IVGCVSW-3181 Add HAL 1.2 support to android-nn-driver
* Updated Android.mk to build HAL 1.2 driver
* Added 1.2 HalPolicy and ArmnnDriver
* Added 1.2 ArmnnPreparedModel
* Updated converters and utilities to accept new HAL 1.2 operands and operand types.
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I62856deab24e106f72cccce09468db4971756fa6
diff --git a/ArmnnPreparedModel_1_2.cpp b/ArmnnPreparedModel_1_2.cpp
new file mode 100644
index 0000000..f03d69d
--- /dev/null
+++ b/ArmnnPreparedModel_1_2.cpp
@@ -0,0 +1,486 @@
+//
+// 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;
+
+static const Timing g_NoTiming = {.timeOnDevice = UINT64_MAX, .timeInDriver = UINT64_MAX};
+
+namespace {
+
+using namespace armnn_driver;
+
+void NotifyCallbackAndCheck(const ::android::sp<V1_0::IExecutionCallback>& callback, ErrorStatus errorStatus,
+ 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::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());
+ }
+}
+
+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> 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)
+{
+ return Execute<V1_0::IExecutionCallback>(request, callback);
+}
+
+template<typename HalVersion>
+Return <ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::execute_1_2(const Request& request,
+ MeasureTiming,
+ const sp<V1_2::IExecutionCallback>& callback)
+{
+ return Execute<V1_2::IExecutionCallback>(request, callback);
+}
+
+template<typename HalVersion>
+Return<void> ArmnnPreparedModel_1_2<HalVersion>::executeSynchronously(const Request& request,
+ MeasureTiming,
+ V1_2::IPreparedModel::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();
+ }
+
+ if (!android::nn::validateRequest(request, m_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();
+ }
+
+ // 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);
+ 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();
+ }
+
+ 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
+ {
+ armnn::Status status = m_Runtime->EnqueueWorkload(m_NetworkId, *pInputTensors, *pOutputTensors);
+
+ 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");
+ cb(ErrorStatus::NONE, {}, g_NoTiming);
+ return Void();
+}
+
+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 sp<V1_2::IBurstContext> burst =
+ ExecutionBurstServer::create(callback, requestChannel, resultChannel, this);
+
+ 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,
+ const ::android::sp<V1_0::IExecutionCallback>& callback)
+{
+ ALOGV("ArmnnPreparedModel_1_2::ExecuteGraph(...)");
+
+ DumpTensorsIfRequired("Input", *pInputTensors);
+
+ // run it
+ try
+ {
+ armnn::Status status = m_Runtime->EnqueueWorkload(m_NetworkId, *pInputTensors, *pOutputTensors);
+ if (status != armnn::Status::Success)
+ {
+ ALOGW("EnqueueWorkload failed");
+ NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE, "ArmnnPreparedModel_1_2::ExecuteGraph");
+ return;
+ }
+ }
+ catch (armnn::Exception& e)
+ {
+ ALOGW("armnn::Exception caught from EnqueueWorkload: %s", e.what());
+ NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE, "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();
+ }
+
+ NotifyCallbackAndCheck(callback, ErrorStatus::NONE, "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>
+template<typename ExecutionCallback>
+Return <ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::Execute(const Request& request,
+ const sp<ExecutionCallback>& callback)
+{
+ ALOGV("ArmnnPreparedModel_1_2::execute(): %s", GetModelSummary(m_Model).c_str());
+ m_RequestCount++;
+
+ if (callback.get() == nullptr)
+ {
+ ALOGE("ArmnnPreparedModel_1_2::execute invalid callback passed");
+ return ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ if (!android::nn::validateRequest(request, m_Model))
+ {
+ NotifyCallbackAndCheck(callback, ErrorStatus::INVALID_ARGUMENT, "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.get()));
+ }
+
+ // 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))
+ {
+ NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE, "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);
+ NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE,
+ "ArmnnPreparedModel_1_2::execute");
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+
+ 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);
+ NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE,
+ "ArmnnPreparedModel_1_2::execute");
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+
+ pOutputTensors->emplace_back(i, outputTensor);
+ }
+ }
+ catch (armnn::Exception& e)
+ {
+ ALOGW("armnn::Exception caught while preparing for EnqueueWorkload: %s", e.what());
+ NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE, "ArmnnPreparedModel_1_2::execute");
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+
+ ALOGV("ArmnnPreparedModel_1_2::execute(...) before PostMsg");
+ // post the request for asynchronous execution
+ m_RequestThread.PostMsg(this, pMemPools, pInputTensors, pOutputTensors, callback);
+ 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