mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
CK Tile FA Training kernels (#1286)
* FA fwd dropout * FA bwd * epilogue reuse * CMakeLists update * [CK_TILE] support alibi (#1269) * add alibi support * fix code * update code based on comment * Support more hdim * fix fp8 bias * support seqlen_k=0 case * remove unused printf * fix format --------- Co-authored-by: rocking <ChunYu.Lai@amd.com> * now fwd/bwd can build * bwd alibi * add bwd validation stream_config * update generated filenames * update bwd kernel launch * CK_TILE_HOST_DEVICE in philox * Transpose -> transpose * format * format * format * Generate the instance for FA required * format * fix error in WarpGemm --------- Co-authored-by: danyao12 <danyao12> Co-authored-by: carlushuang <carlus.huang@amd.com> Co-authored-by: rocking <ChunYu.Lai@amd.com> Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com> Co-authored-by: Jing Zhang <jizhan@amd.com>
This commit is contained in:
Dan Yao
329
include/ck_tile/ops/fmha/block/block_dropout.hpp
Normal file
329
include/ck_tile/ops/fmha/block/block_dropout.hpp
Normal file
@@ -0,0 +1,329 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck_tile/core.hpp"
|
||||
#include "ck_tile/ops/gemm/warp/warp_gemm.hpp"
|
||||
|
||||
namespace ck_tile {
|
||||
|
||||
struct BlockDropout
|
||||
{
|
||||
CK_TILE_HOST_DEVICE BlockDropout(index_t i_batch,
|
||||
index_t i_head,
|
||||
index_t nheads,
|
||||
unsigned long long seed,
|
||||
unsigned long long offset,
|
||||
float rp_undrop_,
|
||||
uint8_t p_undrop_in_uint8_t_,
|
||||
bool is_store_randval_)
|
||||
: ph(seed, offset + (i_batch * nheads + i_head) * get_warp_size() + get_lane_id()),
|
||||
rp_undrop(rp_undrop_),
|
||||
p_undrop_in_uint8_t(p_undrop_in_uint8_t_),
|
||||
is_store_randval(is_store_randval_)
|
||||
{
|
||||
}
|
||||
|
||||
template <typename BlockGemm, bool IsFwd = true, typename RandValDramBlockWindowTmp>
|
||||
CK_TILE_HOST_DEVICE static constexpr auto
|
||||
MakeRandvalDramWindow(RandValDramBlockWindowTmp& randval_dram_block_window_tmp,
|
||||
index_t seqlen_qk_start)
|
||||
{
|
||||
constexpr auto config =
|
||||
BlockGemm::Policy::template GetWarpGemmMWarpNWarp<typename BlockGemm::Problem>();
|
||||
using WG = remove_cvref_t<decltype(config.template at<0>())>;
|
||||
constexpr index_t MWarp = config.template at<1>();
|
||||
constexpr index_t NWarp = config.template at<2>();
|
||||
constexpr index_t kMPerStep = MWarp * WG::kM;
|
||||
constexpr index_t kNPerStep = NWarp * WG::kN;
|
||||
|
||||
const auto block_origin = randval_dram_block_window_tmp.get_window_origin();
|
||||
auto randval_dram_window = [&]() {
|
||||
if constexpr(IsFwd)
|
||||
{
|
||||
return make_tile_window(
|
||||
randval_dram_block_window_tmp.get_bottom_tensor_view(),
|
||||
ck_tile::make_tuple(number<kMPerStep>{}, number<kNPerStep>{}),
|
||||
{block_origin.at(number<0>{}), seqlen_qk_start}); // M/N
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_tile_window(
|
||||
randval_dram_block_window_tmp.get_bottom_tensor_view(),
|
||||
ck_tile::make_tuple(number<kMPerStep>{}, number<kNPerStep>{}),
|
||||
{seqlen_qk_start, block_origin.at(number<1>{})}); // M/N
|
||||
}
|
||||
}();
|
||||
|
||||
return randval_dram_window;
|
||||
}
|
||||
|
||||
template <typename BlockGemm>
|
||||
CK_TILE_HOST_DEVICE static constexpr auto MakeRandValLdsBlockDescriptor()
|
||||
{
|
||||
constexpr auto config =
|
||||
BlockGemm::Policy::template GetWarpGemmMWarpNWarp<typename BlockGemm::Problem>();
|
||||
using WG = remove_cvref_t<decltype(config.template at<0>())>;
|
||||
constexpr index_t MWarp = config.template at<1>();
|
||||
constexpr index_t kMPerStep = MWarp * WG::kM;
|
||||
constexpr index_t kNPerStep = WG::kN;
|
||||
constexpr index_t kN1 = 8;
|
||||
constexpr index_t kN0 = kNPerStep / kN1;
|
||||
|
||||
constexpr auto randval_lds_block_desc_0 = make_naive_tensor_descriptor(
|
||||
ck_tile::make_tuple(number<kN0>{}, number<kMPerStep>{}, number<kN1>{}),
|
||||
ck_tile::make_tuple(number<(kMPerStep + 1) * kN1>{}, number<kN1>{}, number<1>{}),
|
||||
number<kN1>{},
|
||||
number<1>{});
|
||||
|
||||
constexpr auto randval_lds_block_desc = transform_tensor_descriptor(
|
||||
randval_lds_block_desc_0,
|
||||
ck_tile::make_tuple(
|
||||
make_pass_through_transform(number<kMPerStep>{}),
|
||||
make_merge_transform(ck_tile::make_tuple(number<kN0>{}, number<kN1>{}))),
|
||||
ck_tile::make_tuple(sequence<1>{}, sequence<0, 2>{}),
|
||||
ck_tile::make_tuple(sequence<0>{}, sequence<1>{}));
|
||||
|
||||
return randval_lds_block_desc;
|
||||
}
|
||||
|
||||
template <typename BlockGemm>
|
||||
CK_TILE_HOST_DEVICE static constexpr auto MakeRandValTileDistribution()
|
||||
{
|
||||
constexpr auto config =
|
||||
BlockGemm::Policy::template GetWarpGemmMWarpNWarp<typename BlockGemm::Problem>();
|
||||
constexpr index_t MWarp = config.template at<1>();
|
||||
constexpr index_t NWarp = config.template at<2>();
|
||||
|
||||
constexpr index_t MIterPerWarp = 1;
|
||||
constexpr index_t NIterPerWarp = 1;
|
||||
|
||||
constexpr auto randval_block_outer_part_dstr_encoding = tile_distribution_encoding<
|
||||
sequence<>,
|
||||
tuple<sequence<MIterPerWarp, MWarp>, sequence<NIterPerWarp, NWarp>>,
|
||||
tuple<sequence<1, 2>>,
|
||||
tuple<sequence<1, 1>>,
|
||||
sequence<1, 2>,
|
||||
sequence<0, 0>>{};
|
||||
|
||||
// Use Bwd WarpGemm to ensure that Fwd's random values are consistent with Bwd.
|
||||
constexpr auto randval_block_inner_part_dstr_encoding = []() {
|
||||
if constexpr(std::is_same_v<typename BlockGemm::ADataType, half_t> &&
|
||||
std::is_same_v<typename BlockGemm::BDataType, half_t> &&
|
||||
std::is_same_v<typename BlockGemm::CDataType, float>)
|
||||
{
|
||||
return typename WarpGemmMfmaF16F16F32M32N32K16SwizzleA::CWarpDstrEncoding{};
|
||||
}
|
||||
else
|
||||
{
|
||||
return typename WarpGemmMfmaBf16Bf16F32M32N32K16SwizzleA::CWarpDstrEncoding{};
|
||||
}
|
||||
}();
|
||||
|
||||
constexpr auto randval_block_part_dstr_encode =
|
||||
detail::make_embed_tile_distribution_encoding(randval_block_outer_part_dstr_encoding,
|
||||
randval_block_inner_part_dstr_encoding);
|
||||
|
||||
return make_static_tile_distribution(randval_block_part_dstr_encode);
|
||||
}
|
||||
|
||||
template <typename BlockGemm>
|
||||
CK_TILE_HOST_DEVICE static constexpr auto MakeRandValLdsShuffleTileDistribution()
|
||||
{
|
||||
constexpr auto config =
|
||||
BlockGemm::Policy::template GetWarpGemmMWarpNWarp<typename BlockGemm::Problem>();
|
||||
using WG = remove_cvref_t<decltype(config.template at<0>())>;
|
||||
constexpr index_t MWarp = config.template at<1>();
|
||||
constexpr index_t NWarp = config.template at<2>();
|
||||
|
||||
constexpr index_t MIterPerWarp = 1;
|
||||
constexpr index_t NIterPerWarp = 1;
|
||||
|
||||
constexpr auto randval_block_outer_part_dstr_encoding = tile_distribution_encoding<
|
||||
sequence<>,
|
||||
tuple<sequence<MIterPerWarp, MWarp>, sequence<NIterPerWarp, NWarp>>,
|
||||
tuple<sequence<1, 2>>,
|
||||
tuple<sequence<1, 1>>,
|
||||
sequence<1, 2>,
|
||||
sequence<0, 0>>{};
|
||||
|
||||
constexpr auto randval_block_part_dstr_encode =
|
||||
detail::make_embed_tile_distribution_encoding(randval_block_outer_part_dstr_encoding,
|
||||
typename WG::CWarpDstrEncoding{});
|
||||
|
||||
return make_static_tile_distribution(randval_block_part_dstr_encode);
|
||||
}
|
||||
|
||||
template <typename BlockGemm,
|
||||
typename PComputeDataType,
|
||||
typename RandValOutputDataType,
|
||||
typename PComputeWindow,
|
||||
typename RandValDramWindow>
|
||||
CK_TILE_HOST_DEVICE void Run(void* randval_ptr,
|
||||
const index_t start_n0_idx,
|
||||
PComputeWindow& p_compute,
|
||||
RandValDramWindow& randval_dram_window) const
|
||||
{
|
||||
constexpr auto config =
|
||||
BlockGemm::Policy::template GetWarpGemmMWarpNWarp<typename BlockGemm::Problem>();
|
||||
using WG = remove_cvref_t<decltype(config.template at<0>())>;
|
||||
constexpr index_t MWarp = config.template at<1>();
|
||||
constexpr index_t NWarp = config.template at<2>();
|
||||
using BlockGemmShape = remove_cvref_t<typename BlockGemm::BlockGemmShape>;
|
||||
constexpr index_t kMPerBlock = BlockGemmShape::kM;
|
||||
constexpr index_t kNPerBlock = BlockGemmShape::kN;
|
||||
constexpr index_t kMPerStep = MWarp * WG::kM;
|
||||
constexpr index_t kNPerStep = NWarp * WG::kN;
|
||||
|
||||
// randval tile in LDS
|
||||
auto randval_lds = make_tensor_view<address_space_enum::lds>(
|
||||
reinterpret_cast<uint8_t*>(randval_ptr), MakeRandValLdsBlockDescriptor<BlockGemm>());
|
||||
|
||||
auto randval_lds_window = make_tile_window(
|
||||
randval_lds, MakeRandValLdsBlockDescriptor<BlockGemm>().get_lengths(), {0, 0});
|
||||
|
||||
// register distribute
|
||||
auto randval_dist_generated =
|
||||
make_static_distributed_tensor<uint8_t>(MakeRandValTileDistribution<BlockGemm>());
|
||||
static_assert(randval_dist_generated.kThreadElementSpaceSize == 16);
|
||||
|
||||
auto randval_lds_read_window =
|
||||
make_tile_window(randval_lds_window.get_bottom_tensor_view(),
|
||||
randval_lds_window.get_window_lengths(),
|
||||
randval_lds_window.get_window_origin(),
|
||||
MakeRandValLdsShuffleTileDistribution<BlockGemm>());
|
||||
|
||||
const int start_m0_idx = randval_dram_window.get_window_origin().at(number<0>{});
|
||||
static_for<0, kMPerBlock / kMPerStep, 1>{}([&](auto i_m0) {
|
||||
static_for<0, kNPerBlock / kNPerStep, 1>{}([&](auto i_n0) {
|
||||
int block_row_start = (start_m0_idx / WG::kM) + (i_m0 * MWarp) + get_warp_id();
|
||||
int block_col_start = (start_n0_idx / WG::kN) + i_n0;
|
||||
uint2 rowcol = make_uint2(block_row_start, block_col_start);
|
||||
|
||||
// generate random number
|
||||
uint8_t random_uint8_t[16];
|
||||
ph.get_random_16x8(random_uint8_t, reinterpret_cast<unsigned long long&>(rowcol));
|
||||
|
||||
constexpr auto randval_dist_generated_spans =
|
||||
decltype(randval_dist_generated)::get_distributed_spans();
|
||||
int i_random_idx = 0;
|
||||
sweep_tile_span(randval_dist_generated_spans[number<0>{}], [&](auto idx0) {
|
||||
sweep_tile_span(randval_dist_generated_spans[number<1>{}], [&](auto idx1) {
|
||||
constexpr auto i_j_idx = ck_tile::make_tuple(idx0, idx1);
|
||||
randval_dist_generated(i_j_idx) = random_uint8_t[i_random_idx++];
|
||||
});
|
||||
});
|
||||
// save to LDS
|
||||
store_tile(randval_lds_window, randval_dist_generated);
|
||||
block_sync_lds();
|
||||
// read from LDS to register
|
||||
auto randval = load_tile(randval_lds_read_window);
|
||||
constexpr auto randval_spans = decltype(randval)::get_distributed_spans();
|
||||
sweep_tile_span(randval_spans[number<0>{}], [&](auto idx0) {
|
||||
sweep_tile_span(randval_spans[number<1>{}], [&](auto idx1) {
|
||||
constexpr auto p_idx0 = tile_distributed_index<i_m0>{};
|
||||
constexpr auto p_idx1 =
|
||||
tile_distributed_index<i_n0, idx1.impl_.at(1), idx1.impl_.at(2)>{};
|
||||
constexpr auto p_idx = ck_tile::make_tuple(p_idx0, p_idx1);
|
||||
constexpr auto r_idx = ck_tile::make_tuple(idx0, idx1);
|
||||
p_compute(p_idx) = randval[r_idx] <= p_undrop_in_uint8_t
|
||||
? p_compute[p_idx] * rp_undrop
|
||||
: PComputeDataType(0);
|
||||
});
|
||||
});
|
||||
// save to Global
|
||||
if(is_store_randval)
|
||||
{
|
||||
const auto randval_store = cast_tile<RandValOutputDataType>(randval);
|
||||
store_tile(randval_dram_window, randval_store);
|
||||
move_tile_window(randval_dram_window, {0, kNPerStep});
|
||||
}
|
||||
});
|
||||
if(is_store_randval)
|
||||
{
|
||||
move_tile_window(randval_dram_window, {kMPerStep, -kNPerBlock});
|
||||
}
|
||||
});
|
||||
if(is_store_randval)
|
||||
{
|
||||
move_tile_window(randval_dram_window, {-kMPerBlock, kNPerBlock});
|
||||
}
|
||||
}
|
||||
|
||||
template <typename BlockGemm,
|
||||
typename RandValOutputDataType,
|
||||
typename PComputeWindow,
|
||||
typename RandValDramWindow>
|
||||
CK_TILE_HOST_DEVICE void Run(const index_t start_m0_idx,
|
||||
PComputeWindow& p_compute,
|
||||
RandValDramWindow& randval_dram_window) const
|
||||
{
|
||||
constexpr auto config =
|
||||
BlockGemm::Policy::template GetWarpGemmMWarpNWarp<typename BlockGemm::Problem>();
|
||||
using WG = remove_cvref_t<decltype(config.template at<0>())>;
|
||||
constexpr index_t MWarp = config.template at<1>();
|
||||
constexpr index_t NWarp = config.template at<2>();
|
||||
using BlockGemmShape = remove_cvref_t<typename BlockGemm::BlockGemmShape>;
|
||||
constexpr index_t kMPerBlock = BlockGemmShape::kM;
|
||||
constexpr index_t kNPerBlock = BlockGemmShape::kN;
|
||||
constexpr index_t kMPerStep = MWarp * WG::kM;
|
||||
constexpr index_t kNPerStep = NWarp * WG::kN;
|
||||
|
||||
// register distribute
|
||||
auto randval =
|
||||
make_static_distributed_tensor<uint8_t>(MakeRandValTileDistribution<BlockGemm>());
|
||||
static_assert(randval.kThreadElementSpaceSize == 16);
|
||||
|
||||
const int start_n0_idx = randval_dram_window.get_window_origin().at(number<1>{});
|
||||
static_for<0, kNPerBlock / kNPerStep, 1>{}([&](auto i_n0) {
|
||||
static_for<0, kMPerBlock / kMPerStep, 1>{}([&](auto i_m0) {
|
||||
int block_row_start = (start_m0_idx / WG::kM) + i_m0;
|
||||
int block_col_start = (start_n0_idx / WG::kN) + (i_n0 * NWarp) + get_warp_id();
|
||||
uint2 rowcol = make_uint2(block_row_start, block_col_start);
|
||||
|
||||
// generate random number
|
||||
uint8_t random_uint8_t[16];
|
||||
ph.get_random_16x8(random_uint8_t, reinterpret_cast<unsigned long long&>(rowcol));
|
||||
|
||||
constexpr auto randval_spans = decltype(randval)::get_distributed_spans();
|
||||
int i_random_idx = 0;
|
||||
sweep_tile_span(randval_spans[number<0>{}], [&](auto idx0) {
|
||||
sweep_tile_span(randval_spans[number<1>{}], [&](auto idx1) {
|
||||
constexpr auto r_idx = ck_tile::make_tuple(idx0, idx1);
|
||||
randval(r_idx) = random_uint8_t[i_random_idx++];
|
||||
constexpr auto p_idx0 =
|
||||
tile_distributed_index<i_m0, idx0.impl_.at(1), idx0.impl_.at(2)>{};
|
||||
constexpr auto p_idx1 = tile_distributed_index<i_n0>{};
|
||||
constexpr auto p_idx = ck_tile::make_tuple(p_idx0, p_idx1);
|
||||
p_compute(p_idx) = randval[r_idx] <= p_undrop_in_uint8_t
|
||||
? p_compute[p_idx]
|
||||
: -p_compute[p_idx];
|
||||
});
|
||||
});
|
||||
// save to Global
|
||||
if(is_store_randval)
|
||||
{
|
||||
const auto randval_store = cast_tile<RandValOutputDataType>(randval);
|
||||
store_tile(randval_dram_window, randval_store);
|
||||
move_tile_window(randval_dram_window, {kMPerStep, 0});
|
||||
}
|
||||
});
|
||||
if(is_store_randval)
|
||||
{
|
||||
move_tile_window(randval_dram_window, {-kMPerBlock, kNPerStep});
|
||||
}
|
||||
});
|
||||
if(is_store_randval)
|
||||
{
|
||||
move_tile_window(randval_dram_window, {kMPerBlock, -kNPerBlock});
|
||||
}
|
||||
}
|
||||
|
||||
ck_tile::philox ph;
|
||||
const float rp_undrop;
|
||||
const uint8_t p_undrop_in_uint8_t;
|
||||
const bool is_store_randval;
|
||||
};
|
||||
|
||||
} // namespace ck_tile
|
||||
@@ -1,5 +1,5 @@
|
||||
// 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
|
||||
|
||||
@@ -141,6 +141,36 @@ struct GenericAttentionMask
|
||||
}
|
||||
}
|
||||
|
||||
// to get the loop length along Y axis, return index:[start, end), end-start=length
|
||||
// use this if need loop over Y axis tile by tile (like q-seqlen loopover)
|
||||
// TODO: y_end still could be negative, so end-start could be negative(need check)
|
||||
template <index_t YTile, index_t XTile>
|
||||
CK_TILE_HOST_DEVICE constexpr auto
|
||||
GetTileRangeAlongY(index_t i_x, number<YTile>, number<XTile>) const
|
||||
{
|
||||
if constexpr(!IsMasking)
|
||||
{
|
||||
return ck_tile::make_tuple(0, y_total);
|
||||
}
|
||||
else
|
||||
{
|
||||
// get the tile start/end range assum we loop over along Y tile by tile
|
||||
index_t y_start = [&]() {
|
||||
index_t tmp = max(-x + i_x + 1, 0);
|
||||
return (tmp / YTile) * YTile; // round to tile aligned
|
||||
}();
|
||||
|
||||
// TODO: end could be negative, we ignore clamp here, and let caller to check
|
||||
// ... in which case end-start is negative
|
||||
index_t y_end = [&]() {
|
||||
index_t tmp = min(i_x + XTile - 1 + y, y_total);
|
||||
return ((tmp + YTile - 1) / YTile) * YTile;
|
||||
}();
|
||||
|
||||
return ck_tile::make_tuple(y_start, y_end);
|
||||
}
|
||||
}
|
||||
|
||||
// per-pixel check if out-of-bound, if true, need mask a value(like -INF)
|
||||
CK_TILE_HOST_DEVICE constexpr auto IsOutOfBound(index_t i_y, index_t i_x) const
|
||||
{
|
||||
@@ -160,14 +190,14 @@ struct GenericAttentionMask
|
||||
}
|
||||
else
|
||||
{
|
||||
return i_x >= x_end;
|
||||
return i_x >= x_end || i_y >= y_total;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// if current tile is at the edge, means need per-pixel mask check.
|
||||
// otherwise no need to check per-pixel
|
||||
// Attention! assume the idex passed in this function is with in range of GetTileRangeAlongX()
|
||||
// Attention! assume the idex passed in this function is with in range of GetTileRangeAlongX/Y()
|
||||
// can be used as a fast-path to decide if do per-pixel check or not
|
||||
template <index_t TileHeight, index_t TileWidth>
|
||||
CK_TILE_HOST_DEVICE constexpr auto
|
||||
@@ -269,6 +299,36 @@ struct SimplifiedGenericAttentionMask
|
||||
}
|
||||
}
|
||||
|
||||
// to get the loop length along Y axis, return index:[start, end), end-start=length
|
||||
// use this if need loop over Y axis tile by tile (like q-seqlen loopover)
|
||||
// TODO: y_end still could be negative, so end-start could be negative(need check)
|
||||
template <index_t YTile, index_t XTile>
|
||||
CK_TILE_HOST_DEVICE constexpr auto
|
||||
GetTileRangeAlongY(index_t i_x, number<YTile>, number<XTile>) const
|
||||
{
|
||||
if constexpr(!IsMasking)
|
||||
{
|
||||
return ck_tile::make_tuple(0, y_total);
|
||||
}
|
||||
else
|
||||
{
|
||||
// get the tile start/end range assum we loop over along Y tile by tile
|
||||
index_t y_start = [&]() {
|
||||
index_t tmp = max(-x + i_x + 1, 0);
|
||||
return (tmp / YTile) * YTile; // round to tile aligned
|
||||
}();
|
||||
|
||||
// TODO: end could be negative, we ignore clamp here, and let caller to check
|
||||
// ... in which case end-start is negative
|
||||
index_t y_end = [&]() {
|
||||
index_t tmp = min(i_x + XTile - 1 + y, y_total);
|
||||
return ((tmp + YTile - 1) / YTile) * YTile;
|
||||
}();
|
||||
|
||||
return ck_tile::make_tuple(y_start, y_end);
|
||||
}
|
||||
}
|
||||
|
||||
// per-pixel check if out-of-bound, if true, need mask a value(like -INF)
|
||||
CK_TILE_HOST_DEVICE constexpr auto IsOutOfBound(index_t i_y, index_t i_x) const
|
||||
{
|
||||
@@ -283,13 +343,13 @@ struct SimplifiedGenericAttentionMask
|
||||
index_t x_start = -y + i_y + 1; // this could be negative, but it's fine
|
||||
index_t x_end = min(i_y + x, x_total); // need min in case x is padded
|
||||
|
||||
return i_x < x_start || i_x >= x_end;
|
||||
return i_x < x_start || i_x >= x_end || i_y >= y_total;
|
||||
}
|
||||
}
|
||||
|
||||
// if current tile is at the edge, means need per-pixel mask check.
|
||||
// otherwise no need to check per-pixel
|
||||
// Attention! assume the idex passed in this function is with in range of GetTileRangeAlongX()
|
||||
// Attention! assume the idex passed in this function is with in range of GetTileRangeAlongX/Y()
|
||||
// can be used as a fast-path to decide if do per-pixel check or not
|
||||
template <index_t TileHeight, index_t TileWidth>
|
||||
CK_TILE_HOST_DEVICE constexpr auto
|
||||
@@ -361,6 +421,6 @@ make_generic_attention_mask_from_lr_window(index_t left_size,
|
||||
{
|
||||
auto r = make_generic_attention_mask_coordinates_from_lr_window(
|
||||
left_size, right_size, y_total, x_total, is_top_left);
|
||||
return MaskType{r.at(ck_tile::number<0>{}), r.at(ck_tile::number<1>{}), y_total, x_total};
|
||||
return MaskType{r.at(number<0>{}), r.at(number<1>{}), y_total, x_total};
|
||||
}
|
||||
} // namespace ck_tile
|
||||
|
||||
Reference in New Issue
Block a user