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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>
</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>
<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>
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<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
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<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>
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<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>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</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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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>
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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="../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>
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<div class="section" id="fuse-modules-recipe">
<h1>Fuse Modules Recipe<a class="headerlink" href="#fuse-modules-recipe" title="Permalink to this headline">ยถ</a></h1>
<p>This recipe demonstrates how to fuse a list of PyTorch modules into a single module and how to do the performance test to compare the fused model with its non-fused version.</p>
<div class="section" id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">ยถ</a></h2>
<p>Before quantization is applied to a model to reduce its size and memory footprint (see <a class="reference external" href="quantization.html">Quantization Recipe</a> for details on quantization), the list of modules in the model may be fused first into a single module. Fusion is optional, but it may save on memory access, make the model run faster, and improve its accuracy.</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="steps">
<h2>Steps<a class="headerlink" href="#steps" title="Permalink to this headline">ยถ</a></h2>
<p>Follow the steps below to fuse an example model, quantize it, script it, optimize it for mobile, save it and test it with the Android benchmark tool.</p>
<div class="section" id="define-the-example-model">
<h3>1. Define the Example Model<a class="headerlink" href="#define-the-example-model" title="Permalink to this headline">ยถ</a></h3>
<p>Use the same example model defined in the <a class="reference external" href="https://tutorials.pytorch.kr/recipes/mobile_perf.html">PyTorch Mobile Performance Recipes</a>:</p>
<div class="highlight-default 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">torch.utils.mobile_optimizer</span> <span class="kn">import</span> <span class="n">optimize_for_mobile</span>
<span class="k">class</span> <span class="nc">AnnotatedConvBnReLUModel</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="n">AnnotatedConvBnReLUModel</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">conv</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">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</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">float</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn</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">BatchNorm2d</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">to</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">float</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu</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">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quant</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">QuantStub</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dequant</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">DeQuantStub</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">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quant</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="bp">self</span><span class="o">.</span><span class="n">conv</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="bp">self</span><span class="o">.</span><span class="n">bn</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="bp">self</span><span class="o">.</span><span class="n">relu</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="bp">self</span><span class="o">.</span><span class="n">dequant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
</div>
<div class="section" id="generate-two-models-with-and-without-fuse-modules">
<h3>2. Generate Two Models with and without <cite>fuse_modules</cite><a class="headerlink" href="#generate-two-models-with-and-without-fuse-modules" title="Permalink to this headline">ยถ</a></h3>
<p>Add the following code below the model definition above and run the script:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">AnnotatedConvBnReLUModel</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">prepare_save</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">fused</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">qconfig</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">get_default_qconfig</span><span class="p">(</span><span class="s1">'qnnpack'</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</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">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">torchscript_model_optimized</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>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">torchscript_model_optimized</span><span class="p">,</span> <span class="s2">"model.pt"</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">fused</span> <span class="k">else</span> <span class="s2">"model_fused.pt"</span><span class="p">)</span>
<span class="n">prepare_save</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">AnnotatedConvBnReLUModel</span><span class="p">()</span>
<span class="n">model_fused</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">fuse_modules</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="p">[[</span><span class="s1">'bn'</span><span class="p">,</span> <span class="s1">'relu'</span><span class="p">]],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model_fused</span><span class="p">)</span>
<span class="n">prepare_save</span><span class="p">(</span><span class="n">model_fused</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>The graphs of the original model and its fused version will be printed as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">AnnotatedConvBnReLUModel</span><span class="p">(</span>
<span class="p">(</span><span class="n">conv</span><span class="p">):</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</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">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="p">(</span><span class="n">bn</span><span class="p">):</span> <span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">(</span><span class="n">relu</span><span class="p">):</span> <span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">(</span><span class="n">quant</span><span class="p">):</span> <span class="n">QuantStub</span><span class="p">()</span>
<span class="p">(</span><span class="n">dequant</span><span class="p">):</span> <span class="n">DeQuantStub</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">AnnotatedConvBnReLUModel</span><span class="p">(</span>
<span class="p">(</span><span class="n">conv</span><span class="p">):</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</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">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="p">(</span><span class="n">bn</span><span class="p">):</span> <span class="n">BNReLU2d</span><span class="p">(</span>
<span class="p">(</span><span class="mi">0</span><span class="p">):</span> <span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">(</span><span class="mi">1</span><span class="p">):</span> <span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">)</span>
<span class="p">(</span><span class="n">relu</span><span class="p">):</span> <span class="n">Identity</span><span class="p">()</span>
<span class="p">(</span><span class="n">quant</span><span class="p">):</span> <span class="n">QuantStub</span><span class="p">()</span>
<span class="p">(</span><span class="n">dequant</span><span class="p">):</span> <span class="n">DeQuantStub</span><span class="p">()</span>
<span class="p">)</span>
</pre></div>
</div>
<p>In the second fused model output, the first item <cite>bn</cite> in the list is replaced with the fused module, and the rest of the modules (<cite>relu</cite> in this example) is replaced with identity. In addition, the non-fused and fused versions of the model <cite>model.pt</cite> and <cite>model_fused.pt</cite> are generated.</p>
</div>
<div class="section" id="build-the-android-benchmark-tool">
<h3>3. Build the Android benchmark Tool<a class="headerlink" href="#build-the-android-benchmark-tool" title="Permalink to this headline">ยถ</a></h3>
<p>Get the PyTorch source and build the Android benchmark tool as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="o">--</span><span class="n">recursive</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">pytorch</span><span class="o">/</span><span class="n">pytorch</span>
<span class="n">cd</span> <span class="n">pytorch</span>
<span class="n">git</span> <span class="n">submodule</span> <span class="n">update</span> <span class="o">--</span><span class="n">init</span> <span class="o">--</span><span class="n">recursive</span>
<span class="n">BUILD_PYTORCH_MOBILE</span><span class="o">=</span><span class="mi">1</span> <span class="n">ANDROID_ABI</span><span class="o">=</span><span class="n">arm64</span><span class="o">-</span><span class="n">v8a</span> <span class="o">./</span><span class="n">scripts</span><span class="o">/</span><span class="n">build_android</span><span class="o">.</span><span class="n">sh</span> <span class="o">-</span><span class="n">DBUILD_BINARY</span><span class="o">=</span><span class="n">ON</span>
</pre></div>
</div>
<p>This will generate the Android benchmark binary <cite>speed_benchmark_torch</cite> in the <cite>build_android/bin</cite> folder.</p>
</div>
<div class="section" id="test-compare-the-fused-and-non-fused-models">
<h3>4. Test Compare the Fused and Non-Fused Models<a class="headerlink" href="#test-compare-the-fused-and-non-fused-models" title="Permalink to this headline">ยถ</a></h3>
<p>Connect your Android device, then copy <cite>speed_benchmark_torch</cite> and the model files and run the benchmark tool on them:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">adb</span> <span class="n">push</span> <span class="n">build_android</span><span class="o">/</span><span class="nb">bin</span><span class="o">/</span><span class="n">speed_benchmark_torch</span> <span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">tmp</span>
<span class="n">adb</span> <span class="n">push</span> <span class="n">model</span><span class="o">.</span><span class="n">pt</span> <span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">tmp</span>
<span class="n">adb</span> <span class="n">push</span> <span class="n">model_fused</span><span class="o">.</span><span class="n">pt</span> <span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">tmp</span>
<span class="n">adb</span> <span class="n">shell</span> <span class="s2">"/data/local/tmp/speed_benchmark_torch --model=/data/local/tmp/model.pt"</span> <span class="o">--</span><span class="n">input_dims</span><span class="o">=</span><span class="s2">"1,3,224,224"</span> <span class="o">--</span><span class="n">input_type</span><span class="o">=</span><span class="s2">"float"</span>
<span class="n">adb</span> <span class="n">shell</span> <span class="s2">"/data/local/tmp/speed_benchmark_torch --model=/data/local/tmp/model_fused.pt"</span> <span class="o">--</span><span class="n">input_dims</span><span class="o">=</span><span class="s2">"1,3,224,224"</span> <span class="o">--</span><span class="n">input_type</span><span class="o">=</span><span class="s2">"float"</span>
</pre></div>
</div>
<p>The results from the last two commands should be like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Main</span> <span class="n">run</span> <span class="n">finished</span><span class="o">.</span> <span class="n">Microseconds</span> <span class="n">per</span> <span class="nb">iter</span><span class="p">:</span> <span class="mf">6189.07</span><span class="o">.</span> <span class="n">Iters</span> <span class="n">per</span> <span class="n">second</span><span class="p">:</span> <span class="mf">161.575</span>
</pre></div>
</div>
<p>and</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Main</span> <span class="n">run</span> <span class="n">finished</span><span class="o">.</span> <span class="n">Microseconds</span> <span class="n">per</span> <span class="nb">iter</span><span class="p">:</span> <span class="mf">6216.65</span><span class="o">.</span> <span class="n">Iters</span> <span class="n">per</span> <span class="n">second</span><span class="p">:</span> <span class="mf">160.858</span>
</pre></div>
</div>
<p>For this example model, there is no much performance difference between the fused and non-fused models. But the similar steps can be used to fuse and prepare a real deep model and test to see the performance improvement. Keep in mind that currently <cite>torch.quantization.fuse_modules</cite> only fuses the following sequence of modules:</p>
<ul class="simple">
<li>conv, bn</li>
<li>conv, bn, relu</li>
<li>conv, relu</li>
<li>linear, relu</li>
<li>bn, relu</li>
</ul>
<p>If any other sequence list is provided to the <cite>fuse_modules</cite> call, it will simply be ignored.</p>
</div>
</div>
<div class="section" id="learn-more">
<h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this headline">ยถ</a></h2>
<p>See <a class="reference external" href="https://pytorch.org/docs/stable/quantization.html#preparing-model-for-quantization">here</a> for the official documentation of <cite>torch.quantization.fuse_modules</cite>.</p>
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<ul>
<li><a class="reference internal" href="#">Fuse Modules 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="#steps">Steps</a><ul>
<li><a class="reference internal" href="#define-the-example-model">1. Define the Example Model</a></li>
<li><a class="reference internal" href="#generate-two-models-with-and-without-fuse-modules">2. Generate Two Models with and without <cite>fuse_modules</cite></a></li>
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