| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ArmnnDriverImpl.hpp" |
| #include "ArmnnPreparedModel.hpp" |
| #include "ModelToINetworkTransformer.hpp" |
| #include "SystemPropertiesUtils.hpp" |
| |
| #include <armnnDeserializer/IDeserializer.hpp> |
| |
| #include <log/log.h> |
| #include <sys/stat.h> |
| |
| namespace |
| { |
| |
| Capabilities GenerateCapabilities() |
| { |
| VLOG(DRIVER) << "ArmnnDriverImpl::GenerateCapabilities()"; |
| |
| float defaultPerfValue = .1f; |
| const Capabilities::PerformanceInfo defaultPerfInfo = { /* execTime */ defaultPerfValue, |
| /* powerUsage */ defaultPerfValue |
| }; |
| std::vector<OperandType> operandsTypes({ |
| OperandType::FLOAT32, |
| OperandType::INT32, |
| OperandType::UINT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32, |
| OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::BOOL, |
| OperandType::TENSOR_QUANT16_SYMM, |
| OperandType::TENSOR_FLOAT16, |
| OperandType::TENSOR_BOOL8, |
| OperandType::FLOAT16, |
| OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL, |
| OperandType::TENSOR_QUANT16_ASYMM, |
| OperandType::TENSOR_QUANT8_SYMM, |
| OperandType::TENSOR_QUANT8_ASYMM_SIGNED, |
| }); |
| |
| std::vector<Capabilities::OperandPerformance> operandPerformances; |
| operandPerformances.reserve(operandsTypes.size()); |
| |
| for (auto opType : operandsTypes) |
| { |
| operandPerformances.push_back( |
| Capabilities::OperandPerformance{ /* type */ opType, /* info */ defaultPerfInfo }); |
| } |
| |
| auto operandPerformanceTable = |
| Capabilities::OperandPerformanceTable::create(std::move(operandPerformances)).value(); |
| |
| return { /* relaxedFloat32toFloat16PerformanceScalar */ defaultPerfInfo, |
| /* relaxedFloat32toFloat16PerformanceTensor */ defaultPerfInfo, |
| /* operandPerformance */ std::move(operandPerformanceTable), |
| /* ifPerformance */ defaultPerfInfo, |
| /* whilePerformance */ defaultPerfInfo }; |
| } |
| |
| size_t Hash(std::vector<uint8_t>& cacheData) |
| { |
| std::size_t hash = cacheData.size(); |
| for (auto& i : cacheData) |
| { |
| hash = ((hash << 5) - hash) + i; |
| } |
| return hash; |
| } |
| |
| } // anonymous namespace |
| |
| using namespace android::nn; |
| |
| namespace armnn_driver |
| { |
| |
| bool ArmnnDriverImpl::ValidateSharedHandle(const SharedHandle& sharedHandle) |
| { |
| bool valid = true; |
| |
| if (*sharedHandle < 0) |
| { |
| return !valid; |
| } |
| |
| int dataCacheFileAccessMode = fcntl(*sharedHandle, F_GETFL) & O_ACCMODE; |
| if (dataCacheFileAccessMode != O_RDWR) |
| { |
| return !valid; |
| } |
| |
| return valid; |
| } |
| |
| GeneralResult<SharedPreparedModel> ArmnnDriverImpl::PrepareArmnnModel( |
| const armnn::IRuntimePtr& runtime, |
| const armnn::IGpuAccTunedParametersPtr& clTunedParameters, |
| const DriverOptions& options, |
| const Model& model, |
| const std::vector<SharedHandle>& modelCacheHandle, |
| const std::vector<SharedHandle>& dataCacheHandle, |
| const CacheToken& token, |
| bool float32ToFloat16, |
| Priority priority) |
| { |
| VLOG(DRIVER) << "ArmnnDriverImpl::PrepareArmnnModel()"; |
| |
| if (!runtime) |
| { |
| return NN_ERROR(ErrorStatus::DEVICE_UNAVAILABLE) << "Device unavailable"; |
| } |
| |
| if (const auto result = validate(model); !result.ok()) |
| { |
| return NN_ERROR(ErrorStatus::INVALID_ARGUMENT) << "Invalid model passed as input"; |
| } |
| |
| // Deliberately ignore any unsupported operations requested by the options - |
| // at this point we're being asked to prepare a model that we've already declared support for |
| // and the operation indices may be different to those in getSupportedOperations anyway. |
| std::set<unsigned int> unsupportedOperations; |
| ModelToINetworkTransformer modelConverter(options.GetBackends(), |
| model, |
| unsupportedOperations); |
| |
| if (modelConverter.GetConversionResult() != ConversionResult::Success) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << "ModelToINetworkConverter failed"; |
| } |
| |
| // Serialize the network graph to a .armnn file if an output directory |
| // has been specified in the drivers' arguments. |
| std::vector<uint8_t> dataCacheData; |
| bool serializeToFile = dataCacheHandle.size() < 1 ? false : true; |
| auto serializedNetworkFileName = |
| SerializeNetwork(*modelConverter.GetINetwork(), |
| options.GetRequestInputsAndOutputsDumpDir(), |
| dataCacheData, |
| serializeToFile); |
| |
| // Optimize the network |
| armnn::IOptimizedNetworkPtr optNet(nullptr, nullptr); |
| armnn::OptimizerOptionsOpaque OptOptions; |
| OptOptions.SetReduceFp32ToFp16(float32ToFloat16); |
| OptOptions.SetProfilingEnabled(options.IsGpuProfilingEnabled()); |
| |
| int cachedFd = -1; |
| bool saveCachedNetwork = options.SaveCachedNetwork(); |
| |
| unsigned int numberOfCachedModelFiles = 0; |
| if (modelCacheHandle.size() > 0) |
| { |
| unsigned int index = 0; |
| for (auto& backend : options.GetBackends()) |
| { |
| // modelCacheHandle size should be equal to numberOfCachedModelFiles |
| // modelCacheHandle vector should be in same order as backends |
| auto numberOfCacheFiles = GetNumberOfCacheFiles(backend); |
| if (numberOfCacheFiles > 0) |
| { |
| numberOfCachedModelFiles += numberOfCacheFiles; |
| // For GpuAcc numberOfCachedFiles is 1 |
| if (backend == armnn::Compute::GpuAcc) |
| { |
| cachedFd = *modelCacheHandle[index]; |
| saveCachedNetwork = true; |
| } |
| index += numberOfCachedModelFiles; |
| } |
| } |
| } |
| |
| armnn::BackendOptions gpuAcc("GpuAcc", |
| { |
| { "FastMathEnabled", options.IsFastMathEnabled() }, |
| { "SaveCachedNetwork", saveCachedNetwork }, |
| { "CachedNetworkFilePath", options.GetCachedNetworkFilePath() }, |
| { "MLGOTuningFilePath", options.GetClMLGOTunedParametersFile() }, |
| { "CachedFileDescriptor", cachedFd } |
| }); |
| |
| armnn::BackendOptions cpuAcc("CpuAcc", |
| { |
| { "FastMathEnabled", options.IsFastMathEnabled() }, |
| { "NumberOfThreads", options.GetNumberOfThreads() } |
| }); |
| OptOptions.AddModelOption(gpuAcc); |
| OptOptions.AddModelOption(cpuAcc); |
| |
| std::vector<std::string> errMessages; |
| try |
| { |
| optNet = armnn::Optimize(*modelConverter.GetINetwork(), |
| options.GetBackends(), |
| runtime->GetDeviceSpec(), |
| OptOptions, |
| errMessages); |
| } |
| catch (std::exception& e) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << e.what(); |
| } |
| |
| // Check that the optimized network is valid. |
| if (!optNet) |
| { |
| std::stringstream message; |
| message << "Invalid optimized network"; |
| for (const std::string& msg : errMessages) |
| { |
| message << "\n" << msg; |
| } |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << message.str(); |
| } |
| |
| // Export the optimized network graph to a dot file if an output dump directory |
| // has been specified in the drivers' arguments. |
| std::string dotGraphFileName = ExportNetworkGraphToDotFile(*optNet, |
| options.GetRequestInputsAndOutputsDumpDir()); |
| |
| // Load it into the runtime. |
| armnn::NetworkId netId = 0; |
| std::string msg; |
| armnn::INetworkProperties networkProperties(options.isAsyncModelExecutionEnabled(), |
| MemorySource::Undefined, |
| MemorySource::Undefined, |
| options.IsGpuProfilingEnabled()); |
| auto numInputs = getMainModel(model).inputIndexes.size(); |
| auto numOutputs = getMainModel(model).outputIndexes.size(); |
| try |
| { |
| if (runtime->LoadNetwork(netId, move(optNet), msg, networkProperties) != armnn::Status::Success) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << "Network could not be loaded"; |
| } |
| } |
| catch (std::exception& e) |
| { |
| std::stringstream message; |
| message << "Exception (" << e.what()<< ") caught from LoadNetwork."; |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << message.str(); |
| } |
| |
| // Now that we have a networkId for the graph rename the exported files to use it |
| // so that we can associate the graph file and the input/output tensor exported files |
| RenameExportedFiles(serializedNetworkFileName, |
| dotGraphFileName, |
| options.GetRequestInputsAndOutputsDumpDir(), |
| netId); |
| |
| // Cache the model |
| size_t hashValue = 0; |
| if (dataCacheHandle.size() == 1 ) |
| { |
| hashValue = Hash(dataCacheData); |
| } |
| |
| // Cache the model data |
| if (modelCacheHandle.size() > 0) |
| { |
| if (modelCacheHandle.size() == numberOfCachedModelFiles) |
| { |
| for (uint32_t i = 0; i < modelCacheHandle.size(); ++i) |
| { |
| int modelCacheFileAccessMode = fcntl(*modelCacheHandle[i], F_GETFL) & O_ACCMODE; |
| if (modelCacheFileAccessMode != O_RDONLY) |
| { |
| struct stat statBuffer; |
| if (fstat(*modelCacheHandle[i], &statBuffer) == 0) |
| { |
| long modelDataSize = statBuffer.st_size; |
| if (modelDataSize > 0) |
| { |
| std::vector<uint8_t> modelData(modelDataSize); |
| pread(*modelCacheHandle[i], modelData.data(), modelData.size(), 0); |
| hashValue ^= Hash(modelData); |
| } |
| } |
| } |
| } |
| } |
| } |
| if (dataCacheHandle.size() == 1 && hashValue != 0) |
| { |
| std::vector<uint8_t> theHashValue(sizeof(hashValue)); |
| ::memcpy(theHashValue.data(), &hashValue, sizeof(hashValue)); |
| |
| write(*dataCacheHandle[0], theHashValue.data(), theHashValue.size()); |
| pwrite(*dataCacheHandle[0], dataCacheData.data(), dataCacheData.size(), theHashValue.size()); |
| } |
| |
| bool executeWithDummyInputs = (std::find(options.GetBackends().begin(), |
| options.GetBackends().end(), |
| armnn::Compute::GpuAcc) != options.GetBackends().end()); |
| |
| auto preparedModel = std::make_shared<const ArmnnPreparedModel>(netId, |
| runtime.get(), |
| model, |
| options.GetRequestInputsAndOutputsDumpDir(), |
| options.IsGpuProfilingEnabled(), |
| priority); |
| |
| // Run a single 'dummy' inference of the model. This means that CL kernels will get compiled (and tuned if |
| // this is enabled) before the first 'real' inference which removes the overhead of the first inference. |
| // Only run this if the GpuAcc backend has been added to options |
| if (std::find(options.GetBackends().begin(), |
| options.GetBackends().end(), |
| armnn::Compute::GpuAcc) != options.GetBackends().end()) |
| { |
| if (!preparedModel->ExecuteWithDummyInputs(numInputs, numOutputs)) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << "Network could not be executed"; |
| } |
| |
| if (clTunedParameters && |
| options.GetClTunedParametersMode() == armnn::IGpuAccTunedParameters::Mode::UpdateTunedParameters) |
| { |
| // Now that we've done one inference the CL kernel parameters will have been tuned, |
| // so save the updated file. |
| try |
| { |
| clTunedParameters->Save(options.GetClTunedParametersFile().c_str()); |
| } |
| catch (std::exception& error) |
| { |
| VLOG(DRIVER) << "ArmnnDriverImpl::prepareModel: Failed to save CL tuned parameters file" |
| << options.GetClTunedParametersFile().c_str() << error.what(); |
| } |
| } |
| } |
| return std::move(preparedModel); |
| } |
| |
| GeneralResult<SharedPreparedModel> ArmnnDriverImpl::PrepareArmnnModelFromCache( |
| const armnn::IRuntimePtr& runtime, |
| const armnn::IGpuAccTunedParametersPtr& clTunedParameters, |
| const DriverOptions& options, |
| const std::vector<SharedHandle>& modelCacheHandle, |
| const std::vector<SharedHandle>& dataCacheHandle, |
| const CacheToken& token, |
| bool float32ToFloat16) |
| { |
| VLOG(DRIVER) << "ArmnnDriverImpl::PrepareArmnnModelFromCache()"; |
| |
| if (!runtime) |
| { |
| return NN_ERROR(ErrorStatus::DEVICE_UNAVAILABLE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Device unavailable"; |
| } |
| |
| if (token.size() != ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Token size does not match!"; |
| } |
| |
| // Validate dataCacheHandle |
| if (dataCacheHandle.size() != 1) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Not valid data cache handle!"; |
| } |
| |
| if (!ValidateSharedHandle(dataCacheHandle[0])) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Not valid data cache handle!"; |
| } |
| |
| size_t cachedDataSize = 0; |
| struct stat dataStatBuffer; |
| if (fstat(*dataCacheHandle[0], &dataStatBuffer) == 0) |
| { |
| cachedDataSize = dataStatBuffer.st_size; |
| } |
| if (cachedDataSize == 0) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Not valid cached data!"; |
| } |
| |
| // Check if model files cached they match the expected value |
| unsigned int numberOfCachedModelFiles = 0; |
| for (auto& backend : options.GetBackends()) |
| { |
| numberOfCachedModelFiles += GetNumberOfCacheFiles(backend); |
| } |
| if (modelCacheHandle.size() != numberOfCachedModelFiles) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Model cache handle size does not match."; |
| } |
| |
| // Read the hashValue |
| std::vector<uint8_t> hashValue(sizeof(size_t)); |
| pread(*dataCacheHandle[0], hashValue.data(), hashValue.size(), 0); |
| |
| // Read the model |
| std::vector<uint8_t> dataCacheData(cachedDataSize - hashValue.size()); |
| pread(*dataCacheHandle[0], dataCacheData.data(), dataCacheData.size(), hashValue.size()); |
| auto calculatedHashValue = Hash(dataCacheData); |
| |
| int gpuAccCachedFd = -1; |
| if (modelCacheHandle.size() > 0) |
| { |
| unsigned int index = 0; |
| for (auto& backend : options.GetBackends()) |
| { |
| // modelCacheHandle size should be equal to numberOfCachedModelFiles |
| // modelCacheHandle vector should be in same order as backends |
| auto numberOfCacheFiles = GetNumberOfCacheFiles(backend); |
| if (numberOfCacheFiles > 0) |
| { |
| if (!ValidateSharedHandle(modelCacheHandle[index])) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Invalid model cache handle!"; |
| } |
| int cachedFd = *modelCacheHandle[index]; |
| struct stat statBuffer; |
| if (fstat(cachedFd, &statBuffer) == 0) |
| { |
| long modelDataSize = statBuffer.st_size; |
| if (modelDataSize > 0) |
| { |
| std::vector<uint8_t> modelData(modelDataSize); |
| pread(cachedFd, modelData.data(), modelData.size(), 0); |
| calculatedHashValue ^= Hash(modelData); |
| |
| if (backend == armnn::Compute::GpuAcc) |
| { |
| gpuAccCachedFd = cachedFd; |
| } |
| } |
| } |
| index += numberOfCacheFiles; |
| } |
| } |
| } |
| |
| std::vector<uint8_t> calculatedHashData(sizeof(calculatedHashValue)); |
| ::memcpy(calculatedHashData.data(), &calculatedHashValue, sizeof(calculatedHashValue)); |
| if (hashValue != calculatedHashData) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): ValidateHash() failed!"; |
| } |
| |
| // Deserialize the network.. |
| armnn::INetworkPtr network = armnn::INetworkPtr(nullptr, [](armnn::INetwork*){}); |
| try |
| { |
| network = armnnDeserializer::IDeserializer::Create()->CreateNetworkFromBinary(dataCacheData); |
| } |
| catch (std::exception&) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) |
| << "ArmnnDriverImpl::prepareModelFromCache(): Exception caught from Deserializer!"; |
| } |
| |
| // Optimize the network |
| armnn::IOptimizedNetworkPtr optNet(nullptr, nullptr); |
| armnn::OptimizerOptionsOpaque OptOptions; |
| OptOptions.SetReduceFp32ToFp16(float32ToFloat16); |
| OptOptions.SetProfilingEnabled(options.IsGpuProfilingEnabled()); |
| |
| armnn::BackendOptions gpuAcc("GpuAcc", |
| { |
| { "FastMathEnabled", options.IsFastMathEnabled() }, |
| { "SaveCachedNetwork", false }, |
| { "CachedNetworkFilePath", options.GetCachedNetworkFilePath() }, |
| { "MLGOTuningFilePath", options.GetClMLGOTunedParametersFile() }, |
| { "CachedFileDescriptor", gpuAccCachedFd } |
| }); |
| |
| armnn::BackendOptions cpuAcc("CpuAcc", |
| { |
| { "FastMathEnabled", options.IsFastMathEnabled() }, |
| { "NumberOfThreads", options.GetNumberOfThreads() } |
| }); |
| OptOptions.AddModelOption(gpuAcc); |
| OptOptions.AddModelOption(cpuAcc); |
| |
| std::vector<std::string> errMessages; |
| try |
| { |
| optNet = armnn::Optimize(*network.get(), |
| options.GetBackends(), |
| runtime->GetDeviceSpec(), |
| OptOptions, |
| errMessages); |
| } |
| catch (std::exception& e) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << e.what(); |
| } |
| |
| // Check that the optimized network is valid. |
| if (!optNet) |
| { |
| std::stringstream message; |
| message << "Invalid optimized network"; |
| for (const std::string& msg : errMessages) |
| { |
| message << "\n" << msg; |
| } |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << message.str(); |
| } |
| |
| // Export the optimized network graph to a dot file if an output dump directory |
| // has been specified in the drivers' arguments. |
| std::string dotGraphFileName = ExportNetworkGraphToDotFile(*optNet, |
| options.GetRequestInputsAndOutputsDumpDir()); |
| |
| // Load it into the runtime. |
| armnn::NetworkId netId = 0; |
| std::string msg; |
| armnn::INetworkProperties networkProperties(options.isAsyncModelExecutionEnabled(), |
| MemorySource::Undefined, |
| MemorySource::Undefined, |
| options.IsGpuProfilingEnabled()); |
| try |
| { |
| if (runtime->LoadNetwork(netId, move(optNet), msg, networkProperties) != armnn::Status::Success) |
| { |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << "Network could not be loaded"; |
| } |
| } |
| catch (std::exception& e) |
| { |
| std::stringstream message; |
| message << "Exception (" << e.what()<< ") caught from LoadNetwork."; |
| return NN_ERROR(ErrorStatus::GENERAL_FAILURE) << message.str(); |
| } |
| |
| auto preparedModel = std::make_shared<const ArmnnPreparedModel>(netId, |
| runtime.get(), |
| options.GetRequestInputsAndOutputsDumpDir(), |
| options.IsGpuProfilingEnabled(), |
| Priority::MEDIUM, |
| true); |
| return std::move(preparedModel); |
| } |
| |
| const Capabilities& ArmnnDriverImpl::GetCapabilities(const armnn::IRuntimePtr& runtime) |
| { |
| VLOG(DRIVER) << "ArmnnDriverImpl::GetCapabilities()"; |
| static const Capabilities theCapabilities = GenerateCapabilities(); |
| return theCapabilities; |
| } |
| |
| } // namespace armnn_driver |
