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<title>(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ — PyTorch Tutorials 1.10.2+cu102 documentation</title>
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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/captumyt.html">Model Understanding with Captum</a></li>
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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
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<div class="sphx-glr-example-title section" id="lstm">
<span id="sphx-glr-advanced-dynamic-quantization-tutorial-py"></span><h1>(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ<a class="headerlink" href="#lstm" title="Permalink to this headline">ยถ</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/jamesr66a">James Reed</a></p>
<p><strong>Edited by</strong>: <a class="reference external" href="https://github.com/SethHWeidman/">Seth Weidman</a></p>
<p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/kypark7/">๋ฐ๊ฒฝ๋ฆผ</a> <a class="reference external" href="https://github.com/kwonmha/">Myungha Kwon</a></p>
<div class="section" id="id2">
<h2>์์ํ๊ธฐ<a class="headerlink" href="#id2" title="Permalink to this headline">ยถ</a></h2>
<p>์์ํ๋ ๋ชจ๋ธ์ ํฌ๊ธฐ๋ฅผ ์ค์ด๊ณ ์ถ๋ก ์๋๋ฅผ ๋์ด๋ฉด์๋ ์ ํ๋๋ ๋ณ๋ก ๋ฎ์์ง์ง ์๋๋ก,
๋ชจ๋ธ์ ๊ฐ์ค์น์ ํ์ฑ ํจ์๋ฅผ ์ค์ํ์์ ์ ์ํ์ผ๋ก ๋ณํํฉ๋๋ค.</p>
<p>์ด ํํ ๋ฆฌ์ผ์์๋ PyTorch์ <a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ</a>
์์ ๋ฅผ ๋ฐ๋ผํ๋ฉด์, LSTM ๊ธฐ๋ฐ์ ๋จ์ด ์์ธก ๋ชจ๋ธ์ ๊ฐ์ฅ ๊ฐ๋จํ ์์ํ ๊ธฐ๋ฒ์ธ
<a class="reference external" href="https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic">๋์ ์์ํ</a>
๋ฅผ ์ ์ฉํด ๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ๋ถ๋ฌ์ค๊ธฐ</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
</pre></div>
</div>
</div>
<div class="section" id="id5">
<h2>1. ๋ชจ๋ธ ์ ์ํ๊ธฐ<a class="headerlink" href="#id5" title="Permalink to this headline">ยถ</a></h2>
<p>๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ ์์ ์์ ์ฌ์ฉ๋ <a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/model.py">๋ชจ๋ธ</a> ์
๋ฐ๋ผ LSTM ๋ชจ๋ธ ์ํคํ
์ฒ๋ฅผ ์ ์ํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LSTMModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">"""์ธ์ฝ๋, ๋ฐ๋ณต ๋ชจ๋ ๋ฐ ๋์ฝ๋๊ฐ ์๋ ์ปจํ
์ด๋ ๋ชจ๋."""</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">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">LSTMModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">nhid</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">init_weights</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nhid</span> <span class="o">=</span> <span class="n">nhid</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">=</span> <span class="n">nlayers</span>
<span class="k">def</span> <span class="nf">init_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">initrange</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">initrange</span><span class="p">,</span> <span class="n">initrange</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">initrange</span><span class="p">,</span> <span class="n">initrange</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">):</span>
<span class="n">emb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
<span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">emb</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="n">decoded</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="k">return</span> <span class="n">decoded</span><span class="p">,</span> <span class="n">hidden</span>
<span class="k">def</span> <span class="nf">init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bsz</span><span class="p">):</span>
<span class="n">weight</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="k">return</span> <span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span><span class="p">,</span> <span class="n">bsz</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nhid</span><span class="p">),</span>
<span class="n">weight</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span><span class="p">,</span> <span class="n">bsz</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nhid</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="id7">
<h2>2. ํ
์คํธ ๋ฐ์ดํฐ ๋ถ๋ฌ์ค๊ธฐ<a class="headerlink" href="#id7" title="Permalink to this headline">ยถ</a></h2>
<p>๋ค์์ผ๋ก, ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ ์์ ์ <a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/data.py">์ ์ฒ๋ฆฌ</a>
๊ณผ์ ์ ๋ฐ๋ผ <a class="reference external" href="https://www.google.com/search?q=wikitext+2+data">Wikitext-2 ๋ฐ์ดํฐ์
</a> ์ <cite>Corpus</cite> ์ธ์คํด์ค์ ๋ถ๋ฌ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Dictionary</span><span class="p">(</span><span class="nb">object</span><span class="p">):</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="bp">self</span><span class="o">.</span><span class="n">word2idx</span> <span class="o">=</span> <span class="p">{}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">idx2word</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">def</span> <span class="nf">add_word</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">):</span>
<span class="k">if</span> <span class="n">word</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">word2idx</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">idx2word</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">word2idx</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2word</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">word2idx</span><span class="p">[</span><span class="n">word</span><span class="p">]</span>
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2word</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Corpus</span><span class="p">(</span><span class="nb">object</span><span class="p">):</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">path</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dictionary</span> <span class="o">=</span> <span class="n">Dictionary</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'train.txt'</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">valid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'valid.txt'</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'test.txt'</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="sd">"""ํ
์คํธ ํ์ผ ํ ํฐํ"""</span>
<span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="c1"># ์ฌ์ ์ ๋จ์ด ์ถ๊ฐ</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf8"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
<span class="n">words</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="o">+</span> <span class="p">[</span><span class="s1">'<eos>'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">words</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dictionary</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="c1"># ํ์ผ ๋ด์ฉ ํ ํฐํ</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf8"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">idss</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
<span class="n">words</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="o">+</span> <span class="p">[</span><span class="s1">'<eos>'</span><span class="p">]</span>
<span class="n">ids</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">words</span><span class="p">:</span>
<span class="n">ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dictionary</span><span class="o">.</span><span class="n">word2idx</span><span class="p">[</span><span class="n">word</span><span class="p">])</span>
<span class="n">idss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">))</span>
<span class="n">ids</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">idss</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ids</span>
<span class="n">model_data_filepath</span> <span class="o">=</span> <span class="s1">'data/'</span>
<span class="n">corpus</span> <span class="o">=</span> <span class="n">Corpus</span><span class="p">(</span><span class="n">model_data_filepath</span> <span class="o">+</span> <span class="s1">'wikitext-2'</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id9">
<h2>3. ์ฌ์ ํ์ต๋ ๋ชจ๋ธ ๋ถ๋ฌ์ค๊ธฐ<a class="headerlink" href="#id9" title="Permalink to this headline">ยถ</a></h2>
<p>์ด ํํ ๋ฆฌ์ผ์ ๋ชจ๋ธ์ด ํ์ต๋ ํ ์ ์ฉ๋๋ ์์ํ ๊ธฐ์ ์ธ ๋์ ์์ํ์ ๋ํ ํํ ๋ฆฌ์ผ์
๋๋ค.
๋ฐ๋ผ์ ์ฐ๋ฆฌ๋ ๋ฏธ๋ฆฌ ํ์ต๋ ๊ฐ์ค์น๋ฅผ ๋ชจ๋ธ ์ํคํ
์ฒ์ ๋ก๋ํ ๊ฒ ์
๋๋ค. ์ด ๊ฐ์ค์น๋ word
language ๋ชจ๋ธ ์์ ์ ๊ธฐ๋ณธ ์ค์ ์ ์ฌ์ฉํ์ฌ 5๊ฐ์ epoch ๋์ ํ์ตํ์ฌ ์ป์ ๊ฒ์
๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ntokens</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">corpus</span><span class="o">.</span><span class="n">dictionary</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LSTMModel</span><span class="p">(</span>
<span class="n">ntoken</span> <span class="o">=</span> <span class="n">ntokens</span><span class="p">,</span>
<span class="n">ninp</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span>
<span class="n">nhid</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>
<span class="n">nlayers</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
<span class="n">model_data_filepath</span> <span class="o">+</span> <span class="s1">'word_language_model_quantize.pth'</span><span class="p">,</span>
<span class="n">map_location</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>LSTMModel(
(drop): Dropout(p=0.5, inplace=False)
(encoder): Embedding(33278, 512)
(rnn): LSTM(512, 256, num_layers=5, dropout=0.5)
(decoder): Linear(in_features=256, out_features=33278, bias=True)
)
</pre></div>
</div>
<p>์ด์ ์ฌ์ ํ์ต๋ ๋ชจ๋ธ์ด ์ ๋์ํ๋์ง ํ์ธํด๋ณด๊ธฐ ์ํด ํ
์คํธ๋ฅผ ์์ฑํด ๋ณด๊ฒ ์ต๋๋ค.
์ง๊ธ๊น์ง ํํ ๋ฆฌ์ผ์ ์งํํ๋ ๋ฐฉ์์ฒ๋ผ <a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/generate.py">์ด ์์ </a> ๋ฅผ ๋ฐ๋ผ ํ๊ฒ ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">input_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">ntokens</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">init_hidden</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">temperature</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">num_words</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_data_filepath</span> <span class="o">+</span> <span class="s1">'out.txt'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">outf</span><span class="p">:</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> <span class="c1"># ๊ธฐ๋ก์ ์ถ์ ํ์ง ์์ต๋๋ค.</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_words</span><span class="p">):</span>
<span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
<span class="n">word_weights</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">temperature</span><span class="p">)</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">word_idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">multinomial</span><span class="p">(</span><span class="n">word_weights</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">input_</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="n">word_idx</span><span class="p">)</span>
<span class="n">word</span> <span class="o">=</span> <span class="n">corpus</span><span class="o">.</span><span class="n">dictionary</span><span class="o">.</span><span class="n">idx2word</span><span class="p">[</span><span class="n">word_idx</span><span class="p">]</span>
<span class="n">outf</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">word</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span> <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">20</span> <span class="o">==</span> <span class="mi">19</span> <span class="k">else</span> <span class="s1">' '</span><span class="p">))</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">100</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'| Generated </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1"> words'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">1000</span><span class="p">))</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_data_filepath</span> <span class="o">+</span> <span class="s1">'out.txt'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">outf</span><span class="p">:</span>
<span class="n">all_output</span> <span class="o">=</span> <span class="n">outf</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">all_output</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>| Generated 0/1000 words
| Generated 100/1000 words
| Generated 200/1000 words
| Generated 300/1000 words
| Generated 400/1000 words
| Generated 500/1000 words
| Generated 600/1000 words
| Generated 700/1000 words
| Generated 800/1000 words
| Generated 900/1000 words
b"'s" b'all' b'@-@' b'time' b'wives' b'known' b'as' b'"' b'and' b'so' b'mention' b'spurs' b'.' b'"' b'L\xc3\xbctzow' b'writes' b'Midge' b"'s" b'main' b'motion'
b'to' b'Little' b'cities' b',' b'"' b'one' b'of' b'a' b'precedent' b'is' b'unclear' b'to' b'need' b'to' b'visit' b'a' b'battalion' b'and' b'dancers' b'and'
b'most' b'type' b'of' b'cheek' b'.' b'"' b'<unk>' b'team' b'commented' b'that' b'"' b'a' b'book' b'is' b'going' b'to' b'win' b'account' b'\xe2\x80\x98' b'honor'
b'of' b'forelimbs' b'&' b'<unk>' b'bowling' b'for' b'going' b',' b'Heatseekers' b',' b'and' b'his' b'fishing' b'<unk>' b'in' b'his' b'efforts' b'is' b'starring' b'about'
b'that' b'there' b'has' b'received' b'so' b'.' b'"' b'Fleets' b'felt' b'to' b'be' b'controlling' b'up' b'to' b'this' b'college' b'script' b"'s" b'"' b'total'
b'Lycoming' b'"' b',' b'The' b'translation' b'of' b'Arabia' b'in' b'force' b'The' b'87th' b'show' b',' b'Lewis' b'"' b'the' b'Pitt' b'"' b',' b'his'
b'son' b'(' b'Ottomans' b')' b',' b'a' b'many' b'documentary' b'promising' b',' b'poet' b'Bond' b',' b'coordinated' b'Iraqi' b'television' b'Fantasy' b'VanDerWerff' b'.' b'The'
b'Screen' b'consultancy' b'Evening' b'Post' b'<unk>' b'and' b'core' b'Segers\xc3\xa4ll' b'<unk>' b'the' b'yellow' b'domestic' b'effect' b'In' b'his' b'1960s' b'<eos>' b'In' b'1900' b'Hornung'
b'and' b'Lester' b'Roberts' b'allowed' b'a' b'three' b'@-@' b'act' b'<unk>' b'to' b'retain' b'European' b'PSP' b'programs' b'before' b'Bartlett' b'and' b'his' b'daughter' b'run'
b'<unk>' b'sighting' b'for' b'the' b'book' b'.' b'Known' b'after' b'his' b'death' b'on' b'sequel' b'with' b'Lemon' b'Nelson' b'Shakespeare' b',' b'Alfred' b'<unk>' b','
b'John' b'Price' b',' b'The' b'Jeremy' b'Field' b',' b'and' b'Milton' b'was' b'Veblen' b'.' b'The' b'game' b'was' b'originally' b'declared' b'in' b'November' b'2012'
b',' b'challenged' b'criticism' b'to' b'attribute' b'[' b'of' b'the' b'farewell' b'with' b'John' b'<unk>' b'and' b'his' b'discovery' b'.' b'Eva' b'State' b'was' b'a'
b'distant' b'manufacturer' b',' b'describing' b'in' b'assistance' b'.' b'<eos>' b'<eos>' b'=' b'=' b'=' b'Coaching' b'and' b'fires' b'=' b'=' b'=' b'<eos>' b'<eos>'
b'Fez' b'controlled' b'football' b'\xe2\x80\x93' b'71' b'in' b'2007' b'in' b'reserve' b',' b'to' b'add' b'himself' b'<unk>' b'its' b'20' b'increases' b'later' b',' b'with'
b'40' b'earned' b'1' b'\xc2\xb0' b'miles' b'(' b'72' b'km' b'/' b'h' b')' b'.' b'Despite' b'winds' b'in' b'the' b'year' b'and' b'<unk>' b','
b'it' b'was' b'increasingly' b'misunderstood' b'into' b'by' b'Eric' b'Nights' b'.' b'Those' b'who' b'would' b'be' b'Supervisor' b'to' b'be' b'just' b'positive' b',' b'as'
b'soon' b'force' b'or' b'agricultural' b'pressure' b'as' b'they' b'are' b'.' b'<eos>' b'red' b'technique' b'was' b'attributed' b'to' b'turn' b'reconcile' b'(' b'56' b'@.@'
b'0' b'in' b')' b'at' b'Battle' b"'" b'The' b'Soul' b'of' b'Natural' b'Cross' b'(' b'6' b'point' b')' b',' b'which' b'pass' b'magnitude' b'with'
b'the' b'two' b'habitation' b'of' b'refuse' b'.' b'Three' b'dark' b'individuals' b'were' b'fired' b'around' b'an' b'one' b'of' b'1' b'@,@' b'000' b'levels' b'plan'
b',' b'with' b'a' b'practice' b'pass' b'across' b'creating' b'the' b'2009' b'race' b'and' b'adolescence' b',' b'and' b'it' b'Mountain' b'is' b'strongly' b'three' b'for'
b'first' b'fish' b'.' b'<eos>' b'North' b'Korean' b'language' b'concluded' b'containing' b'Mesolithic' b'251' b'obstacles' b'in' b'the' b'Spanish' b'period' b'.' b'Lyrically' b',' b'as'
b'they' b'were' b'never' b'the' b'<unk>' b',' b'yellow' b'theologians' b',' b'and' b'recently' b'early' b'from' b'August' b'life' b'.' b'"' b'It' b"'s" b'the'
b'largest' b'interests' b'to' b'have' b'its' b'distinctive' b'Boo' b'"' b'.' b'Today' b'is' b'Jellicoe' b"'s" b'first' b'drive' b'to' b'promote' b'transit' b'in' b'flying'
b'horses' b'in' b'1978' b'.' b'Then' b'in' b'the' b'media' b',' b'several' b'tracks' b'were' b'illuminated' b'throughout' b'the' b'summer' b'of' b'2012' b',' b'begin'
b'across' b'a' b'absence' b'and' b'declined' b'in' b'Normally' b'achieve' b'questions' b'and' b'rock' b',' b'to' b'provide' b'such' b'Google' b'calls' b',' b'to' b'help'
b'a' b'compromise' b'to' b'throw' b'besides' b',' b'revenues' b',' b'or' b'Chesham' b'.' b'The' b'first' b'further' b'approval' b'date' b'is' b'based' b'in' b'giant'
b'domestic' b',' b'shooting' b',' b'the' b'stable' b'subject' b"'s" b'fuel' b',' b'symbolically' b',' b'with' b'New' b'York' b',' b'Northern' b'Australia' b',' b'and'
b'R\xc3\xa9union' b'.' b'Some' b'New' b'@-@' b'Korean' b'high' b'sand' b'will' b'have' b'been' b'a' b'official' b'staple' b',' b'with' b'many' b'<unk>' b',' b'dark'
b'or' b'control' b',' b'or' b',' b'while' b'at' b'behest' b'.' b'<eos>' b'3' b':' b'cheats' b'plantations' b'and' b'yellow' b'performances' b'has' b'been' b'widely'
b'carried' b'to' b'circumstances' b'(' b'nearing' b'valued' b'tests' b')' b'.' b'Montgomery' b'associating' b'woods' b'by' b'hearing' b'rendering' b'such' b'the' b'tropical' b'Key' b'demand'
b'as' b'"' b'A' b'tiger' b'"' b'.' b'Flooding' b'belongs' b'to' b'2012' b'in' b'many' b'manner' b',' b'New' b'York' b',' b'reddish' b',' b'and'
b'carvings' b',' b'mixed' b',' b'open' b',' b'and' b'even' b'nesting' b',' b'drunkards' b',' b'and' b'high' b',' b'who' b'had' b'taxonomic' b'fist' b'.'
b'<eos>' b'The' b'typical' b'exact' b'election' b'in' b'Ireland' b'are' b'known' b',' b'which' b'no' b'less' b'than' b'one' b'hundred' b'and' b'western' b'thirteen' b'have'
b'urinary' b'spores' b',' b'and' b'comes' b'on' b'to' b'be' b'discarded' b'.' b'safe' b',' b'Hairan' b'Paul' b'rufipes' b'pointed' b'on' b'to' b'impose' b'other'
b'photographs' b'.' b'Condoms' b'is' b'divided' b'from' b'higher' b'conditions' b'conducted' b'by' b'Welles' b'.' b'<eos>' b'<unk>' b'diving' b'sales' b'in' b'Allosaurus' b'of' b'Prey'
b'memorials' b',' b'is' b'an' b'white' b'<unk>' b'Laz' b',' b'a' b'variety' b'of' b'other' b'coral' b'interpretations' b'houses' b'in' b'the' b'common' b'starling' b','
b'but' b'in' b'mottled' b'of' b'<unk>' b'flow' b',' b'Ireland' b'was' b'redesignated' b'as' b'1913' b'was' b'established' b'by' b'items' b'of' b'entertainment' b',' b'complimenting'
b',' b'concrete' b'<unk>' b',' b'yellow' b'auxiliary' b'and' b'stylized' b'Harbor' b'.' b'The' b'second' b'version' b'(' b'Forms' b',' b'searching' b'to' b'Australia' b')'
b'are' b'that' b'of' b'them' b',' b'before' b'some' b'of' b'those' b'households' b'for' b'Ireland' b'and' b'<unk>' b'red' b'species' b'are' b'the' b'largest' b'little'
b'.' b'In' b'the' b'1995' b'eye' b',' b'the' b'kakapo' b'is' b'currently' b'spreading' b'by' b'light' b'days' b',' b'and' b'distributed' b'not' b'open' b'difficulty'
b'it' b'in' b'agricultural' b'toxic' b',' b'such' b'as' b'Mai' b'<unk>' b',' b'meaning' b'an' b'female' b'tree' b'.' b'The' b'counteract' b'window' b'of' b'black'
b'presence' b'and' b'extrasolar' b'manufacturing' b'species' b'have' b'prank' b';' b'they' b'have' b'their' b'own' b'cause' b'sent' b'by' b'slower' b'administratively' b'trees' b',' b'or'
b'one' b'of' b'her' b'cores' b'through' b'world' b',' b'most' b'obvious' b'or' b'Commonwealth' b',' b'even' b'hard' b'.' b'<eos>' b'Northern' b'Ireland' b'in' b'Europe'
b'stretches' b'to' b'bachelor' b'the' b'River' b'Theoretical' b'-' b'1907' b',' b'with' b'the' b'foreign' b'purposes' b'of' b'toss' b',' b'DAGs' b',' b'CRE' b','
b'and' b'pure' b'human' b'plaques' b',' b'often' b'by' b'Human' b'Burnside' b"'s" b'diet' b'against' b'which' b'assembly' b'them' b',' b'brought' b'promotion' b'.' b'<eos>'
b'As' b'1971' b',' b'males' b'do' b'not' b'meet' b'that' b'when' b'gradient' b'between' b'plants' b',' b'and' b'most' b'birds' b'may' b'be' b'long' b'('
b'late' b'48' b'Ireland' b'repelled' b')' b'.' b'When' b'they' b'will' b'be' b'unlikely' b'due' b'to' b'<unk>' b'or' b'New' b'Zealand' b',' b'<unk>' b','
b'they' b'surprised' b'in' b'Free' b'.' b'It' b'is' b'often' b'barriers' b'to' b'ignore' b'such' b'countries' b'.' b'diagram' b'passed' b'me' b'for' b'several' b'sight'
b',' b'which' b'are' b'more' b'controversial' b',' b'it' b'is' b'active' b'funny' b'.' b'After' b'their' b'numbers' b',' b'they' b'have' b'worked' b'their' b'<unk>'
b'or' b'there' b'is' b'no' b'such' b'@-@' b'profile' b'bowls' b',' b'with' b'creating' b'their' b'properties' b'of' b'endanger' b'Socorro' b'.' b'These' b'starlings' b'have'
</pre></div>
</div>
<p>์ด ๋ชจ๋ธ์ด GPT-2๋ ์๋์ง๋ง, ์ธ์ด์ ๊ตฌ์กฐ๋ฅผ ๋ฐฐ์ฐ๊ธฐ ์์ํ ๊ฒ์ฒ๋ผ ๋ณด์
๋๋ค!</p>
<p>๋์ ์์ํ๋ฅผ ์์ฐํ ์ค๋น๊ฐ ๊ฑฐ์ ๋๋ฌ์ต๋๋ค. ๋ช ๊ฐ์ง helper ํจ์๋ฅผ ์ ์ํ๊ธฐ๋ง ํ๋ฉด ๋ฉ๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">bptt</span> <span class="o">=</span> <span class="mi">25</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">eval_batch_size</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># ํ
์คํธ ๋ฐ์ดํฐ์
๋ง๋ค๊ธฐ</span>
<span class="k">def</span> <span class="nf">batchify</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">bsz</span><span class="p">):</span>
<span class="c1"># ๋ฐ์ดํฐ์
์ bsz ๋ถ๋ถ์ผ๋ก ์ผ๋ง๋ ๊น๋ํ๊ฒ ๋๋ ์ ์๋์ง ๊ณ์ฐํฉ๋๋ค.</span>
<span class="n">nbatch</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">//</span> <span class="n">bsz</span>
<span class="c1"># ๊น๋ํ๊ฒ ๋ง์ง ์๋ ์ถ๊ฐ์ ์ธ ๋ถ๋ถ(๋๋จธ์ง๋ค)์ ์๋ผ๋
๋๋ค.</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">narrow</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">nbatch</span> <span class="o">*</span> <span class="n">bsz</span><span class="p">)</span>
<span class="c1"># ๋ฐ์ดํฐ์ ๋ํ์ฌ bsz ๋ฐฐ์น๋ค๋ก ๋๋ฑํ๊ฒ ๋๋๋๋ค.</span>
<span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">bsz</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">batchify</span><span class="p">(</span><span class="n">corpus</span><span class="o">.</span><span class="n">test</span><span class="p">,</span> <span class="n">eval_batch_size</span><span class="p">)</span>
<span class="c1"># ํ๊ฐ ํจ์๋ค</span>
<span class="k">def</span> <span class="nf">get_batch</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="n">seq_len</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">bptt</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">source</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">i</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">source</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">seq_len</span><span class="p">]</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">source</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">seq_len</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span>
<span class="k">def</span> <span class="nf">repackage_hidden</span><span class="p">(</span><span class="n">h</span><span class="p">):</span>
<span class="sd">"""์๋ ์ํ๋ฅผ ๋ณํ๋ ๊ธฐ๋ก์์ ์ ๊ฑฐ๋ ์๋ก์ด tensor๋ก ๋ง๋ญ๋๋ค."""</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">h</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">repackage_hidden</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">h</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">model_</span><span class="p">,</span> <span class="n">data_source</span><span class="p">):</span>
<span class="c1"># Dropout์ ์ค์ง์ํค๋ ํ๊ฐ ๋ชจ๋๋ก ์คํํฉ๋๋ค.</span>
<span class="n">model_</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">total_loss</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">model_</span><span class="o">.</span><span class="n">init_hidden</span><span class="p">(</span><span class="n">eval_batch_size</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">data_source</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bptt</span><span class="p">):</span>
<span class="n">data</span><span class="p">,</span> <span class="n">targets</span> <span class="o">=</span> <span class="n">get_batch</span><span class="p">(</span><span class="n">data_source</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">model_</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">repackage_hidden</span><span class="p">(</span><span class="n">hidden</span><span class="p">)</span>
<span class="n">output_flat</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">ntokens</span><span class="p">)</span>
<span class="n">total_loss</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output_flat</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="k">return</span> <span class="n">total_loss</span> <span class="o">/</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data_source</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id11">
<h2>4. ๋์ ์์ํ ํ
์คํธํ๊ธฐ<a class="headerlink" href="#id11" title="Permalink to this headline">ยถ</a></h2>
<p>๋ง์ง๋ง์ผ๋ก ๋ชจ๋ธ์์ <code class="docutils literal notranslate"><span class="pre">torch.quantization.quantize_dynamic</span></code> ์ ํธ์ถ ํ ์ ์์ต๋๋ค!
๊ตฌ์ฒด์ ์ผ๋ก,</p>
<ul class="simple">
<li>๋ชจ๋ธ์ <code class="docutils literal notranslate"><span class="pre">nn.LSTM</span></code> ๊ณผ <code class="docutils literal notranslate"><span class="pre">nn.Linear</span></code> ๋ชจ๋์ ์์ํ ํ๋๋ก ๋ช
์ํฉ๋๋ค.</li>
<li>๊ฐ์ค์น๋ค์ด <code class="docutils literal notranslate"><span class="pre">int8</span></code> ๊ฐ์ผ๋ก ๋ณํ๋๋๋ก ๋ช
์ํฉ๋๋ค.</li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.quantization</span>
<span class="n">quantized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">quantize_dynamic</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span> <span class="p">{</span><span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">},</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>LSTMModel(
(drop): Dropout(p=0.5, inplace=False)
(encoder): Embedding(33278, 512)
(rnn): DynamicQuantizedLSTM(512, 256, num_layers=5, dropout=0.5)
(decoder): DynamicQuantizedLinear(in_features=256, out_features=33278, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
)
</pre></div>
</div>
<p>๋ชจ๋ธ์ ๋์ผํ๊ฒ ๋ณด์
๋๋ค. ์ด๊ฒ์ด ์ด๋ป๊ฒ ์ด๋์ ์ฃผ๋ ๊ฒ์ผ๊น์? ์ฒซ์งธ, ๋ชจ๋ธ ํฌ๊ธฐ๊ฐ
์๋นํ ์ค์ด ๋ญ๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">print_size_of_model</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s2">"temp.p"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Size (MB):'</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getsize</span><span class="p">(</span><span class="s2">"temp.p"</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">'temp.p'</span><span class="p">)</span>
<span class="n">print_size_of_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">print_size_of_model</span><span class="p">(</span><span class="n">quantized_model</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size (MB): 113.944608
Size (MB): 79.738778
</pre></div>
</div>
<p>๋ ๋ฒ์งธ๋ก, ํ๊ฐ ์์ค๊ฐ์ ๊ฐ์ผ๋ ์ถ๋ก (inference) ์๋๊ฐ ๋นจ๋ผ์ก์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ๋ฉ๋ชจ: ์์ํ ๋ ๋ชจ๋ธ์ ๋จ์ผ ์ค๋ ๋๋ก ์คํ๋๊ธฐ ๋๋ฌธ์ ๋จ์ผ ์ค๋ ๋ ๋น๊ต๋ฅผ ์ํด</span>
<span class="c1"># ์ค๋ ๋ ์๋ฅผ 1๋ก ์ค์ ํ์ต๋๋ค.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">time_model_evaluation</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_data</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_data</span><span class="p">)</span>
<span class="n">elapsed</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">s</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'''loss: </span><span class="si">{0:.3f}</span><span class="se">\n</span><span class="s1">elapsed time (seconds): </span><span class="si">{1:.1f}</span><span class="s1">'''</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">elapsed</span><span class="p">))</span>
<span class="n">time_model_evaluation</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_data</span><span class="p">)</span>
<span class="n">time_model_evaluation</span><span class="p">(</span><span class="n">quantized_model</span><span class="p">,</span> <span class="n">test_data</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loss: 5.167
elapsed time (seconds): 241.1
loss: 5.168
elapsed time (seconds): 166.4
</pre></div>
</div>
<p>MacBook Pro์์ ๋ก์ปฌ๋ก ์คํํ๋ ๊ฒฝ์ฐ, ์์ํ ์์ด๋ ์ถ๋ก (inference)์ ์ฝ 200์ด๊ฐ ๊ฑธ๋ฆฌ๊ณ
์์ํ๋ฅผ ์ฌ์ฉํ๋ฉด ์ฝ 100์ด๊ฐ ๊ฑธ๋ฆฝ๋๋ค.</p>
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<h2>๋ง์น๋ฉฐ<a class="headerlink" href="#id12" title="Permalink to this headline">ยถ</a></h2>
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์ฌ์ด ๋ฐฉ๋ฒ์ด ๋ ์ ์์ต๋๋ค.</p>
<p>์ฝ์ด์ฃผ์
์ ๊ฐ์ฌํฉ๋๋ค. ์ธ์ ๋์ฒ๋ผ ์ด๋ ํ ํผ๋๋ฐฑ๋ ํ์์ด๋, ์๊ฒฌ์ด ์๋ค๋ฉด
<a class="reference external" href="https://github.com/pytorch/pytorch/issues">์ฌ๊ธฐ</a> ์ ์ด์๋ฅผ ๋จ๊ฒจ ์ฃผ์ธ์.</p>
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