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<section id="writing-dynamo-aten-lowering-passes">
<span id="id1"></span><h1>Writing Dynamo ATen Lowering Passes<a class="headerlink" href="#writing-dynamo-aten-lowering-passes" title="Permalink to this heading">¶</a></h1>
<section id="basics-of-a-lowering-pass">
<h2>Basics of a Lowering Pass<a class="headerlink" href="#basics-of-a-lowering-pass" title="Permalink to this heading">¶</a></h2>
<p>ATen lowering passes are Python functions which take as input a graph of ATen operators, apply some desired modification such as operator coalescing/fusion, operator replacement, subgraph rewriting, custom operator insertion, or other operation on a <cite>torch.fx.GraphModule</cite>, then return the modified graph to the caller. These lowering passes generally modify the graph in-place and return the same input object.</p>
</section>
<section id="lowering-pass-requirements">
<h2>Lowering Pass Requirements<a class="headerlink" href="#lowering-pass-requirements" title="Permalink to this heading">¶</a></h2>
<p>An ATen lowering pass function in Torch-TRT must satisfy two requirements:
- The function must take as input a <cite>torch.fx.GraphModule</cite> and a sequence of torch Tensors, <cite>Sequence[torch.Tensor]</cite>, and return the lowered <cite>torch.fx.GraphModule</cite>
- The function must leave the graph in a valid and invoke-able state, including performing any necessary linting and recompilation</p>
<p>See this link for information on <a class="reference external" href="https://pytorch.org/docs/stable/fx.html#graph-manipulation">Graph Manipulations</a> in FX. See below for an example of a lowering pass which repairs graphs that have inputs which are also outputs, a disallowed configuration for TRT Engines.</p>
</section>
<section id="example-lowering-pass">
<h2>Example Lowering Pass<a class="headerlink" href="#example-lowering-pass" title="Permalink to this heading">¶</a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">repair_input_as_output</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Repair scenarios where inputs are also outputs of the graph</span>
<span class="sd"> TRT does not allow such cases, so we insert a clone (identity) layer</span>
<span class="sd"> """</span>
<span class="n">modified_graph</span> <span class="o">=</span> <span class="kc">False</span>
<span class="c1"># Extract graph placeholder Tensors</span>
<span class="n">placeholders</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">node</span>
<span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span>
<span class="k">if</span> <span class="p">(</span>
<span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">"placeholder"</span>
<span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="nb">type</span><span class="p">)</span>
<span class="ow">and</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
<span class="p">)</span>
<span class="p">]</span>
<span class="k">for</span> <span class="n">placeholder</span> <span class="ow">in</span> <span class="n">placeholders</span><span class="p">:</span>
<span class="c1"># If any placeholder has any users which are direct graph outputs</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">placeholder</span><span class="o">.</span><span class="n">users</span><span class="p">)</span> <span class="o">>=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span>
<span class="n">user</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">"output"</span> <span class="k">for</span> <span class="n">user</span> <span class="ow">in</span> <span class="n">placeholder</span><span class="o">.</span><span class="n">users</span>
<span class="p">):</span>
<span class="n">modified_graph</span> <span class="o">=</span> <span class="kc">True</span>
<span class="c1"># Get direct graph outputs which are direct uses of placeholders</span>
<span class="n">direct_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">user</span> <span class="k">for</span> <span class="n">user</span> <span class="ow">in</span> <span class="n">placeholder</span><span class="o">.</span><span class="n">users</span> <span class="k">if</span> <span class="n">user</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">"output"</span><span class="p">]</span>
<span class="c1"># Insert clone node for placeholder to ensure</span>
<span class="c1"># placeholder is not a direct output</span>
<span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_after</span><span class="p">(</span><span class="n">placeholder</span><span class="p">):</span>
<span class="n">cloned_placeholder</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">clone</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">placeholder</span><span class="p">,),</span>
<span class="p">)</span>
<span class="c1"># Replace placeholder as output with cloned version</span>
<span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">direct_outputs</span><span class="p">:</span>
<span class="n">output</span><span class="o">.</span><span class="n">replace_input_with</span><span class="p">(</span><span class="n">placeholder</span><span class="p">,</span> <span class="n">cloned_placeholder</span><span class="p">)</span>
<span class="c1"># If the graph was modified, clean up the graph and ensure it is up-to-date</span>
<span class="k">if</span> <span class="n">modified_graph</span><span class="p">:</span>
<span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
<span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>
<span class="n">gm</span><span class="o">.</span><span class="n">recompile</span><span class="p">()</span>
<span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Graph after repair_input_as_output:</span><span class="se">\n</span><span class="si">{</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">gm</span>
</pre></div>
</div>
</section>
<section id="registering-lowering-passes">
<h2>Registering Lowering Passes<a class="headerlink" href="#registering-lowering-passes" title="Permalink to this heading">¶</a></h2>
<p>Lowering passes are currently registered in <cite>py/torch_tensorrt/dynamo/lowering/passes/__init__.py</cite>, using the <cite>torch.fx.passes.pass_manager.PassManager</cite> utility to assemble the list of passes in a desired order. New passes added directly to that list will be applied to graphs in the Torch-TensorRT <cite>torch.compile</cite> backend. Currently, we offer an ATen lowering pass registration decorator for convenience, which can be invoked either directly, or with the optional <cite>index</cite> keyword argument which controls where in the pass list the lowering pass will be inserted.</p>
<p>For instance, to insert the pass at the default location (end of the list), the following code can be used:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@_aten_lowering_pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">my_custom_pass</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="o">...</span>
</pre></div>
</div>
<p>Alternatively, to insert the pass at a custom index (such as the front of the list) in the passlist, the following code can be used:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@_aten_lowering_pass</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">my_custom_pass</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="o">...</span>
</pre></div>
</div>
<p>There are also provided utilities in <cite>torch_tensorrt.dynamo.lowering.passes</cite> for displaying the currently-available lowering pass list, applying those passes to an arbitrary <cite>torch.fx.GraphModule</cite>, and removing the lowering pass at a specific index.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Print all lowering passes in the list</span>
<span class="nb">print</span><span class="p">(</span><span class="n">dump_lowering_passes</span><span class="p">())</span>
<span class="c1"># Apply lowering passes to a GraphModule</span>
<span class="n">apply_lowering_passes</span><span class="p">(</span><span class="n">graph_module</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">)</span>
<span class="c1"># Remove the lowering pass at index 1</span>
<span class="n">_remove_lowering_pass</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Note:</strong> The above APIs are subject to change, as the lowering pass system evolves.</p>
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