mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 21:21:22 +00:00
823657ed12
* tweak conv for odd C * update script * clean up elementwise op * fix build * clean up * added example for gemm+bias+relu+add * added example for gemm+bias+relu * add profiler for gemm_s_shuffle; re-org files * add profiler * fix build * clean up * clean up * clean up * fix build
273 lines
12 KiB
C++
273 lines
12 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 "device_tensor.hpp"
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#include "tensor_layout.hpp"
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#include "element_wise_operation.hpp"
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#include "device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp"
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#include "reference_conv_fwd.hpp"
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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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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using InLayout = ck::tensor_layout::convolution::NHWC;
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using WeiLayout = ck::tensor_layout::convolution::KYXC;
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using OutLayout = ck::tensor_layout::convolution::NHWK;
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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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static constexpr auto ConvFwdDefault =
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ck::tensor_operation::device::ConvolutionForwardSpecialization_t::Default;
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// clang-format off
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using DeviceConvFwdInstance = ck::tensor_operation::device::
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DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
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InDataType, // InDataType
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WeiDataType, // WeiDataType
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OutDataType, // OutDataType
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AccDataType, // AccDataType
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InElementOp, // InElementwiseOperation
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WeiElementOp, // WeiElementwiseOperation
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OutElementOp, // OutElementwiseOperation
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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
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32, // MPerXdl
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32, // NPerXdl
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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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1, // CShuffleMXdlPerWavePerShuffle
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1, // CShuffleNXdlPerWavePerShuffle
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S<1, 1, 32, 1, 1, 8>, // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
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8>; // CBlockTransferScalarPerVector_NWaveNPerXdl
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// clang-format on
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using ReferenceConvFwdInstance = ck::tensor_operation::host::
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ReferenceConvFwd<InDataType, WeiDataType, OutDataType, InElementOp, WeiElementOp, OutElementOp>;
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int main(int argc, char* argv[])
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{
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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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// Conv shape
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ck::index_t N = 128;
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ck::index_t K = 256;
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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 conv_stride_h = 2;
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ck::index_t conv_stride_w = 2;
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ck::index_t conv_dilation_h = 1;
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ck::index_t conv_dilation_w = 1;
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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 == 19)
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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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Y = std::stoi(argv[7]);
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X = std::stoi(argv[8]);
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Hi = std::stoi(argv[9]);
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Wi = std::stoi(argv[10]);
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conv_stride_h = std::stoi(argv[11]);
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conv_stride_w = std::stoi(argv[12]);
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conv_dilation_h = std::stoi(argv[13]);
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conv_dilation_w = std::stoi(argv[14]);
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in_left_pad_h = std::stoi(argv[15]);
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in_left_pad_w = std::stoi(argv[16]);
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in_right_pad_h = std::stoi(argv[17]);
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in_right_pad_w = std::stoi(argv[18]);
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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 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, 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 YEff = (Y - 1) * conv_dilation_h + 1;
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const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
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const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
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const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
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const std::vector<ck::index_t> conv_filter_strides{{conv_stride_h, conv_stride_w}};
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const std::vector<ck::index_t> conv_filter_dilations{{conv_dilation_h, conv_dilation_w}};
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const std::vector<ck::index_t> input_left_pads{{in_left_pad_h, in_left_pad_w}};
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const std::vector<ck::index_t> 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 = [](std::size_t N_,
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std::size_t C_,
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std::size_t H,
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std::size_t W,
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auto layout) {
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
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ck::is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
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ck::is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::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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ck::is_same<decltype(layout),
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ck::tensor_layout::convolution::KYXC>::value ||
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ck::is_same<decltype(layout),
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ck::tensor_layout::convolution::NHWK>::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<WeiDataType> wei_k_c_y_x(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
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Tensor<OutDataType> out_n_k_ho_wo_host_result(
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f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
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Tensor<OutDataType> out_n_k_ho_wo_device_result(
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f_host_tensor_descriptor(N, K, 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 << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
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std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo_host_result.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_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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break;
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default:
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in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
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wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
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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 wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
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DeviceMem out_device_buf(sizeof(OutDataType) *
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out_n_k_ho_wo_device_result.mDesc.GetElementSpace());
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in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
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wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
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// do GEMM
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auto conv = DeviceConvFwdInstance{};
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auto invoker = conv.MakeInvoker();
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auto argument = conv.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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std::vector<ck::index_t>{{Hi, Wi}},
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std::vector<ck::index_t>{{Y, X}},
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std::vector<ck::index_t>{{Ho, Wo}},
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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if(!conv.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_conv with the specified compilation parameters does "
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"not support this Conv problem");
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}
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float ave_time = invoker.Run(argument, nrepeat);
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std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
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std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
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sizeof(WeiDataType) * (K * C * Y * X) +
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sizeof(OutDataType) * (N * K * 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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auto ref_conv = ReferenceConvFwdInstance{};
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi,
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wei_k_c_y_x,
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out_n_k_ho_wo_host_result,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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ref_invoker.Run(ref_argument);
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out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
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check_error(out_n_k_ho_wo_host_result, out_n_k_ho_wo_device_result);
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}
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}
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