mirror of https://github.com/alibaba/MNN.git
453 lines
14 KiB
C++
453 lines
14 KiB
C++
//
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// benchmark.cpp
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// MNN
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//
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// Created by MNN on 2019/01/31.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include <errno.h>
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#include <float.h>
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <vector>
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#if defined(_MSC_VER)
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#include <Windows.h>
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#undef min
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#undef max
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#else
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#include <sys/time.h>
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#include <sys/stat.h>
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#include <sys/types.h>
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#include <dirent.h>
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#endif
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#include "core/Backend.hpp"
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#include <MNN/Interpreter.hpp>
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#include <MNN/MNNDefine.h>
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#include <MNN/Tensor.hpp>
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#include <MNN/AutoTime.hpp>
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#include "revertMNNModel.hpp"
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/**
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TODOs:
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1. dynamically get CPU related info.
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2. iOS support
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*/
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struct Model {
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std::string name;
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std::string model_file;
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};
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#if !defined(_MSC_VER)
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inline bool file_exist(const char* file) {
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struct stat buffer;
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return stat(file, &buffer) == 0;
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}
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#endif
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std::vector<Model> findModelFiles(const char* dir) {
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std::vector<Model> models;
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#if defined(_MSC_VER)
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WIN32_FIND_DATA ffd;
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HANDLE hFind = INVALID_HANDLE_VALUE;
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std::string mnn_model_pattern = std::string(dir) + "\\*.mnn";
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hFind = FindFirstFile(mnn_model_pattern.c_str(), &ffd);
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if (INVALID_HANDLE_VALUE == hFind) {
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std::cout << "open " << dir << " failed: " << strerror(errno) << std::endl;
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return models;
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}
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do {
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Model m;
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m.name = ffd.cFileName;
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m.model_file = std::string(dir) + "\\" + m.name;
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if(INVALID_FILE_ATTRIBUTES != GetFileAttributes(m.model_file.c_str()) && GetLastError() != ERROR_FILE_NOT_FOUND) {
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models.push_back(std::move(m));
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}
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} while (FindNextFile(hFind, &ffd) != 0);
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FindClose(hFind);
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#else
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DIR* root;
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if ((root = opendir(dir)) == NULL) {
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std::cout << "open " << dir << " failed: " << strerror(errno) << std::endl;
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return models;
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}
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struct dirent* ent;
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while ((ent = readdir(root)) != NULL) {
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Model m;
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if (ent->d_name[0] != '.') {
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m.name = ent->d_name;
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m.model_file = std::string(dir) + "/" + m.name;
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if (file_exist(m.model_file.c_str())) {
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models.push_back(std::move(m));
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}
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}
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}
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closedir(root);
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#endif
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return models;
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}
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void setInputData(MNN::Tensor* tensor) {
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float* data = tensor->host<float>();
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Revert::fillRandValue(data, tensor->elementSize());
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}
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static inline uint64_t getTimeInUs() {
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uint64_t time;
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#if defined(_MSC_VER)
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LARGE_INTEGER now, freq;
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QueryPerformanceCounter(&now);
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QueryPerformanceFrequency(&freq);
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uint64_t sec = now.QuadPart / freq.QuadPart;
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uint64_t usec = (now.QuadPart % freq.QuadPart) * 1000000 / freq.QuadPart;
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time = sec * 1000000 + usec;
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#else
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struct timeval tv;
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gettimeofday(&tv, nullptr);
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time = static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec;
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#endif
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return time;
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}
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std::vector<float> doBench(Model& model, int loop, int warmup = 10, int forward = MNN_FORWARD_CPU, bool only_inference = true,
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int numberThread = 4, int precision = 2, float sparsity = 0.0f, int sparseBlockOC = 1, bool testQuantModel=false) {
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auto revertor = std::unique_ptr<Revert>(new Revert(model.model_file.c_str()));
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if (testQuantModel) {
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revertor->initialize(0, sparseBlockOC, false, true);
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} else {
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revertor->initialize(sparsity, sparseBlockOC);
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}
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auto modelBuffer = revertor->getBuffer();
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const auto bufferSize = revertor->getBufferSize();
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auto net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromBuffer(modelBuffer, bufferSize), MNN::Interpreter::destroy);
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revertor.reset();
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net->setSessionMode(MNN::Interpreter::Session_Release);
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MNN::ScheduleConfig config;
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config.numThread = numberThread;
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config.type = static_cast<MNNForwardType>(forward);
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MNN::BackendConfig backendConfig;
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backendConfig.precision = (MNN::BackendConfig::PrecisionMode)precision;
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backendConfig.power = MNN::BackendConfig::Power_High;
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config.backendConfig = &backendConfig;
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std::vector<float> costs;
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MNN::Session* session = net->createSession(config);
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MNN::Tensor* input = net->getSessionInput(session, NULL);
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// if the model has not the input dimension, umcomment the below code to set the input dims
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// std::vector<int> dims{1, 3, 224, 224};
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// net->resizeTensor(input, dims);
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// net->resizeSession(session);
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net->releaseModel();
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const MNN::Backend* inBackend = net->getBackend(session, input);
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std::shared_ptr<MNN::Tensor> givenTensor(MNN::Tensor::createHostTensorFromDevice(input, false));
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auto outputTensor = net->getSessionOutput(session, NULL);
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std::shared_ptr<MNN::Tensor> expectTensor(MNN::Tensor::createHostTensorFromDevice(outputTensor, false));
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// Warming up...
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for (int i = 0; i < warmup; ++i) {
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void* host = input->map(MNN::Tensor::MAP_TENSOR_WRITE, input->getDimensionType());
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input->unmap(MNN::Tensor::MAP_TENSOR_WRITE, input->getDimensionType(), host);
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net->runSession(session);
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host = outputTensor->map(MNN::Tensor::MAP_TENSOR_READ, outputTensor->getDimensionType());
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outputTensor->unmap(MNN::Tensor::MAP_TENSOR_READ, outputTensor->getDimensionType(), host);
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}
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for (int round = 0; round < loop; round++) {
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MNN::Timer _t;
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void* host = input->map(MNN::Tensor::MAP_TENSOR_WRITE, input->getDimensionType());
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input->unmap(MNN::Tensor::MAP_TENSOR_WRITE, input->getDimensionType(), host);
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net->runSession(session);
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host = outputTensor->map(MNN::Tensor::MAP_TENSOR_READ, outputTensor->getDimensionType());
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outputTensor->unmap(MNN::Tensor::MAP_TENSOR_READ, outputTensor->getDimensionType(), host);
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auto time = (float)_t.durationInUs() / 1000.0f;
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costs.push_back(time);
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}
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return costs;
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}
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void displayStats(const std::string& name, const std::vector<float>& costs, int quant = 0) {
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float max = 0, min = FLT_MAX, sum = 0, avg;
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for (auto v : costs) {
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max = fmax(max, v);
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min = fmin(min, v);
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sum += v;
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//printf("[ - ] cost：%f ms\n", v);
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}
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avg = costs.size() > 0 ? sum / costs.size() : 0;
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std::string model = name;
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if (quant == 1) {
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model = "quant-" + name;
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}
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printf("[ - ] %-24s max = %8.3f ms min = %8.3f ms avg = %8.3f ms\n", model.c_str(), max, avg == 0 ? 0 : min, avg);
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}
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static inline std::string forwardType(MNNForwardType type) {
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switch (type) {
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case MNN_FORWARD_CPU:
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return "CPU";
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case MNN_FORWARD_VULKAN:
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return "Vulkan";
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case MNN_FORWARD_OPENCL:
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return "OpenCL";
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case MNN_FORWARD_METAL:
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return "Metal";
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default:
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break;
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}
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return "N/A";
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}
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#ifdef __ANDROID__
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#include <errno.h>
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#include <unistd.h>
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#include <sys/prctl.h>
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#include <sys/syscall.h>
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#define BUFFER_SIZE 1024
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static uint32_t getNumberOfCPU() {
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FILE* fp = fopen("/proc/cpuinfo", "rb");
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if (!fp) {
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return 1;
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}
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uint32_t number = 0;
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char buffer[BUFFER_SIZE];
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while (!feof(fp)) {
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char* str = fgets(buffer, BUFFER_SIZE, fp);
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if (!str) {
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break;
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}
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if (memcmp(buffer, "processor", 9) == 0) {
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number++;
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}
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}
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fclose(fp);
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if (number < 1) {
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number = 1;
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}
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return number;
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}
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static int getCPUMaxFreqKHz(int cpuID) {
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char path[256];
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sprintf(path, "/sys/devices/system/cpu/cpufreq/stats/cpu%d/time_in_state", cpuID);
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FILE* fp = fopen(path, "rb");
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if (!fp) {
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sprintf(path, "/sys/devices/system/cpu/cpu%d/cpufreq/stats/time_in_state", cpuID);
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fp = fopen(path, "rb");
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if (!fp) {
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sprintf(path, "/sys/devices/system/cpu/cpu%d/cpufreq/cpuinfo_max_freq", cpuID);
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fp = fopen(path, "rb");
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if (!fp) {
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return -1;
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}
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int maxfrequency = -1;
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fscanf(fp, "%d", &maxfrequency);
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fclose(fp);
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return maxfrequency;
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}
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}
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int maxfrequency = 0;
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while (!feof(fp)) {
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int frequency = 0;
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int history = fscanf(fp, "%d %*d", &frequency);
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if (history != 1) {
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break;
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}
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if (frequency > maxfrequency) {
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maxfrequency = frequency;
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}
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}
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fclose(fp);
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return maxfrequency;
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}
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static int sortCPUIDByMaxFrequency(std::vector<int>& cpuIDs, int* littleClusterOffset) {
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const int cpuNumbers = cpuIDs.size();
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*littleClusterOffset = 0;
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if (cpuNumbers == 0) {
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return 0;
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}
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std::vector<int> cpusFrequency;
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cpusFrequency.resize(cpuNumbers);
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for (int i = 0; i < cpuNumbers; ++i) {
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int frequency = getCPUMaxFreqKHz(i);
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cpuIDs[i] = i;
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cpusFrequency[i] = frequency;
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// MNN_PRINT("cpu fre: %d, %d\n", i, frequency);
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}
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for (int i = 0; i < cpuNumbers; ++i) {
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for (int j = i + 1; j < cpuNumbers; ++j) {
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if (cpusFrequency[i] < cpusFrequency[j]) {
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// id
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int temp = cpuIDs[i];
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cpuIDs[i] = cpuIDs[j];
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cpuIDs[j] = temp;
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// frequency
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temp = cpusFrequency[i];
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cpusFrequency[i] = cpusFrequency[j];
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cpusFrequency[j] = temp;
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}
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}
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}
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int midMaxFrequency = (cpusFrequency.front() + cpusFrequency.back()) / 2;
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if (midMaxFrequency == cpusFrequency.back()) {
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return 0;
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}
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for (int i = 0; i < cpuNumbers; ++i) {
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if (cpusFrequency[i] < midMaxFrequency) {
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*littleClusterOffset = i;
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break;
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}
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}
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return 0;
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}
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//#define CPU_SETSIZE 1024
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#define __NCPUBITS (8 * sizeof (unsigned long))
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#endif
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void set_cpu_affinity()
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{
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#ifdef __ANDROID__
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int cpu_core_num = sysconf(_SC_NPROCESSORS_CONF);
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//LOG_MCNN_CL_INF("cpu core num = %d\n", cpu_core_num);
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int cpu_id = 0;
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cpu_set_t mask;
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CPU_ZERO(&mask);
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auto numberOfCPUs = getNumberOfCPU();
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static std::vector<int> sortedCPUIDs;
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static int littleClusterOffset = 0;
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if (sortedCPUIDs.empty()) {
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sortedCPUIDs.resize(numberOfCPUs);
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for (int i = 0; i < numberOfCPUs; ++i) {
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sortedCPUIDs[i] = i;
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}
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sortCPUIDByMaxFrequency(sortedCPUIDs, &littleClusterOffset);
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}
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printf("max core:");
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for (cpu_id = 0; cpu_id < littleClusterOffset; cpu_id++)
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{
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printf("%d ", sortedCPUIDs[cpu_id]);
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CPU_SET(sortedCPUIDs[cpu_id], &mask);
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}
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printf("\n");
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int sys_call_res = syscall(__NR_sched_setaffinity, gettid(), sizeof(mask), &mask);
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//LOG_MCNN_CL_INF("sys call res = %d\n", sys_call_res);
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if (sys_call_res)
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{
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printf("set_cpu_affinity errno = %d\n", (int)errno);
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}
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#endif
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}
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#if TARGET_OS_IPHONE
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void iosBenchAll(const char* modelPath) {
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std::cout << "MNN benchmark" << std::endl;
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int loop = 20;
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int warmup = 10;
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MNNForwardType forward = MNN_FORWARD_CPU;
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forward = MNN_FORWARD_NN;
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int numberThread = 4;
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int precision = 2;
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std::cout << "Forward type: **" << forwardType(forward) << "** thread=" << numberThread << "** precision=" <<precision << std::endl;
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std::vector<Model> models = findModelFiles(modelPath);
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std::cout << "--------> Benchmarking... loop = " << loop << ", warmup = " << warmup << std::endl;
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for (auto& m : models) {
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std::vector<float> costs = doBench(m, loop, warmup, forward, false, numberThread, precision);
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displayStats(m.name, costs);
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}
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}
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#else
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int main(int argc, const char* argv[]) {
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std::cout << "MNN benchmark" << std::endl;
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int loop = 10;
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int warmup = 10;
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MNNForwardType forward = MNN_FORWARD_CPU;
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int testQuantizedModel = 0;
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int numberThread = 4;
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int precision = 2;
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float sparsity = 0.0f;
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int sparseBlockOC = 1;
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if (argc <= 2) {
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std::cout << "Usage: " << argv[0] << " models_folder [loop_count] [warmup] [forwardtype] [numberThread] [precision] [weightSparsity] [testQuantizedModel]" << std::endl;
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return 1;
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}
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if (argc >= 3) {
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loop = atoi(argv[2]);
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}
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if (argc >= 4) {
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warmup = atoi(argv[3]);
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}
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if (argc >= 5) {
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forward = static_cast<MNNForwardType>(atoi(argv[4]));
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}
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if (argc >= 6) {
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numberThread = atoi(argv[5]);
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}
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if (argc >= 7) {
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precision = atoi(argv[6]);
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}
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if (argc >= 8) {
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sparsity = atof(argv[7]);
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}
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if(argc >= 9) {
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sparseBlockOC = atoi(argv[8]);
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}
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if(argc >= 10) {
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testQuantizedModel = atoi(argv[9]);
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}
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std::cout << "Forward type: " << forwardType(forward) << " thread=" << numberThread << " precision=" <<precision << " sparsity=" <<sparsity << " sparseBlockOC=" << sparseBlockOC << " testQuantizedModel=" << testQuantizedModel << std::endl;
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std::vector<Model> models = findModelFiles(argv[1]);
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std::cout << "--------> Benchmarking... loop = " << argv[2] << ", warmup = " << warmup << std::endl;
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std::string fpInfType = "precision!=2, use fp32 inference.";
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if (precision == 2) {
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fpInfType = "precision=2, use fp16 inference if your device supports and open MNN_ARM82=ON.";
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}
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MNN_PRINT("[-INFO-]: %s\n", fpInfType.c_str());
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if (testQuantizedModel) {
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MNN_PRINT("[-INFO-]: Auto set sparsity=0 when test quantized model in benchmark...\n");
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}
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/* not called yet */
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// set_cpu_affinity();
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if (testQuantizedModel) {
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printf("Auto set sparsity=0 when test quantized model in benchmark...\n");
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}
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for (auto& m : models) {
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std::vector<float> costs = doBench(m, loop, warmup, forward, false, numberThread, precision, sparsity, sparseBlockOC, false);
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displayStats(m.name.c_str(), costs, false);
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if (testQuantizedModel) {
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costs = doBench(m, loop, warmup, forward, false, numberThread, precision, sparsity, sparseBlockOC, true);
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displayStats(m.name, costs, 1);
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}
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}
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}
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#endif
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