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Unverified Commit 171ed358 authored by Illia Silin's avatar Illia Silin Committed by GitHub
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Merge pull request #148 from ROCm/merge_from_public 

Merge from public
parents 829e0eb3 e536d321
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Showing with 1448 additions and 1102 deletions
include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_kernel.hpp
View file @ 171ed358
......@@ -32,8 +32,6 @@ struct FmhaFwdSplitKVKernel
using KDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::KDataType>;
using VDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::VDataType>;
using BiasDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::BiasDataType>;
using RandValOutputDataType =
ck_tile::remove_cvref_t<typename FmhaPipeline::RandValOutputDataType>;
using LSEDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::LSEDataType>;
using SaccDataType = ck_tile::remove_cvref_t<typename FmhaPipeline::SaccDataType>;
using OaccDataType = remove_cvref_t<typename FmhaPipeline::OaccDataType>;
......@@ -46,8 +44,10 @@ struct FmhaFwdSplitKVKernel
static constexpr bool kPadHeadDimQ = FmhaPipeline::kPadHeadDimQ;
static constexpr bool kPadHeadDimV = FmhaPipeline::kPadHeadDimV;
static constexpr auto BiasEnum = FmhaPipeline::BiasEnum;
static constexpr bool kHasDropout = FmhaPipeline::kHasDropout;
static constexpr bool kDoFp8StaticQuant = FmhaPipeline::Problem::kDoFp8StaticQuant;
static constexpr bool kIsPagedKV = FmhaPipeline::Problem::kIsPagedKV;
static_assert(!kIsGroupMode || (kIsGroupMode && !kIsPagedKV),
"paged-kvcache only supported by batch mode kernels");
using FmhaMask = ck_tile::remove_cvref_t<typename FmhaPipeline::FmhaMask>;
static constexpr bool kHasMask = FmhaMask::IsMasking;
......@@ -86,7 +86,7 @@ struct FmhaFwdSplitKVKernel
(kBlockPerCuInput == -1 ? "" : ("o" + _TS_(kBlockPerCu) + "_")) + _SS_(FmhaPipeline::name) + "_" +
"v" + (std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor> ? "r" : "c") + (pn.empty() ? "" : "_" + pn) +
(BiasEnum == BlockAttentionBiasEnum::NO_BIAS ? _SS_("") : (_SS_("_") + BlockAttentionBiasEnumToStr<BiasEnum>::name)) +
(kHasMask ? "_" + _SS_(FmhaMask::name) : "") + (kHasDropout ? "_dropout" : "" ) + (kDoFp8StaticQuant ? "_squant" : "" );
(kHasMask ? "_" + _SS_(FmhaMask::name) : "") + (kDoFp8StaticQuant ? "_squant" : "") + (kIsPagedKV ? "_pagedkv" : "" );
#undef _SS_
#undef _TS_
// clang-format on
......@@ -110,7 +110,6 @@ struct FmhaFwdSplitKVKernel
void* o_acc_ptr;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
......@@ -136,6 +135,7 @@ 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;
......@@ -173,32 +173,16 @@ struct FmhaFwdSplitKVKernel
float scale_p;
};
struct CommonDropoutKargs
{
void init_dropout(const float p_drop,
const std::tuple<uint64_t, uint64_t>& drop_seed_offset)
struct PageBlockTableKargs
{
float p_undrop = 1.0 - p_drop;
p_undrop_in_uint8_t =
uint8_t(std::floor(p_undrop * std::numeric_limits<uint8_t>::max()));
rp_undrop = 1.0 / p_undrop;
drop_seed = std::get<0>(drop_seed_offset);
drop_offset = std::get<1>(drop_seed_offset);
}
float rp_undrop = 1;
uint8_t p_undrop_in_uint8_t = std::numeric_limits<uint8_t>::max();
bool is_store_randval = false;
uint64_t drop_seed = 1;
uint64_t drop_offset = 0;
void* rand_val_ptr = nullptr;
ck_tile::index_t stride_randval = 0;
ck_tile::index_t nhead_stride_randval = 0;
const int32_t* block_table_ptr;
ck_tile::index_t batch_stride_block_table;
ck_tile::index_t page_block_size;
};
struct BatchModeDropoutKargs : CommonDropoutKargs
struct CacheBatchIdxKargs
{
ck_tile::index_t batch_stride_randval = 0;
const int32_t* cache_batch_idx;
};
struct BatchModeKargs
......@@ -210,12 +194,13 @@ struct FmhaFwdSplitKVKernel
EmptyKargs<0>>>,
std::conditional_t<kHasMask, MaskKargs, EmptyKargs<1>>,
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<2>>,
std::conditional_t<kHasDropout, BatchModeDropoutKargs, EmptyKargs<3>>
std::conditional_t<kIsPagedKV, PageBlockTableKargs, CacheBatchIdxKargs>
{
const int32_t* seqlen_k_ptr;
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
......@@ -226,12 +211,14 @@ struct FmhaFwdSplitKVKernel
AlibiKargs,
EmptyKargs<0>>>,
std::conditional_t<kHasMask, MaskKargs, EmptyKargs<1>>,
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<2>>,
std::conditional_t<kHasDropout, CommonDropoutKargs, EmptyKargs<3>>
std::conditional_t<kDoFp8StaticQuant, Fp8StaticQuantKargs, EmptyKargs<2>>
{
const int32_t* seqstart_q_ptr;
const int32_t* seqstart_k_ptr;
const int32_t* seqlen_k_ptr;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
};
using Kargs = std::conditional_t<kIsGroupMode, GroupModeKargs, BatchModeKargs>;
......@@ -242,48 +229,45 @@ struct FmhaFwdSplitKVKernel
const void* k_ptr,
const void* v_ptr,
const void* bias_ptr,
void* rand_val_ptr,
void* lse_acc_ptr,
void* o_acc_ptr,
ck_tile::index_t batch,
ck_tile::index_t max_seqlen_q,
ck_tile::index_t seqlen_q,
ck_tile::index_t seqlen_k,
ck_tile::index_t seqlen_k, // only used if 'seqlen_k_ptr' is not specified
const void* seqlen_k_ptr, // only used for (paged-) kvcache
ck_tile::index_t hdim_q,
ck_tile::index_t hdim_v,
ck_tile::index_t num_head_q,
ck_tile::index_t nhead_ratio_qk,
ck_tile::index_t num_splits,
const void* block_table_ptr,
ck_tile::index_t batch_stride_block_table,
ck_tile::index_t page_block_size,
const void* cache_batch_idx,
float scale_s,
float scale_p,
ck_tile::index_t stride_q,
ck_tile::index_t stride_k,
ck_tile::index_t stride_v,
ck_tile::index_t stride_bias,
ck_tile::index_t stride_randval,
ck_tile::index_t stride_o_acc,
ck_tile::index_t nhead_stride_q,
ck_tile::index_t nhead_stride_k,
ck_tile::index_t nhead_stride_v,
ck_tile::index_t nhead_stride_bias,
ck_tile::index_t nhead_stride_randval,
ck_tile::index_t nhead_stride_lse_acc,
ck_tile::index_t nhead_stride_o_acc,
ck_tile::index_t batch_stride_q,
ck_tile::index_t batch_stride_k,
ck_tile::index_t batch_stride_v,
ck_tile::index_t batch_stride_bias,
ck_tile::index_t batch_stride_randval,
ck_tile::index_t batch_stride_lse_acc,
ck_tile::index_t batch_stride_o_acc,
ck_tile::index_t split_stride_lse_acc,
ck_tile::index_t split_stride_o_acc,
ck_tile::index_t window_size_left,
ck_tile::index_t window_size_right,
ck_tile::index_t mask_type,
float p_drop,
bool s_randval,
const std::tuple<uint64_t, uint64_t>& drop_seed_offset)
ck_tile::index_t mask_type)
{
Kargs kargs{{q_ptr,
k_ptr,
......@@ -291,7 +275,6 @@ struct FmhaFwdSplitKVKernel
lse_acc_ptr,
o_acc_ptr,
batch,
max_seqlen_q,
seqlen_q,
seqlen_k,
hdim_q,
......@@ -313,17 +296,18 @@ 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
{}, // placeholder for bias
{}, // placeholder for mask
{}, // placeholder for fp8_static_quant args
{}, // placeholder for dropout
{}, // placeholder for paged-block table or cache_batch_idx
reinterpret_cast<const int32_t*>(seqlen_k_ptr),
batch_stride_q,
batch_stride_k,
batch_stride_v,
batch_stride_lse_acc};
batch_stride_v};
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
......@@ -347,14 +331,15 @@ struct FmhaFwdSplitKVKernel
{
kargs.scale_p = scale_p;
}
if constexpr(kHasDropout)
if constexpr(kIsPagedKV)
{
kargs.block_table_ptr = reinterpret_cast<const int32_t*>(block_table_ptr);
kargs.batch_stride_block_table = batch_stride_block_table;
kargs.page_block_size = page_block_size;
}
else
{
kargs.init_dropout(p_drop, drop_seed_offset);
kargs.rand_val_ptr = rand_val_ptr;
kargs.stride_randval = stride_randval;
kargs.nhead_stride_randval = nhead_stride_randval;
kargs.batch_stride_randval = batch_stride_randval;
kargs.is_store_randval = s_randval;
kargs.cache_batch_idx = reinterpret_cast<const int32_t*>(cache_batch_idx);
}
return kargs;
......@@ -366,11 +351,9 @@ struct FmhaFwdSplitKVKernel
const void* k_ptr,
const void* v_ptr,
const void* bias_ptr,
void* rand_val_ptr,
void* lse_acc_ptr,
void* o_acc_ptr,
ck_tile::index_t batch,
ck_tile::index_t max_seqlen_q,
const void* seqstart_q_ptr,
const void* seqstart_k_ptr,
const void* seqlen_k_ptr,
......@@ -385,24 +368,22 @@ struct FmhaFwdSplitKVKernel
ck_tile::index_t stride_k,
ck_tile::index_t stride_v,
ck_tile::index_t stride_bias,
ck_tile::index_t stride_randval,
ck_tile::index_t stride_o_acc,
ck_tile::index_t nhead_stride_q,
ck_tile::index_t nhead_stride_k,
ck_tile::index_t nhead_stride_v,
ck_tile::index_t nhead_stride_bias,
ck_tile::index_t nhead_stride_randval,
ck_tile::index_t nhead_stride_lse_acc,
ck_tile::index_t nhead_stride_o_acc,
ck_tile::index_t batch_stride_k,
ck_tile::index_t batch_stride_v,
ck_tile::index_t batch_stride_lse_acc,
ck_tile::index_t batch_stride_o_acc,
ck_tile::index_t split_stride_lse_acc,
ck_tile::index_t split_stride_o_acc,
ck_tile::index_t window_size_left,
ck_tile::index_t window_size_right,
ck_tile::index_t mask_type,
float p_drop,
bool s_randval,
const std::tuple<uint64_t, uint64_t>& drop_seed_offset)
ck_tile::index_t mask_type)
{
Kargs kargs{{q_ptr,
k_ptr,
......@@ -410,9 +391,8 @@ struct FmhaFwdSplitKVKernel
lse_acc_ptr,
o_acc_ptr,
batch,
max_seqlen_q,
-1, // seqlen will be updated by another pointer
-1, //
-1, // seqlen_q will be updated by another pointer
-1, // seqlen_k will be updated by another pointer
hdim_q,
hdim_v,
num_head_q,
......@@ -432,16 +412,18 @@ 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
{}, // placeholder for bias
{}, // placeholder for mask
{}, // placeholder for fp8_static_quant args
{}, // placeholder for dropout
reinterpret_cast<const int32_t*>(seqstart_q_ptr),
reinterpret_cast<const int32_t*>(seqstart_k_ptr),
reinterpret_cast<const int32_t*>(seqlen_k_ptr)};
reinterpret_cast<const int32_t*>(seqlen_k_ptr),
batch_stride_k,
batch_stride_v};
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
......@@ -464,14 +446,6 @@ struct FmhaFwdSplitKVKernel
{
kargs.scale_p = scale_p;
}
if constexpr(kHasDropout)
{
kargs.init_dropout(p_drop, drop_seed_offset);
kargs.rand_val_ptr = rand_val_ptr;
kargs.stride_randval = stride_randval;
kargs.nhead_stride_randval = nhead_stride_randval;
kargs.is_store_randval = s_randval;
}
return kargs;
}
......@@ -508,7 +482,6 @@ struct FmhaFwdSplitKVKernel
long_index_t batch_offset_k = 0;
long_index_t batch_offset_v = 0;
long_index_t batch_offset_bias = 0;
long_index_t batch_offset_randval = 0;
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;
......@@ -534,14 +507,9 @@ struct FmhaFwdSplitKVKernel
{
batch_offset_bias = query_start * kargs.stride_bias + key_start;
}
if constexpr(kHasDropout)
{
batch_offset_randval = query_start * kargs.stride_randval;
}
// get real # queries & # keys under group mode
const auto adjusted_seqstart_q_ptr = kargs.seqstart_q_ptr + i_batch;
kargs.seqlen_q = adjusted_seqstart_q_ptr[1] - adjusted_seqstart_q_ptr[0];
kargs.seqlen_q = kargs.seqstart_q_ptr[i_batch + 1] - kargs.seqstart_q_ptr[i_batch];
// # of required blocks is different in each groups, terminate unnecessary blocks
// earlier
......@@ -556,24 +524,36 @@ struct FmhaFwdSplitKVKernel
}
else
{
const auto adjusted_seqstart_k_ptr = kargs.seqstart_k_ptr + i_batch;
kargs.seqlen_k = adjusted_seqstart_k_ptr[1] - adjusted_seqstart_k_ptr[0];
kargs.seqlen_k = kargs.seqstart_k_ptr[i_batch + 1] - kargs.seqstart_k_ptr[i_batch];
}
}
else
{
const index_t i_cache_batch = [&, i_batch_ = i_batch] {
if constexpr(kIsPagedKV)
{
return i_batch_;
}
else
{
return (kargs.cache_batch_idx != nullptr ? kargs.cache_batch_idx[i_batch_]
: i_batch_);
}
}();
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_k = static_cast<long_index_t>(i_cache_batch) * kargs.batch_stride_k;
batch_offset_v = static_cast<long_index_t>(i_cache_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;
}
if constexpr(kHasDropout)
if(kargs.seqlen_k_ptr != nullptr)
{
batch_offset_randval =
static_cast<long_index_t>(i_batch) * kargs.batch_stride_randval;
kargs.seqlen_k = kargs.seqlen_k_ptr[i_batch];
}
}
......@@ -589,6 +569,7 @@ struct FmhaFwdSplitKVKernel
reinterpret_cast<const VDataType*>(kargs.v_ptr) +
static_cast<long_index_t>(i_nhead / kargs.nhead_ratio_qk) * kargs.nhead_stride_v +
batch_offset_v;
OaccDataType* o_acc_ptr = reinterpret_cast<OaccDataType*>(kargs.o_acc_ptr) +
static_cast<long_index_t>(i_nhead) * kargs.nhead_stride_o_acc +
batch_offset_o_acc + i_split * kargs.split_stride_o_acc;
......@@ -616,10 +597,11 @@ struct FmhaFwdSplitKVKernel
sequence<kPadSeqLenQ, kPadHeadDimQ>{});
}
}();
const auto k_dram = [&]() {
const auto make_k_dram = [&](const KDataType* data, index_t height) {
const auto k_dram_naive = make_naive_tensor_view<address_space_enum::global>(
k_ptr,
make_tuple(kargs.seqlen_k, kargs.hdim_q),
data, // will update this pointer if using paged-kvcache
make_tuple(height, kargs.hdim_q),
make_tuple(kargs.stride_k, 1),
number<FmhaPipeline::kAlignmentK>{},
number<1>{});
......@@ -628,13 +610,24 @@ struct FmhaFwdSplitKVKernel
k_dram_naive,
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{}),
sequence<kPadSeqLenK, kPadHeadDimQ>{});
};
const auto k_dram = [&]() {
if constexpr(kIsPagedKV)
{
return make_k_dram(nullptr, kargs.page_block_size);
}
else
{
return make_k_dram(k_ptr, kargs.seqlen_k);
}
}();
const auto v_dram = [&]() {
const auto make_v_dram = [&](const VDataType* data, index_t length) {
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
const auto v_dram_naive = make_naive_tensor_view<address_space_enum::global>(
v_ptr,
make_tuple(kargs.seqlen_k, kargs.hdim_v),
data, // will update this pointer if using paged-kvcache
make_tuple(length, kargs.hdim_v),
make_tuple(kargs.stride_v, 1),
number<FmhaPipeline::kAlignmentV>{},
number<1>{});
......@@ -642,7 +635,7 @@ struct FmhaFwdSplitKVKernel
const auto v_dram_transposed =
transform_tensor_view(v_dram_naive,
make_tuple(make_pass_through_transform(kargs.hdim_v),
make_pass_through_transform(kargs.seqlen_k)),
make_pass_through_transform(length)),
make_tuple(sequence<1>{}, sequence<0>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
......@@ -654,8 +647,8 @@ struct FmhaFwdSplitKVKernel
else
{
const auto v_dram_naive = make_naive_tensor_view<address_space_enum::global>(
v_ptr,
make_tuple(kargs.hdim_v, kargs.seqlen_k),
data, // will update this pointer if using paged-kvcache
make_tuple(kargs.hdim_v, length),
make_tuple(kargs.stride_v, 1),
number<FmhaPipeline::kAlignmentV>{},
number<1>{});
......@@ -665,6 +658,76 @@ struct FmhaFwdSplitKVKernel
make_tuple(number<FmhaPipeline::kN1>{}, number<FmhaPipeline::kK1>{}),
sequence<kPadHeadDimV, kPadSeqLenK>{});
}
};
const auto v_dram = [&]() {
if constexpr(kIsPagedKV)
{
return make_v_dram(nullptr, kargs.page_block_size);
}
else
{
return make_v_dram(v_ptr, kargs.seqlen_k);
}
}();
auto k_page_block_navigator = [&, i_batch_ = i_batch, i_nhead_ = i_nhead]() {
if constexpr(kIsPagedKV)
{
const auto* block_indices =
reinterpret_cast<const int32_t*>(kargs.block_table_ptr) +
i_batch_ * kargs.batch_stride_block_table;
const index_t num_blocks =
integer_divide_ceil(kargs.seqlen_k, kargs.page_block_size);
const long_index_t fixed_offset =
static_cast<long_index_t>(i_nhead_ / kargs.nhead_ratio_qk) *
kargs.nhead_stride_k;
return make_page_block_navigator<const KDataType, 0>(
kargs.k_ptr,
kargs.batch_stride_k,
fixed_offset,
block_indices,
num_blocks,
kargs.page_block_size,
k_dram,
make_k_dram(nullptr,
kargs.seqlen_k - (num_blocks - 1) * kargs.page_block_size));
}
else
{
return make_page_block_navigator(k_dram);
}
}();
auto v_page_block_navigator = [&, i_batch_ = i_batch, i_nhead_ = i_nhead]() {
if constexpr(kIsPagedKV)
{
const auto* block_indices =
reinterpret_cast<const int32_t*>(kargs.block_table_ptr) +
i_batch_ * kargs.batch_stride_block_table;
const index_t num_blocks =
integer_divide_ceil(kargs.seqlen_k, kargs.page_block_size);
const long_index_t fixed_offset =
static_cast<long_index_t>(i_nhead_ / kargs.nhead_ratio_qk) *
kargs.nhead_stride_v;
return make_page_block_navigator<const VDataType, 1>(
kargs.v_ptr,
kargs.batch_stride_v,
fixed_offset,
block_indices,
num_blocks,
kargs.page_block_size,
v_dram,
make_v_dram(nullptr,
kargs.seqlen_k - (num_blocks - 1) * kargs.page_block_size));
}
else
{
return make_page_block_navigator(v_dram);
}
}();
auto q_dram_window = make_tile_window(
......@@ -678,13 +741,11 @@ struct FmhaFwdSplitKVKernel
}(),
{i_m0, 0});
auto k_dram_window = make_tile_window(
k_dram, make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{}), {0, 0});
auto k_dram_window_lengths =
make_tuple(number<FmhaPipeline::kN0>{}, number<FmhaPipeline::kK0>{});
auto v_dram_window_lengths =
make_tuple(number<FmhaPipeline::kN1>{}, number<FmhaPipeline::kK1>{});
auto v_dram_window =
make_tile_window(v_dram,
make_tuple(number<FmhaPipeline::kN1>{}, number<FmhaPipeline::kK1>{}),
{i_n1, 0});
/// FIXME: Before C++20, capturing structured binding variables are not supported. Remove
/// following copy capture of the 'i_nhead' if in C++20
const auto bias_dram_window = [&, i_nhead_ = i_nhead]() {
......@@ -741,62 +802,6 @@ struct FmhaFwdSplitKVKernel
return make_tile_window(lse_acc_dram, lse_acc_dram_window_lengths, {i_m0});
}();
// dropout
float rp_undrop = 1;
uint8_t p_undrop_in_uint8_t = std::numeric_limits<uint8_t>::max();
uint64_t drop_seed = 0;
uint64_t drop_offset = 0;
bool is_store_randval = false;
if constexpr(kHasDropout)
{
rp_undrop = kargs.rp_undrop;
p_undrop_in_uint8_t = kargs.p_undrop_in_uint8_t;
drop_seed = kargs.drop_seed;
drop_offset = kargs.drop_offset;
is_store_randval = kargs.is_store_randval;
}
BlockDropout dropout(i_batch,
i_nhead,
kargs.num_head_q,
drop_seed,
drop_offset,
rp_undrop,
p_undrop_in_uint8_t,
is_store_randval);
auto randval_dram_window = [&, i_nhead_ = i_nhead]() {
constexpr auto randval_dram_window_lengths =
make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kN0>{});
if constexpr(kHasDropout)
{
RandValOutputDataType* rand_val_ptr =
reinterpret_cast<RandValOutputDataType*>(kargs.rand_val_ptr) +
static_cast<long_index_t>(i_nhead_) * kargs.nhead_stride_randval +
batch_offset_randval;
const auto randval_dram = [&]() {
const auto randval_dram_naive =
make_naive_tensor_view<address_space_enum::global>(
rand_val_ptr,
make_tuple(kargs.seqlen_q, kargs.seqlen_k),
make_tuple(kargs.stride_randval, 1),
number<1>{},
number<1>{});
return pad_tensor_view(randval_dram_naive,
randval_dram_window_lengths,
sequence<kPadSeqLenQ, kPadSeqLenK>{});
}();
return make_tile_window(randval_dram, randval_dram_window_lengths, {i_m0, 0});
}
else
{
return make_null_tile_window(randval_dram_window_lengths);
}
}();
FmhaMask mask = [&]() {
if constexpr(kHasMask)
return ck_tile::make_generic_attention_mask_from_lr_window<FmhaMask>(
......@@ -823,7 +828,7 @@ struct FmhaFwdSplitKVKernel
#endif
if constexpr(kHasMask)
{
return make_alibi_from_lr_mask<SaccDataType, true>(slope,
return make_alibi_from_lr_mask<SaccDataType, true, 32>(slope,
kargs.window_size_left,
kargs.window_size_right,
kargs.seqlen_q,
......@@ -832,7 +837,7 @@ struct FmhaFwdSplitKVKernel
}
else
{
return Alibi<SaccDataType, true>{
return Alibi<SaccDataType, true, 32>{
slope, kargs.seqlen_q, kargs.seqlen_k, AlibiMode::FROM_BOTTOM_RIGHT};
}
}
......@@ -847,13 +852,14 @@ struct FmhaFwdSplitKVKernel
{
return FmhaPipeline{}(q_dram_window,
identity{}, // q_element_func
k_dram_window,
k_dram_window_lengths,
k_page_block_navigator,
identity{}, // k_element_func
v_dram_window,
v_dram_window_lengths,
v_page_block_navigator,
identity{}, // v_element_func
bias_dram_window,
identity{}, // bias_element_func
randval_dram_window,
lse_acc_dram_window,
identity{}, // lse_element_func
identity{}, // s_acc_element_func
......@@ -864,24 +870,23 @@ struct FmhaFwdSplitKVKernel
mask,
position_encoding,
kargs.scale_s,
smem_ptr,
dropout);
smem_ptr);
}
else
{
return FmhaPipeline{}(q_dram_window,
k_dram_window,
v_dram_window,
k_dram_window_lengths,
k_page_block_navigator,
v_dram_window_lengths,
v_page_block_navigator,
bias_dram_window,
randval_dram_window,
lse_acc_dram_window,
kargs.num_splits,
i_split_,
mask,
position_encoding,
kargs.scale_s,
smem_ptr,
dropout);
smem_ptr);
}
}();
......
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_appendkv_pipeline.hpp 0 → 100644
View file @ 171ed358
// 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_rotary_embedding.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_fwd_appendkv_pipeline_default_policy.hpp"
namespace ck_tile {
template <typename Problem_, typename Policy_ = BlockFmhaFwdAppendKVPipelineDefaultPolicy>
struct BlockFmhaFwdAppendKVPipeline
{
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using QDataType = typename Problem::QDataType;
using KDataType = typename Problem::KDataType;
using VDataType = typename Problem::VDataType;
using VLayout = typename Problem::VLayout;
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t kM0 = Problem::kM0;
static constexpr index_t kN0 = Problem::kN0;
static constexpr index_t kK0 = Problem::kK0;
static constexpr index_t kN1 = Problem::kN1;
static constexpr auto RotaryEnum = Problem::RotaryEnum;
static constexpr bool kIsPagedKV = Problem::kIsPagedKV;
static constexpr bool kPadSeqLenQ = Problem::kPadSeqLenQ;
static constexpr bool kPadSeqLenK = Problem::kPadSeqLenK;
static constexpr bool kPadHeadDimQ = Problem::kPadHeadDimQ;
static constexpr bool kPadHeadDimV = Problem::kPadHeadDimV;
// last dimension vector length used to create tensor view(and decide buffer_load vector length)
// ... together with tensor distribution. tensor dist should able to overwrite this
static constexpr index_t kAlignmentQ =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentQ<Problem>();
static constexpr index_t kAlignmentK =
kPadHeadDimQ ? 1 : Policy::template GetAlignmentK<Problem>();
static constexpr index_t kAlignmentV = []() {
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
return kPadHeadDimV ? 1 : Policy::template GetAlignmentV<Problem>();
else
return kPadSeqLenK ? 1 : Policy::template GetAlignmentV<Problem>();
}();
static constexpr index_t kBlockPerCu = []() {
if constexpr(Problem::kBlockPerCu != -1)
return Problem::kBlockPerCu;
else
{
if constexpr(kK0 <= 32)
{
return 2;
}
else if constexpr(kK0 <= 64)
{
return 3;
}
else if constexpr(kK0 <= 128)
{
return 2;
}
else if constexpr(kK0 <= 256)
{
return 1;
}
}
}();
template <typename QDramBlockWindow,
typename KDramBlockWindow,
typename KPageBlockNavigator,
typename KnewDramBlockWindow,
typename VDramBlockWindow,
typename VPageBlockNavigator,
typename VnewDramBlockWindow,
typename QElementFunction,
typename KnewElementFunction,
typename VnewElementFunction,
typename QRotaryCosDramBlockWindow,
typename QRotarySinDramBlockWindow,
typename KnewRotaryCosDramBlockWindow,
typename KnewRotarySinDramBlockWindow>
CK_TILE_HOST_DEVICE auto
operator()(QDramBlockWindow& q_dram_block_window, // M0*K0 tile
const QElementFunction& q_element_func,
KDramBlockWindow& k_dram_block_window, // N0*K0 tile
index_t i_page_block_k,
const KPageBlockNavigator& k_page_block_navigator,
const KnewDramBlockWindow& knew_dram_block_window, // N0*K0 tile
const KnewElementFunction& knew_element_func,
VDramBlockWindow& v_dram_block_window, // N1*N0 tile
index_t i_page_block_v,
const VPageBlockNavigator& v_page_block_navigator,
const VnewDramBlockWindow& vnew_dram_block_window, // N1*N0 tile
const VnewElementFunction& vnew_element_func,
const QRotaryCosDramBlockWindow q_rotary_cos_dram_block_window,
const QRotarySinDramBlockWindow q_rotary_sin_dram_block_window,
const KnewRotaryCosDramBlockWindow knew_rotary_cos_dram_block_window,
const KnewRotarySinDramBlockWindow knew_rotary_sin_dram_block_window,
index_t rotary_dim,
bool skip_rotate_q,
bool skip_rotate_append_kv) const
{
if(!skip_rotate_append_kv)
{
// append Knew to K
auto knew_window = make_tile_window(
knew_dram_block_window, Policy::template MakeKnewDramTileDistribution<Problem>());
auto knew_tile = [&]() {
auto knew = load_tile(knew_window);
return tile_elementwise_in(knew_element_func, knew);
}();
// optionally apply rotary embedding to Knew
if constexpr(RotaryEnum != RotaryEmbeddingEnum::NONE)
{
auto rotary_cos_window =
make_tile_window(knew_rotary_cos_dram_block_window,
Policy::template MakeRotaryCosSinTileDistribution<
Problem,
/*IsRotaryCosSinForQ=*/false>());
auto rotary_sin_window =
make_tile_window(knew_rotary_sin_dram_block_window,
Policy::template MakeRotaryCosSinTileDistribution<
Problem,
/*IsRotaryCosSinForQ=*/false>());
// We assume that each thread owns contiguous elements on head dimention. And we
// will use the distribution to enable/disable threads in order to override partial
// knew_tile content
auto [thread_start, thread_end] =
Policy::template GetKnewThreadRangeAlongK<Problem>();
ignore = thread_start;
BlockRotaryEmbedding<RotaryEnum>::apply(knew_tile,
knew_window,
rotary_cos_window,
rotary_sin_window,
rotary_dim,
thread_end);
}
store_tile(k_dram_block_window, knew_tile);
// write tile to another block if nesscary
if constexpr(kIsPagedKV)
{
if(k_page_block_navigator.is_cross_block(i_page_block_k, k_dram_block_window))
{
k_page_block_navigator.move_to_block(
i_page_block_k, k_dram_block_window, i_page_block_k + 1);
store_tile(k_dram_block_window, knew_tile);
}
}
// append Vnew to V
auto vnew_window = make_tile_window(
vnew_dram_block_window, Policy::template MakeVnewDramTileDistribution<Problem>());
auto vnew_tile = [&]() {
auto vnew = load_tile(vnew_window);
return tile_elementwise_in(vnew_element_func, vnew);
}();
store_tile(v_dram_block_window, vnew_tile);
// write tile to another block if nesscary
if constexpr(kIsPagedKV)
{
if(v_page_block_navigator.is_cross_block(i_page_block_v, v_dram_block_window))
{
v_page_block_navigator.move_to_block(
i_page_block_v, v_dram_block_window, i_page_block_v + 1);
store_tile(v_dram_block_window, vnew_tile);
}
}
}
if(!skip_rotate_q)
{
// optionally apply rotary embedding to Q
if constexpr(RotaryEnum != RotaryEmbeddingEnum::NONE)
{
auto q_window = make_tile_window(
q_dram_block_window, Policy::template MakeQDramTileDistribution<Problem>());
auto q_tile = [&]() {
auto q = load_tile(q_window);
return tile_elementwise_in(q_element_func, q);
}();
auto rotary_cos_window =
make_tile_window(q_rotary_cos_dram_block_window,
Policy::template MakeRotaryCosSinTileDistribution<
Problem,
/*IsRotaryCosSinForQ=*/true>());
auto rotary_sin_window =
make_tile_window(q_rotary_sin_dram_block_window,
Policy::template MakeRotaryCosSinTileDistribution<
Problem,
/*IsRotaryCosSinForQ=*/true>());
// We assume that each thread owns contiguous elements on head dimention. And we
// will use the distribution to enable/disable threads in order to override partial
// q_tile content
auto [thread_start, thread_end] = Policy::template GetQThreadRangeAlongK<Problem>();
ignore = thread_start;
BlockRotaryEmbedding<RotaryEnum>::apply(
q_tile, q_window, rotary_cos_window, rotary_sin_window, rotary_dim, thread_end);
store_tile(q_dram_block_window, q_tile);
}
}
}
template <typename QDramBlockWindow,
typename KDramBlockWindow,
typename KPageBlockNavigator,
typename KnewDramBlockWindow,
typename VDramBlockWindow,
typename VPageBlockNavigator,
typename VnewDramBlockWindow,
typename QRotaryCosDramBlockWindow,
typename QRotarySinDramBlockWindow,
typename KnewRotaryCosDramBlockWindow,
typename KnewRotarySinDramBlockWindow>
CK_TILE_HOST_DEVICE auto
operator()(QDramBlockWindow& q_dram_block_window,
KDramBlockWindow& k_dram_block_window,
index_t i_page_block_k,
const KPageBlockNavigator& k_page_block_navigator,
const KnewDramBlockWindow& knew_dram_block_window,
VDramBlockWindow& v_dram_block_window,
index_t i_page_block_v,
const VPageBlockNavigator& v_page_block_navigator,
const VnewDramBlockWindow& vnew_dram_block_window,
const QRotaryCosDramBlockWindow& q_rotary_cos_dram_block_window,
const QRotarySinDramBlockWindow& q_rotary_sin_dram_block_window,
const KnewRotaryCosDramBlockWindow& knew_rotary_cos_dram_block_window,
const KnewRotarySinDramBlockWindow& knew_rotary_sin_dram_block_window,
index_t rotary_dim,
bool skip_rotate_q,
bool skip_rotate_append_kv) const
{
return operator()(q_dram_block_window,
identity{},
k_dram_block_window,
i_page_block_k,
k_page_block_navigator,
knew_dram_block_window,
identity{},
v_dram_block_window,
i_page_block_v,
v_page_block_navigator,
vnew_dram_block_window,
identity{},
q_rotary_cos_dram_block_window,
q_rotary_sin_dram_block_window,
knew_rotary_cos_dram_block_window,
knew_rotary_sin_dram_block_window,
rotary_dim,
skip_rotate_q,
skip_rotate_append_kv);
}
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_appendkv_pipeline_default_policy.hpp 0 → 100644
View file @ 171ed358
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
// This pipeline is qkv all located in LDS
struct BlockFmhaFwdAppendKVPipelineDefaultPolicy
{
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentQ()
{
using QDataType = remove_cvref_t<typename Problem::QDataType>;
return 16 / sizeof(QDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentK()
{
using KDataType = remove_cvref_t<typename Problem::KDataType>;
return 16 / sizeof(KDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentV()
{
using VLayout = remove_cvref_t<typename Problem::VLayout>;
using VDataType = remove_cvref_t<typename Problem::VDataType>;
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::kN0;
constexpr index_t kKPerBlock = Problem::kN1;
constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
// TODO: not correct!
if constexpr(total_pixels > 4)
return 4;
else
return 2;
}
else
{
return 16 / sizeof(VDataType);
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetQNumElemsPerRead()
{
using DataType = typename Problem::QDataType;
if constexpr(Problem::RotaryEnum == RotaryEmbeddingEnum::HALF_ROTATED)
{
/// NOTICE: we might need to lower down this to support smaller rotary_dim
return 16 / sizeof(DataType);
}
else
{
return 16 / sizeof(DataType);
}
}
template <typename Problem>
CK_TILE_DEVICE static auto GetQThreadRangeAlongK()
{
static_assert(Problem::RotaryEnum != RotaryEmbeddingEnum::NONE);
if constexpr(Problem::RotaryEnum == RotaryEmbeddingEnum::INTERLEAVED)
{
constexpr index_t KPerThread = GetQNumElemsPerRead<Problem>();
static_assert(Problem::kK0 % KPerThread == 0);
constexpr index_t KThreadPerBlock = Problem::kK0 / KPerThread;
index_t start_pos = (get_thread_id() % KThreadPerBlock) * KPerThread;
return make_tuple(start_pos, start_pos + KPerThread);
}
else
{
constexpr index_t KPerThread = GetQNumElemsPerRead<Problem>();
static_assert(Problem::kK0 % KPerThread == 0);
constexpr index_t KThreadPerBlock = Problem::kK0 / KPerThread;
index_t start_pos = (get_thread_id() % KThreadPerBlock) * KPerThread;
return make_tuple(start_pos, start_pos + KPerThread);
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeQDramTileDistribution()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t kKPerBlock = Problem::kK0;
constexpr index_t KPerThread = GetQNumElemsPerRead<Problem>();
constexpr index_t KThreadPerBlock = kKPerBlock / KPerThread;
constexpr index_t MThreadPerWarp = get_warp_size() / KThreadPerBlock;
constexpr index_t NumWarps = kBlockSize / get_warp_size();
constexpr index_t MPerThread = kMPerBlock / (NumWarps * MThreadPerWarp);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<MPerThread, NumWarps, MThreadPerWarp>,
sequence<KThreadPerBlock, KPerThread>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetKnewNumElemsPerRead()
{
using DataType = typename Problem::KDataType;
if constexpr(Problem::RotaryEnum == RotaryEmbeddingEnum::HALF_ROTATED)
{
/// NOTICE: we might need to lower down this to support smaller rotary_dim
return 16 / sizeof(DataType);
}
else
{
return 16 / sizeof(DataType);
}
}
template <typename Problem>
CK_TILE_DEVICE static auto GetKnewThreadRangeAlongK()
{
static_assert(Problem::RotaryEnum != RotaryEmbeddingEnum::NONE);
if constexpr(Problem::RotaryEnum == RotaryEmbeddingEnum::INTERLEAVED)
{
constexpr index_t KPerThread = GetKnewNumElemsPerRead<Problem>();
constexpr index_t KThreadPerBlock = Problem::kK0 / KPerThread;
index_t start_pos = (get_thread_id() % KThreadPerBlock) * KPerThread;
return make_tuple(start_pos, start_pos + KPerThread);
}
else
{
constexpr index_t KPerThread = GetKnewNumElemsPerRead<Problem>();
constexpr index_t KThreadPerBlock = Problem::kK0 / KPerThread;
index_t start_pos = (get_thread_id() % KThreadPerBlock) * KPerThread;
return make_tuple(start_pos, start_pos + KPerThread);
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeKnewDramTileDistribution()
{
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::kN0;
constexpr index_t kKPerBlock = Problem::kK0;
constexpr index_t KPerThread = GetKnewNumElemsPerRead<Problem>();
constexpr index_t KThreadPerBlock = kKPerBlock / KPerThread;
constexpr index_t NThreadPerWarp = get_warp_size() / KThreadPerBlock;
constexpr index_t NumWarps = kBlockSize / get_warp_size();
constexpr index_t NPerThread = kNPerBlock / (NumWarps * NThreadPerWarp);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<NPerThread, NumWarps, NThreadPerWarp>,
sequence<KThreadPerBlock, KPerThread>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemKPackV()
{
// TODO: this is for 3d layout
using VDataType = remove_cvref_t<typename Problem::VDataType>;
return 16 / sizeof(VDataType);
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeVnewDramTileDistribution()
{
using VLayout = remove_cvref_t<typename Problem::VLayout>;
using VDataType = remove_cvref_t<typename Problem::VDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = Problem::kN1;
constexpr index_t kKPerBlock = Problem::kN0;
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
constexpr index_t NPerThread = 16 / sizeof(VDataType);
constexpr index_t NThreadPerBlock = kNPerBlock / NPerThread;
constexpr index_t KThreadPerWarp = get_warp_size() / NThreadPerBlock;
constexpr index_t NumWarps = kBlockSize / get_warp_size();
constexpr index_t KPerThread = kKPerBlock / (NumWarps * KThreadPerWarp);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<NThreadPerBlock, NPerThread>,
sequence<KPerThread, NumWarps, KThreadPerWarp>>,
tuple<sequence<2>, sequence<1, 2>>,
tuple<sequence<1>, sequence<0, 2>>,
sequence<1, 2>,
sequence<1, 0>>{});
}
else
{
constexpr index_t KPerThread = 16 / sizeof(VDataType);
constexpr index_t KThreadPerBlock = kKPerBlock / KPerThread;
constexpr index_t NThreadPerWarp = get_warp_size() / KThreadPerBlock;
constexpr index_t NumWarps = kBlockSize / get_warp_size();
constexpr index_t NPerThread = kNPerBlock / (NumWarps * NThreadPerWarp);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<NPerThread, NumWarps, NThreadPerWarp>,
sequence<KThreadPerBlock, KPerThread>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
}
template <typename Problem, bool IsRotaryCosSinForQ>
CK_TILE_HOST_DEVICE static constexpr auto GetRotaryCosSinTileSize()
{
constexpr index_t height = (IsRotaryCosSinForQ ? Problem::kM0 : Problem::kN0);
if constexpr(Problem::RotaryEnum == RotaryEmbeddingEnum::HALF_ROTATED)
{
return make_tuple(number<height>{}, number<Problem::kK0>{});
}
else
{
return make_tuple(number<height>{}, number<Problem::kK0 / 2>{});
}
}
template <typename Problem, bool IsRotaryCosSinForQ>
CK_TILE_HOST_DEVICE static constexpr auto MakeRotaryCosSinTileDistribution()
{
using DataType = std::conditional_t<IsRotaryCosSinForQ,
typename Problem::QDataType,
typename Problem::KDataType>;
constexpr auto TileSize = GetRotaryCosSinTileSize<Problem, IsRotaryCosSinForQ>();
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = TileSize[number<0>{}];
constexpr index_t kKPerBlock = TileSize[number<1>{}];
constexpr index_t KPerThread = []() {
if constexpr(Problem::RotaryEnum == RotaryEmbeddingEnum::HALF_ROTATED)
{
/// NOTICE: we might need to lower down this to support smaller rotary_dim
return 16 / sizeof(DataType);
}
else
{
return 8 / sizeof(DataType);
}
}();
constexpr index_t KThreadPerBlock = kKPerBlock / KPerThread;
constexpr index_t NThreadPerWarp = get_warp_size() / KThreadPerBlock;
constexpr index_t NumWarps = kBlockSize / get_warp_size();
constexpr index_t NPerThread = kNPerBlock / (NumWarps * NThreadPerWarp);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<NPerThread, NumWarps, NThreadPerWarp>,
sequence<KThreadPerBlock, KPerThread>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_qr_ks_vs.hpp
View file @ 171ed358
......@@ -6,7 +6,6 @@
#include "ck_tile/core.hpp"
#include "ck_tile/ops/fmha/block/block_attention_bias_enum.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_qr_ks_vs_default_policy.hpp"
#include "ck_tile/ops/fmha/block/block_dropout.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
namespace ck_tile {
......@@ -23,7 +22,6 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
using SaccDataType = remove_cvref_t<typename Problem::SaccDataType>;
using SMPLComputeDataType = remove_cvref_t<typename Problem::SMPLComputeDataType>;
using BiasDataType = remove_cvref_t<typename Problem::BiasDataType>;
using RandValOutputDataType = remove_cvref_t<typename Problem::RandValOutputDataType>;
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
using PDataType = remove_cvref_t<typename Problem::PDataType>;
using OaccDataType = remove_cvref_t<typename Problem::OaccDataType>;
......@@ -50,7 +48,7 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
static constexpr bool kPadHeadDimV = Problem::kPadHeadDimV;
static constexpr auto BiasEnum = Problem::BiasEnum;
static constexpr bool kStoreLSE = true; // always store LSE (acc)
static constexpr bool kHasDropout = false; // ignore this flag
static constexpr bool kIsPagedKV = Problem::kIsPagedKV;
static constexpr bool kHasUnevenSplits = Problem::kHasUnevenSplits;
// last dimension vector length used to create tensor view(and decide buffer_load vector length)
......@@ -106,10 +104,11 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
}
template <typename QDramBlockWindowTmp,
typename KDramBlockWindowTmp,
typename VDramBlockWindowTmp,
typename KDramBlockWindowLengths,
typename KPageBlockNavigator,
typename VDramBlockWindowLengths,
typename VPageBlockNavigator,
typename BiasDramBlockWindowTmp,
typename RandValDramBlockWindowTmp,
typename LSEaccDramBlockWindowTmp,
typename QElementFunction,
typename KElementFunction,
......@@ -123,13 +122,14 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp, // M0*K0 tile
const QElementFunction& q_element_func,
const KDramBlockWindowTmp& k_dram_block_window_tmp, // N0*K0 tile
const KDramBlockWindowLengths& k_dram_block_window_lengths, // N0*K0 tile
const KPageBlockNavigator& k_page_block_navigator,
const KElementFunction& k_element_func,
const VDramBlockWindowTmp& v_dram_block_window_tmp, // N1*K1 tile
const VDramBlockWindowLengths& v_dram_block_window_lengths, // N1*K1 tile
const VPageBlockNavigator& v_page_block_navigator,
const VElementFunction& v_element_func,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp, // M0*N0 tile
const BiasElementFunction& bias_element_func,
RandValDramBlockWindowTmp& randval_dram_block_window_tmp,
LSEaccDramBlockWindowTmp& lse_acc_dram_window_tmp, // M0*1 tile
const LSEaccElementFunction& lse_acc_element_func,
const SAccElementFunction& s_acc_element_func,
......@@ -140,20 +140,19 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
FmhaMask mask,
PositionEncoding position_encoding,
float scale_s,
void* smem_ptr,
BlockDropout& dropout) const
void* smem_ptr) const
{
static_assert(
std::is_same_v<QDataType, remove_cvref_t<typename QDramBlockWindowTmp::DataType>> &&
std::is_same_v<KDataType, remove_cvref_t<typename KDramBlockWindowTmp::DataType>> &&
std::is_same_v<VDataType, remove_cvref_t<typename VDramBlockWindowTmp::DataType>>,
std::is_same_v<KDataType, remove_cvref_t<typename KPageBlockNavigator::DataType>> &&
std::is_same_v<VDataType, remove_cvref_t<typename VPageBlockNavigator::DataType>>,
"wrong!");
static_assert(kM0 == QDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == KDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kK0 == KDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
kN1 == VDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kK1 == VDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
kN0 == KDramBlockWindowLengths{}[number<0>{}] &&
kK0 == KDramBlockWindowLengths{}[number<1>{}] &&
kN1 == VDramBlockWindowLengths{}[number<0>{}] &&
kK1 == VDramBlockWindowLengths{}[number<1>{}] &&
kM0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<1>{}],
"wrong!");
......@@ -213,12 +212,12 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
const auto [seqlen_k_start, seqlen_k_end] = mask.GetTileRangeAlongX(
q_origin.at(number<0>{}), number<kM0>{}, number<kN0>{}, num_splits, i_split);
const auto num_total_loop = integer_divide_ceil(seqlen_k_end - seqlen_k_start, kN0);
// check early exit if masked and no work to do.
if constexpr(FmhaMask::IsMasking || kHasUnevenSplits)
{
if(num_total_loop <= 0)
const index_t original_num_total_loop =
integer_divide_ceil(seqlen_k_end - seqlen_k_start, kN0);
if(original_num_total_loop <= 0)
{
if constexpr(kStoreLSE)
{
......@@ -237,25 +236,33 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
}
}
auto k_dram_block_window =
make_tile_window(k_dram_block_window_tmp.get_bottom_tensor_view(),
k_dram_block_window_tmp.get_window_lengths(),
{seqlen_k_start, 0});
// make sure the first tile is completely located in page-block
const index_t adjusted_seqlen_k_start = [&, seqlen_k_start_ = seqlen_k_start] {
if constexpr(kIsPagedKV)
{
return kN0 * integer_divide_floor(seqlen_k_start_, kN0);
}
else
{
return seqlen_k_start_;
}
}();
const index_t num_total_loop =
integer_divide_ceil(seqlen_k_end - adjusted_seqlen_k_start, kN0);
auto [i_page_block_k, k_dram_block_window] = k_page_block_navigator.make_tile_window(
k_dram_block_window_lengths, {adjusted_seqlen_k_start, 0});
const auto bias_origin = bias_dram_block_window_tmp.get_window_origin();
auto bias_dram_window = make_tile_window(
bias_dram_block_window_tmp.get_bottom_tensor_view(),
bias_dram_block_window_tmp.get_window_lengths(),
{bias_origin.at(number<0>{}), seqlen_k_start}, // M/N
{bias_origin.at(number<0>{}), adjusted_seqlen_k_start}, // M/N
Policy::template MakeBiasDramTileDistribution<Problem, decltype(gemm_0)>());
auto randval_dram_window = dropout.MakeRandvalDramWindow<decltype(gemm_0)>(
randval_dram_block_window_tmp, seqlen_k_start);
auto v_dram_window =
make_tile_window(v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
{0, seqlen_k_start}, // TODO: hdim split?
auto [i_page_block_v, v_dram_window] = v_page_block_navigator.make_tile_window(
v_dram_block_window_lengths,
{0, adjusted_seqlen_k_start}, // TODO: hdim split?
Policy::template MakeVDramTileDistribution<Problem>());
auto q_tile = tile_elementwise_in(q_element_func, q);
......@@ -271,14 +278,14 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
{
// STAGE 1, QK gemm
auto k_dram_window = make_tile_window(
k_dram_block_window.get_bottom_tensor_view(),
k_dram_block_window.get_window_lengths(),
k_dram_block_window.get_window_origin(),
k_dram_block_window,
Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window for
// load
auto k_block_tile = load_tile(k_dram_window);
{
// moving k_dram_window is an in-page-block operation, so there is
// no need to invoke k_page_block_navigator.move_tile_window() here.
move_tile_window(k_dram_window, {0, kK0});
clear_tile(s_acc); // initialize C
store_tile(k_lds_window, tile_elementwise_in(k_element_func, k_block_tile));
......@@ -355,7 +362,8 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
const auto k_origin = k_dram_block_window.get_window_origin();
const auto k_origin = k_page_block_navigator.to_global_window_origin(
i_page_block_k, k_dram_block_window.get_window_origin());
constexpr auto s_spans = decltype(s_acc)::get_distributed_spans();
s_acc = tile_elementwise_in(s_acc_element_func, s_acc);
sweep_tile_span(s_spans[number<0>{}], [&](auto idx0) {
......@@ -381,22 +389,32 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
}
move_tile_window(bias_dram_window, {0, kN0});
/// TODO: only check in last iteration without increasing code size
/// TODO: only check in first/last iteration without increasing code size
if constexpr(kHasUnevenSplits)
{
const auto k_origin = k_dram_block_window.get_window_origin();
const auto k_origin = k_page_block_navigator.to_global_window_origin(
i_page_block_k, k_dram_block_window.get_window_origin());
set_tile_if(s_acc,
-numeric<SMPLComputeDataType>::infinity(),
[&, seqlen_k_end_ = seqlen_k_end](auto tile_idx) {
[&, seqlen_k_start_ = seqlen_k_start, seqlen_k_end_ = seqlen_k_end](
auto tile_idx) {
const auto col =
k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
if constexpr(kIsPagedKV)
{
return col < seqlen_k_start_ || seqlen_k_end_ <= col;
}
else
{
return seqlen_k_end_ <= col;
}
});
}
if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
{
const auto k_origin = k_dram_block_window.get_window_origin();
const auto k_origin = k_page_block_navigator.to_global_window_origin(
i_page_block_k, k_dram_block_window.get_window_origin());
bool need_perpixel_check = mask.IsEdgeTile(q_origin.at(number<0>{}),
k_origin.at(number<0>{}),
number<kM0>{},
......@@ -501,12 +519,6 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
});
});
if constexpr(kHasDropout)
{
dropout.Run<decltype(gemm_0), SMPLComputeDataType, RandValOutputDataType>(
smem_ptr, seqlen_k_start + i_total_loops * kN0, p_compute, randval_dram_window);
}
block_sync_lds();
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
......@@ -522,7 +534,8 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
store_tile(v_lds_window,
tile_elementwise_in(v_element_func, v_prefetch)); // store the prefetch
}
move_tile_window(v_dram_window, {0, kK1});
i_page_block_v =
v_page_block_navigator.move_tile_window(i_page_block_v, v_dram_window, {0, kK1});
const auto p =
cast_tile<PDataType>(tile_elementwise_in(p_compute_element_func, p_compute));
......@@ -530,8 +543,10 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
// STAGE 3, KV gemm
if constexpr(k1_loops > 1)
{
static_for<0, k1_loops - 1, 1>{}([&](auto i_k1) {
const auto v = load_tile(v_dram_window); // load next v
static_for<0, k1_loops - 1, 1>{}([&,
&i_page_block_v_ = i_page_block_v,
&v_dram_window_ = v_dram_window](auto i_k1) {
const auto v = load_tile(v_dram_window_); // load next v
block_sync_lds();
gemm_1(o_acc,
get_slice_tile(
......@@ -552,11 +567,13 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
store_tile(v_lds_window,
tile_elementwise_in(v_element_func, v)); // store next v
}
move_tile_window(v_dram_window, {0, kK1});
i_page_block_v_ = v_page_block_navigator.move_tile_window(
i_page_block_v_, v_dram_window_, {0, kK1});
});
}
// move K tile windows
move_tile_window(k_dram_block_window, {kN0, 0});
i_page_block_k = k_page_block_navigator.move_tile_window(
i_page_block_k, k_dram_block_window, {kN0, 0});
// tail
{
block_sync_lds();
......@@ -618,36 +635,38 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
}
template <typename QDramBlockWindowTmp,
typename KDramBlockWindowTmp,
typename VDramBlockWindowTmp,
typename KDramBlockWindowLengths,
typename KPageBlockNavigator,
typename VDramBlockWindowLengths,
typename VPageBlockNavigator,
typename BiasDramBlockWindowTmp,
typename RandValDramBlockWindowTmp,
typename LSEaccDramBlockWindowTmp,
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp, // M0*K0 tile
const KDramBlockWindowTmp& k_dram_block_window_tmp, // N0*K0 tile
const VDramBlockWindowTmp& v_dram_block_window_tmp, // N1*K1 tile
const KDramBlockWindowLengths& k_dram_block_window_lengths, // N0*K0 tile
const KPageBlockNavigator& k_page_block_navigator,
const VDramBlockWindowLengths& v_dram_block_window_lengths, // N1*K1 tile
const VPageBlockNavigator& v_page_block_navigator,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp, // M0*N0 tile
RandValDramBlockWindowTmp& randval_dram_block_window_tmp, // M0*N0 tile
LSEaccDramBlockWindowTmp& lse_acc_dram_block_window_tmp, // M0*1 tile
index_t num_splits,
index_t i_split,
FmhaMask mask,
PositionEncoding position_encoding,
float scale_s,
void* smem_ptr,
BlockDropout& dropout) const
void* smem_ptr) const
{
return operator()(q_dram_block_window_tmp,
identity{},
k_dram_block_window_tmp,
k_dram_block_window_lengths,
k_page_block_navigator,
identity{},
v_dram_block_window_tmp,
v_dram_block_window_lengths,
v_page_block_navigator,
identity{},
bias_dram_block_window_tmp,
identity{},
randval_dram_block_window_tmp,
lse_acc_dram_block_window_tmp,
identity{},
identity{},
......@@ -658,8 +677,7 @@ struct BlockFmhaFwdSplitKVPipelineQRKSVS
mask,
position_encoding,
scale_s,
smem_ptr,
dropout);
smem_ptr);
}
};
......
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_qr_ks_vs_async.hpp deleted 100644 → 0
View file @ 829e0eb3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/fmha/block/block_attention_bias_enum.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_qr_ks_vs_async_default_policy.hpp"
#include "ck_tile/ops/fmha/block/block_dropout.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
namespace ck_tile {
// a variation of qr/ks/vs, where we use async copy to load k (potentially v in the future)
template <typename Problem_, typename Policy_ = BlockFmhaFwdSplitKVPipelineQRKSVSAsyncDefaultPolicy>
struct BlockFmhaFwdSplitKVPipelineQRKSVSAsync
{
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using QDataType = remove_cvref_t<typename Problem::QDataType>;
using KDataType = remove_cvref_t<typename Problem::KDataType>;
using VDataType = remove_cvref_t<typename Problem::VDataType>;
using SaccDataType = remove_cvref_t<typename Problem::SaccDataType>;
using SMPLComputeDataType = remove_cvref_t<typename Problem::SMPLComputeDataType>;
using BiasDataType = remove_cvref_t<typename Problem::BiasDataType>;
using RandValOutputDataType = remove_cvref_t<typename Problem::RandValOutputDataType>;
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
using PDataType = remove_cvref_t<typename Problem::PDataType>;
using OaccDataType = remove_cvref_t<typename Problem::OaccDataType>;
using FmhaMask = remove_cvref_t<typename Problem::FmhaMask>;
using BlockFmhaShape = remove_cvref_t<typename Problem::BlockFmhaShape>;
using VLayout = remove_cvref_t<typename BlockFmhaShape::VLayout>;
static constexpr bool kQLoadOnce = true; // if q_tile load whole block length (hdim) at once
static_assert(kQLoadOnce == Policy::QLoadOnce);
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t kM0 = BlockFmhaShape::kM0;
static constexpr index_t kN0 = BlockFmhaShape::kN0;
static constexpr index_t kK0 = BlockFmhaShape::kK0;
static constexpr index_t kN1 = BlockFmhaShape::kN1;
static constexpr index_t kK1 = BlockFmhaShape::kK1;
static constexpr index_t kK0BlockLength = BlockFmhaShape::kK0BlockLength;
static constexpr bool kIsGroupMode = Problem::kIsGroupMode;
// TODO: seq_q always support padding, hdim_q/v support multiple of vector(like 8x)
// only need special care about seq_k padding (oob need set -INF of p instead of zero)
static_assert(Problem::kPadSeqLenQ == true && Problem::kPadHeadDimQ == true &&
Problem::kPadHeadDimV == true);
static constexpr bool kPadSeqLenQ = true;
static constexpr bool kPadSeqLenK = Problem::kPadSeqLenK;
static constexpr bool kPadHeadDimQ = true; // support multiple of vector(like 8x)
static constexpr bool kPadHeadDimV = true; // support multiple of vector(like 8x)
static constexpr auto BiasEnum = Problem::BiasEnum;
static constexpr bool kStoreLSE = true; // always store LSE (acc)
static constexpr bool kHasDropout = false; // ignore this flag
static constexpr bool kHasUnevenSplits = Problem::kHasUnevenSplits;
// last dimension vector length used to create tensor view(and decide buffer_load vector length)
// ... together with tensor distribution. tensor dist should able to overwrite this
static constexpr index_t kAlignmentQ = Policy::template GetAlignmentQ<Problem>();
static constexpr index_t kAlignmentK = Policy::template GetAlignmentK<Problem>();
static constexpr index_t kAlignmentV = []() {
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
return Policy::template GetAlignmentV<Problem>();
else
return kPadSeqLenK ? 1 : Policy::template GetAlignmentV<Problem>();
}();
static constexpr index_t kAlignmentO = Policy::template GetAlignmentO<Problem>();
static constexpr index_t kAlignmentBias =
kPadSeqLenK ? 1 : Policy::template GetAlignmentBias<Problem>();
#if CK_TILE_FMHA_FWD_FAST_EXP2
static constexpr auto R_LOG2E = 1.0 / log2e_v<SaccDataType>;
#endif
static constexpr index_t kBlockPerCu = []() {
if constexpr(Problem::kBlockPerCu != -1)
return Problem::kBlockPerCu;
else
{
if constexpr(kK0BlockLength <= 32)
{
if constexpr(kPadSeqLenK && BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS &&
FmhaMask::IsMasking)
return 1;
else
return 2;
}
else if constexpr(kK0BlockLength <= 64)
{
if constexpr(kPadSeqLenK && BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
return 2;
else
return 3;
}
else if constexpr(kK0BlockLength <= 128)
{
if constexpr(kPadSeqLenK && BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
return 1;
else
return 2;
}
else if constexpr(kK0BlockLength <= 256)
{
return 1;
}
}
}();
static constexpr const char* name = "qr_async";
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
}
template <typename QDramBlockWindowTmp,
typename KDramBlockWindowTmp,
typename VDramBlockWindowTmp,
typename BiasDramBlockWindowTmp,
typename RandValDramBlockWindowTmp,
typename LSEaccDramBlockWindowTmp,
typename QElementFunction,
typename KElementFunction,
typename VElementFunction,
typename BiasElementFunction,
typename LSEaccElementFunction,
typename SAccElementFunction,
typename PComputeElementFunction,
typename OAccElementFunction,
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp, // M0*K0 tile
const QElementFunction& q_element_func,
const KDramBlockWindowTmp& k_dram_block_window_tmp, // N0*K0 tile
const KElementFunction& /*k_element_func*/,
const VDramBlockWindowTmp& v_dram_block_window_tmp, // N1*K1 tile
const VElementFunction& v_element_func,
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp, // M0*N0 tile
const BiasElementFunction& bias_element_func,
RandValDramBlockWindowTmp& randval_dram_block_window_tmp,
LSEaccDramBlockWindowTmp& lse_acc_dram_window_tmp, // M0*1 tile
const LSEaccElementFunction& lse_acc_element_func,
const SAccElementFunction& s_acc_element_func,
const PComputeElementFunction& p_compute_element_func,
const OAccElementFunction& o_acc_element_func,
index_t num_splits,
index_t i_split,
FmhaMask mask,
PositionEncoding position_encoding,
float scale_s,
void* smem_ptr,
BlockDropout& dropout) const
{
static_assert(
std::is_same_v<QDataType, remove_cvref_t<typename QDramBlockWindowTmp::DataType>> &&
std::is_same_v<KDataType, remove_cvref_t<typename KDramBlockWindowTmp::DataType>> &&
std::is_same_v<VDataType, remove_cvref_t<typename VDramBlockWindowTmp::DataType>>,
"wrong!");
static_assert(kM0 == QDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == KDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kK0 == KDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
kN1 == VDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kK1 == VDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
kM0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
kN0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<1>{}],
"wrong!");
constexpr auto LdsSeq = Policy::template GetLdsBufferSequence<Problem>();
// K tile in LDS
auto k_lds_ptr = reinterpret_cast<KDataType*>(smem_ptr);
auto k_lds_store = generate_tuple(
[&](auto i_buf) {
return make_tile_window(
make_tensor_view<address_space_enum::lds>(
k_lds_ptr, Policy::template MakeKLdsStoreBlockDescriptor<Problem>(i_buf)),
Policy::template MakeKLdsStoreBlockDescriptor<Problem>(i_buf).get_lengths(),
{0, 0, 0});
},
number<Policy::NumPrefetchK>{});
#if K_LDS_LOAD_USE_OFFSET_TRANSFORM
auto k_lds_load = generate_tuple(
[&](auto i_buf) {
return make_tile_window(
make_tensor_view<address_space_enum::lds>(
k_lds_ptr, Policy::template MakeKLdsLoadBlockDescriptor<Problem>(i_buf)),
Policy::template MakeKLdsLoadBlockDescriptor<Problem>(i_buf).get_lengths(),
{0, 0});
},
number<Policy::NumPrefetchK>{});
#else
auto k_lds_Load_view = make_tensor_view<address_space_enum::lds>(
k_lds_ptr, Policy::template MakeKLdsLoadBlockDescriptor<Problem>());
auto k_lds_load =
make_tile_window(k_lds_Load_view,
Policy::template MakeKLdsLoadBlockDescriptor<Problem>().get_lengths(),
{0, 0});
#endif
// V tile in LDS
auto v_lds = make_tensor_view<address_space_enum::lds>(
reinterpret_cast<VDataType*>(smem_ptr),
Policy::template MakeVLdsBlockDescriptor<Problem>());
auto v_lds_window = make_tile_window(
v_lds, Policy::template MakeVLdsBlockDescriptor<Problem>().get_lengths(), {0, 0});
// Block GEMM
constexpr auto gemm_0 = Policy::template GetQKBlockGemm<Problem>();
constexpr auto gemm_1 = Policy::template GetKVBlockGemm<Problem>();
auto q_dram_window = make_tile_window(
q_dram_block_window_tmp.get_bottom_tensor_view(),
q_dram_block_window_tmp.get_window_lengths(),
q_dram_block_window_tmp.get_window_origin(),
Policy::template MakeQDramTileDistribution<Problem, decltype(gemm_0)>());
// TODO: we use async Copy for K, which is inline asm
// a side effect is we have to use inline asm for q as well
auto q = decltype(load_tile(q_dram_window)){};
set_tile(q, number<0>{}); // use per-dword clear to avoid scratch
load_tile_raw(q, q_dram_window);
__builtin_amdgcn_sched_barrier(0);
using SaccBlockTileType = decltype(gemm_0.MakeCBlockTile());
auto s_acc = SaccBlockTileType{};
// reduction function for softmax
const auto f_max = [](auto e0, auto e1) { return max(e0, e1); };
const auto f_sum = [](auto e0, auto e1) { return e0 + e1; };
// infer Sacc, S, P, M, L, Oacc type
using SBlockTileType = decltype(cast_tile<SMPLComputeDataType>(s_acc));
using MLBlockTileType = decltype(block_tile_reduce<SMPLComputeDataType>(
SBlockTileType{}, sequence<1>{}, f_max, SMPLComputeDataType{0}));
using OaccBlockTileType = decltype(gemm_1.MakeCBlockTile());
// init Oacc, M, L
auto o_acc = OaccBlockTileType{};
auto m = MLBlockTileType{};
auto l = MLBlockTileType{};
clear_tile(o_acc);
set_tile(m, -numeric<SMPLComputeDataType>::infinity());
clear_tile(l);
__builtin_amdgcn_sched_barrier(0);
const auto q_origin = q_dram_window.get_window_origin();
const auto [seqlen_k_start, seqlen_k_end] = mask.GetTileRangeAlongX(
q_origin.at(number<0>{}), number<kM0>{}, number<kN0>{}, num_splits, i_split);
const auto num_total_loop = integer_divide_ceil(seqlen_k_end - seqlen_k_start, kN0);
// check early exit if masked and no work to do.
if constexpr(FmhaMask::IsMasking || kPadSeqLenK || kHasUnevenSplits)
{
if(num_total_loop <= 0)
{
if constexpr(kStoreLSE)
{
auto lse_acc =
make_static_distributed_tensor<LSEDataType>(m.get_tile_distribution());
set_tile(lse_acc, -numeric<SMPLComputeDataType>::infinity());
store_tile(lse_acc_dram_window_tmp,
tile_elementwise_in(lse_acc_element_func, lse_acc));
}
buffer_load_fence(0); // rocm-6.1, if whole tile is masked out, need to fence(0)
// otherwise will have compute error(maybe compiler bug?)
// Note: here occ are all cleard, return it
return o_acc;
}
__builtin_amdgcn_sched_barrier(0); // make sure sched_barrier(0) for this check
}
auto k_dram_block_window =
make_tile_window(k_dram_block_window_tmp.get_bottom_tensor_view(),
k_dram_block_window_tmp.get_window_lengths(),
{seqlen_k_start, 0});
auto k_dram_window = make_tile_window(
k_dram_block_window.get_bottom_tensor_view(),
k_dram_block_window.get_window_lengths(),
k_dram_block_window.get_window_origin(),
Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window for
// load
const auto bias_origin = bias_dram_block_window_tmp.get_window_origin();
auto bias_dram_window = make_tile_window(
bias_dram_block_window_tmp.get_bottom_tensor_view(),
bias_dram_block_window_tmp.get_window_lengths(),
{bias_origin.at(number<0>{}), seqlen_k_start}, // M/N
Policy::template MakeBiasDramTileDistribution<Problem, decltype(gemm_0)>());
auto randval_dram_window = dropout.MakeRandvalDramWindow<decltype(gemm_0)>(
randval_dram_block_window_tmp, seqlen_k_start);
auto v_dram_window =
make_tile_window(v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
{0, seqlen_k_start}, // TODO: hdim split?
Policy::template MakeVDramTileDistribution<Problem>());
// prefetch K tile
async_load_tile_raw(k_lds_store(LdsSeq.at(number<0>{})), k_dram_window);
move_tile_window(k_dram_window, {0, kK0});
__builtin_amdgcn_sched_barrier(0);
buffer_load_fence(k_dram_window.get_num_access(), q.get_thread_buffer());
(void)q_element_func; // ??? rocm-6.x if use q element func will have scratch on hdim=64/32
// auto q_tile = q; // tile_elementwise_in(q_element_func, q);
index_t i_total_loops = 0;
constexpr index_t k0_loops = kK0BlockLength / kK0;
constexpr index_t k1_loops = kN0 / kK1;
static_assert(1 <= k0_loops);
static_assert(1 <= k1_loops);
// main loop
do
{
// STAGE 1, QK gemm
clear_tile(s_acc); // initialize C
if constexpr(k0_loops > 1)
{
static_for<0, k0_loops - 1, 1>{}([&](auto i_k0) {
async_load_tile_raw(k_lds_store(number<LdsSeq.at(number<i_k0 + 1>{})>{}),
k_dram_window);
if constexpr(i_k0 < k0_loops - 1)
move_tile_window(k_dram_window, {0, kK0});
async_load_fence(k_dram_window.get_num_access());
__builtin_amdgcn_s_barrier();
__builtin_amdgcn_sched_barrier(0);
gemm_0(s_acc,
get_slice_tile(
q, sequence<0, i_k0 * kK0>{}, sequence<kM0, (i_k0 + 1) * kK0>{}),
#if K_LDS_LOAD_USE_OFFSET_TRANSFORM
k_lds_load[number<LdsSeq.at(number<i_k0>{})>{}]);
#else
get_slice_tile(k_lds_load,
sequence<(LdsSeq.at(number<i_k0>{})) * kN0, 0>{},
sequence<(LdsSeq.at(number<i_k0>{}) + 1) * kN0, kK0>{}));
#endif
});
}
// TODO: this to fix a bug when loop smaller than 2,
// the following fence/barrier will be scheduled inside 1st loop
if constexpr(k0_loops <= 2)
__builtin_amdgcn_sched_barrier(0);
async_load_fence();
__builtin_amdgcn_s_barrier();
const auto bias_tile = load_tile(bias_dram_window); // load bias tile
auto v_buf = load_tile(v_dram_window, bool_constant<false>{});
__builtin_amdgcn_sched_barrier(0);
{ // tail
gemm_0(s_acc,
get_slice_tile(
q, sequence<0, (k0_loops - 1) * kK0>{}, sequence<kM0, k0_loops * kK0>{}),
#if K_LDS_LOAD_USE_OFFSET_TRANSFORM
k_lds_load[number<LdsSeq.at(number<k0_loops - 1>{})>{}]);
#else
get_slice_tile(
k_lds_load,
sequence<(LdsSeq.at(number<k0_loops - 1>{})) * kN0, 0>{},
sequence<(LdsSeq.at(number<k0_loops - 1>{}) + 1) * kN0, kK0>{}));
#endif
}
__builtin_amdgcn_sched_barrier(1);
// STAGE 2, scale_s, add bias, mask, softmax
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS)
{
s_acc = tile_elementwise_in(s_acc_element_func, s_acc);
tile_elementwise_inout([&scale_s](auto& x) { x = x * scale_s; }, s_acc);
tile_elementwise_inout(
[&](auto& x, const auto& y) {
#if !CK_TILE_FMHA_FWD_FAST_EXP2
x += type_convert<SaccDataType>(bias_element_func(y));
#else
x += log2e_v<SaccDataType> *
type_convert<SaccDataType>(bias_element_func(y));
#endif
},
s_acc,
bias_tile);
}
else if constexpr(BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
const auto k_origin = k_dram_block_window.get_window_origin();
constexpr auto s_spans = decltype(s_acc)::get_distributed_spans();
s_acc = tile_elementwise_in(s_acc_element_func, s_acc);
sweep_tile_span(s_spans[number<0>{}], [&](auto idx0) {
sweep_tile_span(s_spans[number<1>{}], [&](auto idx1) {
const auto tile_idx = get_x_indices_from_distributed_indices(
s_acc.get_tile_distribution(), make_tuple(idx0, idx1));
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
constexpr auto i_j_idx = make_tuple(idx0, idx1);
s_acc(i_j_idx) *= scale_s;
position_encoding.update(s_acc(i_j_idx), row, col);
});
});
}
else
{
s_acc = tile_elementwise_in(s_acc_element_func, s_acc);
#if !CK_TILE_FMHA_FWD_FAST_EXP2
tile_elementwise_inout([&scale_s](auto& x) { x = x * scale_s; }, s_acc);
#endif
}
move_tile_window(bias_dram_window, {0, kN0});
/// TODO: only check in last iteration without increasing code size
if constexpr(kHasUnevenSplits)
{
const auto k_origin = k_dram_block_window.get_window_origin();
set_tile_if(s_acc,
-numeric<SMPLComputeDataType>::infinity(),
[&, seqlen_k_end_ = seqlen_k_end](auto tile_idx) {
const auto col =
k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
return seqlen_k_end_ <= col;
});
}
if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
{
const auto k_origin = k_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(
s_acc, -numeric<SMPLComputeDataType>::infinity(), [&](auto tile_idx) {
const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
return mask.IsOutOfBound(row, col);
});
}
}
const auto s = cast_tile<SMPLComputeDataType>(s_acc); // S{j}
auto m_local = block_tile_reduce<SMPLComputeDataType>(
s,
sequence<1>{},
f_max,
-numeric<SMPLComputeDataType>::infinity()); // m_local = rowmax(S{j})
block_tile_reduce_sync(m_local, f_max, bool_constant<false>{});
const auto m_old = m; // m{j-1}
tile_elementwise_inout(
[](auto& e0, auto e1, auto e2) { e0 = max(e1, e2); }, m, m_old, m_local); // m{j}
auto p_compute = make_static_distributed_tensor<SMPLComputeDataType>(
s.get_tile_distribution()); // Pcompute{j}
__builtin_amdgcn_sched_barrier(0x7F);
// store & prefetch next v, after the max reduction
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
auto v_shuffle_tmp = make_static_distributed_tensor<VDataType>(
Policy::template MakeShuffledVRegBlockDescriptor<Problem>());
shuffle_tile(v_shuffle_tmp, v_buf);
auto v_lds_window_tmp =
get_slice_tile(v_lds_window,
sequence<(LdsSeq.at(number<k0_loops>{})) * kN1, 0>{},
sequence<(LdsSeq.at(number<k0_loops>{}) + 1) * kN1, kK1>{});
store_tile(
v_lds_window_tmp,
tile_elementwise_in(v_element_func, v_shuffle_tmp)); // store the prefetch
}
else
{
auto v_lds_window_tmp =
get_slice_tile(v_lds_window,
sequence<(LdsSeq.at(number<k0_loops>{})) * kN1, 0>{},
sequence<(LdsSeq.at(number<k0_loops>{}) + 1) * kN1, kK1>{});
store_tile(v_lds_window_tmp,
tile_elementwise_in(v_element_func, v_buf)); // store the prefetch
}
if constexpr(k1_loops > 1)
{
move_tile_window(
v_dram_window,
{0, kK1}); // will have scratch if move this right after load_tile(v_dram)...
v_buf = load_tile(v_dram_window, bool_constant<false>{}); // load next v_buf
}
__builtin_amdgcn_sched_barrier(0);
static const auto get_validated_m = [](SMPLComputeDataType raw_m) {
/// NOTICE: bias might be materialized mask including -inf values, need
/// consideration. alibi does not have this problem
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
FmhaMask::IsMasking)
{
return raw_m == -numeric<SMPLComputeDataType>::infinity()
? type_convert<SMPLComputeDataType>(0.f)
: raw_m;
}
else
{
return raw_m;
}
};
constexpr auto p_spans = decltype(p_compute)::get_distributed_spans();
sweep_tile_span(p_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
auto row_max = scale_s * get_validated_m(m[i_idx]);
#endif
sweep_tile_span(p_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
#if CK_TILE_FMHA_FWD_FAST_EXP2
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
p_compute(i_j_idx) = exp2(s[i_j_idx] - get_validated_m(m[i_idx]));
}
else
{
p_compute(i_j_idx) = exp2(scale_s * s[i_j_idx] - row_max);
}
#else
p_compute(i_j_idx) = exp(s[i_j_idx] - get_validated_m(m[i_idx]));
#endif
});
});
auto rowsum_p = block_tile_reduce<SMPLComputeDataType>(
p_compute, sequence<1>{}, f_sum, SMPLComputeDataType{0}); // rowsum(Pcompute{j})
block_tile_reduce_sync(rowsum_p, f_sum, bool_constant<false>{});
// l{j}, Oacc{j}
constexpr auto o_spans = decltype(o_acc)::get_distributed_spans();
sweep_tile_span(o_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
const auto tmp = [&]() {
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
return exp2(m_old[i_idx] - get_validated_m(m[i_idx]));
}
else
{
auto row_max = scale_s * get_validated_m(m[i_idx]);
return exp2(scale_s * m_old[i_idx] - row_max);
}
}();
#else
const auto tmp = exp(m_old[i_idx] - get_validated_m(m[i_idx]));
#endif
l(i_idx) = tmp * l[i_idx] + rowsum_p[i_idx];
sweep_tile_span(o_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
// FIXME: this use different equation from FA v2 paper,
// but produce correc result.
// Is the equation wrong?
o_acc(i_j_idx) *= tmp;
});
});
if constexpr(kHasDropout)
{
auto randval_ptr =
reinterpret_cast<char*>(smem_ptr) + Policy::template GetSmemSizeKV<Problem>();
dropout.Run<decltype(gemm_0), SMPLComputeDataType, RandValOutputDataType>(
randval_ptr,
seqlen_k_start + i_total_loops * kN0,
p_compute,
randval_dram_window);
}
const auto p =
cast_tile<PDataType>(tile_elementwise_in(p_compute_element_func, p_compute));
// STAGE 3, KV gemm
if constexpr(k1_loops > 1)
{
static_for<0, k1_loops - 1, 1>{}([&](auto i_k1) {
if constexpr(i_k1 != 0 && i_k1 < k1_loops - 1)
{
v_buf = load_tile(v_dram_window, bool_constant<false>{}); // load next v_buf
}
block_sync_lds();
gemm_1(o_acc,
get_slice_tile(
p, sequence<0, i_k1 * kK1>{}, sequence<kM0, (i_k1 + 1) * kK1>{}),
get_slice_tile(
v_lds_window,
sequence<(LdsSeq.at(number<k0_loops + i_k1>{})) * kN1, 0>{},
sequence<(LdsSeq.at(number<k0_loops + i_k1>{}) + 1) * kN1, kK1>{}));
if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
auto v_shuffle_tmp = make_static_distributed_tensor<VDataType>(
Policy::template MakeShuffledVRegBlockDescriptor<Problem>());
shuffle_tile(v_shuffle_tmp, v_buf);
auto v_lds_window_tmp = get_slice_tile(
v_lds_window,
sequence<(LdsSeq.at(number<k0_loops + i_k1 + 1>{})) * kN1, 0>{},
sequence<(LdsSeq.at(number<k0_loops + i_k1 + 1>{}) + 1) * kN1, kK1>{});
store_tile(v_lds_window_tmp,
tile_elementwise_in(v_element_func,
v_shuffle_tmp)); // store the prefetch
}
else
{
auto v_lds_window_tmp = get_slice_tile(
v_lds_window,
sequence<(LdsSeq.at(number<k0_loops + i_k1 + 1>{})) * kN1, 0>{},
sequence<(LdsSeq.at(number<k0_loops + i_k1 + 1>{}) + 1) * kN1, kK1>{});
store_tile(v_lds_window_tmp,
tile_elementwise_in(v_element_func, v_buf)); // store next v_buf
}
if constexpr(i_k1 < k1_loops - 1)
move_tile_window(v_dram_window, {0, kK1});
});
}
i_total_loops++;
if(i_total_loops < num_total_loop)
{
// move K tile windows
move_tile_window(k_dram_block_window, {kN0, 0});
k_dram_window =
make_tile_window(k_dram_block_window.get_bottom_tensor_view(),
k_dram_block_window.get_window_lengths(),
k_dram_block_window.get_window_origin(),
Policy::template MakeKDramTileDistribution<Problem>());
if constexpr(k1_loops >= 2 &&
LdsSeq.at(number<0>{}) == LdsSeq.at(number<k0_loops + k1_loops - 2>{}))
__builtin_amdgcn_s_barrier();
async_load_tile_raw(k_lds_store(LdsSeq.at(number<0>{})), k_dram_window);
move_tile_window(k_dram_window, {0, kK0});
}
// tail
{
block_sync_lds();
gemm_1(
o_acc,
get_slice_tile(p, sequence<0, (k1_loops - 1) * kK1>{}, sequence<kM0, kN0>{}),
get_slice_tile(
v_lds_window,
sequence<(LdsSeq.at(number<k0_loops + k1_loops - 1>{})) * kN1, 0>{},
sequence<(LdsSeq.at(number<k0_loops + k1_loops - 1>{}) + 1) * kN1, kK1>{}));
}
} while(i_total_loops < num_total_loop);
// store lse acc
if constexpr(kStoreLSE)
{
auto lse_acc = make_static_distributed_tensor<LSEDataType>(m.get_tile_distribution());
constexpr auto lse_acc_spans = decltype(lse_acc)::get_distributed_spans();
sweep_tile_span(lse_acc_spans[number<0>{}], [&, m_ = m, l_ = l](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
if constexpr(BiasEnum == BlockAttentionBiasEnum::ELEMENTWISE_BIAS ||
BiasEnum == BlockAttentionBiasEnum::ALIBI)
{
lse_acc(i_idx) = m_[i_idx] * R_LOG2E + log(l_[i_idx]);
}
else
{
lse_acc(i_idx) = m_[i_idx] * scale_s * R_LOG2E + log(l_[i_idx]);
}
#else
lse_acc(i_idx) = m_[i_idx] + log(l_[i_idx]);
#endif
});
store_tile(lse_acc_dram_window_tmp, tile_elementwise_in(lse_acc_element_func, lse_acc));
}
// finally, O
constexpr auto o_spans = decltype(o_acc)::get_distributed_spans();
sweep_tile_span(o_spans[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
const auto tmp = [&]() {
if constexpr(FmhaMask::IsMasking)
{
return l[i_idx] == 0.f ? 0.f : 1 / l[i_idx];
}
else
return 1 / l[i_idx];
}();
sweep_tile_span(o_spans[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
o_acc(i_j_idx) *= tmp;
});
});
o_acc = tile_elementwise_in(o_acc_element_func, o_acc);
return o_acc;
}
template <typename QDramBlockWindowTmp,
typename KDramBlockWindowTmp,
typename VDramBlockWindowTmp,
typename BiasDramBlockWindowTmp,
typename RandValDramBlockWindowTmp,
typename LSEaccDramBlockWindowTmp,
typename PositionEncoding>
CK_TILE_HOST_DEVICE auto
operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp, // M0*K0 tile
const KDramBlockWindowTmp& k_dram_block_window_tmp, // N0*K0 tile
const VDramBlockWindowTmp& v_dram_block_window_tmp, // N1*K1 tile
const BiasDramBlockWindowTmp& bias_dram_block_window_tmp, // M0*N0 tile
RandValDramBlockWindowTmp& randval_dram_block_window_tmp, // M0*N0 tile
LSEaccDramBlockWindowTmp& lse_acc_dram_block_window_tmp, // M0*1 tile
index_t num_splits,
index_t i_split,
FmhaMask mask,
PositionEncoding position_encoding,
float scale_s,
void* smem_ptr,
BlockDropout& dropout) const
{
return operator()(q_dram_block_window_tmp,
identity{},
k_dram_block_window_tmp,
identity{},
v_dram_block_window_tmp,
identity{},
bias_dram_block_window_tmp,
identity{},
randval_dram_block_window_tmp,
lse_acc_dram_block_window_tmp,
identity{},
identity{},
identity{},
identity{},
num_splits,
i_split,
mask,
position_encoding,
scale_s,
smem_ptr,
dropout);
}
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_qr_ks_vs_async_default_policy.hpp deleted 100644 → 0
View file @ 829e0eb3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qx_ks_vs_custom_policy.hpp"
namespace ck_tile {
// This pipeline is qkv all located in LDS
using BlockFmhaFwdSplitKVPipelineQRKSVSAsyncDefaultPolicy =
BlockFmhaPipelineQXKSVSCustomPolicy</* QLoadOnce = */ true,
/* AsyncCopyK = */ true,
/* AsyncCopyV = */ false,
/* NumPrefetchK = */ 3,
/* NumPrefetchV = */ 3>;
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_pipeline_problem.hpp
View file @ 171ed358
......@@ -54,38 +54,50 @@ struct BlockFmhaPipelineProblem
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
};
template <typename QDataType,
typename KDataType,
typename VDataType,
typename SaccDataType,
typename SMPLComputeDataType,
typename BiasDataType,
typename RandValOutputDataType,
typename LSEDataType,
typename PDataType,
typename OaccDataType,
typename ODataType,
typename BlockFmhaShape,
bool kIsGroupMode,
typename FmhaMask,
typename Traits>
struct BlockFmhaFwdSplitKVPipelineProblem : BlockFmhaPipelineProblem<QDataType,
KDataType,
VDataType,
SaccDataType,
SMPLComputeDataType,
BiasDataType,
RandValOutputDataType,
LSEDataType,
PDataType,
OaccDataType,
ODataType,
BlockFmhaShape,
kIsGroupMode,
FmhaMask,
Traits>
template <typename QDataType_,
typename KDataType_,
typename VDataType_,
typename SaccDataType_,
typename SMPLComputeDataType_,
typename BiasDataType_,
typename LSEDataType_,
typename PDataType_,
typename OaccDataType_,
typename ODataType_,
typename BlockFmhaShape_,
bool kIsGroupMode_,
typename FmhaMask_,
typename Traits_>
struct BlockFmhaFwdSplitKVPipelineProblem
{
using QDataType = remove_cvref_t<QDataType_>;
using KDataType = remove_cvref_t<KDataType_>;
using VDataType = remove_cvref_t<VDataType_>;
using SaccDataType = remove_cvref_t<SaccDataType_>;
using SMPLComputeDataType = remove_cvref_t<SMPLComputeDataType_>;
using BiasDataType = remove_cvref_t<BiasDataType_>;
using LSEDataType = remove_cvref_t<LSEDataType_>;
using PDataType = remove_cvref_t<PDataType_>;
using OaccDataType = remove_cvref_t<OaccDataType_>;
using ODataType = remove_cvref_t<ODataType_>;
using BlockFmhaShape = remove_cvref_t<BlockFmhaShape_>;
using FmhaMask = remove_cvref_t<FmhaMask_>;
using Traits = remove_cvref_t<Traits_>;
static constexpr index_t kBlockSize = BlockFmhaShape::NumWarps * get_warp_size();
static constexpr bool kIsGroupMode = kIsGroupMode_;
// attributes from traits
static constexpr bool kPadSeqLenQ = Traits::kPadSeqLenQ;
static constexpr bool kPadSeqLenK = Traits::kPadSeqLenK;
static constexpr bool kPadHeadDimQ = Traits::kPadHeadDimQ;
static constexpr bool kPadHeadDimV = Traits::kPadHeadDimV;
static constexpr auto BiasEnum = Traits::BiasEnum;
static constexpr bool kStoreLSE = Traits::kStoreLSE;
static constexpr bool kDoFp8StaticQuant = Traits::kDoFp8StaticQuant;
static constexpr bool kIsPagedKV = Traits::kIsPagedKV;
static constexpr bool kHasUnevenSplits = kIsGroupMode || Traits::kHasUnevenSplits;
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
};
template <typename LSEDataType_,
......@@ -119,4 +131,44 @@ struct BlockFmhaSplitKVCombinePipelineProblem
static constexpr index_t kMaxSplits = Traits::kMaxSplits;
};
template <typename QDataType_,
typename KDataType_,
typename VDataType_,
index_t kM0_,
index_t kN0_,
index_t kK0_,
index_t kN1_,
bool kIsVLayoutRowMajor_,
RotaryEmbeddingEnum RotaryEnum_,
bool kIsPagedKV_,
typename Traits_>
struct BlockFmhaFwdAppendKVPipelineProblem
{
using QDataType = remove_cvref_t<QDataType_>;
using KDataType = remove_cvref_t<KDataType_>;
using VDataType = remove_cvref_t<VDataType_>;
using Traits = remove_cvref_t<Traits_>;
static constexpr index_t kBlockSize = 256;
static constexpr index_t kM0 = kM0_;
static constexpr index_t kN0 = kN0_;
static constexpr index_t kK0 = kK0_;
static constexpr index_t kN1 = kN1_;
using VLayout = std::conditional_t<kIsVLayoutRowMajor_,
ck_tile::tensor_layout::gemm::RowMajor,
ck_tile::tensor_layout::gemm::ColumnMajor>;
static constexpr auto RotaryEnum = RotaryEnum_;
static constexpr bool kIsPagedKV = kIsPagedKV_;
// attributes from traits
static constexpr bool kPadSeqLenQ = Traits::kPadSeqLenQ;
static constexpr bool kPadSeqLenK = Traits::kPadSeqLenK;
static constexpr bool kPadHeadDimQ = Traits::kPadHeadDimQ;
static constexpr bool kPadHeadDimV = Traits::kPadHeadDimV;
static constexpr index_t kBlockPerCu = Traits::kBlockPerCu;
};
} // namespace ck_tile
include/ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qx_ks_vs_custom_policy.hpp
View file @ 171ed358
......@@ -707,16 +707,19 @@ struct BlockFmhaPipelineQXKSVSCustomPolicy : BlockFmhaPipelineQXCustomPolicy<QLo
{
if constexpr(AsyncCopyK)
{
return GetSmemSizeKV<Problem>() + GetSmemSizeDropout<Problem>();
return GetSmemSizeKV<Problem>() + GetSmemSizeDropout<Problem>(0);
}
else
{
return ck_tile::max(GetSmemSizeKV<Problem>(), GetSmemSizeDropout<Problem>());
return ck_tile::max(GetSmemSizeKV<Problem>(), GetSmemSizeDropout<Problem>(0));
}
}
// this method is only available when Problem::kHasDropout is present
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeDropout()
CK_TILE_HOST_DEVICE static constexpr std::
enable_if_t<std::is_convertible_v<decltype(Problem::kHasDropout), bool>, ck_tile::index_t>
GetSmemSizeDropout(int)
{
if constexpr(Problem::kHasDropout)
{
......@@ -736,6 +739,13 @@ struct BlockFmhaPipelineQXKSVSCustomPolicy : BlockFmhaPipelineQXCustomPolicy<QLo
}
}
// fallback version if Problem::kHasDropout is not exist
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSizeDropout(...)
{
return 0;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeKDramTileDistribution()
{
......
include/ck_tile/ops/fmha/pipeline/tile_fmha_traits.hpp
View file @ 171ed358
......@@ -5,6 +5,7 @@
#include "ck_tile/core.hpp"
#include "ck_tile/ops/fmha/block/block_attention_bias_enum.hpp"
#include "ck_tile/ops/fmha/block/block_rotary_embedding.hpp"
namespace ck_tile {
......@@ -32,30 +33,31 @@ struct TileFmhaTraits
static constexpr index_t kBlockPerCu = kBlockPerCu_;
};
template <bool kPadSeqLenQ /* padding for seqlen_q */,
bool kPadSeqLenK /* padding for seqlen_k */,
bool kPadHeadDimQ /* paddding for hdim_q */,
bool kPadHeadDimV /* paddding for hdim_v */,
BlockAttentionBiasEnum BiasEnum,
bool kHasBiasGrad,
bool kStoreLSE,
bool kHasDropout,
bool kDoFp8StaticQuant,
bool kHasUnevenSplits_ = true,
index_t kBlockPerCu = -1 /* overwrite occupancy if not -1 */>
struct TileFmhaFwdSplitKVTraits : TileFmhaTraits<kPadSeqLenQ,
kPadSeqLenK,
kPadHeadDimQ,
kPadHeadDimV,
BiasEnum,
kHasBiasGrad,
kStoreLSE,
kHasDropout,
kDoFp8StaticQuant,
kBlockPerCu>
template <bool kPadSeqLenQ_ /* padding for seqlen_q */,
bool kPadSeqLenK_ /* padding for seqlen_k */,
bool kPadHeadDimQ_ /* paddding for hdim_q */,
bool kPadHeadDimV_ /* paddding for hdim_v */,
BlockAttentionBiasEnum BiasEnum_,
bool kHasBiasGrad_,
bool kStoreLSE_,
bool kDoFp8StaticQuant_,
bool kIsPagedKV_,
bool kHasUnevenSplits_,
index_t kBlockPerCu_ = -1 /* overwrite occupancy if not -1 */>
struct TileFmhaFwdSplitKVTraits
{
static constexpr bool kPadSeqLenQ = kPadSeqLenQ_;
static constexpr bool kPadSeqLenK = kPadSeqLenK_;
static constexpr bool kPadHeadDimQ = kPadHeadDimQ_;
static constexpr bool kPadHeadDimV = kPadHeadDimV_;
static constexpr auto BiasEnum = BiasEnum_;
static constexpr bool kHasBiasGrad = kHasBiasGrad_;
static constexpr bool kStoreLSE = kStoreLSE_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
static constexpr bool kIsPagedKV = kIsPagedKV_;
// determine if some split (length) is not divisible by tile size
static constexpr bool kHasUnevenSplits = kHasUnevenSplits_;
static constexpr index_t kBlockPerCu = kBlockPerCu_;
};
template <bool kPadSeqLenQ_ /* padding for seqlen_q */,
......@@ -76,6 +78,20 @@ struct TileFmhaFwdSplitKVCombineTraits
static constexpr index_t kBlockPerCu = kBlockPerCu_;
};
template <bool kPadSeqLenQ_ /* padding for seqlen_q */,
bool kPadSeqLenK_ /* padding for seqlen_k */,
bool kPadHeadDimQ_ /* paddding for hdim_q */,
bool kPadHeadDimV_ /* paddding for hdim_v */,
index_t kBlockPerCu_ = -1 /* overwrite occupancy if not -1 */>
struct TileFmhaFwdAppendKVTraits
{
static constexpr bool kPadSeqLenQ = kPadSeqLenQ_;
static constexpr bool kPadSeqLenK = kPadSeqLenK_;
static constexpr bool kPadHeadDimQ = kPadHeadDimQ_;
static constexpr bool kPadHeadDimV = kPadHeadDimV_;
static constexpr index_t kBlockPerCu = kBlockPerCu_;
};
template <bool kPadSeqLenQ_ /* padding for seqlen_q */,
bool kPadHeadDimV_ /* paddding for hdim_v */,
index_t kBlockPerCu_ = 2 /* hint to occupancy */>
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 171ed358
......@@ -74,6 +74,10 @@ using GNWK = ck::tensor_layout::convolution::GNWK;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
using NGKW = ck::tensor_layout::convolution::NGKW;
using NGKHW = ck::tensor_layout::convolution::NGKHW;
using NGKDHW = ck::tensor_layout::convolution::NGKDHW;
//
using NWGC = ck::tensor_layout::convolution::NWGC;
using NHWGC = ck::tensor_layout::convolution::NHWGC;
......@@ -87,6 +91,10 @@ using NWGK = ck::tensor_layout::convolution::NWGK;
using NHWGK = ck::tensor_layout::convolution::NHWGK;
using NDHWGK = ck::tensor_layout::convolution::NDHWGK;
using NGCW = ck::tensor_layout::convolution::NGCW;
using NGCHW = ck::tensor_layout::convolution::NGCHW;
using NGCDHW = ck::tensor_layout::convolution::NGCDHW;
//
using G_K = ck::tensor_layout::convolution::G_K;
using GK_Tuple = ck::Tuple<G_K>;
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_two_stage_xdl_instance.hpp
View file @ 171ed358
......@@ -56,6 +56,46 @@ using device_grouped_conv_bwd_weight_two_stage_xdl_c_shuffle_f16_instances = std
// clang-format on
>;
// NGCHW requires transpose, we use vector loads and stores params for them
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_instances = std::tuple<
// clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
//#########################################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl| Scheduler| Version| |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| | | | |
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1, F16, F16, 1, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 32, 32, 8, 32, 32, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 2, F16, F16, 2, 2>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 64, 32, 8, 32, 32, 1, 2, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 4, F16, F16, 4, 4>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 128, 32, 8, 32, 32, 1, 4, S<4, 4, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, 1, 1, S<1, 4, 1, 8>, 1, Scheduler, PipelineVersion, 8, F16, F16, 8, 8>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 32, 32, 8, 32, 32, 1, 1, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 2, F16, F16, 2, 2>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 32, 32, 8, 32, 32, 2, 1, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 4, F16, F16, 4, 4>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 128, 32, 32, 8, 32, 32, 4, 1, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, S<4, 4, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, 1, 1, S<1, 8, 1, 4>, 1, Scheduler, PipelineVersion, 8, F16, F16, 8, 8>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 32, 32, 8, 32, 32, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 2, F16, F16, 1, 2>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 64, 32, 8, 32, 32, 1, 2, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 4, F16, F16, 1, 4>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 128, 32, 8, 32, 32, 1, 4, S<4, 4, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, 1, 1, S<1, 4, 1, 8>, 1, Scheduler, PipelineVersion, 8, F16, F16, 1, 8>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 32, 32, 8, 32, 32, 2, 1, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 4, F16, F16, 1, 4>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 128, 32, 32, 8, 32, 32, 4, 1, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, S<4, 4, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, 1, 1, S<1, 8, 1, 4>, 1, Scheduler, PipelineVersion, 8, F16, F16, 1, 8>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 32, 32, 8, 32, 32, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 2, 2, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 2, F16, F16, 2, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 64, 32, 8, 32, 32, 1, 2, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 4, F16, F16, 4, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 32, 128, 32, 8, 32, 32, 1, 4, S<4, 4, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, 1, 1, S<1, 4, 1, 8>, 1, Scheduler, PipelineVersion, 8, F16, F16, 8 ,1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 32, 32, 8, 32, 32, 2, 1, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 4, F16, F16, 4, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 128, 32, 32, 8, 32, 32, 4, 1, S<4, 16, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, S<4, 4, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 8, 8, false, 1, 1, S<1, 8, 1, 4>, 1, Scheduler, PipelineVersion, 8, F16, F16, 8, 1>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp 0 → 100644
View file @ 171ed358
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using Empty_Tuple = ck::Tuple<>;
using namespace ck::tensor_layout::convolution;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvFwdDefault =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto ConvFwd3x3 = ConvolutionForwardSpecialization::Filter3x3;
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_merged_groups_bf16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| ACompute| BCompute| BlockGemm| NumGroups|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Type| Type| Pipeline| ToMerge|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| | | Scheduler| |
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// Instances with NumGroupsPerBatch > 1
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsLayout, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, BF16, BF16, LoopScheduler::Default, 8>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsLayout, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, BF16, BF16, LoopScheduler::Default, 16>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsLayout, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, BF16, BF16, LoopScheduler::Default, 32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_merged_groups_f16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// Instances with NumGroupsPerBatch > 1
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, DsLayout, F16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, F16, F16, LoopScheduler::Default, 8>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, DsLayout, F16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, F16, F16, LoopScheduler::Default, 16>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, DsLayout, F16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, F16, F16, LoopScheduler::Default, 32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_merged_groups_f32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// Instances with NumGroupsPerBatch > 1
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsLayout, F32, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, F32, F32, LoopScheduler::Default, 8>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsLayout, F32, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, F32, F32, LoopScheduler::Default, 16>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsLayout, F32, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, F32, F32, LoopScheduler::Default, 32>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp
View file @ 171ed358
......@@ -367,6 +367,21 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instances(
op_ptrs);
}
#endif
}
if constexpr(is_same_v<InLayout, NGCHW> && is_same_v<WeiLayout, GKYXC> &&
is_same_v<OutLayout, NGKHW>)
{
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> &&
is_same_v<ComputeTypeB, half_t>)
{
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instances(
op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev5_instances(
op_ptrs);
}
#endif
}
}
......@@ -447,6 +462,21 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(
op_ptrs);
}
#endif
}
if constexpr(is_same_v<InLayout, NGCDHW> && is_same_v<WeiLayout, GKZYXC> &&
is_same_v<OutLayout, NGKDHW>)
{
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> &&
is_same_v<ComputeTypeB, half_t>)
{
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev2_instances(
op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev5_instances(
op_ptrs);
}
#endif
}
}
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_xdl.inc
View file @ 171ed358
......@@ -137,6 +137,29 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pi
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev5_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
......@@ -240,6 +263,29 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW,
GKZYXC,
NGKDHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev5_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW,
GKZYXC,
NGKDHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp
View file @ 171ed358
......@@ -18,6 +18,7 @@
#ifdef CK_USE_XDL
#include "grouped_convolution_forward_xdl.inc"
#include "grouped_convolution_forward_xdl_large_tensor.inc"
#include "grouped_convolution_forward_xdl_merged_groups.inc"
#include "grouped_convolution_forward_comp_xdl.inc"
#include "grouped_convolution_forward_mem_inter_xdl.inc"
#include "grouped_convolution_forward_mem_intra_xdl.inc"
......@@ -202,6 +203,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances(
op_ptrs);
......@@ -217,6 +220,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_comp_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_mem_intra_instances(
op_ptrs);
......@@ -234,6 +239,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_comp_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_mem_intra_instances(
op_ptrs);
......@@ -293,6 +300,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
op_ptrs);
......@@ -349,6 +358,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_mem_intra_instances(
op_ptrs);
......@@ -366,6 +377,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_comp_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_mem_intra_instances(
op_ptrs);
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_xdl_merged_groups.inc 0 → 100644
View file @ 171ed358
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// grouped conv2d forward, NHWGC/GKYXC/NHWGK
#ifdef CK_ENABLE_BF16
void add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP16
void add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_BF16
// grouped conv3d forward, NDHWGC/GKZYXC/NDHWGK
void add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP16
void add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/utility/convolution_host_tensor_descriptor_helper.hpp
View file @ 171ed358
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -46,6 +46,21 @@ std::vector<std::size_t> get_layout_transpose_gnchw_to_old()
{
return {0, 1, 2, 3};
}
else if constexpr(ck::is_same_v<OldLayout, ck::tensor_layout::convolution::NGCW> ||
ck::is_same_v<OldLayout, ck::tensor_layout::convolution::NGKW>)
{
return {1, 0, 2, 3};
}
else if constexpr(ck::is_same_v<OldLayout, ck::tensor_layout::convolution::NGCHW> ||
ck::is_same_v<OldLayout, ck::tensor_layout::convolution::NGKHW>)
{
return {1, 0, 2, 3, 4};
}
else if constexpr(ck::is_same_v<OldLayout, ck::tensor_layout::convolution::NGCDHW> ||
ck::is_same_v<OldLayout, ck::tensor_layout::convolution::NGKDHW>)
{
return {1, 0, 2, 3, 4, 5};
}
else if constexpr(ck::is_same_v<OldLayout, ck::tensor_layout::convolution::GNCHW> ||
ck::is_same_v<OldLayout, ck::tensor_layout::convolution::GKCYX> ||
ck::is_same_v<OldLayout, ck::tensor_layout::convolution::GNKHW>)
......@@ -132,6 +147,18 @@ make_input_host_tensor_descriptor_g_n_c_wis_packed(const ck::utils::conv::ConvPa
param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
}
// separate from legacy code above
else if constexpr(ck::is_same_v<InLayout, ck::tensor_layout::convolution::NGCW> ||
ck::is_same_v<InLayout, ck::tensor_layout::convolution::NGCHW> ||
ck::is_same_v<InLayout, ck::tensor_layout::convolution::NGCDHW>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.end(),
param.input_spatial_lengths_.begin(),
param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(ck::is_same_v<InLayout, ck::tensor_layout::convolution::GNCW> ||
ck::is_same_v<InLayout, ck::tensor_layout::convolution::GNCHW> ||
ck::is_same_v<InLayout, ck::tensor_layout::convolution::GNCDHW>)
......@@ -314,6 +341,19 @@ make_output_host_tensor_descriptor_g_n_k_wos_packed(const ck::utils::conv::ConvP
param.output_spatial_lengths_.begin(),
param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
}
// separate from legacy code above
else if constexpr(ck::is_same_v<OutLayout, ck::tensor_layout::convolution::NGKW> ||
ck::is_same_v<OutLayout, ck::tensor_layout::convolution::NGKHW> ||
ck::is_same_v<OutLayout, ck::tensor_layout::convolution::NGKDHW>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.K_)};
physical_lengths.insert(physical_lengths.end(),
param.output_spatial_lengths_.begin(),
param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(ck::is_same_v<OutLayout, ck::tensor_layout::convolution::GNWK> ||
ck::is_same_v<OutLayout, ck::tensor_layout::convolution::GNHWK> ||
ck::is_same_v<OutLayout, ck::tensor_layout::convolution::GNDHWK>)
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/CMakeLists.txt
View file @ 171ed358
......@@ -8,6 +8,8 @@ set(GROUPED_CONV2D_BWD_WEIGHT
xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev5_instance.cpp
)
if(DL_KERNELS)
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instance.cpp 0 → 100644
View file @ 171ed358
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_two_stage_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_instances<
2,
NGCHW,
GKYXC,
NGKHW,
ConvBwdWeightDefault,
BlockGemmPipelineScheduler::Intrawave,
BlockGemmPipelineVersion::v2>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev5_instance.cpp 0 → 100644
View file @ 171ed358
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_two_stage_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev5_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_instances<
2,
NGCHW,
GKYXC,
NGKHW,
ConvBwdWeightDefault,
BlockGemmPipelineScheduler::Intrawave,
BlockGemmPipelineVersion::v5>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
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