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<a href="../../_sources/functionlib/dsplib/tensorliststack.rst.txt" rel="nofollow"> 查看页面源码</a>
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<section id="tensorliststack">
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<h1>Tensorliststack<a class="headerlink" href="#tensorliststack" title="Link to this heading"></a></h1>
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<p>将多个张量（Tensor）堆叠成一个更大的张量。此算子可以处理不同数据类型的张量，将它们按顺序拼接成一个连续的内存块。</p>
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<div class="math notranslate nohighlight">
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\[\text{output\_data} = [\text{tensor}_1, \text{tensor}_2, \ldots, \text{tensor}_n]\]</div>
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<p>其中每个张量的数据类型和元素数量可以不同。</p>
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<dl class="simple">
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<dt>输入：</dt><dd><ul class="simple">
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<li><p><strong>tensor_num</strong> - 张量数量，tensor_num > 0</p></li>
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<li><p><strong>tensor_element_nums</strong> - 每个张量的元素数量（int* 类型）</p></li>
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<li><p><strong>tensor_data_type</strong> - 每个张量元素的数据类型，以字节数表示</p></li>
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<li><p><strong>tensor_data</strong> - 每个张量数据的起始地址（void** 类型）</p></li>
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<li><p><strong>output_data</strong> - 输出结果的数组起始位置（void* 类型）</p></li>
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<li><p><strong>unknown_type_offset</strong> - 未知类型数据在输出结果中的偏移量</p></li>
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<li><p><strong>core_mask</strong> - 核掩码（int），仅共享存储版本需要</p></li>
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</ul>
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</dd>
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<dt>输出：</dt><dd><ul class="simple">
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<li><p><strong>output_data</strong> - 堆叠后的张量数据，按输入顺序连续存储</p></li>
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</ul>
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</dd>
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<dt>支持平台：</dt><dd><p><code class="docutils literal notranslate"><span class="pre">FT78NE</span></code>
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<code class="docutils literal notranslate"><span class="pre">MT7004</span></code></p>
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</dd>
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</dl>
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<div class="admonition note">
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<p class="admonition-title">备注</p>
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<ul class="simple">
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<li><p>该算子不区分具体的数据类型，数据类型信息通过tensor_data_type参数传递</p></li>
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<li><p>当tensor_data_type[i]为0（kTypeUnknown）时，算子会将输出内存清零</p></li>
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<li><p>当tensor_data_type[i]不为0时，算子会按字节复制数据</p></li>
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<li><p>调用前需要确保output_data指向的内存空间足够大以容纳所有张量数据</p></li>
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<li><p>TensorList中不同的Tensor数据类型可能不同，类型信息已经在算子中包含</p></li>
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</ul>
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</div>
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<p><strong>共享存储版本：</strong></p>
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<dl class="c function">
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<dt class="sig sig-object c" id="c.tensorliststack_s">
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<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">tensorliststack_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">tensor_num</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">tensor_element_nums</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">tensor_data_type</span></span>, <span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">tensor_data</span></span>, <span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output_data</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">unknown_type_offset</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.tensorliststack_s" title="Link to this definition"></a><br /></dt>
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<dd></dd></dl>
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<p><strong>C调用示例（共享存储版本）：</strong></p>
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<div class="highlight-c notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1">//FT78NE示例</span>
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<span class="linenos"> 2</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><stdio.h></span>
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<span class="linenos"> 3</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><tensorliststack.h></span>
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<span class="linenos"> 4</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><string.h></span>
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<span class="linenos"> 5</span>
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<span class="linenos"> 6</span><span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
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<span class="linenos"> 7</span><span class="w"> </span><span class="c1">// 假设在DDR空间</span>
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<span class="linenos"> 8</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">tensor_num</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
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<span class="linenos"> 9</span>
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<span class="linenos">10</span><span class="w"> </span><span class="c1">// 每个张量的元素数量</span>
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<span class="linenos">11</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">tensor_element_nums</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">};</span>
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<span class="linenos">12</span>
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<span class="linenos">13</span><span class="w"> </span><span class="c1">// 每个张量的数据类型（以字节数表示）</span>
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<span class="linenos">14</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">tensor_data_type</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">};</span><span class="w"> </span><span class="c1">// 32位int(4字节), 64位double(8字节)</span>
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<span class="linenos">15</span>
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<span class="linenos">16</span><span class="w"> </span><span class="c1">// 张量数据</span>
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<span class="linenos">17</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="o">*</span><span class="n">tensor1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xA0000000</span><span class="p">;</span>
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<span class="linenos">18</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="o">*</span><span class="n">tensor2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">double</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xA0100000</span><span class="p">;</span>
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<span class="linenos">19</span>
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||
<span class="linenos">20</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">tensor_data</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">tensor1</span><span class="p">,</span><span class="w"> </span><span class="n">tensor2</span><span class="p">};</span>
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<span class="linenos">21</span>
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<span class="linenos">22</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">output_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xB0000000</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输出数据</span>
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<span class="linenos">23</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">unknown_type_offset</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
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<span class="linenos">24</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">core_mask</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mh">0xff</span><span class="p">;</span>
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<span class="linenos">25</span>
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<span class="linenos">26</span><span class="w"> </span><span class="c1">// 调用共享存储版本的函数</span>
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<span class="hll"><span class="linenos">27</span><span class="w"> </span><span class="n">tensorliststack_s</span><span class="p">(</span><span class="n">tensor_num</span><span class="p">,</span><span class="w"> </span><span class="n">tensor_element_nums</span><span class="p">,</span><span class="w"> </span><span class="n">tensor_data_type</span><span class="p">,</span>
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</span><span class="hll"><span class="linenos">28</span><span class="w"> </span><span class="n">tensor_data</span><span class="p">,</span><span class="w"> </span><span class="n">output_data</span><span class="p">,</span><span class="w"> </span><span class="n">unknown_type_offset</span><span class="p">,</span><span class="w"> </span><span class="n">core_mask</span><span class="p">);</span>
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</span><span class="linenos">29</span>
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<span class="linenos">30</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
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<span class="linenos">31</span><span class="p">}</span>
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</pre></div>
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</div>
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<p><strong>私有存储版本：</strong></p>
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<dl class="c function">
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<dt class="sig sig-object c" id="c.tensorliststack_p">
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<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">tensorliststack_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">tensor_num</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">tensor_element_nums</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">tensor_data_type</span></span>, <span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">tensor_data</span></span>, <span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output_data</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">unknown_type_offset</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.tensorliststack_p" title="Link to this definition"></a><br /></dt>
|
||
<dd></dd></dl>
|
||
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<p><strong>C调用示例（私有存储版本）：</strong></p>
|
||
<div class="highlight-c notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1">//FT78NE示例</span>
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||
<span class="linenos"> 2</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><stdio.h></span>
|
||
<span class="linenos"> 3</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><tensorliststack.h></span>
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||
<span class="linenos"> 4</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><string.h></span>
|
||
<span class="linenos"> 5</span>
|
||
<span class="linenos"> 6</span><span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
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<span class="linenos"> 7</span><span class="w"> </span><span class="c1">// 假设在L2空间</span>
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||
<span class="linenos"> 8</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">tensor_num</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
|
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<span class="linenos"> 9</span>
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<span class="linenos">10</span><span class="w"> </span><span class="c1">// 每个张量的元素数量</span>
|
||
<span class="linenos">11</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">tensor_element_nums</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">};</span>
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<span class="linenos">12</span>
|
||
<span class="linenos">13</span><span class="w"> </span><span class="c1">// 每个张量的数据类型（以字节数表示）</span>
|
||
<span class="linenos">14</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">tensor_data_type</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">};</span><span class="w"> </span><span class="c1">// 32位int(4字节), 64位double(8字节)</span>
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<span class="linenos">15</span>
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<span class="linenos">16</span><span class="w"> </span><span class="c1">// 张量数据</span>
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<span class="linenos">17</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="o">*</span><span class="n">tensor1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x10000000</span><span class="p">;</span>
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<span class="linenos">18</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="o">*</span><span class="n">tensor2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">double</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x10100000</span><span class="p">;</span>
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<span class="linenos">19</span>
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<span class="linenos">20</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">tensor_data</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">tensor1</span><span class="p">,</span><span class="w"> </span><span class="n">tensor2</span><span class="p">};</span>
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<span class="linenos">21</span>
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<span class="linenos">22</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">output_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x10200000</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输出数据</span>
|
||
<span class="linenos">23</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">unknown_type_offset</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
|
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<span class="linenos">24</span>
|
||
<span class="linenos">25</span><span class="w"> </span><span class="c1">// 调用私有存储版本的函数</span>
|
||
<span class="hll"><span class="linenos">26</span><span class="w"> </span><span class="n">tensorliststack_p</span><span class="p">(</span><span class="n">tensor_num</span><span class="p">,</span><span class="w"> </span><span class="n">tensor_element_nums</span><span class="p">,</span><span class="w"> </span><span class="n">tensor_data_type</span><span class="p">,</span>
|
||
</span><span class="hll"><span class="linenos">27</span><span class="w"> </span><span class="n">tensor_data</span><span class="p">,</span><span class="w"> </span><span class="n">output_data</span><span class="p">,</span><span class="w"> </span><span class="n">unknown_type_offset</span><span class="p">);</span>
|
||
</span><span class="linenos">28</span>
|
||
<span class="linenos">29</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
|
||
<span class="linenos">30</span><span class="p">}</span>
|
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</pre></div>
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