device_pool3d_fwd_ndhwc_ndhwc.hpp Source File

device_pool3d_fwd_ndhwc_ndhwc.hpp Source File#

Composable Kernel: device_pool3d_fwd_ndhwc_ndhwc.hpp Source File
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
#pragma once
 
#include <iostream>
#include <sstream>
 
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/device_pool_fwd.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_threadwise.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
 
namespace ck {
namespace tensor_operation {
namespace device {
 
template <typename InDataType,
          typename OutDataType,
          typename IndexDataType, // enable if OutputIndex == true
          typename ComputeDataType,
          ck::ReduceTensorOp ReduceOpId,
          bool OutputIndex,
          ck::index_t BlockSize,
          ck::index_t MThreadClusterSize,
          ck::index_t KThreadClusterSize,
          ck::index_t MThreadSliceSize,
          ck::index_t KThreadSliceSize,
          ck::index_t InSrcOutDstVectorSize>
struct DevicePool3dFwd_NDHWC_NDHWC : public DevicePoolFwd<5,
                                                          3,
                                                          InDataType,
                                                          OutDataType,
                                                          IndexDataType,
                                                          tensor_layout::convolution::NDHWC,
                                                          tensor_layout::convolution::NDHWC,
                                                          ReduceOpId,
                                                          OutputIndex>
{
    static constexpr auto I0 = Number<0>{};
    static constexpr auto I1 = Number<1>{};
    static constexpr auto I2 = Number<2>{};
    static constexpr auto I3 = Number<3>{};
    static constexpr auto I4 = Number<4>{};
    static constexpr auto I5 = Number<5>{};
 
    static constexpr index_t InOutRank  = 5;
    static constexpr index_t WindowRank = 3;
 
    using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
 
    using InElementwiseOperation =
        typename reduce_unary_operator<ReduceOpId, true, true>::InElementwiseOperation;
 
    using AccElementwiseOperation =
        typename reduce_unary_operator<ReduceOpId, true, true>::AccElementwiseOperation;
 
    static constexpr ck::index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
    static constexpr ck::index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
 
    static auto MakeABGridDescriptor_A_M_K_B_M(std::vector<ck::index_t> input_ncdhw_lengths,
                                               std::vector<ck::index_t> output_ncdhw_lengths,
                                               std::vector<ck::index_t> input_ncdhw_stride,
                                               std::vector<ck::index_t> output_ncdhw_stride,
                                               std::vector<ck::index_t> window_spatial_zyx_lengths,
                                               std::vector<ck::index_t> window_zyx_strides,
                                               std::vector<ck::index_t> window_zyx_dilations,
                                               std::vector<ck::index_t> input_left_dhw_pads,
                                               std::vector<ck::index_t> input_right_dhw_pads)
    {
        const index_t N  = input_ncdhw_lengths[0];
        const index_t C  = input_ncdhw_lengths[1];
        const index_t Di = input_ncdhw_lengths[2];
        const index_t Hi = input_ncdhw_lengths[3];
        const index_t Wi = input_ncdhw_lengths[4];
 
        const index_t Do = output_ncdhw_lengths[2];
        const index_t Ho = output_ncdhw_lengths[3];
        const index_t Wo = output_ncdhw_lengths[4];
 
        const index_t Z = window_spatial_zyx_lengths[0];
        const index_t Y = window_spatial_zyx_lengths[1];
        const index_t X = window_spatial_zyx_lengths[2];
 
        const index_t WindowStrideD = window_zyx_strides[0];
        const index_t WindowStrideH = window_zyx_strides[1];
        const index_t WindowStrideW = window_zyx_strides[2];
 
        const index_t WindowDilationD = window_zyx_dilations[0];
        const index_t WindowDilationH = window_zyx_dilations[1];
        const index_t WindowDilationW = window_zyx_dilations[2];
 
        const index_t InLeftPadD = input_left_dhw_pads[0];
        const index_t InLeftPadH = input_left_dhw_pads[1];
        const index_t InLeftPadW = input_left_dhw_pads[2];
 
        const index_t InRightPadD = input_right_dhw_pads[0];
        const index_t InRightPadH = input_right_dhw_pads[1];
        const index_t InRightPadW = input_right_dhw_pads[2];
 
        const index_t MRaw = N * Do * Ho * Wo * C;
        const index_t MPad = math::integer_least_multiple(MRaw, M_BlockTileSize) - MRaw;
 
        const index_t KRaw = Z * Y * X;
        const index_t KPad = math::integer_least_multiple(KRaw, K_BlockTileSize) - KRaw;
 
        // A[ReduceM, ReduceK]
        const index_t Ni_stride = input_ncdhw_stride[0];
        const index_t Ci_stride = input_ncdhw_stride[1];
        const index_t Di_stride = input_ncdhw_stride[2];
        const index_t Hi_stride = input_ncdhw_stride[3];
        const index_t Wi_stride = input_ncdhw_stride[4];
 
        const auto in_grid_desc_n_di_hi_wi_c = make_naive_tensor_descriptor(
            make_tuple(N, Di, Hi, Wi, C),
            make_tuple(Ni_stride, Di_stride, Hi_stride, Wi_stride, Ci_stride));
 
        const auto in_grid_desc_n_dip_hip_wip_c = transform_tensor_descriptor(
            in_grid_desc_n_di_hi_wi_c,
            make_tuple(make_pass_through_transform(N),
                       make_pad_transform(Di, InLeftPadD, InRightPadD),
                       make_pad_transform(Hi, InLeftPadH, InRightPadH),
                       make_pad_transform(Wi, InLeftPadW, InRightPadW),
                       make_pass_through_transform(C)),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
 
        const auto in_grid_desc_n_z_do_y_ho_x_wo_c = transform_tensor_descriptor(
            in_grid_desc_n_dip_hip_wip_c,
            make_tuple(
                make_pass_through_transform(N),
                make_embed_transform(make_tuple(Z, Do), make_tuple(WindowDilationD, WindowStrideD)),
                make_embed_transform(make_tuple(Y, Ho), make_tuple(WindowDilationH, WindowStrideH)),
                make_embed_transform(make_tuple(X, Wo), make_tuple(WindowDilationW, WindowStrideW)),
                make_pass_through_transform(C)),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
            make_tuple(Sequence<0>{},
                       Sequence<1, 2>{},
                       Sequence<3, 4>{},
                       Sequence<5, 6>{},
                       Sequence<7>{}));
 
        const auto in_grid_desc_reducemraw_reducekraw = transform_tensor_descriptor(
            in_grid_desc_n_z_do_y_ho_x_wo_c,
            make_tuple(make_merge_transform(make_tuple(N, Do, Ho, Wo, C)),
                       make_merge_transform(make_tuple(Z, Y, X))),
            make_tuple(Sequence<0, 2, 4, 6, 7>{}, Sequence<1, 3, 5>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}));
 
        const auto in_grid_desc_reducem_reducek = transform_tensor_descriptor(
            in_grid_desc_reducemraw_reducekraw,
            make_tuple(make_right_pad_transform(MRaw, MPad), make_right_pad_transform(KRaw, KPad)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}));
 
        // B[ReduceM]
        const index_t No_stride = output_ncdhw_stride[0];
        const index_t Co_stride = output_ncdhw_stride[1];
        const index_t Do_stride = output_ncdhw_stride[2];
        const index_t Ho_stride = output_ncdhw_stride[3];
        const index_t Wo_stride = output_ncdhw_stride[4];
 
        const auto out_grid_desc_n_do_ho_wo_c = make_naive_tensor_descriptor(
            make_tuple(N, Di, Hi, Wi, C),
            make_tuple(No_stride, Do_stride, Ho_stride, Wo_stride, Co_stride));
 
        const auto out_grid_desc_reducemraw = transform_tensor_descriptor(
            out_grid_desc_n_do_ho_wo_c,
            make_tuple(make_merge_transform(make_tuple(N, Do, Ho, Wo, C))),
            make_tuple(Sequence<0, 1, 2, 3, 4>{}),
            make_tuple(Sequence<0>{}));
 
        const auto out_grid_desc_reducem =
            transform_tensor_descriptor(out_grid_desc_reducemraw,
                                        make_tuple(make_right_pad_transform(MRaw, MPad)),
                                        make_tuple(Sequence<0>{}),
                                        make_tuple(Sequence<0>{}));
 
        return make_tuple(in_grid_desc_reducem_reducek, out_grid_desc_reducem);
    }
 
    using ABGridDescs =
        decltype(MakeABGridDescriptor_A_M_K_B_M({}, {}, {}, {}, {}, {}, {}, {}, {}));
 
    using AGridDesc_M_K = remove_cvref_t<decltype(ABGridDescs{}[I0])>;
    using BGridDesc_M   = remove_cvref_t<decltype(ABGridDescs{}[I1])>;
 
    struct Argument : public BaseArgument
    {
        Argument(const InDataType* p_in_dev,
                 OutDataType* p_out_dev,
                 IndexDataType* p_out_indices_dev,
                 std::vector<ck::index_t>& input_ncdhw_lengths,
                 std::vector<ck::index_t>& output_ncdhw_lengths,
                 std::vector<ck::index_t>& input_ncdhw_stride,
                 std::vector<ck::index_t>& output_ncdhw_stride,
                 std::vector<ck::index_t>&, // indices_ncdhw_stride
                 std::vector<ck::index_t>& window_spatial_zyx_lengths,
                 std::vector<ck::index_t>& window_zyx_strides,
                 std::vector<ck::index_t>& window_zyx_dilations,
                 std::vector<ck::index_t>& input_left_dhw_pads,
                 std::vector<ck::index_t>& input_right_dhw_pads)
            : p_in_dev_{p_in_dev},
              p_out_dev_{p_out_dev},
              p_out_indices_dev_{p_out_indices_dev},
              a_grid_desc_m_k_{},
              b_grid_desc_m_{},
              input_ncdhw_lengths_{input_ncdhw_lengths},
              output_ncdhw_lengths_{output_ncdhw_lengths},
              input_ncdhw_stride_{input_ncdhw_stride},
              output_ncdhw_stride_{output_ncdhw_stride}
        {
            const auto descs = MakeABGridDescriptor_A_M_K_B_M(input_ncdhw_lengths,
                                                              output_ncdhw_lengths,
                                                              input_ncdhw_stride,
                                                              output_ncdhw_stride,
                                                              window_spatial_zyx_lengths,
                                                              window_zyx_strides,
                                                              window_zyx_dilations,
                                                              input_left_dhw_pads,
                                                              input_right_dhw_pads);
 
            a_grid_desc_m_k_ = descs[I0];
            b_grid_desc_m_   = descs[I1];
 
            int32_t reduceLength = window_spatial_zyx_lengths[0] * window_spatial_zyx_lengths[1] *
                                   window_spatial_zyx_lengths[2];
 
            std::tie(in_element_op_, acc_element_op_) =
                reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(reduceLength);
        }
 
        const InDataType* p_in_dev_;
        OutDataType* p_out_dev_;
        IndexDataType* p_out_indices_dev_;
        AGridDesc_M_K a_grid_desc_m_k_;
        BGridDesc_M b_grid_desc_m_;
 
        InElementwiseOperation in_element_op_;
        AccElementwiseOperation acc_element_op_;
 
        // for checking vector load/store
        std::vector<ck::index_t> input_ncdhw_lengths_;
        std::vector<ck::index_t> output_ncdhw_lengths_;
        std::vector<ck::index_t> input_ncdhw_stride_;
        std::vector<ck::index_t> output_ncdhw_stride_;
    };
 
    struct Invoker : public BaseInvoker
    {
        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
            // for NDHWC, the dim C is the fastest dimension, and is not reduced.
            // Hence, it is in M dimension for reduction kernel.
            static constexpr index_t InSrcOutDstVectorDim = 0; // 0: M, 1: K
 
            using gridwise_reduce =
                GridwiseReduction_mk_to_m_threadwise<InDataType,
                                                     OutDataType,
                                                     ComputeDataType,
                                                     IndexDataType,
                                                     AGridDesc_M_K,
                                                     BGridDesc_M,
                                                     ReduceOperation,
                                                     InElementwiseOperation,
                                                     AccElementwiseOperation,
                                                     InMemoryDataOperationEnum::Set,
                                                     false, // propagate_nan
                                                     BlockSize,
                                                     MThreadSliceSize,
                                                     KThreadSliceSize,
                                                     InSrcOutDstVectorDim,
                                                     InSrcOutDstVectorSize,
                                                     InSrcOutDstVectorSize>;
 
            const auto kernel =
                kernel_reduce_threadwise<gridwise_reduce,
                                         OutputIndex,
                                         true,  // pooling need to return global index
                                         false, // don't have index input
                                         InDataType,
                                         OutDataType,
                                         ComputeDataType,
                                         IndexDataType,
                                         AGridDesc_M_K,
                                         BGridDesc_M,
                                         InElementwiseOperation,
                                         AccElementwiseOperation>;
 
            ck::index_t M = arg.a_grid_desc_m_k_.GetLength(I0);
 
            const index_t grid_size = (M / M_BlockTileSize);
 
            return launch_and_time_kernel(stream_config,
                                          kernel,
                                          dim3(grid_size),
                                          dim3(BlockSize),
                                          0,
                                          arg.a_grid_desc_m_k_,
                                          arg.b_grid_desc_m_,
                                          arg.in_element_op_,
                                          arg.acc_element_op_,
                                          float(1),
                                          arg.p_in_dev_,
                                          nullptr,
                                          float(0),
                                          arg.p_out_dev_,
                                          arg.p_out_indices_dev_);
        }
 
        float Run(const BaseArgument* p_arg,
                  const StreamConfig& stream_config = StreamConfig{}) override
        {
            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
        }
    };
 
    bool IsSupportedArgument(const BaseArgument* p_arg) override
    {
        const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
 
        // C should be fastest dimension
        if(pArg->input_ncdhw_stride_[1] != 1)
            return false;
 
        for(int i = 0; i < InOutRank; ++i)
        {
            if(pArg->input_ncdhw_stride_[i] == 1 &&
               pArg->input_ncdhw_lengths_[i] % InSrcOutDstVectorSize != 0)
                return false;
 
            if(pArg->output_ncdhw_stride_[i] == 1 &&
               pArg->output_ncdhw_lengths_[i] % InSrcOutDstVectorSize != 0)
                return false;
        }
 
        return true;
    }
 
    virtual std::unique_ptr<BaseArgument>
    MakeArgumentPointer(const void* p_in_dev,
                        void* p_out_dev,
                        void* p_out_indices_dev,
                        std::vector<ck::index_t> input_ncdhw_lengths,
                        std::vector<ck::index_t> window_zyx_lengths,
                        std::vector<ck::index_t> output_ncdhw_lengths,
                        std::vector<ck::index_t> input_ncdhw_stride,
                        std::vector<ck::index_t> output_ncdhw_stride,
                        std::vector<ck::index_t> indices_ncdhw_stride,
                        std::vector<ck::index_t> window_zyx_strides,
                        std::vector<ck::index_t> window_zyx_dilations,
                        std::vector<ck::index_t> input_left_dhw_pads,
                        std::vector<ck::index_t> input_right_dhw_pads,
                        std::vector<ck::index_t> pooling_dims) override
    {
        if(input_ncdhw_lengths.size() != InOutRank || window_zyx_lengths.size() != WindowRank ||
           input_ncdhw_lengths.size() != InOutRank || window_zyx_strides.size() != WindowRank ||
           window_zyx_dilations.size() != WindowRank || input_left_dhw_pads.size() != WindowRank ||
           input_right_dhw_pads.size() != WindowRank)
            throw std::runtime_error("dimension is incorrect");
 
        if(pooling_dims != std::vector<ck::index_t>{2, 3, 4})
            throw std::runtime_error("pooling_dims only support {2, 3, 4} in pool3d so far");
 
        if(output_ncdhw_stride != indices_ncdhw_stride)
            throw std::runtime_error(
                "output_ncdhw_stride need to be equal to indices_ncdhw_stride for now");
 
        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_dev),
                                          static_cast<OutDataType*>(p_out_dev),
                                          static_cast<IndexDataType*>(p_out_indices_dev),
                                          input_ncdhw_lengths,
                                          output_ncdhw_lengths,
                                          input_ncdhw_stride,
                                          output_ncdhw_stride,
                                          indices_ncdhw_stride,
                                          window_zyx_lengths,
                                          window_zyx_strides,
                                          window_zyx_dilations,
                                          input_left_dhw_pads,
                                          input_right_dhw_pads);
    }
 
    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
    {
        return std::make_unique<Invoker>(Invoker{});
    }
 
    std::string GetTypeString() const override
    {
        auto str = std::stringstream();
 
        // clang-format off
        str << "DevicePool3dFwd_NDHWC_NDHWC<" << BlockSize << ",";
        str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
        str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
        str <<"InSrcOutDstVectorSize_" << InSrcOutDstVectorSize << ">";
        // clang-format on
 
        return str.str();
    }
};
 
} // namespace device
} // namespace tensor_operation
} // namespace ck