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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_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>
</ul>
<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>
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<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>
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<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>
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<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>
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<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>
</ul>
<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<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="../advanced/cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_conv_bn_fuser.html">(๋ฒ ํ) FX์์ ํฉ์ฑ๊ณฑ/๋ฐฐ์น ์ ๊ทํ(Convolution/Batch Norm) ๊ฒฐํฉ๊ธฐ(Fuser) ๋ง๋ค๊ธฐ</a></li>
<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>
</ul>
<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<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="../advanced/cpp_frontend.html">PyTorch C++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_classes.html">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
</ul>
<p class="caption"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<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="../advanced/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="../advanced/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="../advanced/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="../advanced/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="../advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
</ul>
<p class="caption"><span class="caption-text">Mobile</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deeplabv3_on_ios.html">iOS์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deeplabv3_on_android.html">์๋๋ก์ด๋์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
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<div class="section" id="script-and-optimize-for-mobile-recipe">
<h1>Script and Optimize for Mobile Recipe<a class="headerlink" href="#script-and-optimize-for-mobile-recipe" title="Permalink to this headline">ยถ</a></h1>
<p>This recipe demonstrates how to convert a PyTorch model to TorchScript which can run in a high-performance C++ environment such as iOS and Android, and how to optimize the converted TorchScript model for mobile deployment.</p>
<div class="section" id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">ยถ</a></h2>
<p>After a PyTorch model is trained and optionally but preferably quantized (see <a class="reference external" href="quantization.html">Quantization Recipe</a> for more details), one essential step before the model can be used in iOS and Android apps is to convert the Python-dependent model to TorchScript, which can then further be optimized for mobile apps. Conversion to TorchScript can be as simple as a single call, or as complicated as changing the original model in many different places.</p>
</div>
<div class="section" id="pre-requisites">
<h2>Pre-requisites<a class="headerlink" href="#pre-requisites" title="Permalink to this headline">ยถ</a></h2>
<p>PyTorch 1.6.0 or 1.7.0</p>
</div>
<div class="section" id="conversion-to-torchscript">
<h2>Conversion to TorchScript<a class="headerlink" href="#conversion-to-torchscript" title="Permalink to this headline">ยถ</a></h2>
<p>There are two basic ways to convert a PyTorch model to TorchScript, using <cite>trace</cite> and <cite>script</cite>. Mixing <cite>trace</cite> and <cite>script</cite> may also be needed in some cases - see <a class="reference external" href="https://tutorials.pytorch.kr/beginner/Intro_to_TorchScript_tutorial.html#mixing-scripting-and-tracing">here</a> for more information.</p>
<div class="section" id="use-the-trace-method">
<h3>Use the <cite>trace</cite> Method<a class="headerlink" href="#use-the-trace-method" title="Permalink to this headline">ยถ</a></h3>
<p>To use the <cite>trace</cite> method on a model, an example or dummy input for the model needs to be specified, the actual input size needs to be the same as the example input size, and the model definition cannot have control flow such as <cite>if</cite> or <cite>for</cite>. The reason for these constraints is that running <cite>trace</cite> on a model with an example input simply calls the modelโs <cite>forward</cite> method with the input and all operations executed in the model layers are recorded, creating the trace of the model.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">dummy_input</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="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="n">torchscript_model</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">trace</span><span class="p">(</span><span class="n">model_quantized</span><span class="p">,</span> <span class="n">dummy_input</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="use-the-script-method">
<h3>Use the <cite>script</cite> Method<a class="headerlink" href="#use-the-script-method" title="Permalink to this headline">ยถ</a></h3>
<p>For the example above, calling <cite>script</cite> below makes no difference:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torchscript_model</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">model_quantized</span><span class="p">)</span>
</pre></div>
</div>
<p>But if a model has some flow control, then <cite>trace</cite> wonโt correctly record all the possible traces. Take some code snippet of an example model definition from <a class="reference external" href="https://tutorials.pytorch.kr/beginner/Intro_to_TorchScript_tutorial.html">here</a> for example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">class</span> <span class="nc">MyDecisionGate</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="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="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="o">-</span><span class="n">x</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="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">traced_cell</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">trace</span><span class="p">(</span><span class="n">MyDecisionGate</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="n">traced_cell</span><span class="o">.</span><span class="n">code</span><span class="p">)</span>
</pre></div>
</div>
<p>The code above will output:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can''t record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if x.sum() > 0:
def forward(self,
x: Tensor) -> Tensor:
return x
</pre></div>
</div>
<p>Note that โthe trace might not generalize to other inputsโ warning above means that if the model has any kind of data-dependent control flow, <cite>trace</cite> is not the right answer. But if we replace the last two lines of the Python code snippet above (before the code output) with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">scripted_cell</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">MyDecisionGate</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scripted_cell</span><span class="o">.</span><span class="n">code</span><span class="p">)</span>
</pre></div>
</div>
<p>The scripted model as shown by the <cite>print</cite> result below will be covering all possible inputs, thus generalizing to other inputs:</p>
<div class="highlight-default 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">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="n">_0</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="mi">0</span><span class="p">))</span>
<span class="k">if</span> <span class="n">_0</span><span class="p">:</span>
<span class="n">_1</span> <span class="o">=</span> <span class="n">x</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">_1</span> <span class="o">=</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="k">return</span> <span class="n">_1</span>
</pre></div>
</div>
<p>This is another example of using <cite>trace</cite> and <cite>script</cite> - it converts the model trained in the PyTorch tutorial <a class="reference external" href="https://tutorials.pytorch.kr/intermediate/seq2seq_translation_tutorial.html">NLP FROM SCRATCH: TRANSLATION WITH A SEQUENCE TO SEQUENCE NETWORK AND ATTENTION</a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">encoder</span> <span class="o">=</span> <span class="n">EncoderRNN</span><span class="p">(</span><span class="n">input_lang</span><span class="o">.</span><span class="n">n_words</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">)</span>
<span class="n">decoder</span> <span class="o">=</span> <span class="n">AttnDecoderRNN</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_lang</span><span class="o">.</span><span class="n">n_words</span><span class="p">)</span>
<span class="c1"># method 1: using trace with example inputs</span>
<span class="n">encoder_input</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="mi">1</span><span class="p">])</span>
<span class="n">encoder_hidden</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</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">hidden_size</span><span class="p">)</span>
<span class="n">decoder_input1</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="mi">0</span><span class="p">]])</span>
<span class="n">decoder_input2</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</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">hidden_size</span><span class="p">)</span>
<span class="n">decoder_input3</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">MAX_LENGTH</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">)</span>
<span class="n">traced_encoder</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">trace</span><span class="p">(</span><span class="n">encoder</span><span class="p">,</span> <span class="p">(</span><span class="n">encoder_input</span><span class="p">,</span> <span class="n">encoder_hidden</span><span class="p">))</span>
<span class="n">traced_decoder</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">trace</span><span class="p">(</span><span class="n">decoder</span><span class="p">,</span> <span class="p">(</span><span class="n">decoder_input1</span><span class="p">,</span> <span class="n">decoder_input2</span><span class="p">,</span> <span class="n">decoder_input3</span><span class="p">))</span>
<span class="c1"># method 2: using script</span>
<span class="n">scripted_encoder</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">encoder</span><span class="p">)</span>
<span class="n">scripted_decoder</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">decoder</span><span class="p">)</span>
</pre></div>
</div>
<p>So is it true that one can simply always use the <cite>script</cite> call and the model is converted to TorchScript? The answer is no, because TorchScript is actually a subset of Python and to make <cite>script</cite> work, the PyTorch model definition must only use the language features of that TorchScript subset of Python. <a class="reference external" href="https://pytorch.org/docs/master/jit_language_reference.html#language-reference">TorchScript Language Reference</a> covers all the details of what is supported in TorchScript. Below we will describe some of the common errors when using the <cite>script</cite> method.</p>
</div>
</div>
<div class="section" id="fix-common-errors-when-using-the-script-method">
<h2>Fix Common Errors When Using the <cite>script</cite> Method<a class="headerlink" href="#fix-common-errors-when-using-the-script-method" title="Permalink to this headline">ยถ</a></h2>
<p>If you apply the <cite>script</cite> method to a non-trivial model, chances are you may encounter several types of errors. Check out <a class="reference external" href="https://tutorials.pytorch.kr/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html">this tutorial</a> for a complete example of converting a chatbot model to TorchScript. But follow the steps below to fix common errors when you run the <cite>script</cite> method:</p>
<div class="section" id="runtimeerror-attribute-lookup-is-not-defined-on-python-value-of-type">
<h3>1. RuntimeError <cite>attribute lookup is not defined on python value of type</cite><a class="headerlink" href="#runtimeerror-attribute-lookup-is-not-defined-on-python-value-of-type" title="Permalink to this headline">ยถ</a></h3>
<p>For this error, pass the value of the model as a parameter in the constructor. This is because when calling <cite>script</cite> on a model that accepts another model as a parameter, the model passed is actually of type <cite>TracedModule</cite> or <cite>ScriptModule</cite>, not of type <cite>Module</cite>, making the the model attribute not defined when scripting.</p>
<p>For example, the <cite>LuongAttnDecoderRNN</cite> module in the tutorial above has an attribute <cite>n_layers</cite>, and the <cite>GreedySearchDecoder</cite> module refers to the <cite>n_layers</cite> attribute of a <cite>decoder</cite> instance of the <cite>LuongAttnDecoderRNN</cite> module, so in order to make <cite>script</cite> work, the <cite>GreedySearchDecoder</cite> moduleโs constructor needs to be changed from:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">encoder</span><span class="p">,</span> <span class="n">decoder</span><span class="p">):</span>
</pre></div>
</div>
<p>to:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">encoder</span><span class="p">,</span> <span class="n">decoder</span><span class="p">,</span> <span class="n">decoder_n_layers</span><span class="p">):</span>
<span class="o">...</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_decoder_n_layers</span> <span class="o">=</span> <span class="n">decoder_n_layers</span>
</pre></div>
</div>
<p>and the <cite>GreedySearchDecoder</cite>โs <cite>forward</cite> method needs to refer <cite>self._decoder_n_layers</cite> instead of <cite>decoder.n_layers</cite>.</p>
</div>
<div class="section" id="runtimeerror-python-value-of-type-cannot-be-used-as-a-value">
<h3>2. RuntimeError <cite>python value of type โโฆโ cannot be used as a value.</cite><a class="headerlink" href="#runtimeerror-python-value-of-type-cannot-be-used-as-a-value" title="Permalink to this headline">ยถ</a></h3>
<p>The complete error message for this one continues with <cite>Perhaps it is a closed over global variable? If so, please consider passing it in as an argument or use a local variable instead.</cite>, store global variablesโ values as attributes in the model constructor (thereโs no need to add them to a special list called <cite>__constants__</cite>). The reason is that global values can be used conveniently in normal model training and inference, but the global values are not accessible during the scripting.</p>
<p>For example, <cite>device</cite> and <cite>SOS_token</cite> are global variables, and to make <cite>script</cite> work, they need to be added to the <cite>GreedySearchDecoder</cite>โs constructor:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">_device</span> <span class="o">=</span> <span class="n">device</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_SOS_token</span> <span class="o">=</span> <span class="n">SOS_token</span>
</pre></div>
</div>
<p>and referred to as <cite>self._device</cite> and <cite>self._SOS_token</cite> instead of <cite>device</cite> and <cite>SOS_token</cite> in the <cite>GreedySearchDecoder</cite>โs <cite>forward</cite> method.</p>
</div>
<div class="section" id="runtimeerror-all-inputs-of-range-must-be-found-tensor-inferred-in-argument">
<h3>3. RuntimeError <cite>all inputs of range must be โโฆโ, found Tensor (inferred) in argument</cite><a class="headerlink" href="#runtimeerror-all-inputs-of-range-must-be-found-tensor-inferred-in-argument" title="Permalink to this headline">ยถ</a></h3>
<p>The error message continues with: <cite>add type definitions for each of the moduleโs forward method arguments. Because all parameters to a TorchScript function are of the `torch.Tensor</cite> type by default, you need to specifically declare the type for each parameter that is not of type โTensorโ. For a complete list of TorchScript-supported types, see <a class="reference external" href="https://pytorch.org/docs/master/jit_language_reference.html#supported-type">here</a>.</p>
<p>For example, the <cite>GreedySearchDecoder</cite>โs <cite>forward</cite> method signature needs to be changed from:</p>
<div class="highlight-default 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">input_seq</span><span class="p">,</span> <span class="n">input_length</span><span class="p">,</span> <span class="n">max_length</span><span class="p">):</span>
</pre></div>
</div>
<p>to:</p>
<div class="highlight-default 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">input_seq</span><span class="p">,</span> <span class="n">input_length</span><span class="p">,</span> <span class="n">max_length</span> <span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
</pre></div>
</div>
<p>After using the <cite>trace</cite> or <cite>script</cite> method above, and fixing possible errors, you should have a TorchScript model ready to be optimized for mobile.</p>
</div>
</div>
<div class="section" id="optimize-a-torchscript-model">
<h2>Optimize a TorchScript Model<a class="headerlink" href="#optimize-a-torchscript-model" title="Permalink to this headline">ยถ</a></h2>
<p>Simply run the following code snippet to optimize a TorchScript model generated with the <cite>trace</cite> and/or <cite>script</cite> method:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.mobile_optimizer</span> <span class="kn">import</span> <span class="n">optimize_for_mobile</span>
<span class="n">optimized_torchscript_model</span> <span class="o">=</span> <span class="n">optimize_for_mobile</span><span class="p">(</span><span class="n">torchscript_model</span><span class="p">)</span>
</pre></div>
</div>
<p>The optimized model can then be saved and deployed in mobile apps:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">optimized_torchscript_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"optimized_torchscript_model.pth"</span><span class="p">)</span>
</pre></div>
</div>
<p>By default, <cite>optimize_for_mobile</cite> will perform the following types of optimizations:</p>
<ul class="simple">
<li>Conv2D and BatchNorm fusion which folds Conv2d-BatchNorm2d into Conv2d;</li>
<li>Insert and fold prepacked ops which rewrites the model graph to replace 2D convolutions and linear ops with their prepacked counterparts.</li>
<li>ReLU and hardtanh fusion which rewrites graph by finding ReLU/hardtanh ops and fuses them together.</li>
<li>Dropout removal which removes dropout nodes from this module when training is false.</li>
</ul>
</div>
<div class="section" id="learn-more">
<h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this headline">ยถ</a></h2>
<ol class="arabic simple">
<li>The official <a class="reference external" href="https://pytorch.org/docs/stable/jit_language_reference.html">TorchScript Language Reference</a>.</li>
<li>The <cite>torch.utils.mobile_optimizer</cite> <a class="reference external" href="https://pytorch.org/docs/stable/mobile_optimizer.html">API documentation</a>.</li>
</ol>
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<ul>
<li><a class="reference internal" href="#">Script and Optimize for Mobile Recipe</a><ul>
<li><a class="reference internal" href="#introduction">Introduction</a></li>
<li><a class="reference internal" href="#pre-requisites">Pre-requisites</a></li>
<li><a class="reference internal" href="#conversion-to-torchscript">Conversion to TorchScript</a><ul>
<li><a class="reference internal" href="#use-the-trace-method">Use the <cite>trace</cite> Method</a></li>
<li><a class="reference internal" href="#use-the-script-method">Use the <cite>script</cite> Method</a></li>
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<li><a class="reference internal" href="#fix-common-errors-when-using-the-script-method">Fix Common Errors When Using the <cite>script</cite> Method</a><ul>
<li><a class="reference internal" href="#runtimeerror-attribute-lookup-is-not-defined-on-python-value-of-type">1. RuntimeError <cite>attribute lookup is not defined on python value of type</cite></a></li>
<li><a class="reference internal" href="#runtimeerror-python-value-of-type-cannot-be-used-as-a-value">2. RuntimeError <cite>python value of type โโฆโ cannot be used as a value.</cite></a></li>
<li><a class="reference internal" href="#runtimeerror-all-inputs-of-range-must-be-found-tensor-inferred-in-argument">3. RuntimeError <cite>all inputs of range must be โโฆโ, found Tensor (inferred) in argument</cite></a></li>
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