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introductionMobileBERT被设计成和$BERT_{large}$一样深，而每一">
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MobileBERT a Compact Task-Agnostic BERT for Resource-Limited Devices
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<p>论文<br><a target="_blank" rel="noopener" href="https://arxiv.org/pdf/2004.02984.pdf">MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices</a></p>
<h1 id="introduction"><a href="#introduction" class="headerlink" title="introduction"></a>introduction</h1><p>MobileBERT被设计成和$BERT_{large}$一样深，而每一层都通过采用bottleneck结构和平衡注意力机制和前馈网络进而使其变得更加狭窄（下图）。为了训练MobileBERT，我们首先训练了一个特殊设计的教师模型，一个包含$BERT_{large}$模型（IB-BERT）的inverted-bottleneck模型。然后，我们将知识从IB-BERT迁移到MobileBERT。我们在实证研究中仔细研究了各种知识迁移策略。<br><img src="https://github.com/iMountTai/iMountTai.github.io/tree/main/MobileBERT/f1.jpg" alt><br>实证评估表明，MobileBERT比BERT（BASE）小4.3倍、快5.5倍，并且可以在知名的NLP基准上获得可观的结果。在GLUE的自然语言推理任务中，MobileBERT的GLUE得分为77.7，仅比BERT（BASE）低0.6，在Pixel 4手机上的延迟为62ms。在SQuAD v1.1/v2.0问答任务中，MobileBER获得的dev F1得分为90.3/80.2，甚至比BERT（BASE）高出1.5/2.1。</p>
<h1 id="MobileBERT"><a href="#MobileBERT" class="headerlink" title="MobileBERT"></a>MobileBERT</h1><p><img src="https://github.com/iMountTai/iMountTai.github.io/tree/main/MobileBERT/t1.jpg" alt></p>
<h2 id="瓶颈层和倒瓶颈层"><a href="#瓶颈层和倒瓶颈层" class="headerlink" title="瓶颈层和倒瓶颈层"></a>瓶颈层和倒瓶颈层</h2><p>MobileBERT的体系结构如图1（c）所示。它和$BERT_{large}$一样深，但每个构建块都小得多。如表1所示，每个构建块的隐状态大小仅为128。另一方面，我们为每个构建块引入两个线性变换，以将其输入和输出维度调整为512。根据（He等人，2016）中的术语，我们将这种架构称为瓶颈层。</p>
<p>训练这样一个深并且瘦小的网络是很大的挑战。为了解决这一问题，我们首先构建了一个教师网络，并对其进行训练直至收敛，然后将知识从这个教师网络迁移到MobileBERT。我们发现这比从头开始直接训练MobileBERT的效果要好得多。各种训练策略将在后面的章节中讨论。这里，我们介绍教师网络的架构设计，如图1（b）所示。事实上，教师网络就是$BERT_{large}$，同时增加了倒瓶颈结构（Sandler等人，2018），将其feature map大小调整为512。在下文中，我们将教师网络称为IB-BERT（LARGE）。请注意，IB-BERT和MobileBERT具有相同的feature map大小，即512。因此，我们可以直接比较IB-BERT和MobileBERT的layer-wise输出的差异。我们的知识迁移战略需要这样一种直接的比较。</p>
<p>值得指出的是，同时引入瓶颈和倒瓶颈结构使得体系结构设计具有相当的灵活性。我们可以只使用MobileBERT的瓶颈（相应地，教师变为BERT）或者只使用IB-BERT的倒瓶颈（MobileBERT中没有瓶颈）来对齐它们的特征映射。然而，当使用这两种方法时，我们可以允许IB-BERT（LARGE）在保证MobileBERT足够紧凑的同时保持$BERT_{large}$的性能。</p>
<h2 id="堆叠式前馈网络"><a href="#堆叠式前馈网络" class="headerlink" title="堆叠式前馈网络"></a>堆叠式前馈网络</h2><p>MobileBERT的瓶颈结构带来的一个问题是，多头注意力（MHA）模块和前馈网络（FFN）模块之间的平衡被打破。MHA和FFN在Transformer结构中扮演着不同的角色：前者允许模型共同处理来自不同子空间的信息，而后者增加了模型的非线性。在原始的BERT中，MHA和FFN中的参数之比总是1:2。但在瓶颈结构中，MHA的输入来自更宽的特征映射（块间大小），而FFN的输入来自更窄的瓶颈（块内大小）。这导致MobileBERT中的MHA模块相对包含更多的参数。</p>
<p>为了解决这个问题，我们建议在MobileBERT中使用堆叠式前馈网络来重新平衡MHA和FFN之间的相对大小。如图1（c）所示，每个MobileBERT层包含一个MHA，但多个堆叠的FFN。在MobileBERT中，我们在每个MHA之后使用4个堆叠FFN。</p>
<h1 id="操作优化"><a href="#操作优化" class="headerlink" title="操作优化"></a>操作优化</h1><p><strong>移除层归一化</strong> 将n-channel的隐状态h的层归一化替换为element-wise的线性变换：<br>$\text{NoNORm(h)} = \gamma \circ h+β$<br><strong>使用relu激活</strong> 我们用更简单的relu激活代替了gelu激活（Nair和Hinton，2010）。</p>
<h2 id="Embedding因子分解"><a href="#Embedding因子分解" class="headerlink" title="Embedding因子分解"></a>Embedding因子分解</h2><p>BERT模型中的嵌入表占模型大小的很大一部分。为了压缩嵌入层，如表1所示，我们将MobileBERT中的嵌入维度减少到128。然后，我们对原始token嵌入应用核大小为3的一维卷积来产生512维的输出。</p>
<p>训练目标</p>
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