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https://github.com/ROCm/composable_kernel.git
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5d37d7bff4
* delete obselete files * move files * build * update cmake * update cmake * fix build * reorg examples * update cmake for example and test
282 lines
11 KiB
C++
282 lines
11 KiB
C++
#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 <half.hpp>
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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_gemm.hpp"
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#include "device_tensor.hpp"
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#include "device_base.hpp"
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#include "device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp"
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#include "device_conv3d_fwd_naive_ndhwc_kzyxc_ndhwk.hpp"
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#include "convolution_utility.hpp"
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// convolution data type
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using InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using AccDataType = float;
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using InElementOp = ck::tensor_operation::element_wise::PassThrough;
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using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
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using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
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using F16 = ck::half_t;
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using F32 = float;
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using InLayout = ck::tensor_layout::convolution::NDHWC;
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using WeiLayout = ck::tensor_layout::convolution::KZYXC;
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using OutLayout = ck::tensor_layout::convolution::NDHWK;
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static constexpr auto ConvFwdDefault =
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ck::tensor_operation::device::ConvolutionForwardSpecialization_t::Default;
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using DeviceConv3dFwdInstance = ck::tensor_operation::device::
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DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<
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InDataType, // InData
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WeiDataType, // WeiData
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OutDataType, // OutData
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AccDataType, // AccData
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InElementOp, // InElementwise Operation
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WeiElementOp, // WeiElementwise Operation
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OutElementOp, // OutElementwise Operation
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ConvFwdDefault, // ConvForwardSpecialization
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256, // BlockSize
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128, // MPerBlock
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256, // NPerBlock
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4, // K0PerBlock
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8, // K1. K0PerBlock * K1 = KPerBlock
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32, // MPerXDL
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32, // NPerXDL. Each XDL computes a matrix of size (MPerXDL, NPerBlock)
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2, // MXdlPerWave
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4, // NXdlPerWave
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S<4, 64, 1>, // ABlockTransferThreadClusterLengths_K0_M_K1
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S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // ABlockTransferSrcAccessOrder
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2, // ABlockTransferSrcVectorDim
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8, // ABlockTransferSrcScalarPerVector
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8, // ABlockTransferDstScalarPerVector_K1
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true, // ABlockLdsAddExtraM
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S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
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S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // BBlockTransferSrcAccessOrder
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2, // BBlockTransferSrcVectorDim
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8, // BBlockTransferSrcScalarPerVector
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8, // BBlockTransferDstScalarPerVector_K1
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true, // BBlockLdsAddExtraN
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7, // CThreadTransferSrcDstVectorDim
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1>; // CThreadTransferDstScalarPerVector
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int main(int argc, char* argv[])
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{
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bool do_verification = false;
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int init_method = 0;
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int nrepeat = 5;
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// convolution shape
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ck::index_t N = 4;
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ck::index_t K = 256;
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ck::index_t C = 192;
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std::vector<ck::index_t> in_spatial_lengths = {71, 71, 71};
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std::vector<ck::index_t> filter_spatial_lengths = {3, 3, 3};
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std::vector<ck::index_t> conv_filter_strides = {2, 2, 2};
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std::vector<ck::index_t> conv_filter_dilations = {1, 1, 1};
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std::vector<ck::index_t> in_left_pads = {1, 1, 1};
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std::vector<ck::index_t> in_right_pads = {1, 1, 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 == 25)
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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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K = std::stoi(argv[5]);
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C = std::stoi(argv[6]);
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filter_spatial_lengths[0] = std::stoi(argv[7]);
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filter_spatial_lengths[1] = std::stoi(argv[8]);
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filter_spatial_lengths[2] = std::stoi(argv[9]);
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in_spatial_lengths[0] = std::stoi(argv[10]);
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in_spatial_lengths[1] = std::stoi(argv[11]);
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in_spatial_lengths[2] = std::stoi(argv[12]);
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conv_filter_strides[0] = std::stoi(argv[13]);
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conv_filter_strides[1] = std::stoi(argv[14]);
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conv_filter_strides[2] = std::stoi(argv[15]);
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conv_filter_dilations[0] = std::stoi(argv[16]);
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conv_filter_dilations[1] = std::stoi(argv[17]);
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conv_filter_dilations[2] = std::stoi(argv[18]);
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in_left_pads[0] = std::stoi(argv[19]);
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in_left_pads[1] = std::stoi(argv[20]);
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in_left_pads[2] = std::stoi(argv[21]);
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in_right_pads[0] = std::stoi(argv[22]);
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in_right_pads[1] = std::stoi(argv[23]);
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in_right_pads[2] = std::stoi(argv[24]);
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}
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else
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{
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printf("Usage: 3 or 24 input arguments\n");
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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 24: N, K, C, Z, Y, X, Di, Hi, Wi, Sz, Sy, Sz, Dz, Dy, Dx, LeftPz, LeftPy, "
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"LeftPz, RightPz, RightPy, RightPx\n");
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exit(0);
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}
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auto conv3d = DeviceConv3dFwdInstance{};
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const auto out_spatial_lengths =
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ck::tensor_operation::ConvolutionUtility::ComputeOutputSpatialLengths(
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in_spatial_lengths,
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filter_spatial_lengths,
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conv_filter_strides,
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conv_filter_dilations,
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in_left_pads,
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in_right_pads);
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Tensor<InDataType> in(
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{N, in_spatial_lengths[0], in_spatial_lengths[1], in_spatial_lengths[2], C});
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Tensor<WeiDataType> wei(
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{K, filter_spatial_lengths[0], filter_spatial_lengths[1], filter_spatial_lengths[2], C});
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Tensor<OutDataType> out(
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{N, out_spatial_lengths[0], out_spatial_lengths[1], out_spatial_lengths[2], K});
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std::cout << "in: " << in.mDesc << std::endl;
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std::cout << "wei: " << wei.mDesc << std::endl;
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std::cout << "out: " << out.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:
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in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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break;
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default:
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in.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
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wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpace());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpace());
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DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpace());
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in_device_buf.ToDevice(in.mData.data());
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wei_device_buf.ToDevice(wei.mData.data());
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// do Convolution
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auto invoker = conv3d.MakeInvoker();
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auto argument = conv3d.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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N,
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K,
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C,
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in_spatial_lengths,
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filter_spatial_lengths,
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out_spatial_lengths,
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conv_filter_strides,
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conv_filter_dilations,
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in_left_pads,
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in_right_pads,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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if(!conv3d.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_conv3d with the specified compilation parameters does "
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"not support this GEMM problem");
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}
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float ave_time = invoker.Run(argument, nrepeat);
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const auto Di = in_spatial_lengths[0];
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const auto Hi = in_spatial_lengths[1];
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const auto Wi = in_spatial_lengths[2];
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const auto Do = out_spatial_lengths[0];
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const auto Ho = out_spatial_lengths[1];
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const auto Wo = out_spatial_lengths[2];
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const auto Z = filter_spatial_lengths[0];
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const auto Y = filter_spatial_lengths[1];
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const auto X = filter_spatial_lengths[2];
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std::size_t flop = std::size_t(2) * N * K * Do * Ho * Wo * C * Z * Y * X;
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std::size_t num_btype = sizeof(InDataType) * N * Di * Hi * Wi * C +
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sizeof(WeiDataType) * K * Z * Y * X * C +
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sizeof(OutDataType) * N * Do * Ho * Wo * K;
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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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out_device_buf.FromDevice(out.mData.data());
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if(do_verification)
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{
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DeviceMem out_ref_device_buf(sizeof(OutDataType) * N * Do * Ho * Wo * K);
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using DeviceConv3dFwdNaive = ck::tensor_operation::device::
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DeviceConv3dFwdNaive_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<
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InDataType,
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WeiDataType,
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OutDataType,
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AccDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp>;
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auto conv3d_naive = DeviceConv3dFwdNaive{};
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auto invoker_naive = conv3d_naive.MakeInvoker();
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auto argument_naive = conv3d_naive.MakeArgument(
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static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_ref_device_buf.GetDeviceBuffer()),
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N,
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K,
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C,
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in_spatial_lengths,
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filter_spatial_lengths,
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out_spatial_lengths,
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conv_filter_strides,
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conv_filter_dilations,
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in_left_pads,
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in_right_pads,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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if(!conv3d_naive.IsSupportedArgument(argument_naive))
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{
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throw std::runtime_error(
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"wrong! device_conv3d_naive does NOT support the specified compilation parameters");
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}
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invoker_naive.Run(argument_naive);
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Tensor<OutDataType> out_ref(
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{N, out_spatial_lengths[0], out_spatial_lengths[1], out_spatial_lengths[2], K});
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out_ref_device_buf.FromDevice(out_ref.mData.data());
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check_error(out_ref, out);
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}
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return 0;
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}
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