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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<p class="caption"><span class="caption-text">Python API</span></p>
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<h1>Source code for torch</h1><div class="highlight"><pre>
<span></span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd">The torch package contains data structures for multi-dimensional</span>
<span class="sd">tensors and defines mathematical operations over these tensors.</span>
<span class="sd">Additionally, it provides many utilities for efficient serializing of</span>
<span class="sd">Tensors and arbitrary types, and other useful utilities.</span>
<span class="sd">It has a CUDA counterpart, that enables you to run your tensor computations</span>
<span class="sd">on an NVIDIA GPU with compute capability >= 3.0.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">import</span> <span class="nn">textwrap</span>
<span class="kn">import</span> <span class="nn">ctypes</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o"><</span> <span class="p">(</span><span class="mi">3</span><span class="p">,):</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"Python 2 has reached end-of-life and is no longer supported by PyTorch."</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">_import_dotted_name</span><span class="p">,</span> <span class="n">classproperty</span>
<span class="kn">from</span> <span class="nn">._utils_internal</span> <span class="kn">import</span> <span class="n">get_file_path</span><span class="p">,</span> <span class="n">prepare_multiprocessing_environment</span><span class="p">,</span> \
<span class="n">USE_RTLD_GLOBAL_WITH_LIBTORCH</span><span class="p">,</span> <span class="n">USE_GLOBAL_DEPS</span>
<span class="c1"># TODO(torch_deploy) figure out how to freeze version.py in fbcode build</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">executable</span> <span class="o">==</span> <span class="s1">'torch_deploy'</span><span class="p">:</span>
<span class="n">__version__</span> <span class="o">=</span> <span class="s2">"torch-deploy-1.8"</span>
<span class="k">else</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">.torch_version</span> <span class="kn">import</span> <span class="n">__version__</span> <span class="k">as</span> <span class="n">__version__</span>
<span class="kn">from</span> <span class="nn">._six</span> <span class="kn">import</span> <span class="n">string_classes</span> <span class="k">as</span> <span class="n">_string_classes</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Set</span><span class="p">,</span> <span class="n">Type</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">builtins</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'typename'</span><span class="p">,</span> <span class="s1">'is_tensor'</span><span class="p">,</span> <span class="s1">'is_storage'</span><span class="p">,</span> <span class="s1">'set_default_tensor_type'</span><span class="p">,</span>
<span class="s1">'set_rng_state'</span><span class="p">,</span> <span class="s1">'get_rng_state'</span><span class="p">,</span> <span class="s1">'manual_seed'</span><span class="p">,</span> <span class="s1">'initial_seed'</span><span class="p">,</span> <span class="s1">'seed'</span><span class="p">,</span>
<span class="s1">'save'</span><span class="p">,</span> <span class="s1">'load'</span><span class="p">,</span> <span class="s1">'set_printoptions'</span><span class="p">,</span> <span class="s1">'chunk'</span><span class="p">,</span> <span class="s1">'split'</span><span class="p">,</span> <span class="s1">'stack'</span><span class="p">,</span> <span class="s1">'matmul'</span><span class="p">,</span>
<span class="s1">'no_grad'</span><span class="p">,</span> <span class="s1">'enable_grad'</span><span class="p">,</span> <span class="s1">'rand'</span><span class="p">,</span> <span class="s1">'randn'</span><span class="p">,</span> <span class="s1">'inference_mode'</span><span class="p">,</span>
<span class="s1">'DoubleStorage'</span><span class="p">,</span> <span class="s1">'FloatStorage'</span><span class="p">,</span> <span class="s1">'LongStorage'</span><span class="p">,</span> <span class="s1">'IntStorage'</span><span class="p">,</span>
<span class="s1">'ShortStorage'</span><span class="p">,</span> <span class="s1">'CharStorage'</span><span class="p">,</span> <span class="s1">'ByteStorage'</span><span class="p">,</span> <span class="s1">'BoolStorage'</span><span class="p">,</span>
<span class="s1">'DoubleTensor'</span><span class="p">,</span> <span class="s1">'FloatTensor'</span><span class="p">,</span> <span class="s1">'LongTensor'</span><span class="p">,</span> <span class="s1">'IntTensor'</span><span class="p">,</span>
<span class="s1">'ShortTensor'</span><span class="p">,</span> <span class="s1">'CharTensor'</span><span class="p">,</span> <span class="s1">'ByteTensor'</span><span class="p">,</span> <span class="s1">'BoolTensor'</span><span class="p">,</span> <span class="s1">'Tensor'</span><span class="p">,</span>
<span class="s1">'lobpcg'</span><span class="p">,</span> <span class="s1">'use_deterministic_algorithms'</span><span class="p">,</span>
<span class="s1">'are_deterministic_algorithms_enabled'</span><span class="p">,</span>
<span class="s1">'is_deterministic_algorithms_warn_only_enabled'</span><span class="p">,</span>
<span class="s1">'set_deterministic_debug_mode'</span><span class="p">,</span> <span class="s1">'get_deterministic_debug_mode'</span><span class="p">,</span>
<span class="s1">'set_warn_always'</span><span class="p">,</span> <span class="s1">'is_warn_always_enabled'</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1">################################################################################</span>
<span class="c1"># Load the extension module</span>
<span class="c1">################################################################################</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s1">'win32'</span><span class="p">:</span>
<span class="n">pfiles_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'ProgramFiles'</span><span class="p">,</span> <span class="s1">'C:</span><span class="se">\\</span><span class="s1">Program Files'</span><span class="p">)</span>
<span class="n">py_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">exec_prefix</span><span class="p">,</span> <span class="s1">'Library'</span><span class="p">,</span> <span class="s1">'bin'</span><span class="p">)</span>
<span class="n">th_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="vm">__file__</span><span class="p">),</span> <span class="s1">'lib'</span><span class="p">)</span>
<span class="c1"># When users create a virtualenv that inherits the base environment,</span>
<span class="c1"># we will need to add the corresponding library directory into</span>
<span class="c1"># DLL search directories. Otherwise, it will rely on `PATH` which</span>
<span class="c1"># is dependent on user settings.</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">exec_prefix</span> <span class="o">!=</span> <span class="n">sys</span><span class="o">.</span><span class="n">base_exec_prefix</span><span class="p">:</span>
<span class="n">base_py_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">base_exec_prefix</span><span class="p">,</span> <span class="s1">'Library'</span><span class="p">,</span> <span class="s1">'bin'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">base_py_dll_path</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">dll_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">,</span> <span class="p">[</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="n">py_dll_path</span><span class="p">,</span> <span class="n">base_py_dll_path</span><span class="p">]))</span>
<span class="k">if</span> <span class="nb">all</span><span class="p">([</span><span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="s1">'nvToolsExt64_1.dll'</span><span class="p">))</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dll_paths</span><span class="p">]):</span>
<span class="n">nvtoolsext_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
<span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'NVTOOLSEXT_PATH'</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">pfiles_path</span><span class="p">,</span> <span class="s1">'NVIDIA Corporation'</span><span class="p">,</span> <span class="s1">'NvToolsExt'</span><span class="p">)),</span> <span class="s1">'bin'</span><span class="p">,</span> <span class="s1">'x64'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">nvtoolsext_dll_path</span> <span class="o">=</span> <span class="s1">''</span>
<span class="kn">from</span> <span class="nn">.version</span> <span class="kn">import</span> <span class="n">cuda</span> <span class="k">as</span> <span class="n">cuda_version</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="k">if</span> <span class="n">cuda_version</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">([</span><span class="ow">not</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="s1">'cudart64*.dll'</span><span class="p">))</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dll_paths</span><span class="p">]):</span>
<span class="n">cuda_version_1</span> <span class="o">=</span> <span class="n">cuda_version</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'.'</span><span class="p">,</span> <span class="s1">'_'</span><span class="p">)</span>
<span class="n">cuda_path_var</span> <span class="o">=</span> <span class="s1">'CUDA_PATH_V'</span> <span class="o">+</span> <span class="n">cuda_version_1</span>
<span class="n">default_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">pfiles_path</span><span class="p">,</span> <span class="s1">'NVIDIA GPU Computing Toolkit'</span><span class="p">,</span> <span class="s1">'CUDA'</span><span class="p">,</span> <span class="s1">'v'</span> <span class="o">+</span> <span class="n">cuda_version</span><span class="p">)</span>
<span class="n">cuda_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="n">cuda_path_var</span><span class="p">,</span> <span class="n">default_path</span><span class="p">),</span> <span class="s1">'bin'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cuda_path</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">dll_paths</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">,</span> <span class="p">[</span><span class="n">nvtoolsext_dll_path</span><span class="p">,</span> <span class="n">cuda_path</span><span class="p">]))</span>
<span class="n">kernel32</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinDLL</span><span class="p">(</span><span class="s1">'kernel32.dll'</span><span class="p">,</span> <span class="n">use_last_error</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">with_load_library_flags</span> <span class="o">=</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">kernel32</span><span class="p">,</span> <span class="s1">'AddDllDirectory'</span><span class="p">)</span>
<span class="n">prev_error_mode</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">SetErrorMode</span><span class="p">(</span><span class="mh">0x0001</span><span class="p">)</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryW</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span>
<span class="k">if</span> <span class="n">with_load_library_flags</span><span class="p">:</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">AddDllDirectory</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryExW</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span>
<span class="k">for</span> <span class="n">dll_path</span> <span class="ow">in</span> <span class="n">dll_paths</span><span class="p">:</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">>=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">add_dll_directory</span><span class="p">(</span><span class="n">dll_path</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">with_load_library_flags</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">AddDllDirectory</span><span class="p">(</span><span class="n">dll_path</span><span class="p">)</span>
<span class="k">if</span> <span class="n">res</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">err</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinError</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">get_last_error</span><span class="p">())</span>
<span class="n">err</span><span class="o">.</span><span class="n">strerror</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">' Error adding "</span><span class="si">{</span><span class="n">dll_path</span><span class="si">}</span><span class="s1">" to the DLL directories.'</span>
<span class="k">raise</span> <span class="n">err</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="s1">'vcruntime140.dll'</span><span class="p">)</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="s1">'msvcp140.dll'</span><span class="p">)</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="s1">'vcruntime140_1.dll'</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.</span>
<span class="s1"> It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe'''</span><span class="p">)</span>
<span class="n">dlls</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="s1">'*.dll'</span><span class="p">))</span>
<span class="n">path_patched</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">for</span> <span class="n">dll</span> <span class="ow">in</span> <span class="n">dlls</span><span class="p">:</span>
<span class="n">is_loaded</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">with_load_library_flags</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryExW</span><span class="p">(</span><span class="n">dll</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mh">0x00001100</span><span class="p">)</span>
<span class="n">last_error</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">get_last_error</span><span class="p">()</span>
<span class="k">if</span> <span class="n">res</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">last_error</span> <span class="o">!=</span> <span class="mi">126</span><span class="p">:</span>
<span class="n">err</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinError</span><span class="p">(</span><span class="n">last_error</span><span class="p">)</span>
<span class="n">err</span><span class="o">.</span><span class="n">strerror</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">' Error loading "</span><span class="si">{</span><span class="n">dll</span><span class="si">}</span><span class="s1">" or one of its dependencies.'</span>
<span class="k">raise</span> <span class="n">err</span>
<span class="k">elif</span> <span class="n">res</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">is_loaded</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">is_loaded</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">path_patched</span><span class="p">:</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PATH'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">';'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dll_paths</span> <span class="o">+</span> <span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PATH'</span><span class="p">]])</span>
<span class="n">path_patched</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryW</span><span class="p">(</span><span class="n">dll</span><span class="p">)</span>
<span class="k">if</span> <span class="n">res</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">err</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinError</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">get_last_error</span><span class="p">())</span>
<span class="n">err</span><span class="o">.</span><span class="n">strerror</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">' Error loading "</span><span class="si">{</span><span class="n">dll</span><span class="si">}</span><span class="s1">" or one of its dependencies.'</span>
<span class="k">raise</span> <span class="n">err</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">SetErrorMode</span><span class="p">(</span><span class="n">prev_error_mode</span><span class="p">)</span>
<span class="c1"># See Note [Global dependencies]</span>
<span class="k">def</span> <span class="nf">_load_global_deps</span><span class="p">():</span>
<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'Windows'</span> <span class="ow">or</span> <span class="n">sys</span><span class="o">.</span><span class="n">executable</span> <span class="o">==</span> <span class="s1">'torch_deploy'</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">lib_name</span> <span class="o">=</span> <span class="s1">'libtorch_global_deps'</span> <span class="o">+</span> <span class="p">(</span><span class="s1">'.dylib'</span> <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'Darwin'</span> <span class="k">else</span> <span class="s1">'.so'</span><span class="p">)</span>
<span class="n">here</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="vm">__file__</span><span class="p">)</span>
<span class="n">lib_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">here</span><span class="p">),</span> <span class="s1">'lib'</span><span class="p">,</span> <span class="n">lib_name</span><span class="p">)</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="n">lib_path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">ctypes</span><span class="o">.</span><span class="n">RTLD_GLOBAL</span><span class="p">)</span>
<span class="k">if</span> <span class="p">(</span><span class="n">USE_RTLD_GLOBAL_WITH_LIBTORCH</span> <span class="ow">or</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'TORCH_USE_RTLD_GLOBAL'</span><span class="p">))</span> <span class="ow">and</span> \
<span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s1">'Windows'</span><span class="p">:</span>
<span class="c1"># Do it the hard way. You might want to load libtorch with RTLD_GLOBAL in a</span>
<span class="c1"># few circumstances:</span>
<span class="c1">#</span>
<span class="c1"># 1. You're in a build environment (e.g., fbcode) where</span>
<span class="c1"># libtorch_global_deps is not available, but you still need</span>
<span class="c1"># to get mkl to link in with RTLD_GLOBAL or it will just</span>
<span class="c1"># not work.</span>
<span class="c1">#</span>
<span class="c1"># 2. You're trying to run PyTorch under UBSAN and you need</span>
<span class="c1"># to ensure that only one copy of libtorch is loaded, so</span>
<span class="c1"># vptr checks work properly</span>
<span class="c1">#</span>
<span class="c1"># If you're using this setting, you must verify that all the libraries</span>
<span class="c1"># you load consistently use the same libstdc++, or you may have</span>
<span class="c1"># mysterious segfaults.</span>
<span class="c1">#</span>
<span class="kn">import</span> <span class="nn">os</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">'RTLD_GLOBAL'</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">'RTLD_LAZY'</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># next try if DLFCN exists</span>
<span class="kn">import</span> <span class="nn">DLFCN</span> <span class="k">as</span> <span class="nn">_dl_flags</span> <span class="c1"># type: ignore[import, no-redef]</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="c1"># as a last attempt, use compile-time constants</span>
<span class="kn">import</span> <span class="nn">torch._dl</span> <span class="k">as</span> <span class="nn">_dl_flags</span> <span class="c1"># type: ignore[import, no-redef]</span>
<span class="n">old_flags</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">getdlopenflags</span><span class="p">()</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_GLOBAL</span> <span class="o">|</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_LAZY</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="o">*</span> <span class="c1"># noqa: F403</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">old_flags</span><span class="p">)</span>
<span class="k">del</span> <span class="n">old_flags</span>
<span class="k">del</span> <span class="n">_dl_flags</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Easy way. You want this most of the time, because it will prevent</span>
<span class="c1"># C++ symbols from libtorch clobbering C++ symbols from other</span>
<span class="c1"># libraries, leading to mysterious segfaults.</span>
<span class="c1">#</span>
<span class="c1"># If building in an environment where libtorch_global_deps isn't available</span>
<span class="c1"># like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will</span>
<span class="c1"># want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False</span>
<span class="c1">#</span>
<span class="c1"># See Note [Global dependencies]</span>
<span class="k">if</span> <span class="n">USE_GLOBAL_DEPS</span><span class="p">:</span>
<span class="n">_load_global_deps</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="o">*</span> <span class="c1"># noqa: F403</span>
<span class="c1"># Appease the type checker; ordinarily this binding is inserted by the</span>
<span class="c1"># torch._C module initialization code in C</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">torch._C</span> <span class="k">as</span> <span class="nn">_C</span>
<span class="c1"># Check to see if we can load C extensions, and if not provide some guidance</span>
<span class="c1"># on what the problem might be.</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># _initExtension is chosen (arbitrarily) as a sentinel.</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_initExtension</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">torch._C</span> <span class="k">as</span> <span class="nn">_C_for_compiled_check</span>
<span class="c1"># The __file__ check only works for Python 3.7 and above.</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">>=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span> <span class="ow">and</span> <span class="n">_C_for_compiled_check</span><span class="o">.</span><span class="vm">__file__</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="n">textwrap</span><span class="o">.</span><span class="n">dedent</span><span class="p">(</span><span class="s1">'''</span>
<span class="s1"> Failed to load PyTorch C extensions:</span>
<span class="s1"> It appears that PyTorch has loaded the `torch/_C` folder</span>
<span class="s1"> of the PyTorch repository rather than the C extensions which</span>
<span class="s1"> are expected in the `torch._C` namespace. This can occur when</span>
<span class="s1"> using the `install` workflow. e.g.</span>
<span class="s1"> $ python setup.py install && python -c "import torch"</span>
<span class="s1"> This error can generally be solved using the `develop` workflow</span>
<span class="s1"> $ python setup.py develop && python -c "import torch" # This should succeed</span>
<span class="s1"> or by running Python from a different directory.</span>
<span class="s1"> '''</span><span class="p">)</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span> <span class="kn">from</span> <span class="bp">None</span>
<span class="k">raise</span> <span class="c1"># If __file__ is not None the cause is unknown, so just re-raise.</span>
<span class="n">__all__</span> <span class="o">+=</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="p">)</span>
<span class="k">if</span> <span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'_'</span> <span class="ow">and</span>
<span class="ow">not</span> <span class="n">name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'Base'</span><span class="p">)]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="c1"># issue 38137 and python issue 43367. Submodules of a C extension are</span>
<span class="c1"># non-standard, and attributes of those submodules cannot be pickled since</span>
<span class="c1"># pickle expect to be able to import them as "from _C.sub import attr"</span>
<span class="c1"># which fails with "_C is not a package</span>
<span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="p">):</span>
<span class="n">candidate</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">_C</span><span class="p">,</span> <span class="n">attr</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">candidate</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">type</span><span class="p">(</span><span class="n">_C</span><span class="p">):</span>
<span class="c1"># submodule</span>
<span class="k">if</span> <span class="sa">f</span><span class="s1">'torch._C.</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s1">'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</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="sa">f</span><span class="s1">'torch._C.</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s1">'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span>
<span class="c1">################################################################################</span>
<span class="c1"># Define basic utilities</span>
<span class="c1">################################################################################</span>
<span class="k">def</span> <span class="nf">typename</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">type</span><span class="p">()</span>
<span class="n">module</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="s1">''</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__module__'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">'builtins'</span> \
<span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">'__builtin__'</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">+</span> <span class="s1">'.'</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__qualname__'</span><span class="p">):</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__qualname__</span>
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__name__'</span><span class="p">):</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">return</span> <span class="n">module</span> <span class="o">+</span> <span class="n">class_name</span>
<span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Returns True if `obj` is a PyTorch tensor.</span>
<span class="sd"> Note that this function is simply doing ``isinstance(obj, Tensor)``.</span>
<span class="sd"> Using that ``isinstance`` check is better for typechecking with mypy,</span>
<span class="sd"> and more explicit - so it's recommended to use that instead of</span>
<span class="sd"> ``is_tensor``.</span>
<span class="sd"> Args:</span>
<span class="sd"> obj (Object): Object to test</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> x=torch.tensor([1,2,3])</span>
<span class="sd"> >>> torch.is_tensor(x)</span>
<span class="sd"> True</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Returns True if `obj` is a PyTorch storage object.</span>
<span class="sd"> Args:</span>
<span class="sd"> obj (Object): Object to test</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_storage_classes</span>
<span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Sets the default ``torch.Tensor`` type to floating point tensor type</span>
<span class="sd"> ``t``. This type will also be used as default floating point type for</span>
<span class="sd"> type inference in :func:`torch.tensor`.</span>
<span class="sd"> The default floating point tensor type is initially ``torch.FloatTensor``.</span>
<span class="sd"> Args:</span>
<span class="sd"> t (type or string): the floating point tensor type or its name</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32</span>
<span class="sd"> torch.float32</span>
<span class="sd"> >>> torch.set_default_tensor_type(torch.DoubleTensor)</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor</span>
<span class="sd"> torch.float64</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">_string_classes</span><span class="p">):</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">_import_dotted_name</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Sets the default floating point dtype to :attr:`d`. Supports torch.float32</span>
<span class="sd"> and torch.float64 as inputs. Other dtypes may be accepted without complaint</span>
<span class="sd"> but are not supported and are unlikely to work as expected.</span>
<span class="sd"> When PyTorch is initialized its default floating point dtype is torch.float32,</span>
<span class="sd"> and the intent of set_default_dtype(torch.float64) is to facilitate NumPy-like</span>
<span class="sd"> type inference. The default floating point dtype is used to:</span>
<span class="sd"> 1. Implicitly determine the default complex dtype. When the default floating point</span>
<span class="sd"> type is float32 the default complex dtype is complex64, and when the default</span>
<span class="sd"> floating point type is float64 the default complex type is complex128.</span>
<span class="sd"> 2. Infer the dtype for tensors constructed using Python floats or complex Python</span>
<span class="sd"> numbers. See examples below.</span>
<span class="sd"> 3. Determine the result of type promotion between bool and integer tensors and</span>
<span class="sd"> Python floats and complex Python numbers.</span>
<span class="sd"> Args:</span>
<span class="sd"> d (:class:`torch.dtype`): the floating point dtype to make the default.</span>
<span class="sd"> Either torch.float32 or torch.float64.</span>
<span class="sd"> Example:</span>
<span class="sd"> >>> # initial default for floating point is torch.float32</span>
<span class="sd"> >>> # Python floats are interpreted as float32</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype</span>
<span class="sd"> torch.float32</span>
<span class="sd"> >>> # initial default for floating point is torch.complex64</span>
<span class="sd"> >>> # Complex Python numbers are interpreted as complex64</span>
<span class="sd"> >>> torch.tensor([1.2, 3j]).dtype</span>
<span class="sd"> torch.complex64</span>
<span class="sd"> >>> torch.set_default_dtype(torch.float64)</span>
<span class="sd"> >>> # Python floats are now interpreted as float64</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor</span>
<span class="sd"> torch.float64</span>
<span class="sd"> >>> # Complex Python numbers are now interpreted as complex128</span>
<span class="sd"> >>> torch.tensor([1.2, 3j]).dtype # a new complex tensor</span>
<span class="sd"> torch.complex128</span>
<span class="sd"> """</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">use_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">warn_only</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">""" Sets whether PyTorch operations must use "deterministic"</span>
<span class="sd"> algorithms. That is, algorithms which, given the same input, and when</span>
<span class="sd"> run on the same software and hardware, always produce the same output.</span>
<span class="sd"> When enabled, operations will use deterministic algorithms when available,</span>
<span class="sd"> and if only nondeterministic algorithms are available they will throw a</span>
<span class="sd"> :class:`RuntimeError` when called.</span>
<span class="sd"> .. note:: :func:`torch.set_deterministic_debug_mode` offers an alternative</span>
<span class="sd"> interface for this feature.</span>
<span class="sd"> The following normally-nondeterministic operations will act</span>
<span class="sd"> deterministically when ``mode=True``:</span>
<span class="sd"> * :class:`torch.nn.Conv1d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.Conv2d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.Conv3d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ConvTranspose1d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ConvTranspose2d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ConvTranspose3d` when called on CUDA tensor</span>
<span class="sd"> * :func:`torch.bmm` when called on sparse-dense CUDA tensors</span>
<span class="sd"> * :func:`torch.Tensor.__getitem__` when attempting to differentiate a CPU tensor</span>
<span class="sd"> and the index is a list of tensors</span>
<span class="sd"> * :func:`torch.Tensor.index_put` with ``accumulate=False``</span>
<span class="sd"> * :func:`torch.Tensor.index_put` with ``accumulate=True`` when called on a CPU</span>
<span class="sd"> tensor</span>
<span class="sd"> * :func:`torch.Tensor.put_` with ``accumulate=True`` when called on a CPU</span>
<span class="sd"> tensor</span>
<span class="sd"> * :func:`torch.Tensor.scatter_add_` when ``input`` dimension is one and called</span>
<span class="sd"> on a CUDA tensor</span>
<span class="sd"> * :func:`torch.gather` when ``input`` dimension is one and called</span>
<span class="sd"> on a CUDA tensor that requires grad</span>
<span class="sd"> * :func:`torch.index_add` when called on CUDA tensor</span>
<span class="sd"> * :func:`torch.index_select` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :func:`torch.repeat_interleave` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :func:`torch.Tensor.index_copy` when called on a CPU or CUDA tensor</span>
<span class="sd"> The following normally-nondeterministic operations will throw a</span>
<span class="sd"> :class:`RuntimeError` when ``mode=True``:</span>
<span class="sd"> * :class:`torch.nn.AvgPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.AdaptiveAvgPool2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.AdaptiveAvgPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.MaxPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.AdaptiveMaxPool2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.FractionalMaxPool2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.FractionalMaxPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :func:`torch.nn.functional.interpolate` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> and one of the following modes is used:</span>
<span class="sd"> - ``linear``</span>
<span class="sd"> - ``bilinear``</span>
<span class="sd"> - ``bicubic``</span>
<span class="sd"> - ``trilinear``</span>
<span class="sd"> * :class:`torch.nn.ReflectionPad1d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReflectionPad2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReflectionPad3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReplicationPad1d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReplicationPad2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReplicationPad3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.NLLLoss` when called on a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.CTCLoss` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.EmbeddingBag` when attempting to differentiate a CUDA tensor when</span>
<span class="sd"> ``mode='max'``</span>
<span class="sd"> * :func:`torch.Tensor.scatter_add_` when ``input`` dimension is larger than one</span>
<span class="sd"> and called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.gather` when ``input`` dimension is larger than one</span>
<span class="sd"> and called on a CUDA tensor that requires grad</span>
<span class="sd"> * :func:`torch.Tensor.put_` when ``accumulate=False``</span>
<span class="sd"> * :func:`torch.Tensor.put_` when ``accumulate=True`` and called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.histc` when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.bincount` when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.kthvalue` with called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.median` with indices output when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.nn.functional.grid_sample` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> A handful of CUDA operations are nondeterministic if the CUDA version is</span>
<span class="sd"> 10.2 or greater, unless the environment variable ``CUBLAS_WORKSPACE_CONFIG=:4096:8``</span>
<span class="sd"> or ``CUBLAS_WORKSPACE_CONFIG=:16:8`` is set. See the CUDA documentation for more</span>
<span class="sd"> details: `<https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility>`_</span>
<span class="sd"> If one of these environment variable configurations is not set, a :class:`RuntimeError`</span>
<span class="sd"> will be raised from these operations when called with CUDA tensors:</span>
<span class="sd"> * :func:`torch.mm`</span>
<span class="sd"> * :func:`torch.mv`</span>
<span class="sd"> * :func:`torch.bmm`</span>
<span class="sd"> Note that deterministic operations tend to have worse performance than</span>
<span class="sd"> nondeterministic operations.</span>
<span class="sd"> .. note::</span>
<span class="sd"> This flag does not detect or prevent nondeterministic behavior caused</span>
<span class="sd"> by calling an inplace operation on a tensor with an internal memory</span>
<span class="sd"> overlap or by giving such a tensor as the :attr:`out` argument for an</span>
<span class="sd"> operation. In these cases, multiple writes of different data may target</span>
<span class="sd"> a single memory location, and the order of writes is not guaranteed.</span>
<span class="sd"> Args:</span>
<span class="sd"> mode (:class:`bool`): If True, makes potentially nondeterministic</span>
<span class="sd"> operations switch to a deterministic algorithm or throw a runtime</span>
<span class="sd"> error. If False, allows nondeterministic operations.</span>
<span class="sd"> Keyword args:</span>
<span class="sd"> warn_only (:class:`bool`, optional): If True, operations that do not</span>
<span class="sd"> have a deterministic implementation will throw a warning instead of</span>
<span class="sd"> an error. Default: ``False``</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> torch.use_deterministic_algorithms(True)</span>
<span class="sd"> # Forward mode nondeterministic error</span>
<span class="sd"> >>> torch.randn(10).index_copy(0, torch.tensor([0]), torch.randn(1))</span>
<span class="sd"> ...</span>
<span class="sd"> RuntimeError: index_copy does not have a deterministic implementation...</span>
<span class="sd"> # Backward mode nondeterministic error</span>
<span class="sd"> >>> torch.randn(10, requires_grad=True, device='cuda').index_select(0, torch.tensor([0], device='cuda')).backward()</span>
<span class="sd"> ...</span>
<span class="sd"> RuntimeError: index_add_cuda_ does not have a deterministic implementation...</span>
<span class="sd"> """</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</span><span class="p">,</span> <span class="n">warn_only</span><span class="o">=</span><span class="n">warn_only</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">are_deterministic_algorithms_enabled</span><span class="p">():</span>
<span class="sa">r</span><span class="sd">"""Returns True if the global deterministic flag is turned on. Refer to</span>
<span class="sd"> :func:`torch.use_deterministic_algorithms` documentation for more details.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">is_deterministic_algorithms_warn_only_enabled</span><span class="p">():</span>
<span class="sa">r</span><span class="sd">"""Returns True if the global deterministic flag is set to warn only.</span>
<span class="sd"> Refer to :func:`torch.use_deterministic_algorithms` documentation for more</span>
<span class="sd"> details.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms_warn_only</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">set_deterministic_debug_mode</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">builtins</span><span class="o">.</span><span class="n">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Sets the debug mode for deterministic operations.</span>
<span class="sd"> .. note:: This is an alternative interface for</span>
<span class="sd"> :func:`torch.use_deterministic_algorithms`. Refer to that function's</span>
<span class="sd"> documentation for details about affected operations.</span>
<span class="sd"> Args:</span>
<span class="sd"> debug_mode(str or int): If "default" or 0, don't error or warn on</span>
<span class="sd"> nondeterministic operations. If "warn" or 1, warn on</span>
<span class="sd"> nondeterministic operations. If "error" or 2, error on</span>
<span class="sd"> nondeterministic operations.</span>
<span class="sd"> """</span>
<span class="c1"># NOTE: builtins.int is used here because int in this scope resolves</span>
<span class="c1"># to torch.int</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">,</span> <span class="p">(</span><span class="n">builtins</span><span class="o">.</span><span class="n">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">)):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'debug_mode must be str or int, but got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">if</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span>
<span class="n">debug_mode</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="s1">'warn'</span><span class="p">:</span>
<span class="n">debug_mode</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="s1">'error'</span><span class="p">:</span>
<span class="n">debug_mode</span> <span class="o">=</span> <span class="mi">2</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="s1">'invalid value of debug_mode, expected one of `default`, '</span>
<span class="sa">f</span><span class="s1">'`warn`, `error`, but got </span><span class="si">{</span><span class="n">debug_mode</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">warn_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="kc">True</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="s1">'invalid value of debug_mode, expected 0, 1, or 2, '</span>
<span class="sa">f</span><span class="s1">'but got </span><span class="si">{</span><span class="n">debug_mode</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_deterministic_debug_mode</span><span class="p">()</span> <span class="o">-></span> <span class="n">builtins</span><span class="o">.</span><span class="n">int</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Returns the current value of the debug mode for deterministic</span>
<span class="sd"> operations. Refer to :func:`torch.set_deterministic_debug_mode`</span>
<span class="sd"> documentation for more details.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms</span><span class="p">():</span>
<span class="k">if</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms_warn_only</span><span class="p">():</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">2</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">set_warn_always</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""When this flag is False (default) then some PyTorch warnings may only</span>
<span class="sd"> appear once per process. This helps avoid excessive warning information.</span>
<span class="sd"> Setting it to True causes these warnings to always appear, which may be</span>
<span class="sd"> helpful when debugging.</span>
<span class="sd"> Args:</span>
<span class="sd"> b (:class:`bool`): If True, force warnings to always be emitted</span>
<span class="sd"> If False, set to the default behaviour</span>
<span class="sd"> """</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_warnAlways</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">is_warn_always_enabled</span><span class="p">():</span>
<span class="sa">r</span><span class="sd">"""Returns True if the global warn_always flag is turned on. Refer to</span>
<span class="sd"> :func:`torch.set_warn_always` documentation for more details.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_warnAlways</span><span class="p">()</span>
<span class="c1">################################################################################</span>
<span class="c1"># Define numeric constants</span>
<span class="c1">################################################################################</span>
<span class="c1"># For Python Array API (https://data-apis.org/array-api/latest/API_specification/constants.html) and</span>
<span class="c1"># NumPy consistency (https://numpy.org/devdocs/reference/constants.html)</span>
<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">e</span> <span class="p">,</span> <span class="n">nan</span> <span class="p">,</span> <span class="n">inf</span> <span class="p">,</span> <span class="n">pi</span>
<span class="n">__all__</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="s1">'e'</span><span class="p">,</span> <span class="s1">'pi'</span><span class="p">,</span> <span class="s1">'nan'</span><span class="p">,</span> <span class="s1">'inf'</span><span class="p">])</span>
<span class="c1">################################################################################</span>
<span class="c1"># Define Storage and Tensor classes</span>
<span class="c1">################################################################################</span>
<span class="kn">from</span> <span class="nn">._tensor</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="kn">from</span> <span class="nn">.storage</span> <span class="kn">import</span> <span class="n">_StorageBase</span><span class="p">,</span> <span class="n">_TypedStorage</span>
<span class="c1"># NOTE: New <type>Storage classes should never be added. When adding a new</span>
<span class="c1"># dtype, use torch.storage._TypedStorage directly.</span>