| // |
| // Copyright © 2020 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #define LOG_TAG "ArmnnDriver" |
| |
| #include "ArmnnPreparedModel_1_3.hpp" |
| #include "Utils.hpp" |
| |
| #include <Utils.h> |
| #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, |
| V1_3::ErrorStatus errorStatus, |
| std::vector<OutputShape>, |
| const Timing, |
| std::string callingFunction) |
| { |
| Return<void> returned = callback->notify(convertToV1_0(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, |
| V1_3::ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing timing, |
| std::string callingFunction) |
| { |
| Return<void> returned = callback->notify_1_2(convertToV1_0(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()); |
| } |
| } |
| |
| void NotifyCallbackAndCheck(const ::android::sp<V1_3::IExecutionCallback>& callback, |
| V1_3::ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing timing, |
| std::string callingFunction) |
| { |
| Return<void> returned = callback->notify_1_3(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_3, HalVersion, CallbackContext_1_3> |
| ArmnnPreparedModel_1_3<HalVersion>::m_RequestThread; |
| |
| template<typename HalVersion> |
| template<typename TensorBindingCollection> |
| void ArmnnPreparedModel_1_3<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_3<HalVersion>::ArmnnPreparedModel_1_3(armnn::NetworkId networkId, |
| armnn::IRuntime* runtime, |
| const V1_3::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_3<HalVersion>::~ArmnnPreparedModel_1_3() |
| { |
| // 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 <V1_0::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::execute(const V1_0::Request& request, |
| const ::android::sp<V1_0::IExecutionCallback>& callback) |
| { |
| if (callback.get() == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::execute invalid callback passed"); |
| return V1_0::ErrorStatus::INVALID_ARGUMENT; |
| } |
| |
| auto cb = [callback](V1_3::ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing& timing, |
| std::string callingFunction) |
| { |
| NotifyCallbackAndCheck(callback, errorStatus, outputShapes, timing, callingFunction); |
| }; |
| |
| |
| return convertToV1_0(Execute(convertToV1_3(request), MeasureTiming::NO, cb)); |
| } |
| |
| template<typename HalVersion> |
| Return <V1_0::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::execute_1_2( |
| const V1_0::Request& request, |
| MeasureTiming measureTiming, |
| const sp<V1_2::IExecutionCallback>& callback) |
| { |
| if (callback.get() == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::execute_1_2 invalid callback passed"); |
| return V1_0::ErrorStatus::INVALID_ARGUMENT; |
| } |
| |
| auto cb = [callback](V1_3::ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing& timing, |
| std::string callingFunction) |
| { |
| NotifyCallbackAndCheck(callback, errorStatus, outputShapes, timing, callingFunction); |
| }; |
| |
| return convertToV1_0(Execute(convertToV1_3(request), measureTiming, cb)); |
| } |
| |
| template<typename HalVersion> |
| Return <V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::execute_1_3( |
| const V1_3::Request& request, |
| MeasureTiming measureTiming, |
| const V1_3::OptionalTimePoint&, |
| const V1_3::OptionalTimeoutDuration&, |
| const sp<V1_3::IExecutionCallback>& callback) |
| { |
| if (callback.get() == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::execute_1_3 invalid callback passed"); |
| return V1_3::ErrorStatus::INVALID_ARGUMENT; |
| } |
| |
| auto cb = [callback](V1_3::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_3<HalVersion>::executeFenced(const V1_3::Request&, |
| const hidl_vec<hidl_handle>&, |
| MeasureTiming, |
| const OptionalTimePoint&, |
| const OptionalTimeoutDuration&, |
| const OptionalTimeoutDuration&, |
| executeFenced_cb cb) |
| { |
| cb(ErrorStatus::INVALID_ARGUMENT, hidl_handle(nullptr), nullptr); |
| return Void(); |
| } |
| |
| template<typename HalVersion> |
| Return<V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForInputs( |
| armnn::InputTensors& inputs, |
| const V1_3::Request& request, |
| const std::vector<android::nn::RunTimePoolInfo>& memPools) |
| { |
| inputs.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, memPools); |
| |
| if (inputTensor.GetMemoryArea() == nullptr) |
| { |
| ALOGE("Cannot execute request. Error converting request input %u to tensor", i); |
| return V1_3::ErrorStatus::GENERAL_FAILURE; |
| } |
| |
| inputs.emplace_back(i, inputTensor); |
| } |
| |
| return V1_3::ErrorStatus::NONE; |
| } |
| |
| template<typename HalVersion> |
| Return<V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForOutputs( |
| armnn::OutputTensors& outputs, |
| std::vector<OutputShape> &outputShapes, |
| const V1_3::Request& request, |
| const std::vector<android::nn::RunTimePoolInfo>& memPools) |
| { |
| outputs.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, memPools); |
| if (outputTensor.GetMemoryArea() == nullptr) |
| { |
| ALOGE("Cannot execute request. Error converting request output %u to tensor", i); |
| return V1_3::ErrorStatus::GENERAL_FAILURE; |
| } |
| |
| const size_t outputSize = outputTensorInfo.GetNumBytes(); |
| const size_t bufferSize = memPools.at(outputArg.location.poolIndex).getHidlMemory().size(); |
| if (bufferSize < outputSize) |
| { |
| ALOGW("ArmnnPreparedModel_1_3::Execute failed"); |
| return V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE; |
| } |
| |
| outputs.emplace_back(i, outputTensor); |
| outputShapes[i] = ComputeShape(outputTensorInfo); |
| } |
| |
| return V1_3::ErrorStatus::NONE; |
| } |
| |
| template<typename HalVersion> |
| std::tuple<V1_3::ErrorStatus, hidl_vec<OutputShape>, Timing, std::string> |
| ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForIO(armnn::InputTensors& inputs, |
| armnn::OutputTensors& outputs, |
| std::vector<android::nn::RunTimePoolInfo>& memPools, |
| const V1_3::Request& request) |
| { |
| if (!setRunTimePoolInfosFromMemoryPools(&memPools, request.pools)) |
| { |
| return {ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"}; |
| } |
| |
| // add the inputs and outputs with their data |
| try |
| { |
| if (PrepareMemoryForInputs(inputs, request, memPools) != V1_3::ErrorStatus::NONE) |
| { |
| return {ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"}; |
| } |
| |
| std::vector<OutputShape> outputShapes(request.outputs.size()); |
| |
| auto errorStatus = PrepareMemoryForOutputs(outputs, outputShapes, request, memPools); |
| if (errorStatus != V1_3::ErrorStatus::NONE) |
| { |
| return {errorStatus, outputShapes, g_NoTiming, "ArmnnPreparedModel_1_3::execute"}; |
| } |
| } |
| catch (armnn::Exception& e) |
| { |
| ALOGW("armnn::Exception caught while preparing for EnqueueWorkload: %s", e.what()); |
| return {ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"}; |
| } |
| catch (std::exception& e) |
| { |
| ALOGE("std::exception caught while preparing for EnqueueWorkload: %s", e.what()); |
| return {ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"}; |
| } |
| |
| return {V1_3::ErrorStatus::NONE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"}; |
| } |
| |
| template<typename HalVersion> |
| template<typename CallbackContext> |
| Return<void> ArmnnPreparedModel_1_3<HalVersion>::ExecuteSynchronously(const V1_3::Request& request, |
| CallbackContext cbCtx) |
| { |
| if (cbCtx.ctx.measureTimings == MeasureTiming::YES) |
| { |
| cbCtx.ctx.driverStart = Now(); |
| } |
| |
| if (!android::nn::validateRequest(convertToV1_3(request), m_Model)) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model"); |
| cbCtx.callback(V1_3::ErrorStatus::INVALID_ARGUMENT, |
| {}, |
| g_NoTiming, |
| "ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model"); |
| return Void(); |
| } |
| |
| if (!android::nn::validateRequest(request, m_Model)) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model"); |
| cbCtx.callback(V1_3::ErrorStatus::INVALID_ARGUMENT, |
| {}, |
| g_NoTiming, |
| "ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model"); |
| return Void(); |
| } |
| |
| |
| // 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 memPools = std::make_shared<std::vector<android::nn::RunTimePoolInfo>>(); |
| |
| // allocate the tensors on the heap, as they are passed to the request thread |
| auto inputs = std::make_shared<armnn::InputTensors>(); |
| auto outputs = std::make_shared<armnn::OutputTensors>(); |
| |
| auto [status, outputShapes, timing, message] = PrepareMemoryForIO(*inputs, *outputs, *memPools, request); |
| if (status != V1_3::ErrorStatus::NONE) |
| { |
| cbCtx.callback(status, outputShapes, timing, message); |
| return Void(); |
| } |
| |
| ALOGV("ArmnnPreparedModel_1_3::ExecuteSynchronously() before Execution"); |
| |
| ExecuteGraph(memPools, *inputs, *outputs, cbCtx); |
| return Void(); |
| } |
| |
| template<typename HalVersion> |
| Return<void> ArmnnPreparedModel_1_3<HalVersion>::executeSynchronously(const V1_0::Request& request, |
| MeasureTiming measureTiming, |
| executeSynchronously_cb cb) |
| { |
| ALOGV("ArmnnPreparedModel_1_3::executeSynchronously(): %s", GetModelSummary(m_Model).c_str()); |
| m_RequestCount++; |
| |
| if (cb == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::executeSynchronously invalid callback passed"); |
| return Void(); |
| } |
| |
| auto cbWrapper = [cb](V1_3::ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing& timing, |
| std::string) |
| { |
| cb(convertToV1_0(errorStatus), outputShapes, timing); |
| }; |
| |
| CallbackContext_1_3 cbCtx; |
| cbCtx.callback = cbWrapper; |
| cbCtx.ctx.measureTimings = measureTiming; |
| |
| ExecuteSynchronously(convertToV1_3(request), cbCtx); |
| return Void(); |
| } |
| |
| template<typename HalVersion> |
| Return<void> ArmnnPreparedModel_1_3<HalVersion>::executeSynchronously_1_3( |
| const V1_3::Request& request, |
| MeasureTiming measureTiming, |
| const V1_3::OptionalTimePoint& deadline, |
| const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, |
| executeSynchronously_1_3_cb cb) |
| { |
| ALOGV("ArmnnPreparedModel_1_3::executeSynchronously_1_3(): %s", GetModelSummary(m_Model).c_str()); |
| m_RequestCount++; |
| |
| if (cb == nullptr) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::executeSynchronously_1_3 invalid callback passed"); |
| return Void(); |
| } |
| |
| if (deadline.getDiscriminator() != OptionalTimePoint::hidl_discriminator::none) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::executeSynchronously_1_3 invalid request model"); |
| cb(V1_3::ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming); |
| return Void(); |
| } |
| |
| if (loopTimeoutDuration.getDiscriminator() != OptionalTimeoutDuration::hidl_discriminator::none) |
| { |
| ALOGE("ArmnnPreparedModel_1_3::executeSynchronously_1_3 invalid request model"); |
| cb(V1_3::ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming); |
| return Void(); |
| } |
| |
| auto cbWrapper = [cb](V1_3::ErrorStatus errorStatus, |
| std::vector<OutputShape> outputShapes, |
| const Timing& timing, |
| std::string) |
| { |
| cb(errorStatus, outputShapes, timing); |
| }; |
| |
| CallbackContext_1_3 cbCtx; |
| cbCtx.callback = cbWrapper; |
| cbCtx.ctx.measureTimings = measureTiming; |
| |
| ExecuteSynchronously(request, cbCtx); |
| return Void(); |
| } |
| |
| template<typename HalVersion> |
| Return<void> ArmnnPreparedModel_1_3<HalVersion>::configureExecutionBurst( |
| const sp<V1_2::IBurstCallback>& callback, |
| const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel, |
| const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel, |
| V1_3::IPreparedModel::configureExecutionBurst_cb cb) |
| { |
| ALOGV("ArmnnPreparedModel_1_3::configureExecutionBurst"); |
| const sp<V1_2::IBurstContext> burst = ExecutionBurstServer::create(callback, |
| requestChannel, |
| resultChannel, |
| this); |
| |
| if (burst == nullptr) |
| { |
| cb(V1_0::ErrorStatus::GENERAL_FAILURE, {}); |
| } |
| else |
| { |
| cb(V1_0::ErrorStatus::NONE, burst); |
| } |
| return Void(); |
| } |
| |
| template<typename HalVersion> |
| template<typename CallbackContext> |
| bool ArmnnPreparedModel_1_3<HalVersion>::ExecuteGraph( |
| std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools, |
| armnn::InputTensors& inputTensors, |
| armnn::OutputTensors& outputTensors, |
| CallbackContext cb) |
| { |
| ALOGV("ArmnnPreparedModel_1_3::ExecuteGraph(...)"); |
| |
| TimePoint driverEnd, deviceStart, deviceEnd; |
| |
| DumpTensorsIfRequired("Input", inputTensors); |
| |
| 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(); |
| |
| outputShapes[i] = ComputeShape(outputTensorInfo); |
| } |
| |
| // run it |
| try |
| { |
| if (cb.ctx.measureTimings == MeasureTiming::YES) |
| { |
| deviceStart = Now(); |
| } |
| |
| armnn::Status status = m_Runtime->EnqueueWorkload(m_NetworkId, inputTensors, outputTensors); |
| |
| if (cb.ctx.measureTimings == MeasureTiming::YES) |
| { |
| deviceEnd = Now(); |
| } |
| if (status != armnn::Status::Success) |
| { |
| ALOGW("EnqueueWorkload failed"); |
| cb.callback(V1_3::ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, |
| "ArmnnPreparedModel_1_3::ExecuteGraph"); |
| return false; |
| } |
| } |
| catch (armnn::Exception& e) |
| { |
| ALOGW("armnn:Exception caught from EnqueueWorkload: %s", e.what()); |
| cb.callback(V1_3::ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::ExecuteGraph"); |
| return false; |
| } |
| catch (std::exception& e) |
| { |
| ALOGE("std::exception caught from EnqueueWorkload: %s", e.what()); |
| cb.callback(V1_3::ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::ExecuteGraph"); |
| return false; |
| } |
| |
| CommitPools(*pMemPools); |
| |
| DumpTensorsIfRequired("Output", outputTensors); |
| |
| if (cb.ctx.measureTimings == MeasureTiming::YES) |
| { |
| driverEnd = Now(); |
| Timing timing; |
| timing.timeOnDevice = MicrosecondsDuration(deviceEnd, deviceStart); |
| timing.timeInDriver = MicrosecondsDuration(driverEnd, cb.ctx.driverStart); |
| ALOGV("ArmnnPreparedModel_1_2::execute timing - Device = %lu Driver = %lu", timing.timeOnDevice, |
| timing.timeInDriver); |
| cb.callback(V1_3::ErrorStatus::NONE, outputShapes, timing, "ArmnnPreparedModel_1_3::ExecuteGraph"); |
| } else { |
| cb.callback(V1_3::ErrorStatus::NONE, outputShapes, g_NoTiming, "ArmnnPreparedModel_1_3::ExecuteGraph"); |
| } |
| |
| return true; |
| } |
| |
| template<typename HalVersion> |
| bool ArmnnPreparedModel_1_3<HalVersion>::ExecuteWithDummyInputs() |
| { |
| std::vector<std::vector<char>> storage; |
| armnn::InputTensors inputTensors; |
| for (unsigned int i = 0; i < getMainModel(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 < getMainModel(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); |
| } |
| |
| auto nullCallback = [](V1_3::ErrorStatus, std::vector<OutputShape>, const Timing&, std::string) {}; |
| CallbackContext_1_3 callbackContext; |
| callbackContext.callback = nullCallback; |
| callbackContext.ctx.measureTimings = MeasureTiming::NO; |
| auto memPools = std::make_shared<std::vector<::android::nn::RunTimePoolInfo>>(); |
| return ExecuteGraph(memPools, |
| inputTensors, |
| outputTensors, |
| callbackContext); |
| } |
| |
| template<typename HalVersion> |
| Return <V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::Execute(const V1_3::Request& request, |
| MeasureTiming measureTiming, |
| CallbackAsync_1_3 callback) |
| { |
| ExecutionContext_1_3 ctx; |
| if (measureTiming == MeasureTiming::YES) |
| { |
| ctx.measureTimings = measureTiming; |
| ctx.driverStart = Now(); |
| } |
| |
| ALOGV("ArmnnPreparedModel_1_3::execute(): %s", GetModelSummary(m_Model).c_str()); |
| m_RequestCount++; |
| |
| if (!android::nn::validateRequest(request, m_Model)) |
| { |
| callback(V1_3::ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"); |
| return V1_3::ErrorStatus::INVALID_ARGUMENT; |
| } |
| |
| if (!m_RequestInputsAndOutputsDumpDir.empty()) |
| { |
| ALOGD("Dumping inputs and outputs for request %" PRIuPTR, reinterpret_cast<std::uintptr_t>(&callback)); |
| } |
| |
| // 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 memPools = std::make_shared<std::vector<android::nn::RunTimePoolInfo>>(); |
| |
| // allocate the tensors on the heap, as they are passed to the request thread |
| auto inputTensors = std::make_shared<armnn::InputTensors>(); |
| auto outputTensors = std::make_shared<armnn::OutputTensors>(); |
| |
| auto [status, outShapes, timing, message] = PrepareMemoryForIO(*inputTensors, *outputTensors, |
| *memPools, request); |
| if (status != V1_3::ErrorStatus::NONE) |
| { |
| callback(status, outShapes, timing, message); |
| } |
| |
| switch(status) |
| { |
| case V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE: |
| return V1_3::ErrorStatus::NONE; |
| case V1_3::ErrorStatus::GENERAL_FAILURE: |
| return V1_3::ErrorStatus::GENERAL_FAILURE; |
| default: |
| {} |
| } |
| |
| ALOGV("ArmnnPreparedModel_1_3::execute(...) before PostMsg"); |
| |
| // post the request for asynchronous execution |
| CallbackContext_1_3 cb; |
| cb.callback = callback; |
| cb.ctx = ctx; |
| m_RequestThread.PostMsg(this, memPools, inputTensors, outputTensors, cb); |
| ALOGV("ArmnnPreparedModel_1_3::execute(...) after PostMsg"); |
| return V1_3::ErrorStatus::NONE; |
| } |
| |
| #ifdef ARMNN_ANDROID_NN_V1_3 |
| template class ArmnnPreparedModel_1_3<hal_1_3::HalPolicy>; |
| template bool ArmnnPreparedModel_1_3<hal_1_3::HalPolicy>::ExecuteGraph<CallbackContext_1_3>( |
| std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools, |
| armnn::InputTensors& pInputTensors, |
| armnn::OutputTensors& pOutputTensors, |
| CallbackContext_1_3 cb); |
| #endif |
| |
| } // namespace armnn_driver |
