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<!DOCTYPE html>
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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<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>
</ul>
<p class="caption" role="heading"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<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 class="current">
<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 current"><a class="current reference internal" href="#">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>
<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"><a class="reference internal" href="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">์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>
</ul>
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<div class="section" id="custom-c-and-cuda-extensions">
<h1>Custom C++ and CUDA Extensions<a class="headerlink" href="#custom-c-and-cuda-extensions" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://www.goldsborough.me/">Peter Goldsborough</a></p>
<p>PyTorch provides a plethora of operations related to neural networks, arbitrary
tensor algebra, data wrangling and other purposes. However, you may still find
yourself in need of a more customized operation. For example, you might want to
use a novel activation function you found in a paper, or implement an operation
you developed as part of your research.</p>
<p>The easiest way of integrating such a custom operation in PyTorch is to write it
in Python by extending <code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> as outlined <a class="reference external" href="https://pytorch.org/docs/master/notes/extending.html">here</a>. This gives you the full
power of automatic differentiation (spares you from writing derivative
functions) as well as the usual expressiveness of Python. However, there may be
times when your operation is better implemented in C++. For example, your code
may need to be <em>really</em> fast because it is called very frequently in your model
or is very expensive even for few calls. Another plausible reason is that it
depends on or interacts with other C or C++ libraries. To address such cases,
PyTorch provides a very easy way of writing custom <em>C++ extensions</em>.</p>
<p>C++ extensions are a mechanism we have developed to allow users (you) to create
PyTorch operators defined <em>out-of-source</em>, i.e. separate from the PyTorch
backend. This approach is <em>different</em> from the way native PyTorch operations are
implemented. C++ extensions are intended to spare you much of the boilerplate
associated with integrating an operation with PyTorchโs backend while providing
you with a high degree of flexibility for your PyTorch-based projects.
Nevertheless, once you have defined your operation as a C++ extension, turning
it into a native PyTorch function is largely a matter of code organization,
which you can tackle after the fact if you decide to contribute your operation
upstream.</p>
<div class="section" id="motivation-and-example">
<h2>Motivation and Example<a class="headerlink" href="#motivation-and-example" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>The rest of this note will walk through a practical example of writing and using
a C++ (and CUDA) extension. If you are being chased or someone will fire you if
you donโt get that op done by the end of the day, you can skip this section and
head straight to the implementation details in the next section.</p>
<p>Letโs say youโve come up with a new kind of recurrent unit that you found to
have superior properties compared to the state of the art. This recurrent unit
is similar to an LSTM, but differs in that it lacks a <em>forget gate</em> and uses an
<em>Exponential Linear Unit</em> (ELU) as its internal activation function. Because
this unit never forgets, weโll call it <em>LLTM</em>, or <em>Long-Long-Term-Memory</em> unit.</p>
<p>The two ways in which LLTMs differ from vanilla LSTMs are significant enough
that we canโt configure PyTorchโs <code class="docutils literal notranslate"><span class="pre">LSTMCell</span></code> for our purposes, so weโll have to
create a custom cell. The first and easiest approach for this โ and likely in
all cases a good first step โ is to implement our desired functionality in
plain PyTorch with Python. For this, we need to subclass
<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(PyTorch v2.3์์)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> and implement the forward pass of the LLTM. This would
look something like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LLTM</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">LLTM</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">input_features</span> <span class="o">=</span> <span class="n">input_features</span>
<span class="bp">self</span><span class="o">.</span><span class="n">state_size</span> <span class="o">=</span> <span class="n">state_size</span>
<span class="c1"># 3 * state_size for input gate, output gate and candidate cell gate.</span>
<span class="c1"># input_features + state_size because we will multiply with [input, h].</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">state_size</span><span class="p">,</span> <span class="n">input_features</span> <span class="o">+</span> <span class="n">state_size</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">state_size</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">stdv</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state_size</span><span class="p">)</span>
<span class="k">for</span> <span class="n">weight</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">stdv</span><span class="p">,</span> <span class="o">+</span><span class="n">stdv</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
<span class="n">old_h</span><span class="p">,</span> <span class="n">old_cell</span> <span class="o">=</span> <span class="n">state</span>
<span class="n">X</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">old_h</span><span class="p">,</span> <span class="nb">input</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Compute the input, output and candidate cell gates with one MM.</span>
<span class="n">gate_weights</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
<span class="c1"># Split the combined gate weight matrix into its components.</span>
<span class="n">gates</span> <span class="o">=</span> <span class="n">gate_weights</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">input_gate</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">output_gate</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="c1"># Here we use an ELU instead of the usual tanh.</span>
<span class="n">candidate_cell</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">elu</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="c1"># Compute the new cell state.</span>
<span class="n">new_cell</span> <span class="o">=</span> <span class="n">old_cell</span> <span class="o">+</span> <span class="n">candidate_cell</span> <span class="o">*</span> <span class="n">input_gate</span>
<span class="c1"># Compute the new hidden state and output.</span>
<span class="n">new_h</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">new_cell</span><span class="p">)</span> <span class="o">*</span> <span class="n">output_gate</span>
<span class="k">return</span> <span class="n">new_h</span><span class="p">,</span> <span class="n">new_cell</span>
</pre></div>
</div>
<p>which we could then use as expected:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">X</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="n">batch_size</span><span class="p">,</span> <span class="n">input_features</span><span class="p">)</span>
<span class="n">h</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="n">batch_size</span><span class="p">,</span> <span class="n">state_size</span><span class="p">)</span>
<span class="n">C</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="n">batch_size</span><span class="p">,</span> <span class="n">state_size</span><span class="p">)</span>
<span class="n">rnn</span> <span class="o">=</span> <span class="n">LLTM</span><span class="p">(</span><span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">)</span>
<span class="n">new_h</span><span class="p">,</span> <span class="n">new_C</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">C</span><span class="p">))</span>
</pre></div>
</div>
<p>Naturally, if at all possible and plausible, you should use this approach to
extend PyTorch. Since PyTorch has highly optimized implementations of its
operations for CPU <em>and</em> GPU, powered by libraries such as <a class="reference external" href="https://developer.nvidia.com/cudnn">NVIDIA cuDNN</a>, <a class="reference external" href="https://software.intel.com/en-us/mkl">Intel MKL</a> or <a class="reference external" href="https://github.com/Maratyszcza/NNPACK">NNPACK</a>, PyTorch code like above will often be
fast enough. However, we can also see why, under certain circumstances, there is
room for further performance improvements. The most obvious reason is that
PyTorch has no knowledge of the <em>algorithm</em> you are implementing. It knows only
of the individual operations you use to compose your algorithm. As such, PyTorch
must execute your operations individually, one after the other. Since each
individual call to the implementation (or <em>kernel</em>) of an operation, which may
involve the launch of a CUDA kernel, has a certain amount of overhead, this
overhead may become significant across many function calls. Furthermore, the
Python interpreter that is running our code can itself slow down our program.</p>
<p>A definite method of speeding things up is therefore to rewrite parts in C++ (or
CUDA) and <em>fuse</em> particular groups of operations. Fusing means combining the
implementations of many functions into a single function, which profits from
fewer kernel launches as well as other optimizations we can perform with
increased visibility of the global flow of data.</p>
<p>Letโs see how we can use C++ extensions to implement a <em>fused</em> version of the
LLTM. Weโll begin by writing it in plain C++, using the <a class="reference external" href="https://github.com/zdevito/ATen">ATen</a> library that powers much of PyTorchโs
backend, and see how easily it lets us translate our Python code. Weโll then
speed things up even more by moving parts of the model to CUDA kernel to benefit
from the massive parallelism GPUs provide.</p>
</div>
<div class="section" id="writing-a-c-extension">
<h2>Writing a C++ Extension<a class="headerlink" href="#writing-a-c-extension" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>C++ extensions come in two flavors: They can be built ใahead of timeใ with
<code class="xref py py-mod docutils literal notranslate"><span class="pre">setuptools</span></code>, or ใjust in timeใ via
<a class="reference external" href="https://pytorch.org/docs/stable/cpp_extension.html#torch.utils.cpp_extension.load" title="(PyTorch v2.3์์)"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.utils.cpp_extension.load()</span></code></a>. Weโll begin with the first approach and
discuss the latter later.</p>
<div class="section" id="building-with-setuptools">
<h3>Building with <code class="xref py py-mod docutils literal notranslate"><span class="pre">setuptools</span></code><a class="headerlink" href="#building-with-setuptools" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>For the ใahead of timeใ flavor, we build our C++ extension by writing a
<code class="docutils literal notranslate"><span class="pre">setup.py</span></code> script that uses setuptools to compile our C++ code. For the LLTM, it
looks as simple as this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="kn">import</span> <span class="n">setup</span><span class="p">,</span> <span class="n">Extension</span>
<span class="kn">from</span> <span class="nn">torch.utils</span> <span class="kn">import</span> <span class="n">cpp_extension</span>
<span class="n">setup</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'lltm_cpp'</span><span class="p">,</span>
<span class="n">ext_modules</span><span class="o">=</span><span class="p">[</span><span class="n">cpp_extension</span><span class="o">.</span><span class="n">CppExtension</span><span class="p">(</span><span class="s1">'lltm_cpp'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'lltm.cpp'</span><span class="p">])],</span>
<span class="n">cmdclass</span><span class="o">=</span><span class="p">{</span><span class="s1">'build_ext'</span><span class="p">:</span> <span class="n">cpp_extension</span><span class="o">.</span><span class="n">BuildExtension</span><span class="p">})</span>
</pre></div>
</div>
<p>In this code, <code class="xref py py-class docutils literal notranslate"><span class="pre">CppExtension</span></code> is a convenience wrapper around
<code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> that passes the correct include paths and sets
the language of the extension to C++. The equivalent vanilla <code class="xref py py-mod docutils literal notranslate"><span class="pre">setuptools</span></code>
code would simply be:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Extension</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">'lltm_cpp'</span><span class="p">,</span>
<span class="n">sources</span><span class="o">=</span><span class="p">[</span><span class="s1">'lltm.cpp'</span><span class="p">],</span>
<span class="n">include_dirs</span><span class="o">=</span><span class="n">cpp_extension</span><span class="o">.</span><span class="n">include_paths</span><span class="p">(),</span>
<span class="n">language</span><span class="o">=</span><span class="s1">'c++'</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="xref py py-class docutils literal notranslate"><span class="pre">BuildExtension</span></code> performs a number of required configuration steps and
checks and also manages mixed compilation in the case of mixed C++/CUDA
extensions. And thatโs all we really need to know about building C++ extensions
for now! Letโs now take a look at the implementation of our C++ extension,
which goes into <code class="docutils literal notranslate"><span class="pre">lltm.cpp</span></code>.</p>
</div>
<div class="section" id="writing-the-c-op">
<h3>Writing the C++ Op<a class="headerlink" href="#writing-the-c-op" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>Letโs start implementing the LLTM in C++! One function weโll need for the
backward pass is the derivative of the sigmoid. This is a small enough piece of
code to discuss the overall environment that is available to us when writing C++
extensions:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/extension.h></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><iostream></span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="nf">d_sigmoid</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">z</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">s</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">);</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="p">(</span><span class="mi">1</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">s</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">s</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre"><torch/extension.h></span></code> is the one-stop header to include all the necessary PyTorch
bits to write C++ extensions. It includes:</p>
<ul class="simple">
<li><p>The ATen library, which is our primary API for tensor computation,</p></li>
<li><p><a class="reference external" href="https://github.com/pybind/pybind11">pybind11</a>, which is how we create Python bindings for our C++ code,</p></li>
<li><p>Headers that manage the details of interaction between ATen and pybind11.</p></li>
</ul>
<p>The implementation of <code class="xref py py-func docutils literal notranslate"><span class="pre">d_sigmoid()</span></code> shows how to use the ATen API.
PyTorchโs tensor and variable interface is generated automatically from the
ATen library, so we can more or less translate our Python implementation 1:1
into C++. Our primary datatype for all computations will be
<code class="xref py py-class docutils literal notranslate"><span class="pre">torch::Tensor</span></code>. Its full API can be inspected <a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html">here</a>. Notice
also that we can include <code class="docutils literal notranslate"><span class="pre"><iostream></span></code> or <em>any other C or C++ header</em> โ we have
the full power of C++11 at our disposal.</p>
<p>Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
To workaround the issue, move python binding logic to pure C++ file.
Example use:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><ATen/ATen.h></span>
<span class="n">at</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">SigmoidAlphaBlendForwardCuda</span><span class="p">(....)</span>
</pre></div>
</div>
<p>Instead of:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/extension.h></span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">SigmoidAlphaBlendForwardCuda</span><span class="p">(...)</span>
</pre></div>
</div>
<p>Currently open issue for nvcc bug <a class="reference external" href="https://github.com/pytorch/pytorch/issues/69460">here</a>.
Complete workaround code example <a class="reference external" href="https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48">here</a>.</p>
<div class="section" id="forward-pass">
<h4>Forward Pass<a class="headerlink" href="#forward-pass" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h4>
<p>Next we can port our entire forward pass to C++:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><vector></span>
<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">at</span><span class="o">::</span><span class="n">Tensor</span><span class="o">></span><span class="w"> </span><span class="n">lltm_forward</span><span class="p">(</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">input</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">weights</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">bias</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">old_h</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">old_cell</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">X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">cat</span><span class="p">({</span><span class="n">old_h</span><span class="p">,</span><span class="w"> </span><span class="n">input</span><span class="p">},</span><span class="w"> </span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">gate_weights</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">addmm</span><span class="p">(</span><span class="n">bias</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="p">,</span><span class="w"> </span><span class="n">weights</span><span class="p">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">));</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">gates</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">gate_weights</span><span class="p">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">input_gate</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">output_gate</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">candidate_cell</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">elu</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="w"> </span><span class="cm">/*alpha=*/</span><span class="mf">1.0</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">new_cell</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">old_cell</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">candidate_cell</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">input_gate</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">new_h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">tanh</span><span class="p">(</span><span class="n">new_cell</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">output_gate</span><span class="p">;</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="p">{</span><span class="n">new_h</span><span class="p">,</span>
<span class="w"> </span><span class="n">new_cell</span><span class="p">,</span>
<span class="w"> </span><span class="n">input_gate</span><span class="p">,</span>
<span class="w"> </span><span class="n">output_gate</span><span class="p">,</span>
<span class="w"> </span><span class="n">candidate_cell</span><span class="p">,</span>
<span class="w"> </span><span class="n">X</span><span class="p">,</span>
<span class="w"> </span><span class="n">gate_weights</span><span class="p">};</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="backward-pass">
<h4>Backward Pass<a class="headerlink" href="#backward-pass" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h4>
<p>The C++ extension API currently does not provide a way of automatically
generating a backwards function for us. As such, we have to also implement the
backward pass of our LLTM, which computes the derivative of the loss with
respect to each input of the forward pass. Ultimately, we will plop both the
forward and backward function into a <a class="reference external" href="https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function" title="(PyTorch v2.3์์)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a> to create
a nice Python binding. The backward function is slightly more involved, so
weโll not dig deeper into the code (if you are interested, <a class="reference external" href="https://www.cs.toronto.edu/~graves/phd.pdf">Alex Gravesใ thesis</a> is a good read for more
information on this):</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// tanh'(z) = 1 - tanh^2(z)</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="nf">d_tanh</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">z</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">z</span><span class="p">.</span><span class="n">tanh</span><span class="p">().</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
<span class="p">}</span>
<span class="c1">// elu'(z) = relu'(z) + { alpha * exp(z) if (alpha * (exp(z) - 1)) < 0, else 0}</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="nf">d_elu</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">z</span><span class="p">,</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Scalar</span><span class="w"> </span><span class="n">alpha</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">1.0</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">e</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">z</span><span class="p">.</span><span class="n">exp</span><span class="p">();</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">mask</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">alpha</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="p">(</span><span class="n">e</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">))</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="p">(</span><span class="n">z</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="mi">0</span><span class="p">).</span><span class="n">type_as</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">mask</span><span class="p">.</span><span class="n">type_as</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="p">(</span><span class="n">alpha</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">e</span><span class="p">);</span>
<span class="p">}</span>
<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">></span><span class="w"> </span><span class="n">lltm_backward</span><span class="p">(</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">grad_h</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">grad_cell</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">new_cell</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">input_gate</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">output_gate</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">candidate_cell</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">X</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">gate_weights</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">weights</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">d_output_gate</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">tanh</span><span class="p">(</span><span class="n">new_cell</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">grad_h</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_tanh_new_cell</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_gate</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">grad_h</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_new_cell</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d_tanh</span><span class="p">(</span><span class="n">new_cell</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">d_tanh_new_cell</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">grad_cell</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_old_cell</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d_new_cell</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_candidate_cell</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_gate</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">d_new_cell</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_input_gate</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">candidate_cell</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">d_new_cell</span><span class="p">;</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">gates</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">gate_weights</span><span class="p">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">);</span>
<span class="w"> </span><span class="n">d_input_gate</span><span class="w"> </span><span class="o">*=</span><span class="w"> </span><span class="n">d_sigmoid</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
<span class="w"> </span><span class="n">d_output_gate</span><span class="w"> </span><span class="o">*=</span><span class="w"> </span><span class="n">d_sigmoid</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
<span class="w"> </span><span class="n">d_candidate_cell</span><span class="w"> </span><span class="o">*=</span><span class="w"> </span><span class="n">d_elu</span><span class="p">(</span><span class="n">gates</span><span class="p">[</span><span class="mi">2</span><span class="p">]);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_gates</span><span class="w"> </span><span class="o">=</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">cat</span><span class="p">({</span><span class="n">d_input_gate</span><span class="p">,</span><span class="w"> </span><span class="n">d_output_gate</span><span class="p">,</span><span class="w"> </span><span class="n">d_candidate_cell</span><span class="p">},</span><span class="w"> </span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_weights</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d_gates</span><span class="p">.</span><span class="n">t</span><span class="p">().</span><span class="n">mm</span><span class="p">(</span><span class="n">X</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_bias</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d_gates</span><span class="p">.</span><span class="n">sum</span><span class="p">(</span><span class="cm">/*dim=*/</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="cm">/*keepdim=*/</span><span class="nb">true</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d_gates</span><span class="p">.</span><span class="n">mm</span><span class="p">(</span><span class="n">weights</span><span class="p">);</span>
<span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">state_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">grad_h</span><span class="p">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_old_h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d_X</span><span class="p">.</span><span class="n">slice</span><span class="p">(</span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="n">state_size</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">d_input</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d_X</span><span class="p">.</span><span class="n">slice</span><span class="p">(</span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="n">state_size</span><span class="p">);</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="p">{</span><span class="n">d_old_h</span><span class="p">,</span><span class="w"> </span><span class="n">d_input</span><span class="p">,</span><span class="w"> </span><span class="n">d_weights</span><span class="p">,</span><span class="w"> </span><span class="n">d_bias</span><span class="p">,</span><span class="w"> </span><span class="n">d_old_cell</span><span class="p">};</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="binding-to-python">
<h3>Binding to Python<a class="headerlink" href="#binding-to-python" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>Once you have your operation written in C++ and ATen, you can use pybind11 to
bind your C++ functions or classes into Python in a very simple manner.
Questions or issues you have about this part of PyTorch C++ extensions will
largely be addressed by <a class="reference external" href="https://pybind11.readthedocs.io/en/stable/">pybind11 documentation</a>.</p>
<p>For our extensions, the necessary binding code spans only four lines:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">PYBIND11_MODULE</span><span class="p">(</span><span class="n">TORCH_EXTENSION_NAME</span><span class="p">,</span><span class="w"> </span><span class="n">m</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">m</span><span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"forward"</span><span class="p">,</span><span class="w"> </span><span class="o">&</span><span class="n">lltm_forward</span><span class="p">,</span><span class="w"> </span><span class="s">"LLTM forward"</span><span class="p">);</span>
<span class="w"> </span><span class="n">m</span><span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"backward"</span><span class="p">,</span><span class="w"> </span><span class="o">&</span><span class="n">lltm_backward</span><span class="p">,</span><span class="w"> </span><span class="s">"LLTM backward"</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>One bit to note here is the macro <code class="docutils literal notranslate"><span class="pre">TORCH_EXTENSION_NAME</span></code>. The torch extension
build will define it as the name you give your extension in the <code class="docutils literal notranslate"><span class="pre">setup.py</span></code>
script. In this case, the value of <code class="docutils literal notranslate"><span class="pre">TORCH_EXTENSION_NAME</span></code> would be ใlltm_cppใ.
This is to avoid having to maintain the name of the extension in two places
(the build script and your C++ code), as a mismatch between the two can lead to
nasty and hard to track issues.</p>
</div>
<div class="section" id="using-your-extension">
<h3>Using Your Extension<a class="headerlink" href="#using-your-extension" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>We are now set to import our extension in PyTorch. At this point, your directory
structure could look something like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pytorch</span><span class="o">/</span>
<span class="n">lltm</span><span class="o">-</span><span class="n">extension</span><span class="o">/</span>
<span class="n">lltm</span><span class="o">.</span><span class="n">cpp</span>
<span class="n">setup</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
<p>Now, run <code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">setup.py</span> <span class="pre">install</span></code> to build and install your extension. This
should look something like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>running install
running bdist_egg
running egg_info
creating lltm_cpp.egg-info
writing lltm_cpp.egg-info/PKG-INFO
writing dependency_links to lltm_cpp.egg-info/dependency_links.txt
writing top-level names to lltm_cpp.egg-info/top_level.txt
writing manifest file 'lltm_cpp.egg-info/SOURCES.txt'
reading manifest file 'lltm_cpp.egg-info/SOURCES.txt'
writing manifest file 'lltm_cpp.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_ext
building 'lltm_cpp' extension
creating build
creating build/temp.linux-x86_64-3.7
gcc -pthread -B ~/local/miniconda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I~/local/miniconda/lib/python3.7/site-packages/torch/include -I~/local/miniconda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I~/local/miniconda/lib/python3.7/site-packages/torch/include/TH -I~/local/miniconda/lib/python3.7/site-packages/torch/include/THC -I~/local/miniconda/include/python3.7m -c lltm.cpp -o build/temp.linux-x86_64-3.7/lltm.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=lltm_cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++11
cc1plus: warning: command line option โ-Wstrict-prototypesโ is valid for C/ObjC but not for C++
creating build/lib.linux-x86_64-3.7
g++ -pthread -shared -B ~/local/miniconda/compiler_compat -L~/local/miniconda/lib -Wl,-rpath=~/local/miniconda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/lltm.o -o build/lib.linux-x86_64-3.7/lltm_cpp.cpython-37m-x86_64-linux-gnu.so
creating build/bdist.linux-x86_64
creating build/bdist.linux-x86_64/egg
copying build/lib.linux-x86_64-3.7/lltm_cpp.cpython-37m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg
creating stub loader for lltm_cpp.cpython-37m-x86_64-linux-gnu.so
byte-compiling build/bdist.linux-x86_64/egg/lltm_cpp.py to lltm_cpp.cpython-37.pyc
creating build/bdist.linux-x86_64/egg/EGG-INFO
copying lltm_cpp.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO
copying lltm_cpp.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying lltm_cpp.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying lltm_cpp.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt
zip_safe flag not set; analyzing archive contents...
__pycache__.lltm_cpp.cpython-37: module references __file__
creating 'dist/lltm_cpp-0.0.0-py3.7-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing lltm_cpp-0.0.0-py3.7-linux-x86_64.egg
removing '~/local/miniconda/lib/python3.7/site-packages/lltm_cpp-0.0.0-py3.7-linux-x86_64.egg' (and everything under it)
creating ~/local/miniconda/lib/python3.7/site-packages/lltm_cpp-0.0.0-py3.7-linux-x86_64.egg
Extracting lltm_cpp-0.0.0-py3.7-linux-x86_64.egg to ~/local/miniconda/lib/python3.7/site-packages
lltm-cpp 0.0.0 is already the active version in easy-install.pth
Installed ~/local/miniconda/lib/python3.7/site-packages/lltm_cpp-0.0.0-py3.7-linux-x86_64.egg
Processing dependencies for lltm-cpp==0.0.0
Finished processing dependencies for lltm-cpp==0.0.0
</pre></div>
</div>
<p>A small note on compilers: Due to ABI versioning issues, the compiler you use to
build your C++ extension must be <em>ABI-compatible</em> with the compiler PyTorch was
built with. In practice, this means that you must use GCC version 4.9 and above on Linux.
For Ubuntu 16.04 and other more-recent Linux distributions, this should be the
default compiler already. On MacOS, you must use clang (which does not have any ABI versioning issues). In the worst
case, you can build PyTorch from source with your compiler and then build the
extension with that same compiler.</p>
<p>Once your extension is built, you can simply import it in Python, using the
name you specified in your <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> script. Just be sure to <code class="docutils literal notranslate"><span class="pre">import</span>
<span class="pre">torch</span></code> first, as this will resolve some symbols that the dynamic linker must
see:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="n">In</span> <span class="p">[</span><span class="mi">2</span><span class="p">]:</span> <span class="kn">import</span> <span class="nn">lltm_cpp</span>
<span class="n">In</span> <span class="p">[</span><span class="mi">3</span><span class="p">]:</span> <span class="n">lltm_cpp</span><span class="o">.</span><span class="n">forward</span>
<span class="n">Out</span><span class="p">[</span><span class="mi">3</span><span class="p">]:</span> <span class="o"><</span><span class="n">function</span> <span class="n">lltm</span><span class="o">.</span><span class="n">PyCapsule</span><span class="o">.</span><span class="n">forward</span><span class="o">></span>
</pre></div>
</div>
<p>If we call <code class="docutils literal notranslate"><span class="pre">help()</span></code> on the function or module, we can see that its signature
matches our C++ code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">In</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="n">help</span><span class="p">(</span><span class="n">lltm_cpp</span><span class="o">.</span><span class="n">forward</span><span class="p">)</span>
<span class="n">forward</span><span class="p">(</span><span class="o">...</span><span class="p">)</span> <span class="n">method</span> <span class="n">of</span> <span class="n">builtins</span><span class="o">.</span><span class="n">PyCapsule</span> <span class="n">instance</span>
<span class="n">forward</span><span class="p">(</span><span class="n">arg0</span><span class="p">:</span> <span class="n">torch</span><span class="p">::</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">arg1</span><span class="p">:</span> <span class="n">torch</span><span class="p">::</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">arg2</span><span class="p">:</span> <span class="n">torch</span><span class="p">::</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">arg3</span><span class="p">:</span> <span class="n">torch</span><span class="p">::</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">arg4</span><span class="p">:</span> <span class="n">torch</span><span class="p">::</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="p">::</span><span class="n">Tensor</span><span class="p">]</span>
<span class="n">LLTM</span> <span class="n">forward</span>
</pre></div>
</div>
<p>Since we are now able to call our C++ functions from Python, we can wrap them
with <a class="reference external" href="https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function" title="(PyTorch v2.3์์)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a> and <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(PyTorch v2.3์์)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> to make them first
class citizens of PyTorch:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># Our module!</span>
<span class="kn">import</span> <span class="nn">lltm_cpp</span>
<span class="k">class</span> <span class="nc">LLTMFunction</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">old_h</span><span class="p">,</span> <span class="n">old_cell</span><span class="p">):</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">lltm_cpp</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">old_h</span><span class="p">,</span> <span class="n">old_cell</span><span class="p">)</span>
<span class="n">new_h</span><span class="p">,</span> <span class="n">new_cell</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">variables</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="p">[</span><span class="n">weights</span><span class="p">]</span>
<span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="o">*</span><span class="n">variables</span><span class="p">)</span>
<span class="k">return</span> <span class="n">new_h</span><span class="p">,</span> <span class="n">new_cell</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_h</span><span class="p">,</span> <span class="n">grad_cell</span><span class="p">):</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">lltm_cpp</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span>
<span class="n">grad_h</span><span class="o">.</span><span class="n">contiguous</span><span class="p">(),</span> <span class="n">grad_cell</span><span class="o">.</span><span class="n">contiguous</span><span class="p">(),</span> <span class="o">*</span><span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span><span class="p">)</span>
<span class="n">d_old_h</span><span class="p">,</span> <span class="n">d_input</span><span class="p">,</span> <span class="n">d_weights</span><span class="p">,</span> <span class="n">d_bias</span><span class="p">,</span> <span class="n">d_old_cell</span> <span class="o">=</span> <span class="n">outputs</span>
<span class="k">return</span> <span class="n">d_input</span><span class="p">,</span> <span class="n">d_weights</span><span class="p">,</span> <span class="n">d_bias</span><span class="p">,</span> <span class="n">d_old_h</span><span class="p">,</span> <span class="n">d_old_cell</span>
<span class="k">class</span> <span class="nc">LLTM</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">LLTM</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">input_features</span> <span class="o">=</span> <span class="n">input_features</span>
<span class="bp">self</span><span class="o">.</span><span class="n">state_size</span> <span class="o">=</span> <span class="n">state_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">state_size</span><span class="p">,</span> <span class="n">input_features</span> <span class="o">+</span> <span class="n">state_size</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">state_size</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">stdv</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state_size</span><span class="p">)</span>
<span class="k">for</span> <span class="n">weight</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">stdv</span><span class="p">,</span> <span class="o">+</span><span class="n">stdv</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LLTMFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="o">*</span><span class="n">state</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="performance-comparison">
<h4>Performance Comparison<a class="headerlink" href="#performance-comparison" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h4>
<p>Now that we are able to use and call our C++ code from PyTorch, we can run a
small benchmark to see how much performance we gained from rewriting our op in
C++. Weโll run the LLTM forwards and backwards a few times and measure the
duration:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">16</span>
<span class="n">input_features</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">state_size</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">X</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="n">batch_size</span><span class="p">,</span> <span class="n">input_features</span><span class="p">)</span>
<span class="n">h</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="n">batch_size</span><span class="p">,</span> <span class="n">state_size</span><span class="p">)</span>
<span class="n">C</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="n">batch_size</span><span class="p">,</span> <span class="n">state_size</span><span class="p">)</span>
<span class="n">rnn</span> <span class="o">=</span> <span class="n">LLTM</span><span class="p">(</span><span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">)</span>
<span class="n">forward</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">backward</span> <span class="o">=</span> <span class="mi">0</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">100000</span><span class="p">):</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">new_h</span><span class="p">,</span> <span class="n">new_C</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">C</span><span class="p">))</span>
<span class="n">forward</span> <span class="o">+=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="p">(</span><span class="n">new_h</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="n">new_C</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">backward</span> <span class="o">+=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Forward: </span><span class="si">{:.3f}</span><span class="s1"> s | Backward </span><span class="si">{:.3f}</span><span class="s1"> s'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">forward</span><span class="p">,</span> <span class="n">backward</span><span class="p">))</span>
</pre></div>
</div>
<p>If we run this code with the original LLTM we wrote in pure Python at the start
of this post, we get the following numbers (on my machine):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Forward</span><span class="p">:</span> <span class="mf">506.480</span> <span class="n">us</span> <span class="o">|</span> <span class="n">Backward</span> <span class="mf">444.694</span> <span class="n">us</span>
</pre></div>
</div>
<p>and with our new C++ version:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Forward</span><span class="p">:</span> <span class="mf">349.335</span> <span class="n">us</span> <span class="o">|</span> <span class="n">Backward</span> <span class="mf">443.523</span> <span class="n">us</span>
</pre></div>
</div>
<p>We can already see a significant speedup for the forward function (more than
30%). For the backward function, a speedup is visible, albeit not a major one.
The backward pass I wrote above was not particularly optimized and could
definitely be improved. Also, PyTorchโs automatic differentiation engine can
automatically parallelize computation graphs, may use a more efficient flow of
operations overall, and is also implemented in C++, so itโs expected to be
fast. Nevertheless, this is a good start.</p>
</div>
<div class="section" id="performance-on-gpu-devices">
<h4>Performance on GPU Devices<a class="headerlink" href="#performance-on-gpu-devices" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h4>
<p>A wonderful fact about PyTorchโs <em>ATen</em> backend is that it abstracts the
computing device you are running on. This means the same code we wrote for CPU
can <em>also</em> run on GPU, and individual operations will correspondingly dispatch
to GPU-optimized implementations. For certain operations like matrix multiply
(like <code class="docutils literal notranslate"><span class="pre">mm</span></code> or <code class="docutils literal notranslate"><span class="pre">addmm</span></code>), this is a big win. Letโs take a look at how much
performance we gain from running our C++ code with CUDA tensors. No changes to
our implementation are required, we simply need to put our tensors in GPU
memory from Python, with either adding <code class="docutils literal notranslate"><span class="pre">device=cuda_device</span></code> argument at
creation time or using <code class="docutils literal notranslate"><span class="pre">.to(cuda_device)</span></code> after creation:</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">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
<span class="n">cuda_device</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="s2">"cuda"</span><span class="p">)</span> <span class="c1"># device object representing GPU</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">16</span>
<span class="n">input_features</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">state_size</span> <span class="o">=</span> <span class="mi">128</span>
<span class="c1"># Note the device=cuda_device arguments here</span>
<span class="n">X</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="n">batch_size</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda_device</span><span class="p">)</span>
<span class="n">h</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="n">batch_size</span><span class="p">,</span> <span class="n">state_size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda_device</span><span class="p">)</span>
<span class="n">C</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="n">batch_size</span><span class="p">,</span> <span class="n">state_size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda_device</span><span class="p">)</span>
<span class="n">rnn</span> <span class="o">=</span> <span class="n">LLTM</span><span class="p">(</span><span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda_device</span><span class="p">)</span>
<span class="n">forward</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">backward</span> <span class="o">=</span> <span class="mi">0</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">100000</span><span class="p">):</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">new_h</span><span class="p">,</span> <span class="n">new_C</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">C</span><span class="p">))</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
<span class="n">forward</span> <span class="o">+=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="p">(</span><span class="n">new_h</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="n">new_C</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
<span class="n">backward</span> <span class="o">+=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Forward: </span><span class="si">{:.3f}</span><span class="s1"> us | Backward </span><span class="si">{:.3f}</span><span class="s1"> us'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">forward</span> <span class="o">*</span> <span class="mf">1e6</span><span class="o">/</span><span class="mf">1e5</span><span class="p">,</span> <span class="n">backward</span> <span class="o">*</span> <span class="mf">1e6</span><span class="o">/</span><span class="mf">1e5</span><span class="p">))</span>
</pre></div>
</div>
<p>Once more comparing our plain PyTorch code with our C++ version, now both
running on CUDA devices, we again see performance gains. For Python/PyTorch:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Forward</span><span class="p">:</span> <span class="mf">187.719</span> <span class="n">us</span> <span class="o">|</span> <span class="n">Backward</span> <span class="mf">410.815</span> <span class="n">us</span>
</pre></div>
</div>
<p>And C++/ATen:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Forward</span><span class="p">:</span> <span class="mf">149.802</span> <span class="n">us</span> <span class="o">|</span> <span class="n">Backward</span> <span class="mf">393.458</span> <span class="n">us</span>
</pre></div>
</div>
<p>Thatโs a great overall speedup compared to non-CUDA code. However, we can pull
even more performance out of our C++ code by writing custom CUDA kernels, which
weโll dive into soon. Before that, letโs discuss another way of building your C++
extensions.</p>
</div>
</div>
<div class="section" id="jit-compiling-extensions">
<h3>JIT Compiling Extensions<a class="headerlink" href="#jit-compiling-extensions" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>Previously, I mentioned there were two ways of building C++ extensions: using
<code class="xref py py-mod docutils literal notranslate"><span class="pre">setuptools</span></code> or just in time (JIT). Having covered the former, letโs
elaborate on the latter. The JIT compilation mechanism provides you with a way
of compiling and loading your extensions on the fly by calling a simple
function in PyTorchโs API called <a class="reference external" href="https://pytorch.org/docs/stable/cpp_extension.html#torch.utils.cpp_extension.load" title="(PyTorch v2.3์์)"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.utils.cpp_extension.load()</span></code></a>. For
the LLTM, this would look as simple as this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="kn">import</span> <span class="n">load</span>