Release 18.02

Change-Id: I41a89c149534a7c354a58e2c66a32cba572fc0c1

1 files changed, 353 insertions, 0 deletions

diff --git a/ArmnnPreparedModel.cpp b/ArmnnPreparedModel.cpp
new file mode 100644
index 00000000..1bd72199
--- /dev/null
+++ b/ArmnnPreparedModel.cpp
@@ -0,0 +1,353 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#define LOG_TAG "ArmnnDriver"
+
+#include "ArmnnPreparedModel.hpp"
+#include "Utils.hpp"
+
+#include <boost/format.hpp>
+#include <log/log.h>
+#include <OperationsUtils.h>
+
+#include <cassert>
+#include <cinttypes>
+
+using namespace android;
+
+namespace
+{
+using namespace armnn_driver;
+
+void NotifyCallbackAndCheck(const ::android::sp<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);
+}
+
+}
+
+namespace armnn_driver
+{
+
+RequestThread ArmnnPreparedModel::m_RequestThread;
+
+template <typename TensorBindingCollection>
+void ArmnnPreparedModel::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);
+        }
+    }
+}
+
+ArmnnPreparedModel::ArmnnPreparedModel(armnn::NetworkId networkId,
+    armnn::IRuntime* runtime,
+    const Model& model,
+    const std::string& requestInputsAndOutputsDumpDir)
+: m_NetworkId(networkId)
+, m_Runtime(runtime)
+, m_Model(model)
+, m_RequestCount(0)
+, m_RequestInputsAndOutputsDumpDir(requestInputsAndOutputsDumpDir)
+{
+}
+
+ArmnnPreparedModel::~ArmnnPreparedModel()
+{
+    //unload the network associated with this model
+    m_Runtime->UnloadNetwork(m_NetworkId);
+}
+
+Return<ErrorStatus> ArmnnPreparedModel::execute(const Request& request,
+                                                const ::android::sp<IExecutionCallback>& callback)
+{
+    ALOGV("ArmnnPreparedModel::execute(): %s", GetModelSummary(m_Model).c_str());
+    m_RequestCount++;
+
+    if (callback.get() == nullptr) {
+        ALOGE("ArmnnPreparedModel::execute invalid callback passed");
+        return ErrorStatus::INVALID_ARGUMENT;
+    }
+
+    if (!android::nn::validateRequest(request, m_Model))
+    {
+        NotifyCallbackAndCheck(callback, ErrorStatus::INVALID_ARGUMENT, "ArmnnPreparedModel::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::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);
+                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);
+                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::execute");
+        return ErrorStatus::GENERAL_FAILURE;
+    }
+
+    ALOGV("ArmnnPreparedModel::execute(...) before PostMsg");
+    // post the request for asynchronous execution
+    m_RequestThread.PostMsg(this, pMemPools, pInputTensors, pOutputTensors, callback);
+    ALOGV("ArmnnPreparedModel::execute(...) after PostMsg");
+
+    return ErrorStatus::NONE; // successfully queued
+}
+
+void ArmnnPreparedModel::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<IExecutionCallback>& callback)
+{
+    ALOGV("ArmnnPreparedModel::ExecuteGraph(...)");
+
+    DumpTensorsIfRequired("Input", *pInputTensors);
+
+    // run it
+    try
+    {
+        m_Runtime->EnqueueWorkload(m_NetworkId, *pInputTensors, *pOutputTensors);
+    }
+    catch (armnn::Exception& e)
+    {
+        ALOGW("armnn::Exception caught from EnqueueWorkload: %s", e.what());
+        NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE, "ArmnnPreparedModel::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");
+}
+
+void ArmnnPreparedModel::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
+    {
+        m_Runtime->EnqueueWorkload(m_NetworkId, inputTensors, outputTensors);
+    }
+    catch (armnn::Exception& e)
+    {
+        ALOGW("ExecuteWithDummyInputs: armnn::Exception caught from EnqueueWorkload: %s", e.what());
+    }
+}
+
+AndroidNnCpuExecutorPreparedModel::AndroidNnCpuExecutorPreparedModel(const Model& model,
+    const std::string& requestInputsAndOutputsDumpDir)
+: m_Model(model)
+, m_RequestInputsAndOutputsDumpDir(requestInputsAndOutputsDumpDir)
+, m_RequestCount(0)
+{
+}
+
+bool AndroidNnCpuExecutorPreparedModel::Initialize()
+{
+    return setRunTimePoolInfosFromHidlMemories(&m_ModelPoolInfos, m_Model.pools);
+}
+
+Return<ErrorStatus> AndroidNnCpuExecutorPreparedModel::execute(const Request& request,
+    const ::android::sp<IExecutionCallback>& callback)
+{
+    m_RequestCount++;
+    std::vector<android::nn::RunTimePoolInfo> requestPoolInfos;
+
+    if (!setRunTimePoolInfosFromHidlMemories(&requestPoolInfos, request.pools))
+    {
+        NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE, "AndroidNnCpuExecutorPreparedModel::execute");
+        return ErrorStatus::GENERAL_FAILURE;
+    }
+
+    if (!m_RequestInputsAndOutputsDumpDir.empty())
+    {
+        ALOGD("Dumping inputs and outputs for request %" PRIuPTR, reinterpret_cast<std::uintptr_t>(callback.get()));
+    }
+
+    DumpTensorsIfRequired(
+        "Input",
+        m_Model.inputIndexes,
+        request.inputs,
+        requestPoolInfos);
+
+    android::nn::CpuExecutor executor;
+    const int n = executor.run(m_Model, request, m_ModelPoolInfos, requestPoolInfos);
+    ErrorStatus executionStatus =
+            n == ANEURALNETWORKS_NO_ERROR ? ErrorStatus::NONE : ErrorStatus::GENERAL_FAILURE;
+
+    DumpTensorsIfRequired(
+        "Output",
+        m_Model.outputIndexes,
+        request.outputs,
+        requestPoolInfos);
+
+    NotifyCallbackAndCheck(callback, ErrorStatus::GENERAL_FAILURE, "AndroidNnCpuExecutorPreparedModel::execute");
+    return executionStatus;
+}
+
+void AndroidNnCpuExecutorPreparedModel::DumpTensorsIfRequired(
+    char const* tensorNamePrefix,
+    const hidl_vec<uint32_t>& operandIndices,
+    const hidl_vec<RequestArgument>& requestArgs,
+    const std::vector<android::nn::RunTimePoolInfo>& requestPoolInfos)
+{
+    if (m_RequestInputsAndOutputsDumpDir.empty())
+    {
+        return;
+    }
+
+    for (std::size_t i = 0; i < requestArgs.size(); ++i)
+    {
+        const Operand& operand = m_Model.operands[operandIndices[i]];
+        const armnn::TensorInfo tensorInfo = GetTensorInfoForOperand(operand);
+        const armnn::Tensor tensor = GetTensorForRequestArgument(requestArgs[i], tensorInfo, requestPoolInfos);
+        const std::string tensorName = BuildTensorName(tensorNamePrefix, i);
+        if (tensor.GetMemoryArea() != nullptr)
+        {
+            std::string requestName = boost::str(boost::format("%1%_%2%.dump") % this % m_RequestCount);
+            DumpTensor(m_RequestInputsAndOutputsDumpDir, requestName, tensorName, tensor);
+        }
+        else
+        {
+            ALOGE("Cannot dump tensor %s. An error occurred converting the associated request argument to a tensor.",
+                tensorName.c_str());
+        }
+    }
+}
+
+}