mirror of
https://github.com/ROCm/composable_kernel.git
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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/09_convnd_fwd/CMakeLists.txt
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1
example/09_convnd_fwd/CMakeLists.txt
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add_example_executable(example_convnd_fwd_xdl convnd_fwd_xdl.cpp)
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65
example/09_convnd_fwd/README.md
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65
example/09_convnd_fwd/README.md
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# Instructions for ```convnd_fwd_xdl``` Example
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## Docker script
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```bash
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docker run \
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-it \
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--rm \
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--privileged \
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--group-add sudo \
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-w /root/workspace \
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-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace \
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rocm/tensorflow:rocm4.3.1-tf2.6-dev \
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/bin/bash
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```
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## Build ```convnd_fwd_xdl```
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```bash
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mkdir build && cd build
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```
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```bash
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# Need to specify target ID, example below is gfx908
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cmake \
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-D BUILD_DEV=OFF \
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-D CMAKE_BUILD_TYPE=Release \
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-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 " \
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-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
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-D CMAKE_PREFIX_PATH=/opt/rocm \
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..
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```
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```bash
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make -j convnd_fwd_xdl
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```
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## Run ```convnd_fwd_xdl```
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```bash
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#arg1: verification (0=no, 1=yes)
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#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
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#arg3: run kernel # of times (>1)
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#arg4: N spatial dimensions (default 2)
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#Following arguments (depending on number of spatial dims):
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# N, K, C,
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# <filter spatial dimensions>, (ie Y, X for 2D)
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# <input image spatial dimensions>, (ie Hi, Wi for 2D)
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# <strides>, (ie Sy, Sx for 2D)
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# <dilations>, (ie Dy, Dx for 2D)
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# <left padding>, (ie LeftPy, LeftPx for 2D)
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# <right padding>, (ie RightPy, RightPx for 2D)
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./example/convnd_fwd_xdl 0 1 100
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```
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Result (MI100 @ 1087Mhz, 33.4TFlops peak FP32)
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```
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input: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
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weights: dim 4, lengths {256, 192, 3, 3}, strides {1728, 1, 576, 192}
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output: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
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arg.a_grid_desc_k0_m_k1_{432, 165888, 4}
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arg.b_grid_desc_k0_n_k1_{432, 256, 4}
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arg.c_grid_desc_m_n_{ 165888, 256}
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launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
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Warm up
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Start running 100 times...
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Perf: 4.43736 ms, 33.0753 TFlops, 150.357 GB/s
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```
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378
example/09_convnd_fwd/convnd_fwd_xdl.cpp
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378
example/09_convnd_fwd/convnd_fwd_xdl.cpp
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@@ -0,0 +1,378 @@
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#include <cstdlib>
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#include <iostream>
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#include <numeric>
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#include <type_traits>
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#include "config.hpp"
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#include "conv_utils.hpp"
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#include "device.hpp"
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#include "device_tensor.hpp"
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#include "device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp"
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#include "element_wise_operation.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "reference_conv_fwd.hpp"
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#include "tensor_layout.hpp"
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using InDataType = float;
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using WeiDataType = float;
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using OutDataType = float;
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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 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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using DeviceConvFwdBasePtr =
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ck::tensor_operation::device::DeviceConvFwdPtr<InElementOp, WeiElementOp, OutElementOp>;
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template <ck::index_t NumDimSpatial>
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using DeviceConvNDFwdInstance = ck::tensor_operation::device::
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DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
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// clang-format off
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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, // Input Elementwise Operation
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WeiElementOp, // Weights Elementwise Operation
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OutElementOp, // Output Elementwise Operation
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ConvFwdDefault, // ConvForwardSpecialization
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NumDimSpatial, // NumDimSpatial
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256, // BlockSize
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256, // MPerBlock
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128, // NPerBlock
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4, // K0PerBlock
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4, // K1
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32, // MPerXDL
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32, // NPerXDL
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4, // MXdlPerWave
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2, // 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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4, // ABlockTransferSrcScalarPerVector
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4, // 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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4, // BBlockTransferSrcScalarPerVector
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4, // BBlockTransferDstScalarPerVector_K1
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true, // BBlockTransferAddExtraN
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7, // CThreadTransferSrcDstVectorDim
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1>; // CThreadTransferDstScalarPerVector
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// clang-format on
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template <ck::index_t NumDimSpatial>
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using ReferenceConvNDFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
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WeiDataType,
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OutDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp,
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NumDimSpatial>;
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DeviceConvFwdBasePtr GetConvInstance(int num_dim_spatial)
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{
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switch(num_dim_spatial)
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{
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case 2: {
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return std::make_unique<DeviceConvNDFwdInstance<2>>();
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}
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case 1: {
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return std::make_unique<DeviceConvNDFwdInstance<1>>();
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}
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default: {
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throw std::runtime_error("Unsupported number of spatial dimensions provided!");
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}
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}
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}
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void PrintUseMsg()
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{
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std::cout << "arg1: verification (0=no, 1=yes)\n"
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<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
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<< "arg3: run kernel # of times (>1)\n"
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<< "arg4: N spatial dimensions (default 2)\n"
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<< "Following arguments (depending on number of spatial dims):\n"
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<< " N, K, C, \n"
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<< " <filter spatial dimensions>, (ie Y, X for 2D)\n"
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<< " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
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<< " <strides>, (ie Sy, Sx for 2D)\n"
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<< " <dilations>, (ie Dy, Dx for 2D)\n"
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<< " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
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<< " <right padding>, (ie RightPy, RightPx for 2D)\n"
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<< std::endl;
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}
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ck::conv_util::ConvParams ParseConvParams(int num_dim_spatial, int argc, char* argv[])
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{
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// (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
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int conv_args = 3 + num_dim_spatial * 6;
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int cmdline_nargs = conv_args + 5;
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if(cmdline_nargs != argc)
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{
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PrintUseMsg();
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exit(0);
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}
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ck::conv_util::ConvParams params;
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int arg_idx = 5;
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params.num_dim_spatial = num_dim_spatial;
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params.N = std::stoi(argv[arg_idx++]);
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params.K = std::stoi(argv[arg_idx++]);
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params.C = std::stoi(argv[arg_idx++]);
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params.filter_spatial_lengths.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
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}
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params.input_spatial_lengths.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
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}
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params.conv_filter_strides.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
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}
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params.conv_filter_dilations.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
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}
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params.input_left_pads.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.input_left_pads[i] = std::stoi(argv[arg_idx++]);
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}
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params.input_right_pads.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.input_right_pads[i] = std::stoi(argv[arg_idx++]);
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}
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return params;
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}
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HostTensorDescriptor GetOutputHostTensorDescriptor(const std::vector<std::size_t>& dims,
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int num_dim_spatial = 2)
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{
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namespace tl = ck::tensor_layout::convolution;
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switch(num_dim_spatial)
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{
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case 2: {
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return ck::conv_util::GetHostTensorDescriptor(dims, tl::NHWK{});
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}
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case 1: {
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return ck::conv_util::GetHostTensorDescriptor(dims, tl::NWK{});
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}
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default: {
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throw std::runtime_error("Unsupported number of spatial dimensions provided!");
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}
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}
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}
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HostTensorDescriptor GetFiltersHostTensorDescriptor(const std::vector<std::size_t>& dims,
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int num_dim_spatial = 2)
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{
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namespace tl = ck::tensor_layout::convolution;
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switch(num_dim_spatial)
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{
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case 2: {
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return ck::conv_util::GetHostTensorDescriptor(dims, tl::KYXC{});
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}
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case 1: {
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return ck::conv_util::GetHostTensorDescriptor(dims, tl::KXC{});
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}
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default: {
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throw std::runtime_error("Unsupported number of spatial dimensions provided!");
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}
|
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}
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}
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HostTensorDescriptor GetInputHostTensorDescriptor(const std::vector<std::size_t>& dims,
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int num_dim_spatial = 2)
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{
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namespace tl = ck::tensor_layout::convolution;
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|
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switch(num_dim_spatial)
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{
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case 2: {
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return ck::conv_util::GetHostTensorDescriptor(dims, tl::NHWC{});
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}
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case 1: {
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return ck::conv_util::GetHostTensorDescriptor(dims, tl::NWC{});
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}
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default: {
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throw std::runtime_error("Unsupported number of spatial dimensions provided!");
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}
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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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bool do_verification = 0;
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int init_method = 0;
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int nrepeat = 5;
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int num_dim_spatial = 2;
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ck::conv_util::ConvParams params;
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if(argc >= 5)
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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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num_dim_spatial = std::stoi(argv[4]);
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}
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|
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if(argc >= 6)
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{
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params = ParseConvParams(num_dim_spatial, argc, argv);
|
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}
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std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N),
|
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static_cast<std::size_t>(params.C)};
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input_dims.insert(std::end(input_dims),
|
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std::begin(params.input_spatial_lengths),
|
||||
std::end(params.input_spatial_lengths));
|
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|
||||
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K),
|
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static_cast<std::size_t>(params.C)};
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filter_dims.insert(std::end(filter_dims),
|
||||
std::begin(params.filter_spatial_lengths),
|
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std::end(params.filter_spatial_lengths));
|
||||
|
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const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
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std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N),
|
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static_cast<std::size_t>(params.K)};
|
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output_dims.insert(std::end(output_dims),
|
||||
std::begin(output_spatial_lengths),
|
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std::end(output_spatial_lengths));
|
||||
|
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Tensor<InDataType> input(GetInputHostTensorDescriptor(input_dims, num_dim_spatial));
|
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Tensor<WeiDataType> weights(GetFiltersHostTensorDescriptor(filter_dims, num_dim_spatial));
|
||||
Tensor<OutDataType> host_output(GetOutputHostTensorDescriptor(output_dims, num_dim_spatial));
|
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Tensor<OutDataType> device_output(GetOutputHostTensorDescriptor(output_dims, num_dim_spatial));
|
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|
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std::cout << "input: " << input.mDesc << std::endl;
|
||||
std::cout << "weights: " << weights.mDesc << std::endl;
|
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std::cout << "output: " << host_output.mDesc << std::endl;
|
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|
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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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input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
|
||||
weights.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
|
||||
break;
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default:
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input.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
|
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weights.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
|
||||
}
|
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||||
DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpace());
|
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DeviceMem wei_device_buf(sizeof(WeiDataType) * weights.mDesc.GetElementSpace());
|
||||
DeviceMem out_device_buf(sizeof(OutDataType) * device_output.mDesc.GetElementSpace());
|
||||
|
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in_device_buf.ToDevice(input.mData.data());
|
||||
wei_device_buf.ToDevice(weights.mData.data());
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||||
// do GEMM
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auto conv = GetConvInstance(num_dim_spatial);
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auto invoker = conv->MakeInvokerPointer();
|
||||
auto argument =
|
||||
conv->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
|
||||
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
|
||||
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
|
||||
params.N,
|
||||
params.K,
|
||||
params.C,
|
||||
params.input_spatial_lengths,
|
||||
params.filter_spatial_lengths,
|
||||
output_spatial_lengths,
|
||||
params.conv_filter_strides,
|
||||
params.conv_filter_dilations,
|
||||
params.input_left_pads,
|
||||
params.input_right_pads,
|
||||
InElementOp{},
|
||||
WeiElementOp{},
|
||||
OutElementOp{});
|
||||
|
||||
if(!conv->IsSupportedArgument(argument.get()))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_conv with the specified compilation parameters does "
|
||||
"not support this Conv problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker->Run(argument.get(), nrepeat);
|
||||
|
||||
std::size_t flop = ck::conv_util::GetFlops(
|
||||
params.N, params.C, params.K, params.filter_spatial_lengths, output_spatial_lengths);
|
||||
std::size_t num_btype =
|
||||
ck::conv_util::GetBtype<InDataType, WeiDataType, OutDataType>(params.N,
|
||||
params.C,
|
||||
params.K,
|
||||
params.input_spatial_lengths,
|
||||
params.filter_spatial_lengths,
|
||||
output_spatial_lengths);
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
|
||||
<< std::endl;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
auto verify_f = [&input, &weights, &host_output, ¶ms, &out_device_buf, &device_output](
|
||||
const auto& ref_conv) {
|
||||
auto ref_invoker = ref_conv.MakeInvoker();
|
||||
auto ref_argument = ref_conv.MakeArgument(input,
|
||||
weights,
|
||||
host_output,
|
||||
params.conv_filter_strides,
|
||||
params.conv_filter_dilations,
|
||||
params.input_left_pads,
|
||||
params.input_right_pads,
|
||||
InElementOp{},
|
||||
WeiElementOp{},
|
||||
OutElementOp{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
out_device_buf.FromDevice(device_output.mData.data());
|
||||
check_error(host_output, device_output);
|
||||
};
|
||||
|
||||
switch(num_dim_spatial)
|
||||
{
|
||||
case 2: {
|
||||
auto ref_conv = ReferenceConvNDFwdInstance<2>();
|
||||
verify_f(ref_conv);
|
||||
break;
|
||||
}
|
||||
case 1: {
|
||||
auto ref_conv = ReferenceConvNDFwdInstance<1>();
|
||||
verify_f(ref_conv);
|
||||
break;
|
||||
}
|
||||
default: {
|
||||
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user