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zanzhang
@@ -1,735 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core/arch/utility.hpp"
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#include "ck_tile/core/algorithm/space_filling_curve.hpp"
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#include "ck_tile/core/config.hpp"
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#include "ck_tile/core/container/array.hpp"
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#include "ck_tile/core/container/sequence.hpp"
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#include "ck_tile/core/container/tuple.hpp"
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#include "ck_tile/core/container/container_helper.hpp"
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#include "ck_tile/core/tensor/static_distributed_tensor.hpp"
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#include "ck_tile/core/tensor/tensor_adaptor.hpp"
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#include "ck_tile/core/tensor/tile_distribution.hpp"
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#include "ck_tile/core/utility/functional.hpp"
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#include "ck_tile/core/utility/type_traits.hpp"
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namespace ck_tile {
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/**
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* @brief This class provides tile (windowed) view and access to the device memory.
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*
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* @note This tile window does not support single issue you need to use tile_window_linear
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* structure for this purpose
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*
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* @tparam BottomTensorView_ Class describing & holding device tensor memory.
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* @tparam WindowLengths_ Spatial sizes of windowed view on tensor.
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* @tparam StaticTileDistribution_ Thread distribution (mapping) into Tile dimensions
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* @tparam NumCoord TBD
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*/
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template <typename BottomTensorView_,
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typename WindowLengths_,
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typename StaticTileDistribution_,
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typename StaticPageIndexArray_,
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index_t HsGatherDim = 0,
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index_t NumCoord = 1,
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index_t YsGatherDim = 0>
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struct tile_scatter_gather
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{
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using BottomTensorView = remove_reference_t<BottomTensorView_>;
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using WindowLengths = remove_cvref_t<WindowLengths_>;
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using TileDstr = remove_cvref_t<StaticTileDistribution_>;
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using PageIdxArray = remove_cvref_t<StaticPageIndexArray_>;
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using WindowAdaptor = typename TileDstr::PsYs2XsAdaptor;
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using BottomTensorDesc = typename BottomTensorView::TensorDesc;
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using DataType = remove_cvref_t<typename BottomTensorView::DataType>;
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static constexpr index_t NDimWindowAdaptorTop = WindowAdaptor::get_num_of_top_dimension();
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static constexpr index_t NDimBottomTensor = BottomTensorDesc::get_num_of_dimension();
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static constexpr index_t NDimP = TileDstr::get_num_of_dimension_p();
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static constexpr index_t NDimY = TileDstr::get_num_of_dimension_y();
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static constexpr auto I0 = number<0>{};
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static constexpr auto I1 = number<1>{};
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static_assert(NumCoord == 1);
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// TODO: check WindowLengths and StaticTileDistribution are consistent
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static_assert(ck_tile::is_known_at_compile_time<WindowLengths>::value,
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"wrong! lengths should be static");
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static_assert(TileDstr::is_static(), "wrong!");
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static_assert(NDimBottomTensor == WindowAdaptor::get_num_of_bottom_dimension(),
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"wrong! inconsistent # of diemsnions");
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using AdaptorTopIndex = array<index_t, NDimWindowAdaptorTop>;
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using BottomTensorIndex = array<index_t, NDimBottomTensor>;
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using WindowAdaptorCoord =
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decltype(make_tensor_adaptor_coordinate(WindowAdaptor{}, AdaptorTopIndex{}));
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using BottomTensorCoord =
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decltype(make_tensor_coordinate(BottomTensorDesc{}, BottomTensorIndex{}));
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struct load_store_traits
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{
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private:
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static constexpr auto get_vector_dim_y_scalar_per_vector()
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{
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const auto [ys_vector_lengths, ys_vector_strides] =
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tile_scatter_gather::
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get_window_adaptor_ys_safe_vector_length_strides();
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index_t VectorDimY_ = 0;
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index_t ScalarPerVector_ = 1;
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for(index_t i = 0; i < NDimY; ++i)
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{
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if(ys_vector_strides[i] == 1 && ys_vector_lengths[i] > ScalarPerVector_)
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{
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ScalarPerVector_ = ys_vector_lengths[i];
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VectorDimY_ = i;
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}
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}
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return make_tuple(VectorDimY_, ScalarPerVector_);
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}
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public:
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static constexpr index_t PackedSize =
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ck_tile::numeric_traits<remove_cvref_t<DataType>>::PackedSize;
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static constexpr index_t VectorDimY = get_vector_dim_y_scalar_per_vector().template at<0>();
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static constexpr index_t ScalarPerVector =
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get_vector_dim_y_scalar_per_vector().template at<1>();
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// using vector_type_t = vector_type_maker_t<DataType, ScalarPerVector>;
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// using vector_t = typename vector_type_t::type;
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using vector_t = thread_buffer<DataType, ScalarPerVector / PackedSize>;
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private:
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static constexpr auto scalars_per_access_ = [] {
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constexpr auto scalars_per_access_arr = generate_array(
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[&](auto i) { return (i == VectorDimY) ? ScalarPerVector : 1; }, number<NDimY>{});
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/// TODO: add non-automatic storage argument support to macro TO_SEQUENCE()
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constexpr auto NDimY_ = NDimY;
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return TO_SEQUENCE(scalars_per_access_arr, NDimY_);
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}();
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static constexpr auto get_space_filling_curve()
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{
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constexpr auto tile_dstr = TileDstr{};
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constexpr auto thread_tensor_lengths_ys =
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to_sequence(tile_dstr.get_ys_to_d_descriptor().get_lengths());
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// FIXME: need logic to judge dim access order
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using DimAccessOrder = typename arithmetic_sequence_gen<0, NDimY, 1>::type;
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return space_filling_curve<decltype(thread_tensor_lengths_ys),
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DimAccessOrder,
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decltype(scalars_per_access_)>{};
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}
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public:
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using SFC_Ys = decltype(get_space_filling_curve());
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static constexpr index_t NumAccess = SFC_Ys::get_num_of_access();
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static_assert(0 < NumAccess, "Wrong! NumAccess should be larger than 0");
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static_assert(NumAccess % NumCoord == 0, "wrong! # of access is not divisible by NumCoord");
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};
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static constexpr index_t NumAccessPerCoord = load_store_traits::NumAccess / NumCoord;
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CK_TILE_DEVICE constexpr tile_scatter_gather() = default;
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CK_TILE_DEVICE constexpr tile_scatter_gather(
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const BottomTensorView& bottom_tensor_view,
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const WindowLengths& window_lengths,
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const BottomTensorIndex& window_origin,
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const TileDstr& tile_distribution,
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const PageIdxArray& page_idx)
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: bottom_tensor_view_{bottom_tensor_view},
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window_lengths_{window_lengths},
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window_origin_{window_origin},
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tile_dstr_{tile_distribution},
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page_idx_{page_idx},
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pre_computed_coords_{}
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{
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#if 0 // debug
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// TODO: this use more register for FA, but less register for GEMM
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// need investigation
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// only support warp-tile and block-tile
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static_assert(NDimP == 1 or NDimP == 2, "wrong!");
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WindowAdaptorCoord window_adaptor_thread_coord_tmp;
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if constexpr(NDimP == 1)
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{
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window_adaptor_thread_coord_tmp = make_tensor_adaptor_coordinate(
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tile_distribution.get_ps_ys_to_xs_adaptor(), AdaptorTopIndex{get_lane_id(), 0});
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}
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else if constexpr(NDimP == 2)
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{
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window_adaptor_thread_coord_tmp =
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make_tensor_adaptor_coordinate(tile_distribution.get_ps_ys_to_xs_adaptor(),
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AdaptorTopIndex{get_warp_id(), get_lane_id(), 0});
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}
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#else
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// TODO: this use less register for FA, but more register for GEMM
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// need investigation
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const auto window_adaptor_thread_coord_tmp = make_tensor_adaptor_coordinate(
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tile_distribution.get_ps_ys_to_xs_adaptor(),
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container_concat(detail::get_partition_index(tile_distribution),
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array<index_t, NDimY>{0}));
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#endif
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BottomTensorIndex bottom_tensor_thread_origin_idx_tmp =
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window_origin + window_adaptor_thread_coord_tmp.get_bottom_index();
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bottom_tensor_thread_origin_idx_tmp(HsGatherDim) = 0;
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const auto bottom_tensor_thread_coord_tmp = make_tensor_coordinate(
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bottom_tensor_view_.get_tensor_descriptor(), bottom_tensor_thread_origin_idx_tmp);
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// pre-compute NumCoord (WindowAdaptorCoord, BottomTensorCoord) bundles to speed up
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// future load/store() calls (might allocate more registers)
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using Traits = load_store_traits;
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using SFC_Ys = typename Traits::SFC_Ys;
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static_for<0, NumCoord, 1>{}([&](auto iCoord) {
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auto window_adaptor_thread_coord = window_adaptor_thread_coord_tmp;
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auto bottom_tensor_thread_coord = bottom_tensor_thread_coord_tmp;
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constexpr auto idx_diff_ys =
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SFC_Ys::get_step_between(number<0>{}, number<iCoord * NumAccessPerCoord>{});
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constexpr auto idx_diff_ps_ys = container_concat(
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generate_tuple([&](auto) { return number<0>{}; }, number<NDimP>{}), idx_diff_ys);
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move_window_adaptor_and_bottom_tensor_thread_coordinate(
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window_adaptor_thread_coord, bottom_tensor_thread_coord, idx_diff_ps_ys);
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pre_computed_coords_(iCoord) =
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make_tuple(window_adaptor_thread_coord, bottom_tensor_thread_coord);
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});
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}
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CK_TILE_DEVICE static constexpr index_t get_num_of_dimension() { return NDimBottomTensor; }
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CK_TILE_DEVICE static constexpr bool has_static_tile_distribution()
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{
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return TileDstr::is_static();
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}
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CK_TILE_DEVICE constexpr auto get_window_lengths() const { return window_lengths_; }
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CK_TILE_DEVICE constexpr auto get_tile_distribution() const { return tile_dstr_; }
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CK_TILE_DEVICE constexpr auto get_bottom_tensor_view() const { return bottom_tensor_view_; }
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CK_TILE_DEVICE constexpr auto get_window_origin() const { return window_origin_; }
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CK_TILE_DEVICE constexpr void
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set_bottom_tensor_view_data_ptr(typename BottomTensorView::DataType* data)
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{
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bottom_tensor_view_.buf_.p_data_ = data;
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}
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// move thread's window adaptor coordinate and bottom tensor coordinate
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// [p0, p1, ..., y0, y1, ...] ==> [x0, x1, ...] ==> [x0', x1', ...] ==> [offset]
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template <typename ATopIndex>
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CK_TILE_DEVICE void move_window_adaptor_and_bottom_tensor_thread_coordinate(
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WindowAdaptorCoord& window_adaptor_thread_coord,
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BottomTensorCoord& bottom_tensor_thread_coord,
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const ATopIndex& idx_diff_adaptor_top) const
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{
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array<index_t, NDimBottomTensor> idx_diff_adaptor_bottom;
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move_tensor_adaptor_coordinate(tile_dstr_.get_ps_ys_to_xs_adaptor(),
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window_adaptor_thread_coord,
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idx_diff_adaptor_top,
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idx_diff_adaptor_bottom);
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move_tensor_coordinate(bottom_tensor_view_.get_tensor_descriptor(),
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bottom_tensor_thread_coord,
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idx_diff_adaptor_bottom);
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}
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// return vector dimension among [y0, y1, ...]
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CK_TILE_DEVICE static constexpr auto get_window_adaptor_ys_safe_vector_length_strides()
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{
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// bottom tensor top dimension vector lengths and strides
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const auto [bottom_tensor_top_dim_vector_lengths, bottom_tensor_top_dim_vector_strides] =
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BottomTensorDesc::get_top_dimension_safe_vector_length_strides();
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// window vector lengths/strides
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const auto window_adaptor_bottom_dim_vector_lengths = bottom_tensor_top_dim_vector_lengths;
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const auto window_adaptor_bottom_dim_vector_strides = bottom_tensor_top_dim_vector_strides;
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// window adaptor [p0, p1, ..., y0, y1, ...]
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array<index_t, WindowAdaptor::get_num_of_hidden_dimension()> window_adaptor_vector_lengths{
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-1};
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array<index_t, WindowAdaptor::get_num_of_hidden_dimension()> window_adaptor_vector_strides{
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-1};
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constexpr auto window_adaptor_bottom_dims =
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WindowAdaptor::get_bottom_dimension_hidden_ids();
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set_container_subset(window_adaptor_vector_lengths,
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window_adaptor_bottom_dims,
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window_adaptor_bottom_dim_vector_lengths);
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set_container_subset(window_adaptor_vector_strides,
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window_adaptor_bottom_dims,
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window_adaptor_bottom_dim_vector_strides);
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const auto [window_adaptor_ps_ys_vector_lengths, window_adaptor_ps_ys_vector_strides] =
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WindowAdaptor{}.get_top_dimension_safe_vector_length_strides(
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window_adaptor_vector_lengths, window_adaptor_vector_strides);
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// [y0, y1, ...]
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constexpr auto y_dims = typename arithmetic_sequence_gen<TileDstr::get_num_of_dimension_p(),
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NDimWindowAdaptorTop,
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1>::type{};
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return make_tuple(get_container_subset(window_adaptor_ps_ys_vector_lengths, y_dims),
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get_container_subset(window_adaptor_ps_ys_vector_strides, y_dims));
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}
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CK_TILE_DEVICE constexpr auto get_num_of_access() const { return load_store_traits::NumAccess; }
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template <index_t i_access_unsupport_ = -1, bool oob_conditional_check = true>
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CK_TILE_DEVICE auto load(number<i_access_unsupport_> = {},
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bool_constant<oob_conditional_check> = {}) const
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{
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constexpr auto tile_dstr = TileDstr{};
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auto dst_tensor = make_static_distributed_tensor<DataType>(tile_dstr);
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load(dst_tensor, number<i_access_unsupport_>{}, bool_constant<oob_conditional_check>{});
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return dst_tensor;
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}
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template <typename DistributedTensor,
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index_t i_access_unsupport_ = -1,
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bool oob_conditional_check = true>
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CK_TILE_DEVICE auto load(DistributedTensor& dst_tensor,
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number<i_access_unsupport_> = {},
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bool_constant<oob_conditional_check> = {}) const
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{
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using Traits = load_store_traits;
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using vector_t = typename Traits::vector_t;
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using SFC_Ys = typename Traits::SFC_Ys;
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constexpr auto tile_dstr = TileDstr{};
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// loop over thread tensor space [y0, y1, ...]
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static_for<0, NumCoord, 1>{}([&](auto iCoord) {
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/// TODO: use structure binding (to be captured later) if compiled in C++20
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auto window_adaptor_thread_coord = pre_computed_coords_[iCoord][I0];
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auto bottom_tensor_thread_coord = pre_computed_coords_[iCoord][I1];
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static_for<0, NumAccessPerCoord, 1>{}([&](auto iCoordAccess) {
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constexpr auto iAccess = number<iCoord * NumAccessPerCoord + iCoordAccess>{};
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// data index [y0, y1, ...]
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constexpr auto idx_ys_start = SFC_Ys::get_index(iAccess);
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constexpr auto idx_gather = idx_ys_start[number<YsGatherDim>{}];
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const auto page_offset = page_idx_[idx_gather];
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// read from bottom tensor
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const vector_t vec_value =
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get_bottom_tensor_view().template get_vectorized_elements<vector_t>(
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bottom_tensor_thread_coord, page_offset, bool_constant<oob_conditional_check>{});
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#if 1
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// write into distributed tensor
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static_for<0, Traits::ScalarPerVector, Traits::PackedSize>{}([&](auto j) {
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constexpr auto idx_ys = generate_tuple(
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[&](auto jj) {
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return jj == Traits::VectorDimY ? (idx_ys_start[jj] + j)
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: idx_ys_start[jj];
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},
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number<NDimY>{});
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constexpr index_t d =
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tile_dstr.get_ys_to_d_descriptor().calculate_offset(idx_ys) /
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Traits::PackedSize;
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dst_tensor.get_thread_buffer().template at<d>() =
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vec_value.template get_as<DataType>()[j / Traits::PackedSize];
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});
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#else
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constexpr index_t d =
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tile_dstr.get_ys_to_d_descriptor().calculate_offset(idx_ys_start);
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static_assert(d % Traits::ScalarPerVector == 0);
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dst_tensor.get_thread_buffer().template get_as<vector_t>()(
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number<d / Traits::ScalarPerVector>{}) = bit_cast<vector_t>(vec_value);
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#endif
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// move thread coordinate
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if constexpr(iCoordAccess != (NumAccessPerCoord - 1))
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{
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constexpr auto idx_diff_ys = SFC_Ys::get_forward_step(iAccess);
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constexpr auto forward_step_scatter = generate_tuple(
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[&](auto i) { return i == YsGatherDim ? 0 : idx_diff_ys[i]; }, number<NDimY>{});
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||||
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constexpr auto idx_diff_ps_ys = container_concat(
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generate_tuple([&](auto) { return number<0>{}; }, number<NDimP>{}),
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forward_step_scatter);
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||||
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move_window_adaptor_and_bottom_tensor_thread_coordinate(
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window_adaptor_thread_coord, bottom_tensor_thread_coord, idx_diff_ps_ys);
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}
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||||
});
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||||
});
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||||
}
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||||
|
||||
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||||
// TODO: currently async load only implemented in inline asm
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||||
template <typename LdsTileWindow_,
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index_t i_access_unsupport_ = -1,
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||||
bool oob_conditional_check = true,
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||||
bool pre_nop = false>
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CK_TILE_DEVICE auto async_load_raw(LdsTileWindow_&& lds_tile,
|
||||
number<i_access_unsupport_> = {},
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||||
bool_constant<oob_conditional_check> = {},
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||||
bool_constant<pre_nop> = {}) const
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||||
{
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||||
using LdsTileWindow = remove_cvref_t<LdsTileWindow_>;
|
||||
// using LdsTensorView = typename LdsTileWindow::BottomTensorView;
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||||
using LdsDataType = typename LdsTileWindow::DataType;
|
||||
// using LdsDescriptor = typename LdsTileWindow::BottomTensorDesc;
|
||||
|
||||
// issues * warps * lanes
|
||||
static_assert(LdsTileWindow::get_num_of_dimension() == 3); // TODO: hard coded
|
||||
|
||||
const index_t size_per_buf =
|
||||
lds_tile.get_bottom_tensor_view().get_tensor_descriptor().calculate_offset(
|
||||
make_tuple(number<0>{}, number<0>{}, number<0>{})) *
|
||||
sizeof(LdsDataType);
|
||||
|
||||
const index_t size_per_wave =
|
||||
lds_tile.get_bottom_tensor_view().get_tensor_descriptor().calculate_offset(
|
||||
make_tuple(number<0>{}, number<1>{}, number<0>{})) *
|
||||
sizeof(LdsDataType) -
|
||||
size_per_buf;
|
||||
|
||||
const index_t size_per_issue =
|
||||
lds_tile.get_bottom_tensor_view().get_tensor_descriptor().calculate_offset(
|
||||
make_tuple(number<1>{}, number<0>{}, number<0>{})) *
|
||||
sizeof(LdsDataType) -
|
||||
size_per_buf;
|
||||
|
||||
const index_t m0_init_value = size_per_buf + size_per_wave * get_warp_id();
|
||||
m0_set_with_memory(m0_init_value); // This should be wave independent
|
||||
|
||||
using Traits = load_store_traits;
|
||||
|
||||
// using vector_type_t = typename Traits::vector_type_t;
|
||||
using vector_t = typename Traits::vector_t;
|
||||
using SFC_Ys = typename Traits::SFC_Ys;
|
||||
|
||||
LdsDataType* smem = lds_tile.get_bottom_tensor_view().get_buffer_view().p_data_;
|
||||
|
||||
// loop over thread tensor space [y0, y1, ...]
|
||||
static_for<0, NumCoord, 1>{}([&](auto iCoord) {
|
||||
/// TODO: use structure binding (to be captured later) if compiled in C++20
|
||||
auto window_adaptor_thread_coord = pre_computed_coords_[iCoord][I0];
|
||||
auto bottom_tensor_thread_coord = pre_computed_coords_[iCoord][I1];
|
||||
|
||||
static_for<0, NumAccessPerCoord, 1>{}([&](auto iCoordAccess) {
|
||||
constexpr auto iAccess = number<iCoord * NumAccessPerCoord + iCoordAccess>{};
|
||||
constexpr auto pre_nop_ = [&]() {
|
||||
if constexpr(pre_nop && iCoord == 0 && iCoordAccess == 0)
|
||||
return bool_constant<true>{};
|
||||
else
|
||||
return bool_constant<false>{};
|
||||
}();
|
||||
|
||||
constexpr auto idx_ys_start = SFC_Ys::get_index(iAccess);
|
||||
constexpr auto idx_gather = idx_ys_start[number<YsGatherDim>{}];
|
||||
const auto page_offset = page_idx_[idx_gather];
|
||||
// read from bottom tensor
|
||||
get_bottom_tensor_view().template async_get_vectorized_elements_raw<vector_t>(
|
||||
smem, bottom_tensor_thread_coord, page_offset, 0, pre_nop_);
|
||||
|
||||
// move thread coordinate
|
||||
if constexpr(iCoordAccess != (NumAccessPerCoord - 1))
|
||||
{
|
||||
constexpr auto idx_diff_ys = SFC_Ys::get_forward_step(iAccess);
|
||||
|
||||
constexpr auto forward_step_scatter = generate_tuple(
|
||||
[&](auto i) { return i == YsGatherDim ? 0 : idx_diff_ys[i]; }, number<NDimY>{});
|
||||
|
||||
constexpr auto idx_diff_ps_ys = container_concat(
|
||||
generate_tuple([&](auto) { return number<0>{}; }, number<NDimP>{}),
|
||||
forward_step_scatter);
|
||||
|
||||
move_window_adaptor_and_bottom_tensor_thread_coordinate(
|
||||
window_adaptor_thread_coord, bottom_tensor_thread_coord, idx_diff_ps_ys);
|
||||
|
||||
m0_inc_with_memory(size_per_issue);
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
template <index_t i_access_unsupport_ = -1,
|
||||
bool oob_conditional_check = true>
|
||||
CK_TILE_DEVICE void store(const static_distributed_tensor<DataType, TileDstr>& dstr_tensor,
|
||||
number<i_access_unsupport_> = {},
|
||||
bool_constant<oob_conditional_check> = {}) const
|
||||
{
|
||||
using Traits = load_store_traits;
|
||||
|
||||
// using vector_type_t = typename Traits::vector_type_t;
|
||||
using vector_t = typename Traits::vector_t;
|
||||
using SFC_Ys = typename Traits::SFC_Ys;
|
||||
|
||||
constexpr auto tile_dstr = TileDstr{};
|
||||
// printf("off %d\n", page_idx_[I0]);
|
||||
// loop over thread tensor space [y0, y1, ...]
|
||||
static_for<0, NumCoord, 1>{}([&](auto iCoord) {
|
||||
auto window_adaptor_thread_coord = pre_computed_coords_[iCoord][I0];
|
||||
auto bottom_tensor_thread_coord = pre_computed_coords_[iCoord][I1];
|
||||
|
||||
static_for<0, NumAccessPerCoord, 1>{}([&](auto iCoordAccess) {
|
||||
constexpr auto iAccess = number<iCoord * NumAccessPerCoord + iCoordAccess>{};
|
||||
|
||||
// data index [y0, y1, ...]
|
||||
constexpr auto idx_ys_start = SFC_Ys::get_index(iAccess);
|
||||
constexpr auto idx_gather = idx_ys_start[number<0>{}];
|
||||
const auto page_offset = page_idx_[idx_gather];
|
||||
|
||||
// printf("idx_ys_start[0], idx_ys_start[1](%d, %d) \n",
|
||||
// idx_ys_start[number<0>{}]+0, idx_ys_start[number<1>{}]+0);
|
||||
|
||||
// read from distributed tensor
|
||||
// vector_type_t vec;
|
||||
vector_t vec_value;
|
||||
|
||||
static_for<0, Traits::ScalarPerVector, Traits::PackedSize>{}([&](auto j) {
|
||||
constexpr auto idx_ys = generate_tuple(
|
||||
[&](auto jj) {
|
||||
return jj == Traits::VectorDimY ? (idx_ys_start[jj] + j)
|
||||
: idx_ys_start[jj];
|
||||
},
|
||||
number<NDimY>{});
|
||||
|
||||
constexpr index_t d =
|
||||
tile_dstr.get_ys_to_d_descriptor().calculate_offset(idx_ys) /
|
||||
Traits::PackedSize;
|
||||
// printf("thread_idx_m: %d j: %d\n", idx_ys[number<0>{}] + 0, 0+j);
|
||||
vec_value.template get_as<DataType>()(j / Traits::PackedSize) =
|
||||
dstr_tensor.get_thread_buffer().template at<d>();
|
||||
});
|
||||
|
||||
// const vector_t vec_value = vec.template get_as<vector_t>().template at<0>();
|
||||
|
||||
// write into bottom tensor
|
||||
get_bottom_tensor_view().template set_vectorized_elements<vector_t>(
|
||||
bottom_tensor_thread_coord,
|
||||
page_offset,
|
||||
vec_value,
|
||||
bool_constant<oob_conditional_check>{});
|
||||
// printf("coord_offset:%d, scatter_offset:%d \n",
|
||||
// bottom_tensor_thread_coord.get_offset(), offset); move thread coordinate
|
||||
if constexpr(iCoordAccess != (NumAccessPerCoord - 1))
|
||||
{
|
||||
constexpr auto idx_diff_ys = SFC_Ys::get_forward_step(iAccess);
|
||||
|
||||
constexpr auto forward_step_scatter = generate_tuple(
|
||||
[&](auto i) { return i == YsGatherDim ? 0 : idx_diff_ys[i]; }, number<NDimY>{});
|
||||
|
||||
constexpr auto idx_diff_ps_ys = container_concat(
|
||||
generate_tuple([&](auto) { return number<0>{}; }, number<NDimP>{}),
|
||||
forward_step_scatter);
|
||||
|
||||
move_window_adaptor_and_bottom_tensor_thread_coordinate(
|
||||
window_adaptor_thread_coord, bottom_tensor_thread_coord, idx_diff_ps_ys);
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
// move thread's botom tensor coordiante
|
||||
// [x0', x1', ... ] ==> [offset]
|
||||
// also move window-origin
|
||||
CK_TILE_DEVICE void move(const BottomTensorIndex& step)
|
||||
{
|
||||
window_origin_ += step;
|
||||
BottomTensorIndex step_new = step;
|
||||
step_new(HsGatherDim) = 0;
|
||||
static_for<0, NumCoord, 1>{}([&](auto iCoord) {
|
||||
move_tensor_coordinate(bottom_tensor_view_.get_tensor_descriptor(),
|
||||
pre_computed_coords_(iCoord)(I1),
|
||||
step_new);
|
||||
});
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE void update_page_idx(const PageIdxArray& new_idx)
|
||||
{
|
||||
page_idx_ = new_idx;
|
||||
|
||||
// static_for<0, 2, 1>{}([&](auto k0) {
|
||||
// printf("update tid %d %d \n", threadIdx.x, page_idx_[k0]);
|
||||
// });
|
||||
}
|
||||
CK_TILE_DEVICE void set_window_origin(const BottomTensorIndex& new_window_origin)
|
||||
{
|
||||
window_origin_ = new_window_origin;
|
||||
|
||||
#if 0 // debug
|
||||
// TODO: this use more register for FA, but less register for GEMM
|
||||
// need investigation
|
||||
// only support warp-tile and block-tile
|
||||
static_assert(NDimP == 1 or NDimP == 2, "wrong!");
|
||||
|
||||
WindowAdaptorCoord window_adaptor_thread_coord_tmp;
|
||||
|
||||
if constexpr(NDimP == 1)
|
||||
{
|
||||
window_adaptor_thread_coord_tmp = make_tensor_adaptor_coordinate(
|
||||
tile_dstr_.get_ps_ys_to_xs_adaptor(), AdaptorTopIndex{get_lane_id(), 0});
|
||||
}
|
||||
else if constexpr(NDimP == 2)
|
||||
{
|
||||
window_adaptor_thread_coord_tmp =
|
||||
make_tensor_adaptor_coordinate(tile_dstr_.get_ps_ys_to_xs_adaptor(),
|
||||
AdaptorTopIndex{get_warp_id(), get_lane_id(), 0});
|
||||
}
|
||||
#else
|
||||
// TODO: this use less register for FA, but more register for GEMM
|
||||
// need investigation
|
||||
const auto window_adaptor_thread_coord_tmp = make_tensor_adaptor_coordinate(
|
||||
tile_dstr_.get_ps_ys_to_xs_adaptor(),
|
||||
container_concat(detail::get_partition_index(tile_dstr_), array<index_t, NDimY>{0}));
|
||||
#endif
|
||||
|
||||
BottomTensorIndex bottom_tensor_thread_origin_idx_tmp =
|
||||
window_origin_ + window_adaptor_thread_coord_tmp.get_bottom_index();
|
||||
|
||||
bottom_tensor_thread_origin_idx_tmp(HsGatherDim) = 0;
|
||||
const auto bottom_tensor_thread_coord_tmp = make_tensor_coordinate(
|
||||
bottom_tensor_view_.get_tensor_descriptor(), bottom_tensor_thread_origin_idx_tmp);
|
||||
|
||||
// pre-compute NumCoord (WindowAdaptorCoord, BottomTensorCoord) bundles to speed up
|
||||
// future load/store() calls (might allocate more registers)
|
||||
using Traits = load_store_traits;
|
||||
using SFC_Ys = typename Traits::SFC_Ys;
|
||||
|
||||
static_for<0, NumCoord, 1>{}([&](auto iCoord) {
|
||||
auto window_adaptor_thread_coord = window_adaptor_thread_coord_tmp;
|
||||
auto bottom_tensor_thread_coord = bottom_tensor_thread_coord_tmp;
|
||||
|
||||
constexpr auto idx_diff_ys =
|
||||
SFC_Ys::get_step_between(number<0>{}, number<iCoord * NumAccessPerCoord>{});
|
||||
|
||||
constexpr auto idx_diff_ps_ys = container_concat(
|
||||
generate_tuple([&](auto) { return number<0>{}; }, number<NDimP>{}), idx_diff_ys);
|
||||
|
||||
move_window_adaptor_and_bottom_tensor_thread_coordinate(
|
||||
window_adaptor_thread_coord, bottom_tensor_thread_coord, idx_diff_ps_ys);
|
||||
|
||||
pre_computed_coords_(iCoord) =
|
||||
make_tuple(window_adaptor_thread_coord, bottom_tensor_thread_coord);
|
||||
});
|
||||
}
|
||||
|
||||
CK_TILE_HOST_DEVICE void init_raw() { bottom_tensor_view_.init_raw(); }
|
||||
|
||||
// this is the bottom tensor view
|
||||
// [x0', x1', ...] ==> [offset]
|
||||
BottomTensorView bottom_tensor_view_;
|
||||
|
||||
//
|
||||
WindowLengths window_lengths_;
|
||||
|
||||
// origin ([x0', x1', ...]) of window on bottom tensor
|
||||
BottomTensorIndex window_origin_;
|
||||
|
||||
// Tile tensor distribution, which contains:
|
||||
// 1. adaptor for window: [p0, p1, ..., y0, y1, ...] ==> [x0, x1, ...]
|
||||
// 2. thread descriptor for thread tensor in register: [y0, y1, ...] ==> [d]
|
||||
TileDstr tile_dstr_;
|
||||
|
||||
PageIdxArray page_idx_;
|
||||
|
||||
// this contains:
|
||||
// per-thread coordinate for window adaptor
|
||||
// per-thread coordinate for bottom tensor
|
||||
array<tuple<WindowAdaptorCoord, BottomTensorCoord>, NumCoord> pre_computed_coords_;
|
||||
};
|
||||
|
||||
// TODO: use strategy
|
||||
template <typename TensorView_,
|
||||
typename WindowLengths_,
|
||||
typename StaticTileDistribution_,
|
||||
typename StaticPageIndexArray_,
|
||||
index_t HsGatherDim = 0,
|
||||
index_t NumCoord = 1>
|
||||
CK_TILE_DEVICE constexpr auto
|
||||
make_tile_scatter_gather(const TensorView_& tensor_view,
|
||||
const WindowLengths_& window_lengths,
|
||||
const multi_index<TensorView_::get_num_of_dimension()>& origin,
|
||||
const StaticTileDistribution_& tile_distribution,
|
||||
const StaticPageIndexArray_& page_idx,
|
||||
number<HsGatherDim> = {},
|
||||
number<NumCoord> = {})
|
||||
{
|
||||
return tile_scatter_gather<remove_cvref_t<TensorView_>,
|
||||
remove_cvref_t<WindowLengths_>,
|
||||
remove_cvref_t<StaticTileDistribution_>,
|
||||
remove_cvref_t<StaticPageIndexArray_>,
|
||||
HsGatherDim,
|
||||
NumCoord>{
|
||||
tensor_view, window_lengths, origin, tile_distribution, page_idx};
|
||||
}
|
||||
|
||||
template <typename TensorView, typename WindowLengths, typename StaticTileDistribution, typename StaticPageIndexArray, index_t HsGatherDim>
|
||||
CK_TILE_DEVICE constexpr auto
|
||||
make_tile_scatter_gather(const tile_window_with_static_lengths<TensorView, WindowLengths>& tile_window,
|
||||
const multi_index<TensorView::get_num_of_dimension()>& origin,
|
||||
const StaticTileDistribution& tile_distribution,
|
||||
const StaticPageIndexArray& page_idx,
|
||||
number<HsGatherDim> = {})
|
||||
{
|
||||
return make_tile_scatter_gather(tile_window.get_bottom_tensor_view(),
|
||||
tile_window.get_window_lengths(),
|
||||
origin,
|
||||
tile_distribution,
|
||||
page_idx,
|
||||
number<HsGatherDim>{});
|
||||
}
|
||||
|
||||
template <typename TensorView, typename WindowLengths, typename StaticTileDistribution, typename StaticPageIndexArray, index_t HsGatherDim>
|
||||
CK_TILE_DEVICE constexpr auto
|
||||
make_tile_scatter_gather(const tile_window_with_static_lengths<TensorView, WindowLengths>& tile_window,
|
||||
const StaticTileDistribution& tile_distribution, const StaticPageIndexArray& page_idx,
|
||||
number<HsGatherDim> = {})
|
||||
{
|
||||
return make_tile_scatter_gather(tile_window.get_bottom_tensor_view(),
|
||||
tile_window.get_window_lengths(),
|
||||
tile_window.get_window_origin(),
|
||||
tile_distribution,
|
||||
page_idx,
|
||||
number<HsGatherDim>{});
|
||||
}
|
||||
|
||||
// template <typename TensorView, typename WindowLengths, typename StaticTileDistribution>
|
||||
// CK_TILE_DEVICE constexpr auto
|
||||
// make_tile_window_raw(const tile_window_with_static_lengths<TensorView, WindowLengths>& tile_window,
|
||||
// const StaticTileDistribution& tile_distribution)
|
||||
// {
|
||||
// auto w = make_tile_scatter_gather(tile_window.get_bottom_tensor_view(),
|
||||
// tile_window.get_window_lengths(),
|
||||
// tile_window.get_window_origin(),
|
||||
// tile_distribution);
|
||||
// w.init_raw();
|
||||
// return w;
|
||||
// }
|
||||
|
||||
|
||||
} // namespace ck_tile
|
||||
Reference in New Issue
Block a user