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<div class="section" id="reproducibility">
<h1>Reproducibility<a class="headerlink" href="#reproducibility" title="Permalink to this headline">¶</a></h1>
<p>Completely reproducible results are not guaranteed across PyTorch releases,
individual commits or different platforms. Furthermore, results need not be
reproducible between CPU and GPU executions, even when using identical seeds.</p>
<p>However, in order to make computations deterministic on your specific problem on
one specific platform and PyTorch release, there are a couple of steps to take.</p>
<p>There are two pseudorandom number generators involved in PyTorch, which you will
need to seed manually to make runs reproducible. Furthermore, you should ensure
that all other libraries your code relies on and which use random numbers also
use a fixed seed.</p>
<div class="section" id="pytorch">
<h2>PyTorch<a class="headerlink" href="#pytorch" title="Permalink to this headline">¶</a></h2>
<p>You can use <a class="reference internal" href="../generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.manual_seed()</span></code></a> to seed the RNG for all devices (both
CPU and CUDA):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">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>
</pre></div>
</div>
<p>There are some PyTorch functions that use CUDA functions that can be a source
of nondeterminism. One class of such CUDA functions are atomic operations,
in particular <code class="xref py py-attr docutils literal notranslate"><span class="pre">atomicAdd</span></code>, which can lead to the order of additions being
nondetermnistic. Because floating-point addition is not perfectly associative
for floating-point operands, <code class="xref py py-attr docutils literal notranslate"><span class="pre">atomicAdd</span></code> with floating-point operands can
introduce different floating-point rounding errors on each evaluation, which
introduces a source of nondeterministic variance (aka noise) in the result.</p>
<p>PyTorch functions that use <code class="xref py py-attr docutils literal notranslate"><span class="pre">atomicAdd</span></code> in the forward kernels include
<a class="reference internal" href="../tensors.html#torch.Tensor.index_add_" title="torch.Tensor.index_add_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.index_add_()</span></code></a>, <a class="reference internal" href="../tensors.html#torch.Tensor.scatter_add_" title="torch.Tensor.scatter_add_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.scatter_add_()</span></code></a>,
<a class="reference internal" href="../generated/torch.bincount.html#torch.bincount" title="torch.bincount"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.bincount()</span></code></a>.</p>
<p>A number of operations have backwards kernels that use <code class="xref py py-attr docutils literal notranslate"><span class="pre">atomicAdd</span></code>,
including <a class="reference internal" href="../nn.functional.html#torch.nn.functional.embedding_bag" title="torch.nn.functional.embedding_bag"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.functional.embedding_bag()</span></code></a>,
<a class="reference internal" href="../nn.functional.html#torch.nn.functional.ctc_loss" title="torch.nn.functional.ctc_loss"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.functional.ctc_loss()</span></code></a>, <a class="reference internal" href="../nn.functional.html#torch.nn.functional.interpolate" title="torch.nn.functional.interpolate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.functional.interpolate()</span></code></a>,
and many forms of pooling, padding, and sampling.</p>
<p>There is currently no simple way of avoiding nondeterminism in these functions.</p>
<p>Additionally, the backward path for <code class="xref py py-meth docutils literal notranslate"><span class="pre">repeat_interleave()</span></code> operates
nondeterministically on the CUDA backend because <code class="xref py py-meth docutils literal notranslate"><span class="pre">repeat_interleave()</span></code>
is implemented using <code class="xref py py-meth docutils literal notranslate"><span class="pre">index_select()</span></code>, the backward path for
which is implemented using <code class="xref py py-meth docutils literal notranslate"><span class="pre">index_add_()</span></code>, which is known to operate
nondeterministically (in the forward direction) on the CUDA backend (see above).</p>
</div>
<div class="section" id="cudnn">
<h2>CuDNN<a class="headerlink" href="#cudnn" title="Permalink to this headline">¶</a></h2>
<p>When running on the CuDNN backend, two further options must be set:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">deterministic</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span> <span class="o">=</span> <span class="kc">False</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Deterministic operation may have a negative single-run performance impact,
depending on the composition of your model. Due to different underlying
operations, which may be slower, the processing speed (e.g. the number of
batches trained per second) may be lower than when the model functions
nondeterministically. However, even though single-run speed may be
slower, depending on your application determinism may save time by
facilitating experimentation, debugging, and regression testing.</p>
</div>
</div>
<div class="section" id="numpy">
<h2>Numpy<a class="headerlink" href="#numpy" title="Permalink to this headline">¶</a></h2>
<p>If you or any of the libraries you are using rely on Numpy, you should seed the
Numpy RNG as well. This can be done with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
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