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<section id="reproducibility">
<span id="id1"></span><h1>Reproducibility<a class="headerlink" href="#reproducibility" title="Permalink to this heading">¶</a></h1>
<p>Completely reproducible results are not guaranteed across PyTorch releases,
individual commits, or different platforms. Furthermore, results may not be
reproducible between CPU and GPU executions, even when using identical seeds.</p>
<p>However, there are some steps you can take to limit the number of sources of
nondeterministic behavior for a specific platform, device, and PyTorch release.
First, you can control sources of randomness that can cause multiple executions
of your application to behave differently. Second, you can configure PyTorch
to avoid using nondeterministic algorithms for some operations, so that multiple
calls to those operations, given the same inputs, will produce the same result.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Deterministic operations are often slower than nondeterministic operations, so
single-run performance may decrease for your model. However, determinism may
save time in development by facilitating experimentation, debugging, and
regression testing.</p>
</div>
<section id="controlling-sources-of-randomness">
<h2>Controlling sources of randomness<a class="headerlink" href="#controlling-sources-of-randomness" title="Permalink to this heading">¶</a></h2>
<section id="pytorch-random-number-generator">
<h3>PyTorch random number generator<a class="headerlink" href="#pytorch-random-number-generator" title="Permalink to this heading">¶</a></h3>
<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>Some PyTorch operations may use random numbers internally.
<a class="reference internal" href="../generated/torch.svd_lowrank.html#torch.svd_lowrank" title="torch.svd_lowrank"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.svd_lowrank()</span></code></a> does this, for instance. Consequently, calling it
multiple times back-to-back with the same input arguments may give different
results. However, as long as <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> is set to a constant
at the beginning of an application and all other sources of nondeterminism have
been eliminated, the same series of random numbers will be generated each time
the application is run in the same environment.</p>
<p>It is also possible to obtain identical results from an operation that uses
random numbers by setting <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 the same value between
subsequent calls.</p>
</section>
<section id="python">
<h3>Python<a class="headerlink" href="#python" title="Permalink to this heading">¶</a></h3>
<p>For custom operators, you might need to set python seed as well:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</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>
</section>
<section id="random-number-generators-in-other-libraries">
<h3>Random number generators in other libraries<a class="headerlink" href="#random-number-generators-in-other-libraries" title="Permalink to this heading">¶</a></h3>
<p>If you or any of the libraries you are using rely on NumPy, you can seed the global
NumPy RNG 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>
<p>However, some applications and libraries may use NumPy Random Generator objects,
not the global RNG
(<a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html">https://numpy.org/doc/stable/reference/random/generator.html</a>), and those will
need to be seeded consistently as well.</p>
<p>If you are using any other libraries that use random number generators, refer to
the documentation for those libraries to see how to set consistent seeds for them.</p>
</section>
<section id="cuda-convolution-benchmarking">
<h3>CUDA convolution benchmarking<a class="headerlink" href="#cuda-convolution-benchmarking" title="Permalink to this heading">¶</a></h3>
<p>The cuDNN library, used by CUDA convolution operations, can be a source of nondeterminism
across multiple executions of an application. When a cuDNN convolution is called with a
new set of size parameters, an optional feature can run multiple convolution algorithms,
benchmarking them to find the fastest one. Then, the fastest algorithm will be used
consistently during the rest of the process for the corresponding set of size parameters.
Due to benchmarking noise and different hardware, the benchmark may select different
algorithms on subsequent runs, even on the same machine.</p>
<p>Disabling the benchmarking feature with <code class="code docutils literal notranslate"><span class="pre">torch.backends.cudnn.benchmark</span> <span class="pre">=</span> <span class="pre">False</span></code>
causes cuDNN to deterministically select an algorithm, possibly at the cost of reduced
performance.</p>
<p>However, if you do not need reproducibility across multiple executions of your application,
then performance might improve if the benchmarking feature is enabled with
<code class="code docutils literal notranslate"><span class="pre">torch.backends.cudnn.benchmark</span> <span class="pre">=</span> <span class="pre">True</span></code>.</p>
<p>Note that this setting is different from the <code class="code docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span></code>
setting discussed below.</p>
</section>
</section>
<section id="avoiding-nondeterministic-algorithms">
<h2>Avoiding nondeterministic algorithms<a class="headerlink" href="#avoiding-nondeterministic-algorithms" title="Permalink to this heading">¶</a></h2>
<p><a class="reference internal" href="../generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a> lets you configure PyTorch to use
deterministic algorithms instead of nondeterministic ones where available, and
to throw an error if an operation is known to be nondeterministic (and without
a deterministic alternative).</p>
<p>Please check the documentation for <a class="reference internal" href="../generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a>
for a full list of affected operations. If an operation does not act correctly
according to the documentation, or if you need a deterministic implementation
of an operation that does not have one, please submit an issue:
<a class="reference external" href="https://github.com/pytorch/pytorch/issues?q=label:%22module:%20determinism%22">https://github.com/pytorch/pytorch/issues?q=label:%22module:%20determinism%22</a></p>
<p>For example, running the nondeterministic CUDA implementation of <a class="reference internal" href="../generated/torch.Tensor.index_add_.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>
will throw an error:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>>>> import torch
>>> torch.use_deterministic_algorithms(True)
>>> torch.randn(2, 2).cuda().index_add_(0, torch.tensor([0, 1]), torch.randn(2, 2))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: index_add_cuda_ does not have a deterministic implementation, but you set
'torch.use_deterministic_algorithms(True)'. ...
</pre></div>
</div>
<p>When <a class="reference internal" href="../generated/torch.bmm.html#torch.bmm" title="torch.bmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.bmm()</span></code></a> is called with sparse-dense CUDA tensors it typically uses a
nondeterministic algorithm, but when the deterministic flag is turned on, its alternate
deterministic implementation will be used:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">use_deterministic_algorithms</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">bmm</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">to_sparse</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span>
<span class="go">tensor([[[ 1.1900, -2.3409],</span>
<span class="go"> [ 0.4796, 0.8003]],</span>
<span class="go"> [[ 0.1509, 1.8027],</span>
<span class="go"> [ 0.0333, -1.1444]]], device='cuda:0')</span>
</pre></div>
</div>
<p>Furthermore, if you are using CUDA tensors, and your CUDA version is 10.2 or greater, you
should set the environment variable <cite>CUBLAS_WORKSPACE_CONFIG</cite> according to CUDA documentation:
<a class="reference external" href="https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility">https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility</a></p>
<section id="cuda-convolution-determinism">
<h3>CUDA convolution determinism<a class="headerlink" href="#cuda-convolution-determinism" title="Permalink to this heading">¶</a></h3>
<p>While disabling CUDA convolution benchmarking (discussed above) ensures that
CUDA selects the same algorithm each time an application is run, that algorithm
itself may be nondeterministic, unless either
<code class="code docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms(True)</span></code> or
<code class="code docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span> <span class="pre">True</span></code> is set. The latter setting
controls only this behavior, unlike <a class="reference internal" href="../generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a>
which will make other PyTorch operations behave deterministically, too.</p>
</section>
<section id="cuda-rnn-and-lstm">
<h3>CUDA RNN and LSTM<a class="headerlink" href="#cuda-rnn-and-lstm" title="Permalink to this heading">¶</a></h3>
<p>In some versions of CUDA, RNNs and LSTM networks may have non-deterministic behavior.
See <a class="reference internal" href="../generated/torch.nn.RNN.html#torch.nn.RNN" title="torch.nn.RNN"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.RNN()</span></code></a> and <a class="reference internal" href="../generated/torch.nn.LSTM.html#torch.nn.LSTM" title="torch.nn.LSTM"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.LSTM()</span></code></a> for details and workarounds.</p>
</section>
</section>
<section id="dataloader">
<h2>DataLoader<a class="headerlink" href="#dataloader" title="Permalink to this heading">¶</a></h2>
<p>DataLoader will reseed workers following <a class="reference internal" href="../data.html#data-loading-randomness"><span class="std std-ref">Randomness in multi-process data loading</span></a> algorithm.
Use <code class="xref py py-meth docutils literal notranslate"><span class="pre">worker_init_fn()</span></code> and <cite>generator</cite> to preserve reproducibility:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">seed_worker</span><span class="p">(</span><span class="n">worker_id</span><span class="p">):</span>
<span class="n">worker_seed</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">initial_seed</span><span class="p">()</span> <span class="o">%</span> <span class="mi">2</span><span class="o">**</span><span class="mi">32</span>
<span class="n">numpy</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="n">worker_seed</span><span class="p">)</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">worker_seed</span><span class="p">)</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="n">g</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">DataLoader</span><span class="p">(</span>
<span class="n">train_dataset</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
<span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span>
<span class="n">worker_init_fn</span><span class="o">=</span><span class="n">seed_worker</span><span class="p">,</span>
<span class="n">generator</span><span class="o">=</span><span class="n">g</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
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