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Commit e1396d87 authored by carlushuang's avatar carlushuang
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update SweepTile API

parent 6975cb8f
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Showing with 310 additions and 24 deletions
example/ck_tile/04_topk_softmax/topk_softmax_api.cpp
View file @ e1396d87
......@@ -51,9 +51,9 @@ float topk_softmax(topk_softmax_trait t, topk_softmax_kargs a, ck_tile::stream_c
TOPK_SOFTMAX_DISPATCH(192)
}
#else
if(t.experts <= 16)
if(t.experts <= 128)
{
TOPK_SOFTMAX_DISPATCH(16)
TOPK_SOFTMAX_DISPATCH(128)
}
#endif
}
......
include/ck_tile/core/container/tuple.hpp
View file @ e1396d87
......@@ -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{});
}
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
template <index_t Depth = 0, index_t MaxDepth = -1>
CK_TILE_HOST_DEVICE constexpr auto unroll_nested_tuple(const tuple<>& t)
......
include/ck_tile/core/tensor/static_distributed_tensor.hpp
View file @ e1396d87
......@@ -187,6 +187,20 @@ 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 detail {
// check if 2 static_distributed_tensor has same data type and size of element
......
include/ck_tile/core/tensor/sweep_tile.hpp
View file @ e1396d87
......@@ -42,4 +42,267 @@ CK_TILE_DEVICE void sweep_tile_uspan(TileDistributedSpan_, const F& f, 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 SweepTile
{
using DistributedTensor = remove_cvref_t<DistributedTensor_>;
using F = remove_cvref_t<F_>;
using UnpacksPerXDim = remove_cvref_t<UnpacksPerXDim_>;
CK_TILE_HOST_DEVICE SweepTile(const F& f_, UnpacksPerXDim = {}) : f(f_) {}
CK_TILE_HOST_DEVICE SweepTile(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 SweepTile(const F&, U = {})->SweepTile<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 SweepTile(const T&, const F&, U = {})->SweepTile<T, F, U>;
} // namespace ck_tile
include/ck_tile/core/utility/functional_with_tuple.hpp
View file @ e1396d87
......@@ -105,6 +105,8 @@ struct static_uford_one_shot_impl<sequence<>, sequence<>, Orders>
} // 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
//
......
include/ck_tile/ops/fmha.hpp
View file @ e1396d87
......@@ -33,8 +33,8 @@
#include "ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_qr_ks_vs_default_policy.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_enum.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_problem.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_async_ex.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_async_ex_policy.hpp"
//#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_async_ex.hpp"
//#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_async_ex_policy.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_ks_vs.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_ks_vs_async.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_ks_vs_async_default_policy.hpp"
......
include/ck_tile/ops/softmax/block/block_softmax_2d.hpp
View file @ e1396d87
......@@ -46,15 +46,9 @@ struct BlockSoftmax2D
#else
auto row_max = reduce_row_max(f_max);
#endif
// compute elementwise softmax
constexpr auto span_2d = DistributedTensor::get_distributed_spans();
sweep_tile_span(span_2d[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
sweep_tile_span(span_2d[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
y(i_j_idx) = exp(x[i_j_idx] - row_max(i_idx));
});
sweep_tile<DistributedTensor>([&](auto idx) {
constexpr auto row_id = make_tuple(idx[number<0>{}]);
y(idx) = exp(x[idx] - row_max[row_id]);
});
// compute row sum
......@@ -66,19 +60,12 @@ struct BlockSoftmax2D
#endif
// reciprocal
auto r = make_static_distributed_tensor<DataType>(row_sum.get_tile_distribution());
constexpr auto span_1d = decltype(r)::get_distributed_spans();
sweep_tile_span(span_1d[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
r(i_idx) = DataType{1} / row_sum(i_idx);
});
sweep_tile(row_sum, [&](auto idx) { r(idx) = DataType{1} / row_sum(idx); });
// scale
sweep_tile_span(span_2d[number<0>{}], [&](auto idx0) {
constexpr auto i_idx = make_tuple(idx0);
sweep_tile_span(span_2d[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx0, idx1);
y(i_j_idx) = y(i_j_idx) * r(i_idx);
});
sweep_tile<DistributedTensor>([&](auto idx) {
constexpr auto row_id = make_tuple(idx[number<0>{}]);
y(idx) = y(idx) * r(row_id);
});
}
......
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