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<h1>Source code for torch.backends.cudnn</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</span>
<span class="kn">from</span> <span class="nn">torch.backends</span> <span class="kn">import</span> <span class="n">ContextProp</span><span class="p">,</span> <span class="n">PropModule</span><span class="p">,</span> <span class="n">__allow_nonbracketed_mutation</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_cudnn</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="n">_cudnn</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># type: ignore[assignment]</span>
<span class="c1"># Write:</span>
<span class="c1">#</span>
<span class="c1"># torch.backends.cudnn.enabled = False</span>
<span class="c1">#</span>
<span class="c1"># to globally disable CuDNN/MIOpen</span>
<span class="n">__cudnn_version</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">_cudnn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">_init</span><span class="p">():</span>
<span class="k">global</span> <span class="n">__cudnn_version</span>
<span class="k">if</span> <span class="n">__cudnn_version</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">__cudnn_version</span> <span class="o">=</span> <span class="n">_cudnn</span><span class="o">.</span><span class="n">getVersionInt</span><span class="p">()</span>
<span class="n">runtime_version</span> <span class="o">=</span> <span class="n">_cudnn</span><span class="o">.</span><span class="n">getRuntimeVersion</span><span class="p">()</span>
<span class="n">compile_version</span> <span class="o">=</span> <span class="n">_cudnn</span><span class="o">.</span><span class="n">getCompileVersion</span><span class="p">()</span>
<span class="n">runtime_major</span><span class="p">,</span> <span class="n">runtime_minor</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">runtime_version</span>
<span class="n">compile_major</span><span class="p">,</span> <span class="n">compile_minor</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">compile_version</span>
<span class="c1"># Different major versions are always incompatible</span>
<span class="c1"># Starting with cuDNN 7, minor versions are backwards-compatible</span>
<span class="c1"># Not sure about MIOpen (ROCm), so always do a strict check</span>
<span class="k">if</span> <span class="n">runtime_major</span> <span class="o">!=</span> <span class="n">compile_major</span><span class="p">:</span>
<span class="n">cudnn_compatible</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">elif</span> <span class="n">runtime_major</span> <span class="o"><</span> <span class="mi">7</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">_cudnn</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
<span class="n">cudnn_compatible</span> <span class="o">=</span> <span class="n">runtime_minor</span> <span class="o">==</span> <span class="n">compile_minor</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cudnn_compatible</span> <span class="o">=</span> <span class="n">runtime_minor</span> <span class="o">>=</span> <span class="n">compile_minor</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">cudnn_compatible</span><span class="p">:</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'PYTORCH_SKIP_CUDNN_COMPATIBILITY_CHECK'</span><span class="p">,</span> <span class="s1">'0'</span><span class="p">)</span> <span class="o">==</span> <span class="s1">'1'</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="n">base_error_msg</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s1">'cuDNN version incompatibility: '</span>
<span class="sa">f</span><span class="s1">'PyTorch was compiled against </span><span class="si">{</span><span class="n">compile_version</span><span class="si">}</span><span class="s1"> '</span>
<span class="sa">f</span><span class="s1">'but found runtime version </span><span class="si">{</span><span class="n">runtime_version</span><span class="si">}</span><span class="s1">. '</span>
<span class="sa">f</span><span class="s1">'PyTorch already comes bundled with cuDNN. '</span>
<span class="sa">f</span><span class="s1">'One option to resolving this error is to ensure PyTorch '</span>
<span class="sa">f</span><span class="s1">'can find the bundled cuDNN.'</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'LD_LIBRARY_PATH'</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span>
<span class="n">ld_library_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'LD_LIBRARY_PATH'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">substring</span> <span class="ow">in</span> <span class="n">ld_library_path</span> <span class="k">for</span> <span class="n">substring</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'cuda'</span><span class="p">,</span> <span class="s1">'cudnn'</span><span class="p">]):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">base_error_msg</span><span class="si">}</span><span class="s1">'</span>
<span class="sa">f</span><span class="s1">'Looks like your LD_LIBRARY_PATH contains incompatible version of cudnn'</span>
<span class="sa">f</span><span class="s1">'Please either remove it from the path or install cudnn </span><span class="si">{</span><span class="n">compile_version</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">base_error_msg</span><span class="si">}</span><span class="s1">'</span>
<span class="sa">f</span><span class="s1">'one possibility is that there is a '</span>
<span class="sa">f</span><span class="s1">'conflicting cuDNN in LD_LIBRARY_PATH.'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">base_error_msg</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">_init</span><span class="p">():</span>
<span class="k">return</span> <span class="kc">False</span>
<div class="viewcode-block" id="version"><a class="viewcode-back" href="../../../backends.html#torch.backends.cudnn.version">[docs]</a><span class="k">def</span> <span class="nf">version</span><span class="p">():</span>
<span class="sd">"""Returns the version of cuDNN"""</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">_init</span><span class="p">():</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">__cudnn_version</span></div>
<span class="n">CUDNN_TENSOR_DTYPES</span> <span class="o">=</span> <span class="p">{</span>
<span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">,</span>
<span class="p">}</span>
<div class="viewcode-block" id="is_available"><a class="viewcode-back" href="../../../backends.html#torch.backends.cudnn.is_available">[docs]</a><span class="k">def</span> <span class="nf">is_available</span><span class="p">():</span>
<span class="sa">r</span><span class="sd">"""Returns a bool indicating if CUDNN is currently available."""</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">has_cudnn</span></div>
<span class="k">def</span> <span class="nf">is_acceptable</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_enabled</span><span class="p">():</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s1">'cuda'</span> <span class="ow">or</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">CUDNN_TENSOR_DTYPES</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">is_available</span><span class="p">():</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s2">"PyTorch was compiled without cuDNN/MIOpen support. To use cuDNN/MIOpen, rebuild "</span>
<span class="s2">"PyTorch making sure the library is visible to the build system."</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">_init</span><span class="p">():</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'cuDNN/MIOpen library not found. Check your </span><span class="si">{libpath}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">libpath</span><span class="o">=</span><span class="p">{</span>
<span class="s1">'darwin'</span><span class="p">:</span> <span class="s1">'DYLD_LIBRARY_PATH'</span><span class="p">,</span>
<span class="s1">'win32'</span><span class="p">:</span> <span class="s1">'PATH'</span>
<span class="p">}</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">platform</span><span class="p">,</span> <span class="s1">'LD_LIBRARY_PATH'</span><span class="p">)))</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">def</span> <span class="nf">set_flags</span><span class="p">(</span><span class="n">_enabled</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">_benchmark</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">_benchmark_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">_deterministic</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">_allow_tf32</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">orig_flags</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_enabled</span><span class="p">(),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_benchmark</span><span class="p">(),</span>
<span class="kc">None</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_cuda_get_cudnn_benchmark_limit</span><span class="p">(),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_deterministic</span><span class="p">(),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_allow_tf32</span><span class="p">())</span>
<span class="k">if</span> <span class="n">_enabled</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_enabled</span><span class="p">(</span><span class="n">_enabled</span><span class="p">)</span>
<span class="k">if</span> <span class="n">_benchmark</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_benchmark</span><span class="p">(</span><span class="n">_benchmark</span><span class="p">)</span>
<span class="k">if</span> <span class="n">_benchmark_limit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">is_available</span><span class="p">():</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_cuda_set_cudnn_benchmark_limit</span><span class="p">(</span><span class="n">_benchmark_limit</span><span class="p">)</span>
<span class="k">if</span> <span class="n">_deterministic</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_deterministic</span><span class="p">(</span><span class="n">_deterministic</span><span class="p">)</span>
<span class="k">if</span> <span class="n">_allow_tf32</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_allow_tf32</span><span class="p">(</span><span class="n">_allow_tf32</span><span class="p">)</span>
<span class="k">return</span> <span class="n">orig_flags</span>
<span class="nd">@contextmanager</span>
<span class="k">def</span> <span class="nf">flags</span><span class="p">(</span><span class="n">enabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">benchmark</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">benchmark_limit</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">deterministic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_tf32</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">with</span> <span class="n">__allow_nonbracketed_mutation</span><span class="p">():</span>
<span class="n">orig_flags</span> <span class="o">=</span> <span class="n">set_flags</span><span class="p">(</span><span class="n">enabled</span><span class="p">,</span> <span class="n">benchmark</span><span class="p">,</span> <span class="n">benchmark_limit</span><span class="p">,</span> <span class="n">deterministic</span><span class="p">,</span> <span class="n">allow_tf32</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">yield</span>
<span class="k">finally</span><span class="p">:</span>
<span class="c1"># recover the previous values</span>
<span class="k">with</span> <span class="n">__allow_nonbracketed_mutation</span><span class="p">():</span>
<span class="n">set_flags</span><span class="p">(</span><span class="o">*</span><span class="n">orig_flags</span><span class="p">)</span>
<span class="c1"># The magic here is to allow us to intercept code like this:</span>
<span class="c1">#</span>
<span class="c1"># torch.backends.<cudnn|mkldnn>.enabled = True</span>
<span class="k">class</span> <span class="nc">CudnnModule</span><span class="p">(</span><span class="n">PropModule</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
<span class="n">enabled</span> <span class="o">=</span> <span class="n">ContextProp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_enabled</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_enabled</span><span class="p">)</span>
<span class="n">deterministic</span> <span class="o">=</span> <span class="n">ContextProp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_deterministic</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_deterministic</span><span class="p">)</span>
<span class="n">benchmark</span> <span class="o">=</span> <span class="n">ContextProp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_benchmark</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_benchmark</span><span class="p">)</span>
<span class="n">benchmark_limit</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">is_available</span><span class="p">():</span>
<span class="n">benchmark_limit</span> <span class="o">=</span> <span class="n">ContextProp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_cuda_get_cudnn_benchmark_limit</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_cuda_set_cudnn_benchmark_limit</span><span class="p">)</span>
<span class="n">allow_tf32</span> <span class="o">=</span> <span class="n">ContextProp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cudnn_allow_tf32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cudnn_allow_tf32</span><span class="p">)</span>
<span class="c1"># This is the sys.modules replacement trick, see</span>
<span class="c1"># https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273</span>
<span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="vm">__name__</span><span class="p">]</span> <span class="o">=</span> <span class="n">CudnnModule</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="vm">__name__</span><span class="p">],</span> <span class="vm">__name__</span><span class="p">)</span>
<span class="c1"># Add type annotation for the replaced module</span>
<span class="n">enabled</span><span class="p">:</span> <span class="nb">bool</span>
<span class="n">deterministic</span><span class="p">:</span> <span class="nb">bool</span>
<span class="n">benchmark</span><span class="p">:</span> <span class="nb">bool</span>
<span class="n">allow_tf32</span><span class="p">:</span> <span class="nb">bool</span>
<span class="n">benchmark_limit</span><span class="p">:</span> <span class="nb">int</span>
</pre></div>
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filterTags.bind();
// Add class to links that have code blocks, since we cannot create links in code blocks
$("article.pytorch-article a span.pre").each(function(e) {
$(this).closest("a").addClass("has-code");
});
})
</script>
</body>
</html>