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<meta property="og:title" content="Distributed Training with Uneven Inputs Using the Join Context Manager" />
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<meta property="og:description" content="Author: Andrew Gu In this tutorial, you will see: An overview of the Join context manager., An example of how to use the context manager with DistributedDataParallel., An example of how to use the context manager with both DistributedDataParallel and ZeroRedundancyOptimizer., An example of passin..." />
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<title>Distributed Training with Uneven Inputs Using the Join Context Manager — ํ์ดํ ์น ํ๊ตญ์ด ํํ ๋ฆฌ์ผ (PyTorch tutorials in Korean)</title>
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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
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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 ์๊ฐ - YouTube ์๋ฆฌ์ฆ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">PyTorch ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Pytorch Tensor ์๊ฐ</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">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
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<p class="caption" role="heading"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision Object Detection Finetuning Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tiatoolbox_tutorial.html">Whole Slide Image Classification Using PyTorch and TIAToolbox</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with Tacotron2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forced_alignment_with_torchaudio_tutorial.html">wav2vec2์ ์ด์ฉํ ๊ฐ์ ์ ๋ ฌ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ
์คํธ</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="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html"><code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> ์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</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">๋ฐฑ์๋</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">๊ฐํํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_ppo.html">Reinforcement Learning (PPO) with TorchRL Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">๋ง๋ฆฌ์ค ๊ฒ์ RL ์์ด์ ํธ๋ก ํ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="pendulum.html">Pendulum: Writing your environment and transforms with TorchRL</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</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="../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/realtime_rpi.html">Raspberry Pi 4 ์์ ์ค์๊ฐ ์ถ๋ก (Inference) (30fps!)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ๋กํ์ผ๋ง</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</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="../intermediate/fx_conv_bn_fuser.html">(๋ฒ ํ) FX์์ ํฉ์ฑ๊ณฑ/๋ฐฐ์น ์ ๊ทํ(Convolution/Batch Norm) ๊ฒฐํฉ๊ธฐ(Fuser) ๋ง๋ค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/jacobians_hessians.html">Jacobians, Hessians, hvp, vhp, and more: composing function transforms</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ensembling.html">๋ชจ๋ธ ์์๋ธ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/per_sample_grads.html">Per-sample-gradients</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">PyTorch C++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_classes.html">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="privateuseone.html">Facilitating New Backend Integration by PrivateUse1</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_profiler_tutorial.html">ํ
์๋ณด๋๋ฅผ ์ด์ฉํ ํ์ดํ ์น ํ๋กํ์ผ๋ฌ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Ray Tune์ ์ฌ์ฉํ ํ์ดํผํ๋ผ๋ฏธํฐ ํ๋</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(๋ฒ ํ) PyTorch์์ Eager Mode๋ฅผ ์ด์ฉํ ์ ์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchserve_with_ipex.html">Grokking PyTorch Intel CPU performance from first principles</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/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="../intermediate/nvfuser_intro_tutorial.html">Getting Started - Accelerate Your Scripts with nvFuser</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ax_multiobjective_nas_tutorial.html">Multi-Objective NAS with Ax</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torch_compile_tutorial.html">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/inductor_debug_cpu.html">Inductor CPU backend debugging and profiling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html">(Beta) Scaled Dot Product Attention (SDPA)๋ก ๊ณ ์ฑ๋ฅ ํธ๋์คํฌ๋จธ(Transformers) ๊ตฌํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#torch-compile-sdpa"><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> ๊ณผ ํจ๊ป SDPA ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#sdpa-atteition-bias">SDPA๋ฅผ <code class="docutils literal notranslate"><span class="pre">atteition.bias</span></code> ํ์ ํด๋์ค์ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#id8">๊ฒฐ๋ก </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">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul class="current">
<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="../intermediate/model_parallel_tutorial.html">๋จ์ผ ๋จธ์ ์ ์ฌ์ฉํ ๋ชจ๋ธ ๋ณ๋ ฌํ ๋ชจ๋ฒ ์ฌ๋ก</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ ์์ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch๋ก ๋ถ์ฐ ์ดํ๋ฆฌ์ผ์ด์
๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/FSDP_tutorial.html">Getting Started with Fully Sharded Data Parallel(FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/FSDP_adavnced_tutorial.html">Advanced Model Training with Fully Sharded Data Parallel (FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/TP_tutorial.html">Large Scale Transformer model training with Tensor Parallel (TP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/process_group_cpp_extension_tutorial.html">Cpp ํ์ฅ์ ์ฌ์ฉํ ํ๋ก์ธ์ค ๊ทธ๋ฃน ๋ฐฑ์๋ ์ฌ์ฉ์ ์ ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ์ ๋ณ๋ ฌ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ์ฌ์ฉํ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">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">์ถ์ฒ ์์คํ
</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchrec_tutorial.html">TorchRec ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="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 ํํ ๋ฆฌ์ผ: FLAVA ๋ฏธ์ธ์กฐ์ </a></li>
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<div class="section" id="distributed-training-with-uneven-inputs-using-the-join-context-manager">
<h1>Distributed Training with Uneven Inputs Using the Join Context Manager<a class="headerlink" href="#distributed-training-with-uneven-inputs-using-the-join-context-manager" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/andwgu">Andrew Gu</a></p>
<div class="admonition note">
<p class="admonition-title">์ฐธ๊ณ </p>
<p><a class="reference internal" href="../_images/pencil-16.png"><img alt="edit" src="../_images/pencil-16.png" style="width: 16px; height: 16px;" /></a> View and edit this tutorial in <a class="reference external" href="https://github.com/pytorch/tutorials/blob/main/advanced_source/generic_join.rst">github</a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">์ฐธ๊ณ </p>
<p><code class="docutils literal notranslate"><span class="pre">Join</span></code> is introduced in PyTorch 1.10 as a prototype feature. This
API is subject to change.</p>
</div>
<p>In this tutorial, you will see:</p>
<ul class="simple">
<li><p>An overview of the <a class="reference external" href="https://pytorch.org/docs/master/distributed.algorithms.join.html">Join</a> context manager.</p></li>
<li><p>An example of how to use the context manager with <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code>.</p></li>
<li><p>An example of how to use the context manager with both
<code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> and <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code>.</p></li>
<li><p>An example of passing in keyword arguments to the context manager.</p></li>
<li><p>A dive into how the <a class="reference external" href="https://pytorch.org/docs/master/distributed.algorithms.join.html">Join</a> context manager works.</p></li>
<li><p>An example showing how to make a toy class compatible with the context
manager.</p></li>
</ul>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<ul class="simple">
<li><p>PyTorch 1.10+</p></li>
<li><p><a class="reference external" href="https://tutorials.pytorch.kr/intermediate/ddp_tutorial.html">Getting Started with Distributed Data Parallel</a></p></li>
<li><p><a class="reference external" href="https://tutorials.pytorch.kr/recipes/zero_redundancy_optimizer.html">Shard Optimizer States with ZeroRedundancyOptimizer</a></p></li>
</ul>
</div>
<div class="section" id="what-is-join">
<h2>What is <code class="docutils literal notranslate"><span class="pre">Join</span></code>?<a class="headerlink" href="#what-is-join" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>In <a class="reference external" href="https://tutorials.pytorch.kr/intermediate/ddp_tutorial.html#basic-use-case">Getting Started with Distributed Data Parallel - Basic Use Case</a>, you saw
the general skeleton for using <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a> to perform data
parallel training. This implicitly schedules all-reduces in each backward pass
to synchronize gradients across ranks. Such <a class="reference external" href="https://pytorch.org/docs/stable/distributed.html">collective communications</a> require participation
from all ranks in the process group, so if a rank has fewer inputs, then the
other ranks will hang or error (depending on the backend). More generally, this
problem persists for any class that performs per-iteration synchronous
collective communications.</p>
<p><code class="docutils literal notranslate"><span class="pre">Join</span></code> is a context manager to be used around your per-rank training loop to
facilitate training with uneven inputs. The context manager allows the ranks
that exhaust their inputs early (i.e. <em>join</em> early) to shadow the collective
communications performed by those that have not yet joined. The ways in which
the communications are shadowed are specified by hooks.</p>
</div>
<div class="section" id="using-join-with-distributeddataparallel">
<h2>Using <code class="docutils literal notranslate"><span class="pre">Join</span></code> with <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code><a class="headerlink" href="#using-join-with-distributeddataparallel" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>PyTorchโs <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a> works out-of-the-box with the <code class="docutils literal notranslate"><span class="pre">Join</span></code>
context manager. Here is an example usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">from</span> <span class="nn">torch.distributed.algorithms.join</span> <span class="kn">import</span> <span class="n">Join</span>
<span class="kn">from</span> <span class="nn">torch.nn.parallel</span> <span class="kn">import</span> <span class="n">DistributedDataParallel</span> <span class="k">as</span> <span class="n">DDP</span>
<span class="n">BACKEND</span> <span class="o">=</span> <span class="s2">"nccl"</span>
<span class="n">WORLD_SIZE</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">NUM_INPUTS</span> <span class="o">=</span> <span class="mi">5</span>
<span class="k">def</span> <span class="nf">worker</span><span class="p">(</span><span class="n">rank</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_ADDR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'localhost'</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_PORT'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'29500'</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">BACKEND</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="n">WORLD_SIZE</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">DDP</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">),</span> <span class="n">device_ids</span><span class="o">=</span><span class="p">[</span><span class="n">rank</span><span class="p">])</span>
<span class="c1"># Rank 1 gets one more input than rank 0</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">float</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="n">NUM_INPUTS</span> <span class="o">+</span> <span class="n">rank</span><span class="p">)]</span>
<span class="n">num_inputs</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">with</span> <span class="n">Join</span><span class="p">([</span><span class="n">model</span><span class="p">]):</span>
<span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
<span class="n">num_inputs</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> has exhausted all </span><span class="si">{</span><span class="n">num_inputs</span><span class="si">}</span><span class="s2"> of its inputs!"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">worker</span><span class="p">,</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">WORLD_SIZE</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
<p>This produces the following output (where the <code class="docutils literal notranslate"><span class="pre">print()</span></code> s from rank 0 and
rank 1 may be arbitrarily ordered):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>Rank 0 has exhausted all 5 of its inputs!
Rank 1 has exhausted all 6 of its inputs!
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">์ฐธ๊ณ </p>
<p><a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a> provided its own <a class="reference external" href="https://pytorch.org/docs/stable/_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join">join()</a> context manager
prior to the introduction of this generic <code class="docutils literal notranslate"><span class="pre">Join</span></code> context manager. In the
above example, using <code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">Join([model]):</span></code> is equivalent to using
<code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">model.join():</span></code>. One limitation of the existing
<code class="docutils literal notranslate"><span class="pre">DistributedDataParallel.join()</span></code> is that it does not allow multiple
participating classes, e.g. <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> and
<a class="reference external" href="https://pytorch.org/docs/stable/distributed.optim.html">ZeroRedundancyOptimizer</a> together.</p>
</div>
</div>
<div class="section" id="using-join-with-distributeddataparallel-and-zeroredundancyoptimizer">
<h2>Using <code class="docutils literal notranslate"><span class="pre">Join</span></code> with <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> and <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code><a class="headerlink" href="#using-join-with-distributeddataparallel-and-zeroredundancyoptimizer" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">Join</span></code> context manager works not only with a single class but also with
multiple classes together. PyTorchโs <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> is also
compatible with the context manager, so here, we examine how to modify the
previous example to use both <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> and
<code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.distributed.optim</span> <span class="kn">import</span> <span class="n">ZeroRedundancyOptimizer</span> <span class="k">as</span> <span class="n">ZeRO</span>
<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
<span class="k">def</span> <span class="nf">worker</span><span class="p">(</span><span class="n">rank</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_ADDR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'localhost'</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_PORT'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'29500'</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">BACKEND</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="n">WORLD_SIZE</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">DDP</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">),</span> <span class="n">device_ids</span><span class="o">=</span><span class="p">[</span><span class="n">rank</span><span class="p">])</span>
<span class="n">optim</span> <span class="o">=</span> <span class="n">ZeRO</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">Adam</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="c1"># Rank 1 gets one more input than rank 0</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">float</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="n">NUM_INPUTS</span> <span class="o">+</span> <span class="n">rank</span><span class="p">)]</span>
<span class="n">num_inputs</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Pass both `model` and `optim` into `Join()`</span>
<span class="k">with</span> <span class="n">Join</span><span class="p">([</span><span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="p">]):</span>
<span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
<span class="n">num_inputs</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> has exhausted all </span><span class="si">{</span><span class="n">num_inputs</span><span class="si">}</span><span class="s2"> of its inputs!"</span><span class="p">)</span>
</pre></div>
</div>
<p>This will yield the same output as before. The notable change was
additionally passing in the <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> instance into
<code class="docutils literal notranslate"><span class="pre">Join()</span></code>.</p>
</div>
<div class="section" id="passing-keyword-arguments">
<h2>Passing Keyword Arguments<a class="headerlink" href="#passing-keyword-arguments" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>Classes may provide keyword arguments that modify their behavior in the context
manager at run time. For example, <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> provides an
argument <code class="docutils literal notranslate"><span class="pre">divide_by_initial_world_size</span></code>, which determines if gradients are
divided by the initial world size or by the effective world size (i.e. number
of non-joined ranks). Such keyword arguments can be passed directly into the
context manager.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">Join</span><span class="p">([</span><span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="p">],</span> <span class="n">divide_by_initial_world_size</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
<span class="o">...</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">๊ฒฝ๊ณ </p>
<p>The keyword arguments passed into the context manager are shared across
all participating classes. This should not be a limitation since we do
not expect cases where multiple <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> s need differing settings
of the same argument. Nonetheless, this is something to keep in mind.</p>
</div>
</div>
<div class="section" id="how-does-join-work">
<h2>How Does <code class="docutils literal notranslate"><span class="pre">Join</span></code> Work?<a class="headerlink" href="#how-does-join-work" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>Now that we have seen some preliminary examples of how to use the <code class="docutils literal notranslate"><span class="pre">Join</span></code>
context manager, let us delve deeper into how it works. This will provide a
greater insight into the full capability that it offers and prepare you to make
your own custom classes compatible. Here, we will go over the <code class="docutils literal notranslate"><span class="pre">Join</span></code> class as
well as the supporting classes <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> and <code class="docutils literal notranslate"><span class="pre">JoinHook</span></code>.</p>
<div class="section" id="joinable">
<h3><code class="docutils literal notranslate"><span class="pre">Joinable</span></code><a class="headerlink" href="#joinable" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>To begin, classes compatible with the <code class="docutils literal notranslate"><span class="pre">Join</span></code> context manager must inherit
from the abstract base class <code class="docutils literal notranslate"><span class="pre">Joinable</span></code>. In particular, a <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> must
implement:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">join_hook(self,</span> <span class="pre">**kwargs)</span> <span class="pre">-></span> <span class="pre">JoinHook</span></code></p></li>
</ul>
<p>This returns the <code class="docutils literal notranslate"><span class="pre">JoinHook</span></code> instance for the <code class="docutils literal notranslate"><span class="pre">Joinable</span></code>, determining how
joined processes should shadow the per-iteration collective communications
performed by the <code class="docutils literal notranslate"><span class="pre">Joinable</span></code>.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">join_device(self)</span> <span class="pre">-></span> <span class="pre">torch.device</span></code></p></li>
</ul>
<p>This returns a device to be used by the <code class="docutils literal notranslate"><span class="pre">Join</span></code> context manager to perform
collective communications, e.g. <code class="docutils literal notranslate"><span class="pre">torch.device("cuda:0")</span></code> or
<code class="docutils literal notranslate"><span class="pre">torch.device("cpu")</span></code>.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">join_process_group(self)</span> <span class="pre">-></span> <span class="pre">ProcessGroup</span></code></p></li>
</ul>
<p>This returns the process group to be used by the <code class="docutils literal notranslate"><span class="pre">Join</span></code> context manager to
perform collective communications.</p>
<p>In particular, the <code class="docutils literal notranslate"><span class="pre">join_device</span></code> and <code class="docutils literal notranslate"><span class="pre">join_process_group</span></code> are required
attributes to ensure that the context manager can schedule collective
communications between joined and non-joined processes. One usage is to count
the number of non-joined processes on each iteration using an all-reduce.
Another usage is for implementing the mechanism required for
<code class="docutils literal notranslate"><span class="pre">throw_on_early_termination=True</span></code>, which we will explain later below.</p>
<p><code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> and <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> already inherit
from <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> and implement the above methods, which is why we could
directly use them in the previous examples.</p>
<p><code class="docutils literal notranslate"><span class="pre">Joinable</span></code> classes should make sure to call the <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> constructor
since it initializes a <code class="docutils literal notranslate"><span class="pre">JoinConfig</span></code> instance, which is used internally by
the context manager to ensure correctness. This will be saved in each
<code class="docutils literal notranslate"><span class="pre">Joinable</span></code> as a field <code class="docutils literal notranslate"><span class="pre">_join_config</span></code>.</p>
</div>
<div class="section" id="joinhook">
<h3><code class="docutils literal notranslate"><span class="pre">JoinHook</span></code><a class="headerlink" href="#joinhook" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>Next, let us break down the <code class="docutils literal notranslate"><span class="pre">JoinHook</span></code> class. A <code class="docutils literal notranslate"><span class="pre">JoinHook</span></code> provides two
entry points into a context manager:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">main_hook(self)</span> <span class="pre">-></span> <span class="pre">None</span></code></p></li>
</ul>
<p>This hook is called repeatedly by each joined rank while there exists a rank
that has not yet joined. It is meant to shadow the collective communications
performed by the <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> in each training iteration (e.g. in one forward
pass, backward pass, and optimizer step).</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">post_hook(self,</span> <span class="pre">is_last_joiner:</span> <span class="pre">bool)</span> <span class="pre">-></span> <span class="pre">None</span></code></p></li>
</ul>
<p>This hook is called once all ranks have joined. It is passed an additional
<code class="docutils literal notranslate"><span class="pre">bool</span></code> argument <code class="docutils literal notranslate"><span class="pre">is_last_joiner</span></code>, which indicates if the rank was one of
the last to join. The argument may be useful for synchronization.</p>
<p>To give concrete examples of what these hooks may look like, the provided
<code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> main hook performs an optimizer step per normal
since the joined rank is still responsible for updating and synchronizing its
shard of the parameters, and the provided <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> post-hook
broadcasts the final updated model from one of the last joining ranks to ensure
that it is the same across all ranks.</p>
</div>
<div class="section" id="join">
<h3><code class="docutils literal notranslate"><span class="pre">Join</span></code><a class="headerlink" href="#join" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>Finally, let us examine how these fit into the <code class="docutils literal notranslate"><span class="pre">Join</span></code> class itself.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">__init__(self,</span> <span class="pre">joinables:</span> <span class="pre">List[Joinable],</span> <span class="pre">enable:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">throw_on_early_termination:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False)</span></code></p></li>
</ul>
<p>As we saw in the previous examples, the constructor takes in a list of the
<code class="docutils literal notranslate"><span class="pre">Joinable</span></code> s that participate in the training loop. These should be the
classes that perform collective communications in each iteration.</p>
<p><code class="docutils literal notranslate"><span class="pre">enable</span></code> is a <code class="docutils literal notranslate"><span class="pre">bool</span></code> that can be set to <code class="docutils literal notranslate"><span class="pre">False</span></code> if you know that there
will not be uneven inputs, in which case the context manager becomes vacuous
similar to <code class="docutils literal notranslate"><span class="pre">contextlib.nullcontext()</span></code>. This also may disable join-related
computation in the participating <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> s.</p>
<p><code class="docutils literal notranslate"><span class="pre">throw_on_early_termination</span></code> is a <code class="docutils literal notranslate"><span class="pre">bool</span></code> that can be set to <code class="docutils literal notranslate"><span class="pre">True</span></code> to
have each rank raise an exception the moment that uneven inputs are detected.
This is useful for cases that do not conform to the context managerโs
requirements, which is most typically when there are collective communications
from different classes that may be arbitrarily interleaved, such as when using
<code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> with a model that has <code class="docutils literal notranslate"><span class="pre">SyncBatchNorm</span></code> layers. In
such cases, this argument should be set to <code class="docutils literal notranslate"><span class="pre">True</span></code> so that the application
logic can catch the exception and determine how to proceed.</p>
<ul class="simple">
<li><p>The core logic occurs in the <code class="docutils literal notranslate"><span class="pre">__exit__()</span></code> method, which loops while there
exists a non-joined rank, calling each <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> ใs main hook, and
then once all ranks have joined, calls their post hooks. Both the main hooks
and post-hooks are iterated over in the order that the <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> s are
passed in.</p></li>
<li><p>The context manager requires a heartbeat from non-joined processes. As such,
each <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> class should make a call to <code class="docutils literal notranslate"><span class="pre">Join.notify_join_context()</span></code>
before its per-iteration collective communications. The context manager will
ensure that only the first <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> passed in actually sends the
heartbeat.</p></li>
</ul>
<div class="admonition warning">
<p class="admonition-title">๊ฒฝ๊ณ </p>
<p>As mentioned above regarding <code class="docutils literal notranslate"><span class="pre">throw_on_early_termination</span></code>, the
<code class="docutils literal notranslate"><span class="pre">Join</span></code> context manager is not compatible with certain compositions of
classes. The <code class="docutils literal notranslate"><span class="pre">Joinable</span></code> ใs <code class="docutils literal notranslate"><span class="pre">JoinHook</span></code> s must be serializable since each
hook is fully executed before proceeding to the next. In other words, two
hooks cannot overlap. Moreover, currently, both the main hooks and post-
hooks are iterated over in the same deterministic order. If this appears to
be a major limitation, we may modify the API to permit a customizable
ordering.</p>
</div>
</div>
</div>
<div class="section" id="making-a-toy-class-work-with-join">
<h2>Making a Toy Class Work with <code class="docutils literal notranslate"><span class="pre">Join</span></code><a class="headerlink" href="#making-a-toy-class-work-with-join" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>Since the previous section introduced several concepts, let us see them in
practice with a toy example. Here, we will implement a class that counts the
number of inputs that are seen across all ranks before its rank joins. This
should provide a basic idea of how you may make your own class compatible
with the <code class="docutils literal notranslate"><span class="pre">Join</span></code> context manager.</p>
<p>Specifically, the following code has each rank print out (1) the number of
inputs across all ranks that seen before it joins and (2) the total number
of inputs across all ranks.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">from</span> <span class="nn">torch.distributed.algorithms.join</span> <span class="kn">import</span> <span class="n">Join</span><span class="p">,</span> <span class="n">Joinable</span><span class="p">,</span> <span class="n">JoinHook</span>
<span class="n">BACKEND</span> <span class="o">=</span> <span class="s2">"nccl"</span>
<span class="n">WORLD_SIZE</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">NUM_INPUTS</span> <span class="o">=</span> <span class="mi">5</span>
<span class="k">class</span> <span class="nc">CounterJoinHook</span><span class="p">(</span><span class="n">JoinHook</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Join hook for :class:`Counter`.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> counter (Counter): the :class:`Counter` object using this hook.</span>
<span class="sd"> sync_max_count (bool): whether to sync the max count once all ranks</span>
<span class="sd"> join.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">counter</span><span class="p">,</span>
<span class="n">sync_max_count</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">=</span> <span class="n">counter</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sync_max_count</span> <span class="o">=</span> <span class="n">sync_max_count</span>
<span class="k">def</span> <span class="nf">main_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Shadows the counter's all-reduce by all-reducing a dim-1 zero tensor.</span>
<span class="sd"> """</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">counter</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="n">dist</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">post_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_last_joiner</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Synchronizes the max count across all :class:`Counter` s if</span>
<span class="sd"> ``sync_max_count=True``.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sync_max_count</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">rank</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">counter</span><span class="o">.</span><span class="n">process_group</span><span class="p">)</span>
<span class="n">common_rank</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span><span class="o">.</span><span class="n">find_common_rank</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">is_last_joiner</span><span class="p">)</span>
<span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="n">common_rank</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">counter</span><span class="o">.</span><span class="n">max_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span><span class="o">.</span><span class="n">count</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="n">dist</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">counter</span><span class="o">.</span><span class="n">max_count</span><span class="p">,</span> <span class="n">src</span><span class="o">=</span><span class="n">common_rank</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Counter</span><span class="p">(</span><span class="n">Joinable</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Example :class:`Joinable` that counts the number of training iterations</span>
<span class="sd"> that it participates in.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">process_group</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Counter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
<span class="bp">self</span><span class="o">.</span><span class="n">process_group</span> <span class="o">=</span> <span class="n">process_group</span>
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Counts the number of inputs processed on this iteration by all ranks</span>
<span class="sd"> by all-reducing a dim-1 one tensor; increments its own internal count.</span>
<span class="sd"> """</span>
<span class="n">Join</span><span class="o">.</span><span class="n">notify_join_context</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">dist</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">t</span>
<span class="k">def</span> <span class="nf">join_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-></span> <span class="n">JoinHook</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Return a join hook that shadows the all-reduce in :meth:`__call__`.</span>
<span class="sd"> This join hook supports the following keyword arguments:</span>
<span class="sd"> sync_max_count (bool, optional): whether to synchronize the maximum</span>
<span class="sd"> count across all ranks once all ranks join; default is ``False``.</span>
<span class="sd"> """</span>
<span class="n">sync_max_count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"sync_max_count"</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="k">return</span> <span class="n">CounterJoinHook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sync_max_count</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">join_device</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">join_process_group</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">process_group</span>
<span class="k">def</span> <span class="nf">find_common_rank</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="n">to_consider</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Returns the max rank of the ones to consider over the process group.</span>
<span class="sd"> """</span>
<span class="n">common_rank</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">rank</span> <span class="k">if</span> <span class="n">to_consider</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="n">dist</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">common_rank</span><span class="p">,</span> <span class="n">op</span><span class="o">=</span><span class="n">dist</span><span class="o">.</span><span class="n">ReduceOp</span><span class="o">.</span><span class="n">MAX</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">process_group</span><span class="p">)</span>
<span class="n">common_rank</span> <span class="o">=</span> <span class="n">common_rank</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="k">return</span> <span class="n">common_rank</span>
<span class="k">def</span> <span class="nf">worker</span><span class="p">(</span><span class="n">rank</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device_count</span><span class="p">()</span> <span class="o">>=</span> <span class="n">WORLD_SIZE</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_ADDR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'localhost'</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_PORT'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'29500'</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">BACKEND</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="n">WORLD_SIZE</span><span class="p">)</span>
<span class="n">counter</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="sa">f</span><span class="s2">"cuda:</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span> <span class="n">dist</span><span class="o">.</span><span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">)</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">float</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="n">NUM_INPUTS</span> <span class="o">+</span> <span class="n">rank</span><span class="p">)]</span>
<span class="k">with</span> <span class="n">Join</span><span class="p">([</span><span class="n">counter</span><span class="p">],</span> <span class="n">sync_max_count</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
<span class="n">counter</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">counter</span><span class="o">.</span><span class="n">count</span><span class="o">.</span><span class="n">item</span><span class="p">())</span><span class="si">}</span><span class="s2"> inputs processed before rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> joined!"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">counter</span><span class="o">.</span><span class="n">max_count</span><span class="o">.</span><span class="n">item</span><span class="p">())</span><span class="si">}</span><span class="s2"> inputs processed across all ranks!"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">worker</span><span class="p">,</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">WORLD_SIZE</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
<p>Since rank 0 sees 5 inputs and rank 1 sees 6, this yields the output:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>10 inputs processed before rank 0 joined!
11 inputs processed across all ranks!
11 inputs processed before rank 1 joined!
11 inputs processed across all ranks!
</pre></div>
</div>
<p>Some key points to highlight:</p>
<ul class="simple">
<li><p>A <code class="docutils literal notranslate"><span class="pre">Counter</span></code> instance performs a single all-reduce per iteration, so the
main hook performs a single all-reduce as well to shadow it.</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">Counter</span></code> class makes a call to <code class="docutils literal notranslate"><span class="pre">Join.notify_join_context()</span></code> at the
beginning of its <code class="docutils literal notranslate"><span class="pre">__call__()</span></code> method since that is a place before its per-
iteration collective communications (i.e. its all-reduce).</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">is_last_joiner</span></code> argument is used to determine the broadcast source in
the post-hooks.</p></li>
<li><p>We pass in the <code class="docutils literal notranslate"><span class="pre">sync_max_count</span></code> keyword argument to the context manager,
which is then forwarded to <code class="docutils literal notranslate"><span class="pre">Counter</span></code> ใs join hook.</p></li>
</ul>
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