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<div class="title">CustomMemoryAllocatorSample.cpp</div> </div>
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<p>This example is basically a copy of the SimpleSample example. But it makes use of a CustomAllocator to allocate memory for the inputs, outputs and inter layer memory.</p>
<dl class="section note"><dt>Note</dt><dd>This is currently an experimental interface</dd></dl>
<div class="fragment"><div class="line"><span class="comment">//</span></div><div class="line"><span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><span class="comment">//</span></div><div class="line"></div><div class="line"><span class="preprocessor">#include <<a class="code" href="_arm_n_n_8hpp.xhtml">armnn/ArmNN.hpp</a>></span></div><div class="line"><span class="preprocessor">#include <<a class="code" href="_i_custom_allocator_8hpp.xhtml">armnn/backends/ICustomAllocator.hpp</a>></span></div><div class="line"></div><div class="line"><span class="preprocessor">#include <arm_compute/core/CL/CLKernelLibrary.h></span></div><div class="line"><span class="preprocessor">#include <arm_compute/runtime/CL/CLScheduler.h></span></div><div class="line"></div><div class="line"><span class="preprocessor">#include <iostream></span></div><div class="line"><span class="comment"></span></div><div class="line"><span class="comment">/** Sample implementation of ICustomAllocator for use with the ClBackend.</span></div><div class="line"><span class="comment"> * Note: any memory allocated must be host addressable with write access</span></div><div class="line"><span class="comment"> * in order for ArmNN to be able to properly use it. */</span></div><div class="line"><span class="keyword">class </span>SampleClBackendCustomAllocator : <span class="keyword">public</span> <a name="_a0"></a><a class="code" href="classarmnn_1_1_i_custom_allocator.xhtml">armnn::ICustomAllocator</a></div><div class="line">{</div><div class="line"><span class="keyword">public</span>:</div><div class="line"> SampleClBackendCustomAllocator() = <span class="keywordflow">default</span>;</div><div class="line"></div><div class="line"> <span class="keywordtype">void</span>* <a name="a1"></a><a class="code" href="classarmnn_1_1_i_custom_allocator.xhtml#a6ce9dda753afa9abb498f3d6b673e595">allocate</a>(<span class="keywordtype">size_t</span> size, <span class="keywordtype">size_t</span> alignment)<span class="keyword"> override</span></div><div class="line"><span class="keyword"> </span>{</div><div class="line"> <span class="comment">// If alignment is 0 just use the CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE for alignment</span></div><div class="line"> <span class="keywordflow">if</span> (alignment == 0)</div><div class="line"> {</div><div class="line"> alignment = arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();</div><div class="line"> }</div><div class="line"> <span class="keywordtype">size_t</span> space = size + alignment + alignment;</div><div class="line"> <span class="keyword">auto</span> allocatedMemPtr = std::malloc(space * <span class="keyword">sizeof</span>(<span class="keywordtype">size_t</span>));</div><div class="line"></div><div class="line"> <span class="keywordflow">if</span> (std::align(alignment, size, allocatedMemPtr, space) == <span class="keyword">nullptr</span>)</div><div class="line"> {</div><div class="line"> <span class="keywordflow">throw</span> <a name="_a2"></a><a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"SampleClBackendCustomAllocator::Alignment failed"</span>);</div><div class="line"> }</div><div class="line"> <span class="keywordflow">return</span> allocatedMemPtr;</div><div class="line"> }</div><div class="line"></div><div class="line"> <span class="keywordtype">void</span> <a name="a3"></a><a class="code" href="classarmnn_1_1_i_custom_allocator.xhtml#a7dbeba9295a3f52ea54698c31e555dff">free</a>(<span class="keywordtype">void</span>* ptr)<span class="keyword"> override</span></div><div class="line"><span class="keyword"> </span>{</div><div class="line"> std::free(ptr);</div><div class="line"> }</div><div class="line"></div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277">armnn::MemorySource</a> <a name="a4"></a><a class="code" href="classarmnn_1_1_i_custom_allocator.xhtml#abf02353c52af045a9af48bb40f857ad2">GetMemorySourceType</a>()<span class="keyword"> override</span></div><div class="line"><span class="keyword"> </span>{</div><div class="line"> <span class="keywordflow">return</span> <a name="a5"></a><a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">armnn::MemorySource::Malloc</a>;</div><div class="line"> }</div><div class="line">};</div><div class="line"></div><div class="line"></div><div class="line"><span class="comment">// A simple example application to show the usage of a custom memory allocator. In this sample, the users single</span></div><div class="line"><span class="comment">// input number is multiplied by 1.0f using a fully connected layer with a single neuron to produce an output</span></div><div class="line"><span class="comment">// number that is the same as the input. All memory required to execute this mini network is allocated with</span></div><div class="line"><span class="comment">// the provided custom allocator.</span></div><div class="line"><span class="comment">//</span></div><div class="line"><span class="comment">// Using a Custom Allocator is required for use with Protected Mode and Protected Memory.</span></div><div class="line"><span class="comment">// This example is provided using only unprotected malloc as Protected Memory is platform</span></div><div class="line"><span class="comment">// and implementation specific.</span></div><div class="line"><span class="comment">//</span></div><div class="line"><span class="comment">// Note: This example is similar to the SimpleSample application that can also be found in armnn/samples.</span></div><div class="line"><span class="comment">// The differences are in the use of a custom allocator, the backend is GpuAcc, and the inputs/outputs</span></div><div class="line"><span class="comment">// are being imported instead of copied. (Import must be enabled when using a Custom Allocator)</span></div><div class="line"><span class="comment">// You might find this useful for comparison.</span></div><div class="line"><span class="keywordtype">int</span> <a name="a6"></a><a class="code" href="_armnn_converter_8cpp.xhtml#a0ddf1224851353fc92bfbff6f499fa97">main</a>()</div><div class="line">{</div><div class="line"> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"></div><div class="line"> <span class="keywordtype">float</span> number;</div><div class="line"> std::cout << <span class="stringliteral">"Please enter a number: "</span> << std::endl;</div><div class="line"> std::cin >> number;</div><div class="line"></div><div class="line"> <span class="comment">// Turn on logging to standard output</span></div><div class="line"> <span class="comment">// This is useful in this sample so that users can learn more about what is going on</span></div><div class="line"> <a name="a7"></a><a class="code" href="namespacearmnn.xhtml#aa59f7a819c3e29d10ffc41e5c0616872">ConfigureLogging</a>(<span class="keyword">true</span>, <span class="keyword">false</span>, LogSeverity::Info);</div><div class="line"></div><div class="line"> <span class="comment">// Construct ArmNN network</span></div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkIdentifier;</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = INetwork::Create();</div><div class="line"> <a name="_a8"></a><a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> fullyConnectedDesc;</div><div class="line"> <span class="keywordtype">float</span> weightsData[] = {1.0f}; <span class="comment">// Identity</span></div><div class="line"> <a name="_a9"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo(<a name="_a10"></a><a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 1}), DataType::Float32, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"> weightsInfo.<a name="a11"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"> <a name="_a12"></a><a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"></div><div class="line"> <a name="_a13"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* fullyConnectedLayer =</div><div class="line"> network->AddFullyConnectedLayer(fullyConnectedDesc, <span class="stringliteral">"fully connected"</span>);</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"></div><div class="line"> inputLayer-><a name="a14"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a15"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(fullyConnectedLayer-><a name="a16"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"> weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"> fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"> weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a17"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightsInfo);</div><div class="line"></div><div class="line"> <span class="comment">// Create ArmNN runtime:</span></div><div class="line"> <span class="comment">//</span></div><div class="line"> <span class="comment">// This is the interesting bit when executing a model with a custom allocator.</span></div><div class="line"> <span class="comment">// You can have different allocators for different backends. To support this</span></div><div class="line"> <span class="comment">// the runtime creation option has a map that takes a BackendId and the corresponding</span></div><div class="line"> <span class="comment">// allocator that should be used for that backend.</span></div><div class="line"> <span class="comment">// Only GpuAcc supports a Custom Allocator for now</span></div><div class="line"> <span class="comment">//</span></div><div class="line"> <span class="comment">// Note: This is not covered in this example but if you want to run a model on</span></div><div class="line"> <span class="comment">// protected memory a custom allocator needs to be provided that supports</span></div><div class="line"> <span class="comment">// protected memory allocations and the MemorySource of that allocator is</span></div><div class="line"> <span class="comment">// set to MemorySource::DmaBufProtected</span></div><div class="line"> <a name="_a18"></a><a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"> <span class="keyword">auto</span> customAllocator = std::make_shared<SampleClBackendCustomAllocator>();</div><div class="line"> options.<a name="a19"></a><a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml#a939528f239e70c85f833c87c5fe41d83">m_CustomAllocatorMap</a> = {{<span class="stringliteral">"GpuAcc"</span>, std::move(customAllocator)}};</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime = IRuntime::Create(options);</div><div class="line"></div><div class="line"> <span class="comment">//Set the tensors in the network.</span></div><div class="line"> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 1}), DataType::Float32);</div><div class="line"> inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"></div><div class="line"> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements = inputTensorInfo.<a name="a20"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"> <span class="keywordtype">size_t</span> totalBytes = numElements * <span class="keyword">sizeof</span>(float);</div><div class="line"></div><div class="line"> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 1}), DataType::Float32);</div><div class="line"> fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"></div><div class="line"> <span class="comment">// Optimise ArmNN network</span></div><div class="line"> <a name="_a21"></a><a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"> optOptions.<a name="a22"></a><a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet =</div><div class="line"> <a name="a23"></a><a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<span class="stringliteral">"GpuAcc"</span>}, runtime->GetDeviceSpec(), optOptions);</div><div class="line"> <span class="keywordflow">if</span> (!optNet)</div><div class="line"> {</div><div class="line"> <span class="comment">// This shouldn't happen for this simple sample, with GpuAcc backend.</span></div><div class="line"> <span class="comment">// But in general usage Optimize could fail if the backend at runtime cannot</span></div><div class="line"> <span class="comment">// support the model that has been provided.</span></div><div class="line"> std::cerr << <span class="stringliteral">"Error: Failed to optimise the input network."</span> << std::endl;</div><div class="line"> <span class="keywordflow">return</span> 1;</div><div class="line"> }</div><div class="line"></div><div class="line"> <span class="comment">// Load graph into runtime</span></div><div class="line"> std::string ignoredErrorMessage;</div><div class="line"> <a name="_a24"></a><a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">false</span>, MemorySource::Malloc, MemorySource::Malloc);</div><div class="line"> runtime->LoadNetwork(networkIdentifier, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"></div><div class="line"> <span class="comment">// Creates structures for input & output</span></div><div class="line"> <span class="keyword">const</span> <span class="keywordtype">size_t</span> alignment =</div><div class="line"> arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();</div><div class="line"></div><div class="line"> <span class="keywordtype">void</span>* alignedInputPtr = options.<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml#a939528f239e70c85f833c87c5fe41d83">m_CustomAllocatorMap</a>[<span class="stringliteral">"GpuAcc"</span>]->allocate(totalBytes, alignment);</div><div class="line"></div><div class="line"> <span class="comment">// Input with negative values</span></div><div class="line"> <span class="keyword">auto</span>* inputPtr = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(alignedInputPtr);</div><div class="line"> std::fill_n(inputPtr, numElements, number);</div><div class="line"></div><div class="line"> <span class="keywordtype">void</span>* alignedOutputPtr = options.<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml#a939528f239e70c85f833c87c5fe41d83">m_CustomAllocatorMap</a>[<span class="stringliteral">"GpuAcc"</span>]->allocate(totalBytes, alignment);</div><div class="line"> <span class="keyword">auto</span>* outputPtr = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(alignedOutputPtr);</div><div class="line"> std::fill_n(outputPtr, numElements, -10.0f);</div><div class="line"></div><div class="line"> inputTensorInfo = runtime->GetInputTensorInfo(networkIdentifier, 0);</div><div class="line"> inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"> {</div><div class="line"> {0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(inputTensorInfo, alignedInputPtr)},</div><div class="line"> };</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"> {</div><div class="line"> {0, <a name="_a25"></a><a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runtime->GetOutputTensorInfo(networkIdentifier, 0), alignedOutputPtr)}</div><div class="line"> };</div><div class="line"></div><div class="line"> <span class="comment">// Execute network</span></div><div class="line"> runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"></div><div class="line"> <span class="comment">// Tell the CLBackend to sync memory so we can read the output.</span></div><div class="line"> arm_compute::CLScheduler::get().sync();</div><div class="line"> <span class="keyword">auto</span>* outputResult = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(alignedOutputPtr);</div><div class="line"> std::cout << <span class="stringliteral">"Your number was "</span> << outputResult[0] << std::endl;</div><div class="line"> runtime->UnloadNetwork(networkIdentifier);</div><div class="line"> <span class="keywordflow">return</span> 0;</div><div class="line"></div><div class="line">}</div></div><!-- fragment --> </div><!-- contents -->
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