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<div class="section" id="frequently-asked-questions">
<h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Permalink to this headline">¶</a></h1>
<div class="section" id="my-model-reports-cuda-runtime-error-2-out-of-memory">
<h2>My model reports “cuda runtime error(2): out of memory”<a class="headerlink" href="#my-model-reports-cuda-runtime-error-2-out-of-memory" title="Permalink to this headline">¶</a></h2>
<p>As the error message suggests, you have run out of memory on your
GPU. Since we often deal with large amounts of data in PyTorch,
small mistakes can rapidly cause your program to use up all of your
GPU; fortunately, the fixes in these cases are often simple.
Here are a few common things to check:</p>
<p><strong>Don’t accumulate history across your training loop.</strong>
By default, computations involving variables that require gradients
will keep history. This means that you should avoid using such
variables in computations which will live beyond your training loops,
e.g., when tracking statistics. Instead, you should detach the variable
or access its underlying data.</p>
<p>Sometimes, it can be non-obvious when differentiable variables can
occur. Consider the following training loop (abridged from <a class="reference external" href="https://discuss.pytorch.org/t/high-memory-usage-while-training/162">source</a>):</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10000</span><span class="p">):</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">output</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">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</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">total_loss</span> <span class="o">+=</span> <span class="n">loss</span>
</pre></div>
</div>
<p>Here, <code class="docutils literal notranslate"><span class="pre">total_loss</span></code> is accumulating history across your training loop, since
<code class="docutils literal notranslate"><span class="pre">loss</span></code> is a differentiable variable with autograd history. You can fix this by
writing <cite>total_loss += float(loss)</cite> instead.</p>
<p>Other instances of this problem:
<a class="reference external" href="https://discuss.pytorch.org/t/resolved-gpu-out-of-memory-error-with-batch-size-1/3719">1</a>.</p>
<p><strong>Don’t hold onto tensors and variables you don’t need.</strong>
If you assign a Tensor or Variable to a local, Python will not
deallocate until the local goes out of scope. You can free
this reference by using <code class="docutils literal notranslate"><span class="pre">del</span> <span class="pre">x</span></code>. Similarly, if you assign
a Tensor or Variable to a member variable of an object, it will
not deallocate until the object goes out of scope. You will
get the best memory usage if you don’t hold onto temporaries
you don’t need.</p>
<p>The scopes of locals can be larger than you expect. For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="n">intermediate</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">result</span> <span class="o">+=</span> <span class="n">g</span><span class="p">(</span><span class="n">intermediate</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">h</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
</pre></div>
</div>
<p>Here, <code class="docutils literal notranslate"><span class="pre">intermediate</span></code> remains live even while <code class="docutils literal notranslate"><span class="pre">h</span></code> is executing,
because its scope extrudes past the end of the loop. To free it
earlier, you should <code class="docutils literal notranslate"><span class="pre">del</span> <span class="pre">intermediate</span></code> when you are done with it.</p>
<p><strong>Don’t run RNNs on sequences that are too large.</strong>
The amount of memory required to backpropagate through an RNN scales
linearly with the length of the RNN input; thus, you will run out of memory
if you try to feed an RNN a sequence that is too long.</p>
<p>The technical term for this phenomenon is <a class="reference external" href="https://en.wikipedia.org/wiki/Backpropagation_through_time">backpropagation through time</a>,
and there are plenty of references for how to implement truncated
BPTT, including in the <a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">word language model</a> example; truncation is handled by the
<code class="docutils literal notranslate"><span class="pre">repackage</span></code> function as described in
<a class="reference external" href="https://discuss.pytorch.org/t/help-clarifying-repackage-hidden-in-word-language-model/226">this forum post</a>.</p>
<p><strong>Don’t use linear layers that are too large.</strong>
A linear layer <code class="docutils literal notranslate"><span class="pre">nn.Linear(m,</span> <span class="pre">n)</span></code> uses <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>O</mi><mo stretchy="false">(</mo><mi>n</mi><mi>m</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">O(nm)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathdefault" style="margin-right:0.02778em;">O</span><span class="mopen">(</span><span class="mord mathdefault">n</span><span class="mord mathdefault">m</span><span class="mclose">)</span></span></span></span>
</span> memory: that is to say,
the memory requirements of the weights
scales quadratically with the number of features. It is very easy
to <a class="reference external" href="https://github.com/pytorch/pytorch/issues/958">blow through your memory</a>
this way (and remember that you will need at least twice the size of the
weights, since you also need to store the gradients.)</p>
</div>
<div class="section" id="my-gpu-memory-isn-t-freed-properly">
<h2>My GPU memory isn’t freed properly<a class="headerlink" href="#my-gpu-memory-isn-t-freed-properly" title="Permalink to this headline">¶</a></h2>
<p>PyTorch uses a caching memory allocator to speed up memory allocations. As a
result, the values shown in <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code> usually don’t reflect the true
memory usage. See <a class="reference internal" href="cuda.html#cuda-memory-management"><span class="std std-ref">Memory management</span></a> for more details about GPU
memory management.</p>
<p>If your GPU memory isn’t freed even after Python quits, it is very likely that
some Python subprocesses are still alive. You may find them via
<code class="docutils literal notranslate"><span class="pre">ps</span> <span class="pre">-elf</span> <span class="pre">|</span> <span class="pre">grep</span> <span class="pre">python</span></code> and manually kill them with <code class="docutils literal notranslate"><span class="pre">kill</span> <span class="pre">-9</span> <span class="pre">[pid]</span></code>.</p>
</div>
<div class="section" id="my-data-loader-workers-return-identical-random-numbers">
<span id="dataloader-workers-random-seed"></span><h2>My data loader workers return identical random numbers<a class="headerlink" href="#my-data-loader-workers-return-identical-random-numbers" title="Permalink to this headline">¶</a></h2>
<p>You are likely using other libraries to generate random numbers in the dataset.
For example, NumPy’s RNG is duplicated when worker subprocesses are started via
<code class="docutils literal notranslate"><span class="pre">fork</span></code>. See <a class="reference internal" href="../data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>’s documentation for how to
properly set up random seeds in workers with its <code class="xref py py-attr docutils literal notranslate"><span class="pre">worker_init_fn</span></code> option.</p>
</div>
<div class="section" id="my-recurrent-network-doesn-t-work-with-data-parallelism">
<span id="pack-rnn-unpack-with-data-parallelism"></span><h2>My recurrent network doesn’t work with data parallelism<a class="headerlink" href="#my-recurrent-network-doesn-t-work-with-data-parallelism" title="Permalink to this headline">¶</a></h2>
<p>There is a subtlety in using the
<code class="docutils literal notranslate"><span class="pre">pack</span> <span class="pre">sequence</span> <span class="pre">-></span> <span class="pre">recurrent</span> <span class="pre">network</span> <span class="pre">-></span> <span class="pre">unpack</span> <span class="pre">sequence</span></code> pattern in a
<a class="reference internal" href="../nn.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> with <a class="reference internal" href="../nn.html#torch.nn.DataParallel" title="torch.nn.DataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataParallel</span></code></a> or
<a class="reference internal" href="../nn.functional.html#torch.nn.parallel.data_parallel" title="torch.nn.parallel.data_parallel"><code class="xref py py-func docutils literal notranslate"><span class="pre">data_parallel()</span></code></a>. Input to each the <code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code> on
each device will only be part of the entire input. Because the unpack operation
<a class="reference internal" href="../nn.html#torch.nn.utils.rnn.pad_packed_sequence" title="torch.nn.utils.rnn.pad_packed_sequence"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.utils.rnn.pad_packed_sequence()</span></code></a> by default only pads up to the
longest input it sees, i.e., the longest on that particular device, size
mismatches will happen when results are gathered together. Therefore, you can
instead take advantage of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">total_length</span></code> argument of
<a class="reference internal" href="../nn.html#torch.nn.utils.rnn.pad_packed_sequence" title="torch.nn.utils.rnn.pad_packed_sequence"><code class="xref py py-func docutils literal notranslate"><span class="pre">pad_packed_sequence()</span></code></a> to make sure that the
<code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code> calls return sequences of same length. For example, you can
write:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn.utils.rnn</span> <span class="k">import</span> <span class="n">pack_padded_sequence</span><span class="p">,</span> <span class="n">pad_packed_sequence</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="c1"># ... __init__, other methods, etc.</span>
<span class="c1"># padded_input is of shape [B x T x *] (batch_first mode) and contains</span>
<span class="c1"># the sequences sorted by lengths</span>
<span class="c1"># B is the batch size</span>
<span class="c1"># T is max sequence length</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="n">padded_input</span><span class="p">,</span> <span class="n">input_lengths</span><span class="p">):</span>
<span class="n">total_length</span> <span class="o">=</span> <span class="n">padded_input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># get the max sequence length</span>
<span class="n">packed_input</span> <span class="o">=</span> <span class="n">pack_padded_sequence</span><span class="p">(</span><span class="n">padded_input</span><span class="p">,</span> <span class="n">input_lengths</span><span class="p">,</span>
<span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">packed_output</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">my_lstm</span><span class="p">(</span><span class="n">packed_input</span><span class="p">)</span>
<span class="n">output</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">pad_packed_sequence</span><span class="p">(</span><span class="n">packed_output</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">total_length</span><span class="o">=</span><span class="n">total_length</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">dp_m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
</pre></div>
</div>
<p>Additionally, extra care needs to be taken when batch dimension is dim <code class="docutils literal notranslate"><span class="pre">1</span></code>
(i.e., <code class="docutils literal notranslate"><span class="pre">batch_first=False</span></code>) with data parallelism. In this case, the first
argument of pack_padded_sequence <code class="docutils literal notranslate"><span class="pre">padding_input</span></code> will be of shape
<code class="docutils literal notranslate"><span class="pre">[T</span> <span class="pre">x</span> <span class="pre">B</span> <span class="pre">x</span> <span class="pre">*]</span></code> and should be scattered along dim <code class="docutils literal notranslate"><span class="pre">1</span></code>, but the second argument
<code class="docutils literal notranslate"><span class="pre">input_lengths</span></code> will be of shape <code class="docutils literal notranslate"><span class="pre">[B]</span></code> and should be scattered along dim
<code class="docutils literal notranslate"><span class="pre">0</span></code>. Extra code to manipulate the tensor shapes will be needed.</p>
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