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<li class="toctree-l1"><a class="reference internal" href="../../../notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/autograd.html">Autograd mechanics</a></li>
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<h1>Source code for torch.fx.interpreter</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">.graph_module</span> <span class="kn">import</span> <span class="n">GraphModule</span>
<span class="kn">from</span> <span class="nn">.graph</span> <span class="kn">import</span> <span class="n">Graph</span>
<span class="kn">from</span> <span class="nn">.node</span> <span class="kn">import</span> <span class="n">Argument</span><span class="p">,</span> <span class="n">Node</span><span class="p">,</span> <span class="n">Target</span><span class="p">,</span> <span class="n">map_arg</span><span class="p">,</span> <span class="n">map_aggregate</span>
<span class="kn">from</span> <span class="nn">.proxy</span> <span class="kn">import</span> <span class="n">Proxy</span>
<span class="kn">from</span> <span class="nn">._symbolic_trace</span> <span class="kn">import</span> <span class="n">Tracer</span>
<span class="kn">from</span> <span class="nn">._compatibility</span> <span class="kn">import</span> <span class="n">compatibility</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">config</span>
<span class="kn">import</span> <span class="nn">torch.fx.traceback</span> <span class="k">as</span> <span class="nn">fx_traceback</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">Dict</span><span class="p">,</span> <span class="n">Iterator</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">inspect</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</span>
<span class="kn">from</span> <span class="nn">torch.hub</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Interpreter'</span><span class="p">,</span> <span class="s1">'Transformer'</span><span class="p">]</span>
<div class="viewcode-block" id="Interpreter"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter">[docs]</a><span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Interpreter</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> An Interpreter executes an FX graph Node-by-Node. This pattern</span>
<span class="sd"> can be useful for many things, including writing code</span>
<span class="sd"> transformations as well as analysis passes.</span>
<span class="sd"> Methods in the Interpreter class can be overridden to customize</span>
<span class="sd"> the behavior of execution. The map of overrideable methods</span>
<span class="sd"> in terms of call hierarchy::</span>
<span class="sd"> run()</span>
<span class="sd"> +-- run_node</span>
<span class="sd"> +-- placeholder()</span>
<span class="sd"> +-- get_attr()</span>
<span class="sd"> +-- call_function()</span>
<span class="sd"> +-- call_method()</span>
<span class="sd"> +-- call_module()</span>
<span class="sd"> +-- output()</span>
<span class="sd"> Example:</span>
<span class="sd"> Suppose we want to swap all instances of ``torch.neg`` with</span>
<span class="sd"> ``torch.sigmoid`` and vice versa (including their ``Tensor``</span>
<span class="sd"> method equivalents). We could subclass Interpreter like so::</span>
<span class="sd"> class NegSigmSwapInterpreter(Interpreter):</span>
<span class="sd"> def call_function(self, target : Target,</span>
<span class="sd"> args : Tuple, kwargs : Dict) -> Any:</span>
<span class="sd"> if target == torch.sigmoid:</span>
<span class="sd"> return torch.neg(*args, **kwargs)</span>
<span class="sd"> return super().call_function(n)</span>
<span class="sd"> def call_method(self, target : Target,</span>
<span class="sd"> args : Tuple, kwargs : Dict) -> Any:</span>
<span class="sd"> if target == 'neg':</span>
<span class="sd"> call_self, *args_tail = args</span>
<span class="sd"> return call_self.sigmoid(*args_tail, **kwargs)</span>
<span class="sd"> return super().call_method(n)</span>
<span class="sd"> def fn(x):</span>
<span class="sd"> return torch.sigmoid(x).neg()</span>
<span class="sd"> gm = torch.fx.symbolic_trace(fn)</span>
<span class="sd"> input = torch.randn(3, 4)</span>
<span class="sd"> result = NegSigmSwapInterpreter(gm).run(input)</span>
<span class="sd"> torch.testing.assert_close(result, torch.neg(input).sigmoid())</span>
<span class="sd"> Args:</span>
<span class="sd"> module (GraphModule): The module to be executed</span>
<span class="sd"> garbage_collect_values (bool): Whether to delete values after their last</span>
<span class="sd"> use within the Module's execution. This ensures optimal memory usage during</span>
<span class="sd"> execution. This can be disabled to, for example, examine all of the intermediate</span>
<span class="sd"> values in the execution by looking at the ``Interpreter.env`` attribute.</span>
<span class="sd"> """</span>
<span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span> <span class="p">:</span> <span class="n">GraphModule</span><span class="p">,</span> <span class="n">garbage_collect_values</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">GraphModule</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">module</span> <span class="o">=</span> <span class="n">module</span>
<span class="bp">self</span><span class="o">.</span><span class="n">submodules</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">named_modules</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">env</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">Node</span><span class="p">,</span> <span class="n">Any</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">name</span> <span class="o">=</span> <span class="s2">"Interpreter"</span>
<span class="bp">self</span><span class="o">.</span><span class="n">garbage_collect_values</span> <span class="o">=</span> <span class="n">garbage_collect_values</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">garbage_collect_values</span><span class="p">:</span>
<span class="c1"># Run through reverse nodes and record the first instance of a use</span>
<span class="c1"># of a given node. This represents the *last* use of the node in the</span>
<span class="c1"># execution order of the program, which we will use to free unused</span>
<span class="c1"># values</span>
<span class="n">node_to_last_use</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">Node</span><span class="p">,</span> <span class="n">Node</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">user_to_last_uses</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">Node</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">Node</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">def</span> <span class="nf">register_last_uses</span><span class="p">(</span><span class="n">n</span> <span class="p">:</span> <span class="n">Node</span><span class="p">,</span> <span class="n">user</span> <span class="p">:</span> <span class="n">Node</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node_to_last_use</span><span class="p">:</span>
<span class="n">node_to_last_use</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">user</span>
<span class="bp">self</span><span class="o">.</span><span class="n">user_to_last_uses</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">user</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
<span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">):</span>
<span class="n">map_arg</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">n</span><span class="p">:</span> <span class="n">register_last_uses</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">node</span><span class="p">))</span>
<span class="n">map_arg</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">kwargs</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">n</span><span class="p">:</span> <span class="n">register_last_uses</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">node</span><span class="p">))</span>
<div class="viewcode-block" id="Interpreter.run"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.run">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">initial_env</span> <span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="n">Node</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">enable_io_processing</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Run `module` via interpretation and return the result.</span>
<span class="sd"> Args:</span>
<span class="sd"> *args: The arguments to the Module to run, in positional order</span>
<span class="sd"> initial_env (Optional[Dict[Node, Any]]): An optional starting environment for execution.</span>
<span class="sd"> This is a dict mapping `Node` to any value. This can be used, for example, to</span>
<span class="sd"> pre-populate results for certain `Nodes` so as to do only partial evaluation within</span>
<span class="sd"> the interpreter.</span>
<span class="sd"> enable_io_processing (bool): If true, we process the inputs and outputs with graph's process_inputs and</span>
<span class="sd"> process_outputs function first before using them.</span>
<span class="sd"> Returns:</span>
<span class="sd"> Any: The value returned from executing the Module</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">env</span> <span class="o">=</span> <span class="n">initial_env</span> <span class="k">if</span> <span class="n">initial_env</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">{}</span>
<span class="c1"># Positional function args are consumed left-to-right by</span>
<span class="c1"># `placeholder` nodes. Use an iterator to keep track of</span>
<span class="c1"># position and extract those values.</span>
<span class="k">if</span> <span class="n">enable_io_processing</span><span class="p">:</span>
<span class="n">args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">process_inputs</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">args_iter</span> <span class="p">:</span> <span class="n">Iterator</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="n">pbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">),</span>
<span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">))</span> <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">verbose_progress</span> <span class="k">else</span> <span class="s1">''</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
<span class="n">initial</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">disable_progress</span><span class="p">,</span> <span class="n">delay</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
<span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="p">:</span>
<span class="c1"># Short circuit if we have this value. This could</span>
<span class="c1"># be used, for example, for partial evaluation</span>
<span class="c1"># where the caller has pre-populated `env` with</span>
<span class="c1"># values for a subset of the program.</span>
<span class="k">continue</span>
<span class="k">try</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_node</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"While executing </span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">format_node</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span>
<span class="n">msg</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{}</span><span class="se">\n\n</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">msg</span><span class="p">)</span> <span class="k">if</span> <span class="n">e</span><span class="o">.</span><span class="n">args</span> <span class="k">else</span> <span class="nb">str</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
<span class="n">msg</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Original traceback:</span><span class="se">\n</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">stack_trace</span><span class="si">}</span><span class="s2">"</span>
<span class="n">e</span><span class="o">.</span><span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="n">msg</span><span class="p">,)</span> <span class="o">+</span> <span class="n">e</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="ne">KeyError</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="o">*</span><span class="n">e</span><span class="o">.</span><span class="n">args</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span>
<span class="k">raise</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">garbage_collect_values</span><span class="p">:</span>
<span class="k">for</span> <span class="n">to_delete</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">user_to_last_uses</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="p">[]):</span>
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="p">[</span><span class="n">to_delete</span><span class="p">]</span>
<span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s1">'output'</span><span class="p">:</span>
<span class="n">output_val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="p">[</span><span class="n">node</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">process_outputs</span><span class="p">(</span><span class="n">output_val</span><span class="p">)</span> <span class="k">if</span> <span class="n">enable_io_processing</span> <span class="k">else</span> <span class="n">output_val</span></div>
<span class="nd">@contextmanager</span>
<span class="k">def</span> <span class="nf">_set_current_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node</span><span class="p">):</span>
<span class="k">with</span> <span class="n">fx_traceback</span><span class="o">.</span><span class="n">set_current_meta</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">):</span>
<span class="k">yield</span>
<div class="viewcode-block" id="Interpreter.run_node"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.run_node">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">run_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span> <span class="p">:</span> <span class="n">Node</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Run a specific node ``n`` and return the result.</span>
<span class="sd"> Calls into placeholder, get_attr, call_function,</span>
<span class="sd"> call_method, call_module, or output depending</span>
<span class="sd"> on ``node.op``</span>
<span class="sd"> Args:</span>
<span class="sd"> n (Node): The Node to execute</span>
<span class="sd"> Returns:</span>
<span class="sd"> Any: The result of executing ``n``</span>
<span class="sd"> """</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_current_node</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_args_kwargs_from_env</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kwargs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="o">.</span><span class="n">op</span><span class="p">)(</span><span class="n">n</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<span class="c1"># Main Node running APIs</span>
<div class="viewcode-block" id="Interpreter.placeholder"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.placeholder">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">placeholder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute a ``placeholder`` node. Note that this is stateful:</span>
<span class="sd"> ``Interpreter`` maintains an internal iterator over</span>
<span class="sd"> arguments passed to ``run`` and this method returns</span>
<span class="sd"> next() on that iterator.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> Returns:</span>
<span class="sd"> Any: The argument value that was retrieved.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="k">if</span> <span class="n">target</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'*'</span><span class="p">):</span>
<span class="c1"># For a starred parameter e.g. `*args`, retrieve all</span>
<span class="c1"># remaining values from the args list.</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args_iter</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args_iter</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">StopIteration</span> <span class="k">as</span> <span class="n">si</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Expected positional argument for parameter </span><span class="si">{</span><span class="n">target</span><span class="si">}</span><span class="s1">, but one was not passed in!'</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">si</span></div>
<div class="viewcode-block" id="Interpreter.get_attr"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.get_attr">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_attr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute a ``get_attr`` node. Will retrieve an attribute</span>
<span class="sd"> value from the ``Module`` hierarchy of ``self.module``.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> Return:</span>
<span class="sd"> Any: The value of the attribute that was retrieved</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">target</span><span class="p">)</span></div>
<div class="viewcode-block" id="Interpreter.call_function"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.call_function">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">call_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute a ``call_function`` node and return the result.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> Return</span>
<span class="sd"> Any: The value returned by the function invocation</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="c1"># Execute the function and return the result</span>
<span class="k">return</span> <span class="n">target</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></div>
<div class="viewcode-block" id="Interpreter.call_method"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.call_method">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">call_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute a ``call_method`` node and return the result.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> Return</span>
<span class="sd"> Any: The value returned by the method invocation</span>
<span class="sd"> """</span>
<span class="c1"># args[0] is the `self` object for this method call</span>
<span class="n">self_obj</span><span class="p">,</span> <span class="o">*</span><span class="n">args_tail</span> <span class="o">=</span> <span class="n">args</span>
<span class="c1"># Execute the method and return the result</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">self_obj</span><span class="p">,</span> <span class="n">target</span><span class="p">)(</span><span class="o">*</span><span class="n">args_tail</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="Interpreter.call_module"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.call_module">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">call_module</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute a ``call_module`` node and return the result.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> Return</span>
<span class="sd"> Any: The value returned by the module invocation</span>
<span class="sd"> """</span>
<span class="c1"># Retrieve executed args and kwargs values from the environment</span>
<span class="c1"># Execute the method and return the result</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
<span class="k">return</span> <span class="n">submod</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></div>
<div class="viewcode-block" id="Interpreter.output"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.output">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute an ``output`` node. This really just retrieves</span>
<span class="sd"> the value referenced by the ``output`` node and returns it.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> Return:</span>
<span class="sd"> Any: The return value referenced by the output node</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>
<span class="c1"># Helper methods</span>
<div class="viewcode-block" id="Interpreter.fetch_attr"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.fetch_attr">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">fetch_attr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Fetch an attribute from the ``Module`` hierarchy of ``self.module``.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (str): The fully-qualified name of the attribute to fetch</span>
<span class="sd"> Return:</span>
<span class="sd"> Any: The value of the attribute.</span>
<span class="sd"> """</span>
<span class="n">target_atoms</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'.'</span><span class="p">)</span>
<span class="n">attr_itr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">atom</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">target_atoms</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">attr_itr</span><span class="p">,</span> <span class="n">atom</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">"Node referenced nonexistent target </span><span class="si">{</span><span class="s1">'.'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">target_atoms</span><span class="p">[:</span><span class="n">i</span><span class="p">])</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">attr_itr</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">attr_itr</span><span class="p">,</span> <span class="n">atom</span><span class="p">)</span>
<span class="k">return</span> <span class="n">attr_itr</span></div>
<div class="viewcode-block" id="Interpreter.fetch_args_kwargs_from_env"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.fetch_args_kwargs_from_env">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">fetch_args_kwargs_from_env</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span> <span class="p">:</span> <span class="n">Node</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tuple</span><span class="p">,</span> <span class="n">Dict</span><span class="p">]:</span>
<span class="sd">"""</span>
<span class="sd"> Fetch the concrete values of ``args`` and ``kwargs`` of node ``n``</span>
<span class="sd"> from the current execution environment.</span>
<span class="sd"> Args:</span>
<span class="sd"> n (Node): The node for which ``args`` and ``kwargs`` should be fetched.</span>
<span class="sd"> Return:</span>
<span class="sd"> Tuple[Tuple, Dict]: ``args`` and ``kwargs`` with concrete values for ``n``.</span>
<span class="sd"> """</span>
<span class="n">args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">map_nodes_to_values</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">map_nodes_to_values</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">kwargs</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kwargs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span>
<span class="k">return</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span></div>
<div class="viewcode-block" id="Interpreter.map_nodes_to_values"><a class="viewcode-back" href="../../../fx.html#torch.fx.Interpreter.map_nodes_to_values">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">map_nodes_to_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Argument</span><span class="p">,</span> <span class="n">n</span> <span class="p">:</span> <span class="n">Node</span><span class="p">)</span> <span class="o">-></span> <span class="n">Argument</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Recursively descend through ``args`` and look up the concrete value</span>
<span class="sd"> for each ``Node`` in the current execution environment.</span>
<span class="sd"> Args:</span>
<span class="sd"> args (Argument): Data structure within which to look up concrete values</span>
<span class="sd"> n (Node): Node to which ``args`` belongs. This is only used for error reporting.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">load_arg</span><span class="p">(</span><span class="n">n_arg</span> <span class="p">:</span> <span class="n">Node</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="k">if</span> <span class="n">n_arg</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Node </span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s1"> referenced nonexistent value </span><span class="si">{</span><span class="n">n_arg</span><span class="si">}</span><span class="s1">! Run Graph.lint() '</span>
<span class="sa">f</span><span class="s1">'to diagnose such issues'</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="p">[</span><span class="n">n_arg</span><span class="p">]</span>
<span class="k">return</span> <span class="n">map_arg</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">load_arg</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="Transformer"><a class="viewcode-back" href="../../../fx.html#torch.fx.Transformer">[docs]</a><span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Transformer</span><span class="p">(</span><span class="n">Interpreter</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> ``Transformer`` is a special type of interpreter that produces a</span>
<span class="sd"> new ``Module``. It exposes a ``transform()`` method that returns</span>
<span class="sd"> the transformed ``Module``. ``Transformer`` does not require</span>
<span class="sd"> arguments to run, as ``Interpreter`` does. ``Transformer`` works</span>
<span class="sd"> entirely symbolically.</span>
<span class="sd"> Example:</span>
<span class="sd"> Suppose we want to swap all instances of ``torch.neg`` with</span>
<span class="sd"> ``torch.sigmoid`` and vice versa (including their ``Tensor``</span>
<span class="sd"> method equivalents). We could subclass ``Transformer`` like so::</span>
<span class="sd"> class NegSigmSwapXformer(Transformer):</span>
<span class="sd"> def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:</span>
<span class="sd"> if target == torch.sigmoid:</span>
<span class="sd"> return torch.neg(*args, **kwargs)</span>
<span class="sd"> return super().call_function(n)</span>
<span class="sd"> def call_method(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:</span>
<span class="sd"> if target == 'neg':</span>
<span class="sd"> call_self, *args_tail = args</span>
<span class="sd"> return call_self.sigmoid(*args_tail, **kwargs)</span>
<span class="sd"> return super().call_method(n)</span>
<span class="sd"> def fn(x):</span>
<span class="sd"> return torch.sigmoid(x).neg()</span>
<span class="sd"> gm = torch.fx.symbolic_trace(fn)</span>
<span class="sd"> transformed : torch.nn.Module = NegSigmSwapXformer(gm).transform()</span>
<span class="sd"> input = torch.randn(3, 4)</span>
<span class="sd"> torch.testing.assert_close(transformed(input), torch.neg(input).sigmoid())</span>
<span class="sd"> Args:</span>
<span class="sd"> module (GraphModule): The ``Module`` to be transformed.</span>
<span class="sd"> """</span>
<span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">new_graph</span> <span class="o">=</span> <span class="n">Graph</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">new_graph</span><span class="o">.</span><span class="n">set_codegen</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">_codegen</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">TransformerTracer</span><span class="p">(</span><span class="n">Tracer</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">:</span> <span class="n">Graph</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">graph</span>
<span class="k">def</span> <span class="nf">is_leaf_module</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">__</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracer</span> <span class="o">=</span> <span class="n">TransformerTracer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">new_graph</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracer</span><span class="o">.</span><span class="n">root</span> <span class="o">=</span> <span class="n">module</span>
<div class="viewcode-block" id="Transformer.placeholder"><a class="viewcode-back" href="../../../fx.html#torch.fx.Transformer.placeholder">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">placeholder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Proxy</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute a ``placeholder`` node. In ``Transformer``, this is</span>
<span class="sd"> overridden to insert a new ``placeholder`` into the output</span>
<span class="sd"> graph.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="n">default_value</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">args</span><span class="p">))</span> <span class="k">if</span> <span class="n">args</span> <span class="k">else</span> <span class="n">inspect</span><span class="o">.</span><span class="n">Signature</span><span class="o">.</span><span class="n">empty</span>
<span class="k">return</span> <span class="n">Proxy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">new_graph</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">default_value</span><span class="o">=</span><span class="n">default_value</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracer</span><span class="p">)</span></div>
<div class="viewcode-block" id="Transformer.get_attr"><a class="viewcode-back" href="../../../fx.html#torch.fx.Transformer.get_attr">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_attr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Proxy</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Execute a ``get_attr`` node. In ``Transformer``, this is</span>
<span class="sd"> overridden to insert a new ``get_attr`` node into the output</span>
<span class="sd"> graph.</span>
<span class="sd"> Args:</span>
<span class="sd"> target (Target): The call target for this node. See</span>
<span class="sd"> `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for</span>
<span class="sd"> details on semantics</span>
<span class="sd"> args (Tuple): Tuple of positional args for this invocation</span>
<span class="sd"> kwargs (Dict): Dict of keyword arguments for this invocation</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Proxy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">new_graph</span><span class="o">.</span><span class="n">get_attr</span><span class="p">(</span><span class="n">target</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracer</span><span class="p">)</span></div>
<div class="viewcode-block" id="Transformer.call_module"><a class="viewcode-back" href="../../../fx.html#torch.fx.Transformer.call_module">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">call_module</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="c1"># Override so that the leaf module policy from `self.tracer` is respected.</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracer</span><span class="o">.</span><span class="n">call_module</span><span class="p">(</span><span class="n">submod</span><span class="p">,</span> <span class="n">submod</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="Transformer.call_function"><a class="viewcode-back" href="../../../fx.html#torch.fx.Transformer.call_function">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">call_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span> <span class="p">:</span> <span class="s1">'Target'</span><span class="p">,</span> <span class="n">args</span> <span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">kwargs</span> <span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="c1"># Override so that functions that were wrapped are still wrapped.</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracer</span><span class="o">.</span><span class="n">create_proxy</span><span class="p">(</span><span class="s1">'call_function'</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="Transformer.transform"><a class="viewcode-back" href="../../../fx.html#torch.fx.Transformer.transform">[docs]</a> <span class="nd">@compatibility</span><span class="p">(</span><span class="n">is_backward_compatible</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">GraphModule</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Transform ``self.module`` and return the transformed</span>
<span class="sd"> ``GraphModule``.</span>
<span class="sd"> """</span>
<span class="k">with</span> <span class="n">fx_traceback</span><span class="o">.</span><span class="n">preserve_node_meta</span><span class="p">():</span>
<span class="n">result</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">enable_io_processing</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">strip_proxy</span><span class="p">(</span><span class="n">a</span> <span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="n">Proxy</span><span class="p">])</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">node</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">Proxy</span><span class="p">)</span> <span class="k">else</span> <span class="n">a</span>
<span class="bp">self</span><span class="o">.</span><span class="n">new_graph</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">map_aggregate</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">strip_proxy</span><span class="p">))</span>
<span class="k">return</span> <span class="n">GraphModule</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_graph</span><span class="p">)</span></div></div>
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