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<div class="title">GpuFsaPreCompiledWorkload.cpp</div> </div>
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<a href="_gpu_fsa_pre_compiled_workload_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2024 Arm Ltd and Contributors. All rights reserved.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div>
<div class="line"><a name="l00005"></a><span class="lineno"> 5</span>  </div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_gpu_fsa_pre_compiled_workload_8hpp.html">GpuFsaPreCompiledWorkload.hpp</a>"</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_gpu_fsa_workload_utils_8hpp.html">GpuFsaWorkloadUtils.hpp</a>"</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_polymorphic_downcast_8hpp.html">armnn/utility/PolymorphicDowncast.hpp</a>"</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  </div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_gpu_fsa_tensor_handle_8hpp.html">gpuFsa/GpuFsaTensorHandle.hpp</a>></span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_gpu_fsa_backend_8hpp.html">gpuFsa/GpuFsaBackend.hpp</a>></span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_arm_compute_tensor_utils_8hpp.html">aclCommon/ArmComputeTensorUtils.hpp</a>></span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <fmt/format.h></span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  </div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_arm_compute_tensor_utils_8hpp.html">aclCommon/ArmComputeTensorUtils.hpp</a>></span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <arm_compute/runtime/CL/CLTensor.h></span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <arm_compute/core/ITensorInfo.h></span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <arm_compute/core/TensorInfo.h></span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <arm_compute/core/TensorShape.h></span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <arm_compute/core/CL/CLKernelLibrary.h></span></div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <arm_compute/core/CL/CLCompileContext.h></span></div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  </div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include <arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h></span></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h></span></div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h></span></div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h></span></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h></span></div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  </div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a> {</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  </div>
<div class="line"><a name="l00031"></a><span class="lineno"><a class="line" href="classarmnn_1_1_gpu_fsa_pre_compiled_workload.html#aeef6e7488b495adc96f8abd7d29e3d5f"> 31</a></span> <a class="code" href="classarmnn_1_1_gpu_fsa_pre_compiled_workload.html#aeef6e7488b495adc96f8abd7d29e3d5f">GpuFsaPreCompiledWorkload::GpuFsaPreCompiledWorkload</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.html">PreCompiledQueueDescriptor</a> &descriptor,</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &info)</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  : <a class="code" href="classarmnn_1_1_base_workload.html">BaseWorkload</a><<a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.html">PreCompiledQueueDescriptor</a>>(descriptor, <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>), m_workloadInfo(<a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> {</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// Check that the workload is holding a pointer to a valid pre-compiled object</span></div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.html#aa1fedd1a6371526cb68cc5cc58c87465">m_PreCompiledObject</a> == <span class="keyword">nullptr</span>)</div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  {</div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="stringliteral">"GpuFsaPrecompiledWorkload requires a valid pre-compiled object (GpuWorkloadSketch)."</span>);</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  }</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> }</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  </div>
<div class="line"><a name="l00043"></a><span class="lineno"><a class="line" href="classarmnn_1_1_gpu_fsa_pre_compiled_workload.html#ae071e8822437c78baea75c3aef3a263a"> 43</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_gpu_fsa_pre_compiled_workload.html#ae071e8822437c78baea75c3aef3a263a">GpuFsaPreCompiledWorkload::Execute</a>()<span class="keyword"> const</span></div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword"></span>{</div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment">/*</span></div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment"> * The Execute function of the GpuFsa Backends PreCompiled workload needs to jump through various hoops in order to</span></div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment"> * create a valid sketch and runtime that can execute the kernel</span></div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment"> * First we need all of the data stored within the PreCompiled blob which was used to setup the workload, namely:</span></div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="comment"> * The GpuWorkloadContext, this is a context which contains the TensorInfos and is unique to the graph being run</span></div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment"> * The Sketch, this can contain one or many ops and acts as a subgraph within the context</span></div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment"> * The inputTensorInfos / outputTensorInfos, These are vectors containing the TensorInfos used when creating the sketch</span></div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment"> *</span></div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment"> * It is very important that the Tensors passed into the Runtime being used to execute this sketch are created with</span></div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="comment"> * the same TensorInfos as used when creating the sketch. We do this by creating new tensors, getting the original</span></div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment"> * TensorInfos from the vectors of tensorInfos stored in the blob, and then importing the buffers from our own</span></div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment"> * TensorHandles directly into these newly created Tensors. This allows us to link the externally visible Tensors</span></div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="comment"> * from ArmNN to the Tensors which are needed to execute with the Sketch.</span></div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="comment"> *</span></div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="comment"> */</span></div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">using namespace </span>arm_compute::experimental::dynamic_fusion;</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Get the runtime and configure it with the precompiled sketch</span></div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  ClWorkloadRuntime runtime;</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html">GpuFsaPreCompiledBlob</a> *preCompiledBlob = <span class="keyword">static_cast<</span><a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html">GpuFsaPreCompiledBlob</a>*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.html#aa1fedd1a6371526cb68cc5cc58c87465">m_PreCompiledObject</a>);</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">auto</span> sketch = preCompiledBlob-><a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a14f92a9f65e32c3da896e7b1d45abd02">sketch</a>.release();</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">auto</span> status = runtime.configure(*sketch);</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  </div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Get the TensorInfos stored within the PreCompiledBlob and check they're the right size</span></div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">auto</span> inputTensorInfos = preCompiledBlob-><a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a479b90f0b24c427502d94b716117e034">inputTensorInfos</a>.get();</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">auto</span> outputTensorInfos = preCompiledBlob-><a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#ac49bf679a23aa84f06a6bde3440a4c40">outputTensorInfos</a>.get();</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">if</span> (inputTensorInfos->size() != <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size())</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  {</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"GpuFsaPreCompiledWorkload::Execute: The number of inputTensorInfos"</span></div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="stringliteral">" {} does not match the number of inputs {}."</span>,</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  inputTensorInfos->size(), <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size()));</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  }</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span> (outputTensorInfos->size() != <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size())</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  {</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"GpuFsaPreCompiledWorkload::Execute: The number of outputTensorInfos"</span></div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="stringliteral">" {} does not match the number of outputs {}."</span>,</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  outputTensorInfos->size(), <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size()));</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  </div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// (Important) Allocate auxiliary tensor memory if there are any</span></div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &data : runtime.get_auxiliary_tensors())</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  {</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  arm_compute::CLTensor* tensor = std::get<0>(data);</div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  arm_compute::TensorInfo <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = std::get<1>(data);</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  arm_compute::experimental::dynamic_fusion::AuxMemoryInfo aux_mem_req = std::get<2>(data);</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  tensor->allocator()->init(<a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, aux_mem_req.alignment);</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  tensor->allocator()->allocate(); <span class="comment">// Use ACL allocated memory</span></div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  }</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  </div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// Create and initialize user tensors</span></div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  std::vector<arm_compute::CLTensor*> inputsWeightsOutputs;</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  inputsWeightsOutputs.reserve(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() + <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size());</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  </div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">for</span> (uint32_t inputSlotIdx = 0; inputSlotIdx < <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(); ++inputSlotIdx)</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  {</div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  arm_compute::CLTensor* input = <span class="keyword">new</span> arm_compute::CLTensor{};</div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="comment">// inputTensorInfos is a ptr to a vector of ptrs, so we need to do a double dereference</span></div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  input->allocator()->init(*((*inputTensorInfos)[inputSlotIdx]));</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">auto</span>* inputHandle = PolymorphicDowncast<GpuFsaTensorHandle*>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[inputSlotIdx]);</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  input->allocator()->import_memory(inputHandle->GetTensor().cl_buffer());</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  inputsWeightsOutputs.emplace_back(std::move(input));</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  }</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// Set the outputs</span></div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">for</span> (uint32_t outputSlotIdx = 0; outputSlotIdx < <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size(); ++outputSlotIdx)</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  {</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  arm_compute::CLTensor* output = <span class="keyword">new</span> arm_compute::CLTensor{};</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">// outputTensorInfos is a ptr to a vector of ptrs, so we need to do a double dereference</span></div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  output->allocator()->init(*((*outputTensorInfos)[outputSlotIdx]));</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">auto</span>* outputHandle = PolymorphicDowncast<GpuFsaTensorHandle*>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[outputSlotIdx]);</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  output->allocator()->import_memory(outputHandle->GetTensor().cl_buffer());</div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  inputsWeightsOutputs.emplace_back(std::move(output));</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  runtime.run(inputsWeightsOutputs);</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span> }</div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span> } <span class="comment">// namespace armnn</span></div>
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<div class="ttc" id="astructarmnn_1_1_gpu_fsa_pre_compiled_blob_html_ac49bf679a23aa84f06a6bde3440a4c40"><div class="ttname"><a href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#ac49bf679a23aa84f06a6bde3440a4c40">armnn::GpuFsaPreCompiledBlob::outputTensorInfos</a></div><div class="ttdeci">std::unique_ptr< std::vector< arm_compute::ITensorInfo * > > outputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_backend_8hpp_source.html#l00038">GpuFsaBackend.hpp:38</a></div></div>
<div class="ttc" id="astructarmnn_1_1_queue_descriptor_html_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00026">WorkloadData.hpp:26</a></div></div>
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