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Diffstat (limited to 'src/backends/cl/test/ClCustomAllocatorTests.cpp')
| -rw-r--r-- | src/backends/cl/test/ClCustomAllocatorTests.cpp | 160 |
1 files changed, 160 insertions, 0 deletions
diff --git a/src/backends/cl/test/ClCustomAllocatorTests.cpp b/src/backends/cl/test/ClCustomAllocatorTests.cpp new file mode 100644 index 0000000000..4d1a0e1cfb --- /dev/null +++ b/src/backends/cl/test/ClCustomAllocatorTests.cpp @@ -0,0 +1,160 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/backends/ICustomAllocator.hpp> +#include <armnn/Descriptors.hpp> +#include <armnn/Exceptions.hpp> +#include <armnn/INetwork.hpp> +#include <armnn/IRuntime.hpp> +#include <armnn/Utils.hpp> +#include <armnn/BackendRegistry.hpp> +#include <cl/ClBackend.hpp> + +#include <doctest/doctest.h> + +// Contains the OpenCl interfaces for mapping memory in the Gpu Page Tables +// Requires the OpenCl backend to be included (GpuAcc) +#include <arm_compute/core/CL/CLKernelLibrary.h> +#include <CL/cl_ext.h> +#include <arm_compute/runtime/CL/CLScheduler.h> + + +/** Sample implementation of ICustomAllocator for use with the ClBackend. + * Note: any memory allocated must be host accessible with write access to allow for weights and biases + * to be passed in. Read access is not required.. */ +class SampleClBackendCustomAllocator : public armnn::ICustomAllocator +{ +public: + SampleClBackendCustomAllocator() = default; + + void* allocate(size_t size, size_t alignment) + { + // 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; + } + + /** Interface to be implemented by the child class to free the allocated tensor */ + void free(void* ptr) + { + std::free(ptr); + } + + armnn::MemorySource GetMemorySourceType() + { + return armnn::MemorySource::Malloc; + } +}; + +TEST_SUITE("ClCustomAllocatorTests") +{ + +// This is a copy of the SimpleSample app modified to use a custom +// allocator for the clbackend. It creates a FullyConnected network with a single layer +// taking a single number as an input +TEST_CASE("ClCustomAllocatorTest") +{ + using namespace armnn; + + float number = 3; + + // 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); + 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 + IRuntime::CreationOptions options; // default options + auto customAllocator = std::make_shared<SampleClBackendCustomAllocator>(); + options.m_CustomAllocatorMap = {{"GpuAcc", std::move(customAllocator)}}; + IRuntimePtr run = IRuntime::Create(options); + + //Set the tensors in the network. + TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32); + InputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + + 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"}, run->GetDeviceSpec(), optOptions); + CHECK(optNet); + + // Load graph into runtime + std::string ignoredErrorMessage; + INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); + run->LoadNetwork(networkIdentifier, std::move(optNet), ignoredErrorMessage, networkProperties); + + // Creates structures for input & output + unsigned int numElements = inputTensorInfo.GetNumElements(); + size_t totalBytes = numElements * sizeof(float); + + 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); + + armnn::InputTensors inputTensors + { + {0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), alignedInputPtr)}, + }; + armnn::OutputTensors outputTensors + { + {0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), alignedOutputPtr)} + }; + + // Execute network + run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors); + run->UnloadNetwork(networkIdentifier); + + + // Tell the CLBackend to sync memory so we can read the output. + arm_compute::CLScheduler::get().sync(); + auto* outputResult = reinterpret_cast<float*>(alignedOutputPtr); + + run->UnloadNetwork(networkIdentifier); + CHECK(outputResult[0] == number); + auto& backendRegistry = armnn::BackendRegistryInstance(); + backendRegistry.DeregisterAllocator(ClBackend::GetIdStatic()); +} + +} // test suite ClCustomAllocatorTests
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