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Diffstat (limited to 'ArmnnPreparedModel_1_3.hpp')
| -rw-r--r-- | ArmnnPreparedModel_1_3.hpp | 137 |
1 files changed, 137 insertions, 0 deletions
diff --git a/ArmnnPreparedModel_1_3.hpp b/ArmnnPreparedModel_1_3.hpp new file mode 100644 index 00000000..247149c8 --- /dev/null +++ b/ArmnnPreparedModel_1_3.hpp @@ -0,0 +1,137 @@ +// +// Copyright © 2020 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "ArmnnDriver.hpp" +#include "ArmnnDriverImpl.hpp" +#include "RequestThread.hpp" +#include "ModelToINetworkConverter.hpp" + +#include <NeuralNetworks.h> +#include <armnn/ArmNN.hpp> + +#include <string> +#include <vector> + +namespace armnn_driver +{ +using CallbackAsync_1_3 = std::function< + void(V1_3::ErrorStatus errorStatus, + std::vector<::android::hardware::neuralnetworks::V1_2::OutputShape> outputShapes, + const ::android::hardware::neuralnetworks::V1_2::Timing& timing, + std::string callingFunction)>; + +struct ExecutionContext_1_3 +{ + ::android::hardware::neuralnetworks::V1_2::MeasureTiming measureTimings = + ::android::hardware::neuralnetworks::V1_2::MeasureTiming::NO; + TimePoint driverStart; +}; + +using CallbackContext_1_3 = CallbackContext<CallbackAsync_1_3, ExecutionContext_1_3>; + +using executeFenced_cb = std::function<void(::android::hardware::neuralnetworks::V1_3::ErrorStatus status, + const ::android::hardware::hidl_handle& syncFence, + const ::android::sp<::android::hardware::neuralnetworks::V1_3::IFencedExecutionCallback>& callback)>; + +template <typename HalVersion> +class ArmnnPreparedModel_1_3 : public V1_3::IPreparedModel +{ +public: + using HalModel = typename V1_3::Model; + + ArmnnPreparedModel_1_3(armnn::NetworkId networkId, + armnn::IRuntime* runtime, + const HalModel& model, + const std::string& requestInputsAndOutputsDumpDir, + const bool gpuProfilingEnabled); + + virtual ~ArmnnPreparedModel_1_3(); + + Return<V1_0::ErrorStatus> execute(const V1_0::Request& request, + const sp<V1_0::IExecutionCallback>& callback) override; + + Return<V1_0::ErrorStatus> execute_1_2(const V1_0::Request& request, MeasureTiming measure, + const sp<V1_2::IExecutionCallback>& callback) override; + + Return<V1_3::ErrorStatus> execute_1_3(const V1_3::Request& request, + V1_2::MeasureTiming measure, + const V1_3::OptionalTimePoint&, + const sp<V1_3::IExecutionCallback>& callback) override; + + Return<void> executeSynchronously(const V1_0::Request &request, + MeasureTiming measure, + V1_3::IPreparedModel::executeSynchronously_cb cb) override; + + Return<void> executeSynchronously_1_3(const V1_3::Request &request, + MeasureTiming measure, + const V1_3::OptionalTimePoint& deadline, + V1_3::IPreparedModel::executeSynchronously_1_3_cb cb) override; + + Return<void> executeFenced(const V1_3::Request& request, + const android::hardware::hidl_vec<android::hardware::hidl_handle>& wait_for, + MeasureTiming measure, + const V1_3::OptionalTimePoint& deadline, + const V1_3::OptionalTimeoutDuration& duration, + executeFenced_cb callback) override; + + Return<void> configureExecutionBurst( + const sp<V1_2::IBurstCallback>& callback, + const android::hardware::MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel, + const android::hardware::MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel, + configureExecutionBurst_cb cb) override; + + template<typename CallbackContext> + Return<void> ExecuteSynchronously(const V1_3::Request& request, CallbackContext cbCtx); + + /// execute the graph prepared from the request + template<typename CallbackContext> + bool ExecuteGraph(std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools, + armnn::InputTensors& inputTensors, + armnn::OutputTensors& outputTensors, + CallbackContext callback); + + /// Executes this model with dummy inputs (e.g. all zeroes). + /// \return false on failure, otherwise true + bool ExecuteWithDummyInputs(); + +private: + Return <V1_3::ErrorStatus> Execute(const V1_3::Request& request, + MeasureTiming measureTiming, + CallbackAsync_1_3 callback); + + Return<V1_3::ErrorStatus> PrepareMemoryForInputs( + armnn::InputTensors& inputs, + const V1_3::Request& request, + const std::vector<android::nn::RunTimePoolInfo>& memPools); + + Return<V1_3::ErrorStatus> PrepareMemoryForOutputs( + armnn::OutputTensors& outputs, + std::vector<OutputShape> &outputShapes, + const V1_3::Request& request, + const std::vector<android::nn::RunTimePoolInfo>& memPools); + + std::tuple<V1_3::ErrorStatus, hidl_vec<OutputShape>, Timing, std::string> PrepareMemoryForIO( + armnn::InputTensors& inputs, + armnn::OutputTensors& outputs, + std::vector<android::nn::RunTimePoolInfo>& memPools, + const V1_3::Request& request); + + template <typename TensorBindingCollection> + void DumpTensorsIfRequired(char const* tensorNamePrefix, const TensorBindingCollection& tensorBindings); + + armnn::NetworkId m_NetworkId; + armnn::IRuntime* m_Runtime; + V1_3::Model m_Model; + // There must be a single RequestThread for all ArmnnPreparedModel objects to ensure serial execution of workloads + // It is specific to this class, so it is declared as static here + static RequestThread<ArmnnPreparedModel_1_3, HalVersion, CallbackContext_1_3> m_RequestThread; + uint32_t m_RequestCount; + const std::string& m_RequestInputsAndOutputsDumpDir; + const bool m_GpuProfilingEnabled; +}; + +} |