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<div class="section" id="hip-rocm-semantics">
<span id="hip-semantics"></span><h1>HIP (ROCm) semantics<a class="headerlink" href="#hip-rocm-semantics" title="Permalink to this headline">¶</a></h1>
<p>ROCm™ is AMD’s open source software platform for GPU-accelerated high
performance computing and machine learning. HIP is ROCm’s C++ dialect designed
to ease conversion of CUDA applications to portable C++ code. HIP is used when
converting existing CUDA applications like PyTorch to portable C++ and for new
projects that require portability between AMD and NVIDIA.</p>
<div class="section" id="hip-interfaces-reuse-the-cuda-interfaces">
<span id="hip-as-cuda"></span><h2>HIP Interfaces Reuse the CUDA Interfaces<a class="headerlink" href="#hip-interfaces-reuse-the-cuda-interfaces" title="Permalink to this headline">¶</a></h2>
<p>PyTorch for HIP intentionally reuses the existing <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> interfaces.
This helps to accelerate the porting of existing PyTorch code and models because
very few code changes are necessary, if any.</p>
<p>The example from <a class="reference internal" href="cuda.html#cuda-semantics"><span class="std std-ref">CUDA semantics</span></a> will work exactly the same for HIP:</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 HIP 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="c1"># 'rocm' or 'hip' are not valid, use 'cuda'</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>
<div class="section" id="checking-for-hip">
<span id="id1"></span><h2>Checking for HIP<a class="headerlink" href="#checking-for-hip" title="Permalink to this headline">¶</a></h2>
<p>Whether you are using PyTorch for CUDA or HIP, the result of calling
<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> will be the same. If you are using a PyTorch
that has been built with GPU support, it will return <cite>True</cite>. If you must check
which version of PyTorch you are using, refer to this example below:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</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="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">hip</span><span class="p">:</span>
<span class="c1"># do something specific for HIP</span>
<span class="k">elif</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="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">cuda</span><span class="p">:</span>
<span class="c1"># do something specific for CUDA</span>
</pre></div>
</div>
</div>
<div class="section" id="tensorfloat-32-tf32-on-rocm">
<span id="tf32-on-rocm"></span><h2>TensorFloat-32(TF32) on ROCm<a class="headerlink" href="#tensorfloat-32-tf32-on-rocm" title="Permalink to this headline">¶</a></h2>
<p>TF32 is not supported on ROCm.</p>
</div>
<div class="section" id="memory-management">
<span id="rocm-memory-management"></span><h2>Memory management<a class="headerlink" href="#memory-management" title="Permalink to this headline">¶</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">rocm-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>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>
<p>To debug memory errors, set
<code class="docutils literal notranslate"><span class="pre">PYTORCH_NO_CUDA_MEMORY_CACHING=1</span></code> in your environment to disable caching.</p>
</div>
<div class="section" id="hipfft-rocfft-plan-cache">
<span id="hipfft-plan-cache"></span><h2>hipFFT/rocFFT plan cache<a class="headerlink" href="#hipfft-rocfft-plan-cache" title="Permalink to this headline">¶</a></h2>
<p>Setting the size of the cache for hipFFT/rocFFT plans is not supported.</p>
</div>
<div class="section" id="refer-to-cuda-semantics-doc">
<h2>Refer to CUDA Semantics doc<a class="headerlink" href="#refer-to-cuda-semantics-doc" title="Permalink to this headline">¶</a></h2>
<p>For any sections not listed here, please refer to the CUDA semantics doc: <a class="reference internal" href="cuda.html#cuda-semantics"><span class="std std-ref">CUDA semantics</span></a></p>
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