Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include <armnn/ArmNN.hpp> |
| 7 | #include <armnn/backends/ICustomAllocator.hpp> |
| 8 | |
| 9 | #include <arm_compute/core/CL/CLKernelLibrary.h> |
| 10 | #include <arm_compute/runtime/CL/CLScheduler.h> |
| 11 | |
| 12 | #include <iostream> |
| 13 | |
| 14 | /** Sample implementation of ICustomAllocator for use with the ClBackend. |
| 15 | * Note: any memory allocated must be host addressable with write access |
| 16 | * in order for ArmNN to be able to properly use it. */ |
| 17 | class SampleClBackendCustomAllocator : public armnn::ICustomAllocator |
| 18 | { |
| 19 | public: |
| 20 | SampleClBackendCustomAllocator() = default; |
| 21 | |
Francis Murtagh | e8d7ccb | 2021-10-14 17:30:24 +0100 | [diff] [blame] | 22 | void* allocate(size_t size, size_t alignment) override |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 23 | { |
| 24 | // If alignment is 0 just use the CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE for alignment |
| 25 | if (alignment == 0) |
| 26 | { |
| 27 | alignment = arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>(); |
| 28 | } |
| 29 | size_t space = size + alignment + alignment; |
| 30 | auto allocatedMemPtr = std::malloc(space * sizeof(size_t)); |
| 31 | |
| 32 | if (std::align(alignment, size, allocatedMemPtr, space) == nullptr) |
| 33 | { |
| 34 | throw armnn::Exception("SampleClBackendCustomAllocator::Alignment failed"); |
| 35 | } |
| 36 | return allocatedMemPtr; |
| 37 | } |
David Monahan | 6642b8a | 2021-11-04 16:31:46 +0000 | [diff] [blame] | 38 | |
| 39 | void free(void* ptr) override |
| 40 | { |
| 41 | std::free(ptr); |
| 42 | } |
| 43 | |
| 44 | armnn::MemorySource GetMemorySourceType() override |
| 45 | { |
| 46 | return armnn::MemorySource::Malloc; |
| 47 | } |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 48 | }; |
| 49 | |
| 50 | |
| 51 | // A simple example application to show the usage of a custom memory allocator. In this sample, the users single |
| 52 | // input number is multiplied by 1.0f using a fully connected layer with a single neuron to produce an output |
| 53 | // number that is the same as the input. All memory required to execute this mini network is allocated with |
| 54 | // the provided custom allocator. |
| 55 | // |
| 56 | // Using a Custom Allocator is required for use with Protected Mode and Protected Memory. |
| 57 | // This example is provided using only unprotected malloc as Protected Memory is platform |
| 58 | // and implementation specific. |
| 59 | // |
| 60 | // Note: This example is similar to the SimpleSample application that can also be found in armnn/samples. |
| 61 | // The differences are in the use of a custom allocator, the backend is GpuAcc, and the inputs/outputs |
| 62 | // are being imported instead of copied. (Import must be enabled when using a Custom Allocator) |
| 63 | // You might find this useful for comparison. |
| 64 | int main() |
| 65 | { |
| 66 | using namespace armnn; |
| 67 | |
| 68 | float number; |
| 69 | std::cout << "Please enter a number: " << std::endl; |
| 70 | std::cin >> number; |
| 71 | |
| 72 | // Turn on logging to standard output |
| 73 | // This is useful in this sample so that users can learn more about what is going on |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 74 | ConfigureLogging(true, false, LogSeverity::Info); |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 75 | |
| 76 | // Construct ArmNN network |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 77 | NetworkId networkIdentifier; |
| 78 | INetworkPtr network = INetwork::Create(); |
| 79 | FullyConnectedDescriptor fullyConnectedDesc; |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 80 | float weightsData[] = {1.0f}; // Identity |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 81 | TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32, 0.0f, 0, true); |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 82 | weightsInfo.SetConstant(true); |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 83 | ConstTensor weights(weightsInfo, weightsData); |
| 84 | |
| 85 | IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| 86 | IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights"); |
| 87 | IConnectableLayer* fullyConnectedLayer = |
| 88 | network->AddFullyConnectedLayer(fullyConnectedDesc, "fully connected"); |
| 89 | IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| 90 | |
| 91 | inputLayer->GetOutputSlot(0).Connect(fullyConnectedLayer->GetInputSlot(0)); |
| 92 | weightsLayer->GetOutputSlot(0).Connect(fullyConnectedLayer->GetInputSlot(1)); |
| 93 | fullyConnectedLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| 94 | weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo); |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 95 | |
| 96 | // Create ArmNN runtime: |
| 97 | // |
| 98 | // This is the interesting bit when executing a model with a custom allocator. |
| 99 | // You can have different allocators for different backends. To support this |
| 100 | // the runtime creation option has a map that takes a BackendId and the corresponding |
| 101 | // allocator that should be used for that backend. |
| 102 | // Only GpuAcc supports a Custom Allocator for now |
| 103 | // |
| 104 | // Note: This is not covered in this example but if you want to run a model on |
| 105 | // protected memory a custom allocator needs to be provided that supports |
| 106 | // protected memory allocations and the MemorySource of that allocator is |
| 107 | // set to MemorySource::DmaBufProtected |
| 108 | IRuntime::CreationOptions options; |
| 109 | auto customAllocator = std::make_shared<SampleClBackendCustomAllocator>(); |
| 110 | options.m_CustomAllocatorMap = {{"GpuAcc", std::move(customAllocator)}}; |
| 111 | IRuntimePtr runtime = IRuntime::Create(options); |
| 112 | |
| 113 | //Set the tensors in the network. |
| 114 | TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 115 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 116 | |
| 117 | unsigned int numElements = inputTensorInfo.GetNumElements(); |
| 118 | size_t totalBytes = numElements * sizeof(float); |
| 119 | |
| 120 | TensorInfo outputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 121 | fullyConnectedLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 122 | |
| 123 | // Optimise ArmNN network |
| 124 | OptimizerOptions optOptions; |
| 125 | optOptions.m_ImportEnabled = true; |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 126 | IOptimizedNetworkPtr optNet = |
| 127 | Optimize(*network, {"GpuAcc"}, runtime->GetDeviceSpec(), optOptions); |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 128 | if (!optNet) |
| 129 | { |
| 130 | // This shouldn't happen for this simple sample, with GpuAcc backend. |
| 131 | // But in general usage Optimize could fail if the backend at runtime cannot |
| 132 | // support the model that has been provided. |
| 133 | std::cerr << "Error: Failed to optimise the input network." << std::endl; |
| 134 | return 1; |
| 135 | } |
| 136 | |
| 137 | // Load graph into runtime |
| 138 | std::string ignoredErrorMessage; |
| 139 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
| 140 | runtime->LoadNetwork(networkIdentifier, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 141 | |
| 142 | // Creates structures for input & output |
| 143 | const size_t alignment = |
| 144 | arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>(); |
| 145 | |
| 146 | void* alignedInputPtr = options.m_CustomAllocatorMap["GpuAcc"]->allocate(totalBytes, alignment); |
| 147 | |
| 148 | // Input with negative values |
| 149 | auto* inputPtr = reinterpret_cast<float*>(alignedInputPtr); |
| 150 | std::fill_n(inputPtr, numElements, number); |
| 151 | |
| 152 | void* alignedOutputPtr = options.m_CustomAllocatorMap["GpuAcc"]->allocate(totalBytes, alignment); |
| 153 | auto* outputPtr = reinterpret_cast<float*>(alignedOutputPtr); |
| 154 | std::fill_n(outputPtr, numElements, -10.0f); |
| 155 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 156 | inputTensorInfo = runtime->GetInputTensorInfo(networkIdentifier, 0); |
| 157 | inputTensorInfo.SetConstant(true); |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 158 | InputTensors inputTensors |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 159 | { |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 160 | {0, ConstTensor(inputTensorInfo, alignedInputPtr)}, |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 161 | }; |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 162 | OutputTensors outputTensors |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 163 | { |
Francis Murtagh | bb6c649 | 2022-02-09 15:13:38 +0000 | [diff] [blame] | 164 | {0, Tensor(runtime->GetOutputTensorInfo(networkIdentifier, 0), alignedOutputPtr)} |
Jan Eilers | c1c872f | 2021-07-22 13:17:04 +0100 | [diff] [blame] | 165 | }; |
| 166 | |
| 167 | // Execute network |
| 168 | runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors); |
| 169 | |
| 170 | // Tell the CLBackend to sync memory so we can read the output. |
| 171 | arm_compute::CLScheduler::get().sync(); |
| 172 | auto* outputResult = reinterpret_cast<float*>(alignedOutputPtr); |
| 173 | std::cout << "Your number was " << outputResult[0] << std::endl; |
| 174 | runtime->UnloadNetwork(networkIdentifier); |
| 175 | return 0; |
| 176 | |
| 177 | } |