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๋๋ค. PyTorch ํ์ด์ฌ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ API ๋๋ถ๋ถ์ C++ ํ๋ก ํธ์๋์์๋ ์ฌ์ฉํ ์ ์์ผ๋ฉฐ, ํ์ด์ฌ์์ C++๋ก ์๋ ๋ฏธ๋ถ ์ฝ๋๋ฅผ ์ฝ๊ฒ ๋ณํํ ์ ์์ต๋๋ค. ์ด ํํ ๋ฆฌ์ผ์์๋ PyTorch C++ ํ๋ก ํธ์๋์์ ์๋ ๋ฏธ๋ถ์ ์ํํ๋ ๋ช ๊ฐ์ง ์๋ฅผ ์ดํด๋ณด๊ฒ ์ต๋๋ค. ์ด ํํ ๋ฆฌ์ผ์ ์ฌ๋ฌ๋ถ์ด ํ์ด์ฌ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ์ ๋ํด ๊ธฐ๋ณธ์ ์ผ๋ก ์ดํดํ๊ณ ์๋ค๊ณ ๊ฐ์ ํฉ๋๋ค. ๊ทธ๋ ์ง ์์ ๊ฒฝ์ฐ ๋จผ์ Autograd: A..." />
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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
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<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>
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<p class="caption" role="heading"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<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>
</ul>
<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 class="current">
<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 current"><a class="current reference internal" href="#">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_classes.html">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="privateuseone.html">Facilitating New Backend Integration by PrivateUse1</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_profiler_tutorial.html">ํ
์๋ณด๋๋ฅผ ์ด์ฉํ ํ์ดํ ์น ํ๋กํ์ผ๋ฌ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Ray Tune์ ์ฌ์ฉํ ํ์ดํผํ๋ผ๋ฏธํฐ ํ๋</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(๋ฒ ํ) PyTorch์์ Eager Mode๋ฅผ ์ด์ฉํ ์ ์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchserve_with_ipex.html">Grokking PyTorch Intel CPU performance from first principles</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchserve_with_ipex_2.html">Grokking PyTorch Intel CPU performance from first principles (Part 2)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/nvfuser_intro_tutorial.html">Getting Started - Accelerate Your Scripts with nvFuser</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ax_multiobjective_nas_tutorial.html">Multi-Objective NAS with Ax</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torch_compile_tutorial.html">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/inductor_debug_cpu.html">Inductor CPU backend debugging and profiling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html">(Beta) Scaled Dot Product Attention (SDPA)๋ก ๊ณ ์ฑ๋ฅ ํธ๋์คํฌ๋จธ(Transformers) ๊ตฌํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#torch-compile-sdpa"><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> ๊ณผ ํจ๊ป SDPA ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#sdpa-atteition-bias">SDPA๋ฅผ <code class="docutils literal notranslate"><span class="pre">atteition.bias</span></code> ํ์ ํด๋์ค์ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#id8">๊ฒฐ๋ก </a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/knowledge_distillation_tutorial.html">Knowledge Distillation Tutorial</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<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>
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<div class="section" id="c-autograd">
<h1>C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)<a class="headerlink" href="#c-autograd" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h1>
<p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/yoosful">์ ์ฉํ</a></p>
<p><code class="docutils literal notranslate"><span class="pre">autograd</span></code> ๋ PyTorch๋ก ์ ์ฐํ๊ณ ์ญ๋์ ์ธ ์ ๊ฒฝ๋ง์ ๊ตฌ์ถํ๊ธฐ ์ํด
ํ์์ ์ธ ํจํค์ง์
๋๋ค. PyTorch ํ์ด์ฌ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ API ๋๋ถ๋ถ์ C++ ํ๋ก ํธ์๋์์๋
์ฌ์ฉํ ์ ์์ผ๋ฉฐ, ํ์ด์ฌ์์ C++๋ก ์๋ ๋ฏธ๋ถ ์ฝ๋๋ฅผ ์ฝ๊ฒ ๋ณํํ ์ ์์ต๋๋ค.</p>
<p>์ด ํํ ๋ฆฌ์ผ์์๋ PyTorch C++ ํ๋ก ํธ์๋์์ ์๋ ๋ฏธ๋ถ์ ์ํํ๋ ๋ช ๊ฐ์ง ์๋ฅผ ์ดํด๋ณด๊ฒ ์ต๋๋ค.
์ด ํํ ๋ฆฌ์ผ์ ์ฌ๋ฌ๋ถ์ด ํ์ด์ฌ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ์ ๋ํด ๊ธฐ๋ณธ์ ์ผ๋ก ์ดํดํ๊ณ ์๋ค๊ณ
๊ฐ์ ํฉ๋๋ค. ๊ทธ๋ ์ง ์์ ๊ฒฝ์ฐ ๋จผ์ <a class="reference external" href="https://tutorials.pytorch.kr/beginner/blitz/autograd_tutorial.html">Autograd: Automatic Differentiation</a> ์ ์ฝ์ด๋ณด์ธ์.</p>
<div class="section" id="id2">
<h2>๊ธฐ์ด ์๋ ๋ฏธ๋ถ ์ฐ์ฐ<a class="headerlink" href="#id2" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>(<a class="reference external" href="https://tutorials.pytorch.kr/beginner/blitz/autograd_tutorial.html#autograd-automatic-differentiation">์ด ํํ ๋ฆฌ์ผ</a> ์ ๋ด์ฉ์ ๊ธฐ๋ฐํจ)</p>
<p>ํ
์๋ฅผ ์์ฑํ๊ณ ๊ทธ๊ฒ์ ๊ณ์ฐ์ ์ถ์ ํ๊ธฐ ์ํด <code class="docutils literal notranslate"><span class="pre">torch::requires_grad()</span></code> ๋ฅผ ์คํํด๋ด
์๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></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">ones</span><span class="p">({</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">requires_grad</span><span class="p">());</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</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">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="m">1</span><span class="w"> </span><span class="m">1</span>
<span class="m">1</span><span class="w"> </span><span class="m">1</span>
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">2</span>,2<span class="o">}</span><span class="w"> </span><span class="o">]</span>
</pre></div>
</div>
<p>ํ
์ ์ฐ์ฐ์ ์ํํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="m">3</span>
<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="m">3</span>
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">2</span>,2<span class="o">}</span><span class="w"> </span><span class="o">]</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">y</span></code> ๋ ์ฐ์ฐ์ ๊ฒฐ๊ณผ๋ก ์์ฑ๋์์ผ๋ฏ๋ก <code class="docutils literal notranslate"><span class="pre">grad_fn</span></code> ๋ฅผ ๊ฐ๊ณ ์์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">y</span><span class="p">.</span><span class="n">grad_fn</span><span class="p">()</span><span class="o">-></span><span class="n">name</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>AddBackward1
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">y</span></code> ์ ๋ํด ๋ ๋ง์ ์ฐ์ฐ์ ์ํํด๋ด
์๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">z</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">3</span><span class="p">;</span>
<span class="k">auto</span><span class="w"> </span><span class="n">out</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">mean</span><span class="p">();</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">z</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</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">grad_fn</span><span class="p">()</span><span class="o">-></span><span class="n">name</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">out</span><span class="p">.</span><span class="n">grad_fn</span><span class="p">()</span><span class="o">-></span><span class="n">name</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="w"> </span><span class="m">27</span><span class="w"> </span><span class="m">27</span>
<span class="w"> </span><span class="m">27</span><span class="w"> </span><span class="m">27</span>
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">2</span>,2<span class="o">}</span><span class="w"> </span><span class="o">]</span>
MulBackward1
<span class="m">27</span>
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{}</span><span class="w"> </span><span class="o">]</span>
MeanBackward0
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">.requires_grad_(</span> <span class="pre">...</span> <span class="pre">)</span></code> ๋ in-place๋ก ํ
์์ ๊ธฐ์กด <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> ํ๋๊ทธ๋ฅผ ๋ฐ๊ฟ๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">a</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">randn</span><span class="p">({</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">});</span>
<span class="n">a</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">((</span><span class="n">a</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">3</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="p">(</span><span class="n">a</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">));</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">a</span><span class="p">.</span><span class="n">requires_grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">a</span><span class="p">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="nb">true</span><span class="p">);</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">a</span><span class="p">.</span><span class="n">requires_grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="k">auto</span><span class="w"> </span><span class="n">b</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">a</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">a</span><span class="p">).</span><span class="n">sum</span><span class="p">();</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">b</span><span class="p">.</span><span class="n">grad_fn</span><span class="p">()</span><span class="o">-></span><span class="n">name</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">false</span>
<span class="nb">true</span>
SumBackward0
</pre></div>
</div>
<p>์ด์ ์ญ์ ํ๋ฅผ ์ํํด๋ด
์๋ค. <code class="docutils literal notranslate"><span class="pre">out</span></code> ์ด ๋จ์ผ ์ค์นผ๋ผ๋ง์ ํฌํจํ๋ฏ๋ก, <code class="docutils literal notranslate"><span class="pre">out.backward()</span></code> ๋
<code class="docutils literal notranslate"><span class="pre">out.backward(torch::tensor(1.))</span></code> ์ ๊ฐ์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">out</span><span class="p">.</span><span class="n">backward</span><span class="p">();</span>
</pre></div>
</div>
<p>๋ณํ๋ d(out)/dx๋ฅผ ์ถ๋ ฅํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="w"> </span><span class="m">4</span>.5000<span class="w"> </span><span class="m">4</span>.5000
<span class="w"> </span><span class="m">4</span>.5000<span class="w"> </span><span class="m">4</span>.5000
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">2</span>,2<span class="o">}</span><span class="w"> </span><span class="o">]</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">4.5</span></code> ํ๋ ฌ์ด ์ถ๋ ฅ๋ผ์ผ ํฉ๋๋ค. ์ด ๊ฐ์ ์ป๋ ๊ณผ์ ์ ๋ํ ์ค๋ช
์ <a class="reference external" href="https://tutorials.pytorch.kr/beginner/blitz/autograd_tutorial.html#gradients">์ด ํํ ๋ฆฌ์ผ์ ํด๋น ์น์
</a> ์์ ํ์ธํ์ธ์.</p>
<p>์ด์ ๋ฒกํฐ-์ผ์ฝ๋น์ ๊ณฑ์ ์๋ฅผ ์ดํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></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">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">requires_grad</span><span class="p">());</span>
<span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
<span class="k">while</span><span class="w"> </span><span class="p">(</span><span class="n">y</span><span class="p">.</span><span class="n">norm</span><span class="p">().</span><span class="n">item</span><span class="o"><</span><span class="kt">double</span><span class="o">></span><span class="p">()</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="mi">1000</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
<span class="p">}</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">y</span><span class="p">.</span><span class="n">grad_fn</span><span class="p">()</span><span class="o">-></span><span class="n">name</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>-1021.4020
<span class="w"> </span><span class="m">314</span>.6695
<span class="w"> </span>-613.4944
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">3</span><span class="o">}</span><span class="w"> </span><span class="o">]</span>
MulBackward1
</pre></div>
</div>
<p>๋ฒกํฐ-์ผ์ฝ๋น์ ๊ณฑ์ ์ป๊ธฐ ์ํด ๋ฒกํฐ๋ฅผ <code class="docutils literal notranslate"><span class="pre">backward</span></code> ์ ์ธ์๋ก ๋ฃ์ด์ค๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">v</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">tensor</span><span class="p">({</span><span class="mf">0.1</span><span class="p">,</span><span class="w"> </span><span class="mf">1.0</span><span class="p">,</span><span class="w"> </span><span class="mf">0.0001</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kFloat</span><span class="p">);</span>
<span class="n">y</span><span class="p">.</span><span class="n">backward</span><span class="p">(</span><span class="n">v</span><span class="p">);</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="w"> </span><span class="m">102</span>.4000
<span class="w"> </span><span class="m">1024</span>.0000
<span class="w"> </span><span class="m">0</span>.1024
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">3</span><span class="o">}</span><span class="w"> </span><span class="o">]</span>
</pre></div>
</div>
<p>๋ํ ์ฝ๋์ <code class="docutils literal notranslate"><span class="pre">torch::NoGradGuard</span></code> ๋ฅผ ๋ฃ์ด์ฃผ๋ฉด ์๋ ๋ฏธ๋ถ์ผ๋ก ํ์ฌ๊ธ ๊ทธ๋๋์ธํธ๊ฐ
ํ์ํ ํ
์๋ฅผ ์ถ์ ํ์ง ์๋๋ก ํ ์ ์์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">requires_grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</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="n">requires_grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="p">{</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">NoGradGuard</span><span class="w"> </span><span class="n">no_grad</span><span class="p">;</span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</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="n">requires_grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">true</span>
<span class="nb">true</span>
<span class="nb">false</span>
</pre></div>
</div>
<p>ํน์ <code class="docutils literal notranslate"><span class="pre">.detach()</span></code> ๋ฅผ ์ฌ์ฉํ์ฌ ๋ด์ฉ์ ๋์ผํ์ง๋ง ๊ทธ๋๋์ธํธ๊ฐ ํ์ ์๋
์ ํ
์๋ฅผ ์ป์ ์๋ ์์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">requires_grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">detach</span><span class="p">();</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">y</span><span class="p">.</span><span class="n">requires_grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">eq</span><span class="p">(</span><span class="n">y</span><span class="p">).</span><span class="n">all</span><span class="p">().</span><span class="n">item</span><span class="o"><</span><span class="kt">bool</span><span class="o">></span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">true</span>
<span class="nb">false</span>
<span class="nb">true</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">grad</span></code> / <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> / <code class="docutils literal notranslate"><span class="pre">is_leaf</span></code> / <code class="docutils literal notranslate"><span class="pre">backward</span></code> / <code class="docutils literal notranslate"><span class="pre">detach</span></code> / <code class="docutils literal notranslate"><span class="pre">detach_</span></code> /
<code class="docutils literal notranslate"><span class="pre">register_hook</span></code> / <code class="docutils literal notranslate"><span class="pre">retain_grad</span></code> ๋ฑ C++ ํ
์ ์๋ ๋ฏธ๋ถ API์ ๋ํ ์์ธํ ๋ด์ฉ์ <a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html">ํด๋น C++ API ๋ฌธ์</a> ์์ ํ์ธํ์ธ์.</p>
</div>
<div class="section" id="c">
<h2>C++๋ก ๊ณ ์ฐจ์ ๊ทธ๋๋์ธํธ ๊ณ์ฐํ๊ธฐ<a class="headerlink" href="#c" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>๊ณ ์ฐจ์ ๊ทธ๋๋์ธํธ๋ฅผ ์ฌ์ฉํ๋ ์ฌ๋ก๋ก ๊ทธ๋๋์ธํธ ํจ๋ํฐ ๊ณ์ฐ์ด ์์ต๋๋ค.
<code class="docutils literal notranslate"><span class="pre">torch::autograd::grad</span></code> ๋ฅผ ์ฌ์ฉํ๋ ์๋ฅผ ์ดํด๋ด
์๋ค.</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/torch.h></span>
<span class="k">auto</span><span class="w"> </span><span class="n">model</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">nn</span><span class="o">::</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">);</span>
<span class="k">auto</span><span class="w"> </span><span class="n">input</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">randn</span><span class="p">({</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">4</span><span class="p">}).</span><span class="n">requires_grad_</span><span class="p">(</span><span class="nb">true</span><span class="p">);</span>
<span class="k">auto</span><span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model</span><span class="p">(</span><span class="n">input</span><span class="p">);</span>
<span class="c1">// Calculate loss</span>
<span class="k">auto</span><span class="w"> </span><span class="n">target</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">randn</span><span class="p">({</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">});</span>
<span class="k">auto</span><span class="w"> </span><span class="n">loss</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">nn</span><span class="o">::</span><span class="n">MSELoss</span><span class="p">()(</span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">target</span><span class="p">);</span>
<span class="c1">// Use norm of gradients as penalty</span>
<span class="k">auto</span><span class="w"> </span><span class="n">grad_output</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">ones_like</span><span class="p">(</span><span class="n">output</span><span class="p">);</span>
<span class="k">auto</span><span class="w"> </span><span class="n">gradient</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">autograd</span><span class="o">::</span><span class="n">grad</span><span class="p">({</span><span class="n">output</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">input</span><span class="p">},</span><span class="w"> </span><span class="cm">/*grad_outputs=*/</span><span class="p">{</span><span class="n">grad_output</span><span class="p">},</span><span class="w"> </span><span class="cm">/*create_graph=*/</span><span class="nb">true</span><span class="p">)[</span><span class="mi">0</span><span class="p">];</span>
<span class="k">auto</span><span class="w"> </span><span class="n">gradient_penalty</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">pow</span><span class="p">((</span><span class="n">gradient</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="mi">2</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="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">),</span><span class="w"> </span><span class="mi">2</span><span class="p">).</span><span class="n">mean</span><span class="p">();</span>
<span class="c1">// Add gradient penalty to loss</span>
<span class="k">auto</span><span class="w"> </span><span class="n">combined_loss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">loss</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">gradient_penalty</span><span class="p">;</span>
<span class="n">combined_loss</span><span class="p">.</span><span class="n">backward</span><span class="p">();</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">input</span><span class="p">.</span><span class="n">grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>-0.1042<span class="w"> </span>-0.0638<span class="w"> </span><span class="m">0</span>.0103<span class="w"> </span><span class="m">0</span>.0723
-0.2543<span class="w"> </span>-0.1222<span class="w"> </span><span class="m">0</span>.0071<span class="w"> </span><span class="m">0</span>.0814
-0.1683<span class="w"> </span>-0.1052<span class="w"> </span><span class="m">0</span>.0355<span class="w"> </span><span class="m">0</span>.1024
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">3</span>,4<span class="o">}</span><span class="w"> </span><span class="o">]</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">torch::autograd::backward</span></code>
(<a class="reference external" href="https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1afa9b5d4329085df4b6b3d4b4be48914b.html">๋งํฌ</a>) ๋ฐ
<code class="docutils literal notranslate"><span class="pre">torch::autograd::grad</span></code>
(<a class="reference external" href="https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1e03c42b14b40c306f9eb947ef842d9c.html">๋งํฌ</a>) ๋ฌธ์์์
์ด ํจ์๋ค์ ์ฌ์ฉ๋ฒ์ ๋ํด ๋ ์์๋ณด์ธ์.</p>
</div>
<div class="section" id="id7">
<h2>C++์์ ์ฌ์ฉ์ ์ง์ ์๋ ๋ฏธ๋ถ ํจ์ ์ฌ์ฉํ๊ธฐ<a class="headerlink" href="#id7" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>(<a class="reference external" href="https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd">์ด ํํ ๋ฆฌ์ผ</a> ์ ๋ด์ฉ์ ๊ธฐ๋ฐํจ)</p>
<p><code class="docutils literal notranslate"><span class="pre">torch::autograd</span></code> ์ ์๋ก์ด ๊ธฐ๋ณธ(elementary) ์ฐ์ฐ์ ์ถ๊ฐํ๋ ค๋ฉด ๊ฐ ์ฐ์ฐ์ ๋ํด ์๋ก์ด <code class="docutils literal notranslate"><span class="pre">torch::autograd::Function</span></code>
ํ์ ํด๋์ค(subclass)๋ฅผ ๊ตฌํํด์ผ ํฉ๋๋ค. <code class="docutils literal notranslate"><span class="pre">torch::autograd</span></code> ๋ ๊ฒฐ๊ณผ์ ๊ทธ๋๋์ธํธ๋ฅผ ๊ณ์ฐํ๊ณ ์ฐ์ฐ ๊ธฐ๋ก์ ์ธ์ฝ๋ฉํ๊ธฐ ์ํด ์ํด
์ด <code class="docutils literal notranslate"><span class="pre">torch::autograd::Function</span></code> ๋ค์ ์ฌ์ฉํฉ๋๋ค. ๋ชจ๋ ์๋ก์ด ํจ์์๋ ๋ ๊ฐ์ง ๋ฐฉ๋ฒ, ์ฆ <code class="docutils literal notranslate"><span class="pre">forward</span></code> ์ <code class="docutils literal notranslate"><span class="pre">backward</span></code> ๋ฅผ
๊ตฌํํด์ผ ํ๋ฉฐ ์์ธํ ์๊ตฌ์ฌํญ์ <a class="reference external" href="https://pytorch.org/cppdocs/api/structtorch_1_1autograd_1_1_function.html">์ด ๋งํฌ</a>
์์ ํ์ธํ์ธ์.</p>
<p>์๋ ์ฝ๋๋ <code class="docutils literal notranslate"><span class="pre">torch::nn</span></code> ์ <code class="docutils literal notranslate"><span class="pre">Linear</span></code> ํจ์๋ฅผ ์ฌ์ฉํฉ๋๋ค.</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/torch.h></span>
<span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">torch</span><span class="o">::</span><span class="nn">autograd</span><span class="p">;</span>
<span class="c1">// Inherit from Function</span>
<span class="k">class</span><span class="w"> </span><span class="nc">LinearFunction</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">Function</span><span class="o"><</span><span class="n">LinearFunction</span><span class="o">></span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">public</span><span class="o">:</span>
<span class="w"> </span><span class="c1">// Note that both forward and backward are static functions</span>
<span class="w"> </span><span class="c1">// bias is an optional argument</span>
<span class="w"> </span><span class="k">static</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">forward</span><span class="p">(</span>
<span class="w"> </span><span class="n">AutogradContext</span><span class="w"> </span><span class="o">*</span><span class="n">ctx</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">weight</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="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">ctx</span><span class="o">-></span><span class="n">save_for_backward</span><span class="p">({</span><span class="n">input</span><span class="p">,</span><span class="w"> </span><span class="n">weight</span><span class="p">,</span><span class="w"> </span><span class="n">bias</span><span class="p">});</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input</span><span class="p">.</span><span class="n">mm</span><span class="p">(</span><span class="n">weight</span><span class="p">.</span><span class="n">t</span><span class="p">());</span>
<span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">bias</span><span class="p">.</span><span class="n">defined</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">+=</span><span class="w"> </span><span class="n">bias</span><span class="p">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">).</span><span class="n">expand_as</span><span class="p">(</span><span class="n">output</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="n">output</span><span class="p">;</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="k">static</span><span class="w"> </span><span class="n">tensor_list</span><span class="w"> </span><span class="n">backward</span><span class="p">(</span><span class="n">AutogradContext</span><span class="w"> </span><span class="o">*</span><span class="n">ctx</span><span class="p">,</span><span class="w"> </span><span class="n">tensor_list</span><span class="w"> </span><span class="n">grad_outputs</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">saved</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-></span><span class="n">get_saved_variables</span><span class="p">();</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">input</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">saved</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">weight</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">saved</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">bias</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">saved</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">grad_output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">grad_outputs</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">grad_input</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">grad_output</span><span class="p">.</span><span class="n">mm</span><span class="p">(</span><span class="n">weight</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">grad_weight</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">grad_output</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">input</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">grad_bias</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">Tensor</span><span class="p">();</span>
<span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">bias</span><span class="p">.</span><span class="n">defined</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">grad_bias</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">grad_output</span><span class="p">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</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="p">{</span><span class="n">grad_input</span><span class="p">,</span><span class="w"> </span><span class="n">grad_weight</span><span class="p">,</span><span class="w"> </span><span class="n">grad_bias</span><span class="p">};</span>
<span class="w"> </span><span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p>์ด์ ์๋์ ๊ฐ์ด <code class="docutils literal notranslate"><span class="pre">LinearFunction</span></code> ์ ์ฌ์ฉํ ์ ์์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></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">randn</span><span class="p">({</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">}).</span><span class="n">requires_grad_</span><span class="p">();</span>
<span class="k">auto</span><span class="w"> </span><span class="n">weight</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">randn</span><span class="p">({</span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">}).</span><span class="n">requires_grad_</span><span class="p">();</span>
<span class="k">auto</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">LinearFunction</span><span class="o">::</span><span class="n">apply</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">weight</span><span class="p">);</span>
<span class="n">y</span><span class="p">.</span><span class="n">sum</span><span class="p">().</span><span class="n">backward</span><span class="p">();</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">weight</span><span class="p">.</span><span class="n">grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="w"> </span><span class="m">0</span>.5314<span class="w"> </span><span class="m">1</span>.2807<span class="w"> </span><span class="m">1</span>.4864
<span class="w"> </span><span class="m">0</span>.5314<span class="w"> </span><span class="m">1</span>.2807<span class="w"> </span><span class="m">1</span>.4864
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">2</span>,3<span class="o">}</span><span class="w"> </span><span class="o">]</span>
<span class="w"> </span><span class="m">3</span>.7608<span class="w"> </span><span class="m">0</span>.9101<span class="w"> </span><span class="m">0</span>.0073
<span class="w"> </span><span class="m">3</span>.7608<span class="w"> </span><span class="m">0</span>.9101<span class="w"> </span><span class="m">0</span>.0073
<span class="w"> </span><span class="m">3</span>.7608<span class="w"> </span><span class="m">0</span>.9101<span class="w"> </span><span class="m">0</span>.0073
<span class="w"> </span><span class="m">3</span>.7608<span class="w"> </span><span class="m">0</span>.9101<span class="w"> </span><span class="m">0</span>.0073
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">4</span>,3<span class="o">}</span><span class="w"> </span><span class="o">]</span>
</pre></div>
</div>
<p>์ฌ๊ธฐ์, ํ
์๊ฐ ์๋ ์ธ์๋ฅผ ๋งค๊ฐ๋ณ์๋ก ๊ฐ๋ ๋ ๋ค๋ฅธ ํจ์๋ฅผ ์๋ก ๋ค์ด ๋ณด๊ฒ ์ต๋๋ค.</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/torch.h></span>
<span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">torch</span><span class="o">::</span><span class="nn">autograd</span><span class="p">;</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MulConstant</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">Function</span><span class="o"><</span><span class="n">MulConstant</span><span class="o">></span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">public</span><span class="o">:</span>
<span class="w"> </span><span class="k">static</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">forward</span><span class="p">(</span><span class="n">AutogradContext</span><span class="w"> </span><span class="o">*</span><span class="n">ctx</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">tensor</span><span class="p">,</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="n">constant</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// ctx is a context object that can be used to stash information</span>
<span class="w"> </span><span class="c1">// for backward computation</span>
<span class="w"> </span><span class="n">ctx</span><span class="o">-></span><span class="n">saved_data</span><span class="p">[</span><span class="s">"constant"</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">constant</span><span class="p">;</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">tensor</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">constant</span><span class="p">;</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="k">static</span><span class="w"> </span><span class="n">tensor_list</span><span class="w"> </span><span class="n">backward</span><span class="p">(</span><span class="n">AutogradContext</span><span class="w"> </span><span class="o">*</span><span class="n">ctx</span><span class="p">,</span><span class="w"> </span><span class="n">tensor_list</span><span class="w"> </span><span class="n">grad_outputs</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// We return as many input gradients as there were arguments.</span>
<span class="w"> </span><span class="c1">// Gradients of non-tensor arguments to forward must be `torch::Tensor()`.</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="p">{</span><span class="n">grad_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">ctx</span><span class="o">-></span><span class="n">saved_data</span><span class="p">[</span><span class="s">"constant"</span><span class="p">].</span><span class="n">toDouble</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="p">()};</span>
<span class="w"> </span><span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p>์ด์ ์๋์ ๊ฐ์ด <code class="docutils literal notranslate"><span class="pre">MulConstant</span></code> ๋ฅผ ์ฌ์ฉํ ์ ์์ต๋๋ค.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></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">randn</span><span class="p">({</span><span class="mi">2</span><span class="p">}).</span><span class="n">requires_grad_</span><span class="p">();</span>
<span class="k">auto</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">MulConstant</span><span class="o">::</span><span class="n">apply</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="mf">5.5</span><span class="p">);</span>
<span class="n">y</span><span class="p">.</span><span class="n">sum</span><span class="p">().</span><span class="n">backward</span><span class="p">();</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">x</span><span class="p">.</span><span class="n">grad</span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
</pre></div>
</div>
<p>Out:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="w"> </span><span class="m">5</span>.5000
<span class="w"> </span><span class="m">5</span>.5000
<span class="o">[</span><span class="w"> </span>CPUFloatType<span class="o">{</span><span class="m">2</span><span class="o">}</span><span class="w"> </span><span class="o">]</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">torch::autograd::Function</span></code> ์ ๋ํ ๋ ๋ง์ ๋ด์ฉ์
<a class="reference external" href="https://pytorch.org/cppdocs/api/structtorch_1_1autograd_1_1_function.html">์ด ๋ฌธ์</a> ์์ ํ์ธํ ์ ์์ต๋๋ค.</p>
</div>
<div class="section" id="id10">
<h2>ํ์ด์ฌ ์๋ ๋ฏธ๋ถ ์ฝ๋๋ฅผ C++๋ก ๋ณํํ๊ธฐ<a class="headerlink" href="#id10" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>๊ฐ๋ต์ ์ผ๋ก ๋งํ๋ฉด, C++์์ ์๋ ๋ฏธ๋ถ์ ์ฌ์ฉํ๋ ๊ฐ์ฅ ์ฌ์ด ๋ฐฉ๋ฒ์ ๋จผ์
ํ์ด์ฌ์์ ๋์ํ๋ ์๋ ๋ฏธ๋ถ ์ฝ๋๋ฅผ ์์ฑํ ํ, ์๋ ํ๋ฅผ ์ฐธ๊ณ ํด C++ ์ฝ๋๋ก
๋ณํํ๋ ๊ฒ์
๋๋ค.</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 16%" />
<col style="width: 84%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Python</p></th>
<th class="head"><p>C++</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.autograd.backward</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::autograd::backward</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1afa9b5d4329085df4b6b3d4b4be48914b.html">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.autograd.grad</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::autograd::grad</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1e03c42b14b40c306f9eb947ef842d9c.html">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.detach</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::detach</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor6detachEv">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.detach_</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::detach_</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor7detach_Ev">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.backward</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::backward</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor8backwardERK6Tensorbb">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.register_hook</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::register_hook</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4I0ENK2at6Tensor13register_hookE18hook_return_void_tI1TERR1T">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.requires_grad</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::requires_grad_</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor14requires_grad_Eb">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.retain_grad</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::retain_grad</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor11retain_gradEv">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.grad</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::grad</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor4gradEv">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.grad_fn</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::grad_fn</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor7grad_fnEv">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.set_data</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::set_data</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor8set_dataERK6Tensor">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.data</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::data</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor4dataEv">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.output_nr</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::output_nr</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor9output_nrEv">๋งํฌ</a>)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch.Tensor.is_leaf</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor::is_leaf</span></code> (<a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor7is_leafEv">๋งํฌ</a>)</p></td>
</tr>
</tbody>
</table>
<p>๋๋ถ๋ถ์ ๋ณํ๋ ํ์ด์ฌ ์๋ ๋ฏธ๋ถ ์ฝ๋๊ฐ C++์์๋ ์ ๋์ํ ๊ฒ์
๋๋ค.
๋์ํ์ง ์์ ๊ฒฝ์ฐ, <a class="reference external" href="https://github.com/pytorch/pytorch/issues">GitHub issues</a> ์ ๋ฒ๊ทธ ๋ฆฌํฌํธ๋ฅผ ์ ์ถํด ์ฃผ์๋ฉด
์ต๋ํ ๋นจ๋ฆฌ ๊ณ ์ณ๋๋ฆฌ๊ฒ ์ต๋๋ค.</p>
</div>
<div class="section" id="id25">
<h2>๊ฒฐ๋ก <a class="headerlink" href="#id25" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์ด์ PyTorch์ C++ ์๋ ๋ฏธ๋ถ API์ ๋ํ ๊ฐ๊ด์ ์ธ ์ดํด๊ฐ ์๊ฒผ์ ๊ฒ์
๋๋ค.
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<a class="reference external" href="https://discuss.pytorch.org/">ํฌ๋ผ</a> ์ ์ด์ฉํ๊ฑฐ๋ <a class="reference external" href="https://github.com/pytorch/pytorch/issues">Github ์ด์</a> ๋ก ์ฐ๋ฝ์ฃผ์ธ์.</p>
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