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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
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<li class="toctree-l1 current"><a class="reference internal" href="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="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</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>
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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</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 ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
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<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/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="../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">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>
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<div class="section" id="shard-optimizer-states-with-zeroredundancyoptimizer">
<h1>Shard Optimizer States with ZeroRedundancyOptimizer<a class="headerlink" href="#shard-optimizer-states-with-zeroredundancyoptimizer" title="Permalink to this headline">ยถ</a></h1>
<p>In this recipe, you will learn:</p>
<ul class="simple">
<li>The high-level idea of <a class="reference external" href="https://pytorch.org/docs/master/distributed.optim.html">ZeroRedundancyOptimizer</a>.</li>
<li>How to use <a class="reference external" href="https://pytorch.org/docs/master/distributed.optim.html">ZeroRedundancyOptimizer</a>
in distributed training and its impact.</li>
</ul>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline">ยถ</a></h2>
<ul class="simple">
<li>PyTorch 1.8+</li>
<li><a class="reference external" href="https://tutorials.pytorch.kr/intermediate/ddp_tutorial.html">Getting Started With Distributed Data Parallel</a></li>
</ul>
</div>
<div class="section" id="what-is-zeroredundancyoptimizer">
<h2>What is <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code>?<a class="headerlink" href="#what-is-zeroredundancyoptimizer" title="Permalink to this headline">ยถ</a></h2>
<p>The idea of <a class="reference external" href="https://pytorch.org/docs/master/distributed.optim.html">ZeroRedundancyOptimizer</a>
comes from <a class="reference external" href="https://github.com/microsoft/DeepSpeed">DeepSpeed/ZeRO project</a> and
<a class="reference external" href="https://github.com/marian-nmt/marian-dev">Marian</a> that shard
optimizer states across distributed data-parallel processes to
reduce per-process memory footprint. In the
<a class="reference external" href="https://tutorials.pytorch.kr/intermediate/ddp_tutorial.html">Getting Started With Distributed Data Parallel</a>
tutorial, we have shown how to use
<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a>
(DDP) to train models. In that tutorial, each process keeps a dedicated replica
of the optimizer. Since DDP has already synchronized gradients in the
backward pass, all optimizer replicas will operate on the same parameter and
gradient values in every iteration, and this is how DDP keeps model replicas in
the same state. Oftentimes, optimizers also maintain local states. For example,
the <code class="docutils literal notranslate"><span class="pre">Adam</span></code> optimizer uses per-parameter <code class="docutils literal notranslate"><span class="pre">exp_avg</span></code> and <code class="docutils literal notranslate"><span class="pre">exp_avg_sq</span></code> states. As a
result, the <code class="docutils literal notranslate"><span class="pre">Adam</span></code> optimizerโs memory consumption is at least twice the model
size. Given this observation, we can reduce the optimizer memory footprint by
sharding optimizer states across DDP processes. More specifically, instead of
creating per-param states for all parameters, each optimizer instance in
different DDP processes only keeps optimizer states for a shard of all model
parameters. The optimizer <code class="docutils literal notranslate"><span class="pre">step()</span></code> function only updates the parameters in its
shard and then broadcasts its updated parameters to all other peer DDP
processes, so that all model replicas still land in the same state.</p>
</div>
<div class="section" id="how-to-use-zeroredundancyoptimizer">
<h2>How to use <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code>?<a class="headerlink" href="#how-to-use-zeroredundancyoptimizer" title="Permalink to this headline">ยถ</a></h2>
<p>The code below demonstrates how to use
<a class="reference external" href="https://pytorch.org/docs/master/distributed.optim.html">ZeroRedundancyOptimizer</a>.
The majority of the code is similar to the simple DDP example presented in
<a class="reference external" href="https://pytorch.org/docs/stable/notes/ddp.html">Distributed Data Parallel notes</a>.
The main difference is the <code class="docutils literal notranslate"><span class="pre">if-else</span></code> clause in the <code class="docutils literal notranslate"><span class="pre">example</span></code> function which
wraps optimizer constructions, toggling between
<a class="reference external" href="https://pytorch.org/docs/master/distributed.optim.html">ZeroRedundancyOptimizer</a>
and <code class="docutils literal notranslate"><span class="pre">Adam</span></code> optimizer.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">from</span> <span class="nn">torch.distributed.optim</span> <span class="kn">import</span> <span class="n">ZeroRedundancyOptimizer</span>
<span class="kn">from</span> <span class="nn">torch.nn.parallel</span> <span class="kn">import</span> <span class="n">DistributedDataParallel</span> <span class="k">as</span> <span class="n">DDP</span>
<span class="k">def</span> <span class="nf">print_peak_memory</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
<span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">prefix</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">max_memory_allocated</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="o">//</span> <span class="mf">1e6</span><span class="si">}</span><span class="s2">MB "</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">example</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">use_zero</span><span class="p">):</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_ADDR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'localhost'</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_PORT'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'29500'</span>
<span class="c1"># create default process group</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="s2">"gloo"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span><span class="p">)</span>
<span class="c1"># create local model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">)])</span>
<span class="n">print_peak_memory</span><span class="p">(</span><span class="s2">"Max memory allocated after creating local model"</span><span class="p">,</span> <span class="n">rank</span><span class="p">)</span>
<span class="c1"># construct DDP model</span>
<span class="n">ddp_model</span> <span class="o">=</span> <span class="n">DDP</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="p">[</span><span class="n">rank</span><span class="p">])</span>
<span class="n">print_peak_memory</span><span class="p">(</span><span class="s2">"Max memory allocated after creating DDP"</span><span class="p">,</span> <span class="n">rank</span><span class="p">)</span>
<span class="c1"># define loss function and optimizer</span>
<span class="n">loss_fn</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="k">if</span> <span class="n">use_zero</span><span class="p">:</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">ZeroRedundancyOptimizer</span><span class="p">(</span>
<span class="n">ddp_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span>
<span class="n">optimizer_class</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">,</span>
<span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">ddp_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="c1"># forward pass</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">ddp_model</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">))</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span>
<span class="c1"># backward pass</span>
<span class="n">loss_fn</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="c1"># update parameters</span>
<span class="n">print_peak_memory</span><span class="p">(</span><span class="s2">"Max memory allocated before optimizer step()"</span><span class="p">,</span> <span class="n">rank</span><span class="p">)</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">print_peak_memory</span><span class="p">(</span><span class="s2">"Max memory allocated after optimizer step()"</span><span class="p">,</span> <span class="n">rank</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"params sum is: </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">world_size</span> <span class="o">=</span> <span class="mi">2</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"=== Using ZeroRedundancyOptimizer ==="</span><span class="p">)</span>
<span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">example</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="kc">True</span><span class="p">),</span>
<span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span>
<span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"=== Not Using ZeroRedundancyOptimizer ==="</span><span class="p">)</span>
<span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">example</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="kc">False</span><span class="p">),</span>
<span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span>
<span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span><span class="o">==</span><span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
<p>The output is shown below. When enabling <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> with <code class="docutils literal notranslate"><span class="pre">Adam</span></code>,
the optimizer <code class="docutils literal notranslate"><span class="pre">step()</span></code> peak memory consumption is half of vanilla <code class="docutils literal notranslate"><span class="pre">Adam</span></code>โs
memory consumption. This agrees with our expectation, as we are sharding
<code class="docutils literal notranslate"><span class="pre">Adam</span></code> optimizer states across two processes. The output also shows that, with
<code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code>, the model parameters still end up with the same
values after one iterations (the parameters sum is the same with and without
<code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code>).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">===</span> <span class="n">Using</span> <span class="n">ZeroRedundancyOptimizer</span> <span class="o">===</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">after</span> <span class="n">creating</span> <span class="n">local</span> <span class="n">model</span><span class="p">:</span> <span class="mf">335.0</span><span class="n">MB</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">after</span> <span class="n">creating</span> <span class="n">DDP</span><span class="p">:</span> <span class="mf">656.0</span><span class="n">MB</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">before</span> <span class="n">optimizer</span> <span class="n">step</span><span class="p">():</span> <span class="mf">992.0</span><span class="n">MB</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">after</span> <span class="n">optimizer</span> <span class="n">step</span><span class="p">():</span> <span class="mf">1361.0</span><span class="n">MB</span>
<span class="n">params</span> <span class="nb">sum</span> <span class="ow">is</span><span class="p">:</span> <span class="o">-</span><span class="mf">3453.6123046875</span>
<span class="n">params</span> <span class="nb">sum</span> <span class="ow">is</span><span class="p">:</span> <span class="o">-</span><span class="mf">3453.6123046875</span>
<span class="o">===</span> <span class="n">Not</span> <span class="n">Using</span> <span class="n">ZeroRedundancyOptimizer</span> <span class="o">===</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">after</span> <span class="n">creating</span> <span class="n">local</span> <span class="n">model</span><span class="p">:</span> <span class="mf">335.0</span><span class="n">MB</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">after</span> <span class="n">creating</span> <span class="n">DDP</span><span class="p">:</span> <span class="mf">656.0</span><span class="n">MB</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">before</span> <span class="n">optimizer</span> <span class="n">step</span><span class="p">():</span> <span class="mf">992.0</span><span class="n">MB</span>
<span class="n">Max</span> <span class="n">memory</span> <span class="n">allocated</span> <span class="n">after</span> <span class="n">optimizer</span> <span class="n">step</span><span class="p">():</span> <span class="mf">1697.0</span><span class="n">MB</span>
<span class="n">params</span> <span class="nb">sum</span> <span class="ow">is</span><span class="p">:</span> <span class="o">-</span><span class="mf">3453.6123046875</span>
<span class="n">params</span> <span class="nb">sum</span> <span class="ow">is</span><span class="p">:</span> <span class="o">-</span><span class="mf">3453.6123046875</span>
</pre></div>
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