/* * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ #ifndef TENSORRT_COMMON_H #define TENSORRT_COMMON_H #include "NvInfer.h" #if !TRT_WINML #include "NvInferPlugin.h" #endif #include "logger.h" #include "safeCommon.h" #include "utils/timingCache.h" #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #ifdef _MSC_VER // For loadLibrary // Needed so that the max/min definitions in windows.h do not conflict with std::max/min. #define NOMINMAX #include #undef NOMINMAX #else #include #endif #ifdef _MSC_VER #define FN_NAME __FUNCTION__ #else #define FN_NAME __func__ #endif #if defined(__aarch64__) || defined(__QNX__) #define ENABLE_DLA_API 1 #endif #define CHECK_RETURN_W_MSG(status, val, errMsg) \ do \ { \ if (!(status)) \ { \ sample::gLogError << errMsg << " Error in " << __FILE__ << ", function " << FN_NAME << "(), line " << __LINE__ \ << std::endl; \ return val; \ } \ } while (0) #undef ASSERT #define ASSERT(condition) \ do \ { \ if (!(condition)) \ { \ sample::gLogError << "Assertion failure: " << #condition << std::endl; \ exit(EXIT_FAILURE); \ } \ } while (0) #define CHECK_RETURN(status, val) CHECK_RETURN_W_MSG(status, val, "") #define OBJ_GUARD(A) std::unique_ptr template OBJ_GUARD(T) makeObjGuard(T_* t) { CHECK(!(std::is_base_of::value || std::is_same::value)); auto deleter = [](T* t) { delete t; }; return std::unique_ptr{static_cast(t), deleter}; } constexpr long double operator"" _GiB(long double val) { return val * (1 << 30); } constexpr long double operator"" _MiB(long double val) { return val * (1 << 20); } constexpr long double operator"" _KiB(long double val) { return val * (1 << 10); } struct SimpleProfiler : public nvinfer1::IProfiler { struct Record { float time{0}; int count{0}; }; void reportLayerTime(const char* layerName, float ms) noexcept override { mProfile[layerName].count++; mProfile[layerName].time += ms; if (std::find(mLayerNames.begin(), mLayerNames.end(), layerName) == mLayerNames.end()) { mLayerNames.push_back(layerName); } } SimpleProfiler(const char* name, const std::vector& srcProfilers = std::vector()) : mName(name) { for (const auto& srcProfiler : srcProfilers) { for (const auto& rec : srcProfiler.mProfile) { auto it = mProfile.find(rec.first); if (it == mProfile.end()) { mProfile.insert(rec); } else { it->second.time += rec.second.time; it->second.count += rec.second.count; } } } } friend std::ostream& operator<<(std::ostream& out, const SimpleProfiler& value) { out << "========== " << value.mName << " profile ==========" << std::endl; float totalTime = 0; std::string layerNameStr = "TensorRT layer name"; int maxLayerNameLength = std::max(static_cast(layerNameStr.size()), 70); for (const auto& elem : value.mProfile) { totalTime += elem.second.time; maxLayerNameLength = std::max(maxLayerNameLength, static_cast(elem.first.size())); } auto old_settings = out.flags(); auto old_precision = out.precision(); // Output header { out << std::setfill(' ') << std::setw(maxLayerNameLength) << layerNameStr << " "; out << std::setw(12) << "Runtime, " << "%" << " "; out << std::setw(12) << "Invocations" << " "; out << std::setw(12) << "Runtime, ms" << std::endl; } for (size_t i = 0; i < value.mLayerNames.size(); i++) { const std::string layerName = value.mLayerNames[i]; auto elem = value.mProfile.at(layerName); out << std::setw(maxLayerNameLength) << layerName << " "; out << std::setw(12) << std::fixed << std::setprecision(1) << (elem.time * 100.0F / totalTime) << "%" << " "; out << std::setw(12) << elem.count << " "; out << std::setw(12) << std::fixed << std::setprecision(2) << elem.time << std::endl; } out.flags(old_settings); out.precision(old_precision); out << "========== " << value.mName << " total runtime = " << totalTime << " ms ==========" << std::endl; return out; } private: std::string mName; std::vector mLayerNames; std::map mProfile; }; namespace samplesCommon { using nvinfer1::utils::loadTimingCacheFile; using nvinfer1::utils::buildTimingCacheFromFile; using nvinfer1::utils::saveTimingCacheFile; using nvinfer1::utils::updateTimingCacheFile; // Swaps endianness of an integral type. template ::value, int>::type = 0> inline T swapEndianness(const T& value) { uint8_t bytes[sizeof(T)]; for (int i = 0; i < static_cast(sizeof(T)); ++i) { bytes[sizeof(T) - 1 - i] = *(reinterpret_cast(&value) + i); } return *reinterpret_cast(bytes); } class HostMemory { public: HostMemory() = delete; virtual void* data() const noexcept { return mData; } virtual std::size_t size() const noexcept { return mSize; } virtual nvinfer1::DataType type() const noexcept { return mType; } virtual ~HostMemory() {} protected: HostMemory(std::size_t size, nvinfer1::DataType type) : mData{nullptr} , mSize(size) , mType(type) { } void* mData; std::size_t mSize; nvinfer1::DataType mType; }; template class TypedHostMemory : public HostMemory { public: explicit TypedHostMemory(std::size_t size) : HostMemory(size, dataType) { mData = new ElemType[size]; }; ~TypedHostMemory() noexcept override { delete[](ElemType*) mData; } ElemType* raw() noexcept { return static_cast(data()); } }; using FloatMemory = TypedHostMemory; using HalfMemory = TypedHostMemory; using ByteMemory = TypedHostMemory; inline void* safeCudaMalloc(size_t memSize) { void* deviceMem; CHECK(cudaMalloc(&deviceMem, memSize)); if (deviceMem == nullptr) { std::cerr << "Out of memory" << std::endl; exit(EXIT_FAILURE); } return deviceMem; } inline bool isDebug() { return (std::getenv("TENSORRT_DEBUG") ? true : false); } struct InferDeleter { template void operator()(T* obj) const { delete obj; } }; template using SampleUniquePtr = std::unique_ptr; static auto StreamDeleter = [](cudaStream_t* pStream) { if (pStream) { static_cast(cudaStreamDestroy(*pStream)); delete pStream; } }; inline std::unique_ptr makeCudaStream() { std::unique_ptr pStream(new cudaStream_t, StreamDeleter); if (cudaStreamCreateWithFlags(pStream.get(), cudaStreamNonBlocking) != cudaSuccess) { pStream.reset(nullptr); } return pStream; } //! Return vector of indices that puts magnitudes of sequence in descending order. template std::vector argMagnitudeSort(Iter begin, Iter end) { std::vector indices(end - begin); std::iota(indices.begin(), indices.end(), 0); std::sort(indices.begin(), indices.end(), [&begin](size_t i, size_t j) { return std::abs(begin[j]) < std::abs(begin[i]); }); return indices; } inline bool readReferenceFile(const std::string& fileName, std::vector& refVector) { std::ifstream infile(fileName); if (!infile.is_open()) { std::cout << "ERROR: readReferenceFile: Attempting to read from a file that is not open." << std::endl; return false; } std::string line; while (std::getline(infile, line)) { if (line.empty()) continue; refVector.push_back(line); } infile.close(); return true; } template std::vector classify( const std::vector& refVector, const std::vector& output, const size_t topK) { const auto inds = samplesCommon::argMagnitudeSort(output.cbegin(), output.cend()); std::vector result; result.reserve(topK); for (size_t k = 0; k < topK; ++k) { result.push_back(refVector[inds[k]]); } return result; } // Returns indices of highest K magnitudes in v. template std::vector topKMagnitudes(const std::vector& v, const size_t k) { std::vector indices = samplesCommon::argMagnitudeSort(v.cbegin(), v.cend()); indices.resize(k); return indices; } template bool readASCIIFile(const std::string& fileName, const size_t size, std::vector& out) { std::ifstream infile(fileName); if (!infile.is_open()) { std::cout << "ERROR readASCIIFile: Attempting to read from a file that is not open." << std::endl; return false; } out.clear(); out.reserve(size); out.assign(std::istream_iterator(infile), std::istream_iterator()); infile.close(); return true; } template bool writeASCIIFile(const std::string& fileName, const std::vector& in) { std::ofstream outfile(fileName); if (!outfile.is_open()) { std::cout << "ERROR: writeASCIIFile: Attempting to write to a file that is not open." << std::endl; return false; } for (auto fn : in) { outfile << fn << "\n"; } outfile.close(); return true; } inline void print_version() { std::cout << " TensorRT version: " << NV_TENSORRT_MAJOR << "." << NV_TENSORRT_MINOR << "." << NV_TENSORRT_PATCH << "." << NV_TENSORRT_BUILD << std::endl; } inline std::string getFileType(const std::string& filepath) { return filepath.substr(filepath.find_last_of(".") + 1); } inline std::string toLower(const std::string& inp) { std::string out = inp; std::transform(out.begin(), out.end(), out.begin(), ::tolower); return out; } inline float getMaxValue(const float* buffer, int64_t size) { assert(buffer != nullptr); assert(size > 0); return *std::max_element(buffer, buffer + size); } // Ensures that every tensor used by a network has a dynamic range set. // // All tensors in a network must have a dynamic range specified if a calibrator is not used. // This function is just a utility to globally fill in missing scales and zero-points for the entire network. // // If a tensor does not have a dyanamic range set, it is assigned inRange or outRange as follows: // // * If the tensor is the input to a layer or output of a pooling node, its dynamic range is derived from inRange. // * Otherwise its dynamic range is derived from outRange. // // The default parameter values are intended to demonstrate, for final layers in the network, // cases where dynamic ranges are asymmetric. // // The default parameter values choosen arbitrarily. Range values should be choosen such that // we avoid underflow or overflow. Also range value should be non zero to avoid uniform zero scale tensor. inline void setAllDynamicRanges(nvinfer1::INetworkDefinition* network, float inRange = 2.0F, float outRange = 4.0F) { // Ensure that all layer inputs have a scale. for (int i = 0; i < network->getNbLayers(); i++) { auto layer = network->getLayer(i); for (int j = 0; j < layer->getNbInputs(); j++) { nvinfer1::ITensor* input{layer->getInput(j)}; // Optional inputs are nullptr here and are from RNN layers. if (input != nullptr && !input->dynamicRangeIsSet()) { ASSERT(input->setDynamicRange(-inRange, inRange)); } } } // Ensure that all layer outputs have a scale. // Tensors that are also inputs to layers are ingored here // since the previous loop nest assigned scales to them. for (int i = 0; i < network->getNbLayers(); i++) { auto layer = network->getLayer(i); for (int j = 0; j < layer->getNbOutputs(); j++) { nvinfer1::ITensor* output{layer->getOutput(j)}; // Optional outputs are nullptr here and are from RNN layers. if (output != nullptr && !output->dynamicRangeIsSet()) { // Pooling must have the same input and output scales. if (layer->getType() == nvinfer1::LayerType::kPOOLING) { ASSERT(output->setDynamicRange(-inRange, inRange)); } else { ASSERT(output->setDynamicRange(-outRange, outRange)); } } } } } inline void setDummyInt8DynamicRanges(const nvinfer1::IBuilderConfig* c, nvinfer1::INetworkDefinition* n) { // Set dummy per-tensor dynamic range if Int8 mode is requested. if (c->getFlag(nvinfer1::BuilderFlag::kINT8)) { sample::gLogWarning << "Int8 calibrator not provided. Generating dummy per-tensor dynamic range. Int8 accuracy " "is not guaranteed." << std::endl; setAllDynamicRanges(n); } } inline void enableDLA( nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config, int useDLACore, bool allowGPUFallback = true) { if (useDLACore >= 0) { if (builder->getNbDLACores() == 0) { std::cerr << "Trying to use DLA core " << useDLACore << " on a platform that doesn't have any DLA cores" << std::endl; assert("Error: use DLA core on a platfrom that doesn't have any DLA cores" && false); } if (allowGPUFallback) { config->setFlag(nvinfer1::BuilderFlag::kGPU_FALLBACK); } if (!config->getFlag(nvinfer1::BuilderFlag::kINT8)) { // User has not requested INT8 Mode. // By default run in FP16 mode. FP32 mode is not permitted. config->setFlag(nvinfer1::BuilderFlag::kFP16); } config->setDefaultDeviceType(nvinfer1::DeviceType::kDLA); config->setDLACore(useDLACore); } } inline int32_t parseDLA(int32_t argc, char** argv) { for (int32_t i = 1; i < argc; i++) { if (strncmp(argv[i], "--useDLACore=", 13) == 0) { return std::stoi(argv[i] + 13); } } return -1; } inline uint32_t getElementSize(nvinfer1::DataType t) noexcept { switch (t) { case nvinfer1::DataType::kINT64: return 8; case nvinfer1::DataType::kINT32: case nvinfer1::DataType::kFLOAT: return 4; case nvinfer1::DataType::kBF16: case nvinfer1::DataType::kHALF: return 2; case nvinfer1::DataType::kBOOL: case nvinfer1::DataType::kUINT8: case nvinfer1::DataType::kINT8: case nvinfer1::DataType::kFP8: return 1; case nvinfer1::DataType::kINT4: ASSERT(false && "Element size is not implemented for sub-byte data-types"); } return 0; } inline int64_t volume(nvinfer1::Dims const& dims, int32_t start, int32_t stop) { ASSERT(start >= 0); ASSERT(start <= stop); ASSERT(stop <= dims.nbDims); ASSERT(std::all_of(dims.d + start, dims.d + stop, [](int32_t x) { return x >= 0; })); return std::accumulate(dims.d + start, dims.d + stop, int64_t{1}, std::multiplies{}); } template struct PPM { std::string magic, fileName; int h, w, max; uint8_t buffer[C * H * W]; }; // New vPPM(variable sized PPM) class with variable dimensions. struct vPPM { std::string magic, fileName; int h, w, max; std::vector buffer; }; struct BBox { float x1, y1, x2, y2; }; template void readPPMFile(const std::string& filename, samplesCommon::PPM& ppm) { ppm.fileName = filename; std::ifstream infile(filename, std::ifstream::binary); assert(infile.is_open() && "Attempting to read from a file that is not open."); infile >> ppm.magic >> ppm.w >> ppm.h >> ppm.max; infile.seekg(1, infile.cur); infile.read(reinterpret_cast(ppm.buffer), ppm.w * ppm.h * 3); } inline void readPPMFile(const std::string& filename, vPPM& ppm, std::vector& input_dir) { ppm.fileName = filename; std::ifstream infile(locateFile(filename, input_dir), std::ifstream::binary); infile >> ppm.magic >> ppm.w >> ppm.h >> ppm.max; infile.seekg(1, infile.cur); for (int i = 0; i < ppm.w * ppm.h * 3; ++i) { ppm.buffer.push_back(0); } infile.read(reinterpret_cast(&ppm.buffer[0]), ppm.w * ppm.h * 3); } template void writePPMFileWithBBox(const std::string& filename, PPM& ppm, const BBox& bbox) { std::ofstream outfile("./" + filename, std::ofstream::binary); assert(!outfile.fail()); outfile << "P6" << "\n" << ppm.w << " " << ppm.h << "\n" << ppm.max << "\n"; auto round = [](float x) -> int { return int(std::floor(x + 0.5F)); }; const int x1 = std::min(std::max(0, round(int(bbox.x1))), W - 1); const int x2 = std::min(std::max(0, round(int(bbox.x2))), W - 1); const int y1 = std::min(std::max(0, round(int(bbox.y1))), H - 1); const int y2 = std::min(std::max(0, round(int(bbox.y2))), H - 1); for (int x = x1; x <= x2; ++x) { // bbox top border ppm.buffer[(y1 * ppm.w + x) * 3] = 255; ppm.buffer[(y1 * ppm.w + x) * 3 + 1] = 0; ppm.buffer[(y1 * ppm.w + x) * 3 + 2] = 0; // bbox bottom border ppm.buffer[(y2 * ppm.w + x) * 3] = 255; ppm.buffer[(y2 * ppm.w + x) * 3 + 1] = 0; ppm.buffer[(y2 * ppm.w + x) * 3 + 2] = 0; } for (int y = y1; y <= y2; ++y) { // bbox left border ppm.buffer[(y * ppm.w + x1) * 3] = 255; ppm.buffer[(y * ppm.w + x1) * 3 + 1] = 0; ppm.buffer[(y * ppm.w + x1) * 3 + 2] = 0; // bbox right border ppm.buffer[(y * ppm.w + x2) * 3] = 255; ppm.buffer[(y * ppm.w + x2) * 3 + 1] = 0; ppm.buffer[(y * ppm.w + x2) * 3 + 2] = 0; } outfile.write(reinterpret_cast(ppm.buffer), ppm.w * ppm.h * 3); } inline void writePPMFileWithBBox(const std::string& filename, vPPM ppm, std::vector& dets) { std::ofstream outfile("./" + filename, std::ofstream::binary); assert(!outfile.fail()); outfile << "P6" << "\n" << ppm.w << " " << ppm.h << "\n" << ppm.max << "\n"; auto round = [](float x) -> int { return int(std::floor(x + 0.5F)); }; for (auto bbox : dets) { for (int x = int(bbox.x1); x < int(bbox.x2); ++x) { // bbox top border ppm.buffer[(round(bbox.y1) * ppm.w + x) * 3] = 255; ppm.buffer[(round(bbox.y1) * ppm.w + x) * 3 + 1] = 0; ppm.buffer[(round(bbox.y1) * ppm.w + x) * 3 + 2] = 0; // bbox bottom border ppm.buffer[(round(bbox.y2) * ppm.w + x) * 3] = 255; ppm.buffer[(round(bbox.y2) * ppm.w + x) * 3 + 1] = 0; ppm.buffer[(round(bbox.y2) * ppm.w + x) * 3 + 2] = 0; } for (int y = int(bbox.y1); y < int(bbox.y2); ++y) { // bbox left border ppm.buffer[(y * ppm.w + round(bbox.x1)) * 3] = 255; ppm.buffer[(y * ppm.w + round(bbox.x1)) * 3 + 1] = 0; ppm.buffer[(y * ppm.w + round(bbox.x1)) * 3 + 2] = 0; // bbox right border ppm.buffer[(y * ppm.w + round(bbox.x2)) * 3] = 255; ppm.buffer[(y * ppm.w + round(bbox.x2)) * 3 + 1] = 0; ppm.buffer[(y * ppm.w + round(bbox.x2)) * 3 + 2] = 0; } } outfile.write(reinterpret_cast(&ppm.buffer[0]), ppm.w * ppm.h * 3); } class TimerBase { public: virtual void start() {} virtual void stop() {} float microseconds() const noexcept { return mMs * 1000.F; } float milliseconds() const noexcept { return mMs; } float seconds() const noexcept { return mMs / 1000.F; } void reset() noexcept { mMs = 0.F; } protected: float mMs{0.0F}; }; class GpuTimer : public TimerBase { public: explicit GpuTimer(cudaStream_t stream) : mStream(stream) { CHECK(cudaEventCreate(&mStart)); CHECK(cudaEventCreate(&mStop)); } ~GpuTimer() { CHECK(cudaEventDestroy(mStart)); CHECK(cudaEventDestroy(mStop)); } void start() override { CHECK(cudaEventRecord(mStart, mStream)); } void stop() override { CHECK(cudaEventRecord(mStop, mStream)); float ms{0.0F}; CHECK(cudaEventSynchronize(mStop)); CHECK(cudaEventElapsedTime(&ms, mStart, mStop)); mMs += ms; } private: cudaEvent_t mStart, mStop; cudaStream_t mStream; }; // class GpuTimer template class CpuTimer : public TimerBase { public: using clock_type = Clock; void start() override { mStart = Clock::now(); } void stop() override { mStop = Clock::now(); mMs += std::chrono::duration{mStop - mStart}.count(); } private: std::chrono::time_point mStart, mStop; }; // class CpuTimer using PreciseCpuTimer = CpuTimer; inline std::vector splitString(std::string str, char delimiter = ',') { std::vector splitVect; std::stringstream ss(str); std::string substr; while (ss.good()) { getline(ss, substr, delimiter); splitVect.emplace_back(std::move(substr)); } return splitVect; } inline int getC(nvinfer1::Dims const& d) { return d.nbDims >= 3 ? d.d[d.nbDims - 3] : 1; } inline int getH(const nvinfer1::Dims& d) { return d.nbDims >= 2 ? d.d[d.nbDims - 2] : 1; } inline int getW(const nvinfer1::Dims& d) { return d.nbDims >= 1 ? d.d[d.nbDims - 1] : 1; } //! Platform-agnostic wrapper around dynamic libraries. class DynamicLibrary { public: explicit DynamicLibrary(std::string const& name) : mLibName{name} { #if defined(_WIN32) mHandle = LoadLibraryA(name.c_str()); #else // defined(_WIN32) int32_t flags{RTLD_LAZY}; #if ENABLE_ASAN // https://github.com/google/sanitizers/issues/89 // asan doesn't handle module unloading correctly and there are no plans on doing // so. In order to get proper stack traces, don't delete the shared library on // close so that asan can resolve the symbols correctly. flags |= RTLD_NODELETE; #endif // ENABLE_ASAN mHandle = dlopen(name.c_str(), flags); #endif // defined(_WIN32) if (mHandle == nullptr) { std::string errorStr{}; #if !defined(_WIN32) errorStr = std::string{" due to "} + std::string{dlerror()}; #endif throw std::runtime_error("Unable to open library: " + name + errorStr); } } DynamicLibrary(DynamicLibrary const&) = delete; DynamicLibrary(DynamicLibrary const&&) = delete; //! //! Retrieve a function symbol from the loaded library. //! //! \return the loaded symbol on success //! \throw std::invalid_argument if loading the symbol failed. //! template std::function symbolAddress(char const* name) { if (mHandle == nullptr) { throw std::runtime_error("Handle to library is nullptr."); } void* ret; #if defined(_MSC_VER) ret = static_cast(GetProcAddress(static_cast(mHandle), name)); #else ret = dlsym(mHandle, name); #endif if (ret == nullptr) { std::string const kERROR_MSG(mLibName + ": error loading symbol: " + std::string(name)); throw std::invalid_argument(kERROR_MSG); } return reinterpret_cast(ret); } ~DynamicLibrary() { try { #if defined(_WIN32) ASSERT(static_cast(FreeLibrary(static_cast(mHandle)))); #else ASSERT(dlclose(mHandle) == 0); #endif } catch (...) { sample::gLogError << "Unable to close library: " << mLibName << std::endl; } } private: std::string mLibName{}; //!< Name of the DynamicLibrary void* mHandle{}; //!< Handle to the DynamicLibrary }; inline std::unique_ptr loadLibrary(std::string const& path) { // make_unique not available until C++14 - we still need to support C++11 builds. return std::unique_ptr(new DynamicLibrary{path}); } inline int32_t getMaxPersistentCacheSize() { int32_t deviceIndex{}; CHECK(cudaGetDevice(&deviceIndex)); int32_t maxPersistentL2CacheSize{}; #if CUDART_VERSION >= 11030 && !TRT_WINML CHECK(cudaDeviceGetAttribute(&maxPersistentL2CacheSize, cudaDevAttrMaxPersistingL2CacheSize, deviceIndex)); #endif return maxPersistentL2CacheSize; } inline bool isDataTypeSupported(nvinfer1::DataType dataType) { auto builder = SampleUniquePtr(createBuilder()); if (!builder) { return false; } if ((dataType == nvinfer1::DataType::kINT8 && !builder->platformHasFastInt8()) || (dataType == nvinfer1::DataType::kHALF && !builder->platformHasFastFp16())) { return false; } return true; } } // namespace samplesCommon inline std::ostream& operator<<(std::ostream& os, const nvinfer1::Dims& dims) { os << "("; for (int i = 0; i < dims.nbDims; ++i) { os << (i ? ", " : "") << dims.d[i]; } return os << ")"; } #endif // TENSORRT_COMMON_H