wrong!");
static_assert(kM0 == QGradDramBlockWindowTmp{}.get_window_lengths()[number<0>{}], "wrong!");
auto dq_acc_dram_window =
make_tile_window(dq_acc_dram_block_window_tmp.get_bottom_tensor_view(),
dq_acc_dram_block_window_tmp.get_window_lengths(),
dq_acc_dram_block_window_tmp.get_window_origin(),
Policy::template MakePostQGradAccDramTileDistribution<Problem>());
auto dq_acc = decltype(load_tile(dq_acc_dram_window)){};
clear_tile(dq_acc);
constexpr auto dq_acc_spans = decltype(dq_acc)::get_distributed_spans();
index_t i_total_loops = 0;
auto dq_acc_buf = load_tile(dq_acc_dram_window);
move_tile_window(dq_acc_dram_window, {1, 0, 0});
do
{
sweep_tile_span(dq_acc_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(dq_acc_spans[number<1>{}], [&](auto idx1) {
sweep_tile_span(dq_acc_spans[number<2>{}], [&](auto idx2) {
constexpr auto n_i_j_idx = make_tuple(idx0, idx1, idx2);
dq_acc(n_i_j_idx) += dq_acc_buf(n_i_j_idx);
});
});
});
dq_acc_buf = load_tile(dq_acc_dram_window);
move_tile_window(dq_acc_dram_window, {1, 0, 0});
i_total_loops += 1;
} while(i_total_loops < (nsplits - 1));
sweep_tile_span(dq_acc_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(dq_acc_spans[number<1>{}], [&](auto idx1) {
sweep_tile_span(dq_acc_spans[number<2>{}], [&](auto idx2) {
constexpr auto n_i_j_idx = make_tuple(idx0, idx1, idx2);
dq_acc(n_i_j_idx) += dq_acc_buf(n_i_j_idx);
});
});
});
// declare dq
constexpr auto dq_converted_dstr =
Policy::template MakePostQGradAccDramTileDistribution<Problem>();
auto dq_converted = make_static_distributed_tensor<QGradDataType>(dq_converted_dstr);
sweep_tile_span(dq_acc_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(dq_acc_spans[number<1>{}], [&](auto idx1) {
sweep_tile_span(dq_acc_spans[number<2>{}], [&](auto idx2) {
constexpr auto n_i_j_idx = make_tuple(idx0, idx1, idx2);
dq_converted(n_i_j_idx) = type_convert<QGradDataType>(dq_acc[n_i_j_idx]);
});
});
});
constexpr auto dq_dstr = Policy::template MakePostQGradDramTileDistribution<Problem>();
auto dq = make_static_distributed_tensor<QGradDataType>(dq_dstr);
dq.get_thread_buffer() = dq_converted.get_thread_buffer();
store_tile(dq_dram_block_window_tmp, dq);
}
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dot_do_o.hpp
View file @ f84e2020
......@@ -4,11 +4,11 @@
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_dot_do_o_default_policy.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp"
namespace ck_tile {
template <typename Problem, typename Policy = BlockFmhaBwdOGradDotODefaultPolicy>
template <typename Problem, typename Policy = BlockFmhaBwdPipelineDefaultPolicy>
struct BlockFmhaBwdOGradDotO
{
using ODataType = remove_cvref_t<typename Problem::ODataType>;
......@@ -26,7 +26,7 @@ struct BlockFmhaBwdOGradDotO
static constexpr index_t kAlignmentO =
kPadHeadDimV ? 1 : Policy::template GetAlignmentO<Problem>();
static constexpr index_t kAlignmentOGrad =
kPadHeadDimV ? 1 : Policy::template GetAlignmentOGrad<Problem>();
kPadHeadDimV ? 1 : Policy::template GetAlignmentO<Problem>();
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize() { return 0; }
......
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dot_do_o_default_policy.hpp deleted 100644 → 0
View file @ 408534d4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp"
namespace ck_tile {
// These templates are not used here.
using BlockFmhaBwdOGradDotODefaultPolicy =
BlockFmhaBwdPipelineDefaultPolicy</* QLoadOnce_ = */ false,
/* QTLoadOnce_ = */ false,
/* KLoadOnce_ = */ false,
/* KTLoadOnce_ = */ false,
/* VLoadOnce_ = */ false,
/* OGradLoadOnce_ = */ false,
/* OGradTLoadOnce_ = */ false>;
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_qs_ks_vr_dos.hpp → include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_kr_ktr_vr.hpp
View file @ f84e2020
......@@ -6,13 +6,13 @@
#include "ck_tile/core.hpp"
#include "ck_tile/ops/fmha/block/block_attention_bias_enum.hpp"
#include "ck_tile/ops/fmha/block/block_dropout.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_qs_ks_vr_dos_default_policy.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
namespace ck_tile {
template <typename Problem, typename Policy = BlockFmhaBwdDQDKDVPipelineQSKSVROGradSDefaultPolicy>
struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
template <typename Problem, typename Policy = BlockFmhaBwdPipelineDefaultPolicy>
struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
{
using QDataType = remove_cvref_t<typename Problem::QDataType>;
using KDataType = remove_cvref_t<typename Problem::KDataType>;
......@@ -30,6 +30,8 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
using VGradDataType = remove_cvref_t<typename Problem::VGradDataType>;
using BiasGradDataType = remove_cvref_t<typename Problem::BiasGradDataType>;
using FmhaMask = remove_cvref_t<typename Problem::FmhaMask>;
using FmhaDropout = remove_cvref_t<typename Problem::FmhaDropout>;
using HotLoopScheduler = typename Policy::template HotLoopScheduler<Problem>;
using BlockFmhaShape = remove_cvref_t<typename Problem::BlockFmhaShape>;
......@@ -46,14 +48,6 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
static constexpr index_t kQKHeaddim = BlockFmhaShape::kQKHeaddim;
static constexpr index_t kVHeaddim = BlockFmhaShape::kVHeaddim;
static constexpr bool kQLoadOnce = true;
static constexpr bool kQTLoadOnce = false;
static constexpr bool kKLoadOnce = true;
static constexpr bool kKTLoadOnce = false;
static constexpr bool kVLoadOnce = true;
static constexpr bool kOGradLoadOnce = true;
static constexpr bool kOGradTLoadOnce = false;
static constexpr bool kIsGroupMode = Problem::kIsGroupMode;
static constexpr bool kPadSeqLenQ = Problem::kPadSeqLenQ;
static constexpr bool kPadSeqLenK = Problem::kPadSeqLenK;
......@@ -61,7 +55,7 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
static constexpr bool kPadHeadDimV = Problem::kPadHeadDimV;
static constexpr auto BiasEnum = Problem::BiasEnum;
static constexpr bool kHasBiasGrad = Problem::kHasBiasGrad;
static constexpr bool kHasDropout = Problem::kHasDropout;
static constexpr bool kIsDeterministic = Problem::kIsDeterministic;
// 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
......@@ -71,12 +65,9 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
kPadHeadDimQ ? 1 : Policy::template GetAlignmentK<Problem>();
static constexpr index_t kAlignmentV =
kPadHeadDimV ? 1 : Policy::template GetAlignmentV<Problem>();
static constexpr index_t kAlignmentO =
kPadHeadDimV ? 1 : Policy::template GetAlignmentO<Problem>();
static constexpr index_t kAlignmentOGrad =
kPadHeadDimV ? 1 : Policy::template GetAlignmentOGrad<Problem>();
static constexpr index_t kAlignmentQGrad =
kPadHeadDimQ ? 2 : Policy::template GetAlignmentQGrad<Problem>();
static constexpr index_t kAlignmentQGrad = 1;
static constexpr index_t kAlignmentKGrad =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentKGrad<Problem>();
static constexpr index_t kAlignmentVGrad =
......@@ -84,7 +75,7 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
static constexpr index_t kAlignmentBias =
kPadSeqLenK ? 1 : Policy::template GetTransposedAlignmentBias<Problem>();
static constexpr const char* name = "qs_ks_vr_dos";
static constexpr const char* name = "kr_ktr_vr";
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
......@@ -92,14 +83,11 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
}
template <typename QDramBlockWindowTmp,
typename QTDramBlockWindowTmp,
typename KDramBlockWindowTmp,
typename KTDramBlockWindowTmp,
typename VDramBlockWindowTmp,
typename BiasDramBlockWindowTmp,
typename RandValDramBlockWindowTmp,
typename OGradDramBlockWindowTmp,
typename OGradTDramBlockWindowTmp,
typename LSEDramBlockWindowTmp,
typename DDramBlockWindowTmp,
typename QGradDramBlockWindowTmp,
......@@ -107,14 +95,11 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp,
const QTDramBlockWindowTmp& /*qt_dram_block_window_tmp*/,
const KDramBlockWindowTmp& k_dram_block_window_tmp,
const KTDramBlockWindowTmp& /*kt_dram_block_window_tmp*/,
const VDramBlockWindowTmp& v_dram_block_window_tmp,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp,
const RandValDramBlockWindowTmp& randval_dram_block_window_tmp,
const OGradDramBlockWindowTmp& do_dram_block_window_tmp,
const OGradTDramBlockWindowTmp& /*dot_dram_block_window_tmp*/,
const LSEDramBlockWindowTmp& lse_dram_block_window_tmp,
const DDramBlockWindowTmp& d_dram_block_window_tmp,
const QGradDramBlockWindowTmp& dq_dram_block_window_tmp,
......@@ -122,13 +107,11 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
FmhaMask mask,
PositionEncoding position_encoding,
float raw_scale,
#if CK_TILE_FMHA_FWD_FAST_EXP2
float scale,
#endif
float rp_undrop,
float scale_rp_undrop,
void* smem_ptr,
BlockDropout& dropout) const
FmhaDropout& dropout) const
{
static_assert(
std::is_same_v<QDataType, remove_cvref_t<typename QDramBlockWindowTmp::DataType>> &&
......@@ -138,9 +121,7 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
remove_cvref_t<typename OGradDramBlockWindowTmp::DataType>> &&
std::is_same_v<LSEDataType,
remove_cvref_t<typename LSEDramBlockWindowTmp::DataType>> &&
std::is_same_v<DDataType, remove_cvref_t<typename DDramBlockWindowTmp::DataType>> &&
std::is_same_v<QGradDataType,
remove_cvref_t<typename QGradDramBlockWindowTmp::DataType>>,
std::is_same_v<DDataType, remove_cvref_t<typename DDramBlockWindowTmp::DataType>>,
"wrong!");
static_assert(kM0 == QDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
......@@ -156,77 +137,6 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
kN0 == BiasGradDramBlockWindowTmp{}.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) + Policy::template GetSmemSizeK<Problem>()));
auto q_lds = make_tensor_view<address_space_enum::lds>(
q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
auto q_lds_window =
make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kQKHeaddim>{}), {0, 0});
// QT tile in LDS
auto qt_lds = make_tensor_view<address_space_enum::lds>(
q_lds_ptr, Policy::template MakeQLdsBlockDescriptorAsQT<Problem>());
auto qt_lds_window =
make_tile_window(qt_lds, make_tuple(number<kQKHeaddim>{}, number<kM0>{}), {0, 0});
// K tile in LDS
auto k_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<KDataType*>(smem_ptr),
Policy::template MakeKLdsBlockDescriptor<Problem>());
auto k_lds_window =
make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kQKHeaddim>{}), {0, 0});
// KT tile in LDS
auto kt_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<KDataType*>(smem_ptr),
Policy::template MakeKLdsBlockDescriptorAsKT<Problem>());
auto kt_lds_window =
make_tile_window(kt_lds, make_tuple(number<kQKHeaddim>{}, number<kN0>{}), {0, 0});
// OGrad tile in LDS
OGradDataType* do_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeQ<Problem>()));
auto do_lds = make_tensor_view<address_space_enum::lds>(
do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptor<Problem>());
auto do_lds_window =
make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kVHeaddim>{}), {0, 0});
// OGradT tile in LDS
auto dot_lds = make_tensor_view<address_space_enum::lds>(
do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptorAsOGradT<Problem>());
auto dot_lds_window =
make_tile_window(dot_lds, make_tuple(number<kVHeaddim>{}, number<kM0>{}), {0, 0});
// SGrad tile in LDS
GemmDataType* ds_lds_ptr = static_cast<GemmDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeQ<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>()));
auto ds_lds = make_tensor_view<address_space_enum::lds>(
ds_lds_ptr, Policy::template MakeSGradLdsBlockDescriptor<Problem>());
auto ds_lds_window =
make_tile_window(ds_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
// BiasT/BiasGradT tile in LDS, use the same size and layout
BiasDataType* biast_lds_ptr = static_cast<BiasDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeQ<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>()));
auto biast_lds = make_tensor_view<address_space_enum::lds>(
biast_lds_ptr, Policy::template MakeBiasTLdsBlockDescriptor<Problem>());
auto biast_lds_shuffle_window =
make_tile_window(biast_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
auto dbiast_lds_shuffle_window =
make_tile_window(biast_lds,
make_tuple(number<kM0>{}, number<kN0>{}),
{0, 0},
Policy::template MakeShuffledBiasTileDistribution<Problem>());
static_assert(std::is_same_v<BiasDataType, BiasGradDataType>,
"BiasDataType and BiasGradDataType should be the same!");
// Block GEMM
constexpr auto gemm_0 = Policy::template GetQKBlockGemm<Problem>();
constexpr auto gemm_1 = Policy::template GetPTOGradTBlockGemm<Problem>();
......@@ -234,34 +144,19 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
constexpr auto gemm_3 = Policy::template GetSGradTQTBlockGemm<Problem>();
constexpr auto gemm_4 = Policy::template GetSGradKTBlockGemm<Problem>();
auto v_dram_window = make_tile_window(
v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
v_dram_block_window_tmp.get_window_origin(),
Policy::template MakeVInRegDramTileDistribution<Problem, decltype(gemm_2)>());
auto v = load_tile(v_dram_window); // persistent V register tile
using SPTBlockTileType = decltype(gemm_0.MakeCBlockTile());
using SPGradTBlockTileType = decltype(gemm_2.MakeCBlockTile());
using QGradBlockTileType = decltype(gemm_4.MakeCBlockTile());
// init VGrad & KGrad
auto dv_acc = decltype(gemm_1.MakeCBlockTile()){};
auto dk_acc = decltype(gemm_3.MakeCBlockTile()){};
clear_tile(dv_acc);
clear_tile(dk_acc);
auto k_dram_window = make_tile_window(
k_dram_block_window_tmp.get_bottom_tensor_view(),
// K, HBM ->LDS ->Reg
auto k_dram_window =
make_tile_window(k_dram_block_window_tmp.get_bottom_tensor_view(),
k_dram_block_window_tmp.get_window_lengths(),
k_dram_block_window_tmp.get_window_origin(),
Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window for
// load
Policy::template MakeKDramTileDistribution<Problem>());
__builtin_amdgcn_sched_barrier(0);
const auto k_origin = k_dram_window.get_window_origin();
// Early termination
const auto [seqlen_q_start, seqlen_q_end] =
mask.GetTileRangeAlongY(k_origin.at(number<0>{}), number<kM0>{}, number<kN0>{});
......@@ -274,217 +169,408 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
{
// Note: here dk_acc&dv_acc are all cleard, return it
// Note: v loaded but no fence, ignore it.
return ck_tile::make_tuple(dk_acc, dv_acc);
return make_tuple(dk_acc, dv_acc);
}
}
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 MakeKLdsWriteBlockDescriptor<Problem>());
auto k_lds_write_window =
make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
auto k_lds_read_window =
make_tile_window(k_lds_write_window.get_bottom_tensor_view(),
make_tuple(number<kN0>{}, number<kK0>{}),
k_lds_write_window.get_window_origin(),
Policy::template MakeKRegSliceBlockDescriptor<Problem>());
auto k_reg_tensor = make_static_distributed_tensor<KDataType>(
Policy::template MakeKRegBlockDescriptor<Problem>());
//------------------------------------------------------------------
// V, HBM ->LDS ->Reg
auto v_dram_window =
make_tile_window(v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
v_dram_block_window_tmp.get_window_origin(),
Policy::template MakeVDramTileDistribution<Problem>());
VDataType* v_lds_ptr =
static_cast<VDataType*>(static_cast<void*>(static_cast<char*>(smem_ptr)));
auto v_lds = make_tensor_view<address_space_enum::lds>(
v_lds_ptr, Policy::template MakeVLdsWriteBlockDescriptor<Problem>());
auto v_lds_write_window =
make_tile_window(v_lds, make_tuple(number<kN0>{}, number<kK2>{}), {0, 0});
auto v_lds_read_window =
make_tile_window(v_lds_write_window.get_bottom_tensor_view(),
make_tuple(number<kN0>{}, number<kK2>{}),
v_lds_write_window.get_window_origin(),
Policy::template MakeVRegSliceBlockDescriptor<Problem>());
auto v_reg_tensor = make_static_distributed_tensor<VDataType>(
Policy::template MakeVRegBlockDescriptor<Problem>());
//------------------------------------------------------------------
// KT, Reg ->LDS ->Reg
auto shuffled_k_block_tile = make_static_distributed_tensor<KDataType>(
Policy::template MakeShuffledKRegWriteBlockDescriptor<Problem>());
KDataType* kt_lds_ptr = static_cast<KDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto shuffled_k_lds_write = make_tensor_view<address_space_enum::lds>(
kt_lds_ptr, Policy::template MakeShuffledKLdsWriteBlockDescriptor<Problem>());
auto shuffled_k_lds_write_window = make_tile_window(
shuffled_k_lds_write, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
auto kt_lds_read = make_tensor_view<address_space_enum::lds>(
kt_lds_ptr, Policy::template MakeKTLdsReadBlockDescriptor<Problem>());
auto kt_lds_read_window =
make_tile_window(kt_lds_read,
make_tuple(number<kQKHeaddim>{}, number<kN0>{}),
{0, 0},
Policy::template MakeKTRegBlockDescriptor<Problem>());
//------------------------------------------------------------------
// Pre-Load KV into Registers
auto k_block_tile = load_tile(k_dram_window);
auto v_block_tile = load_tile(v_dram_window);
store_tile(k_lds_write_window, k_block_tile);
shuffle_tile(shuffled_k_block_tile, k_block_tile);
store_tile(shuffled_k_lds_write_window, shuffled_k_block_tile);
block_sync_lds();
k_reg_tensor = load_tile(k_lds_read_window);
block_sync_lds();
auto kt_reg_tensor = load_tile(kt_lds_read_window);
store_tile(k_lds_window, k_block_tile); // // persistent K in LDS
store_tile(v_lds_write_window, v_block_tile);
auto q_dram_block_window =
block_sync_lds();
v_reg_tensor = load_tile(v_lds_read_window);
block_sync_lds();
//---------------------------- Loop Load in ----------------------------//
// Q: HBM ->Reg ->LDS
auto q_dram_window =
make_tile_window(q_dram_block_window_tmp.get_bottom_tensor_view(),
q_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
{seqlen_q_start, 0},
Policy::template MakeQDramTileDistribution<Problem>());
auto do_dram_block_window =
QDataType* q_lds_ptr = static_cast<QDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>()));
auto q_lds = make_tensor_view<address_space_enum::lds>(
q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
auto q_lds_window =
make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
auto q_lds_read_window =
make_tile_window(q_lds_window.get_bottom_tensor_view(),
make_tuple(number<kM0>{}, number<kK0>{}),
q_lds_window.get_window_origin(),
Policy::template MakeQRegSliceBlockDescriptor<Problem>());
auto pt_reg_tensor = make_static_distributed_tensor<GemmDataType>(
Policy::template MakePTRegSliceBlockDescriptor<Problem>());
// QT: Reg -> Reg-> LDS
auto shuffled_q_block_tile = make_static_distributed_tensor<QDataType>(
Policy::template MakeShuffledQRegWriteBlockDescriptor<Problem>());
QDataType* qt_lds_ptr =
static_cast<QDataType*>(static_cast<void*>(static_cast<char*>(smem_ptr)));
auto shuffled_q_lds_write = make_tensor_view<address_space_enum::lds>(
qt_lds_ptr, Policy::template MakeShuffledQLdsWriteBlockDescriptor<Problem>());
auto shuffled_q_lds_write_window = make_tile_window(
shuffled_q_lds_write, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
auto qt_lds_read = make_tensor_view<address_space_enum::lds>(
qt_lds_ptr, Policy::template MakeQTLdsReadBlockDescriptor<Problem>());
auto qt_lds_read_window =
make_tile_window(qt_lds_read,
make_tuple(number<kQKHeaddim>{}, number<kM0>{}),
{0, 0},
Policy::template MakeQTRegSliceBlockDescriptor<Problem>());
// dO: HBM ->Reg ->LDS
auto do_dram_window =
make_tile_window(do_dram_block_window_tmp.get_bottom_tensor_view(),
do_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
{seqlen_q_start, 0},
Policy::template MakeOGradDramTileDistribution<Problem>());
auto dq_dram_block_window =
make_tile_window(dq_dram_block_window_tmp.get_bottom_tensor_view(),
dq_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
OGradDataType* do_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>()));
auto lse_dram_block_window =
make_tile_window(lse_dram_block_window_tmp.get_bottom_tensor_view(),
lse_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start});
auto do_lds = make_tensor_view<address_space_enum::lds>(
do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptor<Problem>());
auto d_dram_block_window =
make_tile_window(d_dram_block_window_tmp.get_bottom_tensor_view(),
d_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start});
auto do_lds_window =
make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
auto do_lds_read_window =
make_tile_window(do_lds_window.get_bottom_tensor_view(),
make_tuple(number<kM0>{}, number<kK2>{}),
do_lds_window.get_window_origin(),
Policy::template MakeOGradRegSliceBlockDescriptor<Problem>());
// dOT: Reg ->Reg ->LDS
auto shuffled_do_block_tile = make_static_distributed_tensor<OGradDataType>(
Policy::template MakeShuffledOGradRegWriteBlockDescriptor<Problem>());
OGradDataType* dot_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>()));
auto shuffled_do_lds_write = make_tensor_view<address_space_enum::lds>(
dot_lds_ptr, Policy::template MakeShuffledOGradLdsWriteBlockDescriptor<Problem>());
auto shuffled_do_lds_write_window = make_tile_window(
shuffled_do_lds_write, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
auto dot_read_lds = make_tensor_view<address_space_enum::lds>(
dot_lds_ptr, Policy::template MakeOGradTLdsReadBlockDescriptor<Problem>());
auto dot_lds_read_window =
make_tile_window(dot_read_lds,
make_tuple(number<kVHeaddim>{}, number<kM0>{}),
{0, 0},
Policy::template MakeOGradTRegSliceBlockDescriptor<Problem>());
// dS: Reg -> Reg -> LDS
GemmDataType* ds_lds_ptr = static_cast<GemmDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>() + Policy::template GetSmemSizeLSE<Problem>() +
Policy::template GetSmemSizeD<Problem>()));
auto ds_lds = make_tensor_view<address_space_enum::lds>(
ds_lds_ptr, Policy::template MakeSGradLdsBlockDescriptor<Problem>());
auto ds_lds_window =
make_tile_window(ds_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
auto ds_lds_read_window =
make_tile_window(ds_lds_window.get_bottom_tensor_view(),
make_tuple(number<kM0>{}, number<kK4>{}),
ds_lds_window.get_window_origin(),
Policy::template MakeSGradRegSliceBlockDescriptor<Problem>());
auto dst_reg_tensor = make_static_distributed_tensor<GemmDataType>(
Policy::template MakeSGradTRegSliceBlockDescriptor<Problem>());
// Bias: HBM ->Reg ->Reg ->LDS
const auto bias_origin = bias_dram_block_window_tmp.get_window_origin();
auto bias_dram_block_window =
auto bias_dram_window =
make_tile_window(bias_dram_block_window_tmp.get_bottom_tensor_view(),
bias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, bias_origin.at(number<1>{})}); // M/N
{seqlen_q_start, bias_origin.at(number<1>{})},
Policy::template MakeBiasTileDistribution<Problem>());
const auto dbias_origin = dbias_dram_block_window_tmp.get_window_origin();
auto dbias_dram_block_window =
make_tile_window(dbias_dram_block_window_tmp.get_bottom_tensor_view(),
dbias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, dbias_origin.at(number<1>{})}); // M/N
BiasDataType* bias_lds_ptr = static_cast<BiasDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>() + Policy::template GetSmemSizeLSE<Problem>() +
Policy::template GetSmemSizeD<Problem>()));
auto bias_lds = make_tensor_view<address_space_enum::lds>(
bias_lds_ptr, Policy::template MakeBiasLdsBlockDescriptor<Problem>());
auto bias_lds_write_window =
make_tile_window(bias_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
auto bias_s_lds_read_window =
make_tile_window(bias_lds_write_window.get_bottom_tensor_view(),
bias_lds_write_window.get_window_lengths(),
bias_lds_write_window.get_window_origin(),
Policy::template MakeBiasSTileDistribution<decltype(gemm_0)>());
static_assert(std::is_same_v<BiasDataType, BiasGradDataType>,
"BiasDataType and BiasGradDataType should be the same!");
// LSE: HBM -> LDS ->Reg
auto lse_dram_window = make_tile_window(
lse_dram_block_window.get_bottom_tensor_view(),
lse_dram_block_window.get_window_lengths(),
lse_dram_block_window.get_window_origin(),
lse_dram_block_window_tmp.get_bottom_tensor_view(),
lse_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start},
Policy::template MakeLSEDDramTileDistribution<Problem, decltype(gemm_0)>());
LSEDataType* lse_lds_ptr = static_cast<LSEDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>()));
auto lse_lds = make_tensor_view<address_space_enum::lds>(
lse_lds_ptr, Policy::template MakeLSEDLdsWriteBlockDescriptor<Problem>());
auto lse_lds_write_window = make_tile_window(lse_lds, make_tuple(number<kM0>{}), {0});
auto lse_lds_read_window = make_tile_window(
lse_lds,
make_tuple(number<kM0>{}),
{0},
Policy::template MakeLSEDLdsReadBlockDescriptor<Problem, decltype(gemm_0)>());
// D: HBM ->Reg
auto d_dram_window = make_tile_window(
d_dram_block_window.get_bottom_tensor_view(),
d_dram_block_window.get_window_lengths(),
d_dram_block_window.get_window_origin(),
d_dram_block_window_tmp.get_bottom_tensor_view(),
d_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start},
Policy::template MakeLSEDDramTileDistribution<Problem, decltype(gemm_0)>());
auto bias_dram_window =
make_tile_window(bias_dram_block_window.get_bottom_tensor_view(),
bias_dram_block_window.get_window_lengths(),
bias_dram_block_window.get_window_origin(),
Policy::template MakeBiasTileDistribution<Problem>());
DDataType* d_lds_ptr = static_cast<DDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>() + Policy::template GetSmemSizeLSE<Problem>()));
auto d_lds = make_tensor_view<address_space_enum::lds>(
d_lds_ptr, Policy::template MakeLSEDLdsWriteBlockDescriptor<Problem>());
auto biast_lds_window =
make_tile_window(biast_lds_shuffle_window.get_bottom_tensor_view(),
biast_lds_shuffle_window.get_window_lengths(),
biast_lds_shuffle_window.get_window_origin(),
Policy::template MakeBiasTTileDistribution<decltype(gemm_0)>());
auto d_lds_write_window = make_tile_window(d_lds, make_tuple(number<kM0>{}), {0});
auto randval_dram_window = dropout.MakeRandvalDramWindow<decltype(gemm_0), false>(
auto d_lds_read_window = make_tile_window(
d_lds,
make_tuple(number<kM0>{}),
{0},
Policy::template MakeLSEDLdsReadBlockDescriptor<Problem, decltype(gemm_0)>());
// RandVal: HBM ->Reg
auto randval_dram_window = dropout.template MakeRandvalDramWindow<decltype(gemm_0), false>(
randval_dram_block_window_tmp, seqlen_q_start);
// BiasGrad
// Reg ->LDS ->Reg ->HBM
const auto dbias_origin = dbias_dram_block_window_tmp.get_window_origin();
auto dbias_dram_window =
make_tile_window(dbias_dram_block_window_tmp.get_bottom_tensor_view(),
dbias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, dbias_origin.at(number<1>{})}); // M/N
auto dbias_lds_read_window =
make_tile_window(bias_lds,
make_tuple(number<kM0>{}, number<kN0>{}),
{0, 0},
Policy::template MakeShuffledBiasTileDistribution<Problem>());
// ----------------------------Loop write out------------------------------//
auto dq_dram_window = make_tile_window(dq_dram_block_window_tmp.get_bottom_tensor_view(),
dq_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
using SPBlockTileType = decltype(gemm_0.MakeCBlockTile());
using SPGradBlockTileType = decltype(gemm_2.MakeCBlockTile());
using QGradBlockTileType = decltype(gemm_4.MakeCBlockTile());
index_t i_total_loops = 0;
constexpr index_t k0_loops = kQKHeaddim / kK0;
constexpr index_t k1_loops = kM0 / kK1;
constexpr index_t k2_loops = kVHeaddim / kK2;
constexpr index_t k3_loops = kM0 / kK3;
index_t seqlen_q_step = seqlen_q_start;
static_assert(kQKHeaddim == kK0, "kQKHeaddim should equal to kK0");
static_assert(kM0 == kK1, "kM0 should equal to kK1");
static_assert(kVHeaddim == kK2, "kVHeaddim should equal to kK2");
static_assert(kM0 == kK3, "kM0 should equal to kK3");
constexpr index_t k4_loops = kN0 / kK4;
do
{
auto q_dram_window = make_tile_window(
q_dram_block_window.get_bottom_tensor_view(),
q_dram_block_window.get_window_lengths(),
q_dram_block_window.get_window_origin(),
Policy::template MakeQDramTileDistribution<Problem>()); // Q DRAM tile window for
// load
auto do_dram_window = make_tile_window(
do_dram_block_window.get_bottom_tensor_view(),
do_dram_block_window.get_window_lengths(),
do_dram_block_window.get_window_origin(),
Policy::template MakeOGradDramTileDistribution<Problem>()); // OGrad DRAM tile
// window for load
// STAGE 1, Q@K Gemm0
auto st_acc = SPTBlockTileType{};
clear_tile(dv_acc);
clear_tile(dk_acc);
__builtin_amdgcn_sched_barrier(0);
// Hot loop
while(i_total_loops < num_total_loop)
{
auto q_block_tile = load_tile(q_dram_window);
clear_tile(st_acc); // Initialize S^T
store_tile(q_lds_window, q_block_tile); // LDS write
move_tile_window(q_dram_window, {kM0, 0});
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
}
auto lse_block_tile = load_tile(lse_dram_window);
move_tile_window(lse_dram_window, {kM0});
if constexpr(k0_loops > 1)
{
static_for<0, k0_loops - 1, 1>{}([&](auto i_k0) {
block_sync_lds();
gemm_0(st_acc,
get_slice_tile(q_lds_window,
sequence<0, i_k0 * kK0>{},
sequence<kM0, (i_k0 + 1) * kK0>{}),
get_slice_tile(k_lds_window,
sequence<0, i_k0 * kK0>{},
sequence<kN0, (i_k0 + 1) * kK0>{}));
block_sync_lds();
});
}
store_tile(q_lds_window, q_block_tile);
shuffle_tile(shuffled_q_block_tile, q_block_tile);
store_tile(shuffled_q_lds_write_window, shuffled_q_block_tile);
store_tile(lse_lds_write_window, lse_block_tile);
auto do_block_tile = load_tile(do_dram_window); // prefetch load OGrad tile
{ // tail
block_sync_lds();
gemm_0(st_acc,
get_slice_tile(q_lds_window,
sequence<0, (k0_loops - 1) * kK0>{},
sequence<kM0, k0_loops * kK0>{}),
get_slice_tile(k_lds_window,
sequence<0, (k0_loops - 1) * kK0>{},
sequence<kN0, k0_loops * kK0>{}));
auto q_reg_tensor = load_tile(q_lds_read_window);
auto lse = load_tile(lse_lds_read_window);
block_sync_lds();
}
// STAGE 1, Q@K Gemm0
auto s_acc = SPBlockTileType{};
s_acc = gemm_0(q_reg_tensor, k_reg_tensor);
// STAGE 2, Scale, Add bias, Mask, Softmax, Dropout
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
block_sync_lds();
auto bias_shuffle_tmp = make_static_distributed_tensor<BiasDataType>(
const auto bias_tile = load_tile(bias_dram_window);
auto shuffled_bias_tile = make_static_distributed_tensor<BiasDataType>(
Policy::template MakeShuffledBiasTileDistribution<Problem>());
shuffle_tile(bias_shuffle_tmp, bias_tile);
store_tile(biast_lds_shuffle_window, bias_shuffle_tmp);
shuffle_tile(shuffled_bias_tile, bias_tile);
store_tile(bias_lds_write_window, shuffled_bias_tile);
block_sync_lds();
auto biast_tile = load_tile(biast_lds_window);
auto bias_s_tile = load_tile(bias_s_lds_read_window);
tile_elementwise_inout(
[&](auto& x, const auto& y) {
#if !CK_TILE_FMHA_FWD_FAST_EXP2
x = raw_scale * x + type_convert<AccDataType>(y);
#else
x = scale * x + log2e_v<AccDataType> * type_convert<AccDataType>(y);
#endif
},
st_acc,
biast_tile);
s_acc,
bias_s_tile);
move_tile_window(bias_dram_window, {kM0, 0});
__builtin_amdgcn_sched_barrier(0);
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
const auto q_origin = q_dram_block_window.get_window_origin();
constexpr auto st_spans = decltype(st_acc)::get_distributed_spans();
sweep_tile_span(st_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(st_spans[number<1>{}], [&](auto idx1) {
constexpr auto s_spans = decltype(s_acc)::get_distributed_spans();
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(
st_acc.get_tile_distribution(), make_tuple(idx0, idx1));
s_acc.get_tile_distribution(), make_tuple(idx0, idx1));
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
const auto row = seqlen_q_step + 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);
#if !CK_TILE_FMHA_FWD_FAST_EXP2
st_acc(i_j_idx) *= raw_scale;
#else
st_acc(i_j_idx) *= scale;
#endif
position_encoding.update(st_acc(i_j_idx), row, col);
s_acc(i_j_idx) *= scale;
position_encoding.update(s_acc(i_j_idx), row, col);
});
});
}
else
{
#if !CK_TILE_FMHA_FWD_FAST_EXP2
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, st_acc);
#endif
}
if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
{
const auto q_origin = q_dram_block_window.get_window_origin();
bool need_perpixel_check = mask.IsEdgeTile(q_origin.at(number<0>{}),
k_origin.at(number<0>{}),
number<kM0>{},
number<kN0>{});
bool need_perpixel_check = mask.IsEdgeTile(
seqlen_q_step, k_origin.at(number<0>{}), number<kM0>{}, number<kN0>{});
if(need_perpixel_check)
{
set_tile_if(st_acc, -numeric<AccDataType>::infinity(), [&](auto tile_idx) {
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
set_tile_if(s_acc, -numeric<AccDataType>::infinity(), [&](auto tile_idx) {
const auto row = seqlen_q_step + tile_idx.at(number<0>{});
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
return mask.IsOutOfBound(row, col);
});
}
}
const auto lse = load_tile(lse_dram_window);
static const auto get_validated_lse = [](LSEDataType raw_lse) {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
FmhaMask::IsMasking)
......@@ -499,157 +585,162 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
}
};
auto pt = SPTBlockTileType{};
constexpr auto pt_spans = decltype(pt)::get_distributed_spans();
sweep_tile_span(pt_spans[number<0>{}], [&](auto idx0) {
auto p = SPBlockTileType{};
constexpr auto p_spans = decltype(p)::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_lse = log2e_v<LSEDataType> * get_validated_lse(lse[i_idx]);
#endif
sweep_tile_span(pt_spans[number<1>{}], [&](auto idx1) {
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)
{
pt(i_j_idx) = exp2(st_acc[i_j_idx] - row_lse);
p(i_j_idx) = exp2(s_acc[i_j_idx] - row_lse);
}
else
{
pt(i_j_idx) = exp2(scale * st_acc[i_j_idx] - row_lse);
p(i_j_idx) = exp2(scale * s_acc[i_j_idx] - row_lse);
}
#else
pt(i_j_idx) = exp(st_acc[i_j_idx] - get_validated_lse(lse[i_idx]));
#endif
});
});
if constexpr(kHasDropout)
if constexpr(FmhaDropout::IsDropout)
{
dropout.Run<decltype(gemm_0), RandValOutputDataType>(
seqlen_q_start + i_total_loops * kM0, pt, randval_dram_window);
dropout.template Run<decltype(gemm_0), RandValOutputDataType>(
seqlen_q_step, k_origin.at(number<0>{}), p, randval_dram_window);
}
// STAGE 3, P^T@OGrad^T Gemm1
block_sync_lds();
store_tile(do_lds_window, do_block_tile); // store the prefetch
const auto pt_gemm = [&]() {
if constexpr(kHasDropout)
const auto p_gemm = [&]() {
if constexpr(FmhaDropout::IsDropout)
{
return tile_elementwise_in(
[](const auto& x) { return type_convert<GemmDataType>(x > 0.f ? x : 0.f); },
pt);
p);
}
else
{
return cast_tile<GemmDataType>(pt);
return cast_tile<GemmDataType>(p);
}
}();
static_for<0, k1_loops, 1>{}([&](auto i_k1) {
block_sync_lds();
gemm_1(dv_acc,
get_slice_tile(
pt_gemm, sequence<i_k1 * kK1, 0>{}, sequence<(i_k1 + 1) * kK1, kN0>{}),
get_slice_tile(dot_lds_window,
sequence<0, i_k1 * kK1>{},
sequence<kVHeaddim, (i_k1 + 1) * kK1>{}));
// STAGE 3, P^T@OGrad^T Gemm1
auto do_block_tile = load_tile(do_dram_window);
move_tile_window(do_dram_window, {kM0, 0});
auto d_block_tile = load_tile(d_dram_window);
move_tile_window(d_dram_window, {kM0});
store_tile(do_lds_window, do_block_tile);
shuffle_tile(shuffled_do_block_tile, do_block_tile);
store_tile(shuffled_do_lds_write_window, shuffled_do_block_tile);
store_tile(d_lds_write_window, d_block_tile);
block_sync_lds();
});
// STAGE 4, OGrad@V Gemm2
auto dpt_acc = SPGradTBlockTileType{};
clear_tile(dpt_acc); // Initialize PGrad^T
auto dot_reg_tensor = load_tile(dot_lds_read_window);
static_for<0, k2_loops, 1>{}([&](auto i_k2) {
block_sync_lds();
gemm_2(dpt_acc,
get_slice_tile(do_lds_window,
sequence<0, i_k2 * kK2>{},
sequence<kM0, (i_k2 + 1) * kK2>{}),
get_slice_tile(
v, sequence<0, i_k2 * kK2>{}, sequence<kN0, (i_k2 + 1) * kK2>{}));
Policy::template PTFromGemm0CToGemm1A<Problem,
decltype(pt_reg_tensor),
decltype(p_gemm)>(pt_reg_tensor, p_gemm);
gemm_1(dv_acc, pt_reg_tensor, dot_reg_tensor);
// STAGE 4, OGrad@V Gemm2
auto do_reg_tensor = load_tile(do_lds_read_window);
auto d = load_tile(d_lds_read_window);
block_sync_lds();
});
// STAGE 5, P^T(PGrad^T - D)
const auto d = load_tile(d_dram_window);
auto dp_acc = SPGradBlockTileType{};
dp_acc = gemm_2(do_reg_tensor, v_reg_tensor);
auto dst = SPGradTBlockTileType{};
constexpr auto dst_spans = decltype(dst)::get_distributed_spans();
sweep_tile_span(dst_spans[number<0>{}], [&](auto idx0) {
// STAGE 5, P^T(PGrad^T - D)
auto ds = SPGradBlockTileType{};
constexpr auto ds_spans = decltype(ds)::get_distributed_spans();
sweep_tile_span(ds_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
sweep_tile_span(dst_spans[number<1>{}], [&](auto idx1) {
sweep_tile_span(ds_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
bool undrop_flag = pt[i_j_idx] >= 0;
dst(i_j_idx) =
pt[i_j_idx] *
(!kHasDropout || undrop_flag ? (dpt_acc[i_j_idx] - d[i_idx]) : d[i_idx]);
bool undrop_flag = p[i_j_idx] >= 0;
ds(i_j_idx) = p[i_j_idx] * (!FmhaDropout::IsDropout || undrop_flag
? (dp_acc[i_j_idx] - d[i_idx])
: d[i_idx]);
});
});
if constexpr(kHasBiasGrad)
{
const auto dbiast = [&]() {
if constexpr(kHasDropout)
const auto dbias = [&]() {
if constexpr(FmhaDropout::IsDropout)
{
return tile_elementwise_in(
[&rp_undrop](const auto& x) {
return type_convert<BiasGradDataType>(x * rp_undrop);
},
dst);
ds);
}
else
{
return cast_tile<BiasGradDataType>(dst);
return cast_tile<BiasGradDataType>(ds);
}
}();
store_tile(biast_lds_shuffle_window, dbiast);
store_tile(bias_lds_write_window, dbias);
block_sync_lds();
auto dbiast_tile = load_tile(dbiast_lds_shuffle_window);
auto dbiast_shuffle_tmp = make_static_distributed_tensor<BiasGradDataType>(
auto shuffled_dbias_tile = load_tile(dbias_lds_read_window);
auto dbias_tile = make_static_distributed_tensor<BiasGradDataType>(
Policy::template MakeBiasTileDistribution<Problem>());
shuffle_tile(dbiast_shuffle_tmp, dbiast_tile);
store_tile(dbias_dram_block_window, dbiast_shuffle_tmp);
move_tile_window(dbias_dram_block_window, {kM0, 0});
shuffle_tile(dbias_tile, shuffled_dbias_tile);
store_tile(dbias_dram_window, dbias_tile);
move_tile_window(dbias_dram_window, {kM0, 0});
__builtin_amdgcn_sched_barrier(0);
}
// STAGE 6, SGrad^T@Q^T Gemm3
auto qt_reg_tensor = load_tile(qt_lds_read_window);
block_sync_lds();
const auto dst_gemm = cast_tile<GemmDataType>(dst);
static_for<0, k3_loops, 1>{}([&](auto i_k3) {
block_sync_lds();
gemm_3(dk_acc,
get_slice_tile(
dst_gemm, sequence<i_k3 * kK3, 0>{}, sequence<(i_k3 + 1) * kK3, kN0>{}),
get_slice_tile(qt_lds_window,
sequence<0, i_k3 * kK3>{},
sequence<kQKHeaddim, (i_k3 + 1) * kK3>{}));
block_sync_lds();
});
const auto ds_gemm = cast_tile<GemmDataType>(ds);
// STAGE 7, SGrad@K^T Gemm4
store_tile(ds_lds_window, dst_gemm);
Policy::template SGradTFromGemm2CToGemm3A<Problem,
decltype(dst_reg_tensor),
decltype(ds_gemm)>(dst_reg_tensor, ds_gemm);
auto dq_acc = QGradBlockTileType{};
clear_tile(dq_acc); // Initialize QGrad
gemm_3(dk_acc, dst_reg_tensor, qt_reg_tensor);
store_tile(ds_lds_window, ds_gemm);
block_sync_lds();
auto ds_reg_tensor = load_tile(ds_lds_read_window);
auto ds_reg_tensor_next = decltype(ds_reg_tensor){};
move_tile_window(ds_lds_read_window, {0, kK4});
// STAGE7 SGrad@K^T Gemm4
auto dq_acc = QGradBlockTileType{};
clear_tile(dq_acc);
static_for<0, k4_loops, 1>{}([&](auto i_k4) {
gemm_4(dq_acc,
get_slice_tile(ds_lds_window,
sequence<0, i_k4 * kK4>{},
sequence<kM0, (i_k4 + 1) * kK4>{}),
get_slice_tile(kt_lds_window,
if constexpr(i_k4 < k4_loops - 1)
{
ds_reg_tensor_next = load_tile(ds_lds_read_window);
move_tile_window(ds_lds_read_window, {0, kK4});
}
auto kt_reg_tensor_slice = get_slice_tile(kt_reg_tensor,
sequence<0, i_k4 * kK4>{},
sequence<kQKHeaddim, (i_k4 + 1) * kK4>{}));
});
sequence<kQKHeaddim, (i_k4 + 1) * kK4>{});
gemm_4(dq_acc, ds_reg_tensor, kt_reg_tensor_slice);
if constexpr(i_k4 < k4_loops - 1)
{
ds_reg_tensor.get_thread_buffer() = ds_reg_tensor_next.get_thread_buffer();
}
});
move_tile_window(ds_lds_read_window, {0, -kN0});
// QGrad Scale
if constexpr(kHasDropout)
if constexpr(FmhaDropout::IsDropout)
{
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dq_acc);
......@@ -658,34 +749,33 @@ struct BlockFmhaBwdDQDKDVPipelineQSKSVROGradS
{
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dq_acc);
}
const auto dq = cast_tile<QGradDataType>(dq_acc);
update_tile(dq_dram_block_window, dq);
if constexpr(kIsDeterministic)
{
store_tile(dq_dram_window, dq_acc);
}
else
{
update_tile(dq_dram_window, dq_acc);
}
move_tile_window(dq_dram_window, {kM0, 0});
// move tile windows
move_tile_window(q_dram_block_window, {kM0, 0});
move_tile_window(dq_dram_block_window, {kM0, 0});
move_tile_window(do_dram_block_window, {kM0, 0});
move_tile_window(lse_dram_window, {kM0});
move_tile_window(d_dram_window, {kM0});
} while(++i_total_loops < num_total_loop);
i_total_loops += 1;
seqlen_q_step += kM0;
}
// KGrad Scale
if constexpr(kHasDropout)
// Results Scale
if constexpr(FmhaDropout::IsDropout)
{
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dk_acc);
tile_elementwise_inout([&rp_undrop](auto& x) { x = x * rp_undrop; }, dv_acc);
}
else
{
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dk_acc);
}
// VGrad Scale
if constexpr(kHasDropout)
{
tile_elementwise_inout([&rp_undrop](auto& x) { x = x * rp_undrop; }, dv_acc);
}
return ck_tile::make_tuple(dk_acc, dv_acc);
return make_tuple(dk_acc, dv_acc);
}
};
......
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_ks_kts_vr.hpp → include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_kr_ktr_vr_iglp.hpp
View file @ f84e2020
......@@ -6,13 +6,13 @@
#include "ck_tile/core.hpp"
#include "ck_tile/ops/fmha/block/block_attention_bias_enum.hpp"
#include "ck_tile/ops/fmha/block/block_dropout.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_ks_kts_vr_default_policy.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
namespace ck_tile {
template <typename Problem, typename Policy = BlockFmhaBwdDQDKDVPipelineKSKTSVRDefaultPolicy>
struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
template <typename Problem, typename Policy = BlockFmhaBwdPipelineDefaultPolicy>
struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
{
using QDataType = remove_cvref_t<typename Problem::QDataType>;
using KDataType = remove_cvref_t<typename Problem::KDataType>;
......@@ -30,6 +30,8 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
using VGradDataType = remove_cvref_t<typename Problem::VGradDataType>;
using BiasGradDataType = remove_cvref_t<typename Problem::BiasGradDataType>;
using FmhaMask = remove_cvref_t<typename Problem::FmhaMask>;
using FmhaDropout = remove_cvref_t<typename Problem::FmhaDropout>;
using HotLoopScheduler = typename Policy::template HotLoopScheduler<Problem>;
using BlockFmhaShape = remove_cvref_t<typename Problem::BlockFmhaShape>;
......@@ -46,14 +48,6 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
static constexpr index_t kQKHeaddim = BlockFmhaShape::kQKHeaddim;
static constexpr index_t kVHeaddim = BlockFmhaShape::kVHeaddim;
static constexpr bool kQLoadOnce = false;
static constexpr bool kQTLoadOnce = false;
static constexpr bool kKLoadOnce = true;
static constexpr bool kKTLoadOnce = true;
static constexpr bool kVLoadOnce = true;
static constexpr bool kOGradLoadOnce = false;
static constexpr bool kOGradTLoadOnce = false;
static constexpr bool kIsGroupMode = Problem::kIsGroupMode;
static constexpr bool kPadSeqLenQ = Problem::kPadSeqLenQ;
static constexpr bool kPadSeqLenK = Problem::kPadSeqLenK;
......@@ -61,7 +55,7 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
static constexpr bool kPadHeadDimV = Problem::kPadHeadDimV;
static constexpr auto BiasEnum = Problem::BiasEnum;
static constexpr bool kHasBiasGrad = Problem::kHasBiasGrad;
static constexpr bool kHasDropout = Problem::kHasDropout;
static constexpr bool kIsDeterministic = Problem::kIsDeterministic;
// 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
......@@ -71,12 +65,9 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
kPadHeadDimQ ? 1 : Policy::template GetAlignmentK<Problem>();
static constexpr index_t kAlignmentV =
kPadHeadDimV ? 1 : Policy::template GetAlignmentV<Problem>();
static constexpr index_t kAlignmentO =
kPadHeadDimV ? 1 : Policy::template GetAlignmentO<Problem>();
static constexpr index_t kAlignmentOGrad =
kPadHeadDimV ? 1 : Policy::template GetAlignmentOGrad<Problem>();
static constexpr index_t kAlignmentQGrad =
kPadHeadDimQ ? 2 : Policy::template GetAlignmentQGrad<Problem>();
static constexpr index_t kAlignmentQGrad = 1;
static constexpr index_t kAlignmentKGrad =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentKGrad<Problem>();
static constexpr index_t kAlignmentVGrad =
......@@ -84,7 +75,7 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
static constexpr index_t kAlignmentBias =
kPadSeqLenK ? 1 : Policy::template GetTransposedAlignmentBias<Problem>();
static constexpr const char* name = "ks_kts_vr";
static constexpr const char* name = "kr_ktr_vr_iglp";
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
......@@ -92,14 +83,11 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
}
template <typename QDramBlockWindowTmp,
typename QTDramBlockWindowTmp,
typename KDramBlockWindowTmp,
typename KTDramBlockWindowTmp,
typename VDramBlockWindowTmp,
typename BiasDramBlockWindowTmp,
typename RandValDramBlockWindowTmp,
typename OGradDramBlockWindowTmp,
typename OGradTDramBlockWindowTmp,
typename LSEDramBlockWindowTmp,
typename DDramBlockWindowTmp,
typename QGradDramBlockWindowTmp,
......@@ -107,14 +95,11 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp,
const QTDramBlockWindowTmp& qt_dram_block_window_tmp,
const KDramBlockWindowTmp& k_dram_block_window_tmp,
const KTDramBlockWindowTmp& kt_dram_block_window_tmp,
const VDramBlockWindowTmp& v_dram_block_window_tmp,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp,
const RandValDramBlockWindowTmp& randval_dram_block_window_tmp,
const OGradDramBlockWindowTmp& do_dram_block_window_tmp,
const OGradTDramBlockWindowTmp& dot_dram_block_window_tmp,
const LSEDramBlockWindowTmp& lse_dram_block_window_tmp,
const DDramBlockWindowTmp& d_dram_block_window_tmp,
const QGradDramBlockWindowTmp& dq_dram_block_window_tmp,
......@@ -122,43 +107,29 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
FmhaMask mask,
PositionEncoding position_encoding,
float raw_scale,
#if CK_TILE_FMHA_FWD_FAST_EXP2
float scale,
#endif
float rp_undrop,
float scale_rp_undrop,
void* smem_ptr,
BlockDropout& dropout) const
FmhaDropout& dropout) const
{
static_assert(
std::is_same_v<QDataType, remove_cvref_t<typename QDramBlockWindowTmp::DataType>> &&
std::is_same_v<QDataType,
remove_cvref_t<typename QTDramBlockWindowTmp::DataType>> &&
std::is_same_v<KDataType, remove_cvref_t<typename KDramBlockWindowTmp::DataType>> &&
std::is_same_v<KDataType,
remove_cvref_t<typename KTDramBlockWindowTmp::DataType>> &&
std::is_same_v<VDataType, remove_cvref_t<typename VDramBlockWindowTmp::DataType>> &&
std::is_same_v<OGradDataType,
remove_cvref_t<typename OGradDramBlockWindowTmp::DataType>> &&
std::is_same_v<OGradDataType,
remove_cvref_t<typename OGradTDramBlockWindowTmp::DataType>> &&
std::is_same_v<LSEDataType,
remove_cvref_t<typename LSEDramBlockWindowTmp::DataType>> &&
std::is_same_v<DDataType, remove_cvref_t<typename DDramBlockWindowTmp::DataType>> &&
std::is_same_v<QGradDataType,
remove_cvref_t<typename QGradDramBlockWindowTmp::DataType>>,
std::is_same_v<DDataType, remove_cvref_t<typename DDramBlockWindowTmp::DataType>>,
"wrong!");
static_assert(kM0 == QDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kQKHeaddim == QTDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == KDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kQKHeaddim == KTDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == VDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
kM0 == OGradDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kVHeaddim ==
OGradTDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == LSEDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == DDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == QGradDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
......@@ -166,83 +137,6 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
kN0 == BiasGradDramBlockWindowTmp{}.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) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeKT<Problem>()));
auto q_lds = make_tensor_view<address_space_enum::lds>(
q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
auto q_lds_window =
make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
// QT tile in LDS
QDataType* qt_lds_ptr = static_cast<QDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeKT<Problem>()));
auto qt_lds = make_tensor_view<address_space_enum::lds>(
qt_lds_ptr, Policy::template MakeQTLdsBlockDescriptor<Problem>());
auto qt_lds_window =
make_tile_window(qt_lds, make_tuple(number<kQKHeaddim>{}, number<kK3>{}), {0, 0});
// K tile in LDS
auto k_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<KDataType*>(smem_ptr),
Policy::template MakeKLdsBlockDescriptor<Problem>());
auto k_lds_window =
make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kQKHeaddim>{}), {0, 0});
// KT tile in LDS
KDataType* kt_lds_ptr = static_cast<KDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto kt_lds = make_tensor_view<address_space_enum::lds>(
kt_lds_ptr, Policy::template MakeKTLdsBlockDescriptor<Problem>());
auto kt_lds_window =
make_tile_window(kt_lds, make_tuple(number<kQKHeaddim>{}, number<kN0>{}), {0, 0});
// OGrad tile in LDS
OGradDataType* do_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeKT<Problem>()));
auto do_lds = make_tensor_view<address_space_enum::lds>(
do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptor<Problem>());
auto do_lds_window =
make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
// OGradT tile in LDS
OGradDataType* dot_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeKT<Problem>()));
auto dot_lds = make_tensor_view<address_space_enum::lds>(
dot_lds_ptr, Policy::template MakeOGradTLdsBlockDescriptor<Problem>());
auto dot_lds_window =
make_tile_window(dot_lds, make_tuple(number<kVHeaddim>{}, number<kK1>{}), {0, 0});
// SGrad tile in LDS
GemmDataType* ds_lds_ptr = static_cast<GemmDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeKT<Problem>()));
auto ds_lds = make_tensor_view<address_space_enum::lds>(
ds_lds_ptr, Policy::template MakeSGradLdsBlockDescriptor<Problem>());
auto ds_lds_window =
make_tile_window(ds_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
// BiasT/BiasGradT tile in LDS, use the same size and layout
BiasDataType* biast_lds_ptr = static_cast<BiasDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>() +
Policy::template GetSmemSizeKT<Problem>()));
auto biast_lds = make_tensor_view<address_space_enum::lds>(
biast_lds_ptr, Policy::template MakeBiasTLdsBlockDescriptor<Problem>());
auto biast_lds_shuffle_window =
make_tile_window(biast_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
auto dbiast_lds_shuffle_window =
make_tile_window(biast_lds,
make_tuple(number<kM0>{}, number<kN0>{}),
{0, 0},
Policy::template MakeShuffledBiasTileDistribution<Problem>());
static_assert(std::is_same_v<BiasDataType, BiasGradDataType>,
"BiasDataType and BiasGradDataType should be the same!");
// Block GEMM
constexpr auto gemm_0 = Policy::template GetQKBlockGemm<Problem>();
constexpr auto gemm_1 = Policy::template GetPTOGradTBlockGemm<Problem>();
......@@ -250,34 +144,19 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
constexpr auto gemm_3 = Policy::template GetSGradTQTBlockGemm<Problem>();
constexpr auto gemm_4 = Policy::template GetSGradKTBlockGemm<Problem>();
auto v_dram_window = make_tile_window(
v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
v_dram_block_window_tmp.get_window_origin(),
Policy::template MakeVInRegDramTileDistribution<Problem, decltype(gemm_2)>());
auto v = load_tile(v_dram_window); // persistent V register tile
using SPTBlockTileType = decltype(gemm_0.MakeCBlockTile());
using SPGradTBlockTileType = decltype(gemm_2.MakeCBlockTile());
using QGradBlockTileType = decltype(gemm_4.MakeCBlockTile());
// init VGrad & KGrad
auto dv_acc = decltype(gemm_1.MakeCBlockTile()){};
auto dk_acc = decltype(gemm_3.MakeCBlockTile()){};
clear_tile(dv_acc);
clear_tile(dk_acc);
auto k_dram_window = make_tile_window(
k_dram_block_window_tmp.get_bottom_tensor_view(),
// K, HBM ->LDS ->Reg
auto k_dram_window =
make_tile_window(k_dram_block_window_tmp.get_bottom_tensor_view(),
k_dram_block_window_tmp.get_window_lengths(),
k_dram_block_window_tmp.get_window_origin(),
Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window for
// load
Policy::template MakeKDramTileDistribution<Problem>());
__builtin_amdgcn_sched_barrier(0);
const auto k_origin = k_dram_window.get_window_origin();
// Early termination
const auto [seqlen_q_start, seqlen_q_end] =
mask.GetTileRangeAlongY(k_origin.at(number<0>{}), number<kM0>{}, number<kN0>{});
......@@ -290,272 +169,444 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
{
// Note: here dk_acc&dv_acc are all cleard, return it
// Note: v loaded but no fence, ignore it.
return ck_tile::make_tuple(dk_acc, dv_acc);
return make_tuple(dk_acc, dv_acc);
}
}
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 MakeKLdsWriteBlockDescriptor<Problem>());
auto k_block_tile = load_tile(k_dram_window);
auto k_lds_write_window =
make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
auto k_lds_read_window =
make_tile_window(k_lds_write_window.get_bottom_tensor_view(),
make_tuple(number<kN0>{}, number<kK0>{}),
k_lds_write_window.get_window_origin(),
Policy::template MakeKRegSliceBlockDescriptor<Problem>());
auto k_reg_tensor = make_static_distributed_tensor<KDataType>(
Policy::template MakeKRegBlockDescriptor<Problem>());
store_tile(k_lds_window, k_block_tile); // // persistent K in LDS
//------------------------------------------------------------------
// V, HBM ->LDS ->Reg
auto v_dram_window =
make_tile_window(v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
v_dram_block_window_tmp.get_window_origin(),
Policy::template MakeVDramTileDistribution<Problem>());
VDataType* v_lds_ptr =
static_cast<VDataType*>(static_cast<void*>(static_cast<char*>(smem_ptr)));
auto v_lds = make_tensor_view<address_space_enum::lds>(
v_lds_ptr, Policy::template MakeVLdsWriteBlockDescriptor<Problem>());
auto v_lds_write_window =
make_tile_window(v_lds, make_tuple(number<kN0>{}, number<kK2>{}), {0, 0});
auto v_lds_read_window =
make_tile_window(v_lds_write_window.get_bottom_tensor_view(),
make_tuple(number<kN0>{}, number<kK2>{}),
v_lds_write_window.get_window_origin(),
Policy::template MakeVRegSliceBlockDescriptor<Problem>());
auto v_reg_tensor = make_static_distributed_tensor<VDataType>(
Policy::template MakeVRegBlockDescriptor<Problem>());
//------------------------------------------------------------------
// KT, Reg ->LDS ->Reg
auto shuffled_k_block_tile = make_static_distributed_tensor<KDataType>(
Policy::template MakeShuffledKRegWriteBlockDescriptor<Problem>());
KDataType* kt_lds_ptr = static_cast<KDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto shuffled_k_lds_write = make_tensor_view<address_space_enum::lds>(
kt_lds_ptr, Policy::template MakeShuffledKLdsWriteBlockDescriptor<Problem>());
auto kt_dram_block_window = kt_dram_block_window_tmp;
auto shuffled_k_lds_write_window = make_tile_window(
shuffled_k_lds_write, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
auto kt_dram_window = make_tile_window(
kt_dram_block_window.get_bottom_tensor_view(),
kt_dram_block_window.get_window_lengths(),
kt_dram_block_window.get_window_origin(),
Policy::template MakeKTDramTileDistribution<Problem>()); // K^T DRAM tile window for
// load
auto kt_lds_read = make_tensor_view<address_space_enum::lds>(
kt_lds_ptr, Policy::template MakeKTLdsReadBlockDescriptor<Problem>());
auto kt_block_tile = load_tile(kt_dram_window);
auto kt_lds_read_window =
make_tile_window(kt_lds_read,
make_tuple(number<kQKHeaddim>{}, number<kN0>{}),
{0, 0},
Policy::template MakeKTRegBlockDescriptor<Problem>());
//------------------------------------------------------------------
// Pre-Load KV into Registers
auto k_block_tile = load_tile(k_dram_window);
auto v_block_tile = load_tile(v_dram_window);
store_tile(k_lds_write_window, k_block_tile);
shuffle_tile(shuffled_k_block_tile, k_block_tile);
store_tile(shuffled_k_lds_write_window, shuffled_k_block_tile);
block_sync_lds();
k_reg_tensor = load_tile(k_lds_read_window);
block_sync_lds();
auto kt_reg_tensor = load_tile(kt_lds_read_window);
auto kt_shuffle_tmp = make_static_distributed_tensor<KDataType>(
Policy::template MakeShuffledKTRegBlockDescriptor<Problem>());
shuffle_tile(kt_shuffle_tmp, kt_block_tile);
store_tile(v_lds_write_window, v_block_tile);
store_tile(kt_lds_window, kt_shuffle_tmp); // persistent K^T in LDS
block_sync_lds();
auto q_dram_block_window =
v_reg_tensor = load_tile(v_lds_read_window);
//---------------------------- Loop Load in ----------------------------//
// Q: HBM ->Reg ->LDS
auto q_dram_window =
make_tile_window(q_dram_block_window_tmp.get_bottom_tensor_view(),
q_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
{seqlen_q_start, 0},
Policy::template MakeQDramTileDistribution<Problem>());
QDataType* q_lds_ptr = static_cast<QDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>()));
auto qt_dram_block_window =
make_tile_window(qt_dram_block_window_tmp.get_bottom_tensor_view(),
qt_dram_block_window_tmp.get_window_lengths(),
{0, seqlen_q_start});
auto q_lds = make_tensor_view<address_space_enum::lds>(
q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
auto q_lds_window =
make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
auto q_lds_read_window =
make_tile_window(q_lds_window.get_bottom_tensor_view(),
make_tuple(number<kM0>{}, number<kK0>{}),
q_lds_window.get_window_origin(),
Policy::template MakeQRegSliceBlockDescriptor<Problem>());
auto pt_reg_tensor = make_static_distributed_tensor<GemmDataType>(
Policy::template MakePTRegSliceBlockDescriptor<Problem>());
// QT: Reg -> Reg-> LDS
auto shuffled_q_block_tile = make_static_distributed_tensor<QDataType>(
Policy::template MakeShuffledQRegWriteBlockDescriptor<Problem>());
QDataType* qt_lds_ptr =
static_cast<QDataType*>(static_cast<void*>(static_cast<char*>(smem_ptr)));
auto shuffled_q_lds_write = make_tensor_view<address_space_enum::lds>(
qt_lds_ptr, Policy::template MakeShuffledQLdsWriteBlockDescriptor<Problem>());
auto shuffled_q_lds_write_window = make_tile_window(
shuffled_q_lds_write, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
auto qt_lds_read = make_tensor_view<address_space_enum::lds>(
qt_lds_ptr, Policy::template MakeQTLdsReadBlockDescriptor<Problem>());
auto qt_lds_read_window =
make_tile_window(qt_lds_read,
make_tuple(number<kQKHeaddim>{}, number<kM0>{}),
{0, 0},
Policy::template MakeQTRegSliceBlockDescriptor<Problem>());
auto do_dram_block_window =
// dO: HBM ->Reg ->LDS
auto do_dram_window =
make_tile_window(do_dram_block_window_tmp.get_bottom_tensor_view(),
do_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
{seqlen_q_start, 0},
Policy::template MakeOGradDramTileDistribution<Problem>());
auto dot_dram_block_window =
make_tile_window(dot_dram_block_window_tmp.get_bottom_tensor_view(),
dot_dram_block_window_tmp.get_window_lengths(),
{0, seqlen_q_start});
OGradDataType* do_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>()));
auto dq_dram_block_window =
make_tile_window(dq_dram_block_window_tmp.get_bottom_tensor_view(),
dq_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
auto do_lds = make_tensor_view<address_space_enum::lds>(
do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptor<Problem>());
auto lse_dram_block_window =
make_tile_window(lse_dram_block_window_tmp.get_bottom_tensor_view(),
lse_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start});
auto do_lds_window =
make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
auto d_dram_block_window =
make_tile_window(d_dram_block_window_tmp.get_bottom_tensor_view(),
d_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start});
auto do_lds_read_window =
make_tile_window(do_lds_window.get_bottom_tensor_view(),
make_tuple(number<kM0>{}, number<kK2>{}),
do_lds_window.get_window_origin(),
Policy::template MakeOGradRegSliceBlockDescriptor<Problem>());
// dOT: Reg ->Reg ->LDS
auto shuffled_do_block_tile = make_static_distributed_tensor<OGradDataType>(
Policy::template MakeShuffledOGradRegWriteBlockDescriptor<Problem>());
OGradDataType* dot_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>()));
auto shuffled_do_lds_write = make_tensor_view<address_space_enum::lds>(
dot_lds_ptr, Policy::template MakeShuffledOGradLdsWriteBlockDescriptor<Problem>());
auto shuffled_do_lds_write_window = make_tile_window(
shuffled_do_lds_write, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
auto dot_read_lds = make_tensor_view<address_space_enum::lds>(
dot_lds_ptr, Policy::template MakeOGradTLdsReadBlockDescriptor<Problem>());
auto dot_lds_read_window =
make_tile_window(dot_read_lds,
make_tuple(number<kVHeaddim>{}, number<kM0>{}),
{0, 0},
Policy::template MakeOGradTRegSliceBlockDescriptor<Problem>());
// dS: Reg -> Reg -> LDS
GemmDataType* ds_lds_ptr = static_cast<GemmDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>() + Policy::template GetSmemSizeLSE<Problem>() +
Policy::template GetSmemSizeD<Problem>()));
auto ds_lds = make_tensor_view<address_space_enum::lds>(
ds_lds_ptr, Policy::template MakeSGradLdsBlockDescriptor<Problem>());
auto ds_lds_window =
make_tile_window(ds_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
auto ds_lds_read_window =
make_tile_window(ds_lds_window.get_bottom_tensor_view(),
make_tuple(number<kM0>{}, number<kK4>{}),
ds_lds_window.get_window_origin(),
Policy::template MakeSGradRegSliceBlockDescriptor<Problem>());
auto dst_reg_tensor = make_static_distributed_tensor<GemmDataType>(
Policy::template MakeSGradTRegSliceBlockDescriptor<Problem>());
// Bias: HBM ->Reg ->Reg ->LDS
const auto bias_origin = bias_dram_block_window_tmp.get_window_origin();
auto bias_dram_block_window =
auto bias_dram_window =
make_tile_window(bias_dram_block_window_tmp.get_bottom_tensor_view(),
bias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, bias_origin.at(number<1>{})}); // M/N
{seqlen_q_start, bias_origin.at(number<1>{})},
Policy::template MakeBiasTileDistribution<Problem>());
const auto dbias_origin = dbias_dram_block_window_tmp.get_window_origin();
auto dbias_dram_block_window =
make_tile_window(dbias_dram_block_window_tmp.get_bottom_tensor_view(),
dbias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, dbias_origin.at(number<1>{})}); // M/N
BiasDataType* bias_lds_ptr = static_cast<BiasDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>() + Policy::template GetSmemSizeLSE<Problem>() +
Policy::template GetSmemSizeD<Problem>()));
auto bias_lds = make_tensor_view<address_space_enum::lds>(
bias_lds_ptr, Policy::template MakeBiasLdsBlockDescriptor<Problem>());
auto bias_lds_write_window =
make_tile_window(bias_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
auto qt_dram_window =
make_tile_window(qt_dram_block_window.get_bottom_tensor_view(),
qt_dram_block_window.get_window_lengths(),
qt_dram_block_window.get_window_origin(),
Policy::template MakeQTDramTileDistribution<Problem>());
auto bias_s_lds_read_window =
make_tile_window(bias_lds_write_window.get_bottom_tensor_view(),
bias_lds_write_window.get_window_lengths(),
bias_lds_write_window.get_window_origin(),
Policy::template MakeBiasSTileDistribution<decltype(gemm_0)>());
auto dot_dram_window =
make_tile_window(dot_dram_block_window.get_bottom_tensor_view(),
dot_dram_block_window.get_window_lengths(),
dot_dram_block_window.get_window_origin(),
Policy::template MakeOGradTDramTileDistribution<Problem>());
static_assert(std::is_same_v<BiasDataType, BiasGradDataType>,
"BiasDataType and BiasGradDataType should be the same!");
// LSE: HBM -> LDS ->Reg
auto lse_dram_window = make_tile_window(
lse_dram_block_window.get_bottom_tensor_view(),
lse_dram_block_window.get_window_lengths(),
lse_dram_block_window.get_window_origin(),
lse_dram_block_window_tmp.get_bottom_tensor_view(),
lse_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start},
Policy::template MakeLSEDDramTileDistribution<Problem, decltype(gemm_0)>());
LSEDataType* lse_lds_ptr = static_cast<LSEDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>()));
auto lse_lds = make_tensor_view<address_space_enum::lds>(
lse_lds_ptr, Policy::template MakeLSEDLdsWriteBlockDescriptor<Problem>());
auto lse_lds_write_window = make_tile_window(lse_lds, make_tuple(number<kM0>{}), {0});
auto lse_lds_read_window = make_tile_window(
lse_lds,
make_tuple(number<kM0>{}),
{0},
Policy::template MakeLSEDLdsReadBlockDescriptor<Problem, decltype(gemm_0)>());
// D: HBM ->Reg
auto d_dram_window = make_tile_window(
d_dram_block_window.get_bottom_tensor_view(),
d_dram_block_window.get_window_lengths(),
d_dram_block_window.get_window_origin(),
d_dram_block_window_tmp.get_bottom_tensor_view(),
d_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start},
Policy::template MakeLSEDDramTileDistribution<Problem, decltype(gemm_0)>());
auto bias_dram_window =
make_tile_window(bias_dram_block_window.get_bottom_tensor_view(),
bias_dram_block_window.get_window_lengths(),
bias_dram_block_window.get_window_origin(),
Policy::template MakeBiasTileDistribution<Problem>());
DDataType* d_lds_ptr = static_cast<DDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQT<Problem>() +
Policy::template GetSmemSizeOGrad<Problem>() +
Policy::template GetSmemSizeOGradT<Problem>() +
Policy::template GetSmemSizeQ<Problem>() + Policy::template GetSmemSizeLSE<Problem>()));
auto d_lds = make_tensor_view<address_space_enum::lds>(
d_lds_ptr, Policy::template MakeLSEDLdsWriteBlockDescriptor<Problem>());
auto d_lds_write_window = make_tile_window(d_lds, make_tuple(number<kM0>{}), {0});
auto biast_lds_window =
make_tile_window(biast_lds_shuffle_window.get_bottom_tensor_view(),
biast_lds_shuffle_window.get_window_lengths(),
biast_lds_shuffle_window.get_window_origin(),
Policy::template MakeBiasTTileDistribution<decltype(gemm_0)>());
auto d_lds_read_window = make_tile_window(
d_lds,
make_tuple(number<kM0>{}),
{0},
Policy::template MakeLSEDLdsReadBlockDescriptor<Problem, decltype(gemm_0)>());
auto randval_dram_window = dropout.MakeRandvalDramWindow<decltype(gemm_0), false>(
// RandVal: HBM ->Reg
auto randval_dram_window = dropout.template MakeRandvalDramWindow<decltype(gemm_0), false>(
randval_dram_block_window_tmp, seqlen_q_start);
// BiasGrad
// Reg ->LDS ->Reg ->HBM
const auto dbias_origin = dbias_dram_block_window_tmp.get_window_origin();
auto dbias_dram_window =
make_tile_window(dbias_dram_block_window_tmp.get_bottom_tensor_view(),
dbias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, dbias_origin.at(number<1>{})}); // M/N
auto dbias_lds_read_window =
make_tile_window(bias_lds,
make_tuple(number<kM0>{}, number<kN0>{}),
{0, 0},
Policy::template MakeShuffledBiasTileDistribution<Problem>());
// ----------------------------Loop write out------------------------------//
auto dq_dram_window = make_tile_window(dq_dram_block_window_tmp.get_bottom_tensor_view(),
dq_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
using SPBlockTileType = decltype(gemm_0.MakeCBlockTile());
using SPGradBlockTileType = decltype(gemm_2.MakeCBlockTile());
using QGradBlockTileType = decltype(gemm_4.MakeCBlockTile());
index_t i_total_loops = 0;
constexpr index_t k0_loops = kQKHeaddim / kK0;
constexpr index_t k1_loops = kM0 / kK1;
constexpr index_t k2_loops = kVHeaddim / kK2;
constexpr index_t k3_loops = kM0 / kK3;
index_t seqlen_q_step = seqlen_q_start;
static_assert(kQKHeaddim == kK0, "kQKHeaddim should equal to kK0");
static_assert(kM0 == kK1, "kM0 should equal to kK1");
static_assert(kVHeaddim == kK2, "kVHeaddim should equal to kK2");
static_assert(kM0 == kK3, "kM0 should equal to kK3");
constexpr index_t k4_loops = kN0 / kK4;
do
{
auto q_dram_window = make_tile_window(
q_dram_block_window.get_bottom_tensor_view(),
q_dram_block_window.get_window_lengths(),
q_dram_block_window.get_window_origin(),
Policy::template MakeQDramTileDistribution<Problem>()); // Q DRAM tile window for
// load
auto do_dram_window = make_tile_window(
do_dram_block_window.get_bottom_tensor_view(),
do_dram_block_window.get_window_lengths(),
do_dram_block_window.get_window_origin(),
Policy::template MakeOGradDramTileDistribution<Problem>()); // OGrad DRAM tile
// window for load
// STAGE 1, Q@K Gemm0
auto st_acc = SPTBlockTileType{};
/*
* Prefetch Q, LSE, dO, D
*/
auto q_block_tile = load_tile(q_dram_window);
{
move_tile_window(q_dram_window, {0, kK0});
clear_tile(st_acc); // Initialize S^T
move_tile_window(q_dram_window, {kM0, 0});
auto lse_block_tile = load_tile(lse_dram_window);
move_tile_window(lse_dram_window, {kM0});
store_tile(q_lds_window, q_block_tile); // LDS write 0
q_block_tile = load_tile(q_dram_window); // global read 1
}
auto do_block_tile = load_tile(do_dram_window);
move_tile_window(do_dram_window, {kM0, 0});
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
}
auto d_block_tile = load_tile(d_dram_window);
move_tile_window(d_dram_window, {kM0});
if constexpr(k0_loops > 2)
{
static_for<0, k0_loops - 2, 1>{}([&](auto i_k0) {
block_sync_lds();
gemm_0(st_acc,
q_lds_window,
get_slice_tile(k_lds_window,
sequence<0, i_k0 * kK0>{},
sequence<kN0, (i_k0 + 1) * kK0>{}));
/*
* Store prefetched data into LDS
*/
block_sync_lds();
move_tile_window(q_dram_window, {0, kK0});
store_tile(q_lds_window, q_block_tile);
shuffle_tile(shuffled_q_block_tile, q_block_tile);
store_tile(shuffled_q_lds_write_window, shuffled_q_block_tile);
store_tile(q_lds_window,
q_block_tile); // LDS write i + 1
q_block_tile = load_tile(q_dram_window); // global read i + 2
});
}
store_tile(lse_lds_write_window, lse_block_tile);
const auto dot_prefetch = load_tile(dot_dram_window); // prefetch load OGrad^T tile
{ // tail
block_sync_lds();
gemm_0(st_acc,
q_lds_window,
get_slice_tile(k_lds_window,
sequence<0, (k0_loops - 2) * kK0>{},
sequence<kN0, (k0_loops - 1) * kK0>{}));
block_sync_lds();
store_tile(do_lds_window, do_block_tile);
shuffle_tile(shuffled_do_block_tile, do_block_tile);
store_tile(shuffled_do_lds_write_window, shuffled_do_block_tile);
store_tile(q_lds_window, q_block_tile);
store_tile(d_lds_write_window, d_block_tile);
block_sync_lds();
gemm_0(st_acc,
q_lds_window,
get_slice_tile(k_lds_window,
sequence<0, (k0_loops - 1) * kK0>{},
sequence<kN0, k0_loops * kK0>{}));
}
/*
* Prefetch LDS data into Reg to Asynchronous Data Movement and MFMA pipeline
*/
auto q_reg_tensor = load_tile(q_lds_read_window);
auto lse = load_tile(lse_lds_read_window);
auto do_reg_tensor = load_tile(do_lds_read_window);
auto d = load_tile(d_lds_read_window);
clear_tile(dv_acc);
clear_tile(dk_acc);
__builtin_amdgcn_sched_barrier(0);
// Hot loop
while(i_total_loops < (num_total_loop - 1))
{
// STAGE 1, Q@K Gemm0
auto s_acc = SPBlockTileType{};
q_block_tile = load_tile(q_dram_window);
move_tile_window(q_dram_window, {kM0, 0});
lse_block_tile = load_tile(lse_dram_window);
move_tile_window(lse_dram_window, {kM0});
do_block_tile = load_tile(do_dram_window);
move_tile_window(do_dram_window, {kM0, 0});
d_block_tile = load_tile(d_dram_window);
move_tile_window(d_dram_window, {kM0});
s_acc = gemm_0(q_reg_tensor, k_reg_tensor);
auto dot_reg_tensor = load_tile(dot_lds_read_window);
HotLoopScheduler::template GemmStagedScheduler<0>();
__builtin_amdgcn_sched_barrier(0);
// STAGE 2, Scale, Add bias, Mask, Softmax, Dropout
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
block_sync_lds();
auto bias_shuffle_tmp = make_static_distributed_tensor<BiasDataType>(
const auto bias_tile = load_tile(bias_dram_window);
auto shuffled_bias_tile = make_static_distributed_tensor<BiasDataType>(
Policy::template MakeShuffledBiasTileDistribution<Problem>());
shuffle_tile(bias_shuffle_tmp, bias_tile);
store_tile(biast_lds_shuffle_window, bias_shuffle_tmp);
shuffle_tile(shuffled_bias_tile, bias_tile);
store_tile(bias_lds_write_window, shuffled_bias_tile);
block_sync_lds();
auto biast_tile = load_tile(biast_lds_window);
auto bias_s_tile = load_tile(bias_s_lds_read_window);
tile_elementwise_inout(
[&](auto& x, const auto& y) {
#if !CK_TILE_FMHA_FWD_FAST_EXP2
x = raw_scale * x + type_convert<AccDataType>(y);
#else
x = scale * x + log2e_v<AccDataType> * type_convert<AccDataType>(y);
#endif
},
st_acc,
biast_tile);
s_acc,
bias_s_tile);
move_tile_window(bias_dram_window, {kM0, 0});
__builtin_amdgcn_sched_barrier(0);
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
const auto q_origin = q_dram_block_window.get_window_origin();
constexpr auto st_spans = decltype(st_acc)::get_distributed_spans();
sweep_tile_span(st_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(st_spans[number<1>{}], [&](auto idx1) {
constexpr auto s_spans = decltype(s_acc)::get_distributed_spans();
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(
st_acc.get_tile_distribution(), make_tuple(idx0, idx1));
s_acc.get_tile_distribution(), make_tuple(idx0, idx1));
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
const auto row = seqlen_q_step + 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);
#if !CK_TILE_FMHA_FWD_FAST_EXP2
st_acc(i_j_idx) *= raw_scale;
#else
st_acc(i_j_idx) *= scale;
#endif
position_encoding.update(st_acc(i_j_idx), row, col);
s_acc(i_j_idx) *= scale;
position_encoding.update(s_acc(i_j_idx), row, col);
});
});
}
else
{
#if !CK_TILE_FMHA_FWD_FAST_EXP2
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, st_acc);
#endif
}
if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
{
const auto q_origin = q_dram_block_window.get_window_origin();
bool need_perpixel_check = mask.IsEdgeTile(q_origin.at(number<0>{}),
k_origin.at(number<0>{}),
number<kM0>{},
number<kN0>{});
bool need_perpixel_check = mask.IsEdgeTile(
seqlen_q_step, k_origin.at(number<0>{}), number<kM0>{}, number<kN0>{});
if(need_perpixel_check)
{
set_tile_if(st_acc, -numeric<AccDataType>::infinity(), [&](auto tile_idx) {
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
set_tile_if(s_acc, -numeric<AccDataType>::infinity(), [&](auto tile_idx) {
const auto row = seqlen_q_step + tile_idx.at(number<0>{});
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
return mask.IsOutOfBound(row, col);
});
}
}
const auto lse = load_tile(lse_dram_window);
static const auto get_validated_lse = [](LSEDataType raw_lse) {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
FmhaMask::IsMasking)
......@@ -570,278 +621,416 @@ struct BlockFmhaBwdDQDKDVPipelineKSKTSVR
}
};
auto pt = SPTBlockTileType{};
constexpr auto pt_spans = decltype(pt)::get_distributed_spans();
sweep_tile_span(pt_spans[number<0>{}], [&](auto idx0) {
auto p = SPBlockTileType{};
constexpr auto p_spans = decltype(p)::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_lse = log2e_v<LSEDataType> * get_validated_lse(lse[i_idx]);
#endif
sweep_tile_span(pt_spans[number<1>{}], [&](auto idx1) {
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)
{
pt(i_j_idx) = exp2(st_acc[i_j_idx] - row_lse);
p(i_j_idx) = exp2(s_acc[i_j_idx] - row_lse);
}
else
{
pt(i_j_idx) = exp2(scale * st_acc[i_j_idx] - row_lse);
p(i_j_idx) = exp2(scale * s_acc[i_j_idx] - row_lse);
}
#else
pt(i_j_idx) = exp(st_acc[i_j_idx] - get_validated_lse(lse[i_idx]));
#endif
});
});
auto dot_shuffle_tmp = make_static_distributed_tensor<OGradDataType>(
Policy::template MakeShuffledOGradTRegBlockDescriptor<Problem>());
block_sync_lds();
{
shuffle_tile(dot_shuffle_tmp, dot_prefetch);
store_tile(dot_lds_window,
dot_shuffle_tmp); // store the prefetch
}
move_tile_window(dot_dram_window, {0, kK1});
if constexpr(kHasDropout)
if constexpr(FmhaDropout::IsDropout)
{
dropout.Run<decltype(gemm_0), RandValOutputDataType>(
seqlen_q_start + i_total_loops * kM0, pt, randval_dram_window);
dropout.template Run<decltype(gemm_0), RandValOutputDataType>(
seqlen_q_step, k_origin.at(number<0>{}), p, randval_dram_window);
}
// STAGE 3, P^T@OGrad^T Gemm1
const auto pt_gemm = [&]() {
if constexpr(kHasDropout)
const auto p_gemm = [&]() {
if constexpr(FmhaDropout::IsDropout)
{
return tile_elementwise_in(
[](const auto& x) { return type_convert<GemmDataType>(x > 0.f ? x : 0.f); },
pt);
p);
}
else
{
return cast_tile<GemmDataType>(pt);
return cast_tile<GemmDataType>(p);
}
}();
if constexpr(k1_loops > 1)
{
static_for<0, k1_loops - 1, 1>{}([&](auto i_k1) {
const auto dot = load_tile(dot_dram_window); // load next OGrad^T
block_sync_lds();
gemm_1(dv_acc,
get_slice_tile(pt_gemm,
sequence<i_k1 * kK1, 0>{},
sequence<(i_k1 + 1) * kK1, kN0>{}),
dot_lds_window);
// STAGE 3, P^T@OGrad^T Gemm1
Policy::template PTFromGemm0CToGemm1A<Problem,
decltype(pt_reg_tensor),
decltype(p_gemm)>(pt_reg_tensor, p_gemm);
gemm_1(dv_acc, pt_reg_tensor, dot_reg_tensor);
auto qt_reg_tensor = load_tile(qt_lds_read_window);
HotLoopScheduler::template GemmStagedScheduler<1>();
__builtin_amdgcn_sched_barrier(0);
// STAGE 4, OGrad@V Gemm2
auto dp_acc = SPGradBlockTileType{};
dp_acc = gemm_2(do_reg_tensor, v_reg_tensor);
block_sync_lds();
shuffle_tile(dot_shuffle_tmp, dot);
store_tile(dot_lds_window,
dot_shuffle_tmp); // store the prefetch
move_tile_window(dot_dram_window, {0, kK1});
store_tile(q_lds_window, q_block_tile);
shuffle_tile(shuffled_q_block_tile, q_block_tile);
store_tile(shuffled_q_lds_write_window, shuffled_q_block_tile);
store_tile(lse_lds_write_window, lse_block_tile);
store_tile(do_lds_window, do_block_tile);
shuffle_tile(shuffled_do_block_tile, do_block_tile);
store_tile(shuffled_do_lds_write_window, shuffled_do_block_tile);
store_tile(d_lds_write_window, d_block_tile);
HotLoopScheduler::template GemmStagedScheduler<2>();
__builtin_amdgcn_sched_barrier(0);
// STAGE 5, P^T(PGrad^T - D)
auto ds = SPGradBlockTileType{};
constexpr auto ds_spans = decltype(ds)::get_distributed_spans();
sweep_tile_span(ds_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
sweep_tile_span(ds_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
bool undrop_flag = p[i_j_idx] >= 0;
ds(i_j_idx) = p[i_j_idx] * (!FmhaDropout::IsDropout || undrop_flag
? (dp_acc[i_j_idx] - d[i_idx])
: d[i_idx]);
});
});
if constexpr(kHasBiasGrad)
{
const auto dbias = [&]() {
if constexpr(FmhaDropout::IsDropout)
{
return tile_elementwise_in(
[&rp_undrop](const auto& x) {
return type_convert<BiasGradDataType>(x * rp_undrop);
},
ds);
}
auto do_block_tile = load_tile(do_dram_window); // prefetch load OGrad tile
// tail
else
{
return cast_tile<BiasGradDataType>(ds);
}
}();
store_tile(bias_lds_write_window, dbias);
block_sync_lds();
gemm_1(dv_acc,
get_slice_tile(
pt_gemm, sequence<(k1_loops - 1) * kK1, 0>{}, sequence<kM0, kN0>{}),
dot_lds_window);
block_sync_lds();
auto shuffled_dbias_tile = load_tile(dbias_lds_read_window);
auto dbias_tile = make_static_distributed_tensor<BiasGradDataType>(
Policy::template MakeBiasTileDistribution<Problem>());
shuffle_tile(dbias_tile, shuffled_dbias_tile);
store_tile(dbias_dram_window, dbias_tile);
move_tile_window(dbias_dram_window, {kM0, 0});
__builtin_amdgcn_sched_barrier(0);
}
// STAGE 4, OGrad@V Gemm2
auto dpt_acc = SPGradTBlockTileType{};
// STAGE 6, SGrad^T@Q^T Gemm3
const auto ds_gemm = cast_tile<GemmDataType>(ds);
Policy::template SGradTFromGemm2CToGemm3A<Problem,
decltype(dst_reg_tensor),
decltype(ds_gemm)>(dst_reg_tensor, ds_gemm);
gemm_3(dk_acc, dst_reg_tensor, qt_reg_tensor);
store_tile(ds_lds_window, ds_gemm);
block_sync_lds();
auto ds_reg_tensor = load_tile(ds_lds_read_window);
auto ds_reg_tensor_next = decltype(ds_reg_tensor){};
move_tile_window(ds_lds_read_window, {0, kK4});
q_reg_tensor = load_tile(q_lds_read_window);
lse = load_tile(lse_lds_read_window);
HotLoopScheduler::template GemmStagedScheduler<3>();
__builtin_amdgcn_sched_barrier(0);
// STAGE7 SGrad@K^T Gemm4
auto dq_acc = QGradBlockTileType{};
clear_tile(dq_acc);
static_for<0, k4_loops, 1>{}([&](auto i_k4) {
if constexpr(i_k4 < k4_loops - 1)
{
ds_reg_tensor_next = load_tile(ds_lds_read_window);
move_tile_window(ds_lds_read_window, {0, kK4});
}
auto kt_reg_tensor_slice = get_slice_tile(kt_reg_tensor,
sequence<0, i_k4 * kK4>{},
sequence<kQKHeaddim, (i_k4 + 1) * kK4>{});
gemm_4(dq_acc, ds_reg_tensor, kt_reg_tensor_slice);
if constexpr(i_k4 < k4_loops - 1)
{
move_tile_window(do_dram_window, {0, kK2});
ds_reg_tensor.get_thread_buffer() = ds_reg_tensor_next.get_thread_buffer();
}
});
move_tile_window(ds_lds_read_window, {0, -kN0});
do_reg_tensor = load_tile(do_lds_read_window);
d = load_tile(d_lds_read_window);
clear_tile(dpt_acc); // Initialize PGrad^T
HotLoopScheduler::template GemmStagedScheduler<4>();
// QGrad Scale
if constexpr(FmhaDropout::IsDropout)
{
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dq_acc);
}
else
{
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dq_acc);
}
if constexpr(kIsDeterministic)
{
store_tile(dq_dram_window, dq_acc);
}
else
{
update_tile(dq_dram_window, dq_acc);
}
move_tile_window(dq_dram_window, {kM0, 0});
store_tile(do_lds_window, do_block_tile); // LDS write 0
do_block_tile = load_tile(do_dram_window); // global read 1
i_total_loops += 1;
seqlen_q_step += kM0;
}
__builtin_amdgcn_sched_barrier(0);
// Tail
auto s_acc = SPBlockTileType{};
// STAGE 1, Q@K Gemm0
s_acc = gemm_0(q_reg_tensor, k_reg_tensor);
if constexpr(k2_loops > 2)
// STAGE 2, Scale, Add bias, Mask, Softmax, Dropout
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
static_for<0, k2_loops - 2, 1>{}([&](auto i_k2) {
block_sync_lds();
gemm_2(dpt_acc,
do_lds_window,
get_slice_tile(
v, sequence<0, i_k2 * kK2>{}, sequence<kN0, (i_k2 + 1) * kK2>{}));
const auto bias_tile = load_tile(bias_dram_window);
auto shuffled_bias_tile = make_static_distributed_tensor<BiasDataType>(
Policy::template MakeShuffledBiasTileDistribution<Problem>());
shuffle_tile(shuffled_bias_tile, bias_tile);
store_tile(bias_lds_write_window, shuffled_bias_tile);
block_sync_lds();
move_tile_window(do_dram_window, {0, kK2});
auto bias_s_tile = load_tile(bias_s_lds_read_window);
tile_elementwise_inout(
[&](auto& x, const auto& y) {
x = scale * x + log2e_v<AccDataType> * type_convert<AccDataType>(y);
},
s_acc,
bias_s_tile);
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
constexpr auto s_spans = decltype(s_acc)::get_distributed_spans();
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));
store_tile(do_lds_window,
do_block_tile); // LDS write i + 1
do_block_tile = load_tile(do_dram_window); // global read i + 2
const auto row = seqlen_q_step + 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;
position_encoding.update(s_acc(i_j_idx), row, col);
});
});
}
const auto qt_prefetch = load_tile(qt_dram_window); // prefetch load Q^T tile
{ // tail
block_sync_lds();
gemm_2(dpt_acc,
do_lds_window,
get_slice_tile(v,
sequence<0, (k2_loops - 2) * kK2>{},
sequence<kN0, (k2_loops - 1) * kK2>{}));
block_sync_lds();
if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
{
bool need_perpixel_check = mask.IsEdgeTile(
seqlen_q_step, k_origin.at(number<0>{}), number<kM0>{}, number<kN0>{});
if(need_perpixel_check)
{
set_tile_if(s_acc, -numeric<AccDataType>::infinity(), [&](auto tile_idx) {
const auto row = seqlen_q_step + tile_idx.at(number<0>{});
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
return mask.IsOutOfBound(row, col);
});
}
}
store_tile(do_lds_window, do_block_tile);
block_sync_lds();
static const auto get_validated_lse = [](LSEDataType raw_lse) {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
FmhaMask::IsMasking)
{
return raw_lse == -numeric<LSEDataType>::infinity() ? type_convert<LSEDataType>(0.f)
: raw_lse;
}
else
{
return raw_lse;
}
};
auto p = SPBlockTileType{};
constexpr auto p_spans = decltype(p)::get_distributed_spans();
sweep_tile_span(p_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
auto row_lse = log2e_v<LSEDataType> * get_validated_lse(lse[i_idx]);
gemm_2(dpt_acc,
do_lds_window,
get_slice_tile(v,
sequence<0, (k2_loops - 1) * kK2>{},
sequence<kN0, k2_loops * kK2>{}));
sweep_tile_span(p_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
p(i_j_idx) = exp2(s_acc[i_j_idx] - row_lse);
}
else
{
p(i_j_idx) = exp2(scale * s_acc[i_j_idx] - row_lse);
}
});
});
// STAGE 5, P^T(PGrad^T - D)
const auto d = load_tile(d_dram_window);
if constexpr(FmhaDropout::IsDropout)
{
dropout.template Run<decltype(gemm_0), RandValOutputDataType>(
seqlen_q_step, k_origin.at(number<0>{}), p, randval_dram_window);
}
auto dst = SPGradTBlockTileType{};
constexpr auto dst_spans = decltype(dst)::get_distributed_spans();
sweep_tile_span(dst_spans[number<0>{}], [&](auto idx0) {
// STAGE 3, P^T@OGrad^T Gemm1
const auto p_gemm = [&]() {
if constexpr(FmhaDropout::IsDropout)
{
return tile_elementwise_in(
[](const auto& x) { return type_convert<GemmDataType>(x > 0.f ? x : 0.f); }, p);
}
else
{
return cast_tile<GemmDataType>(p);
}
}();
Policy::template PTFromGemm0CToGemm1A<Problem, decltype(pt_reg_tensor), decltype(p_gemm)>(
pt_reg_tensor, p_gemm);
auto dot_reg_tensor = load_tile(dot_lds_read_window);
gemm_1(dv_acc, pt_reg_tensor, dot_reg_tensor);
HotLoopScheduler::template GemmStagedScheduler<1>();
// STAGE 4, OGrad@V Gemm2
auto dp_acc = SPGradBlockTileType{};
auto qt_reg_tensor = load_tile(qt_lds_read_window);
dp_acc = gemm_2(do_reg_tensor, v_reg_tensor);
HotLoopScheduler::template GemmStagedScheduler<2>();
// STAGE 5, P^T(PGrad^T - D)
auto ds = SPGradBlockTileType{};
constexpr auto ds_spans = decltype(ds)::get_distributed_spans();
sweep_tile_span(ds_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
sweep_tile_span(dst_spans[number<1>{}], [&](auto idx1) {
sweep_tile_span(ds_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
bool undrop_flag = pt[i_j_idx] >= 0;
dst(i_j_idx) =
pt[i_j_idx] *
(!kHasDropout || undrop_flag ? (dpt_acc[i_j_idx] - d[i_idx]) : d[i_idx]);
bool undrop_flag = p[i_j_idx] >= 0;
ds(i_j_idx) = p[i_j_idx] * (!FmhaDropout::IsDropout || undrop_flag
? (dp_acc[i_j_idx] - d[i_idx])
: d[i_idx]);
});
});
if constexpr(kHasBiasGrad)
{
const auto dbiast = [&]() {
if constexpr(kHasDropout)
const auto dbias = [&]() {
if constexpr(FmhaDropout::IsDropout)
{
return tile_elementwise_in(
[&rp_undrop](const auto& x) {
return type_convert<BiasGradDataType>(x * rp_undrop);
},
dst);
ds);
}
else
{
return cast_tile<BiasGradDataType>(dst);
return cast_tile<BiasGradDataType>(ds);
}
}();
store_tile(biast_lds_shuffle_window, dbiast);
store_tile(bias_lds_write_window, dbias);
block_sync_lds();
auto dbiast_tile = load_tile(dbiast_lds_shuffle_window);
auto dbiast_shuffle_tmp = make_static_distributed_tensor<BiasGradDataType>(
auto shuffled_dbias_tile = load_tile(dbias_lds_read_window);
auto dbias_tile = make_static_distributed_tensor<BiasGradDataType>(
Policy::template MakeBiasTileDistribution<Problem>());
shuffle_tile(dbiast_shuffle_tmp, dbiast_tile);
store_tile(dbias_dram_block_window, dbiast_shuffle_tmp);
move_tile_window(dbias_dram_block_window, {kM0, 0});
shuffle_tile(dbias_tile, shuffled_dbias_tile);
store_tile(dbias_dram_window, dbias_tile);
}
// STAGE 6, SGrad^T@Q^T Gemm3
auto qt_shuffle_tmp = make_static_distributed_tensor<QDataType>(
Policy::template MakeShuffledQTRegBlockDescriptor<Problem>());
block_sync_lds();
{
shuffle_tile(qt_shuffle_tmp, qt_prefetch);
store_tile(qt_lds_window,
qt_shuffle_tmp); // store the prefetch
}
move_tile_window(qt_dram_window, {0, kK3});
const auto ds_gemm = cast_tile<GemmDataType>(ds);
const auto dst_gemm = cast_tile<GemmDataType>(dst);
Policy::template SGradTFromGemm2CToGemm3A<Problem,
decltype(dst_reg_tensor),
decltype(ds_gemm)>(dst_reg_tensor, ds_gemm);
if constexpr(k3_loops > 1)
{
static_for<0, k3_loops - 1, 1>{}([&](auto i_k3) {
const auto qt = load_tile(qt_dram_window); // load next Q^T
block_sync_lds();
gemm_3(dk_acc,
get_slice_tile(dst_gemm,
sequence<i_k3 * kK3, 0>{},
sequence<(i_k3 + 1) * kK3, kN0>{}),
qt_lds_window);
block_sync_lds();
shuffle_tile(qt_shuffle_tmp, qt);
store_tile(qt_lds_window,
qt_shuffle_tmp); // store the prefetch
gemm_3(dk_acc, dst_reg_tensor, qt_reg_tensor);
store_tile(ds_lds_window, ds_gemm);
move_tile_window(qt_dram_window, {0, kK3});
});
}
// tail
{
block_sync_lds();
gemm_3(dk_acc,
get_slice_tile(
dst_gemm, sequence<(k3_loops - 1) * kK3, 0>{}, sequence<kM0, kN0>{}),
qt_lds_window);
block_sync_lds();
}
// STAGE 7, SGrad@K^T Gemm4
store_tile(ds_lds_window, dst_gemm);
auto ds_reg_tensor = load_tile(ds_lds_read_window);
auto ds_reg_tensor_next = decltype(ds_reg_tensor){};
move_tile_window(ds_lds_read_window, {0, kK4});
HotLoopScheduler::template GemmStagedScheduler<3>();
// STAGE 7, SGrad@K^T Gemm4
auto dq_acc = QGradBlockTileType{};
clear_tile(dq_acc); // Initialize QGrad
block_sync_lds();
clear_tile(dq_acc);
static_for<0, k4_loops, 1>{}([&](auto i_k4) {
gemm_4(dq_acc,
get_slice_tile(ds_lds_window,
sequence<0, i_k4 * kK4>{},
sequence<kM0, (i_k4 + 1) * kK4>{}),
get_slice_tile(kt_lds_window,
sequence<0, i_k4 * kK4>{},
sequence<kQKHeaddim, (i_k4 + 1) * kK4>{}));
});
// QGrad Scale
if constexpr(kHasDropout)
if constexpr(i_k4 < k4_loops - 1)
{
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dq_acc);
ds_reg_tensor_next = load_tile(ds_lds_read_window);
move_tile_window(ds_lds_read_window, {0, kK4});
}
else
auto kt_reg_tensor_slice = get_slice_tile(
kt_reg_tensor, sequence<0, i_k4 * kK4>{}, sequence<kQKHeaddim, (i_k4 + 1) * kK4>{});
gemm_4(dq_acc, ds_reg_tensor, kt_reg_tensor_slice);
if constexpr(i_k4 < k4_loops - 1)
{
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dq_acc);
ds_reg_tensor.get_thread_buffer() = ds_reg_tensor_next.get_thread_buffer();
}
const auto dq = cast_tile<QGradDataType>(dq_acc);
update_tile(dq_dram_block_window, dq);
});
// move tile windows
move_tile_window(q_dram_block_window, {kM0, 0});
move_tile_window(dq_dram_block_window, {kM0, 0});
move_tile_window(do_dram_block_window, {kM0, 0});
move_tile_window(lse_dram_window, {kM0});
move_tile_window(d_dram_window, {kM0});
} while(++i_total_loops < num_total_loop);
HotLoopScheduler::template GemmStagedScheduler<4>();
// KGrad Scale
if constexpr(kHasDropout)
// Results Scale
if constexpr(FmhaDropout::IsDropout)
{
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dq_acc);
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dk_acc);
tile_elementwise_inout([&rp_undrop](auto& x) { x = x * rp_undrop; }, dv_acc);
}
else
{
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dq_acc);
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dk_acc);
}
// VGrad Scale
if constexpr(kHasDropout)
if constexpr(kIsDeterministic)
{
tile_elementwise_inout([&rp_undrop](auto& x) { x = x * rp_undrop; }, dv_acc);
store_tile(dq_dram_window, dq_acc);
}
else
{
update_tile(dq_dram_window, dq_acc);
}
return ck_tile::make_tuple(dk_acc, dv_acc);
return make_tuple(dk_acc, dv_acc);
}
};
......
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_ks_kts_vr_default_policy.hpp deleted 100644 → 0
View file @ 408534d4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp"
namespace ck_tile {
// This pipeline is v located in regs, k & k^t located in lds.
using BlockFmhaBwdDQDKDVPipelineKSKTSVRDefaultPolicy =
BlockFmhaBwdPipelineDefaultPolicy</* QLoadOnce_ = */ false,
/* QTLoadOnce_ = */ false,
/* KLoadOnce_ = */ true,
/* KTLoadOnce_ = */ true,
/* VLoadOnce_ = */ true,
/* OGradLoadOnce_ = */ false,
/* OGradTLoadOnce_ = */ false>;
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_ks_vr.hpp deleted 100644 → 0
View file @ 408534d4
// 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/block/block_dropout.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_ks_vr_default_policy.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
namespace ck_tile {
template <typename Problem, typename Policy = BlockFmhaBwdDQDKDVPipelineKSVRDefaultPolicy>
struct BlockFmhaBwdDQDKDVPipelineKSVR
{
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 GemmDataType = remove_cvref_t<typename Problem::GemmDataType>;
using BiasDataType = remove_cvref_t<typename Problem::BiasDataType>;
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
using DDataType = remove_cvref_t<typename Problem::DDataType>;
using RandValOutputDataType = remove_cvref_t<typename Problem::RandValOutputDataType>;
using ODataType = remove_cvref_t<typename Problem::ODataType>;
using OGradDataType = remove_cvref_t<typename Problem::OGradDataType>;
using QGradDataType = remove_cvref_t<typename Problem::QGradDataType>;
using KGradDataType = remove_cvref_t<typename Problem::KGradDataType>;
using VGradDataType = remove_cvref_t<typename Problem::VGradDataType>;
using BiasGradDataType = remove_cvref_t<typename Problem::BiasGradDataType>;
using FmhaMask = remove_cvref_t<typename Problem::FmhaMask>;
using BlockFmhaShape = remove_cvref_t<typename Problem::BlockFmhaShape>;
static constexpr index_t kBlockPerCu = Problem::kBlockPerCu;
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 kK1 = BlockFmhaShape::kK1;
static constexpr index_t kK2 = BlockFmhaShape::kK2;
static constexpr index_t kK3 = BlockFmhaShape::kK3;
static constexpr index_t kK4 = BlockFmhaShape::kK4;
static constexpr index_t kQKHeaddim = BlockFmhaShape::kQKHeaddim;
static constexpr index_t kVHeaddim = BlockFmhaShape::kVHeaddim;
static constexpr bool kQLoadOnce = false;
static constexpr bool kQTLoadOnce = false;
static constexpr bool kKLoadOnce = true;
static constexpr bool kKTLoadOnce = false;
static constexpr bool kVLoadOnce = true;
static constexpr bool kOGradLoadOnce = false;
static constexpr bool kOGradTLoadOnce = false;
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 kHasBiasGrad = Problem::kHasBiasGrad;
static constexpr bool kHasDropout = Problem::kHasDropout;
// 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 =
kPadHeadDimV ? 1 : Policy::template GetAlignmentV<Problem>();
static constexpr index_t kAlignmentO =
kPadHeadDimV ? 1 : Policy::template GetAlignmentO<Problem>();
static constexpr index_t kAlignmentOGrad =
kPadHeadDimV ? 1 : Policy::template GetAlignmentOGrad<Problem>();
static constexpr index_t kAlignmentQGrad =
kPadHeadDimQ ? 2 : Policy::template GetAlignmentQGrad<Problem>();
static constexpr index_t kAlignmentKGrad =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentKGrad<Problem>();
static constexpr index_t kAlignmentVGrad =
kPadHeadDimV ? 1 : Policy::template GetAlignmentVGrad<Problem>();
static constexpr index_t kAlignmentBias =
kPadSeqLenK ? 1 : Policy::template GetTransposedAlignmentBias<Problem>();
static constexpr const char* name = "ks_vr";
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
}
template <typename QDramBlockWindowTmp,
typename QTDramBlockWindowTmp,
typename KDramBlockWindowTmp,
typename KTDramBlockWindowTmp,
typename VDramBlockWindowTmp,
typename BiasDramBlockWindowTmp,
typename RandValDramBlockWindowTmp,
typename OGradDramBlockWindowTmp,
typename OGradTDramBlockWindowTmp,
typename LSEDramBlockWindowTmp,
typename DDramBlockWindowTmp,
typename QGradDramBlockWindowTmp,
typename BiasGradDramBlockWindowTmp,
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp,
const QTDramBlockWindowTmp& qt_dram_block_window_tmp,
const KDramBlockWindowTmp& k_dram_block_window_tmp,
const KTDramBlockWindowTmp& /*kt_dram_block_window_tmp*/,
const VDramBlockWindowTmp& v_dram_block_window_tmp,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp,
const RandValDramBlockWindowTmp& randval_dram_block_window_tmp,
const OGradDramBlockWindowTmp& do_dram_block_window_tmp,
const OGradTDramBlockWindowTmp& dot_dram_block_window_tmp,
const LSEDramBlockWindowTmp& lse_dram_block_window_tmp,
const DDramBlockWindowTmp& d_dram_block_window_tmp,
const QGradDramBlockWindowTmp& dq_dram_block_window_tmp,
const BiasGradDramBlockWindowTmp& dbias_dram_block_window_tmp,
FmhaMask mask,
PositionEncoding position_encoding,
float raw_scale,
#if CK_TILE_FMHA_FWD_FAST_EXP2
float scale,
#endif
float rp_undrop,
float scale_rp_undrop,
void* smem_ptr,
BlockDropout& dropout) const
{
static_assert(
std::is_same_v<QDataType, remove_cvref_t<typename QDramBlockWindowTmp::DataType>> &&
std::is_same_v<QDataType,
remove_cvref_t<typename QTDramBlockWindowTmp::DataType>> &&
std::is_same_v<KDataType, remove_cvref_t<typename KDramBlockWindowTmp::DataType>> &&
std::is_same_v<VDataType, remove_cvref_t<typename VDramBlockWindowTmp::DataType>> &&
std::is_same_v<OGradDataType,
remove_cvref_t<typename OGradDramBlockWindowTmp::DataType>> &&
std::is_same_v<OGradDataType,
remove_cvref_t<typename OGradTDramBlockWindowTmp::DataType>> &&
std::is_same_v<LSEDataType,
remove_cvref_t<typename LSEDramBlockWindowTmp::DataType>> &&
std::is_same_v<DDataType, remove_cvref_t<typename DDramBlockWindowTmp::DataType>> &&
std::is_same_v<QGradDataType,
remove_cvref_t<typename QGradDramBlockWindowTmp::DataType>>,
"wrong!");
static_assert(kM0 == QDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kQKHeaddim == QTDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == KDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == VDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
kM0 == OGradDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kVHeaddim ==
OGradTDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == LSEDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == DDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == QGradDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kM0 == BiasGradDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == BiasGradDramBlockWindowTmp{}.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) + Policy::template GetSmemSizeK<Problem>()));
auto q_lds = make_tensor_view<address_space_enum::lds>(
q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
auto q_lds_window =
make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
// QT tile in LDS
QDataType* qt_lds_ptr = static_cast<QDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto qt_lds = make_tensor_view<address_space_enum::lds>(
qt_lds_ptr, Policy::template MakeQTLdsBlockDescriptor<Problem>());
auto qt_lds_window =
make_tile_window(qt_lds, make_tuple(number<kQKHeaddim>{}, number<kK3>{}), {0, 0});
// K tile in LDS
auto k_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<KDataType*>(smem_ptr),
Policy::template MakeKLdsBlockDescriptor<Problem>());
auto k_lds_window =
make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kQKHeaddim>{}), {0, 0});
// KT tile in LDS
auto kt_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<KDataType*>(smem_ptr),
Policy::template MakeKLdsBlockDescriptorAsKT<Problem>());
auto kt_lds_window =
make_tile_window(kt_lds, make_tuple(number<kQKHeaddim>{}, number<kN0>{}), {0, 0});
// OGrad tile in LDS
OGradDataType* do_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto do_lds = make_tensor_view<address_space_enum::lds>(
do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptor<Problem>());
auto do_lds_window =
make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
// OGradT tile in LDS
OGradDataType* dot_lds_ptr = static_cast<OGradDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto dot_lds = make_tensor_view<address_space_enum::lds>(
dot_lds_ptr, Policy::template MakeOGradTLdsBlockDescriptor<Problem>());
auto dot_lds_window =
make_tile_window(dot_lds, make_tuple(number<kVHeaddim>{}, number<kK1>{}), {0, 0});
// SGrad tile in LDS
GemmDataType* ds_lds_ptr = static_cast<GemmDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto ds_lds = make_tensor_view<address_space_enum::lds>(
ds_lds_ptr, Policy::template MakeSGradLdsBlockDescriptor<Problem>());
auto ds_lds_window =
make_tile_window(ds_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
// BiasT/BiasGradT tile in LDS, use the same size and layout
BiasDataType* biast_lds_ptr = static_cast<BiasDataType*>(static_cast<void*>(
static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeK<Problem>()));
auto biast_lds = make_tensor_view<address_space_enum::lds>(
biast_lds_ptr, Policy::template MakeBiasTLdsBlockDescriptor<Problem>());
auto biast_lds_shuffle_window =
make_tile_window(biast_lds, make_tuple(number<kM0>{}, number<kN0>{}), {0, 0});
auto dbiast_lds_shuffle_window =
make_tile_window(biast_lds,
make_tuple(number<kM0>{}, number<kN0>{}),
{0, 0},
Policy::template MakeShuffledBiasTileDistribution<Problem>());
static_assert(std::is_same_v<BiasDataType, BiasGradDataType>,
"BiasDataType and BiasGradDataType should be the same!");
// Block GEMM
constexpr auto gemm_0 = Policy::template GetQKBlockGemm<Problem>();
constexpr auto gemm_1 = Policy::template GetPTOGradTBlockGemm<Problem>();
constexpr auto gemm_2 = Policy::template GetOGradVBlockGemm<Problem>();
constexpr auto gemm_3 = Policy::template GetSGradTQTBlockGemm<Problem>();
constexpr auto gemm_4 = Policy::template GetSGradKTBlockGemm<Problem>();
auto v_dram_window = make_tile_window(
v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
v_dram_block_window_tmp.get_window_origin(),
Policy::template MakeVInRegDramTileDistribution<Problem, decltype(gemm_2)>());
auto v = load_tile(v_dram_window); // persistent V register tile
using SPTBlockTileType = decltype(gemm_0.MakeCBlockTile());
using SPGradTBlockTileType = decltype(gemm_2.MakeCBlockTile());
using QGradBlockTileType = decltype(gemm_4.MakeCBlockTile());
// init VGrad & KGrad
auto dv_acc = decltype(gemm_1.MakeCBlockTile()){};
auto dk_acc = decltype(gemm_3.MakeCBlockTile()){};
clear_tile(dv_acc);
clear_tile(dk_acc);
auto k_dram_window = make_tile_window(
k_dram_block_window_tmp.get_bottom_tensor_view(),
k_dram_block_window_tmp.get_window_lengths(),
k_dram_block_window_tmp.get_window_origin(),
Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window for
// load
__builtin_amdgcn_sched_barrier(0);
const auto k_origin = k_dram_window.get_window_origin();
const auto [seqlen_q_start, seqlen_q_end] =
mask.GetTileRangeAlongY(k_origin.at(number<0>{}), number<kM0>{}, number<kN0>{});
const auto num_total_loop = integer_divide_ceil(seqlen_q_end - seqlen_q_start, kM0);
// check early exit if masked and no work to do.
if constexpr(FmhaMask::IsMasking)
{
if(num_total_loop <= 0)
{
// Note: here dk_acc&dv_acc are all cleard, return it
// Note: v loaded but no fence, ignore it.
return ck_tile::make_tuple(dk_acc, dv_acc);
}
}
auto k_block_tile = load_tile(k_dram_window);
store_tile(k_lds_window, k_block_tile); // // persistent K in LDS
auto q_dram_block_window =
make_tile_window(q_dram_block_window_tmp.get_bottom_tensor_view(),
q_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
auto qt_dram_block_window =
make_tile_window(qt_dram_block_window_tmp.get_bottom_tensor_view(),
qt_dram_block_window_tmp.get_window_lengths(),
{0, seqlen_q_start});
auto do_dram_block_window =
make_tile_window(do_dram_block_window_tmp.get_bottom_tensor_view(),
do_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
auto dot_dram_block_window =
make_tile_window(dot_dram_block_window_tmp.get_bottom_tensor_view(),
dot_dram_block_window_tmp.get_window_lengths(),
{0, seqlen_q_start});
auto dq_dram_block_window =
make_tile_window(dq_dram_block_window_tmp.get_bottom_tensor_view(),
dq_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, 0});
auto lse_dram_block_window =
make_tile_window(lse_dram_block_window_tmp.get_bottom_tensor_view(),
lse_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start});
auto d_dram_block_window =
make_tile_window(d_dram_block_window_tmp.get_bottom_tensor_view(),
d_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start});
const auto bias_origin = bias_dram_block_window_tmp.get_window_origin();
auto bias_dram_block_window =
make_tile_window(bias_dram_block_window_tmp.get_bottom_tensor_view(),
bias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, bias_origin.at(number<1>{})}); // M/N
const auto dbias_origin = dbias_dram_block_window_tmp.get_window_origin();
auto dbias_dram_block_window =
make_tile_window(dbias_dram_block_window_tmp.get_bottom_tensor_view(),
dbias_dram_block_window_tmp.get_window_lengths(),
{seqlen_q_start, dbias_origin.at(number<1>{})}); // M/N
auto qt_dram_window =
make_tile_window(qt_dram_block_window.get_bottom_tensor_view(),
qt_dram_block_window.get_window_lengths(),
qt_dram_block_window.get_window_origin(),
Policy::template MakeQTDramTileDistribution<Problem>());
auto dot_dram_window =
make_tile_window(dot_dram_block_window.get_bottom_tensor_view(),
dot_dram_block_window.get_window_lengths(),
dot_dram_block_window.get_window_origin(),
Policy::template MakeOGradTDramTileDistribution<Problem>());
auto lse_dram_window = make_tile_window(
lse_dram_block_window.get_bottom_tensor_view(),
lse_dram_block_window.get_window_lengths(),
lse_dram_block_window.get_window_origin(),
Policy::template MakeLSEDDramTileDistribution<Problem, decltype(gemm_0)>());
auto d_dram_window = make_tile_window(
d_dram_block_window.get_bottom_tensor_view(),
d_dram_block_window.get_window_lengths(),
d_dram_block_window.get_window_origin(),
Policy::template MakeLSEDDramTileDistribution<Problem, decltype(gemm_0)>());
auto bias_dram_window =
make_tile_window(bias_dram_block_window.get_bottom_tensor_view(),
bias_dram_block_window.get_window_lengths(),
bias_dram_block_window.get_window_origin(),
Policy::template MakeBiasTileDistribution<Problem>());
auto biast_lds_window =
make_tile_window(biast_lds_shuffle_window.get_bottom_tensor_view(),
biast_lds_shuffle_window.get_window_lengths(),
biast_lds_shuffle_window.get_window_origin(),
Policy::template MakeBiasTTileDistribution<decltype(gemm_0)>());
auto randval_dram_window = dropout.MakeRandvalDramWindow<decltype(gemm_0), false>(
randval_dram_block_window_tmp, seqlen_q_start);
index_t i_total_loops = 0;
constexpr index_t k0_loops = kQKHeaddim / kK0;
constexpr index_t k1_loops = kM0 / kK1;
constexpr index_t k2_loops = kVHeaddim / kK2;
constexpr index_t k3_loops = kM0 / kK3;
constexpr index_t k4_loops = kN0 / kK4;
do
{
auto q_dram_window = make_tile_window(
q_dram_block_window.get_bottom_tensor_view(),
q_dram_block_window.get_window_lengths(),
q_dram_block_window.get_window_origin(),
Policy::template MakeQDramTileDistribution<Problem>()); // Q DRAM tile window for
// load
auto do_dram_window = make_tile_window(
do_dram_block_window.get_bottom_tensor_view(),
do_dram_block_window.get_window_lengths(),
do_dram_block_window.get_window_origin(),
Policy::template MakeOGradDramTileDistribution<Problem>()); // OGrad DRAM tile
// window for load
// STAGE 1, Q@K Gemm0
auto st_acc = SPTBlockTileType{};
auto q_block_tile = load_tile(q_dram_window);
{
move_tile_window(q_dram_window, {0, kK0});
clear_tile(st_acc); // Initialize S^T
store_tile(q_lds_window, q_block_tile); // LDS write 0
q_block_tile = load_tile(q_dram_window); // global read 1
}
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(st_acc,
q_lds_window,
get_slice_tile(k_lds_window,
sequence<0, i_k0 * kK0>{},
sequence<kN0, (i_k0 + 1) * kK0>{}));
block_sync_lds();
move_tile_window(q_dram_window, {0, kK0});
store_tile(q_lds_window,
q_block_tile); // LDS write i + 1
q_block_tile = load_tile(q_dram_window); // global read i + 2
});
}
const auto dot_prefetch = load_tile(dot_dram_window); // prefetch load OGrad^T tile
{ // tail
block_sync_lds();
gemm_0(st_acc,
q_lds_window,
get_slice_tile(k_lds_window,
sequence<0, (k0_loops - 2) * kK0>{},
sequence<kN0, (k0_loops - 1) * kK0>{}));
block_sync_lds();
store_tile(q_lds_window, q_block_tile);
block_sync_lds();
gemm_0(st_acc,
q_lds_window,
get_slice_tile(k_lds_window,
sequence<0, (k0_loops - 1) * kK0>{},
sequence<kN0, k0_loops * kK0>{}));
}
// STAGE 2, Scale, Add bias, Mask, Softmax, Dropout
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
block_sync_lds();
auto bias_shuffle_tmp = make_static_distributed_tensor<BiasDataType>(
Policy::template MakeShuffledBiasTileDistribution<Problem>());
shuffle_tile(bias_shuffle_tmp, bias_tile);
store_tile(biast_lds_shuffle_window, bias_shuffle_tmp);
block_sync_lds();
auto biast_tile = load_tile(biast_lds_window);
tile_elementwise_inout(
[&](auto& x, const auto& y) {
#if !CK_TILE_FMHA_FWD_FAST_EXP2
x = raw_scale * x + type_convert<AccDataType>(y);
#else
x = scale * x + log2e_v<AccDataType> * type_convert<AccDataType>(y);
#endif
},
st_acc,
biast_tile);
move_tile_window(bias_dram_window, {kM0, 0});
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
const auto q_origin = q_dram_block_window.get_window_origin();
constexpr auto st_spans = decltype(st_acc)::get_distributed_spans();
sweep_tile_span(st_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(st_spans[number<1>{}], [&](auto idx1) {
const auto tile_idx = get_x_indices_from_distributed_indices(
st_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);
#if !CK_TILE_FMHA_FWD_FAST_EXP2
st_acc(i_j_idx) *= raw_scale;
#else
st_acc(i_j_idx) *= scale;
#endif
position_encoding.update(st_acc(i_j_idx), row, col);
});
});
}
else
{
#if !CK_TILE_FMHA_FWD_FAST_EXP2
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, st_acc);
#endif
}
if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
{
const auto q_origin = q_dram_block_window.get_window_origin();
bool need_perpixel_check = mask.IsEdgeTile(q_origin.at(number<0>{}),
k_origin.at(number<0>{}),
number<kM0>{},
number<kN0>{});
if(need_perpixel_check)
{
set_tile_if(st_acc, -numeric<AccDataType>::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);
});
}
}
const auto lse = load_tile(lse_dram_window);
static const auto get_validated_lse = [](LSEDataType raw_lse) {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
FmhaMask::IsMasking)
{
return raw_lse == -numeric<LSEDataType>::infinity()
? type_convert<LSEDataType>(0.f)
: raw_lse;
}
else
{
return raw_lse;
}
};
auto pt = SPTBlockTileType{};
constexpr auto pt_spans = decltype(pt)::get_distributed_spans();
sweep_tile_span(pt_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
auto row_lse = log2e_v<LSEDataType> * get_validated_lse(lse[i_idx]);
#endif
sweep_tile_span(pt_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)
{
pt(i_j_idx) = exp2(st_acc[i_j_idx] - row_lse);
}
else
{
pt(i_j_idx) = exp2(scale * st_acc[i_j_idx] - row_lse);
}
#else
pt(i_j_idx) = exp(st_acc[i_j_idx] - get_validated_lse(lse[i_idx]));
#endif
});
});
auto dot_shuffle_tmp = make_static_distributed_tensor<OGradDataType>(
Policy::template MakeShuffledOGradTRegBlockDescriptor<Problem>());
block_sync_lds();
{
shuffle_tile(dot_shuffle_tmp, dot_prefetch);
store_tile(dot_lds_window,
dot_shuffle_tmp); // store the prefetch
}
move_tile_window(dot_dram_window, {0, kK1});
if constexpr(kHasDropout)
{
dropout.Run<decltype(gemm_0), RandValOutputDataType>(
seqlen_q_start + i_total_loops * kM0, pt, randval_dram_window);
}
// STAGE 3, P^T@OGrad^T Gemm1
const auto pt_gemm = [&]() {
if constexpr(kHasDropout)
{
return tile_elementwise_in(
[](const auto& x) { return type_convert<GemmDataType>(x > 0.f ? x : 0.f); },
pt);
}
else
{
return cast_tile<GemmDataType>(pt);
}
}();
if constexpr(k1_loops > 1)
{
static_for<0, k1_loops - 1, 1>{}([&](auto i_k1) {
const auto dot = load_tile(dot_dram_window); // load next OGrad^T
block_sync_lds();
gemm_1(dv_acc,
get_slice_tile(pt_gemm,
sequence<i_k1 * kK1, 0>{},
sequence<(i_k1 + 1) * kK1, kN0>{}),
dot_lds_window);
block_sync_lds();
shuffle_tile(dot_shuffle_tmp, dot);
store_tile(dot_lds_window,
dot_shuffle_tmp); // store the prefetch
move_tile_window(dot_dram_window, {0, kK1});
});
}
auto do_block_tile = load_tile(do_dram_window); // prefetch load OGrad tile
// tail
{
block_sync_lds();
gemm_1(dv_acc,
get_slice_tile(
pt_gemm, sequence<(k1_loops - 1) * kK1, 0>{}, sequence<kM0, kN0>{}),
dot_lds_window);
block_sync_lds();
}
// STAGE 4, OGrad@V Gemm2
auto dpt_acc = SPGradTBlockTileType{};
{
move_tile_window(do_dram_window, {0, kK2});
clear_tile(dpt_acc); // Initialize PGrad^T
store_tile(do_lds_window, do_block_tile); // LDS write 0
do_block_tile = load_tile(do_dram_window); // global read 1
}
if constexpr(k2_loops > 2)
{
static_for<0, k2_loops - 2, 1>{}([&](auto i_k2) {
block_sync_lds();
gemm_2(dpt_acc,
do_lds_window,
get_slice_tile(
v, sequence<0, i_k2 * kK2>{}, sequence<kN0, (i_k2 + 1) * kK2>{}));
block_sync_lds();
move_tile_window(do_dram_window, {0, kK2});
store_tile(do_lds_window,
do_block_tile); // LDS write i + 1
do_block_tile = load_tile(do_dram_window); // global read i + 2
});
}
const auto qt_prefetch = load_tile(qt_dram_window); // prefetch load Q^T tile
{ // tail
block_sync_lds();
gemm_2(dpt_acc,
do_lds_window,
get_slice_tile(v,
sequence<0, (k2_loops - 2) * kK2>{},
sequence<kN0, (k2_loops - 1) * kK2>{}));
block_sync_lds();
store_tile(do_lds_window, do_block_tile);
block_sync_lds();
gemm_2(dpt_acc,
do_lds_window,
get_slice_tile(v,
sequence<0, (k2_loops - 1) * kK2>{},
sequence<kN0, k2_loops * kK2>{}));
}
// STAGE 5, P^T(PGrad^T - D)
const auto d = load_tile(d_dram_window);
auto dst = SPGradTBlockTileType{};
constexpr auto dst_spans = decltype(dst)::get_distributed_spans();
sweep_tile_span(dst_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
sweep_tile_span(dst_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
bool undrop_flag = pt[i_j_idx] >= 0;
dst(i_j_idx) =
pt[i_j_idx] *
(!kHasDropout || undrop_flag ? (dpt_acc[i_j_idx] - d[i_idx]) : d[i_idx]);
});
});
if constexpr(kHasBiasGrad)
{
const auto dbiast = [&]() {
if constexpr(kHasDropout)
{
return tile_elementwise_in(
[&rp_undrop](const auto& x) {
return type_convert<BiasGradDataType>(x * rp_undrop);
},
dst);
}
else
{
return cast_tile<BiasGradDataType>(dst);
}
}();
store_tile(biast_lds_shuffle_window, dbiast);
block_sync_lds();
auto dbiast_tile = load_tile(dbiast_lds_shuffle_window);
auto dbiast_shuffle_tmp = make_static_distributed_tensor<BiasGradDataType>(
Policy::template MakeBiasTileDistribution<Problem>());
shuffle_tile(dbiast_shuffle_tmp, dbiast_tile);
store_tile(dbias_dram_block_window, dbiast_shuffle_tmp);
move_tile_window(dbias_dram_block_window, {kM0, 0});
}
// STAGE 6, SGrad^T@Q^T Gemm3
auto qt_shuffle_tmp = make_static_distributed_tensor<QDataType>(
Policy::template MakeShuffledQTRegBlockDescriptor<Problem>());
block_sync_lds();
{
shuffle_tile(qt_shuffle_tmp, qt_prefetch);
store_tile(qt_lds_window,
qt_shuffle_tmp); // store the prefetch
}
move_tile_window(qt_dram_window, {0, kK3});
const auto dst_gemm = cast_tile<GemmDataType>(dst);
if constexpr(k3_loops > 1)
{
static_for<0, k3_loops - 1, 1>{}([&](auto i_k3) {
const auto qt = load_tile(qt_dram_window); // load next Q^T
block_sync_lds();
gemm_3(dk_acc,
get_slice_tile(dst_gemm,
sequence<i_k3 * kK3, 0>{},
sequence<(i_k3 + 1) * kK3, kN0>{}),
qt_lds_window);
block_sync_lds();
shuffle_tile(qt_shuffle_tmp, qt);
store_tile(qt_lds_window,
qt_shuffle_tmp); // store the prefetch
move_tile_window(qt_dram_window, {0, kK3});
});
}
// tail
{
block_sync_lds();
gemm_3(dk_acc,
get_slice_tile(
dst_gemm, sequence<(k3_loops - 1) * kK3, 0>{}, sequence<kM0, kN0>{}),
qt_lds_window);
block_sync_lds();
}
// STAGE 7, SGrad@K^T Gemm4
store_tile(ds_lds_window, dst_gemm);
auto dq_acc = QGradBlockTileType{};
clear_tile(dq_acc); // Initialize QGrad
block_sync_lds();
static_for<0, k4_loops, 1>{}([&](auto i_k4) {
gemm_4(dq_acc,
get_slice_tile(ds_lds_window,
sequence<0, i_k4 * kK4>{},
sequence<kM0, (i_k4 + 1) * kK4>{}),
get_slice_tile(kt_lds_window,
sequence<0, i_k4 * kK4>{},
sequence<kQKHeaddim, (i_k4 + 1) * kK4>{}));
});
// QGrad Scale
if constexpr(kHasDropout)
{
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dq_acc);
}
else
{
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dq_acc);
}
const auto dq = cast_tile<QGradDataType>(dq_acc);
update_tile(dq_dram_block_window, dq);
// move tile windows
move_tile_window(q_dram_block_window, {kM0, 0});
move_tile_window(dq_dram_block_window, {kM0, 0});
move_tile_window(do_dram_block_window, {kM0, 0});
move_tile_window(lse_dram_window, {kM0});
move_tile_window(d_dram_window, {kM0});
} while(++i_total_loops < num_total_loop);
// KGrad Scale
if constexpr(kHasDropout)
{
tile_elementwise_inout([&scale_rp_undrop](auto& x) { x = x * scale_rp_undrop; },
dk_acc);
}
else
{
tile_elementwise_inout([&raw_scale](auto& x) { x = x * raw_scale; }, dk_acc);
}
// VGrad Scale
if constexpr(kHasDropout)
{
tile_elementwise_inout([&rp_undrop](auto& x) { x = x * rp_undrop; }, dv_acc);
}
return ck_tile::make_tuple(dk_acc, dv_acc);
}
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_ks_vr_default_policy.hpp deleted 100644 → 0
View file @ 408534d4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp"
namespace ck_tile {
// This pipeline is v located in regs, k located in lds.
using BlockFmhaBwdDQDKDVPipelineKSVRDefaultPolicy =
BlockFmhaBwdPipelineDefaultPolicy</* QLoadOnce_ = */ false,
/* QTLoadOnce_ = */ false,
/* KLoadOnce_ = */ true,
/* KTLoadOnce_ = */ false,
/* VLoadOnce_ = */ true,
/* OGradLoadOnce_ = */ false,
/* OGradTLoadOnce_ = */ false>;
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_dq_dk_dv_pipeline_qs_ks_vr_dos_default_policy.hpp deleted 100644 → 0
View file @ 408534d4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp"
namespace ck_tile {
// This pipeline is v located in regs, q & k & do located in lds.
using BlockFmhaBwdDQDKDVPipelineQSKSVROGradSDefaultPolicy =
BlockFmhaBwdPipelineDefaultPolicy</* QLoadOnce_ = */ true,
/* QTLoadOnce_ = */ false,
/* KLoadOnce_ = */ true,
/* KTLoadOnce_ = */ false,
/* VLoadOnce_ = */ true,
/* OGradLoadOnce_ = */ true,
/* OGradTLoadOnce_ = */ false>;
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_default_policy.hpp
View file @ f84e2020
......@@ -11,6 +11,8 @@
#include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_bsmem_creg_v1_custom_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_bsmem_creg_v1.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1_custom_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_asmem_breg_creg_v1_custom_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_asmem_breg_creg_v1.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1_custom_policy.hpp"
......@@ -18,60 +20,215 @@
namespace ck_tile {
template <bool QLoadOnce_,
bool QTLoadOnce_,
bool KLoadOnce_,
bool KTLoadOnce_,
bool VLoadOnce_,
bool OGradLoadOnce_,
bool OGradTLoadOnce_>
struct BlockFmhaBwdPipelineDefaultPolicy
{
static constexpr bool QLoadOnce =
QLoadOnce_; // if q load whole block length (qkhdim) to LDS at once
static constexpr bool QTLoadOnce =
QTLoadOnce_; // if q^t load whole block length (qkhdim) to LDS at once
static constexpr bool KLoadOnce =
KLoadOnce_; // if k load whole block length (qkhdim) to LDS at once
static constexpr bool KTLoadOnce =
KTLoadOnce_; // if k^t load whole block length (qkhdim) to LDS at once
static constexpr bool VLoadOnce =
VLoadOnce_; // if v load whole block length (vhdim) to Vgprs at once
static constexpr bool OGradLoadOnce =
OGradLoadOnce_; // if do load whole block length (vhdim) to LDS at once
static constexpr bool OGradTLoadOnce =
OGradTLoadOnce_; // if do^t load whole block length (vhdim) to LDS at once
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetQKBlockGemm()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::QDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kM0,
Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kK0>>;
using WarpGemm = WarpGemmMfmaDispatcher<
typename Problem::QDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm0WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm0WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm0WarpTile::at(number<2>{}),
false,
Problem::BlockFmhaShape::Gemm0WarpTile::at(number<0>{}) == 16 ? false : true>;
using BlockGemmPolicy =
BlockGemmARegBRegCRegV1CustomPolicy<typename Problem::QDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm0BlockWarps,
WarpGemm>;
return BlockGemmARegBRegCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetPTOGradTBlockGemm()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::GemmDataType,
typename Problem::OGradDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kVHeaddim,
Problem::BlockFmhaShape::kK1>>;
using WarpGemm =
WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
typename Problem::OGradDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm1WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm1WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm1WarpTile::at(number<2>{}),
true>;
using BlockGemmPolicy =
BlockGemmARegBRegCRegV1CustomPolicy<typename Problem::GemmDataType,
typename Problem::OGradDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm1BlockWarps,
WarpGemm>;
return BlockGemmARegBRegCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetOGradVBlockGemm()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::OGradDataType,
typename Problem::VDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kM0,
Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kK2>>;
using WarpGemm = WarpGemmMfmaDispatcher<
typename Problem::OGradDataType,
typename Problem::VDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm2WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm2WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm2WarpTile::at(number<2>{}),
false,
Problem::BlockFmhaShape::Gemm0WarpTile::at(number<0>{}) == 16 ? false : true>;
using BlockGemmPolicy =
BlockGemmARegBRegCRegV1CustomPolicy<typename Problem::OGradDataType,
typename Problem::VDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm2BlockWarps,
WarpGemm>;
return BlockGemmARegBRegCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSGradTQTBlockGemm()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::GemmDataType,
typename Problem::QDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kQKHeaddim,
Problem::BlockFmhaShape::kK3>>;
using WarpGemm =
WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
typename Problem::QDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm3WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm3WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm3WarpTile::at(number<2>{}),
true>;
using BlockGemmPolicy =
BlockGemmARegBRegCRegV1CustomPolicy<typename Problem::GemmDataType,
typename Problem::QDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm3BlockWarps,
WarpGemm>;
return BlockGemmARegBRegCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSGradKTBlockGemm()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::GemmDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kM0,
Problem::BlockFmhaShape::kQKHeaddim,
Problem::BlockFmhaShape::kK4>>;
using WarpGemm =
WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm4WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm4WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm4WarpTile::at(number<2>{}),
false>;
using BlockGemmPolicy =
BlockGemmARegBRegCRegV1CustomPolicy<typename Problem::GemmDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm4BlockWarps,
WarpGemm>;
return BlockGemmARegBRegCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
}
// these are for global load
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentQ()
{
using QDataType = remove_cvref_t<typename Problem::QDataType>;
return 16 / sizeof(QDataType);
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMNPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t kMaxVecLoad = 16 / sizeof(QDataType);
constexpr index_t kMinVecLoad = 4 / sizeof(QDataType);
constexpr index_t total_pixels = kMNPerBlock * kKPerBlock / kBlockSize;
constexpr index_t kVecLoad = ((total_pixels / kMaxVecLoad) >= kMinVecLoad)
? kMaxVecLoad
: (total_pixels / kMinVecLoad);
return kVecLoad;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentK()
{
using KDataType = remove_cvref_t<typename Problem::KDataType>;
return 16 / sizeof(KDataType);
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t kMaxVecLoad = 16 / sizeof(KDataType);
constexpr index_t kMinVecLoad = 4 / sizeof(KDataType);
constexpr index_t total_pixels = kMNPerBlock * kKPerBlock / kBlockSize;
constexpr index_t kVecLoad = ((total_pixels / kMaxVecLoad) >= kMinVecLoad)
? kMaxVecLoad
: (total_pixels / kMinVecLoad);
return kVecLoad;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentV()
{
if constexpr(VLoadOnce)
{
using BlockGemm = remove_cvref_t<decltype(GetOGradVBlockGemm<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;
}
else
{
using VDataType = remove_cvref_t<typename Problem::VDataType>;
return 16 / sizeof(VDataType);
}
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t kMaxVecLoad = 16 / sizeof(VDataType);
constexpr index_t total_pixels = kMNPerBlock * kKPerBlock / kBlockSize;
return total_pixels > kMaxVecLoad ? kMaxVecLoad : total_pixels;
}
template <typename Problem>
......@@ -85,19 +242,38 @@ struct BlockFmhaBwdPipelineDefaultPolicy
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentOGrad()
{
using OGradDataType = remove_cvref_t<typename Problem::OGradDataType>;
return 16 / sizeof(OGradDataType);
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMNPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t kMaxVecLoad = 16 / sizeof(OGradDataType);
constexpr index_t kMinVecLoad = 4 / sizeof(OGradDataType);
constexpr index_t total_pixels = kMNPerBlock * kKPerBlock / kBlockSize;
constexpr index_t kVecLoad = ((total_pixels / kMaxVecLoad) >= kMinVecLoad)
? kMaxVecLoad
: (total_pixels / kMinVecLoad);
return kVecLoad;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentQGrad()
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentBias()
{
using BlockGemm = remove_cvref_t<decltype(GetSGradKTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WG = remove_cvref_t<decltype(config.template at<0>())>;
using CWarpDstr = typename WG::CWarpDstr;
constexpr auto vec =
CWarpDstr{}.get_ys_to_d_descriptor().get_lengths().at(number<CWarpDstr::NDimY - 1>{});
return vec;
using BiasDataType = remove_cvref_t<typename Problem::BiasDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kMaxVecLoad = 16 / sizeof(BiasDataType);
constexpr index_t kMinVecLoad = 4 / sizeof(BiasDataType);
constexpr index_t total_pixels = kMPerBlock * kNPerBlock / kBlockSize;
constexpr index_t kVecLoad = ((total_pixels / kMaxVecLoad) >= kMinVecLoad)
? kMaxVecLoad
: (total_pixels / kMinVecLoad);
return kVecLoad;
}
template <typename Problem>
......@@ -128,60 +304,35 @@ struct BlockFmhaBwdPipelineDefaultPolicy
CK_TILE_HOST_DEVICE static constexpr auto GetTransposedAlignmentQ()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(QTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK3;
}();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
// TODO: not correct!
if constexpr(total_pixels > 4)
return 4;
else
return 2;
return total_pixels / GetAlignmentQ<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetTransposedAlignmentK()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(KTLoadOnce)
return Problem::BlockFmhaShape::kN0;
else
return Problem::BlockFmhaShape::kK4;
}();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
// TODO: not correct!
if constexpr(total_pixels > 4)
return 4;
else
return 2;
return total_pixels / GetAlignmentK<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetTransposedAlignmentOGrad()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kVHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(OGradTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK1;
}();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
// TODO: not correct!
if constexpr(total_pixels > 4)
return 4;
else
return 2;
return total_pixels / GetAlignmentOGrad<Problem>();
}
template <typename Problem>
......@@ -193,1151 +344,1577 @@ struct BlockFmhaBwdPipelineDefaultPolicy
constexpr index_t total_pixels = kMPerBlock * kNPerBlock / kBlockSize;
// TODO: not correct!
if constexpr(total_pixels > 32)
return 8;
else
return 4;
return total_pixels / GetAlignmentBias<Problem>();
}
// these are for lds
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackQ()
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentPostQGradAcc()
{
// TODO: this is for 3d layout
using QDataType = remove_cvref_t<typename Problem::QDataType>;
return 16 / sizeof(QDataType);
using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
return 16 / sizeof(AccDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackK()
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentPostQGrad()
{
// TODO: this is for 3d layout
using KDataType = remove_cvref_t<typename Problem::KDataType>;
return 16 / sizeof(KDataType);
return GetAlignmentPostQGradAcc<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackV()
CK_TILE_HOST_DEVICE static constexpr auto MakeKDramTileDistribution()
{
// TODO: this is for 3d layout
using VDataType = remove_cvref_t<typename Problem::VDataType>;
return 16 / sizeof(VDataType);
}
constexpr index_t kBlockSize = Problem::kBlockSize;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackBias()
{
// TODO: this is for 3d layout
using BiasDataType = remove_cvref_t<typename Problem::BiasDataType>;
return 16 / sizeof(BiasDataType);
}
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackOGrad()
{
// TODO: this is for 3d layout
using OGradDataType = remove_cvref_t<typename Problem::OGradDataType>;
return 16 / sizeof(OGradDataType);
constexpr index_t K1 = GetAlignmentK<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t N1 = get_warp_size() / K0;
constexpr index_t N0 = kBlockSize / get_warp_size();
constexpr index_t N2 = kNPerBlock / (N1 * N0);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<1, 0>>,
sequence<1, 2>,
sequence<2, 1>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackSGrad()
CK_TILE_HOST_DEVICE static constexpr auto MakeVDramTileDistribution()
{
// TODO: this is for 3d layout
using GemmDataType = remove_cvref_t<typename Problem::GemmDataType>;
return 16 / sizeof(GemmDataType);
}
constexpr index_t kBlockSize = Problem::kBlockSize;
template <typename Problem, typename BlockGemm>
CK_TILE_HOST_DEVICE static constexpr auto MakeVInRegDramTileDistribution()
{
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
constexpr index_t K1 = GetAlignmentV<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t N2 = get_warp_size() / K0;
constexpr index_t N1 = kBlockSize / get_warp_size();
constexpr index_t N0 = kNPerBlock / (N2 * N1);
using WG = remove_cvref_t<decltype(config.template at<0>())>;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
constexpr index_t MWarp = config.template at<1>();
constexpr index_t NWarp = config.template at<2>();
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQDramTileDistribution()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WG::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WG::kK;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr auto v_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto v_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
v_block_outer_dstr_encoding, typename WG::BWarpDstrEncoding{});
constexpr auto v_block_dstr = make_static_tile_distribution(v_block_dstr_encode);
constexpr index_t K1 = GetAlignmentQ<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t M1 = get_warp_size() / K0;
constexpr index_t M0 = kBlockSize / get_warp_size();
constexpr index_t M2 = kMPerBlock / (M1 * M0);
return v_block_dstr;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<1, 0>>,
sequence<1, 2>,
sequence<2, 1>>{});
}
// 3d + padding
template <index_t MNPerBlock, index_t KPerBlock, index_t KPack>
CK_TILE_HOST_DEVICE static constexpr auto MakeXLdsBlockDescriptor()
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradDramTileDistribution()
{
constexpr auto x_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<KPerBlock / KPack>{}, number<MNPerBlock>{}, number<KPack>{}),
make_tuple(number<(MNPerBlock + 1) * KPack>{}, number<KPack>{}, number<1>{}),
number<8>{},
number<1>{});
constexpr auto x_lds_block_desc = transform_tensor_descriptor(
x_lds_block_desc_0,
make_tuple(make_pass_through_transform(MNPerBlock),
make_merge_transform(make_tuple(KPerBlock / KPack, KPack))),
make_tuple(sequence<1>{}, sequence<0, 2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return x_lds_block_desc;
}
constexpr index_t kBlockSize = Problem::kBlockSize;
// 3d + padding
template <index_t MNPerBlock, index_t KPerBlock, index_t KPack>
CK_TILE_HOST_DEVICE static constexpr auto MakeXLdsBlockDescriptorAsXT()
{
constexpr auto x_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<KPerBlock / KPack>{}, number<MNPerBlock>{}, number<KPack>{}),
make_tuple(number<(MNPerBlock + 1) * KPack>{}, number<KPack>{}, number<1>{}),
number<8>{},
number<1>{});
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr auto xt_lds_block_desc = transform_tensor_descriptor(
x_lds_block_desc_0,
make_tuple(make_pass_through_transform(MNPerBlock),
make_merge_transform(make_tuple(KPerBlock / KPack, KPack))),
make_tuple(sequence<1>{}, sequence<0, 2>{}),
make_tuple(sequence<1>{}, sequence<0>{}));
constexpr index_t K1 = GetAlignmentOGrad<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t M1 = get_warp_size() / K0;
constexpr index_t M0 = kBlockSize / get_warp_size();
constexpr index_t M2 = kMPerBlock / (M1 * M0);
return xt_lds_block_desc;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<1, 0>>,
sequence<1, 2>,
sequence<2, 1>>{});
}
template <index_t MNPerBlock, index_t KPerBlock, index_t KPack, index_t PixelsPerRow>
CK_TILE_HOST_DEVICE static constexpr auto MakeXTLdsBlockDescriptor()
template <typename Problem, typename BlockGemm>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEDDramTileDistribution()
{
static_assert(PixelsPerRow % KPack == 0);
constexpr index_t NPerRow = PixelsPerRow / KPack;
static_assert(MNPerBlock % NPerRow == 0);
static_assert(KPerBlock % KPack == 0);
constexpr auto xt_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<KPerBlock / KPack>{},
number<MNPerBlock / NPerRow>{},
number<NPerRow>{},
number<KPack>{}),
make_tuple(number<(MNPerBlock / NPerRow) * (PixelsPerRow + KPack)>{},
number<PixelsPerRow + KPack>{},
number<KPack>{},
number<1>{}),
number<KPack>{},
number<1>{});
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
constexpr index_t MWarp = config.template at<1>();
constexpr index_t NWarp = config.template at<2>();
constexpr auto xt_lds_block_desc = transform_tensor_descriptor(
xt_lds_block_desc_0,
make_tuple(
make_merge_transform(make_tuple(number<MNPerBlock / NPerRow>{}, number<NPerRow>{})),
make_merge_transform(make_tuple(number<KPerBlock / KPack>{}, number<KPack>{}))),
make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
return xt_lds_block_desc;
}
// Duplicate dimension
constexpr index_t N0 = NWarp;
constexpr index_t N1 =
(get_warp_size() / kMPerBlock) > 1 ? (get_warp_size() / kMPerBlock) : 1;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQLdsBlockDescriptor()
{
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(QLoadOnce)
return Problem::BlockFmhaShape::kQKHeaddim;
else
return Problem::BlockFmhaShape::kK0;
}();
constexpr index_t kKPack = GetSmemKPackQ<Problem>();
constexpr index_t M0 = MWarp;
constexpr index_t M1 = (get_warp_size() / kMPerBlock) > 1 ? kMPerBlock : get_warp_size();
constexpr index_t M2 =
(get_warp_size() / kMPerBlock) > 1 ? 1 : (kMPerBlock / get_warp_size());
return MakeXLdsBlockDescriptor<kMPerBlock, kKPerBlock, kKPack>();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<N0, N1>,
tuple<sequence<M0, M1, M2>>,
tuple<sequence<0, 1>, sequence<0, 1>>,
tuple<sequence<0, 0>, sequence<1, 1>>,
sequence<1>,
sequence<2>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQLdsBlockDescriptorAsQT()
CK_TILE_HOST_DEVICE static constexpr auto MakeBiasTileDistribution()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(QLoadOnce)
return Problem::BlockFmhaShape::kQKHeaddim;
else
return Problem::BlockFmhaShape::kK0;
}();
constexpr index_t kKPack = GetSmemKPackQ<Problem>();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t N1 = GetAlignmentBias<Problem>();
constexpr index_t N0 = kNPerBlock / N1;
constexpr index_t M1 = get_warp_size() / N0;
constexpr index_t M0 = kBlockSize / get_warp_size();
constexpr index_t M2 = kMPerBlock / (M1 * M0);
return MakeXLdsBlockDescriptorAsXT<kMPerBlock, kKPerBlock, kKPack>();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<N0, N1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<1, 0>>,
sequence<1, 2>,
sequence<2, 1>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeKLdsBlockDescriptor()
template <typename DataType, index_t MPerBlock, index_t KPerBlock>
CK_TILE_HOST_DEVICE static constexpr auto MakePreXDramTileDistribution()
{
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(KLoadOnce)
return Problem::BlockFmhaShape::kQKHeaddim;
else
return Problem::BlockFmhaShape::kK0;
}();
constexpr index_t kKPack = GetSmemKPackK<Problem>();
constexpr index_t K1 = 16 / sizeof(DataType);
constexpr index_t K0 = KPerBlock / K1;
constexpr index_t M2 = 1;
constexpr index_t M1 = get_warp_size();
constexpr index_t M0 = MPerBlock / M1;
return MakeXLdsBlockDescriptor<kNPerBlock, kKPerBlock, kKPack>();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1>>,
tuple<sequence<0>, sequence<1>>,
sequence<1, 2, 2>,
sequence<2, 0, 1>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeKLdsBlockDescriptorAsKT()
CK_TILE_HOST_DEVICE static constexpr auto MakePreODramTileDistribution()
{
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(KLoadOnce)
return Problem::BlockFmhaShape::kQKHeaddim;
else
return Problem::BlockFmhaShape::kK0;
}();
constexpr index_t kKPack = GetSmemKPackK<Problem>();
using ODataType = remove_cvref_t<typename Problem::ODataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kKPerBlock = Problem::kVHeaddim;
return MakeXLdsBlockDescriptorAsXT<kNPerBlock, kKPerBlock, kKPack>();
return MakePreXDramTileDistribution<ODataType, kBlockSize, kKPerBlock>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeVLdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakePreOGradDramTileDistribution()
{
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t kKPack = GetSmemKPackV<Problem>();
using OGradDataType = remove_cvref_t<typename Problem::OGradDataType>;
return MakeXLdsBlockDescriptor<kNPerBlock, kKPerBlock, kKPack>();
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kKPerBlock = Problem::kVHeaddim;
return MakePreXDramTileDistribution<OGradDataType, kBlockSize, kKPerBlock>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradLdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakePostQGradAccDramTileDistribution()
{
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(OGradLoadOnce)
return Problem::BlockFmhaShape::kVHeaddim;
else
return Problem::BlockFmhaShape::kK2;
}();
constexpr index_t kKPack = GetSmemKPackOGrad<Problem>();
using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
return MakeXLdsBlockDescriptor<kMPerBlock, kKPerBlock, kKPack>();
}
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t kKPerBlock = Problem::kQKHeaddim;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradLdsBlockDescriptorAsOGradT()
{
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(OGradLoadOnce)
return Problem::BlockFmhaShape::kVHeaddim;
else
return Problem::BlockFmhaShape::kK2;
}();
constexpr index_t kKPack = GetSmemKPackOGrad<Problem>();
constexpr index_t K1 = 16 / sizeof(AccDataType);
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t M2 = get_warp_size() / K0;
constexpr index_t M1 = kBlockSize / get_warp_size();
constexpr index_t M0 = kMPerBlock / (M1 * M2);
return MakeXLdsBlockDescriptorAsXT<kMPerBlock, kKPerBlock, kKPack>();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<1>, sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<2>, sequence<2, 3>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2, 3>,
sequence<0, 0, 1>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeSGradLdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakePostQGradDramTileDistribution()
{
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPack = GetSmemKPackSGrad<Problem>();
using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
return MakeXLdsBlockDescriptor<kMPerBlock, kKPerBlock, kKPack>();
}
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t kKPerBlock = Problem::kQKHeaddim;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQTLdsBlockDescriptor()
{
using QDataType = remove_cvref_t<typename Problem::QDataType>;
constexpr index_t Banks = 32; // TODO: need change based on arch
constexpr index_t PixelsPerRow = Banks * 4 / sizeof(QDataType);
constexpr index_t kKPack = GetSmemKPackQ<Problem>();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(QTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK3;
}();
constexpr index_t K1 = 16 / sizeof(AccDataType);
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t M2 = get_warp_size() / K0;
constexpr index_t M1 = kBlockSize / get_warp_size();
constexpr index_t M0 = kMPerBlock / (M1 * M2);
return MakeXTLdsBlockDescriptor<kNPerBlock, kKPerBlock, kKPack, PixelsPerRow>();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
// these are for lds
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeKTLdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackQ()
{
using KDataType = remove_cvref_t<typename Problem::KDataType>;
constexpr index_t Banks = 32; // TODO: need change based on arch
constexpr index_t PixelsPerRow = Banks * 4 / sizeof(KDataType);
constexpr index_t kKPack = GetSmemKPackK<Problem>();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(KTLoadOnce)
return Problem::BlockFmhaShape::kN0;
else
return Problem::BlockFmhaShape::kK4;
}();
return MakeXTLdsBlockDescriptor<kNPerBlock, kKPerBlock, kKPack, PixelsPerRow>();
return GetAlignmentQ<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradTLdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackQT()
{
using OGradDataType = remove_cvref_t<typename Problem::OGradDataType>;
constexpr index_t Banks = 32; // TODO: need change based on arch
constexpr index_t PixelsPerRow = Banks * 4 / sizeof(OGradDataType);
constexpr index_t kKPack = GetSmemKPackOGrad<Problem>();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kVHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(OGradTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK1;
}();
return MakeXTLdsBlockDescriptor<kNPerBlock, kKPerBlock, kKPack, PixelsPerRow>();
return GetTransposedAlignmentQ<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBiasTLdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackK()
{
using BiasDataType = remove_cvref_t<typename Problem::BiasDataType>;
constexpr index_t Banks = 32; // TODO: need change based on arch
constexpr index_t PixelsPerRow = Banks * 4 / sizeof(BiasDataType);
constexpr index_t kKPack = GetSmemKPackBias<Problem>();
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
static_assert(PixelsPerRow % kKPack == 0);
constexpr index_t NPerRow = PixelsPerRow / kKPack;
static_assert(kNPerBlock % NPerRow == 0);
static_assert(kMPerBlock % kKPack == 0);
constexpr auto biast_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kMPerBlock / kKPack>{},
number<kNPerBlock / NPerRow>{},
number<NPerRow>{},
number<kKPack>{}),
make_tuple(number<(kNPerBlock / NPerRow) * (PixelsPerRow + kKPack)>{},
number<PixelsPerRow + kKPack>{},
number<kKPack>{},
number<1>{}),
number<kKPack>{},
number<1>{});
constexpr auto biast_lds_block_desc = transform_tensor_descriptor(
biast_lds_block_desc_0,
make_tuple(
make_merge_transform(make_tuple(number<kNPerBlock / NPerRow>{}, number<NPerRow>{})),
make_merge_transform(make_tuple(number<kMPerBlock / kKPack>{}, number<kKPack>{}))),
make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<1>{}, sequence<0>{}));
return biast_lds_block_desc;
return GetAlignmentK<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeQ()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackKT()
{
constexpr index_t smem_size_q = sizeof(typename Problem::QDataType) *
MakeQLdsBlockDescriptor<Problem>().get_element_space_size();
return smem_size_q;
return GetTransposedAlignmentK<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeQT()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackV()
{
constexpr index_t smem_size_qt = [&]() {
if constexpr(QLoadOnce && !QTLoadOnce)
return 0;
else
return sizeof(typename Problem::QDataType) *
MakeQTLdsBlockDescriptor<Problem>().get_element_space_size();
}();
return smem_size_qt;
return GetAlignmentV<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeK()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackBias()
{
constexpr index_t smem_size_k = sizeof(typename Problem::KDataType) *
MakeKLdsBlockDescriptor<Problem>().get_element_space_size();
return smem_size_k;
return GetAlignmentBias<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeKT()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackBiasT()
{
constexpr index_t smem_size_kt = [&]() {
if constexpr(KLoadOnce && !KTLoadOnce)
return 0;
else
return sizeof(typename Problem::KDataType) *
MakeKTLdsBlockDescriptor<Problem>().get_element_space_size();
}();
return smem_size_kt;
return GetTransposedAlignmentBias<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeV()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackOGrad()
{
constexpr index_t smem_size_v = [&]() {
if constexpr(VLoadOnce)
return 0;
else
return sizeof(typename Problem::VDataType) *
MakeVLdsBlockDescriptor<Problem>().get_element_space_size();
}();
return smem_size_v;
return GetAlignmentOGrad<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeOGrad()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackOGradT()
{
constexpr index_t smem_size_do =
sizeof(typename Problem::OGradDataType) *
MakeOGradLdsBlockDescriptor<Problem>().get_element_space_size();
return smem_size_do;
return GetTransposedAlignmentOGrad<Problem>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeOGradT()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackSGrad()
{
constexpr index_t smem_size_dot = [&]() {
if constexpr(OGradLoadOnce && !OGradTLoadOnce)
return 0;
else
return sizeof(typename Problem::OGradDataType) *
MakeOGradTLdsBlockDescriptor<Problem>().get_element_space_size();
}();
return smem_size_dot;
// TODO: this is for 3d layout
using GemmDataType = remove_cvref_t<typename Problem::GemmDataType>;
return 16 / sizeof(GemmDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeSGrad()
template <index_t MNPerBlock, index_t KPerBlock, index_t KPack>
CK_TILE_HOST_DEVICE static constexpr auto MakeXLdsBlockDescriptor()
{
constexpr index_t smem_size_ds =
sizeof(typename Problem::GemmDataType) *
MakeSGradLdsBlockDescriptor<Problem>().get_element_space_size();
return smem_size_ds;
constexpr auto DataTypeSize = 2; // sizeof(F16/BF16)
constexpr auto MNLdsLayer =
(32 * 4 / KPerBlock / DataTypeSize) < 1 ? 1 : (32 * 4 / KPerBlock / DataTypeSize);
constexpr auto x_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<KPerBlock / KPack * MNLdsLayer>{},
number<MNPerBlock / MNLdsLayer>{},
number<KPack>{}),
make_tuple(number<KPack>{}, number<KPerBlock * MNLdsLayer>{}, number<1>{}),
number<KPack>{},
number<1>{});
constexpr auto x_lds_block_desc_permuted = transform_tensor_descriptor(
x_lds_block_desc_0,
make_tuple(make_xor_transform(make_tuple(number<MNPerBlock / MNLdsLayer>{},
number<KPerBlock / KPack * MNLdsLayer>{})),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<1, 0>{}, sequence<2>{}),
make_tuple(sequence<1, 0>{}, sequence<2>{}));
constexpr auto x_lds_block_desc_xk0_mnldslayer_mn_xk1 = transform_tensor_descriptor(
x_lds_block_desc_permuted,
make_tuple(make_unmerge_transform(
make_tuple(number<KPerBlock / KPack>{}, number<MNLdsLayer>{})),
make_pass_through_transform(number<MNPerBlock / MNLdsLayer>{}),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{}));
constexpr auto x_lds_block_desc = transform_tensor_descriptor(
x_lds_block_desc_xk0_mnldslayer_mn_xk1,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<MNPerBlock / MNLdsLayer>{}, number<MNLdsLayer>{})),
make_merge_transform_v3_division_mod(
make_tuple(number<KPerBlock / KPack>{}, number<KPack>{}))),
make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return x_lds_block_desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeBias()
template <typename Problem,
index_t MNPerBlock,
index_t KPerBlock,
index_t KPack,
index_t KPackT>
CK_TILE_HOST_DEVICE static constexpr auto MakeXTLdsBlockDescriptor()
{
constexpr index_t smem_size_bias = [&]() {
if constexpr(Problem::BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
return sizeof(typename Problem::BiasDataType) *
MakeBiasTLdsBlockDescriptor<Problem>().get_element_space_size();
else
return 0;
}();
return smem_size_bias;
// kfold and mpair dimension is not always required.
// more dimension in merge_transform increase the difficulty of generating immarg offset
// for compiler.
constexpr auto MNPerXDL = Problem::BlockFmhaShape::Gemm0WarpTile::at(number<0>{});
constexpr auto kBlockSize = Problem::kBlockSize;
constexpr auto MN0 = MNPerBlock / KPack;
constexpr auto MN1 = KPack;
constexpr auto KThreadWrite = kBlockSize / MN0;
constexpr auto K0Number = KPerBlock / KPackT;
constexpr auto K0PerThreadWrite = K0Number / KThreadWrite;
constexpr auto KThreadRead = get_warp_size() / MNPerXDL; // assume 32x32x8 mfma
constexpr auto K0PerThreadRead = K0Number / KThreadRead;
constexpr auto kfold = (KPackT * MN0 * 2 > 128) ? 1 : 128 / (KPackT * MN0 * 2);
constexpr auto KThreadReadPerm =
(kfold * K0PerThreadWrite / K0PerThreadRead) > 1
? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)
: KThreadRead;
// 1<=mnpair<=n0
constexpr auto mnpair =
(KPackT * MNPerXDL * 2 > 128)
? 1
: ((128 / (KPackT * MNPerXDL * 2)) > MN0 ? MN0 : 128 / (KPackT * MNPerXDL * 2));
constexpr auto xt_lds_block_desc_raw = make_naive_tensor_descriptor(
make_tuple(number<KThreadWrite / kfold / KThreadReadPerm>{},
number<K0PerThreadWrite>{},
number<KThreadReadPerm * MN1>{},
number<kfold * MN0 / mnpair>{},
number<mnpair>{},
KPackT),
make_tuple(number<KPackT * kfold * MN0 * KThreadReadPerm * MN1 * K0PerThreadWrite>{},
number<KPackT * kfold * MN0 * KThreadReadPerm * MN1>{},
number<KPackT * kfold * MN0>{},
number<KPackT * mnpair>{},
number<KPackT>{},
number<1>{}),
number<KPackT>{},
number<1>{});
constexpr auto xt_lds_block_desc_permuted = transform_tensor_descriptor(
xt_lds_block_desc_raw,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_xor_transform(
make_tuple(number<KThreadReadPerm * MN1>{}, number<kfold * MN0 / mnpair>{})),
make_pass_through_transform(number<mnpair>{}),
make_pass_through_transform(KPackT)),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}));
constexpr auto xt_lds_block_desc_unmerged = transform_tensor_descriptor(
xt_lds_block_desc_permuted,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_unmerge_transform(make_tuple(number<KThreadReadPerm>{}, number<MN1>{})),
make_unmerge_transform(make_tuple(number<kfold>{}, number<MN0 / mnpair>{})),
make_pass_through_transform(number<mnpair>{}),
make_pass_through_transform(KPackT)),
make_tuple(sequence<0>{},
sequence<1>{},
sequence<2>{},
sequence<3>{},
sequence<4>{},
sequence<5>{}),
make_tuple(sequence<1>{},
sequence<2>{},
sequence<0, 3>{},
sequence<4, 5>{},
sequence<6>{},
sequence<7>{}));
constexpr auto xt_lds_block_desc = transform_tensor_descriptor(
xt_lds_block_desc_unmerged,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<KThreadReadPerm>{},
number<KThreadWrite / kfold / KThreadReadPerm>{},
number<kfold>{},
number<K0PerThreadWrite>{},
number<KPackT>{})),
make_merge_transform_v3_division_mod(
make_tuple(number<MN0 / mnpair>{}, number<mnpair>{}, number<MN1>{}))),
make_tuple(sequence<0, 1, 4, 2, 7>{}, sequence<5, 6, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return xt_lds_block_desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
CK_TILE_HOST_DEVICE static constexpr auto MakeKLdsWriteBlockDescriptor()
{
constexpr index_t smem_size_q = GetSmemSizeQ<Problem>();
constexpr index_t smem_size_qt = GetSmemSizeQT<Problem>();
constexpr index_t smem_size_k = GetSmemSizeK<Problem>();
constexpr index_t smem_size_kt = GetSmemSizeKT<Problem>();
constexpr index_t smem_size_v = GetSmemSizeV<Problem>();
constexpr index_t smem_size_do = GetSmemSizeOGrad<Problem>();
constexpr index_t smem_size_dot = GetSmemSizeOGradT<Problem>();
constexpr index_t smem_size_ds = GetSmemSizeSGrad<Problem>();
constexpr index_t smem_size_bias = GetSmemSizeBias<Problem>();
constexpr index_t smem_size_transpose = max(smem_size_ds, smem_size_bias);
index_t smem_size = 0;
if constexpr(QLoadOnce && OGradLoadOnce)
smem_size += smem_size_q + smem_size_qt + smem_size_do + smem_size_dot +
smem_size_transpose; // 1~4 & 10
else if(QLoadOnce && !OGradLoadOnce && !OGradTLoadOnce)
smem_size += smem_size_q + smem_size_qt +
max(smem_size_do,
smem_size_dot,
smem_size_transpose); // 5/7/11 TODO: Multiple buffers strategy
else if(!QLoadOnce && !QTLoadOnce && OGradLoadOnce)
smem_size += smem_size_do + smem_size_dot +
max(smem_size_q,
smem_size_qt,
smem_size_transpose); // 6/8/12 TODO: Multiple buffers strategy
else if(!QLoadOnce && !QTLoadOnce && !OGradLoadOnce && !OGradTLoadOnce)
smem_size += max(smem_size_q,
smem_size_qt,
smem_size_do,
smem_size_dot,
smem_size_transpose); // 9/13 TODO: Multiple buffers strategy
// 14/15 needs to be adjusted
if constexpr(KLoadOnce)
smem_size += (smem_size_k + smem_size_kt); // 1~13
else
smem_size =
max(smem_size_k, smem_size_kt, smem_size); // 14/15 TODO: Multiple buffers strategy
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t kKPack = GetSmemKPackK<Problem>();
return max(smem_size, smem_size_v); // 15
return MakeXLdsBlockDescriptor<kNPerBlock, kKPerBlock, kKPack>();
}
template <typename Problem, typename BlockGemm>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEDDramTileDistribution()
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeKRegSliceBlockDescriptor()
{
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>())>;
constexpr index_t MWarp = config.template at<1>();
constexpr index_t NWarp = config.template at<2>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<1>{});
constexpr index_t N1 = WG::WarpGemmAttribute::Impl::kCNLane;
constexpr index_t N0 = NWarp;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t M4 = WG::WarpGemmAttribute::Impl::kCM1PerLane * 2;
constexpr index_t M3 = WG::WarpGemmAttribute::Impl::kCMLane;
constexpr index_t M2 = WG::WarpGemmAttribute::Impl::kCM0PerLane / 2;
constexpr index_t M1 = MWarp;
constexpr index_t M0 = kMPerBlock / (M1 * WG::WarpGemmAttribute::Impl::kM);
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<N0, N1>,
tuple<sequence<M0, M1, M2, M3, M4>>,
tuple<sequence<1, 0>, sequence<1, 0>>,
tuple<sequence<1, 0>, sequence<3, 1>>,
sequence<1, 1, 1>,
sequence<0, 2, 4>>{});
constexpr auto k_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto k_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
k_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
constexpr auto k_block_dstr = make_static_tile_distribution(k_block_dstr_encode);
return k_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeVDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeKRegBlockDescriptor()
{
using VDataType = remove_cvref_t<typename Problem::VDataType>;
using BlockGemm = remove_cvref_t<decltype(GetQKBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<1>{});
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t K1 = 16 / sizeof(VDataType);
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t N2 = get_warp_size() / K0;
// coalesce reading for each blocks
constexpr index_t N1 = kBlockSize / get_warp_size();
constexpr index_t N0 = kNPerBlock / (N2 * N1);
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
constexpr auto k_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 1>>{});
sequence<0, 0>>{};
constexpr auto k_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
k_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
constexpr auto k_block_dstr = make_static_tile_distribution(k_block_dstr_encode);
return k_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeVLdsWriteBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(QLoadOnce)
return Problem::BlockFmhaShape::kQKHeaddim;
else
return Problem::BlockFmhaShape::kK0;
}();
constexpr index_t kVPack = GetSmemKPackV<Problem>();
constexpr index_t K1 = GetAlignmentQ<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t M2 = get_warp_size() / K0;
// coalesce reading for each blocks
constexpr index_t M1 = kBlockSize / get_warp_size();
constexpr index_t M0 = kMPerBlock / (M2 * M1);
return MakeXLdsBlockDescriptor<kNPerBlock, kKPerBlock, kVPack>();
}
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeVRegSliceBlockDescriptor()
{
using BlockGemm = remove_cvref_t<decltype(GetOGradVBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<1>{});
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto v_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 1>>{});
sequence<0, 0>>{};
constexpr auto v_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
v_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
constexpr auto v_block_dstr = make_static_tile_distribution(v_block_dstr_encode);
return v_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeKDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeVRegBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
using BlockGemm = remove_cvref_t<decltype(GetOGradVBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<1>{});
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(KLoadOnce)
return Problem::BlockFmhaShape::kQKHeaddim;
else
return Problem::BlockFmhaShape::kK0;
}();
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
constexpr index_t K1 = GetAlignmentK<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t N2 = get_warp_size() / K0;
// coalesce reading for each blocks
constexpr index_t N1 = kBlockSize / get_warp_size();
constexpr index_t N0 = kNPerBlock / (N2 * N1);
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
constexpr auto v_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 1>>{});
sequence<0, 0>>{};
constexpr auto v_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
v_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
constexpr auto v_block_dstr = make_static_tile_distribution(v_block_dstr_encode);
return v_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledKRegWriteBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = [&]() {
if constexpr(OGradLoadOnce)
return Problem::BlockFmhaShape::kVHeaddim;
else
return Problem::BlockFmhaShape::kK2;
}();
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t K1 = GetAlignmentOGrad<Problem>();
constexpr index_t K1 = GetAlignmentK<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t M2 = get_warp_size() / K0;
// coalesce reading for each blocks
constexpr index_t M1 = kBlockSize / get_warp_size();
constexpr index_t M0 = kMPerBlock / (M2 * M1);
constexpr index_t N2 = GetTransposedAlignmentK<Problem>();
constexpr index_t N1 = get_warp_size() / K0;
constexpr index_t N0 = kBlockSize / get_warp_size();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
tuple<sequence<0>, sequence<1, 0>>,
sequence<2, 1>,
sequence<1, 2>>{});
}
template <typename DataType, index_t MPerBlock, index_t KPerBlock>
CK_TILE_HOST_DEVICE static constexpr auto MakePreXDramTileDistribution()
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledKLdsWriteBlockDescriptor()
{
constexpr index_t K1 = 16 / sizeof(DataType);
constexpr index_t K0 = KPerBlock / K1;
constexpr index_t M2 = 1;
constexpr index_t M1 = get_warp_size();
constexpr index_t M0 = MPerBlock / M1;
// Hold all data
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kN0;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1>>,
tuple<sequence<0>, sequence<1>>,
sequence<1, 2, 2>,
sequence<2, 0, 1>>{});
constexpr index_t kKPack = GetSmemKPackK<Problem>();
constexpr index_t kKPackT = GetSmemKPackKT<Problem>();
return MakeXTLdsBlockDescriptor<Problem, kNPerBlock, kKPerBlock, kKPack, kKPackT>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakePreODramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeKTLdsReadBlockDescriptor()
{
using ODataType = remove_cvref_t<typename Problem::ODataType>;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kKPerBlock = Problem::kVHeaddim;
auto shuffled_k_lds_block_desc = MakeShuffledKLdsWriteBlockDescriptor<Problem>();
return MakePreXDramTileDistribution<ODataType, kBlockSize, kKPerBlock>();
return transform_tensor_descriptor(
shuffled_k_lds_block_desc,
make_tuple(make_pass_through_transform(number<kNPerBlock>{}),
make_pass_through_transform(number<kKPerBlock>{})),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakePreOGradDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeKTRegBlockDescriptor()
{
using OGradDataType = remove_cvref_t<typename Problem::OGradDataType>;
using BlockGemm = remove_cvref_t<decltype(GetSGradKTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kKPerBlock = Problem::kVHeaddim;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm4BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm4BlockWarps::at(number<1>{});
return MakePreXDramTileDistribution<OGradDataType, kBlockSize, kKPerBlock>();
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto kt_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto kt_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
kt_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
constexpr auto kt_block_dstr = make_static_tile_distribution(kt_block_dstr_encode);
return kt_block_dstr;
}
template <typename Problem>
CK_TILE_DEVICE static constexpr auto MakeQTDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeQLdsBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(QTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK3;
}();
constexpr index_t N1 = GetTransposedAlignmentQ<Problem>();
constexpr index_t N0 = kNPerBlock / N1; // P
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t K3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackQ<Problem>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = kBlockSize / get_warp_size();
static_assert(kKPerBlock == K0 * K1 * K2 * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
return MakeXLdsBlockDescriptor<kMPerBlock, kKPerBlock, kKPack>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledQTRegBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakeQRegSliceBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(QTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK3;
}();
using BlockGemm = remove_cvref_t<decltype(GetQKBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t N1 = GetTransposedAlignmentQ<Problem>();
constexpr index_t N0 = kNPerBlock / N1;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t K3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackQ<Problem>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = kBlockSize / get_warp_size();
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<1>{});
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WarpGemm::kM);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto q_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<KIterPerWarp>>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<1, 3>>{});
sequence<0, 0>>{};
constexpr auto q_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
q_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
constexpr auto q_block_dstr = make_static_tile_distribution(q_block_dstr_encode);
return q_block_dstr;
}
template <typename Problem>
CK_TILE_DEVICE static constexpr auto MakeKTDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledQRegWriteBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(KTLoadOnce)
return Problem::BlockFmhaShape::kN0;
else
return Problem::BlockFmhaShape::kK4;
}();
constexpr index_t N1 = GetTransposedAlignmentK<Problem>();
constexpr index_t N0 = kNPerBlock / N1; // P
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t K3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackK<Problem>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = kBlockSize / get_warp_size();
static_assert(kKPerBlock == K0 * K1 * K2 * K3);
constexpr index_t K1 = GetAlignmentQ<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t N2 = GetTransposedAlignmentQ<Problem>();
constexpr index_t N1 = get_warp_size() / K0;
constexpr index_t N0 = kBlockSize / get_warp_size();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<1, 0>>,
sequence<2, 1>,
sequence<3, 1>>{});
sequence<1, 2>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledKTRegBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledQLdsWriteBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
// Hold full block data
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(KTLoadOnce)
return Problem::BlockFmhaShape::kN0;
else
return Problem::BlockFmhaShape::kK4;
}();
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t N1 = GetTransposedAlignmentK<Problem>();
constexpr index_t N0 = kNPerBlock / N1;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t K3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackK<Problem>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = kBlockSize / get_warp_size();
constexpr index_t kKPack = GetSmemKPackQ<Problem>();
constexpr index_t kKPackT = GetSmemKPackQT<Problem>();
return MakeXTLdsBlockDescriptor<Problem, kNPerBlock, kKPerBlock, kKPack, kKPackT>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQTLdsReadBlockDescriptor()
{
// Hold full block data
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kM0;
auto shuffled_q_lds_block_desc = MakeShuffledQLdsWriteBlockDescriptor<Problem>();
return transform_tensor_descriptor(
shuffled_q_lds_block_desc,
make_tuple(make_pass_through_transform(number<kNPerBlock>{}),
make_pass_through_transform(number<kKPerBlock>{})),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQTRegSliceBlockDescriptor()
{
using BlockGemm = remove_cvref_t<decltype(GetSGradTQTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm3BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm3BlockWarps::at(number<1>{});
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK3;
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto qt_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto qt_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
qt_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
constexpr auto qt_block_dstr = make_static_tile_distribution(qt_block_dstr_encode);
return qt_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeSGradTRegSliceBlockDescriptor()
{
using BlockGemm = remove_cvref_t<decltype(GetSGradTQTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm3BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm3BlockWarps::at(number<1>{});
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK3;
constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WarpGemm::kM);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto dst_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<KIterPerWarp>>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto dst_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
dst_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
constexpr auto dst_block_dstr = make_static_tile_distribution(dst_block_dstr_encode);
return dst_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEDLdsWriteBlockDescriptor()
{
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
using LSEDType = remove_cvref_t<typename Problem::DDataType>;
constexpr index_t kMPack = 16 / sizeof(LSEDType);
constexpr auto lsed_lds_block_desc =
make_naive_tensor_descriptor(make_tuple(number<kMPerBlock>{}),
make_tuple(number<1>{}),
number<kMPack>{},
number<1>{});
return lsed_lds_block_desc;
}
template <typename Problem, typename BlockGemm>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEDLdsReadBlockDescriptor()
{
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>();
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t N1 = WG::WarpGemmAttribute::Impl::kCNLane;
constexpr index_t N0 = NWarp;
// M4 *2 and M2 /2 when swizzle mode enabled
constexpr index_t SwizzleConfig = WG::kM == 16 ? 1 : 2;
// constexpr index_t SwizzleConfig = 1;
constexpr index_t M4 = WG::WarpGemmAttribute::Impl::kCM1PerLane * SwizzleConfig;
constexpr index_t M3 = WG::WarpGemmAttribute::Impl::kCMLane;
constexpr index_t M2 = WG::WarpGemmAttribute::Impl::kCM0PerLane / SwizzleConfig;
constexpr index_t M1 = MWarp;
constexpr index_t M0 = kMPerBlock / (M1 * WG::WarpGemmAttribute::Impl::kM);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
tile_distribution_encoding<sequence<N0, N1>,
tuple<sequence<M0, M1, M2, M3, M4>>,
tuple<sequence<1, 0>, sequence<1, 0>>,
tuple<sequence<1, 0>, sequence<3, 1>>,
sequence<1, 1, 1>,
sequence<0, 2, 4>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradLdsBlockDescriptor()
{
// Hold full block data
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t kKPack = GetSmemKPackOGrad<Problem>();
return MakeXLdsBlockDescriptor<kMPerBlock, kKPerBlock, kKPack>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradRegSliceBlockDescriptor()
{
using BlockGemm = remove_cvref_t<decltype(GetOGradVBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<1>{});
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WarpGemm::kM);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto do_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<KIterPerWarp>>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<1, 3>>{});
sequence<0, 0>>{};
constexpr auto do_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
do_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
constexpr auto do_block_dstr = make_static_tile_distribution(do_block_dstr_encode);
return do_block_dstr;
}
template <typename Problem>
CK_TILE_DEVICE static constexpr auto MakeOGradTDramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledOGradRegWriteBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kVHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(OGradTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK1;
}();
constexpr index_t N1 = GetTransposedAlignmentOGrad<Problem>();
constexpr index_t N0 = kNPerBlock / N1; // P
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t K3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackOGrad<Problem>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = kBlockSize / get_warp_size();
static_assert(kKPerBlock == K0 * K1 * K2 * K3);
constexpr index_t K1 = GetAlignmentOGrad<Problem>();
constexpr index_t K0 = kKPerBlock / K1;
constexpr index_t N2 = GetTransposedAlignmentOGrad<Problem>();
constexpr index_t N1 = get_warp_size() / K0;
constexpr index_t N0 = kBlockSize / get_warp_size();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<1, 0>>,
sequence<2, 1>,
sequence<3, 1>>{});
sequence<1, 2>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledOGradTRegBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledOGradLdsWriteBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
// Hold all data
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kVHeaddim;
constexpr index_t kKPerBlock = [&]() {
if constexpr(OGradTLoadOnce)
return Problem::BlockFmhaShape::kM0;
else
return Problem::BlockFmhaShape::kK1;
}();
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t N1 = GetTransposedAlignmentOGrad<Problem>();
constexpr index_t N0 = kNPerBlock / N1;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t K3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackOGrad<Problem>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = kBlockSize / get_warp_size();
constexpr index_t kKPackT = GetSmemKPackOGradT<Problem>();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
return MakeXTLdsBlockDescriptor<Problem, kNPerBlock, kKPerBlock, kKPack, kKPackT>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradTLdsReadBlockDescriptor()
{
// Hold all data
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kVHeaddim;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kM0;
auto shuffled_do_lds_block_desc = MakeShuffledOGradLdsWriteBlockDescriptor<Problem>();
return transform_tensor_descriptor(
shuffled_do_lds_block_desc,
make_tuple(make_pass_through_transform(number<kNPerBlock>{}),
make_pass_through_transform(number<kKPerBlock>{})),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOGradTRegSliceBlockDescriptor()
{
using BlockGemm = remove_cvref_t<decltype(GetPTOGradTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm1BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm1BlockWarps::at(number<1>{});
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kVHeaddim;
// constexpr index_t kNPerBlock = 32;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK1;
constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto dot_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto dot_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
dot_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
constexpr auto dot_block_dstr = make_static_tile_distribution(dot_block_dstr_encode);
return dot_block_dstr;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakePTRegSliceBlockDescriptor()
{
using BlockGemm = remove_cvref_t<decltype(GetPTOGradTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm1BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm1BlockWarps::at(number<1>{});
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK1;
constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WarpGemm::kM);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto pt_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<KIterPerWarp>>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<1, 3>>{});
sequence<0, 0>>{};
constexpr auto pt_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
pt_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
constexpr auto pt_block_dstr = make_static_tile_distribution(pt_block_dstr_encode);
return pt_block_dstr;
}
template <typename Problem>
CK_TILE_DEVICE static constexpr auto MakeBiasTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeSGradLdsBlockDescriptor()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPack = GetSmemKPackSGrad<Problem>();
constexpr index_t N1 = GetTransposedAlignmentBias<Problem>();
constexpr index_t N0 = kNPerBlock / N1; // P
return MakeXLdsBlockDescriptor<kMPerBlock, kKPerBlock, kKPack>();
}
constexpr index_t total_pixels = kMPerBlock * kNPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t M3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackBias<Problem>();
static_assert(kKPack % M3 == 0);
constexpr index_t M2 = kKPack / M3; // TODO: this dimention could be outside single wave
constexpr index_t M1 = get_warp_size() / (M2 * N0);
constexpr index_t M0 = kBlockSize / get_warp_size();
static_assert(kMPerBlock == M0 * M1 * M2 * M3);
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeSGradRegSliceBlockDescriptor()
{
using BlockGemm = remove_cvref_t<decltype(GetSGradKTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2, M3>, sequence<N0, N1>>,
tuple<sequence<1>, sequence<1, 2, 1>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm4BlockWarps::at(number<0>{});
constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm4BlockWarps::at(number<1>{});
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK4;
constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WarpGemm::kM);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
constexpr auto ds_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<KIterPerWarp>>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<3, 1>>{});
sequence<0, 0>>{};
constexpr auto ds_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
ds_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
constexpr auto ds_block_dstr = make_static_tile_distribution(ds_block_dstr_encode);
return ds_block_dstr;
}
template <typename Problem, typename PTOutTensor, typename PInTensor>
CK_TILE_DEVICE static constexpr void PTFromGemm0CToGemm1A(PTOutTensor& pt_out,
const PInTensor& p_in)
{
if constexpr(Problem::BlockFmhaShape::Gemm1WarpTile::at(number<0>{}) == 16)
{
using BlockGemm = remove_cvref_t<decltype(GetPTOGradTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm1BlockWarps::at(number<0>{});
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK1;
constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WarpGemm::kM);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
using AWarpDstr = typename WarpGemm::AWarpDstr;
using CWarpDstr = typename WarpGemm::CWarpDstr;
auto pt_warp_tensor =
make_static_distributed_tensor<typename Problem::GemmDataType>(CWarpDstr{});
constexpr auto a_warp_y_lengths =
to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
constexpr auto c_warp_y_lengths =
to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
pt_warp_tensor.get_thread_buffer() = p_in.get_y_sliced_thread_data(
merge_sequences(sequence<kIter, mIter>{}, c_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
pt_out.set_y_sliced_thread_data(
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths),
pt_warp_tensor.get_thread_buffer());
});
});
}
else
{
pt_out.get_thread_buffer() = p_in.get_thread_buffer();
}
}
template <typename Problem, typename SGradTOutTensor, typename SGradInTensor>
CK_TILE_DEVICE static constexpr void SGradTFromGemm2CToGemm3A(SGradTOutTensor& dst_out,
const SGradInTensor& ds_in)
{
if constexpr(Problem::BlockFmhaShape::Gemm3WarpTile::at(number<0>{}) == 16)
{
using BlockGemm = remove_cvref_t<decltype(GetSGradTQTBlockGemm<Problem>())>;
constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm3BlockWarps::at(number<0>{});
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK3;
constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WarpGemm::kM);
constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
using AWarpDstr = typename WarpGemm::AWarpDstr;
using CWarpDstr = typename WarpGemm::CWarpDstr;
auto dst_warp_tensor =
make_static_distributed_tensor<typename Problem::GemmDataType>(CWarpDstr{});
constexpr auto a_warp_y_lengths =
to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
constexpr auto c_warp_y_lengths =
to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
dst_warp_tensor.get_thread_buffer() = ds_in.get_y_sliced_thread_data(
merge_sequences(sequence<kIter, mIter>{}, c_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
dst_out.set_y_sliced_thread_data(
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths),
dst_warp_tensor.get_thread_buffer());
});
});
}
else
{
dst_out.get_thread_buffer() = ds_in.get_thread_buffer();
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledBiasTileDistribution()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t N1 = GetTransposedAlignmentBias<Problem>();
constexpr index_t N1 = GetAlignmentBias<Problem>();
constexpr index_t N0 = kNPerBlock / N1;
constexpr index_t total_pixels = kMPerBlock * kNPerBlock / kBlockSize;
static_assert(total_pixels % N1 == 0); // TODO: this is not always true?
constexpr index_t M3 = total_pixels / N1;
constexpr index_t kKPack = GetSmemKPackBias<Problem>();
static_assert(kKPack % M3 == 0);
constexpr index_t M2 = kKPack / M3; // TODO: this dimention could be outside single wave
constexpr index_t M1 = get_warp_size() / (M2 * N0);
constexpr index_t M2 = GetTransposedAlignmentBias<Problem>();
constexpr index_t M1 = get_warp_size() / N0;
constexpr index_t M0 = kBlockSize / get_warp_size();
return make_static_tile_distribution(
tile_distribution_encoding<sequence<>,
tuple<sequence<M0, M1, M2, M3>, sequence<N0, N1>>,
tuple<sequence<1>, sequence<1, 2, 1>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
tuple<sequence<M0, M1, M2>, sequence<N0, N1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<1, 0>>,
sequence<2, 1>,
sequence<1, 3>>{});
sequence<1, 2>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBiasLdsBlockDescriptor()
{
// Hold full block data
constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
constexpr index_t kKPack = GetSmemKPackBias<Problem>();
constexpr index_t kKPackT = GetSmemKPackBiasT<Problem>();
return MakeXTLdsBlockDescriptor<Problem, kNPerBlock, kMPerBlock, kKPack, kKPackT>();
}
template <typename BlockGemm>
CK_TILE_HOST_DEVICE static constexpr auto MakeBiasTTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeBiasSTileDistribution()
{
using c_block_tensor_type = decltype(BlockGemm{}.MakeCBlockTile());
return c_block_tensor_type::get_tile_distribution();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetQKBlockGemm()
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeQ()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::QDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kM0,
Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kK0>>;
constexpr index_t smem_size_q = sizeof(typename Problem::QDataType) *
MakeQLdsBlockDescriptor<Problem>().get_element_space_size();
return smem_size_q;
}
constexpr auto warp_gemm = []() {
if constexpr(std::is_same_v<typename Problem::QDataType, half_t> &&
std::is_same_v<typename Problem::KDataType, half_t> &&
std::is_same_v<typename Problem::AccDataType, float>)
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeQT()
{
return WarpGemmMfmaF16F16F32M32N32K16SwizzleA{};
constexpr index_t smem_size_qt =
sizeof(typename Problem::QDataType) *
MakeShuffledQLdsWriteBlockDescriptor<Problem>().get_element_space_size();
return smem_size_qt;
}
else if constexpr(std::is_same_v<typename Problem::QDataType, bf16_t> &&
std::is_same_v<typename Problem::KDataType, bf16_t> &&
std::is_same_v<typename Problem::AccDataType, float>)
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeK()
{
return WarpGemmMfmaBf16Bf16F32M32N32K16SwizzleA{};
constexpr index_t smem_size_k =
sizeof(typename Problem::KDataType) *
MakeKLdsWriteBlockDescriptor<Problem>().get_element_space_size();
return smem_size_k;
}
}();
using BlockGemmPolicy =
BlockGemmASmemBSmemCRegV1CustomPolicy<typename Problem::QDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm0BlockWarps,
decltype(warp_gemm)>;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeKT()
{
constexpr index_t smem_size_kt =
sizeof(typename Problem::KDataType) *
MakeKTLdsReadBlockDescriptor<Problem>().get_element_space_size();
return smem_size_kt;
}
return BlockGemmASmemBSmemCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeLSE()
{
constexpr index_t smem_size_lse =
sizeof(typename Problem::LSEDataType) *
MakeLSEDLdsWriteBlockDescriptor<Problem>().get_element_space_size();
return smem_size_lse;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetPTOGradTBlockGemm()
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeD()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::GemmDataType,
typename Problem::OGradDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kVHeaddim,
Problem::BlockFmhaShape::kK1>>;
constexpr index_t smem_size_d =
sizeof(typename Problem::DDataType) *
MakeLSEDLdsWriteBlockDescriptor<Problem>().get_element_space_size();
return smem_size_d;
}
using WarpGemm =
WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
typename Problem::OGradDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm1WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm1WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm1WarpTile::at(number<2>{}),
true>;
using BlockGemmPolicy =
BlockGemmARegBSmemCRegV1CustomPolicy<typename Problem::GemmDataType,
typename Problem::OGradDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm1BlockWarps,
WarpGemm>;
return BlockGemmARegBSmemCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeV()
{
constexpr index_t smem_size_v =
sizeof(typename Problem::VDataType) *
MakeVLdsWriteBlockDescriptor<Problem>().get_element_space_size();
return smem_size_v;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetOGradVBlockGemm()
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeOGrad()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::OGradDataType,
typename Problem::VDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kM0,
Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kK2>>;
constexpr index_t smem_size_do =
sizeof(typename Problem::OGradDataType) *
MakeOGradLdsBlockDescriptor<Problem>().get_element_space_size();
return smem_size_do;
}
constexpr auto warp_gemm = []() {
if constexpr(std::is_same_v<typename Problem::OGradDataType, half_t> &&
std::is_same_v<typename Problem::VDataType, half_t> &&
std::is_same_v<typename Problem::AccDataType, float>)
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeOGradT()
{
return WarpGemmMfmaF16F16F32M32N32K16SwizzleA{};
constexpr index_t smem_size_dot =
sizeof(typename Problem::OGradDataType) *
MakeShuffledOGradLdsWriteBlockDescriptor<Problem>().get_element_space_size();
return smem_size_dot;
}
else if constexpr(std::is_same_v<typename Problem::OGradDataType, bf16_t> &&
std::is_same_v<typename Problem::VDataType, bf16_t> &&
std::is_same_v<typename Problem::AccDataType, float>)
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeSGrad()
{
return WarpGemmMfmaBf16Bf16F32M32N32K16SwizzleA{};
constexpr index_t smem_size_ds =
sizeof(typename Problem::GemmDataType) *
MakeSGradLdsBlockDescriptor<Problem>().get_element_space_size();
return smem_size_ds;
}
}();
using BlockGemmPolicy =
BlockGemmASmemBRegCRegV1CustomPolicy<typename Problem::OGradDataType,
typename Problem::VDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm2BlockWarps,
decltype(warp_gemm)>;
return BlockGemmASmemBRegCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
}
// template <typename Problem>
// CK_TILE_HOST_DEVICE static constexpr auto GetOGradVBlockGemm()
// {
// using BlockGemmProblem =
// BlockGemmPipelineProblem<typename Problem::OGradDataType,
// typename Problem::VDataType,
// typename Problem::AccDataType,
// Problem::kBlockSize,
// TileGemmShape<Problem::BlockFmhaShape::kM0,
// Problem::BlockFmhaShape::kN0,
// Problem::BlockFmhaShape::kK2>>;
// constexpr auto warp_gemm = []() {
// if constexpr(std::is_same_v<typename Problem::OGradDataType, half_t> &&
// std::is_same_v<typename Problem::VDataType, half_t> &&
// std::is_same_v<typename Problem::AccDataType, float>)
// {
// return WarpGemmMfmaF16F16F32M32N32K16SwizzleA{};
// }
// else if constexpr(std::is_same_v<typename Problem::OGradDataType, bf16_t> &&
// std::is_same_v<typename Problem::VDataType, bf16_t> &&
// std::is_same_v<typename Problem::AccDataType, float>)
// {
// return WarpGemmMfmaBf16Bf16F32M32N32K16SwizzleA{};
// }
// }();
// using BlockGemmPolicy =
// BlockGemmASmemBSmemCRegV1CustomPolicy<typename Problem::OGradDataType,
// typename Problem::VDataType,
// typename Problem::AccDataType,
// typename
// Problem::BlockFmhaShape::Gemm2BlockWarps,
// decltype(warp_gemm)>;
// return BlockGemmASmemBSmemCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
// }
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeBias()
{
constexpr index_t smem_size_bias = [&]() {
if constexpr(Problem::BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
return sizeof(typename Problem::BiasDataType) *
MakeBiasLdsBlockDescriptor<Problem>().get_element_space_size();
else
return 0;
}();
return smem_size_bias;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSGradTQTBlockGemm()
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::GemmDataType,
typename Problem::QDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kN0,
Problem::BlockFmhaShape::kQKHeaddim,
Problem::BlockFmhaShape::kK3>>;
constexpr index_t smem_size_q = GetSmemSizeQ<Problem>();
constexpr index_t smem_size_qt = GetSmemSizeQT<Problem>();
constexpr index_t smem_size_lse = GetSmemSizeLSE<Problem>();
constexpr index_t smem_size_k = GetSmemSizeK<Problem>();
constexpr index_t smem_size_kt = GetSmemSizeKT<Problem>();
constexpr index_t smem_size_v = GetSmemSizeV<Problem>();
constexpr index_t smem_size_do = GetSmemSizeOGrad<Problem>();
constexpr index_t smem_size_dot = GetSmemSizeOGradT<Problem>();
constexpr index_t smem_size_d = GetSmemSizeD<Problem>();
constexpr index_t smem_size_ds = GetSmemSizeSGrad<Problem>();
constexpr index_t smem_size_bias = GetSmemSizeBias<Problem>();
using WarpGemm =
WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
typename Problem::QDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm3WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm3WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm3WarpTile::at(number<2>{}),
true>;
using BlockGemmPolicy =
BlockGemmARegBSmemCRegV1CustomPolicy<typename Problem::GemmDataType,
typename Problem::QDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm3BlockWarps,
WarpGemm>;
return BlockGemmARegBSmemCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
constexpr index_t smem_size_stage0_0 = smem_size_k + smem_size_kt;
constexpr index_t smem_size_stage0_1 = smem_size_v;
constexpr index_t smem_size_stage1 = smem_size_qt + smem_size_q + +smem_size_dot +
smem_size_do + smem_size_lse + smem_size_d +
max(smem_size_bias, smem_size_ds);
return max(smem_size_stage0_0, smem_size_stage0_1, smem_size_stage1);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSGradKTBlockGemm()
template <typename Problem_>
struct HotLoopScheduler
{
using BlockGemmProblem =
BlockGemmPipelineProblem<typename Problem::GemmDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
Problem::kBlockSize,
TileGemmShape<Problem::BlockFmhaShape::kM0,
Problem::BlockFmhaShape::kQKHeaddim,
Problem::BlockFmhaShape::kK4>>;
using Problem = Problem_;
using WarpGemm =
WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
Problem::BlockFmhaShape::Gemm4WarpTile::at(number<0>{}),
Problem::BlockFmhaShape::Gemm4WarpTile::at(number<1>{}),
Problem::BlockFmhaShape::Gemm4WarpTile::at(number<2>{}),
true>;
using BlockGemmPolicy =
BlockGemmASmemBSmemCRegV1CustomPolicy<typename Problem::GemmDataType,
typename Problem::KDataType,
typename Problem::AccDataType,
typename Problem::BlockFmhaShape::Gemm4BlockWarps,
WarpGemm>;
return BlockGemmASmemBSmemCRegV1<BlockGemmProblem, BlockGemmPolicy>{};
template <index_t GemmStage>
CK_TILE_DEVICE static constexpr void GemmStagedScheduler()
{
}
template <>
CK_TILE_DEVICE static constexpr void GemmStagedScheduler<0>()
{
// Mem: Q, LSE, OGrad, D global load, OGrad^T LDS load
// Comp: Q x K
constexpr index_t VMEM_READ_INST =
Q_VMEM_READ + OGrad_VMEM_READ + LSE_VMEM_READ + D_VMEM_READ;
constexpr index_t LDS_READ_INST = OGradT_LDS_READ;
constexpr index_t MFMA_INST = Gemm0MFMA;
// Evenly distributed to relieve SQ->TA FIFO pressure
constexpr index_t MFMA_PER_VMEM_READ = MFMA_INST / VMEM_READ_INST;
constexpr index_t MFMA_Remainder = MFMA_INST - MFMA_PER_VMEM_READ * VMEM_READ_INST;
// To hide instruction issue latency
constexpr index_t LDS_READ_PER_MFMA = LDS_READ_INST / MFMA_INST;
static_for<0, VMEM_READ_INST, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
static_for<0, MFMA_PER_VMEM_READ, 1>{}([&](auto j) {
ignore = j;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, LDS_READ_PER_MFMA, 0); // DS read
});
});
static_for<0, MFMA_Remainder, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, LDS_READ_PER_MFMA, 0); // DS read
});
}
template <>
CK_TILE_DEVICE static constexpr void GemmStagedScheduler<1>()
{
// Mem: Q^T LDS load
// Comp: OGrad x V
constexpr index_t LDS_READ_INST = QT_LDS_READ;
constexpr index_t MFMA_INST = Gemm1MFMA;
// To hide instruction issue latency
constexpr index_t LDS_READ_PER_MFMA = LDS_READ_INST / MFMA_INST;
static_for<0, MFMA_INST, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, LDS_READ_PER_MFMA, 0); // DS read
});
}
template <>
CK_TILE_DEVICE static constexpr void GemmStagedScheduler<2>()
{
// Mem: Q, QT, LSE, OGrad, OGradT, D, LDS store
// Comp: PT x OGrad
constexpr index_t LDS_WRITE_INST = Q_LDS_WRITE + QT_LDS_WRITE + OGrad_LDS_WRITE +
OGradT_LDS_WRITE + LSE_LDS_WRITE + D_LDS_WRITE;
constexpr index_t MFMA_INST = Gemm2MFMA;
// To hide instruction issue latency
constexpr index_t LDS_WRITE_PER_MFMA = LDS_WRITE_INST / MFMA_INST;
static_for<0, MFMA_INST, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, LDS_WRITE_PER_MFMA, 0); // DS write
});
}
template <>
CK_TILE_DEVICE static constexpr void GemmStagedScheduler<3>()
{
// Mem: SGradT LDS store, SGrad, Q, LSE LDS load.
// Comp: SGradT x QT
constexpr index_t LDS_WRITE_INST = SGradT_LDS_WRITE;
constexpr index_t LDS_READ_INST = SGradT_LDS_READ_P1 + Q_LDS_READ + LSE_LDS_READ;
constexpr index_t MFMA_INST = Gemm3MFMA;
// To hide instruction issue latency
constexpr index_t LDS_WRITE_PER_MFMA =
LDS_WRITE_INST / MFMA_INST >= 1 ? LDS_WRITE_INST / MFMA_INST : 1;
constexpr index_t MFMA_INST_LDS_WRITE = LDS_WRITE_INST / LDS_WRITE_PER_MFMA;
constexpr index_t LDS_READ_PER_MFMA =
(MFMA_INST - MFMA_INST_LDS_WRITE) > 0
? LDS_READ_INST / (MFMA_INST - MFMA_INST_LDS_WRITE) > 0
? LDS_READ_INST / (MFMA_INST - MFMA_INST_LDS_WRITE)
: 1
: 0;
static_for<0, MFMA_INST_LDS_WRITE, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, LDS_WRITE_PER_MFMA, 0); // DS Write
});
static_for<0, MFMA_INST - MFMA_INST_LDS_WRITE, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, LDS_READ_PER_MFMA, 0); // DS Read
});
}
template <>
CK_TILE_DEVICE static constexpr void GemmStagedScheduler<4>()
{
// Mem: SGrad, OGrad, D LDS load.
// Comp: SGrad x KT
constexpr index_t LDS_READ_INST = SGradT_LDS_READ_P2 + OGrad_LDS_READ + D_LDS_READ;
constexpr index_t MFMA_INST = Gemm4MFMA;
// To hide instruction issue latency
constexpr index_t LDS_READ_PER_MFMA =
LDS_READ_INST / MFMA_INST > 0 ? LDS_READ_INST / MFMA_INST : 1;
static_for<0, MFMA_INST, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, LDS_READ_PER_MFMA, 0); // DS Read
});
}
private:
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t kM0 = Problem::BlockFmhaShape::kM0;
static constexpr index_t kN0 = Problem::BlockFmhaShape::kN0;
static constexpr index_t kQKHeaddim = Problem::BlockFmhaShape::kQKHeaddim;
static constexpr index_t kVHeaddim = Problem::BlockFmhaShape::kVHeaddim;
static constexpr index_t kK4 = Problem::BlockFmhaShape::kK4;
static constexpr index_t WarpGemmM =
Problem::BlockFmhaShape::Gemm0WarpTile::at(number<0>{});
static constexpr index_t WarpGemmN =
Problem::BlockFmhaShape::Gemm0WarpTile::at(number<1>{});
static constexpr index_t WarpGemmK = WarpGemmM == 16 ? 16 : 8;
static constexpr index_t Gemm4MWarp =
Problem::BlockFmhaShape::Gemm4BlockWarps::at(number<0>{});
static constexpr index_t Gemm4NWarp =
Problem::BlockFmhaShape::Gemm4BlockWarps::at(number<1>{});
// Compute
static constexpr index_t Gemm0MFMA =
kM0 * kN0 * kQKHeaddim /
(kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
static constexpr index_t Gemm1MFMA =
kM0 * kN0 * kVHeaddim /
(kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
static constexpr index_t Gemm2MFMA =
kN0 * kVHeaddim * kM0 /
(kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
static constexpr index_t Gemm3MFMA =
kN0 * kQKHeaddim * kM0 /
(kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
static constexpr index_t Gemm4MFMA =
kM0 * kQKHeaddim * kN0 /
(kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
// VMEM
static constexpr index_t Q_VMEM_READ =
kM0 * kQKHeaddim / kBlockSize / GetAlignmentQ<Problem>();
static constexpr index_t OGrad_VMEM_READ =
kM0 * kVHeaddim / kBlockSize / GetAlignmentOGrad<Problem>();
static constexpr index_t LSE_VMEM_READ = 1;
static constexpr index_t D_VMEM_READ = 1;
// LDS Read
static constexpr index_t OGradT_LDS_READ =
kM0 * kVHeaddim / get_warp_size() / GetTransposedAlignmentOGrad<Problem>();
static constexpr index_t QT_LDS_READ =
kM0 * kQKHeaddim / get_warp_size() / GetTransposedAlignmentQ<Problem>();
static constexpr index_t SGradT_LDS_READ_P1 =
kM0 * kK4 / (get_warp_size() * Gemm4MWarp) / GetSmemKPackSGrad<Problem>();
static constexpr index_t Q_LDS_READ =
kM0 * kQKHeaddim / kBlockSize / GetAlignmentQ<Problem>();
static constexpr index_t LSE_LDS_READ = WarpGemmM == 16 ? kM0 / (4 * 4) : kM0 / (2 * 4);
static constexpr index_t SGradT_LDS_READ_P2 =
kM0 * (kN0 - kK4) / (get_warp_size() * Gemm4MWarp) / GetSmemKPackSGrad<Problem>();
static constexpr index_t OGrad_LDS_READ =
kM0 * kVHeaddim / kBlockSize / GetAlignmentOGrad<Problem>();
static constexpr index_t D_LDS_READ = WarpGemmM == 16 ? kM0 / (4 * 4) : kM0 / (2 * 4);
// LDS Write
static constexpr index_t Q_LDS_WRITE =
kM0 * kQKHeaddim / Problem::kBlockSize / GetAlignmentQ<Problem>();
static constexpr index_t QT_LDS_WRITE =
kM0 * kQKHeaddim / kBlockSize / GetTransposedAlignmentQ<Problem>();
static constexpr index_t OGrad_LDS_WRITE =
kM0 * kVHeaddim / kBlockSize / GetAlignmentOGrad<Problem>();
static constexpr index_t OGradT_LDS_WRITE =
kM0 * kVHeaddim / kBlockSize / GetTransposedAlignmentOGrad<Problem>();
static constexpr index_t LSE_LDS_WRITE = 1;
static constexpr index_t D_LDS_WRITE = 1;
static constexpr index_t SGradT_LDS_WRITE = kM0 * kN0 / kBlockSize;
};
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_enum.hpp
View file @ f84e2020
......@@ -8,9 +8,8 @@ namespace ck_tile {
// This class is used for codegen pattern matching
enum class BlockFmhaBwdPipelineEnum
{
KSKTSVR = 0,
QSKSVROGradS,
KSVR,
KRKTRVR_IGLP = 0,
KRKTRVR,
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_bwd_pipeline_problem.hpp
View file @ f84e2020
......@@ -24,7 +24,9 @@ template <typename QDataType_,
typename BiasGradDataType_,
typename BlockFmhaShape_,
bool kIsGroupMode_,
bool kIsDeterministic_,
typename FmhaMask_,
typename FmhaDropout_,
typename Traits_>
struct BlockFmhaBwdPipelineProblem
{
......@@ -45,10 +47,12 @@ struct BlockFmhaBwdPipelineProblem
using BiasGradDataType = remove_cvref_t<BiasGradDataType_>;
using BlockFmhaShape = remove_cvref_t<BlockFmhaShape_>;
using FmhaMask = remove_cvref_t<FmhaMask_>;
using FmhaDropout = remove_cvref_t<FmhaDropout_>;
using Traits = remove_cvref_t<Traits_>;
static constexpr index_t kBlockSize = BlockFmhaShape::NumWarps * get_warp_size();
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr bool kIsDeterministic = kIsDeterministic_;
// attributes from traits
static constexpr bool kPadSeqLenQ = Traits::kPadSeqLenQ;
......@@ -57,7 +61,6 @@ struct BlockFmhaBwdPipelineProblem
static constexpr bool kPadHeadDimV = Traits::kPadHeadDimV;
static constexpr auto BiasEnum = Traits::BiasEnum;
static constexpr bool kHasBiasGrad = Traits::kHasBiasGrad;
static constexpr bool kHasDropout = Traits::kHasDropout;
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
};
......@@ -88,4 +91,35 @@ struct BlockFmhaBwdOGradDotOPipelineProblem
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
};
template <typename AccDataType_,
typename QGradDataType_,
index_t kBlockSize_,
index_t kM0_,
index_t kN0_,
index_t kQKHeaddim_,
bool kIsGroupMode_,
bool kIsDeterministic_,
typename Traits_>
struct BlockFmhaBwdConvertQGradPipelineProblem
{
using AccDataType = remove_cvref_t<AccDataType_>;
using QGradDataType = remove_cvref_t<QGradDataType_>;
using Traits = remove_cvref_t<Traits_>;
static_assert(0 < kBlockSize_ && kBlockSize_ % get_warp_size() == 0,
"kBlockSize should be divisible by get_warp_size()");
static constexpr index_t kBlockSize = kBlockSize_;
static constexpr index_t kM0 = kM0_;
static constexpr index_t kN0 = kN0_;
static constexpr index_t kQKHeaddim = kQKHeaddim_;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr bool kIsDeterministic = kIsDeterministic_;
// attributes from traits
static constexpr bool kPadSeqLenQ = Traits::kPadSeqLenQ;
static constexpr bool kPadHeadDimQ = Traits::kPadHeadDimQ;
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/tile_fmha_traits.hpp
View file @ f84e2020
......@@ -86,4 +86,14 @@ struct TileFmhaBwdOGradDotOTraits
static constexpr index_t kBlockPerCu = kBlockPerCu_;
};
template <bool kPadSeqLenQ_ /* padding for seqlen_q */,
bool kPadHeadDimQ_ /* paddding for hdim_q */,
index_t kBlockPerCu_ = 2 /* hint to occupancy */>
struct TileFmhaBwdConvertQGradTraits
{
static constexpr bool kPadSeqLenQ = kPadSeqLenQ_;
static constexpr bool kPadHeadDimQ = kPadHeadDimQ_;
static constexpr index_t kBlockPerCu = kBlockPerCu_;
};
} // namespace ck_tile
include/ck_tile/ops/gemm.hpp
View file @ f84e2020
......@@ -5,6 +5,9 @@
#include "ck_tile/ops/gemm/block/block_gemm_areg_bgmem_creg_v1.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_bgmem_creg_v1_default_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1_custom_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1_default_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_bsmem_creg_v1.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_bsmem_creg_v1_custom_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_areg_bsmem_creg_v1_default_policy.hpp"
......
include/ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1.hpp 0 → 100644
View file @ f84e2020
// 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/gemm/block/block_gemm_areg_breg_creg_v1_default_policy.hpp"
namespace ck_tile {
// A is block distributed tensor
// B is block distributed tensor
// C is block distributed tensor
template <typename Problem_, typename Policy_ = BlockGemmARegBRegCRegV1DefaultPolicy>
struct BlockGemmARegBRegCRegV1
{
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using CDataType = remove_cvref_t<typename Problem::CDataType>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
static constexpr index_t kBlockSize = Problem::kBlockSize;
// C += A * B
template <typename CBlockTensor, typename ABlockTensor, typename BBlockTensor>
CK_TILE_DEVICE void operator()(CBlockTensor& c_block_tensor,
const ABlockTensor& a_block_tensor,
const BBlockTensor& b_block_tensor) const
{
static_assert(std::is_same_v<ADataType, remove_cv_t<typename ABlockTensor::DataType>> &&
std::is_same_v<BDataType, remove_cv_t<typename BBlockTensor::DataType>> &&
std::is_same_v<CDataType, remove_cv_t<typename CBlockTensor::DataType>>,
"wrong!");
constexpr index_t MPerBlock = BlockGemmShape::kM;
constexpr index_t NPerBlock = BlockGemmShape::kN;
constexpr index_t KPerBlock = BlockGemmShape::kK;
constexpr auto config = 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>();
constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WG::kM);
constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WG::kN);
constexpr index_t KIterPerWarp = KPerBlock / WG::kK;
// M->N Warp
constexpr auto a_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<KIterPerWarp>>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto b_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto c_block_outer_dstr_encoding = tile_distribution_encoding<
sequence<>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<NIterPerWarp, NWarp>>,
tuple<sequence<1, 2>>,
tuple<sequence<1, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto a_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
a_block_outer_dstr_encoding, typename WG::AWarpDstrEncoding{});
constexpr auto b_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
b_block_outer_dstr_encoding, typename WG::BWarpDstrEncoding{});
constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
c_block_outer_dstr_encoding, typename WG::CWarpDstrEncoding{});
// check ABC-block-distribution
static_assert(
std::is_same_v<remove_cvref_t<decltype(a_block_dstr_encode)>,
remove_cvref_t<decltype(ABlockTensor::get_tile_distribution()
.get_static_tile_distribution_encoding())>>,
"A distribution is wrong!");
static_assert(
std::is_same_v<remove_cvref_t<decltype(b_block_dstr_encode)>,
remove_cvref_t<decltype(BBlockTensor::get_tile_distribution()
.get_static_tile_distribution_encoding())>>,
"B distribution is wrong!");
static_assert(
std::is_same_v<remove_cvref_t<decltype(c_block_dstr_encode)>,
remove_cvref_t<decltype(CBlockTensor::get_tile_distribution()
.get_static_tile_distribution_encoding())>>,
"C distribution is wrong!");
using AWarpDstr = typename WG::AWarpDstr;
using BWarpDstr = typename WG::BWarpDstr;
using CWarpDstr = typename WG::CWarpDstr;
using AWarpTensor = typename WG::AWarpTensor;
using BWarpTensor = typename WG::BWarpTensor;
using CWarpTensor = typename WG::CWarpTensor;
constexpr auto a_warp_y_lengths =
to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
constexpr auto b_warp_y_lengths =
to_sequence(BWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
constexpr auto c_warp_y_lengths =
to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
constexpr auto b_warp_y_index_zeros = uniform_sequence_gen_t<BWarpDstr::NDimY, 0>{};
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
// hot loop:
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
// read A warp tensor from A Block window
AWarpTensor a_warp_tensor;
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
// read B warp tensor from B block tensor
BWarpTensor b_warp_tensor;
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
// read C warp tensor from C block tensor
CWarpTensor c_warp_tensor;
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
// warp GEMM
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
// write C warp tensor into C block tensor
c_block_tensor.set_y_sliced_thread_data(
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
c_warp_tensor.get_thread_buffer());
});
});
});
}
CK_TILE_DEVICE constexpr auto MakeCBlockTile() const
{
constexpr index_t MPerBlock = BlockGemmShape::kM;
constexpr index_t NPerBlock = BlockGemmShape::kN;
constexpr auto config = 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>();
constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WG::kM);
constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WG::kN);
// constexpr index_t KIterPerWarp = KPerBlock / WG::kK;
constexpr auto c_block_outer_dstr_encoding = tile_distribution_encoding<
sequence<>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<NIterPerWarp, NWarp>>,
tuple<sequence<1, 2>>,
tuple<sequence<1, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
c_block_outer_dstr_encoding, typename WG::CWarpDstrEncoding{});
constexpr auto c_block_dstr = make_static_tile_distribution(c_block_dstr_encode);
auto c_block_tensor = make_static_distributed_tensor<CDataType>(c_block_dstr);
return c_block_tensor;
}
// C = A * B
template <typename ABlockTensor, typename BBlockTensor>
CK_TILE_DEVICE auto operator()(const ABlockTensor& a_block_tensor,
const BBlockTensor& b_block_tensor) const
{
auto c_block_tensor = MakeCBlockTile();
operator()(c_block_tensor, a_block_tensor, b_block_tensor);
return c_block_tensor;
}
};
} // namespace ck_tile
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