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https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 06:49:15 +00:00
Reorganize files, Part 1 (#119)
* delete obselete files * move files * build * update cmake * update cmake * fix build * reorg examples * update cmake for example and test
This commit is contained in:
Chao Liu
1
example/13_pool2d_fwd/CMakeLists.txt
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1
example/13_pool2d_fwd/CMakeLists.txt
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add_example_executable(example_pool2d_fwd pool2d_fwd.cpp)
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311
example/13_pool2d_fwd/pool2d_fwd.cpp
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example/13_pool2d_fwd/pool2d_fwd.cpp
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <stdlib.h>
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#include "config.hpp"
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#include "print.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "host_reduce_util.hpp"
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#include "device_tensor.hpp"
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#include "tensor_layout.hpp"
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#include "reduction_operator.hpp"
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#include "device_pool2d_fwd_nhwc_nhwc.hpp"
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using InDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using AccDataType = float;
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using InLayout = ck::tensor_layout::convolution::NHWC;
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using OutLayout = ck::tensor_layout::convolution::NHWC;
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#if 1
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static constexpr auto ReduceOpId = ck::ReduceTensorOp_t::MAX;
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#else
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static constexpr auto ReduceOpId = ck::ReduceTensorOp_t::AVG;
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#endif
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static constexpr bool NeedIndices = false;
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static constexpr bool PropagateNan = false;
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using DevicePoolFwdInstance =
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ck::tensor_operation::device::DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C<
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InDataType, // InDataType
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OutDataType, // OutDataType
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AccDataType, // AccDataType
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ReduceOpId,
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NeedIndices,
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64, // BlockSize
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64, // ReduceMThreadClusterSize
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1, // ReduceKThreadClusterSize
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4, // ReduceMThreadSliceSize
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1, // ReduceKThreadSliceSize
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4>; // InSrcOutDstVectorSize
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template <typename InDataType,
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typename OutDataType,
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typename AccDataType,
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ck::ReduceTensorOp_t ReduceOpId,
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bool PropagateNan,
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bool NeedIndices>
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static void pool_host_verify(const Tensor<InDataType>& in,
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Tensor<OutDataType>& out,
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Tensor<int>& out_indices,
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const std::array<ck::index_t, 2>& window_spatial_lengths,
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const std::array<ck::index_t, 2>& window_strides,
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const std::array<ck::index_t, 2>& in_left_pads,
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const std::array<ck::index_t, 2>& /*in_right_pads*/)
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{
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using namespace ck::host_reduce;
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const int divider = window_spatial_lengths[0] * window_spatial_lengths[1];
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const auto PreUnaryOp = PreUnaryOpFn<AccDataType, ReduceOpId>(divider);
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const auto PosUnaryOp = PosUnaryOpFn<AccDataType, ReduceOpId>(divider);
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if constexpr(!NeedIndices)
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{
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auto opReduce = ReduceOpFn<AccDataType, ReduceOpId>();
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auto f_nchw = [&](auto n, auto c, auto ho, auto wo) {
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auto accuVal = ReduceOpZeroVal<AccDataType, ReduceOpId>();
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for(int y = 0; y < window_spatial_lengths[0]; ++y)
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{
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int hi = ho * window_strides[0] + y - in_left_pads[0];
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for(int x = 0; x < window_spatial_lengths[1]; ++x)
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{
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int wi = wo * window_strides[1] + x - in_left_pads[1];
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if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
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wi < in.mDesc.GetLengths()[3])
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{
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AccDataType currVal = static_cast<AccDataType>(in(n, c, hi, wi));
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PreUnaryOp(currVal);
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binop_with_nan_check<AccDataType, PropagateNan>(opReduce, accuVal, currVal);
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}
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}
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}
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PosUnaryOp(accuVal);
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out(n, c, ho, wo) = accuVal;
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};
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make_ParallelTensorFunctor(f_nchw,
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out.mDesc.GetLengths()[0],
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out.mDesc.GetLengths()[1],
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out.mDesc.GetLengths()[2],
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out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
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}
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else
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{
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auto opReduce = ReduceOpFn2<AccDataType, ReduceOpId>();
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auto f_nchw = [&](auto n, auto c, auto ho, auto wo) {
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auto accuVal = ReduceOpZeroVal<AccDataType, ReduceOpId>();
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int accuIndex = 0;
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for(int y = 0; y < window_spatial_lengths[0]; ++y)
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{
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int hi = ho * window_strides[0] + y - in_left_pads[0];
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for(int x = 0; x < window_spatial_lengths[1]; ++x)
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{
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int wi = wo * window_strides[1] + x - in_left_pads[1];
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if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
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wi < in.mDesc.GetLengths()[3])
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{
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AccDataType currVal = static_cast<AccDataType>(in(n, c, hi, wi));
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int currIndex = y * window_spatial_lengths[1] + x;
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PreUnaryOp(currVal);
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binop_with_nan_check2<AccDataType, PropagateNan>(
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opReduce, accuVal, currVal, accuIndex, currIndex);
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}
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}
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}
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PosUnaryOp(accuVal);
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out(n, c, ho, wo) = accuVal;
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out_indices(n, c, ho, wo) = accuIndex;
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};
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make_ParallelTensorFunctor(f_nchw,
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out.mDesc.GetLengths()[0],
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out.mDesc.GetLengths()[1],
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out.mDesc.GetLengths()[2],
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out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
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};
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}
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int main(int argc, char* argv[])
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{
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using namespace ck::host_reduce;
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bool do_verification = 0;
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int init_method = 0;
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int nrepeat = 5;
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// Pool shape
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ck::index_t N = 128;
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ck::index_t C = 192;
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ck::index_t Y = 3;
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ck::index_t X = 3;
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ck::index_t Hi = 71;
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ck::index_t Wi = 71;
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ck::index_t window_stride_h = 2;
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ck::index_t window_stride_w = 2;
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ck::index_t in_left_pad_h = 1;
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ck::index_t in_left_pad_w = 1;
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ck::index_t in_right_pad_h = 1;
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ck::index_t in_right_pad_w = 1;
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if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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nrepeat = std::stoi(argv[3]);
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}
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else if(argc == 16)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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nrepeat = std::stoi(argv[3]);
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N = std::stoi(argv[4]);
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C = std::stoi(argv[5]);
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Y = std::stoi(argv[6]);
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X = std::stoi(argv[7]);
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Hi = std::stoi(argv[8]);
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Wi = std::stoi(argv[9]);
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window_stride_h = std::stoi(argv[10]);
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window_stride_w = std::stoi(argv[11]);
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in_left_pad_h = std::stoi(argv[12]);
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in_left_pad_w = std::stoi(argv[13]);
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in_right_pad_h = std::stoi(argv[14]);
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in_right_pad_w = std::stoi(argv[15]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: run kernel # of times (>1)\n");
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printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, "
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"RightPx\n");
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exit(0);
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}
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const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1;
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const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1;
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const std::array<ck::index_t, 2> window_spatial_lengths{{Y, X}};
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const std::array<ck::index_t, 2> window_strides{{window_stride_h, window_stride_w}};
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const std::array<ck::index_t, 2> input_left_pads{{in_left_pad_h, in_left_pad_w}};
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const std::array<ck::index_t, 2> input_right_pads{{in_right_pad_h, in_right_pad_w}};
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// tensor layout
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auto f_host_tensor_descriptor =
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[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) {
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
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std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
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}
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else if constexpr(ck::is_same<decltype(layout),
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ck::tensor_layout::convolution::NHWC>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
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std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
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}
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};
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Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
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Tensor<OutDataType> out_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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Tensor<int> out_indices_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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Tensor<OutDataType> out_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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Tensor<int> out_indices_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
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std::cout << "out_n_c_ho_wo: " << out_n_c_ho_wo_host.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}); break;
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default: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpace());
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DeviceMem out_device_buf(sizeof(OutDataType) * out_n_c_ho_wo_device.mDesc.GetElementSpace());
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DeviceMem out_indices_device_buf(sizeof(int) *
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out_indices_n_c_ho_wo_device.mDesc.GetElementSpace());
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in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
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auto pool = DevicePoolFwdInstance{};
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auto invoker_ptr = pool.MakeInvokerPointer();
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auto argument_ptr =
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pool.MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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static_cast<int*>(out_indices_device_buf.GetDeviceBuffer()),
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N,
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C,
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std::array<ck::index_t, 2>{{Hi, Wi}},
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std::array<ck::index_t, 2>{{Y, X}},
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std::array<ck::index_t, 2>{{Ho, Wo}},
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window_strides,
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input_left_pads,
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input_right_pads);
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if(!pool.IsSupportedArgument(argument_ptr.get()))
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{
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throw std::runtime_error("wrong! device_op with the specified compilation parameters does "
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"not support this problem");
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}
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float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
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std::size_t flop = std::size_t(2) * N * C * Ho * Wo * Y * X;
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std::size_t num_btype =
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sizeof(InDataType) * (N * C * Hi * Wi) + sizeof(OutDataType) * (N * C * Ho * Wo);
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
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<< std::endl;
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if(do_verification)
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{
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pool_host_verify<InDataType,
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OutDataType,
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AccDataType,
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ReduceOpId,
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PropagateNan,
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NeedIndices>(in_n_c_hi_wi,
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out_n_c_ho_wo_host,
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out_indices_n_c_ho_wo_host,
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window_spatial_lengths,
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window_strides,
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input_left_pads,
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input_right_pads);
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out_device_buf.FromDevice(out_n_c_ho_wo_device.mData.data());
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check_error(out_n_c_ho_wo_host, out_n_c_ho_wo_device);
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if constexpr(NeedIndices)
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{
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out_indices_device_buf.FromDevice(out_indices_n_c_ho_wo_device.mData.data());
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// check_indices(out_indices_n_c_ho_wo_host, out_indices_n_c_ho_wo_device);
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};
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}
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}
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