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https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 14:59:17 +00:00
Add FP64 XDL GEMM built-in function (#199)
* add intrin_mfma_f64_16x16x4f64 * add example * gemm reference add double data type * chang init data * fix M N PerXdlops * fix ifdef * add comparsion config * add conv fwd example * format log out * change rc matrix egister layout * reorganize example * reorganize example 2 * format,because merge develop * fix call impl adding acc data type * lost ; * add compiler warning * change example tunning parameters * add test for fp64 * add instance * add test/gemm/gemm_fp64.cpp * fix get name issue * remove some tunning parameter * fix conflict * format * use integer value for GEMM test * add acc data type * remove typeid because fp16 * fix streamconfig etc bug from merging develop * format * remove test_gemm_xdl_fp64 * add AccDataType * AccDataType problem Co-authored-by: qinletao <letaoqin@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com>
This commit is contained in:
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@@ -1,6 +1,8 @@
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add_example_executable(example_convnd_fwd_xdl_fp32 convnd_fwd_xdl_fp32.cpp)
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add_example_executable(example_convnd_fwd_xdl_int8 convnd_fwd_xdl_int8.cpp)
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add_example_executable(example_convnd_fwd_xdl_fp16 convnd_fwd_xdl_fp16.cpp)
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add_example_executable(example_convnd_fwd_xdl_fp64 convnd_fwd_xdl_fp64.cpp)
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target_link_libraries(example_convnd_fwd_xdl_fp64 PRIVATE conv_util)
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target_link_libraries(example_convnd_fwd_xdl_fp32 PRIVATE conv_util)
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target_link_libraries(example_convnd_fwd_xdl_int8 PRIVATE conv_util)
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target_link_libraries(example_convnd_fwd_xdl_fp16 PRIVATE conv_util)
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344
example/09_convnd_fwd/convnd_fwd_xdl_fp64.cpp
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344
example/09_convnd_fwd/convnd_fwd_xdl_fp64.cpp
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@@ -0,0 +1,344 @@
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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 "check_err.hpp"
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#include "config.hpp"
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#include "conv_util.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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namespace {
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using InDataType = double;
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using WeiDataType = double;
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using OutDataType = double;
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using AccDataType = double;
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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::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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128, // MPerBlock
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128, // NPerBlock
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4, // K0PerBlock
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2, // K1
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16, // MPerXDL
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16, // NPerXDL
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4, // 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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2, // ABlockTransferSrcScalarPerVector
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2, // 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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2, // BBlockTransferSrcScalarPerVector
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2, // 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 get_conv_instance(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 3: {
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return std::make_unique<DeviceConvNDFwdInstance<3>>();
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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 print_use_msg()
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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::utils::conv::ConvParams parse_conv_params(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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print_use_msg();
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exit(0);
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}
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ck::utils::conv::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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} // anonymous namespace
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int main(int argc, char* argv[])
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{
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using namespace ck::utils::conv;
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bool do_verification = 0;
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int init_method = 0;
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bool time_kernel = false;
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int num_dim_spatial = 2;
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ck::utils::conv::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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time_kernel = std::stoi(argv[3]);
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num_dim_spatial = std::stoi(argv[4]);
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}
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if(argc >= 6)
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{
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params = parse_conv_params(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_),
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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),
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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),
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std::begin(output_spatial_lengths),
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std::end(output_spatial_lengths));
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Tensor<InDataType> input(get_input_host_tensor_descriptor(input_dims, num_dim_spatial));
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Tensor<WeiDataType> weights(get_filters_host_tensor_descriptor(filter_dims, num_dim_spatial));
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Tensor<OutDataType> host_output(
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get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
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Tensor<OutDataType> device_output(
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get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
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std::cout << "input: " << input.mDesc << std::endl;
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std::cout << "weights: " << weights.mDesc << std::endl;
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std::cout << "output: " << host_output.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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input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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weights.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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break;
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case 2:
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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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break;
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default:
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input.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
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weights.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
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}
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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());
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DeviceMem out_device_buf(sizeof(OutDataType) * device_output.mDesc.GetElementSpace());
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in_device_buf.ToDevice(input.mData.data());
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wei_device_buf.ToDevice(weights.mData.data());
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// do GEMM
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auto conv = get_conv_instance(num_dim_spatial);
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auto invoker = conv->MakeInvokerPointer();
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auto argument =
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conv->MakeArgumentPointer(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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params.N_,
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params.K_,
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params.C_,
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params.input_spatial_lengths_,
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params.filter_spatial_lengths_,
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output_spatial_lengths,
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params.conv_filter_strides_,
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params.conv_filter_dilations_,
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params.input_left_pads_,
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params.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.get()))
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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.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = get_flops(
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params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);
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std::size_t num_btype =
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get_btype<InDataType, WeiDataType, OutDataType>(params.N_,
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params.C_,
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params.K_,
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params.input_spatial_lengths_,
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params.filter_spatial_lengths_,
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output_spatial_lengths);
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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 verify_f = [&input, &weights, &host_output, ¶ms, &out_device_buf, &device_output](
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const auto& ref_conv) {
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(input,
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weights,
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host_output,
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params.conv_filter_strides_,
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params.conv_filter_dilations_,
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params.input_left_pads_,
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params.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(device_output.mData.data());
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ck::utils::check_err(
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host_output.mData, device_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
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};
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switch(num_dim_spatial)
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{
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case 3: {
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auto ref_conv = ReferenceConvNDFwdInstance<3>();
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verify_f(ref_conv);
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break;
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}
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case 2: {
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auto ref_conv = ReferenceConvNDFwdInstance<2>();
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verify_f(ref_conv);
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break;
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}
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case 1: {
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auto ref_conv = ReferenceConvNDFwdInstance<1>();
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verify_f(ref_conv);
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break;
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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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}
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