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| <div class="title">AsyncExecutionSample.cpp</div> </div> |
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| <p>Yet another variant of the SimpleSample application. In this little sample app you will be shown how to run a network multiple times asynchronously.</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"><span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><span class="preprocessor">#include <<a class="code" href="_i_runtime_8hpp.xhtml">armnn/IRuntime.hpp</a>></span></div><div class="line"><span class="preprocessor">#include <<a class="code" href="_utils_8hpp.xhtml">armnn/Utils.hpp</a>></span></div><div class="line"><span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"></div><div class="line"><span class="preprocessor">#include <iostream></span></div><div class="line"><span class="preprocessor">#include <thread></span></div><div class="line"><span class="comment"></span></div><div class="line"><span class="comment">/// A simple example of using the ArmNN SDK API to run a network multiple times with different inputs in an asynchronous</span></div><div class="line"><span class="comment">/// manner.</span></div><div class="line"><span class="comment">///</span></div><div class="line"><span class="comment">/// Background info: The usual runtime->EnqueueWorkload, which is used to trigger the execution of a network, is not</span></div><div class="line"><span class="comment">/// thread safe. Each workload has memory assigned to it which would be overwritten by each thread.</span></div><div class="line"><span class="comment">/// Before we added support for this you had to load a network multiple times to execute it at the</span></div><div class="line"><span class="comment">/// same time. Every time a network is loaded, it takes up memory on your device. Making the</span></div><div class="line"><span class="comment">/// execution thread safe helps to reduce the memory footprint for concurrent executions significantly.</span></div><div class="line"><span class="comment">/// This example shows you how to execute a model concurrently (multiple threads) while still only</span></div><div class="line"><span class="comment">/// loading it once.</span></div><div class="line"><span class="comment">///</span></div><div class="line"><span class="comment">/// As in most of our simple samples, the network in this example will ask the user for a single input number for each</span></div><div class="line"><span class="comment">/// execution of the network.</span></div><div class="line"><span class="comment">/// The network consists of a single fully connected layer with a single neuron. The neurons weight is set to 1.0f</span></div><div class="line"><span class="comment">/// to produce an output number that is the same as the input.</span></div><div class="line"><span class="comment"></span><span class="keywordtype">int</span> <a name="a0"></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="comment">// The first part of this code is very similar to the SimpleSample.cpp you should check it out for comparison</span></div><div class="line"> <span class="comment">// The interesting part starts when the graph is loaded into the runtime</span></div><div class="line"></div><div class="line"> std::vector<float> inputs;</div><div class="line"> <span class="keywordtype">float</span> number1;</div><div class="line"> std::cout << <span class="stringliteral">"Please enter a number for the first iteration: "</span> << std::endl;</div><div class="line"> std::cin >> number1;</div><div class="line"> <span class="keywordtype">float</span> number2;</div><div class="line"> std::cout << <span class="stringliteral">"Please enter a number for the second iteration: "</span> << std::endl;</div><div class="line"> std::cin >> number2;</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="a1"></a><a class="code" href="namespacearmnn.xhtml#aa59f7a819c3e29d10ffc41e5c0616872">ConfigureLogging</a>(<span class="keyword">true</span>, <span class="keyword">false</span>, <a name="a2"></a><a class="code" href="namespacearmnn.xhtml#a93a3ba385cad27c4774e5fe64c025d3da0eaadb4fcb48a0a0ed7bc9868be9fbaa">LogSeverity::Warning</a>);</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> myNetwork = <a name="a3"></a><a class="code" href="classarmnn_1_1_i_network.xhtml#a41ce159095e95f7cd4174ce5d4662697">INetwork::Create</a>();</div><div class="line"></div><div class="line"> <span class="keywordtype">float</span> weightsData[] = {1.0f}; <span class="comment">// Identity</span></div><div class="line"> <a name="_a4"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo(<a name="_a5"></a><a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 1}), <a name="a6"></a><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"> weightsInfo.<a name="a7"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"> <a name="_a8"></a><a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"></div><div class="line"> <span class="comment">// Constant layer that now holds weights data for FullyConnected</span></div><div class="line"> <a name="_a9"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> constantWeightsLayer = myNetwork->AddConstantLayer(weights, <span class="stringliteral">"const weights"</span>);</div><div class="line"></div><div class="line"> <a name="_a10"></a><a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> fullyConnectedDesc;</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> fullyConnectedLayer = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc,</div><div class="line"> <span class="stringliteral">"fully connected"</span>);</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <a name="_a11"></a><a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a> = myNetwork->AddInputLayer(0);</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <a name="_a12"></a><a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a> = myNetwork->AddOutputLayer(0);</div><div class="line"></div><div class="line"> InputLayer-><a name="a13"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a14"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(fullyConnectedLayer-><a name="a15"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"> constantWeightsLayer-><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"></div><div class="line"> <span class="comment">// Create ArmNN runtime</span></div><div class="line"> <a name="_a16"></a><a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options; <span class="comment">// default options</span></div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> run = <a name="a17"></a><a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(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}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"> InputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a18"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</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}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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"> constantWeightsLayer-><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>(weightsInfo);</div><div class="line"></div><div class="line"> <span class="comment">// Optimise ArmNN network</span></div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a name="a19"></a><a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*myNetwork, {<a name="a20"></a><a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run->GetDeviceSpec());</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 reference backend.</span></div><div class="line"> <span class="comment">// But in general usage Optimize could fail if the hardware 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 errmsg; <span class="comment">// To hold an eventual error message if loading the network fails</span></div><div class="line"> <span class="comment">// Add network properties to enable async execution. The MemorySource::Undefined variables indicate</span></div><div class="line"> <span class="comment">// that neither inputs nor outputs will be imported. Importing will be covered in another example.</span></div><div class="line"> <a name="_a21"></a><a class="code" href="structarmnn_1_1_i_network_properties.xhtml">armnn::INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <a name="a22"></a><a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">MemorySource::Undefined</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">MemorySource::Undefined</a>);</div><div class="line"> run->LoadNetwork(networkIdentifier,</div><div class="line"> std::move(optNet),</div><div class="line"> errmsg,</div><div class="line"> networkProperties);</div><div class="line"></div><div class="line"> <span class="comment">// Creates structures for inputs and outputs. A vector of float for each execution.</span></div><div class="line"> std::vector<std::vector<float>> inputData{{number1}, {number2}};</div><div class="line"> std::vector<std::vector<float>> outputData;</div><div class="line"> outputData.resize(2, std::vector<float>(1));</div><div class="line"></div><div class="line"> inputTensorInfo = run->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"> std::vector<InputTensors> inputTensors</div><div class="line"> {</div><div class="line"> {{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, inputData[0].data())}},</div><div class="line"> {{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, inputData[1].data())}}</div><div class="line"> };</div><div class="line"> std::vector<OutputTensors> outputTensors</div><div class="line"> {</div><div class="line"> {{0, <a name="_a23"></a><a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputData[0].data())}},</div><div class="line"> {{0, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputData[1].data())}}</div><div class="line"> };</div><div class="line"></div><div class="line"> <span class="comment">// Lambda function to execute the network. We use it as thread function.</span></div><div class="line"> <span class="keyword">auto</span> execute = [&](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> executionIndex)</div><div class="line"> {</div><div class="line"> <span class="keyword">auto</span> memHandle = run->CreateWorkingMemHandle(networkIdentifier);</div><div class="line"> run->Execute(*memHandle, inputTensors[executionIndex], outputTensors[executionIndex]);</div><div class="line"> };</div><div class="line"></div><div class="line"> <span class="comment">// Prepare some threads and let each execute the network with a different input</span></div><div class="line"> std::vector<std::thread> threads;</div><div class="line"> <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputTensors.size(); ++i)</div><div class="line"> {</div><div class="line"> threads.emplace_back(std::thread(execute, i));</div><div class="line"> }</div><div class="line"></div><div class="line"> <span class="comment">// Wait for the threads to finish</span></div><div class="line"> <span class="keywordflow">for</span> (std::thread& t : threads)</div><div class="line"> {</div><div class="line"> <span class="keywordflow">if</span>(t.joinable())</div><div class="line"> {</div><div class="line"> t.join();</div><div class="line"> }</div><div class="line"> }</div><div class="line"></div><div class="line"> std::cout << <span class="stringliteral">"Your numbers were "</span> << outputData[0][0] << <span class="stringliteral">" and "</span> << outputData[1][0] << std::endl;</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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