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<p class="caption" role="heading"><span class="caption-text">파이토치(PyTorch) 레시피</span></p>
<ul>
<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>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">모델 저장하고 불러오기</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>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">PyTorch 소개</a></li>
<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>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/captumyt.html">Model Understanding with Captum</a></li>
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<p class="caption" 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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<p class="caption" role="heading"><span class="caption-text">이미지/비디오</span></p>
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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>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="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>
</ul>
<p class="caption" role="heading"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="fx_conv_bn_fuser.html">(베타) FX에서 합성곱/배치 정규화(Convolution/Batch Norm) 결합기(Fuser) 만들기</a></li>
<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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<p class="caption" role="heading"><span class="caption-text">프론트엔드 API</span></p>
<ul>
<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>
</ul>
<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>
</ul>
<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"><a class="reference internal" href="torch_compile_tutorial.html">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">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>
<ul>
<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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<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-inductor-debug-cpu-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
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<div class="sphx-glr-example-title section" id="inductor-cpu-backend-debugging-and-profiling">
<span id="sphx-glr-intermediate-inductor-debug-cpu-py"></span><h1>Inductor CPU backend debugging and profiling<a class="headerlink" href="#inductor-cpu-backend-debugging-and-profiling" title="이 제목에 대한 퍼머링크">¶</a></h1>
<p><strong>Authors</strong>: <a class="reference external" href="https://github.com/Valentine233">Xuan Liao</a>, <a class="reference external" href="https://github.com/zhuhaozhe">Haozhe Zhu</a>, <a class="reference external" href="https://github.com/jgong5">Jiong Gong</a>, <a class="reference external" href="https://github.com/EikanWang">Weihan Wang</a></p>
<div class="section" id="overview">
<h2>Overview<a class="headerlink" href="#overview" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>PyTorch 2.0 introduced the compilation API called <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
This new feature offers a significant speedup over eager mode execution through graph-level optimization powered by the default Inductor backend.</p>
<p>This tutorial is intended to provide an in-depth introduction on the debugging
and performance profiling on Inductor CPU backend by delving into the intricacies of <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.</p>
<p>Meanwhile, you may also find related tutorials about <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>
around <a class="reference external" href="https://tutorials.pytorch.kr/intermediate/torch_compile_tutorial.html">basic usage</a>,
comprehensive <a class="reference external" href="https://pytorch.org/docs/stable/dynamo/troubleshooting.html">troubleshooting</a>
and GPU-specific knowledge like <a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/docs/source/compile/profiling_torch_compile.rst">GPU performance profiling</a>.</p>
<p>We will start debugging with a motivating example that triggers compilation issues and accuracy problems
by demonstrating the process of debugging to pinpoint the problems.</p>
<p>By enabling logging and exploring the underlying generated code,
you can learn how to narrow down the failure step by step and finally figure out the route cause.</p>
<p>Following that, we will proceed to discuss how to profile the compiled code and,
through a performance comparison with eager mode,
elaborate on the reasons why <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can provide an additional performance boost compared to its eager counterpart.</p>
</div>
<div class="section" id="debugging">
<h2>Debugging<a class="headerlink" href="#debugging" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>Here is a simple example to run the <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> using Inductor and compare its result with eager mode:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">foo1</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</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">neg</span><span class="p">(</span><span class="n">x1</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">maximum</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">b</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span>
<span class="n">x1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
<span class="n">x2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="p">(</span><span class="mi">8390</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
<span class="n">compiled_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">foo1</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">compiled_foo1</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</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/inductor_debug_cpu.py"</span>, line <span class="m">54</span>, in <span class="n"><module></span>
<span class="w"> </span><span class="n">result</span> <span class="o">=</span> <span class="n">compiled_foo1</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</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/fq/cfqn54ec6ehs2nkfza4ngx5yorsokicv3iwkxz7ktkbzvqmlxi7c.py"</span>, line <span class="m">56</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/iq/ciqooo53bfagfndmkutjupdd6draw4fwydnuy75nfsda7eflv7lj.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/iq/ciqooo53bfagfndmkutjupdd6draw4fwydnuy75nfsda7eflv7lj.so</span>
<span class="x">Output:</span>
<span class="x">/tmp/torchinductor_root/iq/ciqooo53bfagfndmkutjupdd6draw4fwydnuy75nfsda7eflv7lj.cpp:29:10: fatal error: Python.h: No such file or directory</span>
<span class="x"> 29 | #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>The correct implementation of <code class="docutils literal notranslate"><span class="pre">neg</span></code> in the <code class="docutils literal notranslate"><span class="pre">cpp</span></code> codegen is as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">neg1</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"decltype(</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">)(-</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">)"</span>
</pre></div>
</div>
<p>In order to demonstrate the debugging, we will modify the function to a wrong one later.</p>
<div class="section" id="get-more-logging-information">
<h3>Get more logging information<a class="headerlink" href="#get-more-logging-information" title="이 제목에 대한 퍼머링크">¶</a></h3>
<p>No debugging information would be provided if you run this simple example by default. In order to get more useful debugging and logging information, we usually add a <code class="docutils literal notranslate"><span class="pre">TORCH_COMPILE_DEBUG</span></code> environment variable like below:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">TORCH_COMPILE_DEBUG</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>python<span class="w"> </span>xx.py
</pre></div>
</div>
<p>This would print more debug information in the output logs and also dump the intermediate IRs generated during the codegen process. You can find the dumped file paths in the log like below:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torch._inductor.debug:<span class="w"> </span><span class="o">[</span>WARNING<span class="o">]</span><span class="w"> </span>model___20<span class="w"> </span>debug<span class="w"> </span>trace:<span class="w"> </span>/tmp/torchinductor_root/rx/crxfi2ybd7yp5sbj2pnhw33wfhtdw7wumvrobyp5sjvdui5ktjc2.debug
</pre></div>
</div>
<p>In this directory, the following files are saved for debugging purposes:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 31%" />
<col style="width: 69%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>File</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">fx_graph_runnable.py</span></code></p></td>
<td><p>Executable FX graph, after decomposition, before pattern match</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">fx_graph_transformed.py</span></code></p></td>
<td><p>Transformed FX graph, after pattern match</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">ir_post_fusion.txt</span></code></p></td>
<td><p>Inductor IR before fusion</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">ir_pre_fusion.txt</span></code></p></td>
<td><p>Inductor IR after fusion</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">output_code.py</span></code></p></td>
<td><p>Generated Python code for graph, with C++/Triton kernels</p></td>
</tr>
</tbody>
</table>
<p>Note that <code class="docutils literal notranslate"><span class="pre">fx_graph_runnable.py</span></code> and <code class="docutils literal notranslate"><span class="pre">output_code.py</span></code> are both runnable and editable in order to make debugging easier.
Here are the main parts of code extracted from the files and we correlate the C++ generated line with the FX code line.</p>
<p><code class="docutils literal notranslate"><span class="pre">fx_graph_runnable</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">forward1</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg0_1</span><span class="p">,</span> <span class="n">arg1_1</span><span class="p">):</span>
<span class="n">neg</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">neg</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">arg0_1</span><span class="p">);</span> <span class="n">arg0_1</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">maximum</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">maximum</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">arg1_1</span><span class="p">,</span> <span class="n">neg</span><span class="p">);</span> <span class="n">arg1_1</span> <span class="o">=</span> <span class="n">neg</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">clone</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">clone</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">maximum</span><span class="p">);</span> <span class="n">maximum</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">clone</span><span class="p">,)</span>
</pre></div>
</div>
<p>C++ kernel in <code class="docutils literal notranslate"><span class="pre">output_code</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch._inductor.codecache</span> <span class="kn">import</span> <span class="n">AsyncCompile</span>
<span class="n">async_compile</span> <span class="o">=</span> <span class="n">AsyncCompile</span><span class="p">()</span>
<span class="n">cpp_fused_cat_maximum_neg_0</span> <span class="o">=</span> <span class="n">async_compile</span><span class="o">.</span><span class="n">cpp</span><span class="p">(</span><span class="s1">'''</span>
<span class="s1">#include "/tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h"</span>
<span class="s1">extern "C" void kernel(const unsigned char* in_ptr0,</span>
<span class="s1"> const unsigned char* in_ptr1,</span>
<span class="s1"> unsigned char* out_ptr0)</span>
<span class="s1">{</span>
<span class="s1"> {</span>
<span class="s1"> #pragma GCC ivdep</span>
<span class="s1"> for(long i0=static_cast<long>(0L); i0<static_cast<long>(8390L); i0+=static_cast<long>(1L))</span>
<span class="s1"> {</span>
<span class="s1"> #pragma GCC ivdep</span>
<span class="s1"> for(long i1=static_cast<long>(0L); i1<static_cast<long>(8L); i1+=static_cast<long>(1L))</span>
<span class="s1"> {</span>
<span class="s1"> auto tmp0 = in_ptr0[static_cast<long>(i1 + (8L*i0))];</span>
<span class="s1"> auto tmp1 = in_ptr1[static_cast<long>(i1)];</span>
<span class="s1"> // Corresponding FX code line: neg = torch.ops.aten.neg.default(arg0_1); arg0_1 = None</span>
<span class="s1"> auto tmp2 = decltype(tmp1)(-tmp1);</span>
<span class="s1"> // Corresponding FX code line: maximum = torch.ops.aten.maximum.default(arg1_1, neg); arg1_1 = neg = None</span>
<span class="s1"> auto tmp3 = max_propagate_nan(tmp0, tmp2);</span>
<span class="s1"> // Corresponding FX code line: clone = torch.ops.aten.clone.default(maximum); maximum = None</span>
<span class="s1"> out_ptr0[static_cast<long>(i1 + (8L*i0))] = tmp3;</span>
<span class="s1"> }</span>
<span class="s1"> }</span>
<span class="s1"> }</span>
<span class="s1">}'''</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="determine-component-of-error">
<h3>Determine component of error<a class="headerlink" href="#determine-component-of-error" title="이 제목에 대한 퍼머링크">¶</a></h3>
<p>When encountering errors or accuracy problems, a straightforward solution to find the bug is to narrow down the problem. The first thing to do is to determine the component where the error occurs. Luckily, it can be simply achieved by changing the backend of <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 52%" />
<col style="width: 48%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Code</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.compile(fn,</span> <span class="pre">backend="eager")</span></code></p></td>
<td><p>Enable Dynamo</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.compile(fn,</span> <span class="pre">backend="aot_eager")</span></code></p></td>
<td><p>Enable Dynamo + AOT Autograd</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.compile(fn,</span> <span class="pre">backend="inductor")</span></code></p></td>
<td><p>Enable Dynamo + AOT Autograd + Inductor</p></td>
</tr>
</tbody>
</table>
<p>If the model can successfully run when the backend is set to <code class="docutils literal notranslate"><span class="pre">eager</span></code> or <code class="docutils literal notranslate"><span class="pre">aot_eager</span></code> while it fails with <code class="docutils literal notranslate"><span class="pre">inductor</span></code>, we can narrow down the failure to Inductor.</p>
</div>
<div class="section" id="compilation-error">
<h3>Compilation error<a class="headerlink" href="#compilation-error" title="이 제목에 대한 퍼머링크">¶</a></h3>
<p>As we know, the evolved chain of graph-level optimization is like:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>torch.neg<span class="w"> </span><span class="o">(</span>Python<span class="o">)</span><span class="w"> </span>-><span class="w"> </span>torch.ops.aten.neg.default<span class="w"> </span><span class="o">(</span>within<span class="w"> </span>FX<span class="w"> </span>graph<span class="o">)</span><span class="w"> </span>-><span class="w"> </span>ops.neg<span class="w"> </span><span class="o">(</span>within<span class="w"> </span>IR<span class="w"> </span>node<span class="o">)</span><span class="w"> </span>-><span class="w"> </span><span class="nv">tmp2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>-tmp1<span class="w"> </span><span class="o">(</span>within<span class="w"> </span>C++<span class="w"> </span>kernel<span class="o">)</span>
</pre></div>
</div>
<p>If you encounter a compilation error, there is something wrong when compiling C++ kernels in the output code.
This type of error indicates that bugs are introduced when lowering IR nodes to output code.
The root cause of compilation error is usually shown in the traceback log.</p>
<p>For example, the <code class="docutils literal notranslate"><span class="pre">neg</span></code> function is modified like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">neg2</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"-</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span>
</pre></div>
</div>
<p>The logging gives the following compile error with a rather clear reason.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
CppCompileError: C++ compile error
/tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp: In function ‘void kernel(const unsigned char*, const unsigned char*, unsigned char*)’:
/tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp:17:57: error: no matching function for call to ‘max_propagate_nan(unsigned char&, int&)’
17 | auto tmp3 = max_propagate_nan(tmp0, tmp2);
| ^
In file included from /tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp:2:
/tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h:27:17: note: candidate: ‘template<class scalar_t> scalar_t max_propagate_nan(scalar_t, scalar_t)’
27 | inline scalar_t max_propagate_nan(scalar_t a, scalar_t b) {
| ^~~~~~~~~~~~~~~~~
/tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h:27:17: note: template argument deduction/substitution failed:
/tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp:17:57: note: deduced conflicting types for parameter ‘scalar_t’ (‘unsigned char’ and ‘int’)
17 | auto tmp3 = max_propagate_nan(tmp0, tmp2);
| ^
</pre></div>
</div>
<p>Let us also see the corresponding C++ kernel in output code and IR node.</p>
<p>C++ kernel:</p>
<div class="highlight-c notranslate"><div class="highlight"><pre><span></span><span class="n">include</span><span class="w"> </span><span class="s">"/tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h"</span>
<span class="k">extern</span><span class="w"> </span><span class="s">"C"</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">kernel</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">unsigned</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">in_ptr0</span><span class="p">,</span>
<span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">unsigned</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">in_ptr1</span><span class="p">,</span>
<span class="w"> </span><span class="kt">unsigned</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">out_ptr0</span><span class="p">)</span>
<span class="p">{</span>
<span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="cp">#pragma GCC ivdep</span>
<span class="w"> </span><span class="k">for</span><span class="p">(</span><span class="kt">long</span><span class="w"> </span><span class="n">i0</span><span class="o">=</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="mf">0L</span><span class="p">);</span><span class="w"> </span><span class="n">i0</span><span class="o"><</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="mf">8390L</span><span class="p">);</span><span class="w"> </span><span class="n">i0</span><span class="o">+=</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="mf">1L</span><span class="p">))</span>
<span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="cp">#pragma GCC ivdep</span>
<span class="w"> </span><span class="k">for</span><span class="p">(</span><span class="kt">long</span><span class="w"> </span><span class="n">i1</span><span class="o">=</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="mf">0L</span><span class="p">);</span><span class="w"> </span><span class="n">i1</span><span class="o"><</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="mf">8L</span><span class="p">);</span><span class="w"> </span><span class="n">i1</span><span class="o">+=</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="mf">1L</span><span class="p">))</span>
<span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">tmp0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">in_ptr0</span><span class="p">[</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="n">i1</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="p">(</span><span class="mf">8L</span><span class="o">*</span><span class="n">i0</span><span class="p">))];</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">tmp1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">in_ptr1</span><span class="p">[</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="n">i1</span><span class="p">)];</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">tmp2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">-</span><span class="n">tmp1</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">tmp3</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">max_propagate_nan</span><span class="p">(</span><span class="n">tmp0</span><span class="p">,</span><span class="w"> </span><span class="n">tmp2</span><span class="p">);</span>
<span class="w"> </span><span class="n">out_ptr0</span><span class="p">[</span><span class="n">static_cast</span><span class="o"><</span><span class="kt">long</span><span class="o">></span><span class="p">(</span><span class="n">i1</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="p">(</span><span class="mf">8L</span><span class="o">*</span><span class="n">i0</span><span class="p">))]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">tmp3</span><span class="p">;</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>IR node:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>buf0:<span class="w"> </span>SchedulerNode<span class="o">(</span>ComputedBuffer<span class="o">)</span>
buf0.writes<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[</span>MemoryDep<span class="o">(</span><span class="s1">'buf0'</span>,<span class="w"> </span>c0,<span class="w"> </span><span class="o">{</span>c0:<span class="w"> </span><span class="m">67120</span><span class="o">})]</span>
buf0.unmet_dependencies<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[]</span>
buf0.met_dependencies<span class="w"> </span><span class="o">=</span>
<span class="w"> </span><span class="o">[</span><span class="w"> </span>MemoryDep<span class="o">(</span><span class="s1">'arg0_1'</span>,<span class="w"> </span>c1,<span class="w"> </span><span class="o">{</span>c0:<span class="w"> </span><span class="m">8390</span>,<span class="w"> </span>c1:<span class="w"> </span><span class="m">8</span><span class="o">})</span>,
<span class="w"> </span>MemoryDep<span class="o">(</span><span class="s1">'arg1_1'</span>,<span class="w"> </span>c0,<span class="w"> </span><span class="o">{</span>c0:<span class="w"> </span><span class="m">67120</span><span class="o">})]</span>
buf0.users<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[</span>NodeUser<span class="o">(</span><span class="nv">node</span><span class="o">=</span>OUTPUT,<span class="w"> </span><span class="nv">can_inplace</span><span class="o">=</span>False<span class="o">)]</span>
buf0.group.device<span class="w"> </span><span class="o">=</span><span class="w"> </span>cpu
buf0.group.iteration<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">((</span><span class="m">8390</span>,<span class="w"> </span><span class="m">8</span><span class="o">)</span>,<span class="w"> </span><span class="o">())</span>
buf0.sizes<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">([</span><span class="m">8390</span>,<span class="w"> </span><span class="m">8</span><span class="o">]</span>,<span class="w"> </span><span class="o">[])</span>
class<span class="w"> </span>buf0_loop_body:
<span class="w"> </span><span class="nv">var_ranges</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">{</span>z0:<span class="w"> </span><span class="m">8390</span>,<span class="w"> </span>z1:<span class="w"> </span><span class="m">8</span><span class="o">}</span>
<span class="w"> </span><span class="nv">index0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">8</span>*z0<span class="w"> </span>+<span class="w"> </span>z1
<span class="w"> </span><span class="nv">index1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>z1
<span class="w"> </span>def<span class="w"> </span>body<span class="o">(</span>self,<span class="w"> </span>ops<span class="o">)</span>:
<span class="w"> </span><span class="nv">get_index</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>self.get_index<span class="o">(</span><span class="s1">'index0'</span><span class="o">)</span>
<span class="w"> </span><span class="nv">load</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>ops.load<span class="o">(</span><span class="s1">'arg1_1'</span>,<span class="w"> </span>get_index<span class="o">)</span>
<span class="w"> </span><span class="nv">get_index_1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>self.get_index<span class="o">(</span><span class="s1">'index1'</span><span class="o">)</span>
<span class="w"> </span><span class="nv">load_1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>ops.load<span class="o">(</span><span class="s1">'arg0_1'</span>,<span class="w"> </span>get_index_1<span class="o">)</span>
<span class="w"> </span><span class="nv">neg</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>ops.neg<span class="o">(</span>load_1<span class="o">)</span>
<span class="w"> </span><span class="nv">maximum</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>ops.maximum<span class="o">(</span>load,<span class="w"> </span>neg<span class="o">)</span>
<span class="w"> </span><span class="nv">get_index_2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>self.get_index<span class="o">(</span><span class="s1">'index0'</span><span class="o">)</span>
<span class="w"> </span><span class="nv">store</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>ops.store<span class="o">(</span><span class="s1">'buf0'</span>,<span class="w"> </span>get_index_2,<span class="w"> </span>maximum,<span class="w"> </span>None<span class="o">)</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span>store
</pre></div>
</div>
<p>According to the traceback logging, the compilation error is caused by the data type inconsistency of <code class="docutils literal notranslate"><span class="pre">max_propagate_nan</span></code>’s inputs.
By checking the C++ kernel, we know that <code class="docutils literal notranslate"><span class="pre">tmp2</span></code> is no longer <code class="docutils literal notranslate"><span class="pre">long</span></code> after doing <code class="docutils literal notranslate"><span class="pre">-</span></code> as <code class="docutils literal notranslate"><span class="pre">tmp0</span></code> is <code class="docutils literal notranslate"><span class="pre">long</span></code>.
We can easily match <code class="docutils literal notranslate"><span class="pre">-</span></code> and <code class="docutils literal notranslate"><span class="pre">max_propagate_nan</span></code> in C++ kernel with <code class="docutils literal notranslate"><span class="pre">ops.neg</span></code> and <code class="docutils literal notranslate"><span class="pre">ops.maximum</span></code> in IR node respectively.</p>
<p>Now we successfully find that the root cause is the implementation of <code class="docutils literal notranslate"><span class="pre">ops.neg</span></code> in <code class="docutils literal notranslate"><span class="pre">cpp</span></code> codegen, which silently changes the data type when doing <code class="docutils literal notranslate"><span class="pre">neg</span></code>.</p>
</div>
<div class="section" id="accuracy-debugging">
<h3>Accuracy debugging<a class="headerlink" href="#accuracy-debugging" title="이 제목에 대한 퍼머링크">¶</a></h3>
<p>Otherwise, if the model runs with other errors or accuracy problem, you can use the PyTorch debugging tool called <a class="reference external" href="https://pytorch.org/functorch/stable/notebooks/minifier.html">Minifier</a>.</p>
<p>The core idea of <code class="docutils literal notranslate"><span class="pre">Minifier</span></code> is to keep removing the nodes and inputs of graph until finding the minimal graph with problem.
It helps to automatically generate a minified problematic graph through 4 strategies: truncating suffix, delta debugging, eliminating dead code and removing unused inputs.</p>
<p>We will now show the debugging process for the accuracy problem with the help of <code class="docutils literal notranslate"><span class="pre">Minifer</span></code>.
The accuracy problem refers to the case where the outputs of backends eager and inductor are different.</p>
<p>For instance, we modify the example like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch._dynamo.utils</span> <span class="kn">import</span> <span class="n">same</span>
<span class="k">def</span> <span class="nf">foo2</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</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">neg</span><span class="p">(</span><span class="n">x1</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">maximum</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">b</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span>
<span class="n">x1</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">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">x2</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">8390</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">expected_result</span> <span class="o">=</span> <span class="n">foo2</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
<span class="n">compiled_foo2</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">foo2</span><span class="p">)</span>
<span class="n">actual_result</span> <span class="o">=</span> <span class="n">compiled_foo2</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">same</span><span class="p">(</span><span class="n">expected_result</span><span class="p">,</span> <span class="n">actual_result</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span>
</pre></div>
</div>
<p>And also modify the <code class="docutils literal notranslate"><span class="pre">neg</span></code> function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">neg3</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"decltype(</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">)(2 * </span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">)"</span>
</pre></div>
</div>
<p>An accuracy problem would be raised as follows:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>torch._dynamo.utils:<span class="w"> </span><span class="o">[</span>ERROR<span class="o">]</span><span class="w"> </span>Accuracy<span class="w"> </span>failed:<span class="w"> </span>allclose<span class="w"> </span>not<span class="w"> </span>within<span class="w"> </span><span class="nv">tol</span><span class="o">=</span><span class="m">0</span>.0001
Traceback<span class="w"> </span><span class="o">(</span>most<span class="w"> </span>recent<span class="w"> </span>call<span class="w"> </span>last<span class="o">)</span>:
<span class="w"> </span>File<span class="w"> </span><span class="s2">"test_script.py"</span>,<span class="w"> </span>line<span class="w"> </span><span class="m">18</span>,<span class="w"> </span><span class="k">in</span><span class="w"> </span><module>
<span class="w"> </span>assert<span class="w"> </span>same<span class="o">(</span>expected_result,<span class="w"> </span>actual_result<span class="o">)</span><span class="w"> </span><span class="o">==</span><span class="w"> </span>True
AssertionError
</pre></div>
</div>
<p>To debug an accuracy problem with Minifier, two environment variables are needed:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nv">TORCHDYNAMO_REPRO_AFTER</span><span class="o">=</span><span class="s2">"aot"</span><span class="w"> </span><span class="nv">TORCHDYNAMO_REPRO_LEVEL</span><span class="o">=</span><span class="m">4</span><span class="w"> </span>python<span class="w"> </span>xx.py
</pre></div>
</div>
<p>Which gives us logging information that demonstrates the steps of minifying:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>Started<span class="w"> </span>off<span class="w"> </span>with<span class="w"> </span><span class="m">6</span><span class="w"> </span>nodes
Trying<span class="w"> </span>granularity<span class="w"> </span><span class="m">2</span>
Strategy:<span class="w"> </span>Truncate<span class="w"> </span>suffix<span class="w"> </span><span class="o">(</span>G:<span class="w"> </span><span class="m">2</span><span class="o">)</span><span class="w"> </span><span class="o">(</span><span class="m">6</span><span class="w"> </span>nodes,<span class="w"> </span><span class="m">2</span><span class="w"> </span>inputs<span class="o">)</span>
SUCCESS:<span class="w"> </span>Went<span class="w"> </span>from<span class="w"> </span><span class="m">6</span><span class="w"> </span>to<span class="w"> </span><span class="m">4</span><span class="w"> </span>nodes
Trying<span class="w"> </span>granularity<span class="w"> </span><span class="m">4</span>
Strategy:<span class="w"> </span>Remove<span class="w"> </span>unused<span class="w"> </span>inputs<span class="w"> </span><span class="o">(</span>G:<span class="w"> </span><span class="m">4</span><span class="o">)</span><span class="w"> </span><span class="o">(</span><span class="m">4</span><span class="w"> </span>nodes,<span class="w"> </span><span class="m">2</span><span class="w"> </span>inputs<span class="o">)</span>
SUCCESS:<span class="w"> </span>Went<span class="w"> </span>from<span class="w"> </span><span class="m">4</span><span class="w"> </span>to<span class="w"> </span><span class="m">3</span><span class="w"> </span>nodes
</pre></div>
</div>
<p>After running, we get the final minified graph with the target node <code class="docutils literal notranslate"><span class="pre">neg</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">forward2</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg0_1</span><span class="p">):</span>
<span class="n">neg</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">neg</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">arg0_1</span><span class="p">);</span> <span class="n">arg0_1</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">neg</span><span class="p">,)</span>
</pre></div>
</div>
<p>For more usage details about Minifier, please refer to <a class="reference external" href="https://pytorch.org/docs/stable/dynamo/troubleshooting.html">Troubleshooting</a>.</p>
</div>
</div>
<div class="section" id="performance-profiling">
<h2>Performance profiling<a class="headerlink" href="#performance-profiling" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>Within this section, we will demonstrate the process of conducting performance analysis for a model that has been compiled using the Inductor CPU backend.
In the example below, we benchmark a Hugging Face Transformer model <code class="docutils literal notranslate"><span class="pre">MobileBertForQuestionAnswering</span></code> with both the eager mode and the Inductor graph mode.
The execution time and the speedup ratio of Inductor are printed after the benchmark.
We use Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz and run benchmark on the first socket to demonstrate the optimization within this section.
We set following environment variable as a best practice to benchmark on Intel(R) CPU.</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">KMP_BLOCKTIME</span><span class="o">=</span><span class="m">1</span>
<span class="nb">export</span><span class="w"> </span><span class="nv">KMP_SETTINGS</span><span class="o">=</span><span class="m">1</span>
<span class="nb">export</span><span class="w"> </span><span class="nv">KMP_AFFINITY</span><span class="o">=</span><span class="nv">granularity</span><span class="o">=</span>fine,compact,1,0
<span class="nb">export</span><span class="w"> </span><span class="nv">LD_PRELOAD</span><span class="o">=</span><span class="si">${</span><span class="nv">CONDA_PREFIX</span><span class="k">:-</span><span class="s2">"</span><span class="k">$(</span>dirname<span class="w"> </span><span class="k">$(</span>which<span class="w"> </span>conda<span class="k">))</span><span class="s2">/../"</span><span class="si">}</span>/lib/libiomp5.so:<span class="si">${</span><span class="nv">CONDA_PREFIX</span><span class="k">:-</span><span class="s2">"</span><span class="k">$(</span>dirname<span class="w"> </span><span class="k">$(</span>which<span class="w"> </span>conda<span class="k">))</span><span class="s2">/../"</span><span class="si">}</span>/lib/libjemalloc.so
<span class="nb">export</span><span class="w"> </span><span class="nv">MALLOC_CONF</span><span class="o">=</span><span class="s2">"oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:-1"</span>
numactl<span class="w"> </span>-C<span class="w"> </span><span class="m">0</span>-31<span class="w"> </span>-m<span class="w"> </span><span class="m">0</span><span class="w"> </span>python<span class="w"> </span>bench.py
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># bench.py</span>
<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">MobileBertForQuestionAnswering</span>
<span class="c1"># Initialize an eager model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MobileBertForQuestionAnswering</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">"csarron/mobilebert-uncased-squad-v2"</span><span class="p">)</span>
<span class="n">seq_length</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">bs</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">vocab_size</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="p">(</span><span class="n">bs</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="n">input_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"input_ids"</span><span class="p">:</span> <span class="nb">input</span><span class="p">}</span>
<span class="c1"># Initialize the inductor model</span>
<span class="n">compiled_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">model</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">compiled_model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>
<span class="n">NUM_ITERS</span><span class="o">=</span><span class="mi">50</span>
<span class="kn">import</span> <span class="nn">timeit</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="c1"># warmup</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>
<span class="n">eager_t</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="s2">"model(**input_dict)"</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="n">NUM_ITERS</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">())</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="c1"># warmup</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">compiled_model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>
<span class="n">inductor_t</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="s2">"compiled_model(**input_dict)"</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="n">NUM_ITERS</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">())</span>
<span class="c1"># print(f"eager use: {eager_t * 1000 / NUM_ITERS} ms/iter")</span>
<span class="c1"># print(f"inductor use: {inductor_t * 1000 / NUM_ITERS} ms/iter")</span>
<span class="c1"># print(f"speed up ratio: {eager_t / inductor_t}")</span>
</pre></div>
</div>
<p>Output:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>eager<span class="w"> </span>use:<span class="w"> </span><span class="m">802</span>.1023553796113<span class="w"> </span>ms/iter
inductor<span class="w"> </span>use:<span class="w"> </span><span class="m">339</span>.95180135127157<span class="w"> </span>ms/iter
speed<span class="w"> </span>up<span class="w"> </span>ratio:<span class="w"> </span><span class="m">2</span>.359459053287382
</pre></div>
</div>
<p>In our own testing, we find the Inductor CPU backend speed up the model by around 2.355x.</p>
<p>Next, let’s dive deep into the performance at the operation level to understand where the speed-up comes from.
<a class="reference external" href="https://tutorials.pytorch.kr/recipes/recipes/profiler_recipe.html">Pytorch Profiler</a> is a good tool to help us.
Inductor CPU backend has the support to report the time of the fusion kernels to the profiler with the <code class="docutils literal notranslate"><span class="pre">enable_kernel_profile</span></code> configuration option:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch._inductor</span> <span class="kn">import</span> <span class="n">config</span>
<span class="n">config</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">enable_kernel_profile</span> <span class="o">=</span> <span class="kc">True</span>
</pre></div>
</div>
<p>Following the steps in <a class="reference external" href="https://tutorials.pytorch.kr/recipes/recipes/profiler_recipe.html">Pytorch Profiler</a>
We are able to get the profiling table and trace files.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># bench.py</span>
<span class="kn">from</span> <span class="nn">torch.profiler</span> <span class="kn">import</span> <span class="n">profile</span><span class="p">,</span> <span class="n">schedule</span><span class="p">,</span> <span class="n">ProfilerActivity</span>
<span class="n">RESULT_DIR</span> <span class="o">=</span> <span class="s2">"./prof_trace"</span>
<span class="n">my_schedule</span> <span class="o">=</span> <span class="n">schedule</span><span class="p">(</span>
<span class="n">skip_first</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">wait</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
<span class="n">warmup</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
<span class="n">active</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">repeat</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">trace_handler</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">"self_cpu_time_total"</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="c1"># print(output)</span>
<span class="n">p</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">RESULT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">p</span><span class="o">.</span><span class="n">step_num</span><span class="si">}</span><span class="s2">.json"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span> <span class="c1"># compiled_model(**input_dict) to get inductor model profiling</span>
<span class="n">total</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">with</span> <span class="n">profile</span><span class="p">(</span>
<span class="n">activities</span><span class="o">=</span><span class="p">[</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span><span class="p">],</span>
<span class="n">schedule</span><span class="o">=</span><span class="n">my_schedule</span><span class="p">,</span>
<span class="n">on_trace_ready</span><span class="o">=</span><span class="n">trace_handler</span>
<span class="p">)</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
<span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span> <span class="c1"># compiled_model(**input_dict) to get inductor model profiling</span>
<span class="n">p</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>We get the following performance profiling table for the eager-mode model (omitting some columns):</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>-------------------------<span class="w"> </span>------------<span class="w"> </span>------------<span class="w"> </span>------------
<span class="w"> </span>Name<span class="w"> </span>CPU<span class="w"> </span>total<span class="w"> </span>%<span class="w"> </span>CPU<span class="w"> </span>total<span class="w"> </span><span class="c1"># of Calls</span>
-------------------------<span class="w"> </span>------------<span class="w"> </span>------------<span class="w"> </span>------------
<span class="w"> </span>aten::addmm<span class="w"> </span><span class="m">45</span>.73%<span class="w"> </span><span class="m">370</span>.814ms<span class="w"> </span><span class="m">362</span>
<span class="w"> </span>aten::add<span class="w"> </span><span class="m">19</span>.89%<span class="w"> </span><span class="m">161</span>.276ms<span class="w"> </span><span class="m">363</span>
<span class="w"> </span>aten::copy_<span class="w"> </span><span class="m">14</span>.97%<span class="w"> </span><span class="m">121</span>.416ms<span class="w"> </span><span class="m">488</span>
<span class="w"> </span>aten::mul<span class="w"> </span><span class="m">9</span>.02%<span class="w"> </span><span class="m">73</span>.154ms<span class="w"> </span><span class="m">194</span>
<span class="w"> </span>aten::clamp_min<span class="w"> </span><span class="m">8</span>.81%<span class="w"> </span><span class="m">71</span>.444ms<span class="w"> </span><span class="m">96</span>
<span class="w"> </span>aten::bmm<span class="w"> </span><span class="m">5</span>.46%<span class="w"> </span><span class="m">44</span>.258ms<span class="w"> </span><span class="m">48</span>
<span class="w"> </span>ProfilerStep*<span class="w"> </span><span class="m">100</span>.00%<span class="w"> </span><span class="m">810</span>.920ms<span class="w"> </span><span class="m">1</span>
<span class="w"> </span>aten::div<span class="w"> </span><span class="m">2</span>.89%<span class="w"> </span><span class="m">23</span>.447ms<span class="w"> </span><span class="m">24</span>
<span class="w"> </span>aten::_softmax<span class="w"> </span><span class="m">1</span>.00%<span class="w"> </span><span class="m">8</span>.087ms<span class="w"> </span><span class="m">24</span>
<span class="w"> </span>aten::linear<span class="w"> </span><span class="m">46</span>.48%<span class="w"> </span><span class="m">376</span>.888ms<span class="w"> </span><span class="m">362</span>
<span class="w"> </span>aten::clone<span class="w"> </span><span class="m">2</span>.77%<span class="w"> </span><span class="m">22</span>.430ms<span class="w"> </span><span class="m">98</span>
<span class="w"> </span>aten::t<span class="w"> </span><span class="m">0</span>.31%<span class="w"> </span><span class="m">2</span>.502ms<span class="w"> </span><span class="m">362</span>
<span class="w"> </span>aten::view<span class="w"> </span><span class="m">0</span>.14%<span class="w"> </span><span class="m">1</span>.161ms<span class="w"> </span><span class="m">850</span>
<span class="w"> </span>aten::transpose<span class="w"> </span><span class="m">0</span>.17%<span class="w"> </span><span class="m">1</span>.377ms<span class="w"> </span><span class="m">386</span>
<span class="w"> </span>aten::index_select<span class="w"> </span><span class="m">0</span>.12%<span class="w"> </span><span class="m">952</span>.000us<span class="w"> </span><span class="m">3</span>
<span class="w"> </span>aten::expand<span class="w"> </span><span class="m">0</span>.12%<span class="w"> </span><span class="m">986</span>.000us<span class="w"> </span><span class="m">458</span>
<span class="w"> </span>aten::matmul<span class="w"> </span><span class="m">8</span>.31%<span class="w"> </span><span class="m">67</span>.420ms<span class="w"> </span><span class="m">48</span>
<span class="w"> </span>aten::cat<span class="w"> </span><span class="m">0</span>.09%<span class="w"> </span><span class="m">703</span>.000us<span class="w"> </span><span class="m">1</span>
<span class="w"> </span>aten::as_strided<span class="w"> </span><span class="m">0</span>.08%<span class="w"> </span><span class="m">656</span>.000us<span class="w"> </span><span class="m">963</span>
<span class="w"> </span>aten::relu<span class="w"> </span><span class="m">8</span>.86%<span class="w"> </span><span class="m">71</span>.864ms<span class="w"> </span><span class="m">96</span>
-------------------------<span class="w"> </span>------------<span class="w"> </span>------------<span class="w"> </span>------------
Self<span class="w"> </span>CPU<span class="w"> </span><span class="nb">time</span><span class="w"> </span>total:<span class="w"> </span><span class="m">810</span>.920ms
</pre></div>