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<div class="title">RefGatherNdWorkload.cpp</div> </div>
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<a href="_ref_gather_nd_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>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160; </div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &lt;fmt/format.h&gt;</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_ref_gather_nd_workload_8hpp.html">RefGatherNdWorkload.hpp</a>&quot;</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160; </div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_gather_8hpp.html">Gather.hpp</a>&quot;</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_profiling_8hpp.html">Profiling.hpp</a>&quot;</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_ref_workload_utils_8hpp.html">RefWorkloadUtils.hpp</a>&quot;</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_workload_utils_8hpp.html">backendsCommon/WorkloadUtils.hpp</a>&quot;</span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160; </div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;{</div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160; </div>
<div class="line"><a name="l00017"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_gather_nd_workload.html#ae071e8822437c78baea75c3aef3a263a"> 17</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_gather_nd_workload.html#ae071e8822437c78baea75c3aef3a263a">RefGatherNdWorkload::Execute</a>()<span class="keyword"> const</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="keyword"></span>{</div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160; <a class="code" href="classarmnn_1_1_ref_gather_nd_workload.html#ae071e8822437c78baea75c3aef3a263a">Execute</a>(<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>, <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>);</div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;}</div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160; </div>
<div class="line"><a name="l00022"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_gather_nd_workload.html#ae1c43d025fc90382d7aff7a500937e2c"> 22</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_gather_nd_workload.html#ae1c43d025fc90382d7aff7a500937e2c">RefGatherNdWorkload::ExecuteAsync</a>(<a class="code" href="structarmnn_1_1experimental_1_1_execution_data.html">ExecutionData</a>&amp; executionData)</div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;{</div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160; <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html">WorkingMemDescriptor</a>* workingMemDescriptor = <span class="keyword">static_cast&lt;</span><a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html">WorkingMemDescriptor</a>*<span class="keyword">&gt;</span>(executionData.<a class="code" href="structarmnn_1_1experimental_1_1_execution_data.html#ad2b382076f26f48cd44783cfca2e3642">m_Data</a>);</div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; <a class="code" href="classarmnn_1_1_ref_gather_nd_workload.html#ae071e8822437c78baea75c3aef3a263a">Execute</a>(workingMemDescriptor-&gt;<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>, workingMemDescriptor-&gt;<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>);</div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;}</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; </div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_gather_nd_workload.html#ae071e8822437c78baea75c3aef3a263a">RefGatherNdWorkload::Execute</a>(std::vector&lt;ITensorHandle*&gt; inputs, std::vector&lt;ITensorHandle*&gt; outputs)<span class="keyword"> const</span></div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="keyword"></span>{</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; <a class="code" href="_ref_workload_utils_8hpp.html#a06acca4fd832e0fb179604112c0505af">ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID</a>(<span class="stringliteral">&quot;RefGatherNdWorkload_Execute&quot;</span>);</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; </div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; inputInfo0 = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(inputs[0]);</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; inputInfo1 = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(inputs[1]);</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; outputInfo = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(outputs[0]);</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; </div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; params_decoderPtr = MakeDecoder&lt;float&gt;(inputInfo0, inputs[0]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; </div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; <span class="keyword">const</span> int32_t* indicesDataPtr = <span class="keyword">reinterpret_cast&lt;</span>int32_t*<span class="keyword">&gt;</span>(inputs[1]-&gt;Map());</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; std::vector&lt;int32_t&gt; indices(indicesDataPtr, indicesDataPtr + inputInfo1.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="comment">// Check for negative indices, it could not be checked in validate as we do not have access to the values there</span></div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; inputInfo1.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(); ++i)</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; {</div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="keywordflow">if</span> (indices[i] &lt; 0)</div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; {</div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>((fmt::format(<span class="stringliteral">&quot;GatherNd: indices[{}] &lt; 0&quot;</span>, i)));</div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; }</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; }</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; </div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; output_encoderPtr = MakeEncoder&lt;float&gt;(outputInfo, outputs[0]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; </div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; std::map&lt;std::string, unsigned int&gt; keyIndices = <a class="code" href="namespacearmnn.html#ac40d3e4035af5fbe68d9e126a8d6367c">CalculateGatherNdKeyIndices</a>(inputInfo0, inputInfo1);</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;<span class="comment"></span> </div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;<span class="comment"> /// Calculate flattened indices: flattenedIndices = indices * flattenedCoefficients</span></div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;<span class="comment"></span> <span class="comment">// Calculate the flattened coefficients to use in the multiplication</span></div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="comment">// to calculate the flattened indices needed by gather</span></div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; TensorShape paramsShape = inputInfo0.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; std::vector&lt;unsigned int&gt; flattenedCoeff(keyIndices[<span class="stringliteral">&quot;ND&quot;</span>], 1);</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1; i &lt; keyIndices[<span class="stringliteral">&quot;ND&quot;</span>]; ++i)</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; {</div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; flattenedCoeff[i-1] = paramsShape[i];</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; }</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = keyIndices[<span class="stringliteral">&quot;ND&quot;</span>]-1; i &gt; 0; --i)</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; {</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; flattenedCoeff[i-1] *= flattenedCoeff[i];</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; }</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; </div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="comment">// Prepare the vector to store the output of the matrix multiplication,</span></div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="comment">// which will represent the flattened indices needed by gather</span></div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> flattenedIndices_Info = inputInfo1;</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; flattenedIndices_Info.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">&quot;W&quot;</span>] });</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; std::vector&lt;int32_t&gt; flattenedIndices(flattenedIndices_Info.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0);</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; </div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="comment">// Multiplication to calculate the flattened indices, which are the indices needed by gather.</span></div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; keyIndices[<span class="stringliteral">&quot;W&quot;</span>]; ++i)</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; {</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j &lt; keyIndices[<span class="stringliteral">&quot;ND&quot;</span>]; ++j)</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; {</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; flattenedIndices[i] += indices[i * keyIndices[<span class="stringliteral">&quot;ND&quot;</span>] + j] * <span class="keyword">static_cast&lt;</span>int32_t<span class="keyword">&gt;</span>(flattenedCoeff[j]);</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; }</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; }</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;<span class="comment"></span> </div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160;<span class="comment"> /// Call Gather with adequate shapes</span></div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;<span class="comment"></span> <span class="comment">// Reshape params into {K, C}</span></div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> params_K_C_Info = inputInfo0;</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; params_K_C_Info.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">&quot;K&quot;</span>], keyIndices[<span class="stringliteral">&quot;C&quot;</span>] });</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; </div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="comment">// Reshape indices into {N, W}</span></div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> indices_N_W_Info = inputInfo1;</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; indices_N_W_Info.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">&quot;N&quot;</span>], keyIndices[<span class="stringliteral">&quot;W&quot;</span>] });</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; </div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="comment">// Reshape output to have the shape given by gather {N, W, C}</span></div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="comment">// (the original outputInfo has the shape given by gatherNd)</span></div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputGather_Info = outputInfo;</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; outputGather_Info.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">&quot;N&quot;</span>], keyIndices[<span class="stringliteral">&quot;W&quot;</span>], keyIndices[<span class="stringliteral">&quot;C&quot;</span>] });</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; </div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="comment">// output_gather = gather(params_K_C, indices_N_W)</span></div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <a class="code" href="namespacearmnn.html#a4f1a1b88b01d8dfda3803776e0778a49">Gather</a>(params_K_C_Info, indices_N_W_Info, outputGather_Info,</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; *params_decoderPtr, flattenedIndices.data(), *output_encoderPtr, 0);</div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;}</div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; </div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160;} <span class="comment">//namespace armnn</span></div>
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