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<li class="toctree-l1"><a class="reference internal" href="autograd.html">Autograd mechanics</a></li>
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<div class="section" id="cuda-semantics">
<span id="id1"></span><h1>CUDA semantics<a class="headerlink" href="#cuda-semantics" title="Permalink to this heading">¶</a></h1>
<p><a class="reference internal" href="../cuda.html#module-torch.cuda" title="torch.cuda"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.cuda</span></code></a> is used to set up and run CUDA operations. It keeps track of
the currently selected GPU, and all CUDA tensors you allocate will by default be
created on that device. The selected device can be changed with a
<a class="reference internal" href="../generated/torch.cuda.device.html#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.cuda.device</span></code></a> context manager.</p>
<p>However, once a tensor is allocated, you can do operations on it irrespective
of the selected device, and the results will be always placed on the same
device as the tensor.</p>
<p>Cross-GPU operations are not allowed by default, with the exception of
<a class="reference internal" href="../generated/torch.Tensor.copy_.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">copy_()</span></code></a> and other methods with copy-like functionality
such as <a class="reference internal" href="../generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> and <a class="reference internal" href="../generated/torch.Tensor.cuda.html#torch.Tensor.cuda" title="torch.Tensor.cuda"><code class="xref py py-meth docutils literal notranslate"><span class="pre">cuda()</span></code></a>.
Unless you enable peer-to-peer memory access, any attempts to launch ops on
tensors spread across different devices will raise an error.</p>
<p>Below you can find a small example showcasing this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span> <span class="c1"># Default CUDA device</span>
<span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="n">cuda2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:2'</span><span class="p">)</span> <span class="c1"># GPU 2 (these are 0-indexed)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda0</span><span class="p">)</span>
<span class="c1"># x.device is device(type='cuda', index=0)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># y.device is device(type='cuda', index=0)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
<span class="c1"># allocates a tensor on GPU 1</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="c1"># transfers a tensor from CPU to GPU 1</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># a.device and b.device are device(type='cuda', index=1)</span>
<span class="c1"># You can also use ``Tensor.to`` to transfer a tensor:</span>
<span class="n">b2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="c1"># b.device and b2.device are device(type='cuda', index=1)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="c1"># c.device is device(type='cuda', index=1)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="c1"># z.device is device(type='cuda', index=0)</span>
<span class="c1"># even within a context, you can specify the device</span>
<span class="c1"># (or give a GPU index to the .cuda call)</span>
<span class="n">d</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">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda2</span><span class="p">)</span>
<span class="n">e</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">2</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda2</span><span class="p">)</span>
<span class="n">f</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">2</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="n">cuda2</span><span class="p">)</span>
<span class="c1"># d.device, e.device, and f.device are all device(type='cuda', index=2)</span>
</pre></div>
</div>
<div class="section" id="tensorfloat-32-tf32-on-ampere-devices">
<span id="tf32-on-ampere"></span><h2>TensorFloat-32(TF32) on Ampere devices<a class="headerlink" href="#tensorfloat-32-tf32-on-ampere-devices" title="Permalink to this heading">¶</a></h2>
<p>Starting in PyTorch 1.7, there is a new flag called <cite>allow_tf32</cite>. This flag
defaults to True in PyTorch 1.7 to PyTorch 1.11, and False in PyTorch 1.12 and later.
This flag controls whether PyTorch is allowed to use the TensorFloat32 (TF32) tensor cores,
available on new NVIDIA GPUs since Ampere, internally to compute matmul (matrix multiplies
and batched matrix multiplies) and convolutions.</p>
<p>TF32 tensor cores are designed to achieve better performance on matmul and convolutions on
<cite>torch.float32</cite> tensors by rounding input data to have 10 bits of mantissa, and accumulating
results with FP32 precision, maintaining FP32 dynamic range.</p>
<p>matmuls and convolutions are controlled separately, and their corresponding flags can be accessed at:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># The flag below controls whether to allow TF32 on matmul. This flag defaults to False</span>
<span class="c1"># in PyTorch 1.12 and later.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">True</span>
<span class="c1"># The flag below controls whether to allow TF32 on cuDNN. This flag defaults to 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">allow_tf32</span> <span class="o">=</span> <span class="kc">True</span>
</pre></div>
</div>
<p>Note that besides matmuls and convolutions themselves, functions and nn modules that internally uses
matmuls or convolutions are also affected. These include <cite>nn.Linear</cite>, <cite>nn.Conv*</cite>, cdist, tensordot,
affine grid and grid sample, adaptive log softmax, GRU and LSTM.</p>
<p>To get an idea of the precision and speed, see the example code below:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">a_full</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">10240</span><span class="p">,</span> <span class="mi">10240</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="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">b_full</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">10240</span><span class="p">,</span> <span class="mi">10240</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="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">ab_full</span> <span class="o">=</span> <span class="n">a_full</span> <span class="o">@</span> <span class="n">b_full</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">ab_full</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># 80.7277</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">a_full</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">b_full</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="c1"># Do matmul at TF32 mode.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">ab_tf32</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="n">b</span> <span class="c1"># takes 0.016s on GA100</span>
<span class="n">error</span> <span class="o">=</span> <span class="p">(</span><span class="n">ab_tf32</span> <span class="o">-</span> <span class="n">ab_full</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="c1"># 0.1747</span>
<span class="n">relative_error</span> <span class="o">=</span> <span class="n">error</span> <span class="o">/</span> <span class="n">mean</span> <span class="c1"># 0.0022</span>
<span class="c1"># Do matmul with TF32 disabled.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">ab_fp32</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="n">b</span> <span class="c1"># takes 0.11s on GA100</span>
<span class="n">error</span> <span class="o">=</span> <span class="p">(</span><span class="n">ab_fp32</span> <span class="o">-</span> <span class="n">ab_full</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="c1"># 0.0031</span>
<span class="n">relative_error</span> <span class="o">=</span> <span class="n">error</span> <span class="o">/</span> <span class="n">mean</span> <span class="c1"># 0.000039</span>
</pre></div>
</div>
<p>From the above example, we can see that with TF32 enabled, the speed is ~7x faster, relative error
compared to double precision is approximately 2 orders of magnitude larger. If full FP32 precision
is needed, users can disable TF32 by:</p>
<div class="highlight-python 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">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">False</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">allow_tf32</span> <span class="o">=</span> <span class="kc">False</span>
</pre></div>
</div>
<p>To toggle the TF32 flags off in C++, you can do</p>
<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">setAllowTF32CuBLAS</span><span class="p">(</span><span class="nb">false</span><span class="p">);</span>
<span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">setAllowTF32CuDNN</span><span class="p">(</span><span class="nb">false</span><span class="p">);</span>
</pre></div>
</div>
<p>For more information about TF32, see:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/">TensorFloat-32</a></p></li>
<li><p><a class="reference external" href="https://devblogs.nvidia.com/cuda-11-features-revealed/">CUDA 11</a></p></li>
<li><p><a class="reference external" href="https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/">Ampere architecture</a></p></li>
</ul>
</div>
<div class="section" id="reduced-precision-reduction-in-fp16-gemms">
<span id="fp16reducedprecision"></span><h2>Reduced Precision Reduction in FP16 GEMMs<a class="headerlink" href="#reduced-precision-reduction-in-fp16-gemms" title="Permalink to this heading">¶</a></h2>
<p>fp16 GEMMs are potentially done with some intermediate reduced precision reductions (e.g., in fp16 rather than fp32). These selective reductions in precision can allow for higher performance on certain workloads (particularly those with a large <cite>k</cite> dimension) and GPU architectures at the cost of numerical precision and potential for overflow.</p>
<p>Some example benchmark data on V100:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="o">---------------------------</span> <span class="n">bench_gemm_transformer</span> <span class="o">--------------------------</span><span class="p">]</span>
<span class="p">[</span> <span class="n">m</span> <span class="p">,</span> <span class="n">k</span> <span class="p">,</span> <span class="n">n</span> <span class="p">]</span> <span class="o">|</span> <span class="n">allow_fp16_reduc</span><span class="o">=</span><span class="kc">True</span> <span class="o">|</span> <span class="n">allow_fp16_reduc</span><span class="o">=</span><span class="kc">False</span>
<span class="mi">1</span> <span class="n">threads</span><span class="p">:</span> <span class="o">--------------------------------------------------------------------</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4048</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">1634.6</span> <span class="o">|</span> <span class="mf">1639.8</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4056</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">1670.8</span> <span class="o">|</span> <span class="mf">1661.9</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4080</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">1664.2</span> <span class="o">|</span> <span class="mf">1658.3</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4096</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">1639.4</span> <span class="o">|</span> <span class="mf">1651.0</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4104</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">1677.4</span> <span class="o">|</span> <span class="mf">1674.9</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4128</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">1655.7</span> <span class="o">|</span> <span class="mf">1646.0</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4144</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">1796.8</span> <span class="o">|</span> <span class="mf">2519.6</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">5096</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">2094.6</span> <span class="o">|</span> <span class="mf">3190.0</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">5104</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">2144.0</span> <span class="o">|</span> <span class="mf">2663.5</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">5112</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">2149.1</span> <span class="o">|</span> <span class="mf">2766.9</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">5120</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">2142.8</span> <span class="o">|</span> <span class="mf">2631.0</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">9728</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">3875.1</span> <span class="o">|</span> <span class="mf">5779.8</span>
<span class="p">[</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">16384</span><span class="p">,</span> <span class="mi">4096</span><span class="p">]</span> <span class="o">|</span> <span class="mf">6182.9</span> <span class="o">|</span> <span class="mf">9656.5</span>
<span class="p">(</span><span class="n">times</span> <span class="ow">in</span> <span class="n">microseconds</span><span class="p">)</span><span class="o">.</span>
</pre></div>
</div>
<p>If full precision reductions are needed, users can disable reduced precision reductions in fp16 GEMMs with:</p>
<div class="highlight-python 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">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_fp16_reduced_precision_reduction</span> <span class="o">=</span> <span class="kc">False</span>
</pre></div>
</div>
<p>To toggle the reduced precision reduction flags in C++, one can do</p>
<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">setAllowFP16ReductionCuBLAS</span><span class="p">(</span><span class="nb">false</span><span class="p">);</span>
</pre></div>
</div>
</div>
<div class="section" id="reduced-precision-reduction-in-bf16-gemms">
<span id="bf16reducedprecision"></span><h2>Reduced Precision Reduction in BF16 GEMMs<a class="headerlink" href="#reduced-precision-reduction-in-bf16-gemms" title="Permalink to this heading">¶</a></h2>
<p>A similar flag (as above) exists for BFloat16 GEMMs.
Note that this switch is set to <cite>True</cite> by default for BF16, if you observe
numerical instability in your workload, you may wish to set it to <cite>False</cite>.</p>
<p>If reduced precision reductions are not desired, users can disable reduced
precision reductions in bf16 GEMMs with:</p>
<div class="highlight-python 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">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_bf16_reduced_precision_reduction</span> <span class="o">=</span> <span class="kc">False</span>
</pre></div>
</div>
<p>To toggle the reduced precision reduction flags in C++, one can do</p>
<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">setAllowBF16ReductionCuBLAS</span><span class="p">(</span><span class="nb">true</span><span class="p">);</span>
</pre></div>
</div>
</div>
<div class="section" id="asynchronous-execution">
<h2>Asynchronous execution<a class="headerlink" href="#asynchronous-execution" title="Permalink to this heading">¶</a></h2>
<p>By default, GPU operations are asynchronous. When you call a function that
uses the GPU, the operations are <em>enqueued</em> to the particular device, but not
necessarily executed until later. This allows us to execute more computations
in parallel, including operations on CPU or other GPUs.</p>
<p>In general, the effect of asynchronous computation is invisible to the caller,
because (1) each device executes operations in the order they are queued, and
(2) PyTorch automatically performs necessary synchronization when copying data
between CPU and GPU or between two GPUs. Hence, computation will proceed as if
every operation was executed synchronously.</p>
<p>You can force synchronous computation by setting environment variable
<code class="docutils literal notranslate"><span class="pre">CUDA_LAUNCH_BLOCKING=1</span></code>. This can be handy when an error occurs on the GPU.
(With asynchronous execution, such an error isn’t reported until after the
operation is actually executed, so the stack trace does not show where it was
requested.)</p>
<p>A consequence of the asynchronous computation is that time measurements without
synchronizations are not accurate. To get precise measurements, one should either
call <a class="reference internal" href="../generated/torch.cuda.synchronize.html#torch.cuda.synchronize" title="torch.cuda.synchronize"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cuda.synchronize()</span></code></a> before measuring, or use <a class="reference internal" href="../generated/torch.cuda.Event.html#torch.cuda.Event" title="torch.cuda.Event"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.Event</span></code></a>
to record times as following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">start_event</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Event</span><span class="p">(</span><span class="n">enable_timing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">end_event</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Event</span><span class="p">(</span><span class="n">enable_timing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">start_event</span><span class="o">.</span><span class="n">record</span><span class="p">()</span>
<span class="c1"># Run some things here</span>
<span class="n">end_event</span><span class="o">.</span><span class="n">record</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">synchronize</span><span class="p">()</span> <span class="c1"># Wait for the events to be recorded!</span>
<span class="n">elapsed_time_ms</span> <span class="o">=</span> <span class="n">start_event</span><span class="o">.</span><span class="n">elapsed_time</span><span class="p">(</span><span class="n">end_event</span><span class="p">)</span>
</pre></div>
</div>
<p>As an exception, several functions such as <a class="reference internal" href="../generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> and
<a class="reference internal" href="../generated/torch.Tensor.copy_.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">copy_()</span></code></a> admit an explicit <code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code> argument,
which lets the caller bypass synchronization when it is unnecessary.
Another exception is CUDA streams, explained below.</p>
<div class="section" id="cuda-streams">
<h3>CUDA streams<a class="headerlink" href="#cuda-streams" title="Permalink to this heading">¶</a></h3>
<p>A <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#streams">CUDA stream</a> is a linear sequence of execution that belongs to a specific
device. You normally do not need to create one explicitly: by default, each
device uses its own “default” stream.</p>
<p>Operations inside each stream are serialized in the order they are created,
but operations from different streams can execute concurrently in any
relative order, unless explicit synchronization functions (such as
<a class="reference internal" href="../generated/torch.cuda.synchronize.html#torch.cuda.synchronize" title="torch.cuda.synchronize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">synchronize()</span></code></a> or <a class="reference internal" href="../generated/torch.cuda.Stream.html#torch.cuda.Stream.wait_stream" title="torch.cuda.Stream.wait_stream"><code class="xref py py-meth docutils literal notranslate"><span class="pre">wait_stream()</span></code></a>) are
used. For example, the following code is incorrect:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span> <span class="c1"># Create a new stream.</span>
<span class="n">A</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="c1"># sum() may start execution before normal_() finishes!</span>
<span class="n">B</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
</pre></div>
</div>
<p>When the “current stream” is the default stream, PyTorch automatically performs
necessary synchronization when data is moved around, as explained above.
However, when using non-default streams, it is the user’s responsibility to
ensure proper synchronization.</p>
</div>
<div class="section" id="stream-semantics-of-backward-passes">
<span id="bwd-cuda-stream-semantics"></span><h3>Stream semantics of backward passes<a class="headerlink" href="#stream-semantics-of-backward-passes" title="Permalink to this heading">¶</a></h3>
<p>Each backward CUDA op runs on the same stream that was used for its corresponding forward op.
If your forward pass runs independent ops in parallel on different streams,
this helps the backward pass exploit that same parallelism.</p>
<p>The stream semantics of a backward call with respect to surrounding ops are the same
as for any other call. The backward pass inserts internal syncs to ensure this even when
backward ops run on multiple streams as described in the previous paragraph.
More concretely, when calling
<a class="reference internal" href="../generated/torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.backward</span></code></a>,
<a class="reference internal" href="../generated/torch.autograd.grad.html#torch.autograd.grad" title="torch.autograd.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.grad</span></code></a>, or
<a class="reference internal" href="../generated/torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">tensor.backward</span></code></a>,
and optionally supplying CUDA tensor(s) as the initial gradient(s) (e.g.,
<a class="reference internal" href="../generated/torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.backward(...,</span> <span class="pre">grad_tensors=initial_grads)</span></code></a>,
<a class="reference internal" href="../generated/torch.autograd.grad.html#torch.autograd.grad" title="torch.autograd.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.grad(...,</span> <span class="pre">grad_outputs=initial_grads)</span></code></a>, or
<a class="reference internal" href="../generated/torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">tensor.backward(...,</span> <span class="pre">gradient=initial_grad)</span></code></a>),
the acts of</p>
<ol class="arabic simple">
<li><p>optionally populating initial gradient(s),</p></li>
<li><p>invoking the backward pass, and</p></li>
<li><p>using the gradients</p></li>
</ol>
<p>have the same stream-semantics relationship as any group of ops:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
<span class="c1"># Safe, grads are used in the same stream context as backward()</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</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">use</span> <span class="n">grads</span>
<span class="c1"># Unsafe</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</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">use</span> <span class="n">grads</span>
<span class="c1"># Safe, with synchronization</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</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">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">use</span> <span class="n">grads</span>
<span class="c1"># Safe, populating initial grad and invoking backward are in the same stream context</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</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">gradient</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">loss</span><span class="p">))</span>
<span class="c1"># Unsafe, populating initial_grad and invoking backward are in different stream contexts,</span>
<span class="c1"># without synchronization</span>
<span class="n">initial_grad</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</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">gradient</span><span class="o">=</span><span class="n">initial_grad</span><span class="p">)</span>
<span class="c1"># Safe, with synchronization</span>
<span class="n">initial_grad</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="n">s</span><span class="o">.</span><span class="n">wait_stream</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">current_stream</span><span class="p">())</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="n">initial_grad</span><span class="o">.</span><span class="n">record_stream</span><span class="p">(</span><span class="n">s</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">gradient</span><span class="o">=</span><span class="n">initial_grad</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="bc-note-using-grads-on-the-default-stream">
<h4>BC note: Using grads on the default stream<a class="headerlink" href="#bc-note-using-grads-on-the-default-stream" title="Permalink to this heading">¶</a></h4>
<p>In prior versions of PyTorch (1.9 and earlier), the autograd engine always synced
the default stream with all backward ops, so the following pattern:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</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">use</span> <span class="n">grads</span>
</pre></div>
</div>
<p>was safe as long as <code class="docutils literal notranslate"><span class="pre">use</span> <span class="pre">grads</span></code> happened on the default stream.
In present PyTorch, that pattern is no longer safe. If <code class="docutils literal notranslate"><span class="pre">backward()</span></code>
and <code class="docutils literal notranslate"><span class="pre">use</span> <span class="pre">grads</span></code> are in different stream contexts, you must sync the streams:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</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">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">use</span> <span class="n">grads</span>
</pre></div>
</div>
<p>even if <code class="docutils literal notranslate"><span class="pre">use</span> <span class="pre">grads</span></code> is on the default stream.</p>
</div>
</div>
</div>
<div class="section" id="memory-management">
<span id="cuda-memory-management"></span><h2>Memory management<a class="headerlink" href="#memory-management" title="Permalink to this heading">¶</a></h2>
<p>PyTorch uses a caching memory allocator to speed up memory allocations. This
allows fast memory deallocation without device synchronizations. However, the
unused memory managed by the allocator will still show as if used in
<code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code>. You can use <a class="reference internal" href="../generated/torch.cuda.memory_allocated.html#torch.cuda.memory_allocated" title="torch.cuda.memory_allocated"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_allocated()</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.max_memory_allocated.html#torch.cuda.max_memory_allocated" title="torch.cuda.max_memory_allocated"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max_memory_allocated()</span></code></a> to monitor memory occupied by
tensors, and use <a class="reference internal" href="../generated/torch.cuda.memory_reserved.html#torch.cuda.memory_reserved" title="torch.cuda.memory_reserved"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_reserved()</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.max_memory_reserved.html#torch.cuda.max_memory_reserved" title="torch.cuda.max_memory_reserved"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max_memory_reserved()</span></code></a> to monitor the total amount of memory
managed by the caching allocator. Calling <a class="reference internal" href="../generated/torch.cuda.empty_cache.html#torch.cuda.empty_cache" title="torch.cuda.empty_cache"><code class="xref py py-meth docutils literal notranslate"><span class="pre">empty_cache()</span></code></a>
releases all <strong>unused</strong> cached memory from PyTorch so that those can be used
by other GPU applications. However, the occupied GPU memory by tensors will not
be freed so it can not increase the amount of GPU memory available for PyTorch.</p>
<p>To better understand how CUDA memory is being used over time,
<a class="reference internal" href="../torch_cuda_memory.html#torch-cuda-memory"><span class="std std-ref">Understanding CUDA Memory Usage</span></a> describes tools for capturing and visualizing traces of memory use.</p>
<p>For more advanced users, we offer more comprehensive memory benchmarking via
<a class="reference internal" href="../generated/torch.cuda.memory_stats.html#torch.cuda.memory_stats" title="torch.cuda.memory_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_stats()</span></code></a>. We also offer the capability to capture a
complete snapshot of the memory allocator state via
<a class="reference internal" href="../generated/torch.cuda.memory_snapshot.html#torch.cuda.memory_snapshot" title="torch.cuda.memory_snapshot"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_snapshot()</span></code></a>, which can help you understand the
underlying allocation patterns produced by your code.</p>
<div class="section" id="environment-variables">
<span id="cuda-memory-envvars"></span><h3>Environment variables<a class="headerlink" href="#environment-variables" title="Permalink to this heading">¶</a></h3>
<p>Use of a caching allocator can interfere with memory checking tools such as
<code class="docutils literal notranslate"><span class="pre">cuda-memcheck</span></code>. To debug memory errors using <code class="docutils literal notranslate"><span class="pre">cuda-memcheck</span></code>, set
<code class="docutils literal notranslate"><span class="pre">PYTORCH_NO_CUDA_MEMORY_CACHING=1</span></code> in your environment to disable caching.</p>
<p>The behavior of the caching allocator can be controlled via the environment variable
<code class="docutils literal notranslate"><span class="pre">PYTORCH_CUDA_ALLOC_CONF</span></code>.
The format is <code class="docutils literal notranslate"><span class="pre">PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2>:<value2>...</span></code>
Available options:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">backend</span></code> allows selecting the underlying allocator implementation.
Currently, valid options are <code class="docutils literal notranslate"><span class="pre">native</span></code>, which uses PyTorch’s native
implementation, and <code class="docutils literal notranslate"><span class="pre">cudaMallocAsync</span></code>, which uses
<a class="reference external" href="https://developer.nvidia.com/blog/using-cuda-stream-ordered-memory-allocator-part-1/">CUDA’s built-in asynchronous allocator</a>.
<code class="docutils literal notranslate"><span class="pre">cudaMallocAsync</span></code> requires CUDA 11.4 or newer. The default is <code class="docutils literal notranslate"><span class="pre">native</span></code>.
<code class="docutils literal notranslate"><span class="pre">backend</span></code> applies to all devices used by the process, and can’t be
specified on a per-device basis.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">max_split_size_mb</span></code> prevents the native allocator
from splitting blocks larger than this size (in MB). This can reduce
fragmentation and may allow some borderline workloads to complete without
running out of memory. Performance cost can range from ‘zero’ to ‘substantial’
depending on allocation patterns. Default value is unlimited, i.e. all blocks
can be split. The
<a class="reference internal" href="../generated/torch.cuda.memory_stats.html#torch.cuda.memory_stats" title="torch.cuda.memory_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.memory_summary.html#torch.cuda.memory_summary" title="torch.cuda.memory_summary"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_summary()</span></code></a> methods are useful for tuning. This
option should be used as a last resort for a workload that is aborting
due to ‘out of memory’ and showing a large amount of inactive split blocks.
<code class="docutils literal notranslate"><span class="pre">max_split_size_mb</span></code> is only meaningful with <code class="docutils literal notranslate"><span class="pre">backend:native</span></code>.
With <code class="docutils literal notranslate"><span class="pre">backend:cudaMallocAsync</span></code>, <code class="docutils literal notranslate"><span class="pre">max_split_size_mb</span></code> is ignored.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">roundup_power2_divisions</span></code> helps with rounding the requested allocation
size to nearest power-2 division and making better use of the blocks. In
the native CUDACachingAllocator, the sizes are rounded up in multiple
of blocks size of 512, so this works fine for smaller sizes. However, this
can be inefficient for large near-by allocations as each will go to different
size of blocks and re-use of those blocks are minimized. This might create
lots of unused blocks and will waste GPU memory capacity. This option enables
the rounding of allocation size to nearest power-2 division. For example, if
we need to round-up size of 1200 and if number of divisions is 4,
the size 1200 lies between 1024 and 2048 and if we do 4 divisions between
them, the values are 1024, 1280, 1536, and 1792. So, allocation size of 1200
will be rounded to 1280 as the nearest ceiling of power-2 division.
Specify a single value to apply for all allocation sizes or specify an
array of key value pairs to set power-2 division individually for each
power of two interval. For example to set 1 division for all allocations
under 256MB, 2 division for allocations between 256MB and 512MB, 4 divisions
for allocations between 512MB and 1GB and 8 divisions for any larger allocations,
set the knob value to: [256:1,512:2,1024:4,>:8].
<code class="docutils literal notranslate"><span class="pre">roundup_power2_divisions</span></code> is only meaningful with <code class="docutils literal notranslate"><span class="pre">backend:native</span></code>.
With <code class="docutils literal notranslate"><span class="pre">backend:cudaMallocAsync</span></code>, <code class="docutils literal notranslate"><span class="pre">roundup_power2_divisions</span></code> is ignored.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">garbage_collection_threshold</span></code> helps actively reclaiming unused GPU memory to
avoid triggering expensive sync-and-reclaim-all operation (release_cached_blocks),
which can be unfavorable to latency-critical GPU applications (e.g., servers).
Upon setting this threshold (e.g., 0.8), the allocator will start reclaiming
GPU memory blocks if the GPU memory capacity usage exceeds the threshold (i.e.,
80% of the total memory allocated to the GPU application). The algorithm prefers
to free old & unused blocks first to avoid freeing blocks that are actively being
reused. The threshold value should be between greater than 0.0 and less than 1.0.
<code class="docutils literal notranslate"><span class="pre">garbage_collection_threshold</span></code> is only meaningful with <code class="docutils literal notranslate"><span class="pre">backend:native</span></code>.
With <code class="docutils literal notranslate"><span class="pre">backend:cudaMallocAsync</span></code>, <code class="docutils literal notranslate"><span class="pre">garbage_collection_threshold</span></code> is ignored.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">expandable_segments</span></code> (experimental, default: <cite>False</cite>) If set to <cite>True</cite>, this setting instructs
the allocator to create CUDA allocations that can later be expanded to better handle cases
where a job changing allocation sizes frequently, such as having a changing batch size.
Normally for large (>2MB) allocations, the allocator calls cudaMalloc to get allocations
that are the same size as what the user requests. In the future, parts of these
allocations can be reused for other requests if they are free. This works well
when the program makes many requests of exactly the same size or of sizes that
even multiples of that size. Many deep learning models follow this behavior.
However, one common exception is when the batch size changes slightly from one
iteration to the next, e.g. in batched inference. When the program runs
initially with batch size <cite>N</cite>, it will make allocations appropriate for that size.
If in the future, it runs at size <cite>N - 1</cite>, the existing allocations will still be
big enough. However, if it runs at size <cite>N + 1</cite>, then it will have to make new
allocations that are slightly larger. Not all the tensors are the same size.
Some might be <cite>(N + 1)*A</cite> and others <cite>(N + 1)*A*B</cite> where <cite>A</cite> and <cite>B</cite> are some non-batch
dimensions in the model. Because the allocator reuses existing allocations when
they are big enough, some number of <cite>(N + 1)*A</cite> allocations will actually fit in
the already existing <cite>N*B*A</cite> segments, though not perfectly. As the model runs it
will partially fill up all of these segments leaving unusable free slices of
memory at the end of these segments. The allocator at some point will need to
<cite>cudaMalloc</cite> a new <cite>(N + 1)*A*B</cite> segment. If there is not enough memory, there is
now no way to recover the slices of memory that are free at the end of existing
segments. With models 50+ layers deep, this pattern might repeat 50+ times
creating many slivers.</p>
<p><cite>expandable_segments</cite> allows the allocator to create a segment initially and then
expand its size later when more memory is needed. Instead of making one segment
per allocation, it tries to make one segment (per stream) that grows as
necessary. Now when the <cite>N + 1</cite> case runs, the allocations will tile nicely into
the one large segment until it fills up. Then more memory is requested and
appended to the end of the segment. This process does not create as many slivers
of unusable memory, so it is more likely to succeed at finding this memory.</p>
</li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Some stats reported by the
<a class="reference internal" href="../cuda.html#cuda-memory-management-api"><span class="std std-ref">CUDA memory management API</span></a>
are specific to <code class="docutils literal notranslate"><span class="pre">backend:native</span></code>, and are not meaningful with
<code class="docutils literal notranslate"><span class="pre">backend:cudaMallocAsync</span></code>.
See each function’s docstring for details.</p>
</div>
</div>
</div>
<div class="section" id="using-custom-memory-allocators-for-cuda">
<span id="cuda-memory-custom-allocator"></span><h2>Using custom memory allocators for CUDA<a class="headerlink" href="#using-custom-memory-allocators-for-cuda" title="Permalink to this heading">¶</a></h2>
<p>It is possible to define allocators as simple functions in C/C++ and compile
them as a shared library, the code below shows a basic allocator that just
traces all the memory operations.</p>
<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><sys/types.h></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><cuda_runtime_api.h></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><iostream></span>
<span class="c1">// Compile with g++ alloc.cc -o alloc.so -I/usr/local/cuda/include -shared -fPIC</span>
<span class="k">extern</span><span class="w"> </span><span class="s">"C"</span><span class="w"> </span><span class="p">{</span>
<span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="nf">my_malloc</span><span class="p">(</span><span class="kt">ssize_t</span><span class="w"> </span><span class="n">size</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">device</span><span class="p">,</span><span class="w"> </span><span class="n">cudaStream_t</span><span class="w"> </span><span class="n">stream</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">ptr</span><span class="p">;</span>
<span class="w"> </span><span class="n">cudaMalloc</span><span class="p">(</span><span class="o">&</span><span class="n">ptr</span><span class="p">,</span><span class="w"> </span><span class="n">size</span><span class="p">);</span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="o"><<</span><span class="s">"alloc "</span><span class="o"><<</span><span class="n">ptr</span><span class="o"><<</span><span class="n">size</span><span class="o"><<</span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">ptr</span><span class="p">;</span>
<span class="p">}</span>
<span class="kt">void</span><span class="w"> </span><span class="nf">my_free</span><span class="p">(</span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">ptr</span><span class="p">,</span><span class="w"> </span><span class="kt">ssize_t</span><span class="w"> </span><span class="n">size</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">device</span><span class="p">,</span><span class="w"> </span><span class="n">cudaStream_t</span><span class="w"> </span><span class="n">stream</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="o"><<</span><span class="s">"free "</span><span class="o"><<</span><span class="n">ptr</span><span class="o"><<</span><span class="w"> </span><span class="s">" "</span><span class="o"><<</span><span class="n">stream</span><span class="o"><<</span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="w"> </span><span class="n">cudaFree</span><span class="p">(</span><span class="n">ptr</span><span class="p">);</span>
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>This can be used in python through the <a class="reference internal" href="../generated/torch.cuda.CUDAPluggableAllocator.html#torch.cuda.CUDAPluggableAllocator" title="torch.cuda.memory.CUDAPluggableAllocator"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.memory.CUDAPluggableAllocator</span></code></a>.
The user is responsible for supplying the path to the <cite>.so</cite> file and the name
of the alloc/free functions that match the signatures specified above.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># Load the allocator</span>
<span class="n">new_alloc</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">CUDAPluggableAllocator</span><span class="p">(</span>
<span class="s1">'alloc.so'</span><span class="p">,</span> <span class="s1">'my_malloc'</span><span class="p">,</span> <span class="s1">'my_free'</span><span class="p">)</span>
<span class="c1"># Swap the current allocator</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">change_current_allocator</span><span class="p">(</span><span class="n">new_alloc</span><span class="p">)</span>
<span class="c1"># This will allocate memory in the device using the new allocator</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># Do an initial memory allocator</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="c1"># Load the allocator</span>
<span class="n">new_alloc</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">CUDAPluggableAllocator</span><span class="p">(</span>
<span class="s1">'alloc.so'</span><span class="p">,</span> <span class="s1">'my_malloc'</span><span class="p">,</span> <span class="s1">'my_free'</span><span class="p">)</span>
<span class="c1"># This will error since the current allocator was already instantiated</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">change_current_allocator</span><span class="p">(</span><span class="n">new_alloc</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="cublas-workspaces">
<h2>cuBLAS workspaces<a class="headerlink" href="#cublas-workspaces" title="Permalink to this heading">¶</a></h2>
<p>For each combination of cuBLAS handle and CUDA stream, a cuBLAS workspace will be allocated
if that handle and stream combination executes a cuBLAS kernel that requires a workspace.
In order to avoid repeatedly allocating workspaces, these workspaces are not deallocated unless
<code class="docutils literal notranslate"><span class="pre">torch._C._cuda_clearCublasWorkspaces()</span></code> is called. The workspace size per allocation can be
specified via the environment variable <code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG</span></code> with the format <code class="docutils literal notranslate"><span class="pre">:[SIZE]:[COUNT]</span></code>.
As an example, the default workspace size per allocation is <code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG=:4096:2:16:8</span></code>
which specifies a total size of <code class="docutils literal notranslate"><span class="pre">2</span> <span class="pre">*</span> <span class="pre">4096</span> <span class="pre">+</span> <span class="pre">8</span> <span class="pre">*</span> <span class="pre">16</span> <span class="pre">KiB</span></code>. To force cuBLAS to avoid using workspaces,
set <code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG=:0:0</span></code>.</p>
</div>
<div class="section" id="cufft-plan-cache">
<span id="id2"></span><h2>cuFFT plan cache<a class="headerlink" href="#cufft-plan-cache" title="Permalink to this heading">¶</a></h2>
<p>For each CUDA device, an LRU cache of cuFFT plans is used to speed up repeatedly
running FFT methods (e.g., <a class="reference internal" href="../generated/torch.fft.fft.html#torch.fft.fft" title="torch.fft.fft"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.fft.fft()</span></code></a>) on CUDA tensors of same geometry
with same configuration. Because some cuFFT plans may allocate GPU memory,
these caches have a maximum capacity.</p>
<p>You may control and query the properties of the cache of current device with
the following APIs:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.max_size</span></code> gives the capacity of the
cache (default is 4096 on CUDA 10 and newer, and 1023 on older CUDA versions).
Setting this value directly modifies the capacity.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.size</span></code> gives the number of plans
currently residing in the cache.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.clear()</span></code> clears the cache.</p></li>
</ul>
<p>To control and query plan caches of a non-default device, you can index the
<code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache</span></code> object with either a <a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>
object or a device index, and access one of the above attributes. E.g., to set
the capacity of the cache for device <code class="docutils literal notranslate"><span class="pre">1</span></code>, one can write
<code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache[1].max_size</span> <span class="pre">=</span> <span class="pre">10</span></code>.</p>
</div>
<div class="section" id="just-in-time-compilation">
<span id="cuda-just-in-time-compilation"></span><h2>Just-in-Time Compilation<a class="headerlink" href="#just-in-time-compilation" title="Permalink to this heading">¶</a></h2>
<p>PyTorch just-in-time compiles some operations, like torch.special.zeta, when
performed on CUDA tensors. This compilation can be time consuming
(up to a few seconds depending on your hardware and software)
and may occur multiple times for a single operator since many PyTorch operators actually
select from a variety of kernels, each of which must be compiled once, depending on their input.
This compilation occurs once per process, or just once if a kernel cache is used.</p>
<p>By default, PyTorch creates a kernel cache in $XDG_CACHE_HOME/torch/kernels if
XDG_CACHE_HOME is defined and $HOME/.cache/torch/kernels if it’s not (except on Windows,
where the kernel cache is not yet supported). The caching behavior can be directly
controlled with two environment variables. If USE_PYTORCH_KERNEL_CACHE is set to 0 then no
cache will be used, and if PYTORCH_KERNEL_CACHE_PATH is set then that path will be used
as a kernel cache instead of the default location.</p>
</div>
<div class="section" id="best-practices">
<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this heading">¶</a></h2>
<div class="section" id="device-agnostic-code">
<h3>Device-agnostic code<a class="headerlink" href="#device-agnostic-code" title="Permalink to this heading">¶</a></h3>
<p>Due to the structure of PyTorch, you may need to explicitly write
device-agnostic (CPU or GPU) code; an example may be creating a new tensor as
the initial hidden state of a recurrent neural network.</p>
<p>The first step is to determine whether the GPU should be used or not. A common
pattern is to use Python’s <code class="docutils literal notranslate"><span class="pre">argparse</span></code> module to read in user arguments, and
have a flag that can be used to disable CUDA, in combination with
<a class="reference internal" href="../generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available"><code class="xref py py-meth docutils literal notranslate"><span class="pre">is_available()</span></code></a>. In the following, <code class="docutils literal notranslate"><span class="pre">args.device</span></code> results in a
<a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> object that can be used to move tensors to CPU or CUDA.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s1">'PyTorch Example'</span><span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">'--disable-cuda'</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">'store_true'</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">'Disable CUDA'</span><span class="p">)</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">disable_cuda</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When assessing the availability of CUDA in a given environment (<a class="reference internal" href="../generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available"><code class="xref py py-meth docutils literal notranslate"><span class="pre">is_available()</span></code></a>), PyTorch’s default
behavior is to call the CUDA Runtime API method <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__DEVICE.html#group__CUDART__DEVICE_1g18808e54893cfcaafefeab31a73cc55f">cudaGetDeviceCount</a>. Because this call in turn initializes the
CUDA Driver API (via <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__INITIALIZE.html#group__CUDA__INITIALIZE_1g0a2f1517e1bd8502c7194c3a8c134bc3">cuInit</a>) if it is not already initialized, subsequent forks of a process that has run