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<p class="caption"><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"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="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="basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/captumyt.html">Model Understanding with Captum</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="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"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="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/speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</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="text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
</ul>
<p class="caption"><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/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<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="Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
</ul>
<p class="caption"><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>
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<p class="caption"><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="../advanced/cpp_frontend.html">PyTorch C++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
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<p class="caption"><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="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_classes.html">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
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<p class="caption"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋ง ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_profiler_tutorial.html">PyTorch Profiler With TensorBoard</a></li>
<li class="toctree-l1"><a class="reference internal" href="hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li>
<li class="toctree-l1"><a class="reference internal" href="vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋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="../advanced/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="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
</ul>
<p class="caption"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="dist_overview.html">PyTorch Distributed Overview</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/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="../advanced/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="../intermediate/pipeline_tutorial.html">ํ์ดํ๋ผ์ธ ๋ณ๋ ฌํ๋ก ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต์ํค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/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="../advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
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<li class="toctree-l1"><a class="reference internal" href="deeplabv3_on_android.html">์๋๋ก์ด๋์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
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<div class="sphx-glr-example-title section" id="pytorch">
<span id="sphx-glr-beginner-profiler-py"></span><h1>PyTorch ๋ชจ๋ ํ๋กํ์ผ๋ง ํ๊ธฐ<a class="headerlink" href="#pytorch" title="Permalink to this headline">ยถ</a></h1>
<p><strong>Author:</strong> <a class="reference external" href="https://github.com/suraj813">Suraj Subramanian</a></p>
<p><strong>๋ฒ์ญ:</strong> <a class="reference external" href="http://github.com/zzaebok">์ด์ฌ๋ณต</a></p>
<p>PyTorch๋ ์ฝ๋ ๋ด์ ๋ค์ํ Pytorch ์ฐ์ฐ์ ๋ํ ์๊ฐ๊ณผ ๋ฉ๋ชจ๋ฆฌ ๋น์ฉ์ ํ์
ํ๋ ๋ฐ ์ ์ฉํ ํ๋กํ์ผ๋ฌ(profiler) API๋ฅผ ํฌํจํ๊ณ ์์ต๋๋ค.
ํ๋กํ์ผ๋ฌ๋ ์ฝ๋์ ์ฝ๊ฒ ํตํฉ๋ ์ ์์ผ๋ฉฐ, ํ๋กํ์ผ๋ง ๊ฒฐ๊ณผ๋ ํ๋ก ์ถ๋ ฅ๋๊ฑฐ๋ JSON ํ์์ ์ถ์ (trace) ํ์ผ๋ก ๋ฐํ๋ ์ ์์ต๋๋ค.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">ํ๋กํ์ผ๋ฌ๋ ๋ฉํฐ์ค๋ ๋ํ๋ ๋ชจ๋ธ๋ค์ ์ง์ํฉ๋๋ค.
ํ๋กํ์ผ๋ฌ๋ ์ฐ์ฐ์ด ์ด๋ฃจ์ด์ง๋ ์ค๋ ๋์ ๊ฐ์ ์ค๋ ๋์์ ์คํ๋์ง๋ง ๋ค๋ฅธ ์ค๋ ๋์์ ์คํ๋๋ ์์ ์ฐ์ฐ
๋ํ ํ๋กํ์ผ๋งํ ์ ์์ต๋๋ค.
๋์์ ์คํ๋๋ ํ๋กํ์ผ๋ฌ๋ค์ ๊ฒฐ๊ณผ๊ฐ ์์ด์ง ์๋๋ก ๊ฐ์์ ์ค๋ ๋ ๋ฒ์์ ํ์ ๋ฉ๋๋ค.</p>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Pytorch 1.8์ ๋ฏธ๋์ ๋ฆด๋ฆฌ์ฆ์์ ๊ธฐ์กด์ ํ๋กํ์ผ๋ฌ API๋ฅผ ๋์ฒดํ ์๋ก์ด API๋ฅผ ์๊ฐํ๊ณ ์์ต๋๋ค.
์๋ก์ด API๋ฅผ <a class="reference external" href="https://pytorch.org/docs/master/profiler.html">์ด ํ์ด์ง</a> ์์ ํ์ธํ์ธ์.</p>
</div>
<p>ํ๋กํ์ผ๋ฌ API ์ฌ์ฉ๋ฒ์ ๋ํด ๋น ๋ฅด๊ฒ ์ดํด๋ณด๊ณ ์ถ๋ค๋ฉด <a class="reference external" href="https://tutorials.pytorch.kr/recipes/recipes/profiler_recipe.html">์ด ๋ ์ํผ ๋ฌธ์</a> ๋ฅผ ํ์ธํ์ธ์.</p>
<hr class="docutils" />
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="kn">import</span> <span class="nn">torch.autograd.profiler</span> <span class="k">as</span> <span class="nn">profiler</span>
</pre></div>
</div>
<div class="section" id="id2">
<h2>ํ๋กํ์ผ๋ฌ๋ฅผ ์ด์ฉํ์ฌ ์ฑ๋ฅ ๋๋ฒ๊น
ํ๊ธฐ<a class="headerlink" href="#id2" title="Permalink to this headline">ยถ</a></h2>
<p>ํ๋กํ์ผ๋ฌ๋ ๋ชจ๋ธ์์ ์ฑ๋ฅ์ ๋ณ๋ชฉ์ ํ์
ํ ๋ ์ ์ฉํ ์ ์์ต๋๋ค.
์ด๋ฒ ์์ ์์, ๋ ๊ฐ์ง ํ์ ์์
์ ์ํํ๋ ์ฌ์ฉ์ ์ ์ ๋ชจ๋์ ๋ง๋ค๊ฒ ์ต๋๋ค:</p>
<ul class="simple">
<li>์
๋ ฅ์ ๋ํ ์ ํ ๋ณํ</li>
<li>๋ณํ ๊ฒฐ๊ณผ๋ฅผ ์ด์ฉํ ๋ง์คํฌ ํ
์(mask Tensor)์์ ์ธ๋ฑ์ค ์ถ์ถ</li>
</ul>
<p>๊ฐ ํ์ ์์
๋ค์ ๋ํ ์ฝ๋๋ <code class="docutils literal notranslate"><span class="pre">profiler.record_function("label")</span></code> ์ ์ด์ฉํ์ฌ
๋ ์ด๋ธ๋ ์ปจํ
์คํธ ๋งค๋์ (context manager) ๋ค์ ์ํด ๊ฐ์๋๋ค.
ํ๋กํ์ผ๋ฌ์ ์ถ๋ ฅ์์, ํ์ ์์
๋ค์ ๋ชจ๋ ์ฐ์ฐ์ ๋ํ ์ง๊ณ(aggregate) ์ฑ๋ฅ ์งํ๋ค์ด ํด๋น ๋ ์ด๋ธ ์๋ ๋ํ๋๊ฒ ๋ฉ๋๋ค.</p>
<p>ํ๋กํ์ผ๋ฌ๋ฅผ ์ฌ์ฉํ๋ ๊ฒ์ ์ฝ๊ฐ์ ์ค๋ฒํค๋๊ฐ ๋ฐ์ํ๋ฉฐ, ์ฝ๋๋ฅผ ๋ถ์ํ ๋์๋ง ์ฌ์ฉํ๋ ๊ฒ์ด ๊ฐ์ฅ ์ข์ต๋๋ค.
๋ง์ผ ์คํ์๊ฐ์ ๋ฒค์น๋งํนํ๋ ๊ฒฝ์ฐ์๋ ์ด๋ฅผ ์ ๊ฑฐํ๋ ๊ฒ์ ์์ง ๋ง์ญ์์ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">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">in_features</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_features</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</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">linear</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="n">in_features</span><span class="p">,</span> <span class="n">out_features</span><span class="p">,</span> <span class="n">bias</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">mask</span><span class="p">):</span>
<span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">"LINEAR PASS"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">"MASK INDICES"</span><span class="p">):</span>
<span class="n">threshold</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">hi_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">mask</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="o">></span> <span class="n">threshold</span><span class="p">)</span>
<span class="n">hi_idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">hi_idx</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="n">hi_idx</span>
</pre></div>
</div>
</div>
<div class="section" id="forward-pass">
<h2>์์ ํ ๋จ๊ณ(forward pass) ํ๋กํ์ผ๋งํ๊ธฐ<a class="headerlink" href="#forward-pass" title="Permalink to this headline">ยถ</a></h2>
<p>์
๋ ฅ๊ณผ ๋ง์คํฌ ํ
์, ๊ทธ๋ฆฌ๊ณ ๋ชจ๋ธ์ ์์๋ก ์ด๊ธฐํํฉ๋๋ค.</p>
<p>ํ๋กํ์ผ๋ฌ๋ฅผ ์คํํ๊ธฐ ์ , ์ ํํ ์ฑ๋ฅ ๋ฒค์น๋งํน์ ๋ณด์ฅํ๊ธฐ ์ํด CUDA๋ฅผ ์๋ฐ์
(warm-up) ์ํต๋๋ค.
๋ชจ๋ธ์ ์์ ํ ๋จ๊ณ๋ฅผ <code class="docutils literal notranslate"><span class="pre">profiler.profile</span></code> ์ปจํ
์คํธ ๋งค๋์ ๋ฅผ ํตํด ๊ฐ์๋๋ค.
<code class="docutils literal notranslate"><span class="pre">with_stack=True</span></code> ์ธ์๋ ์ฐ์ฐ์ ์ถ์ (trace) ํ์ผ ๋ด๋ถ์ ํ์ผ๊ณผ ์ค๋ฒํธ๋ฅผ ๋ง๋ถ์
๋๋ค.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last"><code class="docutils literal notranslate"><span class="pre">with_stack=True</span></code> ๋ ์ถ๊ฐ์ ์ธ ์ค๋ฒํค๋๋ฅผ ๋ฐ์์ํค๊ธฐ ๋๋ฌธ์ ์ฝ๋๋ฅผ ๋ถ์ํ ๋์ ์ฌ์ฉํ๋ ๊ฒ์ด ๋ฐ๋์งํฉ๋๋ค.
์ฑ๋ฅ์ ๋ฒค์น๋งํนํ๋ค๋ฉด ์ด๋ฅผ ์ ๊ฑฐํ๋ ๊ฒ์ ์์ง ๋ง์ญ์์ค.</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">500</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">500</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">500</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># ์๋ฐ์
(warm-up)</span>
<span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
<span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span><span class="n">with_stack</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">profile_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
<span class="n">out</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id3">
<h2>ํ๋กํ์ผ๋ฌ์ ๊ฒฐ๊ณผ ์ถ๋ ฅํ๊ธฐ<a class="headerlink" href="#id3" title="Permalink to this headline">ยถ</a></h2>
<p>์ต์ข
์ ์ผ๋ก ํ๋กํ์ผ๋ฌ์ ๊ฒฐ๊ณผ๋ฅผ ์ถ๋ ฅํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">profiler.key_averages</span></code> ๋ ์ฐ์ฐ์์ ์ด๋ฆ์ ๋ฐ๋ผ ๊ฒฐ๊ณผ๋ฅผ ์ง๊ณํ๋๋ฐ,
์ ํ์ ์ผ๋ก ์
๋ ฅ์ shape๊ณผ/๋๋ ์คํ ์ถ์ (stack trace) ์ด๋ฒคํธ์ ๋ฐ๋ผ์๋ ๊ฒฐ๊ณผ๋ฅผ ์ง๊ณํ ์ ์์ต๋๋ค.
์
๋ ฅ์ shape์ ๋ฐ๋ผ์ ๊ทธ๋ฃนํ ํ๋ ๊ฒ์ ์ด๋ ํ shape์ ํ
์๋ค์ด ๋ชจ๋ธ์ ์ํด ์ฌ์ฉ๋๋์ง ํ์
ํ๋ ๋ฐ ์ ์ฉํฉ๋๋ค.</p>
<p>์ฌ๊ธฐ์, <code class="docutils literal notranslate"><span class="pre">group_by_stack_n=5</span></code> ๋ฅผ ์ฌ์ฉํ๋๋ฐ ์ด๋ ์ฐ์ฐ(operation)๊ณผ traceback(๊ฐ์ฅ ์ต๊ทผ 5๊ฐ์ ์ด๋ฒคํธ์ ๋ํ)์
๊ธฐ์ค์ผ๋ก ์คํ์๊ฐ์ ์ง๊ณํ๋ ๊ฒ์ด๊ณ , ์ด๋ฒคํธ๋ค์ด ๋ฑ๋ก๋ ์์๋ก ์ ๋ ฌ๋์ด ํ์๋ฉ๋๋ค.
๊ฒฐ๊ณผ ํ๋ <code class="docutils literal notranslate"><span class="pre">sort_by</span></code> ์ธ์ (์ ํจํ ์ ๋ ฌ ํค๋ <a class="reference external" href="https://pytorch.org/docs/stable/autograd.html#profiler">docs</a> ์์
ํ์ธํ์ธ์) ๋ฅผ ๋๊ฒจ์ค์ผ๋ก์จ ์ ๋ ฌ๋ ์ ์์ต๋๋ค.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">notebook์์ ํ๋กํ์ผ๋ฌ๋ฅผ ์คํํ ๋ ์คํ ์ถ์ (stacktrace)์์ ํ์ผ๋ช
๋์
<code class="docutils literal notranslate"><span class="pre"><ipython-input-18-193a910735e8>(13):</span> <span class="pre">forward</span></code> ์ ๊ฐ์ ํญ๋ชฉ์ ๋ณผ ์ ์์ต๋๋ค.
์ด๋ <code class="docutils literal notranslate"><span class="pre"><notebook-cell>(line</span> <span class="pre">number):</span> <span class="pre">calling-function</span></code> ์ ํ์์ ๋์๋ฉ๋๋ค.</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">(</span><span class="n">group_by_stack_n</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s1">'self_cpu_time_total'</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
<span class="sd">"""</span>
<span class="sd">(Some columns are omitted)</span>
<span class="sd">------------- ------------ ------------ ------------ ---------------------------------</span>
<span class="sd"> Name Self CPU % Self CPU Self CPU Mem Source Location</span>
<span class="sd">------------- ------------ ------------ ------------ ---------------------------------</span>
<span class="sd"> MASK INDICES 87.88% 5.212s -953.67 Mb /mnt/xarfuse/.../torch/au</span>
<span class="sd"> <ipython-input-...>(10): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> aten::copy_ 12.07% 715.848ms 0 b <ipython-input-...>(12): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> LINEAR PASS 0.01% 350.151us -20 b /mnt/xarfuse/.../torch/au</span>
<span class="sd"> <ipython-input-...>(7): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> aten::addmm 0.00% 293.342us 0 b /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(8): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> aten::mean 0.00% 235.095us 0 b <ipython-input-...>(11): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd">----------------------------- ------------ ---------- ----------------------------------</span>
<span class="sd">Self CPU time total: 5.931s</span>
<span class="sd">"""</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h2>๋ฉ๋ชจ๋ฆฌ ์ฑ๋ฅ ํฅ์์ํค๊ธฐ<a class="headerlink" href="#id4" title="Permalink to this headline">ยถ</a></h2>
<p>๋ฉ๋ชจ๋ฆฌ์ ์๊ฐ ์ธก๋ฉด์์ ๊ฐ์ฅ ๋น์ฉ์ด ํฐ ์ฐ์ฐ์ MASK INDICES ๋ด <code class="docutils literal notranslate"><span class="pre">forward(10)</span></code> ์ฐ์ฐ์
๋๋ค.
๋จผ์ ๋ฉ๋ชจ๋ฆฌ ์๋ชจ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํด๋ด
์๋ค.
12๋ฒ์งธ ์ค์ <code class="docutils literal notranslate"><span class="pre">.to()</span></code> ์ฐ์ฐ์ 953.67 Mb๋ฅผ ์๋ชจํ๋ ๊ฒ์ ํ์ธํ ์ ์์ต๋๋ค.
์ด ์ฐ์ฐ์ <code class="docutils literal notranslate"><span class="pre">mask</span></code> ๋ฅผ CPU์ ๋ณต์ฌํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">mask</span></code> ๋ <code class="docutils literal notranslate"><span class="pre">torch.double</span></code> ๋ฐ์ดํฐ ํ์
์ผ๋ก ์ด๊ธฐํ๋ฉ๋๋ค.
์ด๋ฅผ <code class="docutils literal notranslate"><span class="pre">torch.float</span></code> ์ผ๋ก ๋ณํํ์ฌ ๋ฉ๋ชจ๋ฆฌ ์ฌ์ฉ๋์ ์ค์ผ ์ ์์๊น์?</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">500</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">500</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">500</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># ์๋ฐ์
(warm-up)</span>
<span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
<span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span><span class="n">with_stack</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">profile_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
<span class="n">out</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">(</span><span class="n">group_by_stack_n</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s1">'self_cpu_time_total'</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
<span class="sd">"""</span>
<span class="sd">(Some columns are omitted)</span>
<span class="sd">----------------- ------------ ------------ ------------ --------------------------------</span>
<span class="sd"> Name Self CPU % Self CPU Self CPU Mem Source Location</span>
<span class="sd">----------------- ------------ ------------ ------------ --------------------------------</span>
<span class="sd"> MASK INDICES 93.61% 5.006s -476.84 Mb /mnt/xarfuse/.../torch/au</span>
<span class="sd"> <ipython-input-...>(10): forward</span>
<span class="sd"> /mnt/xarfuse/ /torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> aten::copy_ 6.34% 338.759ms 0 b <ipython-input-...>(12): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> aten::as_strided 0.01% 281.808us 0 b <ipython-input-...>(11): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> aten::addmm 0.01% 275.721us 0 b /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(8): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> aten::_local 0.01% 268.650us 0 b <ipython-input-...>(11): forward</span>
<span class="sd"> _scalar_dense /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(9): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd">----------------- ------------ ------------ ------------ --------------------------------</span>
<span class="sd">Self CPU time total: 5.347s</span>
<span class="sd">"""</span>
</pre></div>
</div>
<p>์ด ์ฐ์ฐ์ ์ํ CPU ๋ฉ๋ชจ๋ฆฌ ์ฌ์ฉ๋์ด ์ ๋ฐ์ผ๋ก ์ค์์ต๋๋ค.</p>
</div>
<div class="section" id="id5">
<h2>์๊ฐ ์ฑ๋ฅ ํฅ์์ํค๊ธฐ<a class="headerlink" href="#id5" title="Permalink to this headline">ยถ</a></h2>
<p>์๋ชจ๋ ์๊ฐ์ด ์กฐ๊ธ ์ค๊ธด ํ์ง๋ง, ์ด๋ ์์ง๋ ๋๋ฌด ๋์ ์์น์
๋๋ค.
CUDA ์์ CPU ๋ก ํ๋ ฌ์ ๋ณต์ฌํ๋ ๊ฒ์ด ๊ฝค ๋น์ฉ์ด ํฐ ์ฐ์ฐ์ธ ๊ฒ์ด ๋ฐํ์ก์ต๋๋ค.
<code class="docutils literal notranslate"><span class="pre">forward(12)</span></code> ์ <code class="docutils literal notranslate"><span class="pre">aten::copy_</span></code> ์ฐ์ฐ์ <code class="docutils literal notranslate"><span class="pre">mask</span></code> ๋ฅผ CPU์ ๋ณต์ฌํ์ฌ NumPy ์ <code class="docutils literal notranslate"><span class="pre">argwhere</span></code> ํจ์๋ฅผ ์ฌ์ฉํ ์ ์๊ฒ ํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">forward(13)</span></code> ์ <code class="docutils literal notranslate"><span class="pre">aten::copy_</span></code> ๋ ๋ฐฐ์ด์ ๋ค์ ํ
์๋ก CUDA์ ๋ณต์ฌํฉ๋๋ค.
์ด๊ณณ์์ <code class="docutils literal notranslate"><span class="pre">torch</span></code> ํจ์ <code class="docutils literal notranslate"><span class="pre">nonzero()</span></code> ๋ฅผ ๋์ ์ฌ์ฉํ๋ค๋ฉด ๋ ์ฐ์ฐ์ ๋ชจ๋ ์ ๊ฑฐํ ์ ์์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">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">in_features</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_features</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</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">linear</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="n">in_features</span><span class="p">,</span> <span class="n">out_features</span><span class="p">,</span> <span class="n">bias</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">mask</span><span class="p">):</span>
<span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">"LINEAR PASS"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">"MASK INDICES"</span><span class="p">):</span>
<span class="n">threshold</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">hi_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">mask</span> <span class="o">></span> <span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">as_tuple</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="n">hi_idx</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">500</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">500</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">500</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># ์๋ฐ์
(warm-up)</span>
<span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
<span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span><span class="n">with_stack</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">profile_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
<span class="n">out</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">(</span><span class="n">group_by_stack_n</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s1">'self_cpu_time_total'</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
<span class="sd">"""</span>
<span class="sd">(Some columns are omitted)</span>
<span class="sd">-------------- ------------ ------------ ------------ ---------------------------------</span>
<span class="sd"> Name Self CPU % Self CPU Self CPU Mem Source Location</span>
<span class="sd">-------------- ------------ ------------ ------------ ---------------------------------</span>
<span class="sd"> aten::gt 57.17% 129.089ms 0 b <ipython-input-...>(12): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(25): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> aten::nonzero 37.38% 84.402ms 0 b <ipython-input-...>(12): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(25): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> INDEX SCORE 3.32% 7.491ms -119.21 Mb /mnt/xarfuse/.../torch/au</span>
<span class="sd"> <ipython-input-...>(10): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(25): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd">aten::as_strided 0.20% 441.587us 0 b <ipython-input-...>(12): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(25): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> aten::nonzero</span>
<span class="sd"> _numpy 0.18% 395.602us 0 b <ipython-input-...>(12): forward</span>
<span class="sd"> /mnt/xarfuse/.../torch/nn</span>
<span class="sd"> <ipython-input-...>(25): <module></span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd"> /mnt/xarfuse/.../IPython/</span>
<span class="sd">-------------- ------------ ------------ ------------ ---------------------------------</span>
<span class="sd">Self CPU time total: 225.801ms</span>
<span class="sd">"""</span>
</pre></div>
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