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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>
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<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>
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<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>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
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<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">PyTorch C++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
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<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
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C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dispatcher.html">Registering a Dispatched Operator in C++</a></li>
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<p class="caption"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋ง ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_profiler_tutorial.html">PyTorch Profiler With TensorBoard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</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>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
</ul>
<p class="caption"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">๋จ์ผ ๋จธ์ ์ ์ฌ์ฉํ ๋ชจ๋ธ ๋ณ๋ ฌํ ๋ชจ๋ฒ ์ฌ๋ก</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ ์์ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch๋ก ๋ถ์ฐ ์ดํ๋ฆฌ์ผ์ด์
๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">ํ์ดํ๋ผ์ธ ๋ณ๋ ฌํ๋ก ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต์ํค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ์ ๋ณ๋ ฌ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ์ฌ์ฉํ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต</a></li>
<li class="toctree-l1"><a class="reference internal" href="generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
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<div class="section" id="torchscript-dynamic-parallelism">
<h1>TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)<a class="headerlink" href="#torchscript-dynamic-parallelism" title="Permalink to this headline">ยถ</a></h1>
<p>์ด ํํ ๋ฆฌ์ผ์์๋, TorchScript์์ <em>๋์ inter-op ๋ณ๋ ฌ์ฒ๋ฆฌ</em> ๋ฅผ ํ๋ ๊ตฌ๋ฌธ(syntax)์ ์๊ฐํฉ๋๋ค.
์ด ๋ณ๋ ฌ์ฒ๋ฆฌ์๋ ๋ค์๊ณผ ๊ฐ์ ์์ฑ์ด ์์ต๋๋ค:</p>
<ul class="simple">
<li>๋์ (dynamic) - ์์ฑ๋ ๋ณ๋ ฌ ์์
์ ์์ ์์
๋ถํ๋ ํ๋ก๊ทธ๋จ์ ์ ์ด ํ๋ฆ์ ๋ฐ๋ผ ๋ฌ๋ผ์ง ์ ์์ต๋๋ค.</li>
<li>inter-op - ๋ณ๋ ฌ ์ฒ๋ฆฌ๋ TorchScript ํ๋ก๊ทธ๋จ ์กฐ๊ฐ์ ๋ณ๋ ฌ๋ก ์คํํ๋ ๊ฒ๊ณผ ๊ด๋ จ์ด ์์ต๋๋ค. ์ด๋ ๊ฐ๋ณ ์ฐ์ฐ์๋ฅผ ๋ถํ ํ๊ณ ์ฐ์ฐ์ ์์
์ ํ์ ์งํฉ์ ๋ณ๋ ฌ๋ก ์คํํ๋ ๋ฐฉ์์ธ <em>intra-op parallelism</em> ์๋ ๊ตฌ๋ณ๋ฉ๋๋ค.</li>
</ul>
<div class="section" id="id1">
<h2>๊ธฐ๋ณธ ๊ตฌ๋ฌธ<a class="headerlink" href="#id1" title="Permalink to this headline">ยถ</a></h2>
<p>๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ๋ฅผ ์ํ ๋ ๊ฐ์ง ์ค์ํ API๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค:</p>
<ul class="simple">
<li><code class="docutils literal notranslate"><span class="pre">torch.jit.fork(fn</span> <span class="pre">:</span> <span class="pre">Callable[...,</span> <span class="pre">T],</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span> <span class="pre">-></span> <span class="pre">torch.jit.Future[T]</span></code></li>
<li><code class="docutils literal notranslate"><span class="pre">torch.jit.wait(fut</span> <span class="pre">:</span> <span class="pre">torch.jit.Future[T])</span> <span class="pre">-></span> <span class="pre">T</span></code></li>
</ul>
<p>์ด๋ฌํ ์๋ ๋ฐฉ์์ ๋ค์ ์์ ์์ ์ ์ดํดํ ์ ์์ต๋๋ค:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">neg</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">example</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="c1"># ๋ณ๋ ฌ์ ์ผ๋ก `foo` ๋ฅผ ํธ์ถํฉ๋๋ค.</span>
<span class="c1"># ๋จผ์ , ์์
์ "fork" ํฉ๋๋ค. ์ด ์์
์ `x` ์ธ์(argument)์ ํจ๊ป `foo` ๋ฅผ ์คํํฉ๋๋ค.</span>
<span class="n">future</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">fork</span><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="c1"># ์ผ๋ฐ์ ์ผ๋ก `foo`๋ฅผ ํธ์ถํฉ๋๋ค.</span>
<span class="n">x_normal</span> <span class="o">=</span> <span class="n">foo</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># ๋์งธ, ์์
์ "๊ธฐ๋ค๋ฆฝ๋๋ค".</span>
<span class="c1"># ์์
์ด ๋ณ๋ ฌ๋ก ์คํ ์ค์ผ ์ ์์ผ๋ฏ๋ก ๊ฒฐ๊ณผ๋ฅผ ์ฌ์ฉํ ์ ์์ ๋๊น์ง "๋๊ธฐ" ํด์ผํฉ๋๋ค.</span>
<span class="c1"># ๊ณ์ฐ์ ๋ณ๋ ฌ๋ก ์ํํ๊ธฐ ์ํด์</span>
<span class="c1"># "fork()" ์ "wait()" ์ฌ์ด์์</span>
<span class="c1"># Future๋ฅผ ํธ์ถํ๋ ์ ์ ์ ์ํ์ธ์.</span>
<span class="n">x_parallel</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="n">future</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_normal</span><span class="p">,</span> <span class="n">x_parallel</span>
<span class="nb">print</span><span class="p">(</span><span class="n">example</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span> <span class="c1"># (-1., -1.)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">fork()</span></code> ๋ ํธ์ถ ๊ฐ๋ฅํ(callable) <code class="docutils literal notranslate"><span class="pre">fn</span></code> ,๊ทธ์ ๋ํ ํธ์ถ ๊ฐ๋ฅํ ์ธ์ <code class="docutils literal notranslate"><span class="pre">args</span></code>
๋ฐ <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> ๋ฅผ ์ทจํ๊ณ <code class="docutils literal notranslate"><span class="pre">fn</span></code> ์คํ์ ์ํ ๋น๋๊ธฐ(asynchronous) ์์
์ ์์ฑํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">fn</span></code> ์ ํจ์, ๋ฉ์๋, ๋๋ ๋ชจ๋ ์ธ์คํด์ค์ผ ์ ์์ต๋๋ค.
<code class="docutils literal notranslate"><span class="pre">fork()</span></code> ๋ <code class="docutils literal notranslate"><span class="pre">Future</span></code> ๋ผ๊ณ ๋ถ๋ฆฌ๋ ์ด ์คํ ๊ฒฐ๊ณผ์ ๊ฐ์ ๋ํ ์ฐธ์กฐ(reference)๋ฅผ ๋ฐํํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">fork</span></code> ๋ ๋น๋๊ธฐ ์์
์ ์์ฑํ ์งํ์ ๋ฐํ๋๊ธฐ ๋๋ฌธ์,
<code class="docutils literal notranslate"><span class="pre">fork()</span></code> ํธ์ถ ํ ์ฝ๋ ๋ผ์ธ์ด ์คํ๋ ๋๊น์ง <code class="docutils literal notranslate"><span class="pre">fn</span></code> ์ด ์คํ๋์ง ์์ ์ ์์ต๋๋ค.
๋ฐ๋ผ์, <code class="docutils literal notranslate"><span class="pre">wait()</span></code> ์ ๋น๋๊ธฐ ์์
์ด ์๋ฃ ๋ ๋๊น์ง ๋๊ธฐํ๊ณ ๊ฐ์
๋ฐํํ๋๋ฐ ์ฌ์ฉ๋ฉ๋๋ค.</p>
<p>์ด๋ฌํ ๊ตฌ์กฐ๋ ํจ์ ๋ด์์ ๋ช
๋ น๋ฌธ ์คํ์ ์ค์ฒฉํ๊ฑฐ๋
์์
๋ (์์ ์น์
์ ํ์๋จ) ๋ฃจํ์ ๊ฐ์ ๋ค๋ฅธ
์ธ์ด ๊ตฌ์กฐ๋ก ๊ตฌ์ฑ ๋ ์ ์์ต๋๋ค:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">neg</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">example</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">futures</span> <span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">Future</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="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">futures</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">jit</span><span class="o">.</span><span class="n">fork</span><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">future</span> <span class="ow">in</span> <span class="n">futures</span><span class="p">:</span>
<span class="n">results</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">jit</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="n">future</span><span class="p">))</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">results</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">example</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">([])))</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Future์ ๋น ๋ฆฌ์คํธ(list)๋ฅผ ์ด๊ธฐํํ ๋, ๋ช
์์ ์ธ ์ ํ ์ฃผ์์ <code class="docutils literal notranslate"><span class="pre">futures</span></code> ์ ์ถ๊ฐํด์ผ ํ์ต๋๋ค.
TorchScript์์ ๋น ์ปจํ
์ด๋(container)๋ ๊ธฐ๋ณธ์ ์ผ๋ก tensor ๊ฐ์ ํฌํจํ๋ค๊ณ ๊ฐ์ ํ๋ฏ๋ก
๋ฆฌ์คํธ ์์ฑ์(constructor) #์
<code class="docutils literal notranslate"><span class="pre">List[torch.jit.Future[torch.Tensor]]</span></code> ์ ํ์ ์ฃผ์์ ๋ฌ์์ต๋๋ค.</p>
</div>
<p>์ด ์์ ๋ <code class="docutils literal notranslate"><span class="pre">fork()</span></code> ๋ฅผ ์ฌ์ฉํ์ฌ ํจ์ <code class="docutils literal notranslate"><span class="pre">foo</span></code> ์ ์ธ์คํด์ค 100๊ฐ๋ฅผ ์์ํ๊ณ , 100๊ฐ์ ์์
์ด ์๋ฃ ๋ ๋๊น์ง
๋๊ธฐํ ๋ค์, ๊ฒฐ๊ณผ๋ฅผ ํฉ์ฐํ์ฌ <code class="docutils literal notranslate"><span class="pre">-100.0</span></code> ์ ๋ฐํํฉ๋๋ค.</p>
</div>
<div class="section" id="bidirectional-lstms-ensemble">
<h2>์ ์ฉ๋ ์์: ์๋ฐฉํฅ(bidirectional) LSTMs์ ์์๋ธ(Ensemble)<a class="headerlink" href="#bidirectional-lstms-ensemble" title="Permalink to this headline">ยถ</a></h2>
<p>๋ณด๋ค ํ์ค์ ์ธ ์์์ ๋ณ๋ ฌํ๋ฅผ ์ ์ฉํ๊ณ ์ด๋ค ์ฑ๋ฅ์ ์ป์ ์ ์๋์ง ์ดํด๋ด
์๋ค.
๋จผ์ , ์๋ฐฉํฅ LSTM ๊ณ์ธต์ ์์๋ธ์ธ
๊ธฐ์ค ๋ชจ๋ธ์ ์ ์ํฉ์๋ค.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span><span class="o">,</span> <span class="nn">time</span>
<span class="c1"># RNN ์ฉ์ด์์๋ ์ฐ๋ฆฌ๊ฐ ๊ด์ฌ ๊ฐ๋ ์ฐจ์๋ค์ ์๋์ ๊ฐ์ด ๋ถ๋ฆ
๋๋ค:</span>
<span class="c1"># ๋จ์์๊ฐ์ ๊ฐฏ์ (T)</span>
<span class="c1"># ๋ฐฐ์น ํฌ๊ธฐ (B)</span>
<span class="c1"># "channels"์ ์จ๊ฒจ์ง ํฌ๊ธฐ/์ซ์ (C)</span>
<span class="n">T</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">1024</span>
<span class="c1"># ๋จ์ผ "์๋ฐฉํฅ LSTM"์ ์ ์ํ๋ ๋ชจ๋์
๋๋ค.</span>
<span class="c1"># ์ด๋ ๋จ์ํ ๋์ผํ ์ํ์ค์ ์ ์ฉ๋ ๋ ๊ฐ์ LSTM์ด์ง๋ง ํ๋๋ ๋ฐ๋๋ก ์ ์ฉ๋ฉ๋๋ค.</span>
<span class="k">class</span> <span class="nc">BidirectionalRecurrentLSTM</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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="nb">super</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">cell_f</span> <span class="o">=</span> <span class="n">torch</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">input_size</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="n">C</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_b</span> <span class="o">=</span> <span class="n">torch</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">input_size</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="n">C</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="n">x</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="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="c1"># Forward ๊ณ์ธต</span>
<span class="n">output_f</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># Backward ๊ณ์ธต. ์๊ฐ ์ฐจ์(time dimension)(dim 0)์์ ์
๋ ฅ์ flip (dim 0),</span>
<span class="c1"># ๊ณ์ธต ์ ์ฉํ๊ณ , ์๊ฐ ์ฐจ์์์ ์ถ๋ ฅ์ flip ํฉ๋๋ค.</span>
<span class="n">x_rev</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">output_b</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_b</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">output_b_rev</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">output_b</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">output_f</span><span class="p">,</span> <span class="n">output_b_rev</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># `BidirectionalRecurrentLSTM` ๋ชจ๋์ "ensemble"์
๋๋ค.</span>
<span class="c1"># ์์๋ธ์ ๋ชจ๋์ ๊ฐ์ ์
๋ ฅ์ผ๋ก ํ๋ํ๋์ฉ ์คํ๋๊ณ ,</span>
<span class="c1"># ๋์ ๋๊ณ ํฉ์ฐ๋ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํํฉ๋๋ค.</span>
<span class="k">class</span> <span class="nc">LSTMEnsemble</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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">n_models</span><span class="p">):</span>
<span class="nb">super</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">n_models</span> <span class="o">=</span> <span class="n">n_models</span>
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">([</span>
<span class="n">BidirectionalRecurrentLSTM</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_models</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="n">x</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="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">:</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># fork/wait์ผ๋ก ์คํํ ๊ฒ๋ค์ ์ง์ ๋น๊ต๋ฅผ ์ํด</span>
<span class="c1"># ๋ชจ๋์ ์ธ์คํด์คํํ๊ณ TorchScript๋ฅผ ํตํด ์ปดํ์ผํด ๋ด
์๋ค.</span>
<span class="n">ens</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">LSTMEnsemble</span><span class="p">(</span><span class="n">n_models</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>
<span class="c1"># ์ผ๋ฐ์ ์ผ๋ก ์๋ฒ ๋ฉ ํ
์ด๋ธ(embedding table)์์ ์
๋ ฅ์ ๊ฐ์ ธ์ค์ง๋ง,</span>
<span class="c1"># ๋ฐ๋ชจ๋ฅผ ์ํด ์ฌ๊ธฐ์๋ ๋ฌด์์ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ๊ฒ ์ต๋๋ค.</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
<span class="c1"># ๋ฉ๋ชจ๋ฆฌ ํ ๋น์(memory allocator) ๋ฑ์ ์ค๋น์ํค๊ธฐ ์ํด ๋ชจ๋ธ์ ๋จผ์ ํ๋ฒ ์คํํฉ๋๋ค.</span>
<span class="n">ens</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
<span class="c1"># ์ผ๋ง๋ ๋น ๋ฅด๊ฒ ์คํ๋๋์ง ๋ด
์๋ค!</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">ens</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Inference took'</span><span class="p">,</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="p">,</span> <span class="s1">' seconds'</span><span class="p">)</span>
</pre></div>
</div>
<p>์ ์ปดํจํฐ์์๋ ๋คํธ์ํฌ๊ฐ <code class="docutils literal notranslate"><span class="pre">2.05</span></code> ์ด ๋ง์ ์คํ๋์์ต๋๋ค. ํจ์ฌ ๋ ๋น ๋ฅด๊ฒ ํ ์ ์์ต๋๋ค!</p>
</div>
<div class="section" id="forward-backward">
<h2>Forward, Backward ๊ณ์ธต ๋ณ๋ ฌํ<a class="headerlink" href="#forward-backward" title="Permalink to this headline">ยถ</a></h2>
<p>๊ฐ๋จํ๊ฒ ํ ์ ์๋ ์ผ๋ก๋ <code class="docutils literal notranslate"><span class="pre">BidirectionalRecurrentLSTM</span></code> ๋ด์์ forward, backward ๊ณ์ธต๋ค์ ๋ณ๋ ฌํํ๋ ๊ฒ์ด ์์ต๋๋ค.
์ด ๋, ๊ณ์ฐ ๊ตฌ์กฐ๋ ๊ณ ์ ๋์ด ์์ผ๋ฏ๋ก ์ฐ๋ฆฌ๋ ์ด๋ค ๋ฃจํ๋ ํ์๋ก ํ์ง ์์ต๋๋ค.
<code class="docutils literal notranslate"><span class="pre">BidirectionalRecurrentLSTM</span></code> ์ <code class="docutils literal notranslate"><span class="pre">forward</span></code> ๋ฉ์๋๋ฅผ ๋ค์๊ณผ ๊ฐ์ด ์ฌ์์ฑํด๋ด
์๋ค:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="n">x</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="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="c1"># Backward ๊ณ์ธต๊ณผ ๋ณ๋ ฌ๋ก ์คํ์ํค๊ธฐ ์ํด forward layer๋ฅผ fork()๋ฅผ ํ๋ค.</span>
<span class="n">future_f</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">fork</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cell_f</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="c1"># Backward ๊ณ์ธต. ์๊ฐ ์ฐจ์(time dimension)(dim 0)์์ ์
๋ ฅ์ flip (dim 0),</span>
<span class="c1"># ๊ณ์ธต์ ์ ์ฉํ๊ณ , ๊ทธ๋ฆฌ๊ณ ์๊ฐ ์ฐจ์์์ ์ถ๋ ฅ์ flip ํฉ๋๋ค.</span>
<span class="n">x_rev</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">output_b</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_b</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">output_b_rev</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">output_b</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># Forward ๊ณ์ธต์์ ์ถ๋ ฅ์ ๋ฐ์์ต๋๋ค.</span>
<span class="c1"># ์ด๋ ์ฐ๋ฆฌ๊ฐ ๋ณ๋ ฌํํ๋ ค๋ ์์
*์ดํ*์ ์ผ์ด๋์ผ ํจ์ ์ฃผ์ํด์ผ ํฉ๋๋ค.</span>
<span class="n">output_f</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="n">future_f</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">output_f</span><span class="p">,</span> <span class="n">output_b_rev</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<p>์ด ์์์์, <code class="docutils literal notranslate"><span class="pre">forward()</span></code> ๋ <code class="docutils literal notranslate"><span class="pre">cell_b</span></code> ์ ์คํ์ ๊ณ์ํ๋ ๋์
<code class="docutils literal notranslate"><span class="pre">cell_f</span></code> ๋ฅผ ๋ค๋ฅธ ์ค๋ ๋๋ก ์์ํฉ๋๋ค.
์ด๋ก ์ธํด ๋ ์
์ ์คํ์ด ์๋ก ๊ฒน์นฉ๋๋ค.</p>
<p>์ด ๊ฐ๋จํ ์์ ํ์ ์คํฌ๋ฆฝํธ๋ฅผ ๋ค์ ์คํํ๋ฉด
<code class="docutils literal notranslate"><span class="pre">17%</span></code> ํฅ์๋ <code class="docutils literal notranslate"><span class="pre">1.71</span></code> ์ด์ ๋ฐํ์์ด ๋์ต๋๋ค!</p>
</div>
<div class="section" id="aside-visualizing-parallelism">
<h2>Aside: ๋ณ๋ ฌํ ์๊ฐํ (Visualizing Parallelism)<a class="headerlink" href="#aside-visualizing-parallelism" title="Permalink to this headline">ยถ</a></h2>
<p>์์ง ๋ชจ๋ธ ์ต์ ํ๊ฐ ๋๋์ง ์์์ง๋ง ์ด์ฏค์์ ์ฑ๋ฅ ์๊ฐํ๋ฅผ ์ํ ๋๊ตฌ๋ฅผ ๋์
ํด๋ด
์๋ค.
ํ ๊ฐ์ง ์ค์ํ ๋๊ตฌ๋ <a class="reference external" href="https://pytorch.org/docs/stable/autograd.html#profiler">PyTorch ํ๋กํ์ผ๋ฌ(profiler)</a> ์
๋๋ค.</p>
<p>Chrome์ ์ถ์ ๋ด๋ณด๋ด๊ธฐ ๊ธฐ๋ฅ(trace export functionality)๊ณผ ํจ๊ป ํ๋กํ์ผ๋ฌ๋ฅผ ์ฌ์ฉํด
๋ณ๋ ฌํ๋ ๋ชจ๋ธ์ ์ฑ๋ฅ์ ์๊ฐํํด๋ด
์๋ค:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">()</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
<span class="n">ens</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">prof</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="s1">'parallel.json'</span><span class="p">)</span>
</pre></div>
</div>
<p>์ด ์์ ์ฝ๋ ์กฐ๊ฐ์ <code class="docutils literal notranslate"><span class="pre">parallel.json</span></code> ํ์ผ์ ์์ฑํฉ๋๋ค.
Google Chrome์์ <code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code> ์ผ๋ก ์ด๋ํ์ฌ <code class="docutils literal notranslate"><span class="pre">Load</span></code> ๋ฒํผ์ ํด๋ฆญํ๊ณ
JSON ํ์ผ์ ๋ก๋ํ๋ฉด ๋ค์๊ณผ ๊ฐ์ ํ์๋ผ์ธ์ ๋ณด๊ฒ ๋ ๊ฒ๋๋ค:</p>
<img alt="https://i.imgur.com/rm5hdG9.png" src="https://i.imgur.com/rm5hdG9.png" />
<p>ํ์๋ผ์ธ์ ๊ฐ๋ก์ถ์ ์๊ฐ์, ์ธ๋ก์ถ์ ์คํ ์ค๋ ๋๋ฅผ ๋ํ๋
๋๋ค.
๋ณด๋ค์ํผ ํ ๋ฒ์ ๋ ๊ฐ์ <code class="docutils literal notranslate"><span class="pre">lstm</span></code> ์ ์คํํ๊ณ ์์ต๋๋ค.
์ด๊ฒ์ ์๋ฐฉํฅ(forward, backward) ๊ณ์ธต์ ๋ณ๋ ฌํํ๊ธฐ ์ํด
๋
ธ๋ ฅํ ๊ฒฐ๊ณผ์
๋๋ค!</p>
</div>
<div class="section" id="id2">
<h2>์์๋ธ์์์ ๋ณ๋ ฌํ ๋ชจ๋ธ<a class="headerlink" href="#id2" title="Permalink to this headline">ยถ</a></h2>
<p>์ด ์ฝ๋์ ๋ ๋ง์ ๋ณ๋ ฌํ ๊ธฐํ๊ฐ ์๋ค๋ ๊ฒ์ ๋์น์ฑ์์ง๋ ๋ชจ๋ฆ
๋๋ค:
<code class="docutils literal notranslate"><span class="pre">LSTMEnsemble</span></code> ์ ํฌํจ๋ ๋ชจ๋ธ๋ค์ ์๋ก ๋ณ๋ ฌ๋ก ์คํํ ์๋ ์์ต๋๋ค.
์ด๋ ๊ฒ ํ๊ธฐ ์ํ ๋ฐฉ๋ฒ์ ์์ฃผ ๊ฐ๋จํฉ๋๋ค.
๋ฐ๋ก <code class="docutils literal notranslate"><span class="pre">LSTMEnsemble</span></code> ์ <code class="docutils literal notranslate"><span class="pre">forward</span></code> ๋ฉ์๋๋ฅผ ๋ณ๊ฒฝํ๋ ๋ฐฉ๋ฒ์
๋๋ค:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="n">x</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="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="c1"># ๊ฐ ๋ชจ๋ธ์ ์ํ ์์
์คํํฉ๋๋ค.</span>
<span class="n">futures</span> <span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">Future</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="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">:</span>
<span class="n">futures</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">jit</span><span class="o">.</span><span class="n">fork</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
<span class="c1"># ์คํ๋ ์์
๋ค์์ ๊ฒฐ๊ณผ ์์งํฉ๋๋ค.</span>
<span class="n">results</span> <span class="p">:</span> <span class="n">List</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="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">future</span> <span class="ow">in</span> <span class="n">futures</span><span class="p">:</span>
<span class="n">results</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">jit</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="n">future</span><span class="p">))</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>๋๋, ๋ง์ฝ ๊ฐ๊ฒฐํจ์ ์ค์ํ๊ฒ ์๊ฐํ๋ค๋ฉด ๋ฆฌ์คํธ ์ปดํ๋ฆฌํจ์
(list comprehension)์ ์ฌ์ฉํ ์ ์์ต๋๋ค.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="n">x</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="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="n">futures</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">fork</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">]</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="n">fut</span><span class="p">)</span> <span class="k">for</span> <span class="n">fut</span> <span class="ow">in</span> <span class="n">futures</span><span class="p">]</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>์๋์์ ์ค๋ช
ํ๋ฏ์ด, ์ฐ๋ฆฌ๋ ๋ฃจํ๋ฅผ ์ฌ์ฉํด ์์๋ธ์ ๊ฐ ๋ชจ๋ธ๋ค์ ๋ํ ์์
์ ๋๋ด์ต๋๋ค.
๊ทธ๋ฆฌ๊ณ ๋ชจ๋ ์์
์ด ์๋ฃ๋ ๋๊น์ง ๊ธฐ๋ค๋ฆด ๋ค๋ฅธ ๋ฃจํ๋ฅผ ์ฌ์ฉํ์ต๋๋ค.
์ด๋ ๋ ๋ง์ ๊ณ์ฐ์ ์ค๋ฒ๋ฉ์ ์ ๊ณตํฉ๋๋ค.</p>
<p>์ด ์์ ์
๋ฐ์ดํธ๋ก ์คํฌ๋ฆฝํธ๋ <code class="docutils literal notranslate"><span class="pre">1.4</span></code> ์ด์ ์คํ๋์ด ์ด <code class="docutils literal notranslate"><span class="pre">32%</span></code> ๋งํผ ์๋๊ฐ ํฅ์๋์์ต๋๋ค!
๋จ ๋ ์ค๋ง์ ์ข์ ํจ๊ณผ๋ฅผ ๋ณด์์ต๋๋ค.</p>
<p>๋ํ Chrome ์ถ์ ๊ธฐ(tracer)๋ฅผ ๋ค์ ์ฌ์ฉํด ์งํ ์ํฉ์ ๋ณผ ์ ์์ต๋๋ค:</p>
<img alt="https://i.imgur.com/kA0gyQm.png" src="https://i.imgur.com/kA0gyQm.png" />
<p>์ด์ ๋ชจ๋ <code class="docutils literal notranslate"><span class="pre">LSTM</span></code> ์ธ์คํด์ค๊ฐ ์์ ํ ๋ณ๋ ฌ๋ก ์คํ๋๋ ๊ฒ์ ๋ณผ ์ ์์ต๋๋ค.</p>
</div>
<div class="section" id="id3">
<h2>๊ฒฐ๋ก <a class="headerlink" href="#id3" title="Permalink to this headline">ยถ</a></h2>
<p>์ด ํํ ๋ฆฌ์ผ์์ ์ฐ๋ฆฌ๋ TorchScript์์ ๋์ (dynamic), inter-op ๋ณ๋ ฌ ์ฒ๋ฆฌ๋ฅผ ์ํํ๊ธฐ ์ํ ๊ธฐ๋ณธ API์ธ
<code class="docutils literal notranslate"><span class="pre">fork()</span></code> ์ <code class="docutils literal notranslate"><span class="pre">wait()</span></code> ์ ๋ํด ๋ฐฐ์ ์ต๋๋ค.
์ด๋ฌํ ํจ์๋ค์ ์ฌ์ฉํด TorchScript ์ฝ๋์์ ํจ์, ๋ฉ์๋, ๋๋
<code class="docutils literal notranslate"><span class="pre">Modules</span></code> ์ ์คํ์ ๋ณ๋ ฌํํ๋ ๋ช ๊ฐ์ง ์ผ๋ฐ์ ์ธ ์ฌ์ฉ ํจํด๋ ๋ณด์์ต๋๋ค.
๋ง์ง๋ง์ผ๋ก, ์ด ๊ธฐ์ ์ ์ฌ์ฉํด ๋ชจ๋ธ์ ์ต์ ํํ๋ ์๋ฅผ ํ์ด๋ณด๊ณ , PyTorch์์ ์ฌ์ฉ ๊ฐ๋ฅํ
์ฑ๋ฅ ์ธก์ ๋ฐ ์๊ฐํ ๋๊ตฌ๋ฅผ ์ดํด๋ณด์์ต๋๋ค.</p>
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<li><a class="reference internal" href="#">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a><ul>
<li><a class="reference internal" href="#id1">๊ธฐ๋ณธ ๊ตฌ๋ฌธ</a></li>
<li><a class="reference internal" href="#bidirectional-lstms-ensemble">์ ์ฉ๋ ์์: ์๋ฐฉํฅ(bidirectional) LSTMs์ ์์๋ธ(Ensemble)</a></li>
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