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<p class="caption" role="heading"><span class="caption-text">파이토치(PyTorch) 레시피</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">모든 레시피 보기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">모든 프로토타입 레시피 보기</a></li>
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<p class="caption" role="heading"><span class="caption-text">파이토치(PyTorch) 시작하기</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">파이토치(PyTorch) 기본 익히기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">빠른 시작(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">텐서(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset과 DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">변형(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">신경망 모델 구성하기</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>를 사용한 자동 미분</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">모델 매개변수 최적화하기</a></li>
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<p class="caption" role="heading"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">PyTorch 소개 - YouTube 시리즈</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Pytorch Tensor 소개</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>
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<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" role="heading"><span class="caption-text">파이토치(PyTorch) 배우기</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch로 딥러닝하기: 60분만에 끝장내기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">예제로 배우는 파이토치(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> 이 <em>실제로</em> 무엇인가요?</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard_tutorial.html">TensorBoard로 모델, 데이터, 학습 시각화하기</a></li>
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<li class="toctree-l1"><a class="reference internal" href="torchvision_tutorial.html">TorchVision Object Detection Finetuning Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">적대적 예제 생성(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">배포를 위해 비전 트랜스포머(Vision Transformer) 모델 최적화하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="tiatoolbox_tutorial.html">Whole Slide Image Classification Using PyTorch and TIAToolbox</a></li>
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<p class="caption" role="heading"><span class="caption-text">오디오</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>
<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="speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="text_to_speech_with_torchaudio.html">Text-to-speech with Tacotron2</a></li>
<li class="toctree-l1"><a class="reference internal" href="forced_alignment_with_torchaudio_tutorial.html">wav2vec2을 이용한 강제 정렬</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">텍스트</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/bettertransformer_tutorial.html">Fast Transformer Inference with Better Transformer</a></li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_classification_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_generation_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 생성하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="seq2seq_translation_tutorial.html">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext 라이브러리로 텍스트 분류하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html"><code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> 와 torchtext로 언어 번역하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/torchtext_custom_dataset_tutorial.html">Preprocess custom text dataset using Torchtext</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">백엔드</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">강화학습</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="reinforcement_q_learning.html">강화 학습 (DQN) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="reinforcement_ppo.html">Reinforcement Learning (PPO) with TorchRL Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="mario_rl_tutorial.html">마리오 게임 RL 에이전트로 학습하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/pendulum.html">Pendulum: Writing your environment and transforms with TorchRL</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch 모델을 프로덕션 환경에 배포하기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
<li class="toctree-l1"><a class="reference internal" href="flask_rest_api_tutorial.html">Flask를 사용하여 Python에서 PyTorch를 REST API로 배포하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript 소개</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_export.html">C++에서 TorchScript 모델 로딩하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/super_resolution_with_onnxruntime.html">(선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="realtime_rpi.html">Raspberry Pi 4 에서 실시간 추론(Inference) (30fps!)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch 프로파일링</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch 모듈 프로파일링하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_intro_tutorial.html">Introduction to Holistic Trace Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_trace_diff_tutorial.html">Trace Diff using Holistic Trace Analysis</a></li>
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<p class="caption" role="heading"><span class="caption-text">Code Transforms with FX</span></p>
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<li class="toctree-l1"><a class="reference internal" href="fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
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<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(베타) PyTorch를 사용한 Channels Last 메모리 형식</a></li>
<li class="toctree-l1"><a class="reference internal" href="forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="jacobians_hessians.html">Jacobians, Hessians, hvp, vhp, and more: composing function transforms</a></li>
<li class="toctree-l1"><a class="reference internal" href="ensembling.html">모델 앙상블</a></li>
<li class="toctree-l1"><a class="reference internal" href="per_sample_grads.html">Per-sample-gradients</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">PyTorch C++ 프론트엔드 사용하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript의 동적 병렬 처리(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_autograd.html">C++ 프론트엔드의 자동 미분 (autograd)</a></li>
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<p class="caption" role="heading"><span class="caption-text">PyTorch 확장하기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="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">커스텀 C++ 클래스로 TorchScript 확장하기</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>
<li class="toctree-l1"><a class="reference internal" href="../advanced/privateuseone.html">Facilitating New Backend Integration by PrivateUse1</a></li>
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<p class="caption" role="heading"><span class="caption-text">모델 최적화</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch 모듈 프로파일링하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard_profiler_tutorial.html">텐서보드를 이용한 파이토치 프로파일러</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Ray Tune을 사용한 하이퍼파라미터 튜닝</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">배포를 위해 비전 트랜스포머(Vision Transformer) 모델 최적화하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(베타) PyTorch에서 Eager Mode를 이용한 정적 양자화</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchserve_with_ipex.html">Grokking PyTorch Intel CPU performance from first principles</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchserve_with_ipex_2.html">Grokking PyTorch Intel CPU performance from first principles (Part 2)</a></li>
<li class="toctree-l1"><a class="reference internal" href="nvfuser_intro_tutorial.html">Getting Started - Accelerate Your Scripts with nvFuser</a></li>
<li class="toctree-l1"><a class="reference internal" href="ax_multiobjective_nas_tutorial.html">Multi-Objective NAS with Ax</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="inductor_debug_cpu.html">Inductor CPU backend debugging and profiling</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html">(Beta) Scaled Dot Product Attention (SDPA)로 고성능 트랜스포머(Transformers) 구현하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#torch-compile-sdpa"><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 과 함께 SDPA 사용하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#sdpa-atteition-bias">SDPA를 <code class="docutils literal notranslate"><span class="pre">atteition.bias</span></code> 하위 클래스와 사용하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#id8">결론</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/knowledge_distillation_tutorial.html">Knowledge Distillation Tutorial</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">병렬 및 분산 학습</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../distributed/home.html">Distributed and Parallel Training Tutorials</a></li>
<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="../beginner/ddp_series_intro.html">Distributed Data Parallel in PyTorch - Video Tutorials</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_parallel_tutorial.html">단일 머신을 사용한 모델 병렬화 모범 사례</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_tutorial.html">분산 데이터 병렬 처리 시작하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="dist_tuto.html">PyTorch로 분산 어플리케이션 개발하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="FSDP_tutorial.html">Getting Started with Fully Sharded Data Parallel(FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="FSDP_adavnced_tutorial.html">Advanced Model Training with Fully Sharded Data Parallel (FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="TP_tutorial.html">Large Scale Transformer model training with Tensor Parallel (TP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="process_group_cpp_extension_tutorial.html">Cpp 확장을 사용한 프로세스 그룹 백엔드 사용자 정의</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="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">분산 데이터 병렬(DDP)과 분산 RPC 프레임워크 결합</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/ddp_pipeline.html">분산 데이터 병렬 처리와 병렬 처리 파이프라인을 사용한 트랜스포머 모델 학습</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" role="heading"><span class="caption-text">Edge with ExecuTorch</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html">Exporting to ExecuTorch Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href=" https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html">Running an ExecuTorch Model in C++ Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html">Using the ExecuTorch SDK to Profile a Model</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-ios.html">Building an ExecuTorch iOS Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-android.html">Building an ExecuTorch Android Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html">Lowering a Model as a Delegate</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">추천 시스템</span></p>
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<li class="toctree-l1"><a class="reference internal" href="torchrec_tutorial.html">TorchRec 소개</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/sharding.html">Exploring TorchRec sharding</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Multimodality</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/flava_finetuning_tutorial.html">TorchMultimodal 튜토리얼: FLAVA 미세조정</a></li>
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<article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
<div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">참고</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-intermediate-torch-compile-tutorial-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="introduction-to-torch-compile">
<span id="sphx-glr-intermediate-torch-compile-tutorial-py"></span><h1>Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code><a class="headerlink" href="#introduction-to-torch-compile" title="이 제목에 대한 퍼머링크">¶</a></h1>
<p><strong>Author:</strong> William Wen</p>
<p><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is the latest method to speed up your PyTorch code!
<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> makes PyTorch code run faster by
JIT-compiling PyTorch code into optimized kernels,
all while requiring minimal code changes.</p>
<p>In this tutorial, we cover basic <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> usage,
and demonstrate the advantages of <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> over
previous PyTorch compiler solutions, such as
<a class="reference external" href="https://pytorch.org/docs/stable/jit.html">TorchScript</a> and
<a class="reference external" href="https://pytorch.org/docs/stable/fx.html#torch.fx.symbolic_trace">FX Tracing</a>.</p>
<p><strong>Contents</strong></p>
<div class="contents local topic" id="id1">
<ul class="simple">
<li><p><a class="reference internal" href="#basic-usage" id="id2">Basic Usage</a></p></li>
<li><p><a class="reference internal" href="#demonstrating-speedups" id="id3">Demonstrating Speedups</a></p></li>
<li><p><a class="reference internal" href="#comparison-to-torchscript-and-fx-tracing" id="id4">Comparison to TorchScript and FX Tracing</a></p></li>
<li><p><a class="reference internal" href="#torchdynamo-and-fx-graphs" id="id5">TorchDynamo and FX Graphs</a></p></li>
<li><p><a class="reference internal" href="#conclusion" id="id6">Conclusion</a></p></li>
</ul>
</div>
<p><strong>Required pip Dependencies</strong></p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch</span> <span class="pre">>=</span> <span class="pre">2.0</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torchvision</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">numpy</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">scipy</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">tabulate</span></code></p></li>
</ul>
<p>NOTE: a modern NVIDIA GPU (H100, A100, or V100) is recommended for this tutorial in
order to reproduce the speedup numbers shown below and documented elsewhere.</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">import</span> <span class="nn">warnings</span>
<span class="n">gpu_ok</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
<span class="n">device_cap</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_capability</span><span class="p">()</span>
<span class="k">if</span> <span class="n">device_cap</span> <span class="ow">in</span> <span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">)):</span>
<span class="n">gpu_ok</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">gpu_ok</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s2">"GPU is not NVIDIA V100, A100, or H100. Speedup numbers may be lower "</span>
<span class="s2">"than expected."</span>
<span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/tutorials-kr/intermediate_source/torch_compile_tutorial.py:48: UserWarning:
GPU is not NVIDIA V100, A100, or H100. Speedup numbers may be lower than expected.
</pre></div>
</div>
<div class="section" id="basic-usage">
<h2><a class="toc-backref" href="#id2">Basic Usage</a><a class="headerlink" href="#basic-usage" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is included in the latest PyTorch.
Running TorchInductor on GPU requires Triton, which is included with the PyTorch 2.0 nightly
binary. If Triton is still missing, try installing <code class="docutils literal notranslate"><span class="pre">torchtriton</span></code> via pip
(<code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torchtriton</span> <span class="pre">--extra-index-url</span> <span class="pre">"https://download.pytorch.org/whl/nightly/cu117"</span></code>
for CUDA 11.7).</p>
<p>Arbitrary Python functions can be optimized by passing the callable to
<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. We can then call the returned optimized
function in place of the original function.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="n">y</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">opt_foo1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">foo</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">opt_foo1</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-pytb notranslate"><div class="highlight"><pre><span></span><span class="gt">Traceback (most recent call last):</span>
File <span class="nb">"/workspace/tutorials-kr/intermediate_source/torch_compile_tutorial.py"</span>, line <span class="m">72</span>, in <span class="n"><module></span>
<span class="w"> </span><span class="nb">print</span><span class="p">(</span><span class="n">opt_foo1</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py"</span>, line <span class="m">451</span>, in <span class="n">_fn</span>
<span class="w"> </span><span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py"</span>, line <span class="m">921</span>, in <span class="n">catch_errors</span>
<span class="w"> </span><span class="k">return</span> <span class="n">callback</span><span class="p">(</span><span class="n">frame</span><span class="p">,</span> <span class="n">cache_entry</span><span class="p">,</span> <span class="n">hooks</span><span class="p">,</span> <span class="n">frame_state</span><span class="p">,</span> <span class="n">skip</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py"</span>, line <span class="m">786</span>, in <span class="n">_convert_frame</span>
<span class="w"> </span><span class="n">result</span> <span class="o">=</span> <span class="n">inner_convert</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py"</span>, line <span class="m">400</span>, in <span class="n">_convert_frame_assert</span>
<span class="w"> </span><span class="k">return</span> <span class="n">_compile</span><span class="p">(</span>
File <span class="nb">"/usr/lib/python3.10/contextlib.py"</span>, line <span class="m">79</span>, in <span class="n">inner</span>
<span class="w"> </span><span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py"</span>, line <span class="m">676</span>, in <span class="n">_compile</span>
<span class="w"> </span><span class="n">guarded_code</span> <span class="o">=</span> <span class="n">compile_inner</span><span class="p">(</span><span class="n">code</span><span class="p">,</span> <span class="n">one_graph</span><span class="p">,</span> <span class="n">hooks</span><span class="p">,</span> <span class="n">transform</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py"</span>, line <span class="m">262</span>, in <span class="n">time_wrapper</span>
<span class="w"> </span><span class="n">r</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py"</span>, line <span class="m">535</span>, in <span class="n">compile_inner</span>
<span class="w"> </span><span class="n">out_code</span> <span class="o">=</span> <span class="n">transform_code_object</span><span class="p">(</span><span class="n">code</span><span class="p">,</span> <span class="n">transform</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/bytecode_transformation.py"</span>, line <span class="m">1036</span>, in <span class="n">transform_code_object</span>
<span class="w"> </span><span class="n">transformations</span><span class="p">(</span><span class="n">instructions</span><span class="p">,</span> <span class="n">code_options</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py"</span>, line <span class="m">165</span>, in <span class="n">_fn</span>
<span class="w"> </span><span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py"</span>, line <span class="m">500</span>, in <span class="n">transform</span>
<span class="w"> </span><span class="n">tracer</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py"</span>, line <span class="m">2149</span>, in <span class="n">run</span>
<span class="w"> </span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py"</span>, line <span class="m">810</span>, in <span class="n">run</span>
<span class="w"> </span><span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py"</span>, line <span class="m">773</span>, in <span class="n">step</span>
<span class="w"> </span><span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inst</span><span class="o">.</span><span class="n">opname</span><span class="p">)(</span><span class="n">inst</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py"</span>, line <span class="m">2268</span>, in <span class="n">RETURN_VALUE</span>
<span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">compile_subgraph</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py"</span>, line <span class="m">981</span>, in <span class="n">compile_subgraph</span>
<span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">compile_and_call_fx_graph</span><span class="p">(</span><span class="n">tx</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">stack_values</span><span class="p">)),</span> <span class="n">root</span><span class="p">)</span>
File <span class="nb">"/usr/lib/python3.10/contextlib.py"</span>, line <span class="m">79</span>, in <span class="n">inner</span>
<span class="w"> </span><span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py"</span>, line <span class="m">1178</span>, in <span class="n">compile_and_call_fx_graph</span>
<span class="w"> </span><span class="n">compiled_fn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_user_compiler</span><span class="p">(</span><span class="n">gm</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py"</span>, line <span class="m">262</span>, in <span class="n">time_wrapper</span>
<span class="w"> </span><span class="n">r</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py"</span>, line <span class="m">1251</span>, in <span class="n">call_user_compiler</span>
<span class="w"> </span><span class="k">raise</span> <span class="n">BackendCompilerFailed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">compiler_fn</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span><span class="o">.</span><span class="n">with_traceback</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py"</span>, line <span class="m">1232</span>, in <span class="n">call_user_compiler</span>
<span class="w"> </span><span class="n">compiled_fn</span> <span class="o">=</span> <span class="n">compiler_fn</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">example_inputs</span><span class="p">())</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/repro/after_dynamo.py"</span>, line <span class="m">117</span>, in <span class="n">debug_wrapper</span>
<span class="w"> </span><span class="n">compiled_gm</span> <span class="o">=</span> <span class="n">compiler_fn</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">example_inputs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/__init__.py"</span>, line <span class="m">1731</span>, in <span class="n">__call__</span>
<span class="w"> </span><span class="k">return</span> <span class="n">compile_fx</span><span class="p">(</span><span class="n">model_</span><span class="p">,</span> <span class="n">inputs_</span><span class="p">,</span> <span class="n">config_patches</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">)</span>
File <span class="nb">"/usr/lib/python3.10/contextlib.py"</span>, line <span class="m">79</span>, in <span class="n">inner</span>
<span class="w"> </span><span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py"</span>, line <span class="m">1330</span>, in <span class="n">compile_fx</span>
<span class="w"> </span><span class="k">return</span> <span class="n">aot_autograd</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/backends/common.py"</span>, line <span class="m">58</span>, in <span class="n">compiler_fn</span>
<span class="w"> </span><span class="n">cg</span> <span class="o">=</span> <span class="n">aot_module_simplified</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">example_inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py"</span>, line <span class="m">903</span>, in <span class="n">aot_module_simplified</span>
<span class="w"> </span><span class="n">compiled_fn</span> <span class="o">=</span> <span class="n">create_aot_dispatcher_function</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py"</span>, line <span class="m">262</span>, in <span class="n">time_wrapper</span>
<span class="w"> </span><span class="n">r</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py"</span>, line <span class="m">628</span>, in <span class="n">create_aot_dispatcher_function</span>
<span class="w"> </span><span class="n">compiled_fn</span> <span class="o">=</span> <span class="n">compiler_fn</span><span class="p">(</span><span class="n">flat_fn</span><span class="p">,</span> <span class="n">fake_flat_args</span><span class="p">,</span> <span class="n">aot_config</span><span class="p">,</span> <span class="n">fw_metadata</span><span class="o">=</span><span class="n">fw_metadata</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py"</span>, line <span class="m">443</span>, in <span class="n">aot_wrapper_dedupe</span>
<span class="w"> </span><span class="k">return</span> <span class="n">compiler_fn</span><span class="p">(</span><span class="n">flat_fn</span><span class="p">,</span> <span class="n">leaf_flat_args</span><span class="p">,</span> <span class="n">aot_config</span><span class="p">,</span> <span class="n">fw_metadata</span><span class="o">=</span><span class="n">fw_metadata</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py"</span>, line <span class="m">648</span>, in <span class="n">aot_wrapper_synthetic_base</span>
<span class="w"> </span><span class="k">return</span> <span class="n">compiler_fn</span><span class="p">(</span><span class="n">flat_fn</span><span class="p">,</span> <span class="n">flat_args</span><span class="p">,</span> <span class="n">aot_config</span><span class="p">,</span> <span class="n">fw_metadata</span><span class="o">=</span><span class="n">fw_metadata</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py"</span>, line <span class="m">119</span>, in <span class="n">aot_dispatch_base</span>
<span class="w"> </span><span class="n">compiled_fw</span> <span class="o">=</span> <span class="n">compiler</span><span class="p">(</span><span class="n">fw_module</span><span class="p">,</span> <span class="n">updated_flat_args</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py"</span>, line <span class="m">262</span>, in <span class="n">time_wrapper</span>
<span class="w"> </span><span class="n">r</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py"</span>, line <span class="m">1257</span>, in <span class="n">fw_compiler_base</span>
<span class="w"> </span><span class="k">return</span> <span class="n">inner_compile</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/repro/after_aot.py"</span>, line <span class="m">83</span>, in <span class="n">debug_wrapper</span>
<span class="w"> </span><span class="n">inner_compiled_fn</span> <span class="o">=</span> <span class="n">compiler_fn</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">example_inputs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/debug.py"</span>, line <span class="m">304</span>, in <span class="n">inner</span>
<span class="w"> </span><span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/lib/python3.10/contextlib.py"</span>, line <span class="m">79</span>, in <span class="n">inner</span>
<span class="w"> </span><span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span>
File <span class="nb">"/usr/lib/python3.10/contextlib.py"</span>, line <span class="m">79</span>, in <span class="n">inner</span>
<span class="w"> </span><span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py"</span>, line <span class="m">262</span>, in <span class="n">time_wrapper</span>
<span class="w"> </span><span class="n">r</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py"</span>, line <span class="m">438</span>, in <span class="n">compile_fx_inner</span>
<span class="w"> </span><span class="n">compiled_graph</span> <span class="o">=</span> <span class="n">fx_codegen_and_compile</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py"</span>, line <span class="m">714</span>, in <span class="n">fx_codegen_and_compile</span>
<span class="w"> </span><span class="n">compiled_fn</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">compile_to_fn</span><span class="p">()</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/graph.py"</span>, line <span class="m">1307</span>, in <span class="n">compile_to_fn</span>
<span class="w"> </span><span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">compile_to_module</span><span class="p">()</span><span class="o">.</span><span class="n">call</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py"</span>, line <span class="m">262</span>, in <span class="n">time_wrapper</span>
<span class="w"> </span><span class="n">r</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/graph.py"</span>, line <span class="m">1254</span>, in <span class="n">compile_to_module</span>
<span class="w"> </span><span class="n">mod</span> <span class="o">=</span> <span class="n">PyCodeCache</span><span class="o">.</span><span class="n">load_by_key_path</span><span class="p">(</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py"</span>, line <span class="m">2160</span>, in <span class="n">load_by_key_path</span>
<span class="w"> </span><span class="n">exec</span><span class="p">(</span><span class="n">code</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
File <span class="nb">"/tmp/torchinductor_root/mm/cmmtcbmmfakm37djocu6jtpak3f6yuldla55frjautjluv52xvyz.py"</span>, line <span class="m">59</span>, in <span class="n"><module></span>
<span class="w"> </span><span class="n">async_compile</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="nb">globals</span><span class="p">())</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py"</span>, line <span class="m">2715</span>, in <span class="n">wait</span>
<span class="w"> </span><span class="n">scope</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
File <span class="nb">"/usr/lib/python3.10/concurrent/futures/_base.py"</span>, line <span class="m">458</span>, in <span class="n">result</span>
<span class="w"> </span><span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_result</span><span class="p">()</span>
File <span class="nb">"/usr/lib/python3.10/concurrent/futures/_base.py"</span>, line <span class="m">403</span>, in <span class="n">__get_result</span>
<span class="w"> </span><span class="k">raise</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exception</span>
File <span class="nb">"/usr/lib/python3.10/concurrent/futures/thread.py"</span>, line <span class="m">58</span>, in <span class="n">run</span>
<span class="w"> </span><span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py"</span>, line <span class="m">2074</span>, in <span class="n">load_pybinding</span>
<span class="w"> </span><span class="n">result</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">source_code</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">cuda</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py"</span>, line <span class="m">1948</span>, in <span class="n">load</span>
<span class="w"> </span><span class="n">compile_file</span><span class="p">(</span><span class="n">input_path</span><span class="p">,</span> <span class="n">output_path</span><span class="p">,</span> <span class="n">cmd</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py"</span>, line <span class="m">262</span>, in <span class="n">time_wrapper</span>
<span class="w"> </span><span class="n">r</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
File <span class="nb">"/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py"</span>, line <span class="m">1888</span>, in <span class="n">compile_file</span>
<span class="w"> </span><span class="k">raise</span> <span class="n">exc</span><span class="o">.</span><span class="n">CppCompileError</span><span class="p">(</span><span class="n">cmd</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span>
<span class="gr">torch._dynamo.exc.BackendCompilerFailed</span>: <span class="n">backend='inductor' raised:</span>
<span class="x">CppCompileError: C++ compile error</span>
<span class="x">Command:</span>
<span class="x">g++ /tmp/torchinductor_root/pg/cpg44lbh7nfyakvqvjrcqw4qoozmn277q2j4vwqoke5ugrzrdp45.cpp -shared -fPIC -Wall -std=c++17 -Wno-unused-variable -Wno-unknown-pragmas -D_GLIBCXX_USE_CXX11_ABI=0 -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/include/python3.10 -L/usr/local/lib/python3.10/dist-packages/torch/lib -L/usr/lib/x86_64-linux-gnu -L/usr/local/lib/python3.10/dist-packages/torch/lib -ltorch -ltorch_cpu -lgomp -ltorch_python -lc10 -mavx2 -mfma -DCPU_CAPABILITY_AVX2 -O3 -DNDEBUG -ffast-math -fno-finite-math-only -fno-unsafe-math-optimizations -ffp-contract=off -march=native -fopenmp -D C10_USING_CUSTOM_GENERATED_MACROS -o /tmp/torchinductor_root/pg/cpg44lbh7nfyakvqvjrcqw4qoozmn277q2j4vwqoke5ugrzrdp45.so</span>
<span class="x">Output:</span>
<span class="x">/tmp/torchinductor_root/pg/cpg44lbh7nfyakvqvjrcqw4qoozmn277q2j4vwqoke5ugrzrdp45.cpp:32:10: fatal error: Python.h: No such file or directory</span>
<span class="x"> 32 | #include <Python.h></span>
<span class="x"> | ^~~~~~~~~~</span>
<span class="x">compilation terminated.</span>
<span class="x">Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information</span>
<span class="x">You can suppress this exception and fall back to eager by setting:</span>
<span class="x"> import torch._dynamo</span>
<span class="x"> torch._dynamo.config.suppress_errors = True</span>
</pre></div>
</div>
<p>Alternatively, we can decorate the function.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">compile</span>
<span class="k">def</span> <span class="nf">opt_foo2</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="nb">print</span><span class="p">(</span><span class="n">opt_foo2</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
</pre></div>
</div>
<p>We can also optimize <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> instances.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule</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">lin</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">Linear</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</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">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lin</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
<span class="n">opt_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">opt_mod</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
</pre></div>
</div>
</div>
<div class="section" id="demonstrating-speedups">
<h2><a class="toc-backref" href="#id3">Demonstrating Speedups</a><a class="headerlink" href="#demonstrating-speedups" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>Let’s now demonstrate that using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can speed
up real models. We will compare standard eager mode and
<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> by evaluating and training a <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> model on random data.</p>
<p>Before we start, we need to define some utility functions.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Returns the result of running `fn()` and the time it took for `fn()` to run,</span>
<span class="c1"># in seconds. We use CUDA events and synchronization for the most accurate</span>
<span class="c1"># measurements.</span>
<span class="k">def</span> <span class="nf">timed</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Event</span><span class="p">(</span><span class="n">enable_timing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Event</span><span class="p">(</span><span class="n">enable_timing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">start</span><span class="o">.</span><span class="n">record</span><span class="p">()</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">fn</span><span class="p">()</span>
<span class="n">end</span><span class="o">.</span><span class="n">record</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
<span class="k">return</span> <span class="n">result</span><span class="p">,</span> <span class="n">start</span><span class="o">.</span><span class="n">elapsed_time</span><span class="p">(</span><span class="n">end</span><span class="p">)</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="c1"># Generates random input and targets data for the model, where `b` is</span>
<span class="c1"># batch size.</span>
<span class="k">def</span> <span class="nf">generate_data</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="p">(</span><span class="n">b</span><span class="p">,))</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span>
<span class="p">)</span>
<span class="n">N_ITERS</span> <span class="o">=</span> <span class="mi">10</span>
<span class="kn">from</span> <span class="nn">torchvision.models</span> <span class="kn">import</span> <span class="n">densenet121</span>
<span class="k">def</span> <span class="nf">init_model</span><span class="p">():</span>
<span class="k">return</span> <span class="n">densenet121</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
</pre></div>
</div>
<p>First, let’s compare inference.</p>
<p>Note that in the call to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>, we have have the additional
<code class="docutils literal notranslate"><span class="pre">mode</span></code> argument, which we will discuss below.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">init_model</span><span class="p">()</span>
<span class="c1"># Reset since we are using a different mode.</span>
<span class="kn">import</span> <span class="nn">torch._dynamo</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">model_opt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"reduce-overhead"</span><span class="p">)</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">generate_data</span><span class="p">(</span><span class="mi">16</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"eager:"</span><span class="p">,</span> <span class="n">timed</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">model</span><span class="p">(</span><span class="n">inp</span><span class="p">))[</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"compile:"</span><span class="p">,</span> <span class="n">timed</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">model_opt</span><span class="p">(</span><span class="n">inp</span><span class="p">))[</span><span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
<p>Notice that <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> takes a lot longer to complete
compared to eager. This is because <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> compiles
the model into optimized kernels as it executes. In our example, the
structure of the model doesn’t change, and so recompilation is not
needed. So if we run our optimized model several more times, we should
see a significant improvement compared to eager.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">eager_times</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_ITERS</span><span class="p">):</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">generate_data</span><span class="p">(</span><span class="mi">16</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">_</span><span class="p">,</span> <span class="n">eager_time</span> <span class="o">=</span> <span class="n">timed</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">model</span><span class="p">(</span><span class="n">inp</span><span class="p">))</span>
<span class="n">eager_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">eager_time</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"eager eval time </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">eager_time</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">compile_times</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_ITERS</span><span class="p">):</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">generate_data</span><span class="p">(</span><span class="mi">16</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">_</span><span class="p">,</span> <span class="n">compile_time</span> <span class="o">=</span> <span class="n">timed</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">model_opt</span><span class="p">(</span><span class="n">inp</span><span class="p">))</span>
<span class="n">compile_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">compile_time</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"compile eval time </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">compile_time</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">eager_med</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">eager_times</span><span class="p">)</span>
<span class="n">compile_med</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">compile_times</span><span class="p">)</span>
<span class="n">speedup</span> <span class="o">=</span> <span class="n">eager_med</span> <span class="o">/</span> <span class="n">compile_med</span>
<span class="k">assert</span><span class="p">(</span><span class="n">speedup</span> <span class="o">></span> <span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"(eval) eager median: </span><span class="si">{</span><span class="n">eager_med</span><span class="si">}</span><span class="s2">, compile median: </span><span class="si">{</span><span class="n">compile_med</span><span class="si">}</span><span class="s2">, speedup: </span><span class="si">{</span><span class="n">speedup</span><span class="si">}</span><span class="s2">x"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>And indeed, we can see that running our model with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>
results in a significant speedup. Speedup mainly comes from reducing Python overhead and
GPU read/writes, and so the observed speedup may vary on factors such as model
architecture and batch size. For example, if a model’s architecture is simple
and the amount of data is large, then the bottleneck would be
GPU compute and the observed speedup may be less significant.</p>
<p>You may also see different speedup results depending on the chosen <code class="docutils literal notranslate"><span class="pre">mode</span></code>
argument. The <code class="docutils literal notranslate"><span class="pre">"reduce-overhead"</span></code> mode uses CUDA graphs to further reduce
the overhead of Python. For your own models,
you may need to experiment with different modes to maximize speedup. You can
read more about modes <a class="reference external" href="https://pytorch.org/get-started/pytorch-2.0/#user-experience">here</a>.</p>
<p>You may might also notice that the second time we run our model with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is significantly
slower than the other runs, although it is much faster than the first run. This is because the <code class="docutils literal notranslate"><span class="pre">"reduce-overhead"</span></code>
mode runs a few warm-up iterations for CUDA graphs.</p>
<p>For general PyTorch benchmarking, you can try using <code class="docutils literal notranslate"><span class="pre">torch.utils.benchmark</span></code> instead of the <code class="docutils literal notranslate"><span class="pre">timed</span></code>
function we defined above. We wrote our own timing function in this tutorial to show
<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>’s compilation latency.</p>
<p>Now, let’s consider comparing training.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">init_model</span><span class="p">()</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">opt</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">mod</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">loss</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">CrossEntropyLoss</span><span class="p">()(</span><span class="n">pred</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">eager_times</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_ITERS</span><span class="p">):</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">generate_data</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">eager_time</span> <span class="o">=</span> <span class="n">timed</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inp</span><span class="p">))</span>
<span class="n">eager_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">eager_time</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"eager train time </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">eager_time</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">init_model</span><span class="p">()</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="n">train_opt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"reduce-overhead"</span><span class="p">)</span>
<span class="n">compile_times</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_ITERS</span><span class="p">):</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">generate_data</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">compile_time</span> <span class="o">=</span> <span class="n">timed</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">train_opt</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inp</span><span class="p">))</span>
<span class="n">compile_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">compile_time</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"compile train time </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">compile_time</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">eager_med</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">eager_times</span><span class="p">)</span>
<span class="n">compile_med</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">compile_times</span><span class="p">)</span>
<span class="n">speedup</span> <span class="o">=</span> <span class="n">eager_med</span> <span class="o">/</span> <span class="n">compile_med</span>
<span class="k">assert</span><span class="p">(</span><span class="n">speedup</span> <span class="o">></span> <span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"(train) eager median: </span><span class="si">{</span><span class="n">eager_med</span><span class="si">}</span><span class="s2">, compile median: </span><span class="si">{</span><span class="n">compile_med</span><span class="si">}</span><span class="s2">, speedup: </span><span class="si">{</span><span class="n">speedup</span><span class="si">}</span><span class="s2">x"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>Again, we can see that <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> takes longer in the first
iteration, as it must compile the model, but in subsequent iterations, we see
significant speedups compared to eager.</p>
<p>We remark that the speedup numbers presented in this tutorial are for
demonstration purposes only. Official speedup values can be seen at the
<a class="reference external" href="https://hud.pytorch.org/benchmark/compilers">TorchInductor performance dashboard</a>.</p>
</div>
<div class="section" id="comparison-to-torchscript-and-fx-tracing">
<h2><a class="toc-backref" href="#id4">Comparison to TorchScript and FX Tracing</a><a class="headerlink" href="#comparison-to-torchscript-and-fx-tracing" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>We have seen that <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can speed up PyTorch code.
Why else should we use <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> over existing PyTorch
compiler solutions, such as TorchScript or FX Tracing? Primarily, the
advantage of <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> lies in its ability to handle
arbitrary Python code with minimal changes to existing code.</p>
<p>One case that <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can handle that other compiler
solutions struggle with is data-dependent control flow (the
<code class="docutils literal notranslate"><span class="pre">if</span> <span class="pre">x.sum()</span> <span class="pre"><</span> <span class="pre">0:</span></code> line below).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f1</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</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="o">-</span><span class="n">y</span>
<span class="k">return</span> <span class="n">y</span>
<span class="c1"># Test that `fn1` and `fn2` return the same result, given</span>
<span class="c1"># the same arguments `args`. Typically, `fn1` will be an eager function</span>
<span class="c1"># while `fn2` will be a compiled function (torch.compile, TorchScript, or FX graph).</span>
<span class="k">def</span> <span class="nf">test_fns</span><span class="p">(</span><span class="n">fn1</span><span class="p">,</span> <span class="n">fn2</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">out1</span> <span class="o">=</span> <span class="n">fn1</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
<span class="n">out2</span> <span class="o">=</span> <span class="n">fn2</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">out1</span><span class="p">,</span> <span class="n">out2</span><span class="p">)</span>
<span class="n">inp1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">inp2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<p>TorchScript tracing <code class="docutils literal notranslate"><span class="pre">f1</span></code> results in
silently incorrect results, since only the actual control flow path
is traced.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">traced_f1</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">f1</span><span class="p">,</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"traced 1, 1:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">traced_f1</span><span class="p">,</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"traced 1, 2:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">traced_f1</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)))</span>
</pre></div>
</div>
<p>FX tracing <code class="docutils literal notranslate"><span class="pre">f1</span></code> results in an error due to the presence of
data-dependent control flow.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">traceback</span> <span class="k">as</span> <span class="nn">tb</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">symbolic_trace</span><span class="p">(</span><span class="n">f1</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
</pre></div>
</div>
<p>If we provide a value for <code class="docutils literal notranslate"><span class="pre">x</span></code> as we try to FX trace <code class="docutils literal notranslate"><span class="pre">f1</span></code>, then
we run into the same problem as TorchScript tracing, as the data-dependent
control flow is removed in the traced function.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fx_f1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">symbolic_trace</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">concrete_args</span><span class="o">=</span><span class="p">{</span><span class="s2">"x"</span><span class="p">:</span> <span class="n">inp1</span><span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"fx 1, 1:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">fx_f1</span><span class="p">,</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"fx 1, 2:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">fx_f1</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)))</span>
</pre></div>
</div>
<p>Now we can see that <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> correctly handles
data-dependent control flow.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reset since we are using a different mode.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">compile_f1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">f1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"compile 1, 1:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">compile_f1</span><span class="p">,</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"compile 1, 2:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">compile_f1</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>TorchScript scripting can handle data-dependent control flow, but this
solution comes with its own set of problems. Namely, TorchScript scripting
can require major code changes and will raise errors when unsupported Python
is used.</p>
<p>In the example below, we forget TorchScript type annotations and we receive
a TorchScript error because the input type for argument <code class="docutils literal notranslate"><span class="pre">y</span></code>, an <code class="docutils literal notranslate"><span class="pre">int</span></code>,
does not match with the default argument type, <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f2</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">inp1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">inp2</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">script_f2</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">f2</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">script_f2</span><span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
</pre></div>
</div>
<p>However, <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is easily able to handle <code class="docutils literal notranslate"><span class="pre">f2</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">compile_f2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">f2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"compile 2:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f2</span><span class="p">,</span> <span class="n">compile_f2</span><span class="p">,</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>Another case that <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> handles well compared to
previous compilers solutions is the usage of non-PyTorch functions.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy</span>
<span class="k">def</span> <span class="nf">f3</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="mi">2</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">dct</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</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">from_numpy</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="mi">2</span>
<span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
<p>TorchScript tracing treats results from non-PyTorch function calls
as constants, and so our results can be silently wrong.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">inp1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">inp2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">traced_f3</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">f3</span><span class="p">,</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"traced 3:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f3</span><span class="p">,</span> <span class="n">traced_f3</span><span class="p">,</span> <span class="p">(</span><span class="n">inp2</span><span class="p">,)))</span>
</pre></div>
</div>
<p>TorchScript scripting and FX tracing disallow non-PyTorch function calls.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">try</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">script</span><span class="p">(</span><span class="n">f3</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">symbolic_trace</span><span class="p">(</span><span class="n">f3</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
</pre></div>
</div>
<p>In comparison, <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is easily able to handle
the non-PyTorch function call.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">compile_f3</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">f3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"compile 3:"</span><span class="p">,</span> <span class="n">test_fns</span><span class="p">(</span><span class="n">f3</span><span class="p">,</span> <span class="n">compile_f3</span><span class="p">,</span> <span class="p">(</span><span class="n">inp2</span><span class="p">,)))</span>
</pre></div>
</div>
</div>
<div class="section" id="torchdynamo-and-fx-graphs">
<h2><a class="toc-backref" href="#id5">TorchDynamo and FX Graphs</a><a class="headerlink" href="#torchdynamo-and-fx-graphs" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>One important component of <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is TorchDynamo.
TorchDynamo is responsible for JIT compiling arbitrary Python code into
<a class="reference external" href="https://pytorch.org/docs/stable/fx.html#torch.fx.Graph">FX graphs</a>, which can
then be further optimized. TorchDynamo extracts FX graphs by analyzing Python bytecode
during runtime and detecting calls to PyTorch operations.</p>
<p>Normally, TorchInductor, another component of <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>,
further compiles the FX graphs into optimized kernels,
but TorchDynamo allows for different backends to be used. In order to inspect
the FX graphs that TorchDynamo outputs, let us create a custom backend that
outputs the FX graph and simply returns the graph’s unoptimized forward method.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">custom_backend</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">example_inputs</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="nb">print</span><span class="p">(</span><span class="s2">"custom backend called with FX graph:"</span><span class="p">)</span>
<span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">print_tabular</span><span class="p">()</span>
<span class="k">return</span> <span class="n">gm</span><span class="o">.</span><span class="n">forward</span>
<span class="c1"># Reset since we are using a different backend.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">opt_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">init_model</span><span class="p">(),</span> <span class="n">backend</span><span class="o">=</span><span class="n">custom_backend</span><span class="p">)</span>
<span class="n">opt_model</span><span class="p">(</span><span class="n">generate_data</span><span class="p">(</span><span class="mi">16</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<p>Using our custom backend, we can now see how TorchDynamo is able to handle
data-dependent control flow. Consider the function below, where the line
<code class="docutils literal notranslate"><span class="pre">if</span> <span class="pre">b.sum()</span> <span class="pre"><</span> <span class="pre">0</span></code> is the source of data-dependent control flow.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">bar</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">a</span> <span class="o">/</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">b</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="n">b</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b</span>
<span class="n">opt_bar</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">bar</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">custom_backend</span><span class="p">)</span>
<span class="n">inp1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">inp2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">opt_bar</span><span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="n">inp2</span><span class="p">)</span>
<span class="n">opt_bar</span><span class="p">(</span><span class="n">inp1</span><span class="p">,</span> <span class="o">-</span><span class="n">inp2</span><span class="p">)</span>
</pre></div>
</div>
<p>The output reveals that TorchDynamo extracted 3 different FX graphs
corresponding the following code (order may differ from the output above):</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">a</span> <span class="pre">/</span> <span class="pre">(torch.abs(a)</span> <span class="pre">+</span> <span class="pre">1)</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">b</span> <span class="pre">=</span> <span class="pre">b</span> <span class="pre">*</span> <span class="pre">-1;</span> <span class="pre">return</span> <span class="pre">x</span> <span class="pre">*</span> <span class="pre">b</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">return</span> <span class="pre">x</span> <span class="pre">*</span> <span class="pre">b</span></code></p></li>
</ol>
<p>When TorchDynamo encounters unsupported Python features, such as data-dependent
control flow, it breaks the computation graph, lets the default Python
interpreter handle the unsupported code, then resumes capturing the graph.</p>
<p>Let’s investigate by example how TorchDynamo would step through <code class="docutils literal notranslate"><span class="pre">bar</span></code>.
If <code class="docutils literal notranslate"><span class="pre">b.sum()</span> <span class="pre"><</span> <span class="pre">0</span></code>, then TorchDynamo would run graph 1, let
Python determine the result of the conditional, then run
graph 2. On the other hand, if <code class="docutils literal notranslate"><span class="pre">not</span> <span class="pre">b.sum()</span> <span class="pre"><</span> <span class="pre">0</span></code>, then TorchDynamo
would run graph 1, let Python determine the result of the conditional, then
run graph 3.</p>