| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #define LOG_TAG "ArmnnDriver" |
| |
| #include "ArmnnPreparedModel_1_2.hpp" |
| #include "Utils.hpp" |
| |
| #include <boost/format.hpp> |
| #include <log/log.h> |
| #include <OperationsUtils.h> |
| #include <ExecutionBurstServer.h> |
| #include <ValidateHal.h> |
| |
| #include <cassert> |
| #include <cinttypes> |
| |
| using namespace android; |
| using namespace android::hardware; |
| |
| namespace { |
| |
| static const Timing g_NoTiming = {.timeOnDevice = UINT64_MAX, .timeInDriver = UINT64_MAX}; |
| using namespace armnn_driver; |
| using TimePoint = std::chrono::steady_clock::time_point; |
| |
| TimePoint Now() |
| { |
| return std::chrono::steady_clock::now(); |
| } |
| |
| unsigned long MicrosecondsDuration(TimePoint endPoint, TimePoint startPoint) |
| { |
| return static_cast<unsigned long>(std::chrono::duration_cast<std::chrono::microseconds>( |
| endPoint - startPoint).count()); |
| } |
| |
| void NotifyCallbackAndCheck(const ::android::sp<V1_0::IExecutionCallback>& callback, |
| ErrorStatus errorStatus, |
| std::vector<OutputShape>, |
| const Timing, |
| std::string callingFunction) |
| { |
| Return<void> returned = callback->notify(errorStatus); |
| // This check is required, if the callback fails and it isn't checked it will bring down the service |
| if (!returned.isOk()) |
| { |
| ALOGE("ArmnnDriver::%s: hidl callback failed to return properly: %s", |
| callingFunction.c_str(), returned.description().c_str()); |
| } |
| } |
| |
| void NotifyCallbackAndCheck(const ::android::sp<V1_2::IExecutionCallback>& callback, |
| ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing timing, |
| std::string callingFunction) |
| { |
| Return<void> returned = callback->notify_1_2(errorStatus, outputShapes, timing); |
| // This check is required, if the callback fails and it isn't checked it will bring down the service |
| if (!returned.isOk()) |
| { |
| ALOGE("ArmnnDriver::%s: hidl callback failed to return properly: %s", |
| callingFunction.c_str(), returned.description().c_str()); |
| } |
| } |
| |
| bool ValidateRequestArgument(const RequestArgument& requestArg, const armnn::TensorInfo& tensorInfo) |
| { |
| if (requestArg.dimensions.size() != 0) |
| { |
| if (requestArg.dimensions.size() != tensorInfo.GetNumDimensions()) |
| { |
| ALOGE("Mismatched dimensions (request argument: %zu, expected: %u)", |
| requestArg.dimensions.size(), tensorInfo.GetNumDimensions()); |
| return false; |
| } |
| |
| for (unsigned int d = 0; d < tensorInfo.GetNumDimensions(); ++d) |
| { |
| if (requestArg.dimensions[d] != tensorInfo.GetShape()[d]) |
| { |
| ALOGE("Mismatched size for dimension %d (request argument: %u, expected %u)", |
| d, requestArg.dimensions[d], tensorInfo.GetShape()[d]); |
| return false; |
| } |
| } |
| } |
| |
| return true; |
| } |
| |
| armnn::Tensor GetTensorForRequestArgument(const RequestArgument& requestArg, |
| const armnn::TensorInfo& tensorInfo, |
| const std::vector<::android::nn::RunTimePoolInfo>& requestPools) |
| { |
| if (!ValidateRequestArgument(requestArg, tensorInfo)) |
| { |
| return armnn::Tensor(); |
| } |
| |
| return armnn::Tensor(tensorInfo, GetMemoryFromPool(requestArg.location, requestPools)); |
| } |
| |
| inline std::string BuildTensorName(const char* tensorNamePrefix, std::size_t index) |
| { |
| return tensorNamePrefix + std::to_string(index); |
| } |
| |
| } // anonymous namespace |
| |
| using namespace android::hardware; |
| |
| namespace armnn_driver |
| { |
| |
| template<typename HalVersion> |
| RequestThread<ArmnnPreparedModel_1_2, HalVersion, ArmnnCallback_1_2> |
| ArmnnPreparedModel_1_2<HalVersion>::m_RequestThread; |
| |
| template<typename HalVersion> |
| template<typename TensorBindingCollection> |
| void ArmnnPreparedModel_1_2<HalVersion>::DumpTensorsIfRequired(char const* tensorNamePrefix, |
| const TensorBindingCollection& tensorBindings) |
| { |
| if (!m_RequestInputsAndOutputsDumpDir.empty()) |
| { |
| const std::string requestName = boost::str(boost::format("%1%_%2%.dump") % m_NetworkId % m_RequestCount); |
| for (std::size_t i = 0u; i < tensorBindings.size(); ++i) |
| { |
| DumpTensor(m_RequestInputsAndOutputsDumpDir, |
| requestName, |
| BuildTensorName(tensorNamePrefix, i), |
| tensorBindings[i].second); |
| } |
| } |
| } |
| |
| template<typename HalVersion> |
| ArmnnPreparedModel_1_2<HalVersion>::ArmnnPreparedModel_1_2(armnn::NetworkId networkId, |
| armnn::IRuntime* runtime, |
| const V1_2::Model& model, |
| const std::string& requestInputsAndOutputsDumpDir, |
| const bool gpuProfilingEnabled) |
| : m_NetworkId(networkId) |
| , m_Runtime(runtime) |
| , m_Model(model) |
| , m_RequestCount(0) |
| , m_RequestInputsAndOutputsDumpDir(requestInputsAndOutputsDumpDir) |
| , m_GpuProfilingEnabled(gpuProfilingEnabled) |
| { |
| // Enable profiling if required. |
| m_Runtime->GetProfiler(m_NetworkId)->EnableProfiling(m_GpuProfilingEnabled); |
| } |
| |
| template<typename HalVersion> |
| ArmnnPreparedModel_1_2<HalVersion>::~ArmnnPreparedModel_1_2() |
| { |
| // Get a hold of the profiler used by this model. |
| std::shared_ptr<armnn::IProfiler> profiler = m_Runtime->GetProfiler(m_NetworkId); |
| |
| // Unload the network associated with this model. |
| m_Runtime->UnloadNetwork(m_NetworkId); |
| |
| // Dump the profiling info to a file if required. |
| DumpJsonProfilingIfRequired(m_GpuProfilingEnabled, m_RequestInputsAndOutputsDumpDir, m_NetworkId, profiler.get()); |
| } |
| |
| template<typename HalVersion> |
| Return <ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::execute(const Request& request, |
| const ::android::sp<V1_0::IExecutionCallback>& callback) |
| { |
| if (callback.get() == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_2::execute invalid callback passed"); |
| return ErrorStatus::INVALID_ARGUMENT; |
| } |
| |
| auto cb = [callback](ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing& timing, |
| std::string callingFunction) |
| { |
| NotifyCallbackAndCheck(callback, errorStatus, outputShapes, timing, callingFunction); |
| }; |
| |
| return Execute(request, MeasureTiming::NO, cb); |
| } |
| |
| template<typename HalVersion> |
| Return <ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::execute_1_2(const Request& request, |
| MeasureTiming measureTiming, |
| const sp<V1_2::IExecutionCallback>& callback) |
| { |
| if (callback.get() == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_2::execute_1_2 invalid callback passed"); |
| return ErrorStatus::INVALID_ARGUMENT; |
| } |
| |
| auto cb = [callback](ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing& timing, |
| std::string callingFunction) |
| { |
| NotifyCallbackAndCheck(callback, errorStatus, outputShapes, timing, callingFunction); |
| }; |
| |
| return Execute(request, measureTiming, cb); |
| } |
| |
| template<typename HalVersion> |
| Return<void> ArmnnPreparedModel_1_2<HalVersion>::executeSynchronously(const Request& request, |
| MeasureTiming measureTiming, |
| executeSynchronously_cb cb) |
| { |
| ALOGV("ArmnnPreparedModel_1_2::executeSynchronously(): %s", GetModelSummary(m_Model).c_str()); |
| m_RequestCount++; |
| |
| if (cb == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_2::executeSynchronously invalid callback passed"); |
| return Void(); |
| } |
| |
| TimePoint driverStart, driverEnd, deviceStart, deviceEnd; |
| |
| if (measureTiming == MeasureTiming::YES) |
| { |
| driverStart = Now(); |
| } |
| |
| if (!android::nn::validateRequest(request, m_Model)) |
| { |
| ALOGE("ArmnnPreparedModel_1_2::executeSynchronously invalid request model"); |
| cb(ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming); |
| return Void(); |
| } |
| |
| // allocate the tensors on the heap, as they are passed to the request thread |
| auto pInputTensors = std::make_shared<armnn::InputTensors>(); |
| auto pOutputTensors = std::make_shared<armnn::OutputTensors>(); |
| |
| // map the memory pool into shared pointers |
| // use a shared memory pools vector on the heap, as it is passed to the request thread |
| auto pMemPools = std::make_shared<std::vector<android::nn::RunTimePoolInfo>>(); |
| |
| if (!setRunTimePoolInfosFromHidlMemories(pMemPools.get(), request.pools)) |
| { |
| cb(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming); |
| return Void(); |
| } |
| std::vector<OutputShape> outputShapes(request.outputs.size()); |
| |
| try |
| { |
| pInputTensors->reserve(request.inputs.size()); |
| for (unsigned int i = 0; i < request.inputs.size(); i++) |
| { |
| const auto& inputArg = request.inputs[i]; |
| |
| const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i); |
| const armnn::Tensor inputTensor = GetTensorForRequestArgument(inputArg, inputTensorInfo, *pMemPools); |
| |
| if (inputTensor.GetMemoryArea() == nullptr) |
| { |
| ALOGE("Cannot execute request. Error converting request input %u to tensor", i); |
| cb(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming); |
| return Void(); |
| } |
| |
| pInputTensors->emplace_back(i, inputTensor); |
| } |
| pOutputTensors->reserve(request.outputs.size()); |
| |
| for (unsigned int i = 0; i < request.outputs.size(); i++) |
| { |
| const auto& outputArg = request.outputs[i]; |
| |
| const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i); |
| const armnn::Tensor outputTensor = GetTensorForRequestArgument(outputArg, outputTensorInfo, *pMemPools); |
| |
| if (outputTensor.GetMemoryArea() == nullptr) |
| { |
| ALOGE("Cannot execute request. Error converting request output %u to tensor", i); |
| cb(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming); |
| return Void(); |
| } |
| const size_t outputSize = outputTensorInfo.GetNumBytes(); |
| const size_t bufferSize = pMemPools->at(outputArg.location.poolIndex).getHidlMemory().size(); |
| |
| hidl_vec<uint32_t> dimensions; |
| |
| armnn::TensorShape tensorShape = outputTensorInfo.GetShape(); |
| const unsigned int numDims = tensorShape.GetNumDimensions(); |
| dimensions.resize(numDims); |
| |
| for (unsigned int outputIdx = 0u; outputIdx < numDims; ++outputIdx) |
| { |
| dimensions[outputIdx] = tensorShape[outputIdx]; |
| } |
| outputShapes[i].dimensions = dimensions; |
| outputShapes[i].isSufficient = bufferSize >= outputSize; |
| |
| if (bufferSize < outputSize) |
| { |
| ALOGW("ArmnnPreparedModel_1_2::Execute failed"); |
| cb(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, outputShapes, g_NoTiming); |
| return Void(); |
| } |
| |
| pOutputTensors->emplace_back(i, outputTensor); |
| } |
| } |
| catch (armnn::Exception& e) |
| { |
| ALOGW("armnn::Exception caught while preparing for EnqueueWorkload: %s", e.what()); |
| cb(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming); |
| return Void(); |
| } |
| ALOGV("ArmnnPreparedModel_1_2::executeSynchronously() before Execution"); |
| |
| DumpTensorsIfRequired("Input", *pInputTensors); |
| // run it |
| try |
| { |
| if (measureTiming == MeasureTiming::YES) |
| { |
| deviceStart = Now(); |
| } |
| |
| armnn::Status status = m_Runtime->EnqueueWorkload(m_NetworkId, *pInputTensors, *pOutputTensors); |
| |
| if (measureTiming == MeasureTiming::YES) |
| { |
| deviceEnd = Now(); |
| } |
| |
| if (status != armnn::Status::Success) |
| { |
| ALOGW("EnqueueWorkload failed"); |
| cb(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming); |
| return Void(); |
| } |
| } |
| catch (armnn::Exception& e) |
| { |
| ALOGW("armnn::Exception caught from EnqueueWorkload: %s", e.what()); |
| cb(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming); |
| return Void(); |
| } |
| |
| DumpTensorsIfRequired("Output", *pOutputTensors); |
| |
| // Commit output buffers. |
| // Note that we update *all* pools, even if they aren't actually used as outputs - |
| // this is simpler and is what the CpuExecutor does. |
| for (android::nn::RunTimePoolInfo& pool : *pMemPools) |
| { |
| pool.update(); |
| } |
| ALOGV("ArmnnPreparedModel_1_2::executeSynchronously() after Execution"); |
| |
| if (measureTiming == MeasureTiming::YES) |
| { |
| driverEnd = Now(); |
| Timing timing; |
| timing.timeOnDevice = MicrosecondsDuration(deviceEnd, deviceStart); |
| timing.timeInDriver = MicrosecondsDuration(driverEnd, driverStart); |
| ALOGV("ArmnnPreparedModel_1_2::executeSynchronously timing Device = %lu Driver = %lu", timing.timeOnDevice, |
| timing.timeInDriver); |
| cb(ErrorStatus::NONE, outputShapes, timing); |
| } |
| else |
| { |
| cb(ErrorStatus::NONE, outputShapes, g_NoTiming); |
| } |
| return Void(); |
| } |
| |
| class ArmnnBurstExecutorWithCache : public ExecutionBurstServer::IBurstExecutorWithCache { |
| public: |
| ArmnnBurstExecutorWithCache(IPreparedModel* preparedModel) |
| : m_PreparedModel(preparedModel) |
| {} |
| |
| bool isCacheEntryPresent(int32_t slot) const override |
| { |
| const auto it = m_MemoryCache.find(slot); |
| return (it != m_MemoryCache.end()) && it->second.valid(); |
| } |
| |
| void addCacheEntry(const hidl_memory& memory, int32_t slot) override |
| { |
| m_MemoryCache[slot] = memory; |
| } |
| |
| void removeCacheEntry(int32_t slot) override |
| { |
| m_MemoryCache.erase(slot); |
| } |
| |
| std::tuple<ErrorStatus, hidl_vec<OutputShape>, Timing> execute( |
| const Request& request, const std::vector<int32_t>& slots, |
| MeasureTiming measure) override |
| { |
| ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache::execute"); |
| hidl_vec<hidl_memory> pools(slots.size()); |
| |
| std::transform(slots.begin(), slots.end(), pools.begin(), [this](int32_t slot) |
| { |
| return m_MemoryCache[slot]; |
| }); |
| |
| Request fullRequest = request; |
| fullRequest.pools = std::move(pools); |
| |
| // Setup Callback |
| ErrorStatus returnedStatus = ErrorStatus::GENERAL_FAILURE; |
| hidl_vec<OutputShape> returnedOutputShapes; |
| Timing returnedTiming; |
| auto cb = [&returnedStatus, &returnedOutputShapes, &returnedTiming](ErrorStatus status, |
| const hidl_vec<OutputShape>& outputShapes, |
| const Timing& timing) |
| { |
| returnedStatus = status; |
| returnedOutputShapes = outputShapes; |
| returnedTiming = timing; |
| }; |
| |
| // Execute |
| ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache executing"); |
| const Return<void> ret = m_PreparedModel->executeSynchronously(fullRequest, measure, cb); |
| |
| if (!ret.isOk() || returnedStatus != ErrorStatus::NONE) |
| { |
| ALOGE("ArmnnPreparedModel_1_2::BurstExecutorWithCache::error executing"); |
| } |
| return std::make_tuple(returnedStatus, std::move(returnedOutputShapes), returnedTiming); |
| } |
| |
| private: |
| IPreparedModel* const m_PreparedModel; |
| std::map<int, hidl_memory> m_MemoryCache; |
| }; |
| |
| |
| template<typename HalVersion> |
| Return<void> ArmnnPreparedModel_1_2<HalVersion>::configureExecutionBurst( |
| const sp<V1_2::IBurstCallback>& callback, |
| const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel, |
| const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel, |
| V1_2::IPreparedModel::configureExecutionBurst_cb cb) |
| { |
| ALOGV("ArmnnPreparedModel_1_2::configureExecutionBurst"); |
| const std::shared_ptr<ArmnnBurstExecutorWithCache> executorWithCache = |
| std::make_shared<ArmnnBurstExecutorWithCache>(this); |
| const sp<V1_2::IBurstContext> burst = ExecutionBurstServer::create(callback, |
| requestChannel, |
| resultChannel, |
| executorWithCache); |
| |
| if (burst == nullptr) |
| { |
| cb(ErrorStatus::GENERAL_FAILURE, {}); |
| } |
| else |
| { |
| cb(ErrorStatus::NONE, burst); |
| } |
| return Void(); |
| } |
| |
| template<typename HalVersion> |
| void ArmnnPreparedModel_1_2<HalVersion>::ExecuteGraph( |
| std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools, |
| std::shared_ptr<armnn::InputTensors>& pInputTensors, |
| std::shared_ptr<armnn::OutputTensors>& pOutputTensors, |
| ArmnnCallback_1_2 cb) |
| { |
| ALOGV("ArmnnPreparedModel_1_2::ExecuteGraph(...)"); |
| |
| TimePoint driverEnd, deviceStart, deviceEnd; |
| |
| DumpTensorsIfRequired("Input", *pInputTensors); |
| |
| std::vector<std::pair<int, armnn::Tensor> > outputTensors = *pOutputTensors.get(); |
| std::vector<OutputShape> outputShapes(outputTensors.size()); |
| |
| for (unsigned int i = 0; i < outputTensors.size(); i++) |
| { |
| std::pair<int, armnn::Tensor> outputTensorPair = outputTensors[i]; |
| const armnn::Tensor outputTensor = outputTensorPair.second; |
| const armnn::TensorInfo outputTensorInfo = outputTensor.GetInfo(); |
| |
| hidl_vec<uint32_t> dimensions; |
| |
| armnn::TensorShape tensorShape = outputTensorInfo.GetShape(); |
| const unsigned int numDims = tensorShape.GetNumDimensions(); |
| dimensions.resize(numDims); |
| |
| for (unsigned int outputIdx = 0u; outputIdx < numDims; ++outputIdx) |
| { |
| dimensions[outputIdx] = tensorShape[outputIdx]; |
| } |
| outputShapes[i].dimensions = dimensions; |
| outputShapes[i].isSufficient = true; |
| } |
| |
| // run it |
| try |
| { |
| if (cb.measureTiming == MeasureTiming::YES) |
| { |
| deviceStart = Now(); |
| } |
| |
| armnn::Status status = m_Runtime->EnqueueWorkload(m_NetworkId, *pInputTensors, *pOutputTensors); |
| |
| if (cb.measureTiming == MeasureTiming::YES) |
| { |
| deviceEnd = Now(); |
| } |
| if (status != armnn::Status::Success) |
| { |
| ALOGW("EnqueueWorkload failed"); |
| cb.callback(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, |
| "ArmnnPreparedModel_1_2::ExecuteGraph"); |
| return; |
| } |
| } |
| catch (armnn::Exception& e) |
| { |
| ALOGW("armnn::Exception caught from EnqueueWorkload: %s", e.what()); |
| cb.callback(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, |
| "ArmnnPreparedModel_1_2::ExecuteGraph"); |
| return; |
| } |
| |
| DumpTensorsIfRequired("Output", *pOutputTensors); |
| |
| // Commit output buffers. |
| // Note that we update *all* pools, even if they aren't actually used as outputs - |
| // this is simpler and is what the CpuExecutor does. |
| for (android::nn::RunTimePoolInfo& pool : *pMemPools) |
| { |
| pool.update(); |
| } |
| |
| if (cb.measureTiming == MeasureTiming::YES) |
| { |
| driverEnd = Now(); |
| Timing timing; |
| timing.timeOnDevice = MicrosecondsDuration(deviceEnd, deviceStart); |
| timing.timeInDriver = MicrosecondsDuration(driverEnd, cb.driverStart); |
| cb.callback(ErrorStatus::NONE, outputShapes, timing, "ExecuteGraph"); |
| } else { |
| cb.callback(ErrorStatus::NONE, outputShapes, g_NoTiming, "ExecuteGraph"); |
| } |
| } |
| |
| template<typename HalVersion> |
| bool ArmnnPreparedModel_1_2<HalVersion>::ExecuteWithDummyInputs() |
| { |
| std::vector<std::vector<char>> storage; |
| armnn::InputTensors inputTensors; |
| for (unsigned int i = 0; i < m_Model.inputIndexes.size(); i++) |
| { |
| const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i); |
| storage.emplace_back(inputTensorInfo.GetNumBytes()); |
| const armnn::ConstTensor inputTensor(inputTensorInfo, storage.back().data()); |
| |
| inputTensors.emplace_back(i, inputTensor); |
| } |
| |
| armnn::OutputTensors outputTensors; |
| for (unsigned int i = 0; i < m_Model.outputIndexes.size(); i++) |
| { |
| const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i); |
| storage.emplace_back(outputTensorInfo.GetNumBytes()); |
| const armnn::Tensor outputTensor(outputTensorInfo, storage.back().data()); |
| |
| outputTensors.emplace_back(i, outputTensor); |
| } |
| |
| try |
| { |
| armnn::Status status = m_Runtime->EnqueueWorkload(m_NetworkId, inputTensors, outputTensors); |
| if (status != armnn::Status::Success) |
| { |
| ALOGW("ExecuteWithDummyInputs: EnqueueWorkload failed"); |
| return false; |
| } |
| } |
| catch (armnn::Exception& e) |
| { |
| ALOGW("ExecuteWithDummyInputs: armnn::Exception caught from EnqueueWorkload: %s", e.what()); |
| return false; |
| } |
| return true; |
| } |
| |
| template<typename HalVersion> |
| Return <ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::Execute(const Request& request, |
| MeasureTiming measureTiming, |
| armnnExecuteCallback_1_2 callback) |
| { |
| TimePoint driverStart; |
| |
| if (measureTiming == MeasureTiming::YES) |
| { |
| driverStart = Now(); |
| } |
| |
| ALOGV("ArmnnPreparedModel_1_2::execute(): %s", GetModelSummary(m_Model).c_str()); |
| m_RequestCount++; |
| |
| if (!android::nn::validateRequest(request, m_Model)) |
| { |
| callback(ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming, "ArmnnPreparedModel_1_2::execute"); |
| return ErrorStatus::INVALID_ARGUMENT; |
| } |
| |
| if (!m_RequestInputsAndOutputsDumpDir.empty()) |
| { |
| ALOGD("Dumping inputs and outputs for request %" PRIuPTR, reinterpret_cast<std::uintptr_t>(&callback)); |
| } |
| |
| // allocate the tensors on the heap, as they are passed to the request thread |
| auto pInputTensors = std::make_shared<armnn::InputTensors>(); |
| auto pOutputTensors = std::make_shared<armnn::OutputTensors>(); |
| |
| // map the memory pool into shared pointers |
| // use a shared memory pools vector on the heap, as it is passed to the request thread |
| auto pMemPools = std::make_shared<std::vector<android::nn::RunTimePoolInfo>>(); |
| |
| if (!setRunTimePoolInfosFromHidlMemories(pMemPools.get(), request.pools)) |
| { |
| callback(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_2::execute"); |
| return ErrorStatus::GENERAL_FAILURE; |
| } |
| |
| // add the inputs and outputs with their data |
| try |
| { |
| pInputTensors->reserve(request.inputs.size()); |
| for (unsigned int i = 0; i < request.inputs.size(); i++) |
| { |
| const auto& inputArg = request.inputs[i]; |
| |
| const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i); |
| const armnn::Tensor inputTensor = GetTensorForRequestArgument(inputArg, inputTensorInfo, *pMemPools); |
| |
| if (inputTensor.GetMemoryArea() == nullptr) |
| { |
| ALOGE("Cannot execute request. Error converting request input %u to tensor", i); |
| callback(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_2::execute"); |
| return ErrorStatus::GENERAL_FAILURE; |
| } |
| |
| pInputTensors->emplace_back(i, inputTensor); |
| } |
| |
| pOutputTensors->reserve(request.outputs.size()); |
| std::vector<OutputShape> outputShapes(request.outputs.size()); |
| |
| for (unsigned int i = 0; i < request.outputs.size(); i++) |
| { |
| const auto& outputArg = request.outputs[i]; |
| |
| const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i); |
| const armnn::Tensor outputTensor = GetTensorForRequestArgument(outputArg, outputTensorInfo, *pMemPools); |
| if (outputTensor.GetMemoryArea() == nullptr) |
| { |
| ALOGE("Cannot execute request. Error converting request output %u to tensor", i); |
| callback(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_2::execute"); |
| return ErrorStatus::GENERAL_FAILURE; |
| } |
| |
| const size_t outputSize = outputTensorInfo.GetNumBytes(); |
| const size_t bufferSize = pMemPools->at(outputArg.location.poolIndex).getHidlMemory().size(); |
| pOutputTensors->emplace_back(i, outputTensor); |
| |
| hidl_vec<uint32_t> dimensions; |
| |
| armnn::TensorShape tensorShape = outputTensorInfo.GetShape(); |
| const unsigned int numDims = tensorShape.GetNumDimensions(); |
| dimensions.resize(numDims); |
| |
| for (unsigned int outputIdx = 0u; outputIdx < numDims; ++outputIdx) |
| { |
| dimensions[outputIdx] = tensorShape[outputIdx]; |
| } |
| outputShapes[i].dimensions = dimensions; |
| outputShapes[i].isSufficient = bufferSize >= outputSize; |
| |
| if (bufferSize < outputSize) |
| { |
| ALOGW("ArmnnPreparedModel_1_2::Execute failed"); |
| callback(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, |
| outputShapes, |
| g_NoTiming, |
| "ArmnnPreparedModel_1_2::Execute"); |
| return ErrorStatus::NONE; |
| } |
| } |
| } |
| catch (armnn::Exception& e) |
| { |
| ALOGW("armnn::Exception caught while preparing for EnqueueWorkload: %s", e.what()); |
| callback(ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_2::execute"); |
| return ErrorStatus::GENERAL_FAILURE; |
| } |
| |
| ALOGV("ArmnnPreparedModel_1_2::execute(...) before PostMsg"); |
| // post the request for asynchronous execution |
| ArmnnCallback_1_2 armnnCb; |
| armnnCb.callback = callback; |
| armnnCb.measureTiming = measureTiming; |
| armnnCb.driverStart = driverStart; |
| m_RequestThread.PostMsg(this, pMemPools, pInputTensors, pOutputTensors, armnnCb); |
| ALOGV("ArmnnPreparedModel_1_2::execute(...) after PostMsg"); |
| return ErrorStatus::NONE; |
| } |
| |
| #ifdef ARMNN_ANDROID_NN_V1_2 |
| template class ArmnnPreparedModel_1_2<hal_1_2::HalPolicy>; |
| #endif |
| |
| } // namespace armnn_driver |
