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<h1>Source code for torch.library</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">._ops</span> <span class="kn">import</span> <span class="n">OpOverload</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Set</span><span class="p">,</span> <span class="n">List</span>
<span class="kn">import</span> <span class="nn">traceback</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">weakref</span>
<span class="kn">import</span> <span class="nn">functools</span>
<span class="kn">import</span> <span class="nn">inspect</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'Library'</span><span class="p">,</span>
<span class="s1">'impl'</span><span class="p">,</span>
<span class="s1">'define'</span><span class="p">,</span>
<span class="s1">'fallthrough_kernel'</span><span class="p">,</span>
<span class="s1">'impl_abstract'</span><span class="p">,</span>
<span class="s1">'get_ctx'</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1"># Set containing the combination of (namespace, operator, DispatchKey) for which a new kernel has been registered</span>
<span class="c1"># The keys in the set are of the form `namespace + "/" + op_name + "/" + dispatch_key`.</span>
<span class="c1"># This set is maintained to ensure that two libraries don't try to override the exact same functionality to avoid</span>
<span class="c1"># libraries calling into kernels not intended to be called.</span>
<span class="n">_impls</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="n">_defs</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="c1"># prim is reserved by TorchScript interpreter</span>
<span class="n">_reserved_namespaces</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'prim'</span><span class="p">]</span>
<div class="viewcode-block" id="fallthrough_kernel"><a class="viewcode-back" href="../../library.html#torch.library.fallthrough_kernel">[docs]</a><span class="k">def</span> <span class="nf">fallthrough_kernel</span><span class="p">():</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> A dummy function to pass to ``Library.impl`` in order to register a fallthrough.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"fallthrough_kernel() should never be called."</span><span class="p">)</span></div>
<div class="viewcode-block" id="Library"><a class="viewcode-back" href="../../library.html#torch.library.Library">[docs]</a><span class="k">class</span> <span class="nc">Library</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> A class to create libraries that can be used to register new operators or</span>
<span class="sd"> override operators in existing libraries from Python.</span>
<span class="sd"> A user can optionally pass in a dispatch keyname if they only want to register</span>
<span class="sd"> kernels corresponding to only one specific dispatch key.</span>
<span class="sd"> To create a library to override operators in an existing library (with name ns), set the kind to "IMPL".</span>
<span class="sd"> To create a new library (with name ns) to register new operators, set the kind to "DEF".</span>
<span class="sd"> To create a fragment of a possibly existing library to register operators (and bypass</span>
<span class="sd"> the limitation that there is only one library for a given namespace), set the kind to</span>
<span class="sd"> "FRAGMENT".</span>
<span class="sd"> Args:</span>
<span class="sd"> ns: library name</span>
<span class="sd"> kind: "DEF", "IMPL" (default: "IMPL"), "FRAGMENT"</span>
<span class="sd"> dispatch_key: PyTorch dispatch key (default: "")</span>
<span class="sd"> """</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">ns</span><span class="p">,</span> <span class="n">kind</span><span class="p">,</span> <span class="n">dispatch_key</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span>
<span class="k">if</span> <span class="n">kind</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'IMPL'</span><span class="p">,</span> <span class="s1">'DEF'</span><span class="p">,</span> <span class="s1">'FRAGMENT'</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unsupported kind: "</span><span class="p">,</span> <span class="n">kind</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ns</span> <span class="ow">in</span> <span class="n">_reserved_namespaces</span> <span class="ow">and</span> <span class="p">(</span><span class="n">kind</span> <span class="o">==</span> <span class="s2">"DEF"</span> <span class="ow">or</span> <span class="n">kind</span> <span class="o">==</span> <span class="s1">'FRAGMENT'</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">ns</span><span class="p">,</span> <span class="s2">" is a reserved namespace. Please try creating a library with another name."</span><span class="p">)</span>
<span class="n">frame</span> <span class="o">=</span> <span class="n">traceback</span><span class="o">.</span><span class="n">extract_stack</span><span class="p">(</span><span class="n">limit</span><span class="o">=</span><span class="mi">3</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">filename</span><span class="p">,</span> <span class="n">lineno</span> <span class="o">=</span> <span class="n">frame</span><span class="o">.</span><span class="n">filename</span><span class="p">,</span> <span class="n">frame</span><span class="o">.</span><span class="n">lineno</span>
<span class="bp">self</span><span class="o">.</span><span class="n">m</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dispatch_library</span><span class="p">(</span><span class="n">kind</span><span class="p">,</span> <span class="n">ns</span><span class="p">,</span> <span class="n">dispatch_key</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">lineno</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ns</span> <span class="o">=</span> <span class="n">ns</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_op_defs</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_op_impls</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_registration_handles</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="s2">"torch._library.utils.RegistrationHandle"</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kind</span> <span class="o">=</span> <span class="n">kind</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dispatch_key</span> <span class="o">=</span> <span class="n">dispatch_key</span>
<span class="c1"># Use a finalizer to setup the "destructor" instead of __del__.</span>
<span class="c1"># Python __del__ can lead to weird things (globals and locals may already</span>
<span class="c1"># be gone when __del__ actually gets called!). finalizers help the</span>
<span class="c1"># situation because it lets us capture references and keeps them alive</span>
<span class="n">weakref</span><span class="o">.</span><span class="n">finalize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_del_library</span><span class="p">,</span> <span class="n">_impls</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_impls</span><span class="p">,</span> <span class="n">_defs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_defs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_registration_handles</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"Library(kind=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">kind</span><span class="si">}</span><span class="s2">, ns=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">ns</span><span class="si">}</span><span class="s2">, dispatch_key=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">dispatch_key</span><span class="si">}</span><span class="s2">)>"</span>
<div class="viewcode-block" id="Library.define"><a class="viewcode-back" href="../../library.html#torch.library.Library.define">[docs]</a> <span class="k">def</span> <span class="nf">define</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">schema</span><span class="p">,</span> <span class="n">alias_analysis</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">tags</span><span class="o">=</span><span class="p">()):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">'''Defines a new operator and its semantics in the ns namespace.</span>
<span class="sd"> Args:</span>
<span class="sd"> schema: function schema to define a new operator.</span>
<span class="sd"> alias_analysis (optional): Indicates if the aliasing properties of the operator arguments can be</span>
<span class="sd"> inferred from the schema (default behavior) or not ("CONSERVATIVE").</span>
<span class="sd"> tags (Tag | Sequence[Tag]): one or more torch.Tag to apply to this</span>
<span class="sd"> operator. Tagging an operator changes the operator's behavior</span>
<span class="sd"> under various PyTorch subsystems; please read the docs for the</span>
<span class="sd"> torch.Tag carefully before applying it.</span>
<span class="sd"> Returns:</span>
<span class="sd"> name of the operator as inferred from the schema.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LIBRARY)</span>
<span class="sd"> >>> my_lib = Library("foo", "DEF")</span>
<span class="sd"> >>> my_lib.define("sum(Tensor self) -> Tensor")</span>
<span class="sd"> '''</span>
<span class="c1"># This is added because we also want to disallow PURE_FUNCTION alias analysis which is a valid</span>
<span class="c1"># AliasAnalysis type in C++</span>
<span class="k">if</span> <span class="n">alias_analysis</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">""</span><span class="p">,</span> <span class="s2">"FROM_SCHEMA"</span><span class="p">,</span> <span class="s2">"CONSERVATIVE"</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="s2">"Invalid alias_analysis type </span><span class="si">{</span><span class="n">alias_analysis</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tags</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tag</span><span class="p">):</span>
<span class="n">tags</span> <span class="o">=</span> <span class="p">(</span><span class="n">tags</span><span class="p">,)</span>
<span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="n">schema</span><span class="p">,</span> <span class="n">alias_analysis</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tags</span><span class="p">))</span>
<span class="n">qualname</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ns</span> <span class="o">+</span> <span class="s2">"::"</span> <span class="o">+</span> <span class="n">schema</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"("</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_op_defs</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">qualname</span><span class="p">)</span>
<span class="n">_defs</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">qualname</span><span class="p">)</span>
<span class="k">return</span> <span class="n">result</span></div>
<div class="viewcode-block" id="Library.impl"><a class="viewcode-back" href="../../library.html#torch.library.Library.impl">[docs]</a> <span class="k">def</span> <span class="nf">impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">op_name</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">dispatch_key</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">'''Registers the function implementation for an operator defined in the library.</span>
<span class="sd"> Args:</span>
<span class="sd"> op_name: operator name (along with the overload) or OpOverload object.</span>
<span class="sd"> fn: function that's the operator implementation for the input dispatch key or :func:`~fallthrough_kernel`</span>
<span class="sd"> to register a fallthrough.</span>
<span class="sd"> dispatch_key: dispatch key that the input function should be registered for. By default, it uses</span>
<span class="sd"> the dispatch key that the library was created with.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> my_lib = Library("aten", "IMPL")</span>
<span class="sd"> >>> def div_cpu(self, other):</span>
<span class="sd"> >>> return self * (1 / other)</span>
<span class="sd"> >>> my_lib.impl("div.Tensor", div_cpu, "CPU")</span>
<span class="sd"> '''</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">callable</span><span class="p">(</span><span class="n">fn</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="s2">"Input function is required to be a callable but found type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dispatch_key</span> <span class="o">==</span> <span class="s1">''</span><span class="p">:</span>
<span class="n">dispatch_key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dispatch_key</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">op_name</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">op_name</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">op_name</span><span class="p">,</span> <span class="n">OpOverload</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">op_name</span><span class="o">.</span><span class="n">_schema</span><span class="o">.</span><span class="n">name</span>
<span class="n">overload_name</span> <span class="o">=</span> <span class="n">op_name</span><span class="o">.</span><span class="n">_schema</span><span class="o">.</span><span class="n">overload_name</span>
<span class="k">if</span> <span class="n">overload_name</span> <span class="o">!=</span> <span class="s1">''</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">name</span> <span class="o">+</span> <span class="s1">'.'</span> <span class="o">+</span> <span class="n">overload_name</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="s2">"impl should be passed either a name or an OpOverload object as the first argument"</span><span class="p">)</span>
<span class="n">key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ns</span> <span class="o">+</span> <span class="s2">"/"</span> <span class="o">+</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"::"</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">"/"</span> <span class="o">+</span> <span class="n">dispatch_key</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">_impls</span><span class="p">:</span>
<span class="c1"># TODO: in future, add more info about where the existing function is registered (this info is</span>
<span class="c1"># today already returned by the C++ warning when impl is called but we error out before that)</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"This is not allowed since there's already a kernel registered from python overriding </span><span class="si">{}</span><span class="s2">"</span>
<span class="s2">"'s behavior for </span><span class="si">{}</span><span class="s2"> dispatch key and </span><span class="si">{}</span><span class="s2"> namespace."</span><span class="o">.</span>
<span class="nb">format</span><span class="p">(</span><span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"::"</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dispatch_key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ns</span><span class="p">))</span>
<span class="k">if</span> <span class="n">dispatch_key</span> <span class="o">==</span> <span class="s2">"Meta"</span><span class="p">:</span>
<span class="n">dispatcher_op_name</span> <span class="o">=</span> <span class="n">name</span>
<span class="k">if</span> <span class="s1">'::'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dispatcher_op_name</span><span class="p">:</span>
<span class="n">dispatcher_op_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">ns</span><span class="si">}</span><span class="s1">::</span><span class="si">{</span><span class="n">dispatcher_op_name</span><span class="si">}</span><span class="s1">'</span>
<span class="c1"># Internally, we shouldn't be registering meta kernels for any operators that</span>
<span class="c1"># have CompositeImplicitAutograd kernels.</span>
<span class="c1"># Instead, we should be letting those decompositions run, and writing meta kernels</span>
<span class="c1"># only for the base operators.</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dispatch_has_kernel_for_dispatch_key</span><span class="p">(</span><span class="n">dispatcher_op_name</span><span class="p">,</span> <span class="s2">"CompositeImplicitAutograd"</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="s2">"We should not register a meta kernel directly to the operator '</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">',"</span>
<span class="s2">" because it has a CompositeImplicitAutograd kernel in core."</span>
<span class="s2">" Instead we should let the operator decompose, and ensure that we have meta kernels"</span>
<span class="s2">" for the base ops that it decomposes into."</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">m</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">dispatch_key</span> <span class="k">if</span> <span class="n">dispatch_key</span> <span class="o">!=</span> <span class="s2">""</span> <span class="k">else</span> <span class="s2">"CompositeImplicitAutograd"</span><span class="p">,</span> <span class="n">fn</span><span class="p">)</span>
<span class="n">_impls</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_op_impls</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">key</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_destroy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">for</span> <span class="n">handle</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_registration_handles</span><span class="p">:</span>
<span class="n">handle</span><span class="o">.</span><span class="n">destroy</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_registration_handles</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_del_library</span><span class="p">(</span><span class="n">captured_impls</span><span class="p">,</span> <span class="n">op_impls</span><span class="p">,</span> <span class="n">captured_defs</span><span class="p">,</span> <span class="n">op_defs</span><span class="p">,</span> <span class="n">registration_handles</span><span class="p">):</span>
<span class="n">captured_impls</span> <span class="o">-=</span> <span class="n">op_impls</span>
<span class="n">captured_defs</span> <span class="o">-=</span> <span class="n">op_defs</span>
<span class="k">for</span> <span class="n">handle</span> <span class="ow">in</span> <span class="n">registration_handles</span><span class="p">:</span>
<span class="n">handle</span><span class="o">.</span><span class="n">destroy</span><span class="p">()</span>
<span class="n">_keep_alive</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">NAMELESS_SCHEMA</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s2">"\(.*\) -> .*"</span><span class="p">)</span>
<div class="viewcode-block" id="define"><a class="viewcode-back" href="../../library.html#torch.library.define">[docs]</a><span class="nd">@functools</span><span class="o">.</span><span class="n">singledispatch</span>
<span class="k">def</span> <span class="nf">define</span><span class="p">(</span><span class="n">qualname</span><span class="p">,</span> <span class="n">schema</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">lib</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tags</span><span class="o">=</span><span class="p">()):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Defines a new operator.</span>
<span class="sd"> In PyTorch, defining an op (short for "operator") is a two step-process:</span>
<span class="sd"> - we need to define the op (by providing an operator name and schema)</span>
<span class="sd"> - we need to implement behavior for how the operator interacts with</span>
<span class="sd"> various PyTorch subsystems, like CPU/CUDA Tensors, Autograd, etc.</span>
<span class="sd"> This entrypoint defines the custom operator (the first step)</span>
<span class="sd"> you must then perform the second step by calling various</span>
<span class="sd"> ``impl_*`` APIs, like :func:`torch.library.impl` or</span>
<span class="sd"> :func:`torch.library.impl_abstract`.</span>
<span class="sd"> Args:</span>
<span class="sd"> qualname (str): The qualified name for the operator. Should be</span>
<span class="sd"> a string that looks like "namespace::name", e.g. "aten::sin".</span>
<span class="sd"> Operators in PyTorch need a namespace to</span>
<span class="sd"> avoid name collisions; a given operator may only be created once.</span>
<span class="sd"> If you are writing a Python library, we recommend the namespace to</span>
<span class="sd"> be the name of your top-level module.</span>
<span class="sd"> schema (str): The schema of the operator. E.g. "(Tensor x) -> Tensor"</span>
<span class="sd"> for an op that accepts one Tensor and returns one Tensor. It does</span>
<span class="sd"> not contain the operator name (that is passed in ``qualname``).</span>
<span class="sd"> lib (Optional[Library]): If provided, the lifetime of this operator</span>
<span class="sd"> will be tied to the lifetime of the Library object.</span>
<span class="sd"> tags (Tag | Sequence[Tag]): one or more torch.Tag to apply to this</span>
<span class="sd"> operator. Tagging an operator changes the operator's behavior</span>
<span class="sd"> under various PyTorch subsystems; please read the docs for the</span>
<span class="sd"> torch.Tag carefully before applying it.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LIBRARY)</span>
<span class="sd"> >>> import torch</span>
<span class="sd"> >>> import numpy as np</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Define the operator</span>
<span class="sd"> >>> torch.library.define("mylib::sin", "(Tensor x) -> Tensor")</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Add implementations for the operator</span>
<span class="sd"> >>> @torch.library.impl("mylibrary::sin", "cpu")</span>
<span class="sd"> >>> def f(x):</span>
<span class="sd"> >>> return torch.from_numpy(np.sin(x.numpy()))</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Call the new operator from torch.ops.</span>
<span class="sd"> >>> x = torch.randn(3)</span>
<span class="sd"> >>> y = torch.ops.mylib.sin(x)</span>
<span class="sd"> >>> assert torch.allclose(y, x)</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">qualname</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"define(qualname, schema): expected qualname "</span>
<span class="sa">f</span><span class="s2">"to be instance of str, got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">qualname</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">namespace</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_library</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">parse_namespace</span><span class="p">(</span><span class="n">qualname</span><span class="p">)</span>
<span class="k">if</span> <span class="n">lib</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">lib</span> <span class="o">=</span> <span class="n">Library</span><span class="p">(</span><span class="n">namespace</span><span class="p">,</span> <span class="s2">"FRAGMENT"</span><span class="p">)</span>
<span class="n">_keep_alive</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lib</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">NAMELESS_SCHEMA</span><span class="o">.</span><span class="n">fullmatch</span><span class="p">(</span><span class="n">schema</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"define(qualname, schema, ...): expected schema "</span>
<span class="sa">f</span><span class="s2">"to look like e.g. </span><span class="se">\"</span><span class="s2">(Tensor x) -> Tensor</span><span class="se">\"</span><span class="s2"> but "</span>
<span class="sa">f</span><span class="s2">"got </span><span class="se">\"</span><span class="si">{</span><span class="n">schema</span><span class="si">}</span><span class="se">\"</span><span class="s2">"</span><span class="p">)</span>
<span class="n">lib</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="n">name</span> <span class="o">+</span> <span class="n">schema</span><span class="p">,</span> <span class="n">alias_analysis</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> <span class="n">tags</span><span class="o">=</span><span class="n">tags</span><span class="p">)</span></div>
<span class="nd">@define</span><span class="o">.</span><span class="n">register</span>
<span class="k">def</span> <span class="nf">_</span><span class="p">(</span><span class="n">lib</span><span class="p">:</span> <span class="n">Library</span><span class="p">,</span> <span class="n">schema</span><span class="p">,</span> <span class="n">alias_analysis</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""The old torch.library.define.</span>
<span class="sd"> We're keeping this around for BC reasons</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">wrap</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">lib</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="n">schema</span><span class="p">,</span> <span class="n">alias_analysis</span><span class="p">)</span>
<span class="n">lib</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span>
<span class="k">return</span> <span class="n">wrap</span>
<div class="viewcode-block" id="impl"><a class="viewcode-back" href="../../library.html#torch.library.impl">[docs]</a><span class="nd">@functools</span><span class="o">.</span><span class="n">singledispatch</span>
<span class="k">def</span> <span class="nf">impl</span><span class="p">(</span><span class="n">qualname</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">lib</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Register an implementation for a device type for this operator.</span>
<span class="sd"> You may pass "default" for ``types`` to register this implementation as the</span>
<span class="sd"> default implementation for ALL device types.</span>
<span class="sd"> Please only use this if the implementation truly supports all device types;</span>
<span class="sd"> for example, this is true if it is a composition of built-in PyTorch operators.</span>
<span class="sd"> Some valid types are: "cpu", "cuda", "xla", "mps", "ipu", "xpu".</span>
<span class="sd"> Args:</span>
<span class="sd"> qualname (str): Should be a string that looks like "namespace::operator_name".</span>
<span class="sd"> types (str | Sequence[str]): The device types to register an impl to.</span>
<span class="sd"> lib (Optional[Library]): If provided, the lifetime of this registration</span>
<span class="sd"> will be tied to the lifetime of the Library object.</span>
<span class="sd"> Examples:</span>
<span class="sd"> >>> import torch</span>
<span class="sd"> >>> import numpy as np</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Define the operator</span>
<span class="sd"> >>> torch.library.define("mylibrary::sin", "(Tensor x) -> Tensor")</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Add implementations for the cpu device</span>
<span class="sd"> >>> @torch.library.impl("mylibrary::sin", "cpu")</span>
<span class="sd"> >>> def f(x):</span>
<span class="sd"> >>> return torch.from_numpy(np.sin(x.numpy()))</span>
<span class="sd"> >>></span>
<span class="sd"> >>> x = torch.randn(3)</span>
<span class="sd"> >>> y = torch.ops.mylibrary.sin(x)</span>
<span class="sd"> >>> assert torch.allclose(y, x.sin())</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">types</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">types</span> <span class="o">=</span> <span class="p">(</span><span class="n">types</span><span class="p">,)</span>
<span class="n">keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">({})</span>
<span class="k">for</span> <span class="n">typ</span> <span class="ow">in</span> <span class="n">types</span><span class="p">:</span>
<span class="n">is_dispatch_key</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_parse_dispatch_key</span><span class="p">(</span><span class="n">typ</span><span class="p">)</span>
<span class="k">if</span> <span class="n">is_dispatch_key</span><span class="p">:</span>
<span class="c1"># We also support passing a DispatchKey to impl. Please prefer using</span>
<span class="c1"># the higher-level torch.library APIs and only pass DispatchKey to</span>
<span class="c1"># torch.library.impl with caution (or even better, don't use this</span>
<span class="c1"># option and file an issue on GitHub for what you need).</span>
<span class="c1"># We don't advertise this to users because</span>
<span class="c1"># it is very easy to shoot yourself in the foot.</span>
<span class="n">keys</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">typ</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">keys</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_device_type_to_key</span><span class="p">(</span><span class="n">typ</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">register</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
<span class="n">namespace</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_library</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">parse_namespace</span><span class="p">(</span><span class="n">qualname</span><span class="p">)</span>
<span class="k">if</span> <span class="n">lib</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">use_lib</span> <span class="o">=</span> <span class="n">Library</span><span class="p">(</span><span class="n">namespace</span><span class="p">,</span> <span class="s2">"FRAGMENT"</span><span class="p">)</span>
<span class="n">_keep_alive</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">use_lib</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">use_lib</span> <span class="o">=</span> <span class="n">lib</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
<span class="n">use_lib</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="n">qualname</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span>
<span class="k">if</span> <span class="n">func</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">register</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">register</span><span class="p">(</span><span class="n">func</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_device_type_to_key</span><span class="p">(</span><span class="n">device_type</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
<span class="k">if</span> <span class="n">device_type</span> <span class="o">==</span> <span class="s2">"default"</span><span class="p">:</span>
<span class="c1"># This is technically not correct, because although all device_type</span>
<span class="c1"># DispatchKeys are included in CompositeExplicitAutograd,</span>
<span class="c1"># not everything in CompositeExplicitAutograd is associated with a</span>
<span class="c1"># device_type. I don't really care that much about the difference.</span>
<span class="k">return</span> <span class="s2">"CompositeExplicitAutograd"</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">_dispatch_key_for_device</span><span class="p">(</span><span class="n">device_type</span><span class="p">)</span>
<span class="nd">@impl</span><span class="o">.</span><span class="n">register</span>
<span class="k">def</span> <span class="nf">_</span><span class="p">(</span><span class="n">lib</span><span class="p">:</span> <span class="n">Library</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">dispatch_key</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Legacy torch.library.impl API. Kept around for BC"""</span>
<span class="k">def</span> <span class="nf">wrap</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
<span class="n">lib</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">dispatch_key</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span>
<span class="k">return</span> <span class="n">wrap</span>
<div class="viewcode-block" id="impl_abstract"><a class="viewcode-back" href="../../library.html#torch.library.impl_abstract">[docs]</a><span class="k">def</span> <span class="nf">impl_abstract</span><span class="p">(</span><span class="n">qualname</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">lib</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">_stacklevel</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Register an abstract implementation for this operator.</span>
<span class="sd"> An "abstract implementation" specifies the behavior of this operator on</span>
<span class="sd"> Tensors that carry no data. Given some input Tensors with certain properties</span>
<span class="sd"> (sizes/strides/storage_offset/device), it specifies what the properties of</span>
<span class="sd"> the output Tensors are.</span>
<span class="sd"> The abstract implementation has the same signature as the operator.</span>
<span class="sd"> It is run for both FakeTensors and meta tensors. To write an abstract</span>
<span class="sd"> implementation, assume that all Tensor inputs to the operator are</span>
<span class="sd"> regular CPU/CUDA/Meta tensors, but they do not have storage, and</span>
<span class="sd"> you are trying to return regular CPU/CUDA/Meta tensor(s) as output.</span>
<span class="sd"> The abstract implementation must consist of only PyTorch operations</span>
<span class="sd"> (and may not directly access the storage or data of any input or</span>
<span class="sd"> intermediate Tensors).</span>
<span class="sd"> This API may be used as a decorator (see examples).</span>
<span class="sd"> For a detailed guide on custom ops, please see</span>
<span class="sd"> https://docs.google.com/document/d/1W--T6wz8IY8fOI0Vm8BF44PdBgs283QvpelJZWieQWQ/edit</span>
<span class="sd"> Examples:</span>
<span class="sd"> >>> import torch</span>
<span class="sd"> >>> import numpy as np</span>
<span class="sd"> >>> from torch import Tensor</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Example 1: an operator without data-dependent output shape</span>
<span class="sd"> >>> torch.library.define(</span>
<span class="sd"> >>> "mylib::custom_linear",</span>
<span class="sd"> >>> "(Tensor x, Tensor weight, Tensor bias) -> Tensor")</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @torch.library.impl_abstract("mylib::custom_linear")</span>
<span class="sd"> >>> def custom_linear_abstract(x, weight):</span>
<span class="sd"> >>> assert x.dim() == 2</span>
<span class="sd"> >>> assert weight.dim() == 2</span>
<span class="sd"> >>> assert bias.dim() == 1</span>
<span class="sd"> >>> assert x.shape[1] == weight.shape[1]</span>
<span class="sd"> >>> assert weight.shape[0] == bias.shape[0]</span>
<span class="sd"> >>> assert x.device == weight.device</span>
<span class="sd"> >>></span>
<span class="sd"> >>> return (x @ weight.t()) + bias</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Example 2: an operator with data-dependent output shape</span>
<span class="sd"> >>> torch.library.define("mylib::custom_nonzero", "(Tensor x) -> Tensor")</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @torch.library.impl_abstract("mylib::custom_nonzero")</span>
<span class="sd"> >>> def custom_nonzero_abstract(x):</span>
<span class="sd"> >>> # Number of nonzero-elements is data-dependent.</span>
<span class="sd"> >>> # Since we cannot peek at the data in an abstract impl,</span>
<span class="sd"> >>> # we use the ctx object to construct a new symint that</span>
<span class="sd"> >>> # represents the data-dependent size.</span>
<span class="sd"> >>> ctx = torch.library.get_ctx()</span>
<span class="sd"> >>> nnz = ctx.new_dynamic_size()</span>
<span class="sd"> >>> shape = [nnz, x.dim()]</span>
<span class="sd"> >>> result = x.new_empty(shape, dtype=torch.int64)</span>
<span class="sd"> >>> return result</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @torch.library.impl("mylib::custom_nonzero", "cpu")</span>
<span class="sd"> >>> def custom_nonzero_cpu(x):</span>
<span class="sd"> >>> x_np = x.numpy()</span>
<span class="sd"> >>> res = np.stack(np.nonzero(x_np), axis=1)</span>
<span class="sd"> >>> return torch.tensor(res, device=x.device)</span>
<span class="sd"> """</span>
<span class="n">source</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_library</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">get_source</span><span class="p">(</span><span class="n">_stacklevel</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">frame</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">_getframe</span><span class="p">(</span><span class="n">_stacklevel</span><span class="p">)</span>
<span class="n">caller_module</span> <span class="o">=</span> <span class="n">inspect</span><span class="o">.</span><span class="n">getmodule</span><span class="p">(</span><span class="n">frame</span><span class="p">)</span>
<span class="c1"># Can be none if you call impl_abstract from somewhere there isn't a module</span>
<span class="c1"># (e.g. __main__)</span>
<span class="n">caller_module_name</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">caller_module</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">caller_module</span><span class="o">.</span><span class="vm">__name__</span>
<span class="c1"># TODO(rzou): We're gonna need to stage this change with torchvision,</span>
<span class="c1"># since torchvision is github first.</span>
<span class="k">if</span> <span class="n">caller_module_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">caller_module_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"torchvision."</span><span class="p">):</span>
<span class="n">caller_module_name</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">inner</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
<span class="n">entry</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_library</span><span class="o">.</span><span class="n">simple_registry</span><span class="o">.</span><span class="n">singleton</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">qualname</span><span class="p">)</span>
<span class="k">if</span> <span class="n">caller_module_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">func_to_register</span> <span class="o">=</span> <span class="n">_check_pystubs_once</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">qualname</span><span class="p">,</span> <span class="n">caller_module_name</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">func_to_register</span> <span class="o">=</span> <span class="n">func</span>
<span class="n">handle</span> <span class="o">=</span> <span class="n">entry</span><span class="o">.</span><span class="n">abstract_impl</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">func_to_register</span><span class="p">,</span> <span class="n">source</span><span class="p">)</span>
<span class="k">if</span> <span class="n">lib</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">lib</span><span class="o">.</span><span class="n">_registration_handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
<span class="k">return</span> <span class="n">func</span>
<span class="k">if</span> <span class="n">func</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">inner</span>
<span class="k">return</span> <span class="n">inner</span><span class="p">(</span><span class="n">func</span><span class="p">)</span></div>
<span class="c1"># If the op was defined in C++, then we want to make sure there was an</span>
<span class="c1"># m.impl_abstract_pystub(module, ...) call and that the module is the</span>
<span class="c1"># same as the module that called torch.library.impl_abstract.</span>
<span class="k">def</span> <span class="nf">_check_pystubs_once</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">qualname</span><span class="p">,</span> <span class="n">actual_module_name</span><span class="p">):</span>
<span class="n">checked</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">def</span> <span class="nf">inner</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">nonlocal</span> <span class="n">checked</span>
<span class="k">if</span> <span class="n">checked</span><span class="p">:</span>
<span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">op</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_library</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">lookup_op</span><span class="p">(</span><span class="n">qualname</span><span class="p">)</span>
<span class="k">if</span> <span class="n">op</span><span class="o">.</span><span class="n">_defined_in_python</span><span class="p">:</span>
<span class="n">checked</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">maybe_pystub</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dispatch_pystub</span><span class="p">(</span>
<span class="n">op</span><span class="o">.</span><span class="n">_schema</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
<span class="n">op</span><span class="o">.</span><span class="n">_schema</span><span class="o">.</span><span class="n">overload_name</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">maybe_pystub</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="s2">"Operator '</span><span class="si">{</span><span class="n">qualname</span><span class="si">}</span><span class="s2">' was defined in C++ and has a Python "</span>
<span class="sa">f</span><span class="s2">"abstract impl. In this situation, it is required to have a "</span>
<span class="sa">f</span><span class="s2">"C++ `m.impl_abstract_pystub` call, but we could not find one."</span>
<span class="sa">f</span><span class="s2">"Please add a call to `m.impl_abstract_pystub(</span><span class="se">\"</span><span class="si">{</span><span class="n">actual_module_name</span><span class="si">}</span><span class="se">\"</span><span class="s2">);` "</span>
<span class="sa">f</span><span class="s2">"to the C++ TORCH_LIBRARY block the operator was "</span>
<span class="sa">f</span><span class="s2">"defined in."</span><span class="p">)</span>
<span class="n">pystub_module</span> <span class="o">=</span> <span class="n">maybe_pystub</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">actual_module_name</span> <span class="o">!=</span> <span class="n">pystub_module</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="s2">"Operator '</span><span class="si">{</span><span class="n">qualname</span><span class="si">}</span><span class="s2">' specified that its python abstract impl "</span>
<span class="sa">f</span><span class="s2">"is in the Python module '</span><span class="si">{</span><span class="n">pystub_module</span><span class="si">}</span><span class="s2">' but it was actually found "</span>
<span class="sa">f</span><span class="s2">"in '</span><span class="si">{</span><span class="n">actual_module_name</span><span class="si">}</span><span class="s2">'. Please either move the abstract impl "</span>
<span class="sa">f</span><span class="s2">"or correct the m.impl_abstract_pystub call."</span><span class="p">)</span>
<span class="n">checked</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">inner</span>
<span class="c1"># NOTE [ctx inside the fake implementation]</span>
<span class="c1"># If a user has an operator with data-dependent output shape, then when writing</span>
<span class="c1"># a fake implementation they must query the current ctx and use methods on the</span>
<span class="c1"># ctx to construct a new unbacked symint.</span>
<span class="c1">#</span>
<span class="c1"># This is done via us setting the global_ctx_getter function every time a fake</span>
<span class="c1"># implementation is invoked.</span>
<div class="viewcode-block" id="get_ctx"><a class="viewcode-back" href="../../library.html#torch.library.get_ctx">[docs]</a><span class="k">def</span> <span class="nf">get_ctx</span><span class="p">()</span> <span class="o">-></span> <span class="s2">"torch._library.abstract_impl.AbstractImplCtx"</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""get_ctx() returns the current AbstractImplCtx object.</span>
<span class="sd"> Calling ``get_ctx()`` is only valid inside of an abstract impl</span>
<span class="sd"> (see :func:`torch.library.impl_abstract` for more usage details.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_library</span><span class="o">.</span><span class="n">abstract_impl</span><span class="o">.</span><span class="n">global_ctx_getter</span><span class="p">()</span></div>
</pre></div>
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