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Unverified Commit fe488bf2 authored by rocking's avatar rocking Committed by GitHub
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Merge branch 'develop' into layernorm/instance_support

parents 8bed8529 0394f8a7
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example/ck_tile/05_reduce/reduce.hpp 0 → 100644
View file @ fe488bf2
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
namespace ck_tile {
template <typename ADataType,
typename AccDataType,
typename BDataType,
index_t kBlockSize,
typename BlockWarps, // num warps along seq<M, N>
typename BlockTile, // block size, seq<M, N>
typename WarpTile, // warp size, seq<M, N>
typename ThreadTile> // contiguous pixels(vector size) along seq<M, N>
struct Reduce
{
static constexpr index_t Block_M = BlockTile::at(number<0>{});
static constexpr index_t Block_N = BlockTile::at(number<1>{});
static constexpr index_t Warp_M = WarpTile::at(number<0>{});
static constexpr index_t Warp_N = WarpTile::at(number<1>{});
static constexpr index_t Thread_M = ThreadTile::at(number<0>{});
static constexpr index_t Thread_N = ThreadTile::at(number<1>{});
static constexpr index_t WarpPerBlock_M = BlockWarps::at(number<0>{});
static constexpr index_t WarpPerBlock_N = BlockWarps::at(number<1>{});
static constexpr index_t ThreadPerWarp_M = Warp_M / Thread_M;
static constexpr index_t ThreadPerWarp_N = Warp_N / Thread_N;
static constexpr index_t Repeat_M = Block_M / (WarpPerBlock_M * Warp_M);
static constexpr index_t Repeat_N = Block_N / (WarpPerBlock_N * Warp_N);
__device__ static constexpr auto MakeABlockTileDistribution()
{
return make_static_tile_distribution(
tile_distribution_encoding<
sequence<>,
tuple<sequence<Repeat_M, WarpPerBlock_M, ThreadPerWarp_M, Thread_M>,
sequence<Repeat_N, WarpPerBlock_N, ThreadPerWarp_N, Thread_N>>,
tuple<sequence<1, 2>, sequence<1, 2>>,
tuple<sequence<1, 1>, sequence<2, 2>>,
sequence<1, 1, 2, 2>,
sequence<0, 3, 0, 3>>{});
}
__device__ void operator()(const ADataType* p_a, BDataType* p_b, index_t M, index_t N) const
{
const auto a_m_n = make_naive_tensor_view<address_space_enum::global>(
p_a, make_tuple(M, N), make_tuple(N, 1), number<Thread_N>{}, number<1>{});
const auto iM = get_block_id() * Block_M;
// A window
auto a_block_window = make_tile_window(a_m_n,
make_tuple(number<Block_M>{}, number<Block_N>{}),
{iM, 0},
MakeABlockTileDistribution());
const auto f_reduce = [](const auto& v0, const auto& v1) { return v0 + v1; };
const ADataType reduce_init_value = 0;
constexpr auto reduce_dims = sequence<1>{};
// Acc tile
// TODO: support cross warp reduction
auto acc_block_tensor = decltype(block_tile_reduce<AccDataType>(
load_tile(a_block_window), reduce_dims, f_reduce, reduce_init_value)){};
// init Acc tile
tile_elementwise_inout(
[&](auto& acc) { acc = type_convert<AccDataType>(reduce_init_value); },
acc_block_tensor);
// loop
index_t iN = 0;
do
{
const auto a_block_tensor = load_tile(a_block_window);
// FIXME: support cross warp reduction
block_tile_reduce(acc_block_tensor, a_block_tensor, reduce_dims, f_reduce);
move_tile_window(a_block_window, {0, Block_N});
iN += Block_N;
} while(iN < N);
// FIXME: support cross warp reduction
block_tile_reduce_sync(acc_block_tensor, f_reduce);
// convert acc_block_tensor to b_block_tensor
const auto b_block_tensor = tile_elementwise_in(
[](const auto& acc) { return type_convert<BDataType>(acc); }, acc_block_tensor);
// B
const auto b_m = make_naive_tensor_view_packed<address_space_enum::global>(
p_b, make_tuple(M), number<32>{});
// B window
auto b_block_window = make_tile_window(b_m, make_tuple(number<Block_M>{}), {iM});
// store B tile
store_tile(b_block_window, b_block_tensor);
}
};
} // namespace ck_tile
example/ck_tile/CMakeLists.txt
View file @ fe488bf2
...@@ -6,3 +6,4 @@ add_subdirectory(01_fmha) ...@@ -6,3 +6,4 @@ add_subdirectory(01_fmha)
add_subdirectory(02_layernorm2d) add_subdirectory(02_layernorm2d)
add_subdirectory(03_gemm) add_subdirectory(03_gemm)
add_subdirectory(04_img2col) add_subdirectory(04_img2col)
add_subdirectory(05_reduce)
include/ck_tile/core.hpp
View file @ fe488bf2
...@@ -52,6 +52,7 @@ ...@@ -52,6 +52,7 @@
#include "ck_tile/core/tensor/update_tile.hpp" #include "ck_tile/core/tensor/update_tile.hpp"
#include "ck_tile/core/utility/bit_cast.hpp" #include "ck_tile/core/utility/bit_cast.hpp"
#include "ck_tile/core/utility/functional.hpp" #include "ck_tile/core/utility/functional.hpp"
#include "ck_tile/core/utility/functional_with_tuple.hpp"
#include "ck_tile/core/utility/ignore.hpp" #include "ck_tile/core/utility/ignore.hpp"
#include "ck_tile/core/utility/magic_div.hpp" #include "ck_tile/core/utility/magic_div.hpp"
#include "ck_tile/core/utility/philox_rand.hpp" #include "ck_tile/core/utility/philox_rand.hpp"
......
include/ck_tile/core/arch/utility.hpp
View file @ fe488bf2
...@@ -59,4 +59,47 @@ CK_TILE_DEVICE T warp_shuffle_down(const T& v_local, uint32_t lane_delta) ...@@ -59,4 +59,47 @@ CK_TILE_DEVICE T warp_shuffle_down(const T& v_local, uint32_t lane_delta)
#endif #endif
} }
template <typename T>
CK_TILE_DEVICE T warp_shuffle(const T& v_local, uint32_t src_lane)
{
#if 0
return __shfl(v_local, src_lane);
#elif 1
if constexpr(sizeof(int32_t) > sizeof(T))
{
union packet
{
int32_t x;
T v;
};
packet p;
p.v = v_local;
packet p_remote;
p_remote.x = __builtin_amdgcn_ds_bpermute(src_lane << 2, bit_cast<int32_t>(p));
return p_remote.v;
}
else if constexpr(sizeof(int32_t) == sizeof(T))
{
const int32_t v_remote_tmp =
__builtin_amdgcn_ds_bpermute(src_lane << 2, bit_cast<int32_t>(v_local));
return bit_cast<T>(v_remote_tmp);
}
else
{
static_assert(sizeof(T) % sizeof(int32_t) == 0, "wrong!");
constexpr index_t elm = sizeof(T) / sizeof(int32_t);
using vector_type = thread_buffer<int32_t, elm>;
auto vs = bit_cast<vector_type>(v_local);
auto vs_remote = vector_type{};
static_for<0, elm, 1>{}([&](auto i_e) {
int32_t tmp = __builtin_amdgcn_ds_bpermute(src_lane << 2, bit_cast<int32_t>(vs[i_e]));
vs_remote(i_e) = tmp;
});
return bit_cast<T>(vs_remote);
}
#endif
}
} // namespace ck_tile } // namespace ck_tile
include/ck_tile/core/config.hpp
View file @ fe488bf2
...@@ -32,11 +32,13 @@ ...@@ -32,11 +32,13 @@
#define CK_TILE_DEVICE inline __device__ #define CK_TILE_DEVICE inline __device__
#define CK_TILE_HOST_DEVICE inline __host__ __device__ #define CK_TILE_HOST_DEVICE inline __host__ __device__
#define CK_TILE_DEVICE_EXTERN __device__ #define CK_TILE_DEVICE_EXTERN __device__
#define CK_TILE_HOST_DEVICE_EXTERN __host__ __device__
#else #else
#define CK_TILE_HOST inline #define CK_TILE_HOST inline
#define CK_TILE_DEVICE inline #define CK_TILE_DEVICE inline
#define CK_TILE_HOST_DEVICE inline #define CK_TILE_HOST_DEVICE inline
#define CK_TILE_DEVICE_EXTERN #define CK_TILE_DEVICE_EXTERN
#define CK_TILE_HOST_DEVICE_EXTERN
#endif #endif
#ifndef CK_TILE_USE_CUSTOM_DATA_TYPE #ifndef CK_TILE_USE_CUSTOM_DATA_TYPE
......
include/ck_tile/core/container/sequence.hpp
View file @ fe488bf2
...@@ -1111,4 +1111,126 @@ CK_TILE_HOST_DEVICE constexpr auto generate_array(F&& f, number<N>) ...@@ -1111,4 +1111,126 @@ CK_TILE_HOST_DEVICE constexpr auto generate_array(F&& f, number<N>)
typename arithmetic_sequence_gen<0, N, 1>::type{}); typename arithmetic_sequence_gen<0, N, 1>::type{});
} }
namespace impl {
template <typename, typename, typename, index_t>
struct reverse_slice_sequence_impl;
template <index_t x,
index_t... xs,
index_t m,
index_t... ms,
index_t id,
index_t... ids,
index_t SliceSize>
struct reverse_slice_sequence_impl<sequence<x, xs...>,
sequence<m, ms...>,
sequence<id, ids...>,
SliceSize>
{
using old_scan =
reverse_slice_sequence_impl<sequence<xs...>, sequence<ms...>, sequence<ids...>, SliceSize>;
static constexpr auto slice_size = old_scan::remaining_slice_sizes::front().value;
static constexpr auto slice_length =
std::conditional_t<m, number<gcd(x, slice_size)>, number<x>>::value;
using dim_lengths =
typename sequence_merge<sequence<slice_length>, typename old_scan::dim_lengths>::type;
using dim_slices =
typename sequence_merge<sequence<x / slice_length>, typename old_scan::dim_slices>::type;
using remaining_slice_sizes = typename sequence_merge<
std::conditional_t<m, sequence<slice_size / slice_length>, sequence<slice_size>>,
typename old_scan::remaining_slice_sizes>::type;
// the first idx that sliced length not equal to original length
static constexpr index_t _flag =
slice_length != x && remaining_slice_sizes{}.front().value == 1;
static constexpr index_t _split_flag = std::conditional_t<m, number<_flag>, number<0>>::value;
static constexpr index_t _split_idx =
std::conditional_t<_split_flag, number<id>, number<0>>::value;
static constexpr index_t split_flag = _split_flag || old_scan::split_flag;
static constexpr index_t split_idx = std::
conditional_t<old_scan::split_flag, number<old_scan::split_idx>, number<_split_idx>>::value;
};
template <index_t x, index_t m, index_t id, index_t SliceSize>
struct reverse_slice_sequence_impl<sequence<x>, sequence<m>, sequence<id>, SliceSize>
{
static constexpr auto slice_size = SliceSize;
static constexpr auto slice_length =
std::conditional_t<m, number<gcd(x, slice_size)>, number<x>>::value;
using dim_lengths = sequence<slice_length>;
using dim_slices = sequence<x / slice_length>;
using remaining_slice_sizes =
std::conditional_t<m, sequence<slice_size / slice_length>, sequence<slice_size>>;
// the first idx that sliced length not equal to original length
static constexpr index_t _flag =
slice_length != x && remaining_slice_sizes{}.front().value == 1;
static constexpr index_t split_flag = std::conditional_t<m, number<_flag>, number<0>>::value;
static constexpr index_t split_idx =
std::conditional_t<split_flag, number<id>, number<0>>::value;
};
} // namespace impl
// clang-format off
// input a sequence(with optional mask), and the SliceSize : size per slice
// output the sequence each slice, and number of slices
//
// e.g. <2, 1, 4, 2>, 8 -> lengths:<1, 1, 4, 2> , nums: <2, 1, 1, 1> : 2 slices , slice_idx: 0
// <4, 2, 4, 1, 2>, 4 -> lengths:<1, 1, 2, 1, 2> , nums: <4, 2, 2, 1, 1> : 16 slices , slice_idx: 2
// <4, 2, 4, 1, 6>, 4 -> lengths:<1, 1, 2, 1, 2> , nums: <4, 2, 2, 1, 3> : 48 slices , slice_idx: 2
// <4, 2, 5, 1, 2>, 10 -> lengths:<1, 1, 5, 1, 2> , nums: <4, 2, 1, 1, 1> : 8 slices , slice_idx: 1
//
// <4, 2, 8>, 64 -> lengths:<4, 2, 8> , nums: <1, 1, 1> : 1 slices , slice_idx: 0
// <4, 2, 8>, 32 -> lengths:<2, 2, 8> , nums: <2, 1, 1> : 2 slices , slice_idx: 0
// <4, 2, 8>, 16 -> lengths:<1, 2, 8> , nums: <4, 1, 1> : 4 slices , slice_idx: 0
// <4, 2, 8>, 8 -> lengths:<1, 1, 8> , nums: <4, 2, 1> : 8 slices , slice_idx: 1
// <4, 2, 8>, 4 -> lengths:<1, 1, 4> , nums: <4, 2, 2> : 16 slices , slice_idx: 2
// <4, 2, 8>, 2 -> lengths:<1, 1, 2> , nums: <4, 2, 4> : 32 slices , slice_idx: 2
// <4, 2, 8>, 1 -> lengths:<1, 1, 1> , nums: <4, 2, 8> : 64 slices , slice_idx: 2
//
// <4, 2, 1, 4, 2> / 4 ->
// mask:<1, 1, 1, 0, 1>, -> lengths:<1, 2, 1, 4, 2> , nums: <4, 1, 1, 1, 1> : 8 slices , slice_idx: 0
//
// return tuple<slice_lengths, slice_nums, slice_index>, slice_index is at which index will start
// have split slices (right -> left)
// or the first index that sliced length is different from the original length
// clang-format on
template <typename Seq,
index_t SliceSize,
typename Mask = typename uniform_sequence_gen<Seq::size(), 1>::type>
constexpr auto reverse_slice_sequence(Seq,
number<SliceSize>,
Mask = typename uniform_sequence_gen<Seq::size(), 1>::type{})
{
static_assert(Seq::size() == Mask::size());
using sliced_type =
impl::reverse_slice_sequence_impl<Seq,
Mask,
typename arithmetic_sequence_gen<0, Seq::size(), 1>::type,
SliceSize>;
static_assert(sliced_type::remaining_slice_sizes::front().value == 1,
"can not evenly divide this sequence, please check");
return make_tuple(typename sliced_type::dim_lengths{},
typename sliced_type::dim_slices{},
number<sliced_type::split_idx>{});
}
template <typename Seq,
index_t SliceSize,
typename Mask = typename uniform_sequence_gen<Seq::size(), 1>::type>
constexpr auto slice_sequence(Seq,
number<SliceSize>,
Mask = typename uniform_sequence_gen<Seq::size(), 1>::type{})
{
constexpr auto r =
reverse_slice_sequence(Seq{}.reverse(), number<SliceSize>{}, Mask{}.reverse());
return make_tuple(r[number<0>{}].reverse(),
r[number<1>{}].reverse(),
number<Seq::size() - r[number<2>{}] - 1>{});
}
} // namespace ck_tile } // namespace ck_tile
include/ck_tile/core/container/tuple.hpp
View file @ fe488bf2
...@@ -488,6 +488,26 @@ CK_TILE_HOST_DEVICE constexpr auto transform_tuples(F f, const X& x, const Y& y, ...@@ -488,6 +488,26 @@ CK_TILE_HOST_DEVICE constexpr auto transform_tuples(F f, const X& x, const Y& y,
f, x, y, z, typename arithmetic_sequence_gen<0, X::size(), 1>::type{}); f, x, y, z, typename arithmetic_sequence_gen<0, X::size(), 1>::type{});
} }
namespace detail {
template <typename F, typename X, index_t... Is>
CK_TILE_HOST_DEVICE constexpr auto embed_tuples_impl(F f, const X& x, sequence<Is...>)
{
return concat_tuple(f(x.at(number<Is>{}))...);
}
} // namespace detail
// make sure F return at least a tuple
// e.g. x : tuple<X, Y>, f will return tuple<Z, W>
// this function will return
template <typename F, typename X>
CK_TILE_HOST_DEVICE constexpr auto embed_tuples(F f, const X& x)
{
return detail::embed_tuples_impl(
f, x, typename arithmetic_sequence_gen<0, X::size(), 1>::type{});
}
// By default unroll to the flatten // By default unroll to the flatten
template <index_t Depth = 0, index_t MaxDepth = -1> template <index_t Depth = 0, index_t MaxDepth = -1>
CK_TILE_HOST_DEVICE constexpr auto unroll_nested_tuple(const tuple<>& t) CK_TILE_HOST_DEVICE constexpr auto unroll_nested_tuple(const tuple<>& t)
......
include/ck_tile/core/tensor/static_distributed_tensor.hpp
View file @ fe488bf2
...@@ -187,4 +187,18 @@ set_tile_if(static_distributed_tensor<DataType, StaticTileDistribution>& out_ten ...@@ -187,4 +187,18 @@ set_tile_if(static_distributed_tensor<DataType, StaticTileDistribution>& out_ten
}); });
} }
// this function used inside span loop over
template <typename YLengths, index_t XUnpacks>
CK_TILE_HOST_DEVICE constexpr auto get_y_unpacks_from_x_unpacks(YLengths, number<XUnpacks>)
{
constexpr auto y_size = reduce_on_sequence(YLengths{}, multiplies{}, number<1>{});
constexpr auto y_packs = number<XUnpacks>{};
static_assert(y_size % y_packs == 0);
constexpr auto y_slice_size = y_size / y_packs;
constexpr auto slice_info = slice_sequence(YLengths{}, number<y_slice_size>{});
constexpr auto unpacks = slice_info[number<1>{}];
return unpacks;
}
} // namespace ck_tile } // namespace ck_tile
include/ck_tile/core/tensor/sweep_tile.hpp
View file @ fe488bf2
...@@ -8,6 +8,7 @@ ...@@ -8,6 +8,7 @@
#include "ck_tile/core/numeric/integral_constant.hpp" #include "ck_tile/core/numeric/integral_constant.hpp"
#include "ck_tile/core/tensor/tile_distribution.hpp" #include "ck_tile/core/tensor/tile_distribution.hpp"
#include "ck_tile/core/utility/functional.hpp" #include "ck_tile/core/utility/functional.hpp"
#include "ck_tile/core/utility/functional_with_tuple.hpp"
#include "ck_tile/core/utility/type_traits.hpp" #include "ck_tile/core/utility/type_traits.hpp"
namespace ck_tile { namespace ck_tile {
...@@ -27,4 +28,281 @@ CK_TILE_DEVICE void sweep_tile_span(TileDistributedSpan_, const F& f) ...@@ -27,4 +28,281 @@ CK_TILE_DEVICE void sweep_tile_span(TileDistributedSpan_, const F& f)
}); });
} }
// unpacked span, this version support span with unpack(multi-arg) functor
//
template <
typename TileDistributedSpan_, // tile_distributed_span<...>
typename F, // signature: F(tile_distributed_index<...>)
typename Unpacks = typename uniform_sequence_gen<TileDistributedSpan_::Impl::size(), 1>::type>
CK_TILE_DEVICE void sweep_tile_uspan(TileDistributedSpan_, const F& f, Unpacks = {})
{
using DstrSpan = remove_cvref_t<TileDistributedSpan_>;
static_uford<typename DstrSpan::Impl, Unpacks>{}(
[&](auto... dstr_idx_impl) { f(detail::make_tile_distributed_index(dstr_idx_impl)...); });
}
namespace impl {
template <typename, typename, typename>
struct sweep_tile_impl;
template <typename DistributedTensor, typename UnpacksPerXDim, index_t I, index_t... Is>
struct sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<I, Is...>>
{
CK_TILE_HOST_DEVICE constexpr auto get_y_unpacks() const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
constexpr auto y_lengths = typename decltype(spans[number<I>{}])::Impl{};
constexpr auto x_unpacks = number<UnpacksPerXDim{}.at(number<I>{})>{};
constexpr auto y_unpacks = get_y_unpacks_from_x_unpacks(y_lengths, x_unpacks);
return y_unpacks;
}
CK_TILE_HOST_DEVICE constexpr index_t get_num_of_access() const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
constexpr auto u =
static_uford<typename decltype(spans[number<I>{}])::Impl, decltype(get_y_unpacks())>{};
return u.get_num_of_access() *
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}
.get_num_of_access();
}
template <typename F, typename SpanIdx>
CK_TILE_HOST_DEVICE constexpr void operator()(const F& f, const SpanIdx& span_idx) const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
sweep_tile_uspan(
spans[number<I>{}],
[&](auto... i_idx) {
const auto next_span_idx = embed_tuples(
[&](auto si) { return make_tuple(concat_tuple(si, make_tuple(i_idx))...); },
span_idx);
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}(
f, next_span_idx);
},
get_y_unpacks());
}
template <typename F, typename SpanIdx, index_t i_access>
CK_TILE_HOST_DEVICE constexpr void
operator()(const F& f, const SpanIdx& span_idx, number<i_access>) const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
constexpr auto u =
static_uford<typename decltype(spans[number<I>{}])::Impl, decltype(get_y_unpacks())>{};
constexpr auto access_stride =
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}
.get_num_of_access();
constexpr auto curr_i_access = number<i_access / access_stride>{};
constexpr auto next_i_access = number<i_access % access_stride>{};
u(
[&](auto... i_idx) {
const auto next_span_idx = embed_tuples(
[&](auto si) {
return make_tuple(concat_tuple(
si, make_tuple(detail::make_tile_distributed_index(i_idx)))...);
},
span_idx);
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}(
f, next_span_idx, next_i_access);
},
curr_i_access);
}
};
template <typename DistributedTensor, typename UnpacksPerXDim>
struct sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<>>
{
CK_TILE_HOST_DEVICE constexpr index_t get_num_of_access() const { return 1; }
template <typename F, typename SpanIdx>
CK_TILE_HOST_DEVICE constexpr void operator()(const F& f, const SpanIdx& span_idx) const
{
unpack(f, span_idx);
}
template <typename F, typename SpanIdx, index_t i_access>
CK_TILE_HOST_DEVICE constexpr void
operator()(const F& f, const SpanIdx& span_idx, number<i_access>) const
{
unpack(f, span_idx);
}
};
template <typename, typename, typename>
struct sweep_tile_impl_0;
// TODO: support empty tuple to remove this "entry-point" like function
template <typename DistributedTensor, typename UnpacksPerXDim, index_t I, index_t... Is>
struct sweep_tile_impl_0<DistributedTensor, UnpacksPerXDim, sequence<I, Is...>>
{
CK_TILE_HOST_DEVICE constexpr auto get_y_unpacks() const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
constexpr auto y_lengths = typename decltype(spans[number<I>{}])::Impl{};
constexpr auto x_unpacks = number<UnpacksPerXDim{}.at(number<I>{})>{};
constexpr auto y_unpacks = get_y_unpacks_from_x_unpacks(y_lengths, x_unpacks);
return y_unpacks;
}
CK_TILE_HOST_DEVICE constexpr index_t get_num_of_access() const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
constexpr auto u =
static_uford<typename decltype(spans[number<I>{}])::Impl, decltype(get_y_unpacks())>{};
return u.get_num_of_access() *
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}
.get_num_of_access();
}
template <typename F>
CK_TILE_HOST_DEVICE constexpr void operator()(const F& f) const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
sweep_tile_uspan(
spans[number<I>{}],
[&](auto... i_idx) {
constexpr auto next_span_idx = make_tuple(make_tuple(i_idx)...);
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}(
f, next_span_idx);
},
get_y_unpacks());
}
template <typename F, index_t i_access>
CK_TILE_HOST_DEVICE constexpr void operator()(const F& f, number<i_access>) const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
constexpr auto u =
static_uford<typename decltype(spans[number<I>{}])::Impl, decltype(get_y_unpacks())>{};
constexpr auto access_stride =
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}
.get_num_of_access();
constexpr auto curr_i_access = number<i_access / access_stride>{};
constexpr auto next_i_access = number<i_access % access_stride>{};
u(
[&](auto... i_idx) {
constexpr auto next_span_idx =
make_tuple(make_tuple(detail::make_tile_distributed_index(i_idx))...);
sweep_tile_impl<DistributedTensor, UnpacksPerXDim, sequence<Is...>>{}(
f, next_span_idx, next_i_access);
},
curr_i_access);
}
};
} // namespace impl
/*
* Enhanced sweep-tile utility, can control unpacks along each X-dim
* the lambda function argument is the distributed-idx, which can directly
* plugged into the distributed tensor as setter/getter
*
* e.g. below function, y with the type DistributedTensor, r is row scale
*
* // sweep tile 1 by 1
* sweep_tile<DistributedTensor>([&](auto idx) {
* constexpr auto row_id = make_tuple(idx[number<0>{}]);
* y(idx) = y(idx) * r(row_id);
* });
*
* // sweep tile with 2 pixel from last dim each function call
* sweep_tile<DistributedTensor>(
* [&](auto idx_0, auto idx_1) {
* constexpr auto row_id = make_tuple(idx_0[number<0>{}]);
* y(idx_0) = y(idx_0) * r(row_id);
* y(idx_1) = y(idx_1) * r(row_id);
* },
* sequence<1, 2>{});
*
* // sweep tile with 2x2 pixel each function call
* sweep_tile<DistributedTensor>(
* [&](auto idx_00, auto idx_01, auto idx_10, auto idx_11) {
* constexpr auto row_id0 = make_tuple(idx_00[number<0>{}]);
* constexpr auto row_id1 = make_tuple(idx_10[number<0>{}]);
* y(idx_00) = y(idx_00) * r(row_id0);
* y(idx_01) = y(idx_01) * r(row_id0);
* y(idx_10) = y(idx_10) * r(row_id1);
* y(idx_11) = y(idx_11) * r(row_id1);
* },
* sequence<2, 2>{});
*
* TODO: do we need constexpr? lambda function could be non-constexpr
*/
template <typename DistributedTensor,
typename F,
typename UnpacksPerXDim =
typename uniform_sequence_gen<DistributedTensor::get_num_of_dimension(), 1>::type>
CK_TILE_HOST_DEVICE constexpr void sweep_tile(const F& f, UnpacksPerXDim = {})
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
impl::sweep_tile_impl_0<DistributedTensor,
UnpacksPerXDim,
typename arithmetic_sequence_gen<0, spans.size(), 1>::type>{}(f);
}
template <typename DistributedTensor,
typename F,
typename UnpacksPerXDim =
typename uniform_sequence_gen<DistributedTensor::get_num_of_dimension(), 1>::type>
CK_TILE_HOST_DEVICE constexpr void
sweep_tile(const DistributedTensor&, const F& f, UnpacksPerXDim = {})
{
sweep_tile<DistributedTensor, F, UnpacksPerXDim>(f, UnpacksPerXDim{});
}
/*
* construct a sweep tile instance, which support issue the lambda one by one
* Note that this struct will hold the lambda functor, but will not hold the distributed tensor
* the functionality is the same as sweep_tile()
*/
template <typename DistributedTensor_,
typename F_,
typename UnpacksPerXDim_ =
typename uniform_sequence_gen<DistributedTensor_::get_num_of_dimension(), 1>::type>
struct tile_sweeper
{
using DistributedTensor = remove_cvref_t<DistributedTensor_>;
using F = remove_cvref_t<F_>;
using UnpacksPerXDim = remove_cvref_t<UnpacksPerXDim_>;
CK_TILE_HOST_DEVICE tile_sweeper(const F& f_, UnpacksPerXDim = {}) : f(f_) {}
CK_TILE_HOST_DEVICE tile_sweeper(const DistributedTensor&, const F& f_, UnpacksPerXDim = {})
: f(f_)
{
}
CK_TILE_HOST_DEVICE static constexpr index_t get_num_of_access()
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
constexpr auto tmp =
impl::sweep_tile_impl_0<DistributedTensor,
UnpacksPerXDim,
typename arithmetic_sequence_gen<0, spans.size(), 1>::type>{};
return tmp.get_num_of_access();
}
CK_TILE_HOST_DEVICE void operator()() const
{
sweep_tile<DistributedTensor>(f, UnpacksPerXDim{});
}
template <index_t i_access>
CK_TILE_HOST_DEVICE void operator()(number<i_access>) const
{
constexpr auto spans = DistributedTensor::get_distributed_spans();
impl::sweep_tile_impl_0<DistributedTensor,
UnpacksPerXDim,
typename arithmetic_sequence_gen<0, spans.size(), 1>::type>{}(
f, number<i_access>{});
}
F f;
};
// partial deduction is not allowed
// template <typename T, typename F, typename U>
// CK_TILE_HOST_DEVICE_EXTERN tile_sweeper(const F&, U = {})->tile_sweeper<T, F, U>;
// deduction guide
template <typename T,
typename F,
typename U = typename uniform_sequence_gen<T::get_num_of_dimension(), 1>::type>
CK_TILE_HOST_DEVICE_EXTERN tile_sweeper(const T&, const F&, U = {})->tile_sweeper<T, F, U>;
} // namespace ck_tile } // namespace ck_tile
include/ck_tile/core/tensor/tile_distribution.hpp
View file @ fe488bf2
...@@ -17,6 +17,14 @@ ...@@ -17,6 +17,14 @@
namespace ck_tile { namespace ck_tile {
namespace detail {
template <typename Distribution>
CK_TILE_HOST_DEVICE auto get_partition_index(Distribution)
{
return Distribution::_get_partition_index();
}
} // namespace detail
// distributed span // distributed span
template <index_t... PartialHsLengths> template <index_t... PartialHsLengths>
struct tile_distributed_span struct tile_distributed_span
...@@ -83,6 +91,21 @@ struct tile_distribution ...@@ -83,6 +91,21 @@ struct tile_distribution
CK_TILE_HOST_DEVICE static constexpr index_t get_num_of_dimension_p() { return NDimP; } CK_TILE_HOST_DEVICE static constexpr index_t get_num_of_dimension_p() { return NDimP; }
CK_TILE_HOST_DEVICE static constexpr index_t get_num_of_dimension_r() { return NDimR; } CK_TILE_HOST_DEVICE static constexpr index_t get_num_of_dimension_r() { return NDimR; }
CK_TILE_HOST_DEVICE static auto _get_partition_index()
{
// only support warp-tile and block-tile
static_assert(NDimP == 1 or NDimP == 2, "wrong!");
if constexpr(NDimP == 1)
{
return array<index_t, 1>{get_lane_id()};
}
else if constexpr(NDimP == 2)
{
return array<index_t, 2>{get_warp_id(), get_lane_id()};
}
}
CK_TILE_HOST_DEVICE static constexpr auto get_lengths() CK_TILE_HOST_DEVICE static constexpr auto get_lengths()
{ {
#if 0 #if 0
...@@ -149,6 +172,16 @@ struct tile_distribution ...@@ -149,6 +172,16 @@ struct tile_distribution
} }
#endif #endif
template <typename PartitionIndex = decltype(_get_partition_index())>
CK_TILE_HOST_DEVICE auto
calculate_index(const PartitionIndex& ps_idx = _get_partition_index()) const
{
const auto ps_ys_idx = container_concat(ps_idx, array<index_t, NDimY>{0});
const auto window_adaptor_thread_coord_tmp =
make_tensor_adaptor_coordinate(ps_ys_to_xs_, ps_ys_idx);
return window_adaptor_thread_coord_tmp.get_bottom_index();
}
CK_TILE_HOST_DEVICE static constexpr auto get_distributed_spans() CK_TILE_HOST_DEVICE static constexpr auto get_distributed_spans()
{ {
constexpr auto distributed_spans_impl = DstrEncode::detail::distributed_spans_lengthss_; constexpr auto distributed_spans_impl = DstrEncode::detail::distributed_spans_lengthss_;
...@@ -421,6 +454,7 @@ struct tile_distribution_detail ...@@ -421,6 +454,7 @@ struct tile_distribution_detail
} // namespace detail } // namespace detail
#if 0
// this returns a constexpr tile_distribution // this returns a constexpr tile_distribution
template <typename StaticTileDistributionEncoding_> template <typename StaticTileDistributionEncoding_>
CK_TILE_HOST_DEVICE constexpr auto make_tile_distribution(StaticTileDistributionEncoding_) CK_TILE_HOST_DEVICE constexpr auto make_tile_distribution(StaticTileDistributionEncoding_)
...@@ -457,6 +491,7 @@ CK_TILE_HOST_DEVICE constexpr auto make_tile_distribution(StaticTileDistribution ...@@ -457,6 +491,7 @@ CK_TILE_HOST_DEVICE constexpr auto make_tile_distribution(StaticTileDistribution
detail::tile_distribution_detail<remove_cvref_t<decltype(rh_major_minor_to_hidden_ids)>>>{ detail::tile_distribution_detail<remove_cvref_t<decltype(rh_major_minor_to_hidden_ids)>>>{
ps_ys_to_xs_adaptor, ys_to_d_descriptor}; ps_ys_to_xs_adaptor, ys_to_d_descriptor};
} }
#endif
// this returns a static tile_distribution // this returns a static tile_distribution
template <typename StaticTileDistributionEncoding_> template <typename StaticTileDistributionEncoding_>
...@@ -499,129 +534,6 @@ CK_TILE_HOST_DEVICE constexpr auto make_static_tile_distribution(StaticTileDistr ...@@ -499,129 +534,6 @@ CK_TILE_HOST_DEVICE constexpr auto make_static_tile_distribution(StaticTileDistr
//*********************************************************************************** //***********************************************************************************
namespace detail { namespace detail {
template <typename Distribution>
CK_TILE_HOST_DEVICE auto get_partition_index(Distribution)
{
// only support warp-tile and block-tile
static_assert(Distribution::NDimP == 1 or Distribution::NDimP == 2, "wrong!");
if constexpr(Distribution::NDimP == 1)
{
return array<index_t, 1>{get_lane_id()};
}
else if constexpr(Distribution::NDimP == 2)
{
return array<index_t, 2>{get_warp_id(), get_lane_id()};
}
}
template <typename, typename, typename, index_t>
struct reverse_slice_sequence_impl;
template <index_t x,
index_t... xs,
index_t m,
index_t... ms,
index_t id,
index_t... ids,
index_t SliceSize>
struct reverse_slice_sequence_impl<sequence<x, xs...>,
sequence<m, ms...>,
sequence<id, ids...>,
SliceSize>
{
using old_scan =
reverse_slice_sequence_impl<sequence<xs...>, sequence<ms...>, sequence<ids...>, SliceSize>;
static constexpr auto slice_size = old_scan::remaining_slice_sizes::front().value;
static constexpr auto slice_length =
std::conditional_t<m, number<gcd(x, slice_size)>, number<x>>::value;
using dim_lengths =
typename sequence_merge<sequence<slice_length>, typename old_scan::dim_lengths>::type;
using dim_slices =
typename sequence_merge<sequence<x / slice_length>, typename old_scan::dim_slices>::type;
using remaining_slice_sizes = typename sequence_merge<
std::conditional_t<m, sequence<slice_size / slice_length>, sequence<slice_size>>,
typename old_scan::remaining_slice_sizes>::type;
// the first idx that sliced length not equal to original length
static constexpr index_t _flag =
slice_length != x && remaining_slice_sizes{}.front().value == 1;
static constexpr index_t _split_flag = std::conditional_t<m, number<_flag>, number<0>>::value;
static constexpr index_t _split_idx =
std::conditional_t<_split_flag, number<id>, number<0>>::value;
static constexpr index_t split_flag = _split_flag || old_scan::split_flag;
static constexpr index_t split_idx = std::
conditional_t<old_scan::split_flag, number<old_scan::split_idx>, number<_split_idx>>::value;
};
template <index_t x, index_t m, index_t id, index_t SliceSize>
struct reverse_slice_sequence_impl<sequence<x>, sequence<m>, sequence<id>, SliceSize>
{
static constexpr auto slice_size = SliceSize;
static constexpr auto slice_length =
std::conditional_t<m, number<gcd(x, slice_size)>, number<x>>::value;
using dim_lengths = sequence<slice_length>;
using dim_slices = sequence<x / slice_length>;
using remaining_slice_sizes =
std::conditional_t<m, sequence<slice_size / slice_length>, sequence<slice_size>>;
// the first idx that sliced length not equal to original length
static constexpr index_t _flag =
slice_length != x && remaining_slice_sizes{}.front().value == 1;
static constexpr index_t split_flag = std::conditional_t<m, number<_flag>, number<0>>::value;
static constexpr index_t split_idx =
std::conditional_t<split_flag, number<id>, number<0>>::value;
};
// clang-format off
// input a sequence(with optional mask), and the SliceSize : size per slice
// output the sequence each slice, and number of slices
//
// e.g. <2, 1, 4, 2>, 8 -> lengths:<1, 1, 4, 2> , nums: <2, 1, 1, 1> : 2 slices , slice_idx: 0
// <4, 2, 4, 1, 2>, 4 -> lengths:<1, 1, 2, 1, 2> , nums: <4, 2, 2, 1, 1> : 16 slices , slice_idx: 2
// <4, 2, 4, 1, 6>, 4 -> lengths:<1, 1, 2, 1, 2> , nums: <4, 2, 2, 1, 3> : 48 slices , slice_idx: 2
// <4, 2, 5, 1, 2>, 10 -> lengths:<1, 1, 5, 1, 2> , nums: <4, 2, 1, 1, 1> : 8 slices , slice_idx: 1
//
// <4, 2, 8>, 64 -> lengths:<4, 2, 8> , nums: <1, 1, 1> : 1 slices , slice_idx: 0
// <4, 2, 8>, 32 -> lengths:<2, 2, 8> , nums: <2, 1, 1> : 2 slices , slice_idx: 0
// <4, 2, 8>, 16 -> lengths:<1, 2, 8> , nums: <4, 1, 1> : 4 slices , slice_idx: 0
// <4, 2, 8>, 8 -> lengths:<1, 1, 8> , nums: <4, 2, 1> : 8 slices , slice_idx: 1
// <4, 2, 8>, 4 -> lengths:<1, 1, 4> , nums: <4, 2, 2> : 16 slices , slice_idx: 2
// <4, 2, 8>, 2 -> lengths:<1, 1, 2> , nums: <4, 2, 4> : 32 slices , slice_idx: 2
// <4, 2, 8>, 1 -> lengths:<1, 1, 1> , nums: <4, 2, 8> : 64 slices , slice_idx: 2
//
// <4, 2, 1, 4, 2> / 4 ->
// mask:<1, 1, 1, 0, 1>, -> lengths:<1, 2, 1, 4, 2> , nums: <4, 1, 1, 1, 1> : 8 slices , slice_idx: 0
//
// return tuple<slice_lengths, slice_nums, slice_index>, slice_index is at which index will start
// have split slices (right -> left)
// or the first index that sliced length is different from the original length
// clang-format on
template <typename Seq,
index_t SliceSize,
typename Mask = typename uniform_sequence_gen<Seq::size(), 1>::type>
constexpr auto reverse_slice_sequence(Seq,
number<SliceSize>,
Mask = typename uniform_sequence_gen<Seq::size(), 1>::type{})
{
static_assert(Seq::size() == Mask::size());
using sliced_type =
reverse_slice_sequence_impl<Seq,
Mask,
typename arithmetic_sequence_gen<0, Seq::size(), 1>::type,
SliceSize>;
static_assert(sliced_type::remaining_slice_sizes::front().value == 1,
"can not evenly divide this sequence, please check");
return make_tuple(typename sliced_type::dim_lengths{},
typename sliced_type::dim_slices{},
number<sliced_type::split_idx>{});
}
// //
// slice tensor from x_dim, result in split in y_dim, not p_dim. // slice tensor from x_dim, result in split in y_dim, not p_dim.
// We don't support slice cross p_dim (aka, slice different threads) // We don't support slice cross p_dim (aka, slice different threads)
......
include/ck_tile/core/utility/functional_with_tuple.hpp 0 → 100644
View file @ fe488bf2
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
// This file should not be included inside tuple.hpp!
#include "ck_tile/core/config.hpp"
#include "ck_tile/core/numeric/integer.hpp"
#include "ck_tile/core/numeric/integral_constant.hpp"
#include "ck_tile/core/numeric/math.hpp"
#include "ck_tile/core/container/sequence.hpp"
#include "ck_tile/core/container/tuple.hpp"
#include "ck_tile/core/utility/type_traits.hpp"
#include <stdint.h>
#include <utility>
namespace ck_tile {
namespace detail {
// RemainLengths: sequence<...>
// Orders: sequence<...>
template <class RemainLengths, class RamainUnpacks, class Orders>
struct static_uford_impl
{
CK_TILE_HOST_DEVICE constexpr static_uford_impl()
{
static_assert(RemainLengths::size() > 0, "wrong! should not get here");
static_assert(RamainUnpacks::size() > 0, "wrong! should not get here");
}
template <class F, class CurrentUnpackIds>
CK_TILE_HOST_DEVICE constexpr void operator()(F f, CurrentUnpackIds) const
{
constexpr index_t pack_len = RamainUnpacks::front();
static_for<0, RemainLengths::front(), pack_len>{}([=](auto I) {
constexpr auto new_pack = generate_tuple(
[&](auto idx_) {
constexpr auto i_new_pack = number<I + idx_ % pack_len>{};
constexpr auto i_pre_pack = number<idx_ / pack_len>{};
return CurrentUnpackIds{}.at(i_pre_pack).push_back(i_new_pack);
},
number<CurrentUnpackIds::size() * pack_len>{});
static_uford_impl<decltype(RemainLengths::pop_front()),
decltype(RamainUnpacks::pop_front()),
Orders>{}(f, new_pack);
});
}
};
template <class Orders>
struct static_uford_impl<sequence<>, sequence<>, Orders>
{
template <class F, class PackedId>
CK_TILE_HOST_DEVICE constexpr void operator()(F f, PackedId) const
{
constexpr auto origin_packs = transform_tuples(
[](auto pack_) { return decltype(pack_)::reorder_old_to_new(Orders{}); }, PackedId{});
unpack(f, origin_packs);
}
};
template <class RemainLengths, class RamainUnpacks, class Orders>
struct static_uford_one_shot_impl
{
template <class F, class CurrentUnpackIds, index_t current_acc>
CK_TILE_HOST_DEVICE constexpr void operator()(F f, CurrentUnpackIds, number<current_acc>) const
{
constexpr auto r_lens_stride =
reverse_exclusive_scan_sequence(RemainLengths{}, multiplies{}, number<1>{});
constexpr auto r_upks_stride =
reverse_exclusive_scan_sequence(RamainUnpacks{}, multiplies{}, number<1>{});
constexpr index_t current_stride = r_lens_stride.front() / r_upks_stride.front();
constexpr index_t pack_len = RamainUnpacks::front();
constexpr index_t current_idx = (current_acc / current_stride) * pack_len;
constexpr auto new_pack = generate_tuple(
[&](auto idx_) {
constexpr auto i_new_pack = number<current_idx + idx_ % pack_len>{};
constexpr auto i_pre_pack = number<idx_ / pack_len>{};
return CurrentUnpackIds{}.at(i_pre_pack).push_back(i_new_pack);
},
number<CurrentUnpackIds::size() * pack_len>{});
static_uford_one_shot_impl<decltype(RemainLengths::pop_front()),
decltype(RamainUnpacks::pop_front()),
Orders>{}(f, new_pack, number<current_acc % current_stride>{});
}
};
template <class Orders>
struct static_uford_one_shot_impl<sequence<>, sequence<>, Orders>
{
template <class F, class PackedId, index_t current_acc>
CK_TILE_HOST_DEVICE constexpr void operator()(F f, PackedId, number<current_acc>) const
{
constexpr auto origin_packs = transform_tuples(
[](auto pack_) { return decltype(pack_)::reorder_old_to_new(Orders{}); }, PackedId{});
unpack(f, origin_packs);
}
};
} // namespace detail
// TODO: we may unify static_ford/static_uford in the future
//
// loop over nd space(sequence) with packs
// you must make sure the function passed in has same number of argument
//
// e.g.
// Lengths=seq<2, 3, 4>, Unpacks=<1, 1, 2>
// static_uford<Lengths, Unpacks>{}([&](auto i_0, auto i_1){}); // require 2 args(packs)
//
// loop #0, i_0=seq<0, 0, 0>, i_1=<0, 0, 1>
// loop #1, i_0=seq<0, 0, 2>, i_1=<0, 0, 3>
// loop #2, i_0=seq<0, 1, 0>, i_1=<0, 1, 1>
// loop #3, i_0=seq<0, 1, 2>, i_1=<0, 1, 3>
// loop #4, i_0=seq<0, 2, 0>, i_1=<0, 2, 1>
// loop #5, i_0=seq<0, 2, 2>, i_1=<0, 2, 3>
// loop #6, i_0=seq<1, 0, 0>, i_1=<1, 0, 1>
// ...
template <class Lengths,
class Unpacks = typename uniform_sequence_gen<Lengths::size(), 1>::type,
class Orders = typename arithmetic_sequence_gen<0, Lengths::size(), 1>::type>
struct static_uford
{
static constexpr index_t num_packs = reduce_on_sequence(Unpacks{}, multiplies{}, number<1>{});
CK_TILE_HOST_DEVICE constexpr static_uford()
{
static_assert(Lengths::size() > 0, "wrong! Lengths is empty");
static_assert(Lengths::size() == Unpacks::size(), "wrong! inconsistent size");
static_assert(Lengths::size() == Orders::size(), "wrong! inconsistent size");
static_for<0, Lengths::size(), 1>{}(
[&](auto i) { static_assert(Lengths{}.at(i) % Unpacks{}.at(i) == 0); });
}
CK_TILE_HOST_DEVICE static constexpr index_t get_num_of_access()
{
using L_ = decltype(Lengths{} / Unpacks{});
return reduce_on_sequence(L_{}, multiplies{}, number<1>{});
}
// F signature: F(sequence<...> multi_id...)
// multi_id is the unordered multi-index
template <class F>
CK_TILE_HOST_DEVICE constexpr void operator()(F f) const
{
constexpr auto ordered_lengths = Lengths::reorder_new_to_old(Orders{});
constexpr auto ordered_unpacks = Unpacks::reorder_new_to_old(Orders{});
detail::static_uford_impl<decltype(ordered_lengths), decltype(ordered_unpacks), Orders>{}(
f, make_tuple(sequence<>{}));
}
// this version is friendly for issue function one by one
template <class F, index_t i_access>
CK_TILE_HOST_DEVICE constexpr void operator()(F f, number<i_access>) const
{
static_assert(i_access < get_num_of_access());
constexpr auto ordered_lengths = Lengths::reorder_new_to_old(Orders{});
constexpr auto ordered_unpacks = Unpacks::reorder_new_to_old(Orders{});
detail::static_uford_one_shot_impl<decltype(ordered_lengths),
decltype(ordered_unpacks),
Orders>{}(
f, make_tuple(sequence<>{}), number<i_access>{});
}
};
} // namespace ck_tile
include/ck_tile/host.hpp
View file @ fe488bf2
...@@ -21,7 +21,7 @@ ...@@ -21,7 +21,7 @@
#include "ck_tile/host/reference/reference_batched_softmax.hpp" #include "ck_tile/host/reference/reference_batched_softmax.hpp"
#include "ck_tile/host/reference/reference_gemm.hpp" #include "ck_tile/host/reference/reference_gemm.hpp"
#include "ck_tile/host/reference/reference_im2col.hpp" #include "ck_tile/host/reference/reference_im2col.hpp"
#include "ck_tile/host/reference/reference_layernorm2d.hpp" #include "ck_tile/host/reference/reference_layernorm2d_fwd.hpp"
#include "ck_tile/host/reference/reference_reduce.hpp" #include "ck_tile/host/reference/reference_reduce.hpp"
#include "ck_tile/host/reference/reference_softmax.hpp" #include "ck_tile/host/reference/reference_softmax.hpp"
#include "ck_tile/host/stream_config.hpp" #include "ck_tile/host/stream_config.hpp"
......
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_combine_pipeline.hpp
View file @ fe488bf2
...@@ -12,6 +12,16 @@ namespace detail { ...@@ -12,6 +12,16 @@ namespace detail {
template <index_t N> template <index_t N>
struct log2; struct log2;
template <>
struct log2<4> : std::integral_constant<index_t, 2>
{
};
template <>
struct log2<8> : std::integral_constant<index_t, 3>
{
};
template <> template <>
struct log2<16> : std::integral_constant<index_t, 4> struct log2<16> : std::integral_constant<index_t, 4>
{ {
...@@ -72,18 +82,18 @@ struct BlockFmhaFwdSplitKVCombinePipeline ...@@ -72,18 +82,18 @@ struct BlockFmhaFwdSplitKVCombinePipeline
{ {
if constexpr(kHeadDimV <= 32) if constexpr(kHeadDimV <= 32)
{ {
constexpr std::array<int, 4> occupancy{3, 3, 3, 1}; constexpr std::array occupancy{3, 3, 3, 3, 3, 1};
return occupancy[detail::log2<kMaxSplits>::value - 4]; return occupancy[detail::log2<kMaxSplits>::value - 2];
} }
else if constexpr(kHeadDimV <= 128) else if constexpr(kHeadDimV <= 128)
{ {
constexpr std::array<int, 4> occupancy{3, 3, 2, 1}; constexpr std::array occupancy{3, 3, 3, 3, 2, 1};
return occupancy[detail::log2<kMaxSplits>::value - 4]; return occupancy[detail::log2<kMaxSplits>::value - 2];
} }
else if constexpr(kHeadDimV <= 256) else if constexpr(kHeadDimV <= 256)
{ {
constexpr std::array<int, 4> occupancy{2, 2, 2, 1}; constexpr std::array occupancy{2, 2, 2, 2, 2, 1};
return occupancy[detail::log2<kMaxSplits>::value - 4]; return occupancy[detail::log2<kMaxSplits>::value - 2];
} }
} }
}(); }();
...@@ -138,9 +148,8 @@ struct BlockFmhaFwdSplitKVCombinePipeline ...@@ -138,9 +148,8 @@ struct BlockFmhaFwdSplitKVCombinePipeline
auto lse_accum = make_static_distributed_tensor<LSEDataType>( auto lse_accum = make_static_distributed_tensor<LSEDataType>(
Policy::template MakeLSEaccRegTileDistribution<Problem>()); Policy::template MakeLSEaccRegTileDistribution<Problem>());
// copy LDS (shape=[kM0, kMaxSplits]) to lse_accum (shape=[kM0, max(kMaxSplits, warp_size)]) // copy LDS (shape=[kM0, kMaxSplits]) to lse_accum (shape=[kM0, kMaxSplits])
// this will extend the distributed tensor width so that each thread in wave have data to // and fill up -INF values outside the [kM0, num_splits] region.
// reduce.
{ {
constexpr auto spans = decltype(lse_accum)::get_distributed_spans(); constexpr auto spans = decltype(lse_accum)::get_distributed_spans();
sweep_tile_span(spans[number<0>{}], [&](auto idx0) { sweep_tile_span(spans[number<0>{}], [&](auto idx0) {
......
include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_combine_pipeline_default_policy.hpp
View file @ fe488bf2
...@@ -10,11 +10,26 @@ namespace ck_tile { ...@@ -10,11 +10,26 @@ namespace ck_tile {
struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
{ {
template <index_t BlockSize, index_t M, index_t N, typename DataType>
CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeForTile()
{
constexpr index_t PixelsPerThread = (M * N) / BlockSize;
static_assert(0 < PixelsPerThread);
constexpr index_t MaxNPerThread = 16 / sizeof(DataType);
constexpr index_t NPerThread = min(MaxNPerThread, PixelsPerThread);
return NPerThread;
}
// alignment for dram lse tile (shape=[kMaxSplits, kM0])
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentLSE() CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentLSE()
{ {
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>; return GetVectorSizeForTile<Problem::kBlockSize,
return 16 / sizeof(LSEDataType); Problem::kMaxSplits,
Problem::kM0,
typename Problem::LSEDataType>();
} }
template <typename Problem> template <typename Problem>
...@@ -47,29 +62,31 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy ...@@ -47,29 +62,31 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
MakeLSEaccLdsBlockDescriptor<Problem>().get_element_space_size(); MakeLSEaccLdsBlockDescriptor<Problem>().get_element_space_size();
} }
// shape=[kMaxSplits, kM0]
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccDramTileDistribution() CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccDramTileDistribution()
{ {
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>; using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize; constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNumWarps = Problem::kNumWarps;
constexpr index_t kNPerBlock = Problem::kM0; constexpr index_t kNPerBlock = Problem::kM0;
constexpr index_t kMPerBlock = Problem::kMaxSplits; constexpr index_t kMPerBlock = Problem::kMaxSplits;
constexpr index_t NPerThread = 16 / sizeof(LSEDataType); constexpr index_t NPerThread =
constexpr index_t NThreads = kNPerBlock / NPerThread; GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
constexpr index_t NThreads = kNPerBlock / NPerThread;
constexpr index_t MThreadsPerWarp = get_warp_size() / NThreads; constexpr index_t MThreadsPerWarp = get_warp_size() / NThreads;
constexpr index_t TotalWarps = kBlockSize / get_warp_size(); constexpr index_t MPerThread = kMPerBlock / (kNumWarps * MThreadsPerWarp);
constexpr index_t MPerThread = kMPerBlock / (TotalWarps * MThreadsPerWarp);
static_assert(NThreads * NPerThread == kNPerBlock); static_assert(NThreads * NPerThread == kNPerBlock);
static_assert(MPerThread * TotalWarps * MThreadsPerWarp == kMPerBlock); static_assert(MPerThread * kNumWarps * MThreadsPerWarp == kMPerBlock);
return make_static_tile_distribution( return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>, tile_distribution_encoding<sequence<1>,
tuple<sequence<MPerThread, TotalWarps, MThreadsPerWarp>, tuple<sequence<MPerThread, kNumWarps, MThreadsPerWarp>,
sequence<NThreads, NPerThread>>, sequence<NThreads, NPerThread>>,
tuple<sequence<1>, sequence<1, 2>>, tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>, tuple<sequence<1>, sequence<2, 0>>,
...@@ -77,15 +94,18 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy ...@@ -77,15 +94,18 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
sequence<0, 1>>{}); sequence<0, 1>>{});
} }
// 3d + padding, [kMaxSplits, kM0] // 3d + padding, shape=[kMaxSplits, kM0]
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccLdsStoreBlockDescriptor() CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccLdsStoreBlockDescriptor()
{ {
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>; using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kMaxSplits; constexpr index_t kMPerBlock = Problem::kMaxSplits;
constexpr index_t kNPerBlock = Problem::kM0; constexpr index_t kNPerBlock = Problem::kM0;
constexpr index_t NPack = 16 / sizeof(LSEDataType); constexpr index_t NPack =
GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor( constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}), make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}),
...@@ -103,15 +123,18 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy ...@@ -103,15 +123,18 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
return lse_acc_lds_block_desc; return lse_acc_lds_block_desc;
} }
// 3d + padding, [kM0, kMaxSplits] // 3d + padding, shape=[kM0, kMaxSplits]
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccLdsBlockDescriptor() CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccLdsBlockDescriptor()
{ {
using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>; using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::kMaxSplits; constexpr index_t kMPerBlock = Problem::kMaxSplits;
constexpr index_t kNPerBlock = Problem::kM0; constexpr index_t kNPerBlock = Problem::kM0;
constexpr index_t NPack = 16 / sizeof(LSEDataType); constexpr index_t NPack =
GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor( constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}), make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}),
...@@ -134,26 +157,28 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy ...@@ -134,26 +157,28 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
{ {
constexpr index_t kBlockSize = Problem::kBlockSize; constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kNPerBlock = max(Problem::kMaxSplits, get_warp_size()); constexpr index_t kNPerBlock = Problem::kMaxSplits;
constexpr index_t kMPerBlock = Problem::kM0; constexpr index_t kMPerBlock = Problem::kM0;
constexpr index_t NThreads = get_warp_size(); constexpr index_t NThreads = 4;
constexpr index_t NPerThread = kNPerBlock / NThreads; constexpr index_t NPerThread = kNPerBlock / NThreads;
constexpr index_t MThreads = kBlockSize / NThreads; constexpr index_t MThreads = kBlockSize / NThreads;
constexpr index_t MPerThread = kMPerBlock / MThreads; constexpr index_t MPerThread = kMPerBlock / MThreads;
constexpr index_t MWarps = kBlockSize / get_warp_size();
constexpr index_t MThreadPerWarp = get_warp_size() / NThreads;
static_assert(NThreads * NPerThread == kNPerBlock); static_assert(NThreads * NPerThread == kNPerBlock);
static_assert(MThreads * MPerThread == kMPerBlock); static_assert(MWarps * MThreadPerWarp * MPerThread == kMPerBlock);
return make_static_tile_distribution( return make_static_tile_distribution(
tile_distribution_encoding< tile_distribution_encoding<
sequence<1>, sequence<1>,
tuple<sequence<MThreads, MPerThread>, sequence<NThreads, NPerThread>>, tuple<sequence<MWarps, MThreadPerWarp, MPerThread>, sequence<NThreads, NPerThread>>,
tuple<sequence<1>, sequence<2>>, tuple<sequence<1>, sequence<2, 1>>,
tuple<sequence<0>, sequence<0>>, tuple<sequence<0>, sequence<0, 1>>,
sequence<1, 2>, sequence<1, 2>,
sequence<1, 1>>{}); sequence<2, 1>>{});
} }
template <typename Problem> template <typename Problem>
......
include/ck_tile/ops/fmha/pipeline/block_fmha_pipeline_problem.hpp
View file @ fe488bf2
...@@ -115,7 +115,8 @@ struct BlockFmhaSplitKVCombinePipelineProblem ...@@ -115,7 +115,8 @@ struct BlockFmhaSplitKVCombinePipelineProblem
using ODataType = remove_cvref_t<ODataType_>; using ODataType = remove_cvref_t<ODataType_>;
using Traits = remove_cvref_t<Traits_>; using Traits = remove_cvref_t<Traits_>;
static constexpr index_t kBlockSize = 256; static constexpr index_t kNumWarps = kM0_ / (get_warp_size() / 4);
static constexpr index_t kBlockSize = kNumWarps * get_warp_size();
static constexpr bool kIsGroupMode = kIsGroupMode_; static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr index_t kHeadDimV = HeadDimV_; static constexpr index_t kHeadDimV = HeadDimV_;
......
include/ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qx_ks_vs_custom_policy.hpp
View file @ fe488bf2
...@@ -88,22 +88,33 @@ struct BlockFmhaPipelineQXCustomPolicy</* QLoadOnce = */ true> ...@@ -88,22 +88,33 @@ struct BlockFmhaPipelineQXCustomPolicy</* QLoadOnce = */ true>
typename Problem::BlockFmhaShape::Gemm0WarpTile>>; typename Problem::BlockFmhaShape::Gemm0WarpTile>>;
constexpr auto warp_gemm = []() { constexpr auto warp_gemm = []() {
constexpr index_t WarpGemmM = Problem::BlockFmhaShape::Gemm0WarpTile::at(number<0>{});
static_assert(WarpGemmM == 16 || WarpGemmM == 32);
if constexpr(std::is_same_v<typename Problem::QDataType, half_t> && if constexpr(std::is_same_v<typename Problem::QDataType, half_t> &&
std::is_same_v<typename Problem::KDataType, half_t> && std::is_same_v<typename Problem::KDataType, half_t> &&
std::is_same_v<typename Problem::SaccDataType, float>) std::is_same_v<typename Problem::SaccDataType, float>)
{ {
return WarpGemmMfmaF16F16F32M32N32K16SwizzleBTransposedCDistribution{}; if constexpr(WarpGemmM == 32)
return WarpGemmMfmaF16F16F32M32N32K16SwizzleBTransposedCDistribution{};
else // WarpGemmM == 16
return WarpGemmMfmaF16F16F32M16N16K16TransposedCDistribution{};
} }
else if constexpr(std::is_same_v<typename Problem::QDataType, bf16_t> && else if constexpr(std::is_same_v<typename Problem::QDataType, bf16_t> &&
std::is_same_v<typename Problem::KDataType, bf16_t> && std::is_same_v<typename Problem::KDataType, bf16_t> &&
std::is_same_v<typename Problem::SaccDataType, float>) std::is_same_v<typename Problem::SaccDataType, float>)
{ {
return WarpGemmMfmaBf16Bf16F32M32N32K16SwizzleBTransposedCDistribution{}; if constexpr(WarpGemmM == 32)
return WarpGemmMfmaBf16Bf16F32M32N32K16SwizzleBTransposedCDistribution{};
else // WarpGemmM == 16
return WarpGemmMfmaBf16Bf16F32M16N16K16TransposedCDistribution{};
} }
else if constexpr(std::is_same_v<typename Problem::QDataType, fp8_t> && else if constexpr(std::is_same_v<typename Problem::QDataType, fp8_t> &&
std::is_same_v<typename Problem::KDataType, fp8_t> && std::is_same_v<typename Problem::KDataType, fp8_t> &&
std::is_same_v<typename Problem::SaccDataType, float>) std::is_same_v<typename Problem::SaccDataType, float>)
{ {
static_assert(WarpGemmM == 32);
// TODO: hard coded here. Otherwise, it may incorrect result // TODO: hard coded here. Otherwise, it may incorrect result
constexpr index_t swizzle_factor = 4; constexpr index_t swizzle_factor = 4;
return WarpGemmMfmaFp8Fp8F32M32N32K16SwizzleBTransposedCDistribution< return WarpGemmMfmaFp8Fp8F32M32N32K16SwizzleBTransposedCDistribution<
......
include/ck_tile/ops/gemm.hpp
View file @ fe488bf2
...@@ -23,12 +23,12 @@ ...@@ -23,12 +23,12 @@
#include "ck_tile/ops/gemm/block/block_gemm_problem.hpp" #include "ck_tile/ops/gemm/block/block_gemm_problem.hpp"
#include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp" #include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
#include "ck_tile/ops/gemm/kernel/gemm_tile_partitioner.hpp" #include "ck_tile/ops/gemm/kernel/gemm_tile_partitioner.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1.hpp" #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1_default_policy.hpp" #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1_default_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v2.hpp" #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v2.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v2_default_policy.hpp" #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v2_default_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_problem.hpp" #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_problem.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/tile_gemm_shape.hpp" #include "ck_tile/ops/gemm/pipeline/tile_gemm_shape.hpp"
#include "ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp" #include "ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp"
#include "ck_tile/ops/gemm/warp/warp_gemm.hpp" #include "ck_tile/ops/gemm/warp/warp_gemm.hpp"
......
include/ck_tile/ops/layernorm2d.hpp
View file @ fe488bf2
...@@ -4,6 +4,9 @@ ...@@ -4,6 +4,9 @@
#pragma once #pragma once
#include "ck_tile/ops/layernorm2d/kernel/layernorm2d_fwd_kernel.hpp" #include "ck_tile/ops/layernorm2d/kernel/layernorm2d_fwd_kernel.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/block_layernorm2d_fwd_problem.hpp" #include "ck_tile/ops/layernorm2d/kernel/layernorm2d_fwd_shape.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/tile_layernorm2d_fwd_shape.hpp" #include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_default_policy.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_one_pass.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_problem.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_two_pass.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp" #include "ck_tile/ops/common/tensor_layout.hpp"
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