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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <armnn/ArmNN.hpp>
#include <armnn/Graph.hpp>
#include <armnn/Network.hpp>
#include <backends/reference/RefWorkloadFactory.hpp>
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(RefOptimizedNetwork)
BOOST_AUTO_TEST_CASE(OptimizeValidateCpuRefWorkloads)
{
const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
armnn::Network net;
armnn::NormalizationDescriptor nmDesc;
armnn::ActivationDescriptor acDesc;
// in
// |
// nm
// / |
// ac |
// \ |
// ml
// |
// sm
// |
// ot
armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
layer->GetOutputSlot(0).SetTensorInfo(desc);
armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
normLayer->GetOutputSlot(0).SetTensorInfo(desc);
layer = net.AddActivationLayer(acDesc, "ac");
normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
layer->GetOutputSlot(0).SetTensorInfo(desc);
armnn::IConnectableLayer* prevLayer = layer;
layer = net.AddMultiplicationLayer("ml");
prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
layer->GetOutputSlot(0).SetTensorInfo(desc);
prevLayer = layer;
armnn::SoftmaxDescriptor softmaxDescriptor;
layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
layer->GetOutputSlot(0).SetTensorInfo(desc);
prevLayer = layer;
layer = net.AddOutputLayer(0, "ot");
prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph().AllocateDynamicBuffers();
BOOST_CHECK(optNet);
// Validates workloads.
armnn::RefWorkloadFactory fact;
for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
{
BOOST_CHECK_NO_THROW(
layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
}
}
BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefPermuteLayer)
{
// Create runtime in which test will run
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
// build up the structure of the network
armnn::INetworkPtr net(armnn::INetwork::Create());
armnn::IConnectableLayer* input = net->AddInputLayer(0);
armnn::PermuteDescriptor descriptor({0, 2, 3, 1});
armnn::IConnectableLayer* permute = net->AddPermuteLayer(descriptor);
armnn::IConnectableLayer* output = net->AddOutputLayer(0);
input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));
permute->GetOutputSlot(0).Connect(output->GetInputSlot(0));
input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
permute->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 4, 1, 4 }, armnn::DataType::Float32));
// optimize the network
armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
{
BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
}
}
BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefMeanLayer)
{
// Create runtime in which test will run
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
// build up the structure of the network
armnn::INetworkPtr net(armnn::INetwork::Create());
armnn::IConnectableLayer* input = net->AddInputLayer(0);
armnn::MeanDescriptor descriptor({ 0, 1 }, false);
armnn::IConnectableLayer* meanLayer = net->AddMeanLayer(descriptor);
armnn::IConnectableLayer* output = net->AddOutputLayer(0);
input->GetOutputSlot(0).Connect(meanLayer->GetInputSlot(0));
meanLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 4, 3, 2 }, armnn::DataType::Float32));
meanLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 2 }, armnn::DataType::Float32));
// optimize the network
armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
{
BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
}
}
BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnCpuRef)
{
// Test to check when FP16 Turbo mode set
// it converts the FP32 network to FP16 Network
// add FP32ToFP16 conversion layer after the InputLayer
// add FP16ToFP32 conversion layer after the OutputLayer
// checks the other layers if they are supported in FP16
// if they are not put the conversion layers before and after
// if they are not supported in FP16 use FP32 instead
// if there are inverse conversion layers remove them with optimization
// at the moment FloorLayer is not supported in FP16 so it rolls back to FP32
// and inverse conversion layers are removed by the optimizer
armnn::Network net;
// Defines layers.
auto input = net.AddInputLayer(0);
auto floor = net.AddFloorLayer();
auto output = net.AddOutputLayer(0);
// Connects layers.
input->GetOutputSlot(0).Connect(floor->GetInputSlot(0));
floor->GetOutputSlot(0).Connect(output->GetInputSlot(0));
armnn::TensorShape shape({4});
armnn::TensorInfo info(shape, armnn::DataType::Float32);
input->GetOutputSlot(0).SetTensorInfo(info);
floor->GetOutputSlot(0).SetTensorInfo(info);
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
armnn::OptimizerOptions optimizerOptions;
optimizerOptions.m_ReduceFp32ToFp16 = true;
armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec(),
optimizerOptions);
std::ostringstream ss;
optimizedNet->SerializeToDot(ss);
auto inputId = input->GetGuid();
auto floorId = floor->GetGuid();
auto outputId = output->GetGuid();
std::stringstream expected;
expected <<
"digraph Optimized {\n"
" node [shape=\"record\"];\n"
" edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"
" " << inputId << " [label=\"{Input}\"];\n"
" " << floorId << " [label=\"{Floor}\"];\n"
" " << outputId << " [label=\"{Output}\"];\n"
" " << inputId << " -> " << floorId << " [label=< [4] >];\n"
" " << floorId << " -> " << outputId << " [label=< [4] >];\n"
"}\n";
BOOST_TEST(ss.str() == expected.str());
}
BOOST_AUTO_TEST_SUITE_END()
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