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//
// Copyright © 2019 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <Filesystem.hpp>
#include <LabelsAndEventClasses.hpp>
#include <ProfilingService.hpp>
#include "ProfilingTestUtils.hpp"
#include "PrintPacketHeaderHandler.hpp"
#include <Runtime.hpp>
#include "TestTimelinePacketHandler.hpp"
#include <boost/numeric/conversion/cast.hpp>
#include <boost/test/unit_test.hpp>
#include <cstdio>
#include <sstream>
#include <sys/stat.h>
using namespace armnn::profiling;
using namespace armnn;
using namespace std::chrono_literals;
class FileOnlyHelperService : public ProfilingService
{
public:
// Wait for a notification from the send thread
bool WaitForPacketsSent(uint32_t timeout = 1000)
{
return ProfilingService::WaitForPacketSent(m_ProfilingService, timeout);
}
armnn::profiling::ProfilingService m_ProfilingService;
};
BOOST_AUTO_TEST_SUITE(FileOnlyProfilingDecoratorTests)
BOOST_AUTO_TEST_CASE(TestFileOnlyProfiling)
{
// This test requires at least one backend registry to be enabled
// which can execute a NormalizationLayer
if (!HasSuitableBackendRegistered())
{
return;
}
// Enable m_FileOnly but also provide ILocalPacketHandler which should consume the packets.
// This won't dump anything to file.
armnn::Runtime::CreationOptions creationOptions;
creationOptions.m_ProfilingOptions.m_EnableProfiling = true;
creationOptions.m_ProfilingOptions.m_FileOnly = true;
creationOptions.m_ProfilingOptions.m_CapturePeriod = 100;
creationOptions.m_ProfilingOptions.m_TimelineEnabled = true;
ILocalPacketHandlerSharedPtr localPacketHandlerPtr = std::make_shared<TestTimelinePacketHandler>();
creationOptions.m_ProfilingOptions.m_LocalPacketHandlers.push_back(localPacketHandlerPtr);
armnn::Runtime runtime(creationOptions);
// ensure the GUID generator is reset to zero
GetProfilingService(&runtime).ResetGuidGenerator();
// Load a simple network
// build up the structure of the network
INetworkPtr net(INetwork::Create());
IConnectableLayer* input = net->AddInputLayer(0, "input");
ElementwiseUnaryDescriptor descriptor(UnaryOperation::Sqrt);
IConnectableLayer* normalize = net->AddElementwiseUnaryLayer(descriptor, "normalization");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
normalize->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
// optimize the network
std::vector<armnn::BackendId> backends =
{ armnn::Compute::CpuRef, armnn::Compute::CpuAcc, armnn::Compute::GpuAcc };
IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime.GetDeviceSpec());
// Load it into the runtime. It should succeed.
armnn::NetworkId netId;
BOOST_TEST(runtime.LoadNetwork(netId, std::move(optNet)) == Status::Success);
// Creates structures for input & output.
std::vector<float> inputData(16);
std::vector<float> outputData(16);
for (unsigned int i = 0; i < 16; ++i)
{
inputData[i] = 9.0;
outputData[i] = 3.0;
}
InputTensors inputTensors
{
{0, ConstTensor(runtime.GetInputTensorInfo(netId, 0), inputData.data())}
};
OutputTensors outputTensors
{
{0, Tensor(runtime.GetOutputTensorInfo(netId, 0), outputData.data())}
};
// Does the inference.
runtime.EnqueueWorkload(netId, inputTensors, outputTensors);
static_cast<TestTimelinePacketHandler*>(localPacketHandlerPtr.get())->WaitOnInferenceCompletion(3000);
const TimelineModel& model =
static_cast<TestTimelinePacketHandler*>(localPacketHandlerPtr.get())->GetTimelineModel();
for (auto& error : model.GetErrors())
{
std::cout << error.what() << std::endl;
}
BOOST_TEST(model.GetErrors().empty());
std::vector<std::string> desc = GetModelDescription(model);
std::vector<std::string> expectedOutput;
expectedOutput.push_back("Entity [0] name = input type = layer");
expectedOutput.push_back(" connection [14] from entity [0] to entity [1]");
expectedOutput.push_back(" child: Entity [23] backendId = CpuRef type = workload");
expectedOutput.push_back("Entity [1] name = normalization type = layer");
expectedOutput.push_back(" connection [22] from entity [1] to entity [2]");
expectedOutput.push_back(" child: Entity [15] backendId = CpuRef type = workload");
expectedOutput.push_back("Entity [2] name = output type = layer");
expectedOutput.push_back(" child: Entity [27] backendId = CpuRef type = workload");
expectedOutput.push_back("Entity [6] type = network");
expectedOutput.push_back(" child: Entity [0] name = input type = layer");
expectedOutput.push_back(" child: Entity [1] name = normalization type = layer");
expectedOutput.push_back(" child: Entity [2] name = output type = layer");
expectedOutput.push_back(" execution: Entity [31] type = inference");
expectedOutput.push_back("Entity [15] backendId = CpuRef type = workload");
expectedOutput.push_back(" execution: Entity [44] type = workload_execution");
expectedOutput.push_back("Entity [23] backendId = CpuRef type = workload");
expectedOutput.push_back(" execution: Entity [36] type = workload_execution");
expectedOutput.push_back("Entity [27] backendId = CpuRef type = workload");
expectedOutput.push_back(" execution: Entity [52] type = workload_execution");
expectedOutput.push_back("Entity [31] type = inference");
expectedOutput.push_back(" child: Entity [36] type = workload_execution");
expectedOutput.push_back(" child: Entity [44] type = workload_execution");
expectedOutput.push_back(" child: Entity [52] type = workload_execution");
expectedOutput.push_back(" event: [34] class [start_of_life]");
expectedOutput.push_back(" event: [60] class [end_of_life]");
expectedOutput.push_back("Entity [36] type = workload_execution");
expectedOutput.push_back(" event: [40] class [start_of_life]");
expectedOutput.push_back(" event: [42] class [end_of_life]");
expectedOutput.push_back("Entity [44] type = workload_execution");
expectedOutput.push_back(" event: [48] class [start_of_life]");
expectedOutput.push_back(" event: [50] class [end_of_life]");
expectedOutput.push_back("Entity [52] type = workload_execution");
expectedOutput.push_back(" event: [56] class [start_of_life]");
expectedOutput.push_back(" event: [58] class [end_of_life]");
BOOST_TEST(CompareOutput(desc, expectedOutput));
}
BOOST_AUTO_TEST_CASE(DumpOutgoingValidFileEndToEnd)
{
// This test requires at least one backend registry to be enabled
// which can execute a NormalizationLayer
if (!HasSuitableBackendRegistered())
{
return;
}
// Create a temporary file name.
fs::path tempPath = armnnUtils::Filesystem::NamedTempFile("DumpOutgoingValidFileEndToEnd_CaptureFile.txt");
armnn::Runtime::CreationOptions options;
options.m_ProfilingOptions.m_EnableProfiling = true;
options.m_ProfilingOptions.m_FileOnly = true;
options.m_ProfilingOptions.m_IncomingCaptureFile = "";
options.m_ProfilingOptions.m_OutgoingCaptureFile = tempPath.string();
options.m_ProfilingOptions.m_CapturePeriod = 100;
options.m_ProfilingOptions.m_TimelineEnabled = true;
ILocalPacketHandlerSharedPtr localPacketHandlerPtr = std::make_shared<TestTimelinePacketHandler>();
options.m_ProfilingOptions.m_LocalPacketHandlers.push_back(localPacketHandlerPtr);
// Make sure the file does not exist at this point
BOOST_CHECK(!fs::exists(tempPath));
armnn::Runtime runtime(options);
// ensure the GUID generator is reset to zero
GetProfilingService(&runtime).ResetGuidGenerator();
// Load a simple network
// build up the structure of the network
INetworkPtr net(INetwork::Create());
IConnectableLayer* input = net->AddInputLayer(0, "input");
ElementwiseUnaryDescriptor descriptor(UnaryOperation::Sqrt);
IConnectableLayer* normalize = net->AddElementwiseUnaryLayer(descriptor, "normalization");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
normalize->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
// optimize the network
std::vector<armnn::BackendId> backends =
{ armnn::Compute::CpuRef, armnn::Compute::CpuAcc, armnn::Compute::GpuAcc };
IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime.GetDeviceSpec());
// Load it into the runtime. It should succeed.
armnn::NetworkId netId;
BOOST_TEST(runtime.LoadNetwork(netId, std::move(optNet)) == Status::Success);
// Creates structures for input & output.
std::vector<float> inputData(16);
std::vector<float> outputData(16);
for (unsigned int i = 0; i < 16; ++i)
{
inputData[i] = 9.0;
outputData[i] = 3.0;
}
InputTensors inputTensors
{
{0, ConstTensor(runtime.GetInputTensorInfo(netId, 0), inputData.data())}
};
OutputTensors outputTensors
{
{0, Tensor(runtime.GetOutputTensorInfo(netId, 0), outputData.data())}
};
// Does the inference.
runtime.EnqueueWorkload(netId, inputTensors, outputTensors);
static_cast<TestTimelinePacketHandler*>(localPacketHandlerPtr.get())->WaitOnInferenceCompletion(3000);
// In order to flush the files we need to gracefully close the profiling service.
options.m_ProfilingOptions.m_EnableProfiling = false;
GetProfilingService(&runtime).ResetExternalProfilingOptions(options.m_ProfilingOptions, true);
// The output file size should be greater than 0.
BOOST_CHECK(fs::file_size(tempPath) > 0);
// NOTE: would be an interesting exercise to take this file and decode it
// Delete the tmp file.
BOOST_CHECK(fs::remove(tempPath));
}
BOOST_AUTO_TEST_SUITE_END()
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