chore: import upstream snapshot with attribution
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<meta name="twitter:description" content="来自语言模型的 top-k 采样的 PyTorch 实现。"/>
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<meta property="og:title" content="前 k 个采样"/>
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<meta property="og:description" content="来自语言模型的 top-k 采样的 PyTorch 实现。"/>
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<title>前 k 个采样</title>
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<a class="parent" href="/">home</a>
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<a class="parent" href="index.html">sampling</a>
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<p>
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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations" target="_blank">
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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/sampling/top_k.py" target="_blank">
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View code on Github</a>
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<a href='#section-0'>#</a>
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<h1>前 k 个采样</h1>
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<p>在这里，我们首先从logits分布中挑选top-k代币，然后从中采样。</p>
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<p>这是一个使用这些采样技术的<a href="experiment.html">实验</a>。</p>
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<div class="highlight"><pre><span class="lineno">15</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="lineno">16</span>
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<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml_nn.sampling</span> <span class="kn">import</span> <span class="n">Sampler</span></pre></div>
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<a href='#section-1'>#</a>
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<h2>Top-k 采样器</h2>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">20</span><span class="k">class</span> <span class="nc">TopKSampler</span><span class="p">(</span><span class="n">Sampler</span><span class="p">):</span></pre></div>
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<ul><li><code class="highlight"><span></span><span class="n">k</span></code>
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是要挑选的代币数量</li>
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<li><code class="highlight"><span></span><span class="n">sampler</span></code>
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是用于前 k 个代币的采样器</li></ul>
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<p><code class="highlight"><span></span><span class="n">sampler</span></code>
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可以是任何以 logits 张量作为输入并返回令牌张量的采样器；例如 <a href="temperature.html">“TemperatureSample”</a>。</p>
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<div class="highlight"><pre><span class="lineno">24</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">sampler</span><span class="p">:</span> <span class="n">Sampler</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">32</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">k</span>
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<span class="lineno">33</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampler</span> <span class="o">=</span> <span class="n">sampler</span></pre></div>
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<a href='#section-4'>#</a>
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<p>来自 logits 的样本</p>
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<div class="highlight"><pre><span class="lineno">35</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">logits</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
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<a href='#section-5'>#</a>
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<p>新的 logit 填充了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.66666em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqa" style=""><span class="mord" style="">−</span><span class="mord" style="">∞</span></span></span></span></span></span>；即零概率</p>
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<div class="highlight"><pre><span class="lineno">40</span> <span class="n">zeros</span> <span class="o">=</span> <span class="n">logits</span><span class="o">.</span><span class="n">new_ones</span><span class="p">(</span><span class="n">logits</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'-inf'</span><span class="p">)</span></pre></div>
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<a href='#section-6'>#</a>
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<p>选择最大的对<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span></span></span></span></span>数及其指数</p>
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<div class="highlight"><pre><span class="lineno">42</span> <span class="n">values</span><span class="p">,</span> <span class="n">indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></pre></div>
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<p>将选定前 k 个索引的值设置为实际对数。其他代币的记录仍然存在<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.66666em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqa" style=""><span class="mord" style="">−</span><span class="mord" style="">∞</span></span></span></span></span></span></p>
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<div class="highlight"><pre><span class="lineno">45</span> <span class="n">zeros</span><span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span></pre></div>
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<p>使用指定采样器从 top-k logits 中抽样。</p>
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<div class="highlight"><pre><span class="lineno">48</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampler</span><span class="p">(</span><span class="n">zeros</span><span class="p">)</span></pre></div>
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