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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">Set</span>
<span class="kn">import</span> <span class="nn">traceback</span>
<span class="kn">import</span> <span class="nn">torch</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="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="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="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="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"> Args:</span>
<span class="sd"> ns: library name</span>
<span class="sd"> kind: "DEF", "IMPL" (default: "IMPL")</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="o">!=</span> <span class="s2">"IMPL"</span> <span class="ow">and</span> <span class="n">kind</span> <span class="o">!=</span> <span class="s2">"DEF"</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="n">kind</span> <span class="o">==</span> <span class="s2">"DEF"</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="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_impls</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">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="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="s2">"Library(kind=</span><span class="si">{}</span><span class="s2">, ns=</span><span class="si">{}</span><span class="s2">, dispatch_key=</span><span class="si">{}</span><span class="s2">)>"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kind</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="bp">self</span><span class="o">.</span><span class="n">dispatch_key</span><span class="p">)</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="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"> 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="s2">"Invalid alias_analysis type </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">alias_analysis</span><span class="p">))</span>
<span class="k">return</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></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="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.</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"> >>> # xdoctest: +SKIP</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", "CPU")</span>
<span class="sd"> '''</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">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="s2">"Input function is required to be a callable but found type </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">fn</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="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="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># _op_impls might not have been initialized if an error was thrown in __init__</span>
<span class="n">_op_impls_</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'_op_impls'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">if</span> <span class="n">_op_impls_</span><span class="p">:</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_impls</span><span class="p">:</span>
<span class="n">_impls</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span></div>
<span class="c1"># decorator to register python functions for library ops</span>
<span class="c1"># Note: this decorator API should remain consistent with `Library.impl` API</span>
<span class="k">def</span> <span class="nf">impl</span><span class="p">(</span><span class="n">lib</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="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>
<span class="k">def</span> <span class="nf">define</span><span class="p">(</span><span class="n">lib</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="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>
</pre></div>
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