| // |
| // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <armnn/ArmNN.hpp> |
| #include <armnn/backends/ICustomAllocator.hpp> |
| |
| #include <arm_compute/core/CL/CLKernelLibrary.h> |
| #include <arm_compute/runtime/CL/CLScheduler.h> |
| |
| #include <iostream> |
| |
| /** Sample implementation of ICustomAllocator for use with the ClBackend. |
| * Note: any memory allocated must be host addressable with write access |
| * in order for ArmNN to be able to properly use it. */ |
| class SampleClBackendCustomAllocator : public armnn::ICustomAllocator |
| { |
| public: |
| SampleClBackendCustomAllocator() = default; |
| |
| void* allocate(size_t size, size_t alignment) override |
| { |
| // If alignment is 0 just use the CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE for alignment |
| if (alignment == 0) |
| { |
| alignment = arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>(); |
| } |
| size_t space = size + alignment + alignment; |
| auto allocatedMemPtr = std::malloc(space * sizeof(size_t)); |
| |
| if (std::align(alignment, size, allocatedMemPtr, space) == nullptr) |
| { |
| throw armnn::Exception("SampleClBackendCustomAllocator::Alignment failed"); |
| } |
| return allocatedMemPtr; |
| } |
| |
| void free(void* ptr) override |
| { |
| std::free(ptr); |
| } |
| |
| armnn::MemorySource GetMemorySourceType() override |
| { |
| return armnn::MemorySource::Malloc; |
| } |
| }; |
| |
| |
| // A simple example application to show the usage of a custom memory allocator. In this sample, the users single |
| // input number is multiplied by 1.0f using a fully connected layer with a single neuron to produce an output |
| // number that is the same as the input. All memory required to execute this mini network is allocated with |
| // the provided custom allocator. |
| // |
| // Using a Custom Allocator is required for use with Protected Mode and Protected Memory. |
| // This example is provided using only unprotected malloc as Protected Memory is platform |
| // and implementation specific. |
| // |
| // Note: This example is similar to the SimpleSample application that can also be found in armnn/samples. |
| // The differences are in the use of a custom allocator, the backend is GpuAcc, and the inputs/outputs |
| // are being imported instead of copied. (Import must be enabled when using a Custom Allocator) |
| // You might find this useful for comparison. |
| int main() |
| { |
| using namespace armnn; |
| |
| float number; |
| std::cout << "Please enter a number: " << std::endl; |
| std::cin >> number; |
| |
| // Turn on logging to standard output |
| // This is useful in this sample so that users can learn more about what is going on |
| armnn::ConfigureLogging(true, false, LogSeverity::Info); |
| |
| // Construct ArmNN network |
| armnn::NetworkId networkIdentifier; |
| INetworkPtr myNetwork = INetwork::Create(); |
| armnn::FullyConnectedDescriptor fullyConnectedDesc; |
| float weightsData[] = {1.0f}; // Identity |
| TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32, 0.0f, 0, true); |
| weightsInfo.SetConstant(true); |
| armnn::ConstTensor weights(weightsInfo, weightsData); |
| ARMNN_NO_DEPRECATE_WARN_BEGIN |
| IConnectableLayer *fullyConnected = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc, |
| weights, |
| EmptyOptional(), |
| "fully connected"); |
| ARMNN_NO_DEPRECATE_WARN_END |
| IConnectableLayer *InputLayer = myNetwork->AddInputLayer(0); |
| IConnectableLayer *OutputLayer = myNetwork->AddOutputLayer(0); |
| InputLayer->GetOutputSlot(0).Connect(fullyConnected->GetInputSlot(0)); |
| fullyConnected->GetOutputSlot(0).Connect(OutputLayer->GetInputSlot(0)); |
| |
| // Create ArmNN runtime: |
| // |
| // This is the interesting bit when executing a model with a custom allocator. |
| // You can have different allocators for different backends. To support this |
| // the runtime creation option has a map that takes a BackendId and the corresponding |
| // allocator that should be used for that backend. |
| // Only GpuAcc supports a Custom Allocator for now |
| // |
| // Note: This is not covered in this example but if you want to run a model on |
| // protected memory a custom allocator needs to be provided that supports |
| // protected memory allocations and the MemorySource of that allocator is |
| // set to MemorySource::DmaBufProtected |
| IRuntime::CreationOptions options; |
| auto customAllocator = std::make_shared<SampleClBackendCustomAllocator>(); |
| options.m_CustomAllocatorMap = {{"GpuAcc", std::move(customAllocator)}}; |
| IRuntimePtr runtime = IRuntime::Create(options); |
| |
| //Set the tensors in the network. |
| TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
| InputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| |
| unsigned int numElements = inputTensorInfo.GetNumElements(); |
| size_t totalBytes = numElements * sizeof(float); |
| |
| TensorInfo outputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
| fullyConnected->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // Optimise ArmNN network |
| OptimizerOptions optOptions; |
| optOptions.m_ImportEnabled = true; |
| armnn::IOptimizedNetworkPtr optNet = |
| Optimize(*myNetwork, {"GpuAcc"}, runtime->GetDeviceSpec(), optOptions); |
| if (!optNet) |
| { |
| // This shouldn't happen for this simple sample, with GpuAcc backend. |
| // But in general usage Optimize could fail if the backend at runtime cannot |
| // support the model that has been provided. |
| std::cerr << "Error: Failed to optimise the input network." << std::endl; |
| return 1; |
| } |
| |
| // Load graph into runtime |
| std::string ignoredErrorMessage; |
| INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
| runtime->LoadNetwork(networkIdentifier, std::move(optNet), ignoredErrorMessage, networkProperties); |
| |
| // Creates structures for input & output |
| const size_t alignment = |
| arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>(); |
| |
| void* alignedInputPtr = options.m_CustomAllocatorMap["GpuAcc"]->allocate(totalBytes, alignment); |
| |
| // Input with negative values |
| auto* inputPtr = reinterpret_cast<float*>(alignedInputPtr); |
| std::fill_n(inputPtr, numElements, number); |
| |
| void* alignedOutputPtr = options.m_CustomAllocatorMap["GpuAcc"]->allocate(totalBytes, alignment); |
| auto* outputPtr = reinterpret_cast<float*>(alignedOutputPtr); |
| std::fill_n(outputPtr, numElements, -10.0f); |
| |
| inputTensorInfo = runtime->GetInputTensorInfo(networkIdentifier, 0); |
| inputTensorInfo.SetConstant(true); |
| armnn::InputTensors inputTensors |
| { |
| {0, armnn::ConstTensor(inputTensorInfo, alignedInputPtr)}, |
| }; |
| armnn::OutputTensors outputTensors |
| { |
| {0, armnn::Tensor(runtime->GetOutputTensorInfo(networkIdentifier, 0), alignedOutputPtr)} |
| }; |
| |
| // Execute network |
| runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors); |
| |
| // Tell the CLBackend to sync memory so we can read the output. |
| arm_compute::CLScheduler::get().sync(); |
| auto* outputResult = reinterpret_cast<float*>(alignedOutputPtr); |
| std::cout << "Your number was " << outputResult[0] << std::endl; |
| runtime->UnloadNetwork(networkIdentifier); |
| return 0; |
| |
| } |