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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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<section id="torch-fx">
<h1>torch.fx<a class="headerlink" href="#torch-fx" title="Permalink to this heading">¶</a></h1>
<section id="module-torch.fx">
<span id="overview"></span><h2>Overview<a class="headerlink" href="#module-torch.fx" title="Permalink to this heading">¶</a></h2>
<p>FX is a toolkit for developers to use to transform <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>
instances. FX consists of three main components: a <strong>symbolic tracer,</strong>
an <strong>intermediate representation</strong>, and <strong>Python code generation</strong>. A
demonstration of these components in action:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># Simple module for demonstration</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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="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">param</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">param</span><span class="p">)</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">torch.fx</span> <span class="kn">import</span> <span class="n">symbolic_trace</span>
<span class="c1"># Symbolic tracing frontend - captures the semantics of the module</span>
<span class="n">symbolic_traced</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="o">=</span> <span class="n">symbolic_trace</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
<span class="c1"># High-level intermediate representation (IR) - Graph representation</span>
<span class="nb">print</span><span class="p">(</span><span class="n">symbolic_traced</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
<span class="sd">"""</span>
<span class="sd">graph():</span>
<span class="sd"> %x : [#users=1] = placeholder[target=x]</span>
<span class="sd"> %param : [#users=1] = get_attr[target=param]</span>
<span class="sd"> %add : [#users=1] = call_function[target=operator.add](args = (%x, %param), kwargs = {})</span>
<span class="sd"> %linear : [#users=1] = call_module[target=linear](args = (%add,), kwargs = {})</span>
<span class="sd"> %clamp : [#users=1] = call_method[target=clamp](args = (%linear,), kwargs = {min: 0.0, max: 1.0})</span>
<span class="sd"> return clamp</span>
<span class="sd">"""</span>
<span class="c1"># Code generation - valid Python code</span>
<span class="nb">print</span><span class="p">(</span><span class="n">symbolic_traced</span><span class="o">.</span><span class="n">code</span><span class="p">)</span>
<span class="sd">"""</span>
<span class="sd">def forward(self, x):</span>
<span class="sd"> param = self.param</span>
<span class="sd"> add = x + param; x = param = None</span>
<span class="sd"> linear = self.linear(add); add = None</span>
<span class="sd"> clamp = linear.clamp(min = 0.0, max = 1.0); linear = None</span>
<span class="sd"> return clamp</span>
<span class="sd">"""</span>
</pre></div>
</div>
<p>The <strong>symbolic tracer</strong> performs “symbolic execution” of the Python
code. It feeds fake values, called Proxies, through the code. Operations
on theses Proxies are recorded. More information about symbolic tracing
can be found in the <a class="reference internal" href="#torch.fx.symbolic_trace" title="torch.fx.symbolic_trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">symbolic_trace()</span></code></a> and <a class="reference internal" href="#torch.fx.Tracer" title="torch.fx.Tracer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tracer</span></code></a>
documentation.</p>
<p>The <strong>intermediate representation</strong> is the container for the operations
that were recorded during symbolic tracing. It consists of a list of
Nodes that represent function inputs, callsites (to functions, methods,
or <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> instances), and return values. More information
about the IR can be found in the documentation for <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>. The
IR is the format on which transformations are applied.</p>
<p><strong>Python code generation</strong> is what makes FX a Python-to-Python (or
Module-to-Module) transformation toolkit. For each Graph IR, we can
create valid Python code matching the Graph’s semantics. This
functionality is wrapped up in <a class="reference internal" href="#torch.fx.GraphModule" title="torch.fx.GraphModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">GraphModule</span></code></a>, which is a
<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> instance that holds a <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> as well as a
<code class="docutils literal notranslate"><span class="pre">forward</span></code> method generated from the Graph.</p>
<p>Taken together, this pipeline of components (symbolic tracing ->
intermediate representation -> transforms -> Python code generation)
constitutes the Python-to-Python transformation pipeline of FX. In
addition, these components can be used separately. For example,
symbolic tracing can be used in isolation to capture a form of
the code for analysis (and not transformation) purposes. Code
generation can be used for programmatically generating models, for
example from a config file. There are many uses for FX!</p>
<p>Several example transformations can be found at the
<a class="reference external" href="https://github.com/pytorch/examples/tree/master/fx">examples</a>
repository.</p>
</section>
<section id="writing-transformations">
<span id="id1"></span><h2>Writing Transformations<a class="headerlink" href="#writing-transformations" title="Permalink to this heading">¶</a></h2>
<p>What is an FX transform? Essentially, it’s a function that looks like this.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">m</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
<span class="n">tracer_class</span> <span class="p">:</span> <span class="nb">type</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">Tracer</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="c1"># Step 1: Acquire a Graph representing the code in `m`</span>
<span class="c1"># NOTE: torch.fx.symbolic_trace is a wrapper around a call to</span>
<span class="c1"># fx.Tracer.trace and constructing a GraphModule. We'll</span>
<span class="c1"># split that out in our transform to allow the caller to</span>
<span class="c1"># customize tracing behavior.</span>
<span class="n">graph</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">Graph</span> <span class="o">=</span> <span class="n">tracer_class</span><span class="p">()</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="c1"># Step 2: Modify this Graph or create a new one</span>
<span class="n">graph</span> <span class="o">=</span> <span class="o">...</span>
<span class="c1"># Step 3: Construct a Module to return</span>
<span class="k">return</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">m</span><span class="p">,</span> <span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
<p>Your transform will take in an <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>, acquire a <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>
from it, do some modifications, and return a new
<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>. You should think of the <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> that your FX
transform returns as identical to a regular <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> – you can pass it to another
FX transform, you can pass it to TorchScript, or you can
run it. Ensuring that the inputs and outputs of your FX transform are a
<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> will allow for composability.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It is also possible to modify an existing <a class="reference internal" href="#torch.fx.GraphModule" title="torch.fx.GraphModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">GraphModule</span></code></a> instead of
creating a new one, like so:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">m</span> <span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">)</span> <span class="o">-></span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</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="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">symbolic_trace</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="c1"># Modify gm.graph</span>
<span class="c1"># <...></span>
<span class="c1"># Recompile the forward() method of `gm` from its Graph</span>
<span class="n">gm</span><span class="o">.</span><span class="n">recompile</span><span class="p">()</span>
<span class="k">return</span> <span class="n">gm</span>
</pre></div>
</div>
<p>Note that you MUST call <a class="reference internal" href="#torch.fx.GraphModule.recompile" title="torch.fx.GraphModule.recompile"><code class="xref py py-meth docutils literal notranslate"><span class="pre">GraphModule.recompile()</span></code></a> to bring the generated
<code class="docutils literal notranslate"><span class="pre">forward()</span></code> method on the <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code> in sync with the modified <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>.</p>
</div>
<p>Given that you’ve passed in a <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> that has been traced into a
<a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>, there are now two primary approaches you can take to building a new
<a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>.</p>
<section id="a-quick-primer-on-graphs">
<h3>A Quick Primer on Graphs<a class="headerlink" href="#a-quick-primer-on-graphs" title="Permalink to this heading">¶</a></h3>
<p>Full treatment of the semantics of graphs can be found in the <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>
documentation, but we are going to cover the basics here. A <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> is
a data structure that represents a method on a <a class="reference internal" href="#torch.fx.GraphModule" title="torch.fx.GraphModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">GraphModule</span></code></a>. The
information that this requires is:</p>
<ul class="simple">
<li><p>What are the inputs to the method?</p></li>
<li><p>What are the operations that run inside the method?</p></li>
<li><p>What is the output (i.e. return) value from the method?</p></li>
</ul>
<p>All three of these concepts are represented with <a class="reference internal" href="#torch.fx.Node" title="torch.fx.Node"><code class="xref py py-class docutils literal notranslate"><span class="pre">Node</span></code></a> instances.
Let’s see what we mean by that with a short example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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="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">param</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span><span class="o">.</span><span class="n">relu</span><span class="p">(),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
<span class="n">gm</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">symbolic_trace</span><span class="p">(</span><span class="n">m</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">print_tabular</span><span class="p">()</span>
</pre></div>
</div>
<p>Here we define a module <code class="docutils literal notranslate"><span class="pre">MyModule</span></code> for demonstration purposes, instantiate it,
symbolically trace it, then call the <a class="reference internal" href="#torch.fx.Graph.print_tabular" title="torch.fx.Graph.print_tabular"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.print_tabular()</span></code></a> method to print
out a table showing the nodes of this <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>:</p>
<blockquote>
<div><table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>opcode</p></th>
<th class="head"><p>name</p></th>
<th class="head"><p>target</p></th>
<th class="head"><p>args</p></th>
<th class="head"><p>kwargs</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>placeholder</p></td>
<td><p>x</p></td>
<td><p>x</p></td>
<td><p>()</p></td>
<td><p>{}</p></td>
</tr>
<tr class="row-odd"><td><p>get_attr</p></td>
<td><p>linear_weight</p></td>
<td><p>linear.weight</p></td>
<td><p>()</p></td>
<td><p>{}</p></td>
</tr>
<tr class="row-even"><td><p>call_function</p></td>
<td><p>add_1</p></td>
<td><p><built-in function add></p></td>
<td><p>(x, linear_weight)</p></td>
<td><p>{}</p></td>
</tr>
<tr class="row-odd"><td><p>call_module</p></td>
<td><p>linear_1</p></td>
<td><p>linear</p></td>
<td><p>(add_1,)</p></td>
<td><p>{}</p></td>
</tr>
<tr class="row-even"><td><p>call_method</p></td>
<td><p>relu_1</p></td>
<td><p>relu</p></td>
<td><p>(linear_1,)</p></td>
<td><p>{}</p></td>
</tr>
<tr class="row-odd"><td><p>call_function</p></td>
<td><p>sum_1</p></td>
<td><p><built-in method sum …></p></td>
<td><p>(relu_1,)</p></td>
<td><p>{‘dim’: -1}</p></td>
</tr>
<tr class="row-even"><td><p>call_function</p></td>
<td><p>topk_1</p></td>
<td><p><built-in method topk …></p></td>
<td><p>(sum_1, 3)</p></td>
<td><p>{}</p></td>
</tr>
<tr class="row-odd"><td><p>output</p></td>
<td><p>output</p></td>
<td><p>output</p></td>
<td><p>(topk_1,)</p></td>
<td><p>{}</p></td>
</tr>
</tbody>
</table>
</div></blockquote>
<p>We can use this information to answer the questions we posed above.</p>
<ul class="simple">
<li><p>What are the inputs to the method? In FX, method inputs are specified
via special <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> nodes. In this case, we have a single
<code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node with a <code class="docutils literal notranslate"><span class="pre">target</span></code> of <code class="docutils literal notranslate"><span class="pre">x</span></code>, meaning we have
a single (non-self) argument named x.</p></li>
<li><p>What are the operations within the method? The <code class="docutils literal notranslate"><span class="pre">get_attr</span></code>,
<code class="docutils literal notranslate"><span class="pre">call_function</span></code>, <code class="docutils literal notranslate"><span class="pre">call_module</span></code>, and <code class="docutils literal notranslate"><span class="pre">call_method</span></code> nodes
represent the operations in the method. A full treatment of
the semantics of all of these can be found in the <a class="reference internal" href="#torch.fx.Node" title="torch.fx.Node"><code class="xref py py-class docutils literal notranslate"><span class="pre">Node</span></code></a>
documentation.</p></li>
<li><p>What is the return value of the method? The return value in a
<a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> is specified by a special <code class="docutils literal notranslate"><span class="pre">output</span></code> node.</p></li>
</ul>
<p>Given that we now know the basics of how code is represented in
FX, we can now explore how we would edit a <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>.</p>
</section>
<section id="graph-manipulation">
<h3>Graph Manipulation<a class="headerlink" href="#graph-manipulation" title="Permalink to this heading">¶</a></h3>
<section id="direct-graph-manipulation">
<h4>Direct Graph Manipulation<a class="headerlink" href="#direct-graph-manipulation" title="Permalink to this heading">¶</a></h4>
<p>One approach to building this new <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> is to directly manipulate your old
one. To aid in this, we can simply take the <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> we obtain from symbolic
tracing and modify it. For example, let’s say we desire to replace
<a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.add()</span></code></a> calls with <a class="reference internal" href="generated/torch.mul.html#torch.mul" title="torch.mul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mul()</span></code></a> calls.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="c1"># Sample module</span>
<span class="k">class</span> <span class="nc">M</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">m</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
<span class="n">tracer_class</span> <span class="p">:</span> <span class="nb">type</span> <span class="o">=</span> <span class="n">fx</span><span class="o">.</span><span class="n">Tracer</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="n">graph</span> <span class="p">:</span> <span class="n">fx</span><span class="o">.</span><span class="n">Graph</span> <span class="o">=</span> <span class="n">tracer_class</span><span class="p">()</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="c1"># FX represents its Graph as an ordered list of</span>
<span class="c1"># nodes, so we can iterate through them.</span>
<span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
<span class="c1"># Checks if we're calling a function (i.e:</span>
<span class="c1"># torch.add)</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">'call_function'</span><span class="p">:</span>
<span class="c1"># The target attribute is the function</span>
<span class="c1"># that call_function calls.</span>
<span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">:</span>
<span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span>
<span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span> <span class="c1"># Does some checks to make sure the</span>
<span class="c1"># Graph is well-formed.</span>
<span class="k">return</span> <span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
<p>We can also do more involved <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> rewrites, such as
deleting or appending nodes. To aid in these transformations,
FX has utility functions for transforming the graph that can
be found in the <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> documentation. An
example of using these APIs to append a <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.relu()</span></code> call
can be found below.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specifies the insertion point. Any nodes added to the</span>
<span class="c1"># Graph within this scope will be inserted after `node`</span>
<span class="k">with</span> <span class="n">traced</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">node</span><span class="p">):</span>
<span class="c1"># Insert a new `call_function` node calling `torch.relu`</span>
<span class="n">new_node</span> <span class="o">=</span> <span class="n">traced</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">relu</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">node</span><span class="p">,))</span>
<span class="c1"># We want all places that used the value of `node` to</span>
<span class="c1"># now use that value after the `relu` call we've added.</span>
<span class="c1"># We use the `replace_all_uses_with` API to do this.</span>
<span class="n">node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">new_node</span><span class="p">)</span>
</pre></div>
</div>
<p>For simple transformations that only consist of substitutions, you can also
make use of the <a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/fx/subgraph_rewriter.py">subgraph rewriter.</a></p>
</section>
<section id="subgraph-rewriting-with-replace-pattern">
<h4>Subgraph Rewriting With replace_pattern()<a class="headerlink" href="#subgraph-rewriting-with-replace-pattern" title="Permalink to this heading">¶</a></h4>
<p>FX also provides another level of automation on top of direct graph manipulation.
The <a class="reference internal" href="#torch.fx.replace_pattern" title="torch.fx.replace_pattern"><code class="xref py py-func docutils literal notranslate"><span class="pre">replace_pattern()</span></code></a> API is essentially a “find/replace” tool for editing
<a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>s. It allows you to specify a <code class="docutils literal notranslate"><span class="pre">pattern</span></code> and <code class="docutils literal notranslate"><span class="pre">replacement</span></code> function
and it will trace through those functions, find instances of the group of operations
in the <code class="docutils literal notranslate"><span class="pre">pattern</span></code> graph, and replace those instances with copies of the <code class="docutils literal notranslate"><span class="pre">replacement</span></code>
graph. This can help to greatly automate tedious graph manipulation code, which can
get unwieldy as the transformations get more complex.</p>
</section>
<section id="graph-manipulation-examples">
<h4>Graph Manipulation Examples<a class="headerlink" href="#graph-manipulation-examples" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/examples/blob/master/fx/replace_op.py">Replace one
op</a></p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/40cbf342d3c000712da92cfafeaca651b3e0bd3e/torch/fx/experimental/optimization.py#L50">Conv/Batch Norm
fusion</a></p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/examples/blob/master/fx/subgraph_rewriter_basic_use.py">replace_pattern: Basic usage</a></p></li>
<li><p><a class="reference external" href="https://pytorch.org/docs/master/quantization.html#prototype-fx-graph-mode-quantization">Quantization</a></p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/examples/blob/master/fx/invert.py">Invert Transformation</a></p></li>
</ul>
</section>
</section>
<section id="proxy-retracing">
<h3>Proxy/Retracing<a class="headerlink" href="#proxy-retracing" title="Permalink to this heading">¶</a></h3>
<p>Another way of manipulating <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>s is by reusing the <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a>
machinery used in symbolic tracing. For example, let’s
imagine that we wanted to write a transformation that decomposed
PyTorch functions into smaller operations. It would transform every
<code class="docutils literal notranslate"><span class="pre">F.relu(x)</span></code> call into <code class="docutils literal notranslate"><span class="pre">(x</span> <span class="pre">></span> <span class="pre">0)</span> <span class="pre">*</span> <span class="pre">x</span></code>. One possibility would be to
perform the requisite graph rewriting to insert the comparison and
multiplication after the <code class="docutils literal notranslate"><span class="pre">F.relu</span></code>, and then clean up the original
<code class="docutils literal notranslate"><span class="pre">F.relu</span></code>. However, we can automate this process by using <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a>
objects to automatically record operations into the <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>.</p>
<p>To use this method, we write the operations that we want inserted as regular
PyTorch code and invoke that code with <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a> objects as arguments.
These <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a> objects will capture the operations that are performed
on them and append them to the <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Note that this decomposition rule can be read as regular Python</span>
<span class="k">def</span> <span class="nf">relu_decomposition</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span>
<span class="n">decomposition_rules</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">decomposition_rules</span><span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">]</span> <span class="o">=</span> <span class="n">relu_decomposition</span>
<span class="k">def</span> <span class="nf">decompose</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
<span class="n">tracer_class</span> <span class="p">:</span> <span class="nb">type</span> <span class="o">=</span> <span class="n">fx</span><span class="o">.</span><span class="n">Tracer</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Decompose `model` into smaller constituent operations.</span>
<span class="sd"> Currently,this only supports decomposing ReLU into its</span>
<span class="sd"> mathematical definition: (x > 0) * x</span>
<span class="sd"> """</span>
<span class="n">graph</span> <span class="p">:</span> <span class="n">fx</span><span class="o">.</span><span class="n">Graph</span> <span class="o">=</span> <span class="n">tracer_class</span><span class="p">()</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">new_graph</span> <span class="o">=</span> <span class="n">fx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
<span class="n">env</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">tracer</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">proxy</span><span class="o">.</span><span class="n">GraphAppendingTracer</span><span class="p">(</span><span class="n">new_graph</span><span class="p">)</span>
<span class="k">for</span> <span class="n">node</span> <span class="ow">in</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">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s1">'call_function'</span> <span class="ow">and</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="ow">in</span> <span class="n">decomposition_rules</span><span class="p">:</span>
<span class="c1"># By wrapping the arguments with proxies,</span>
<span class="c1"># we can dispatch to the appropriate</span>
<span class="c1"># decomposition rule and implicitly add it</span>
<span class="c1"># to the Graph by symbolically tracing it.</span>
<span class="n">proxy_args</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">fx</span><span class="o">.</span><span class="n">Proxy</span><span class="p">(</span><span class="n">env</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="p">],</span> <span class="n">tracer</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">fx</span><span class="o">.</span><span class="n">Node</span><span class="p">)</span> <span class="k">else</span> <span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">node</span><span class="o">.</span><span class="n">args</span><span class="p">]</span>
<span class="n">output_proxy</span> <span class="o">=</span> <span class="n">decomposition_rules</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">](</span><span class="o">*</span><span class="n">proxy_args</span><span class="p">)</span>
<span class="c1"># Operations on `Proxy` always yield new `Proxy`s, and the</span>
<span class="c1"># return value of our decomposition rule is no exception.</span>
<span class="c1"># We need to extract the underlying `Node` from the `Proxy`</span>
<span class="c1"># to use it in subsequent iterations of this transform.</span>
<span class="n">new_node</span> <span class="o">=</span> <span class="n">output_proxy</span><span class="o">.</span><span class="n">node</span>
<span class="n">env</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_node</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Default case: we don't have a decomposition rule for this</span>
<span class="c1"># node, so just copy the node over into the new graph.</span>
<span class="n">new_node</span> <span class="o">=</span> <span class="n">new_graph</span><span class="o">.</span><span class="n">node_copy</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">env</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
<span class="n">env</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_node</span>
<span class="k">return</span> <span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">new_graph</span><span class="p">)</span>
</pre></div>
</div>
<p>In addition to avoiding explicit graph manipulation, using <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a>s
also allows you to specify your rewrite rules as native Python code.
For transformations that require a large amount of rewrite rules
(such as vmap or grad), this can often improve readability and
maintainability of the rules. Note that while calling <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a> we also
passed a tracer pointing to the underlying variable <cite>graph</cite>. This is done so
if in case the operations in graph are n-ary (e.g. add is a binary operator)
the call to <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a> does not create multiple instances of a graph
tracer which can lead to unexpected runtime errors. We recommend this method
of using <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a> especially when the underlying operators can not be
safely assumed to be unary.</p>
<p>A worked example of using <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a>s for <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> manipulation
can be found
<a class="reference external" href="https://github.com/pytorch/examples/blob/master/fx/proxy_based_graph_creation.py">here</a>.</p>
</section>
<section id="the-interpreter-pattern">
<h3>The Interpreter Pattern<a class="headerlink" href="#the-interpreter-pattern" title="Permalink to this heading">¶</a></h3>
<p>A useful code organizational pattern in FX is to loop over all the <a class="reference internal" href="#torch.fx.Node" title="torch.fx.Node"><code class="xref py py-class docutils literal notranslate"><span class="pre">Node</span></code></a>s
in a <a class="reference internal" href="#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> and execute them. This can be used for several things including
runtime analysis of values flowing through the graph or transformation of the code
via retracing with <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a>s. For example, suppose we want to run a
<a class="reference internal" href="#torch.fx.GraphModule" title="torch.fx.GraphModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">GraphModule</span></code></a> and record the <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> shape and dtype
properties on the nodes as we see them at runtime. That might look like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="n">Node</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span>
<span class="k">class</span> <span class="nc">ShapeProp</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Shape propagation. This class takes a `GraphModule`.</span>
<span class="sd"> Then, its `propagate` method executes the `GraphModule`</span>
<span class="sd"> node-by-node with the given arguments. As each operation</span>
<span class="sd"> executes, the ShapeProp class stores away the shape and</span>
<span class="sd"> element type for the output values of each operation on</span>
<span class="sd"> the `shape` and `dtype` attributes of the operation's</span>
<span class="sd"> `Node`.</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">mod</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mod</span> <span class="o">=</span> <span class="n">mod</span>
<span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">graph</span>
<span class="bp">self</span><span class="o">.</span><span class="n">modules</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">mod</span><span class="o">.</span><span class="n">named_modules</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">propagate</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">args_iter</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">env</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">Node</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">def</span> <span class="nf">load_arg</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">map_arg</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">n</span><span class="p">:</span> <span class="n">env</span><span class="p">[</span><span class="n">n</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">fetch_attr</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">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">mod</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 nonexistant 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>
<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">graph</span><span class="o">.</span><span class="n">nodes</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">'placeholder'</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">args_iter</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s1">'get_attr'</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">fetch_attr</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s1">'call_function'</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">(</span><span class="o">*</span><span class="n">load_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="o">**</span><span class="n">load_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">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s1">'call_method'</span><span class="p">:</span>
<span class="n">self_obj</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span> <span class="o">=</span> <span class="n">load_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="n">kwargs</span> <span class="o">=</span> <span class="n">load_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="n">result</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">self_obj</span><span class="p">,</span> <span class="n">node</span><span class="o">.</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>
<span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s1">'call_module'</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">](</span><span class="o">*</span><span class="n">load_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="o">**</span><span class="n">load_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="c1"># This is the only code specific to shape propagation.</span>
<span class="c1"># you can delete this `if` branch and this becomes</span>
<span class="c1"># a generic GraphModule interpreter.</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result</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="n">node</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">shape</span>
<span class="n">node</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">dtype</span>
<span class="n">env</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span>
<span class="k">return</span> <span class="n">load_arg</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">result</span><span class="p">)</span>
</pre></div>
</div>
<p>As you can see, a full interpreter for FX is not that complicated
but it can be very useful. To ease using this pattern, we provide
the <a class="reference internal" href="#torch.fx.Interpreter" title="torch.fx.Interpreter"><code class="xref py py-class docutils literal notranslate"><span class="pre">Interpreter</span></code></a> class, which encompasses the above logic
in a way that certain aspects of the interpreter’s execution can
be overridden via method overrides.</p>
<p>In addition to executing operations, we can also generate a new
<cite>Graph</cite> by feeding <a class="reference internal" href="#torch.fx.Proxy" title="torch.fx.Proxy"><code class="xref py py-class docutils literal notranslate"><span class="pre">Proxy</span></code></a> values through an interpreter.
Similarly, we provide the <a class="reference internal" href="#torch.fx.Transformer" title="torch.fx.Transformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transformer</span></code></a> class to encompass
this pattern. <a class="reference internal" href="#torch.fx.Transformer" title="torch.fx.Transformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transformer</span></code></a> behaves similarly to
<a class="reference internal" href="#torch.fx.Interpreter" title="torch.fx.Interpreter"><code class="xref py py-class docutils literal notranslate"><span class="pre">Interpreter</span></code></a>, but instead of calling the <code class="docutils literal notranslate"><span class="pre">run</span></code> method to
get a concrete output value from the Module, you would call the
<a class="reference internal" href="#torch.fx.Transformer.transform" title="torch.fx.Transformer.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Transformer.transform()</span></code></a> method to return a new
<a class="reference internal" href="#torch.fx.GraphModule" title="torch.fx.GraphModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">GraphModule</span></code></a> which was subject to any transformation rules
you installed as overridden methods.</p>
<section id="examples-of-the-interpreter-pattern">
<h4>Examples of the Interpreter Pattern<a class="headerlink" href="#examples-of-the-interpreter-pattern" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/fx/passes/shape_prop.py">Shape
Propagation</a></p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/tutorials/pull/1319">Performance Profiler</a></p></li>
</ul>
</section>
</section>
</section>
<section id="debugging">
<h2>Debugging<a class="headerlink" href="#debugging" title="Permalink to this heading">¶</a></h2>
<section id="introduction">
<h3>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">¶</a></h3>
<p>Often in the course of authoring transformations, our code will not be quite right.
In this case, we may need to do some debugging. The key is to work
backwards: first, check the results of invoking the generated module to prove or
disprove correctness. Then, inspect and debug the generated code. Then, debug the
process of transformations that led to the generated code.</p>
<p>If you’re not familiar with debuggers, please see the auxiliary section
<a class="reference internal" href="#available-debuggers"><span class="std std-ref">Available Debuggers</span></a>.</p>
</section>
<section id="common-pitfalls-in-transform-authoring">
<h3>Common Pitfalls in Transform Authoring<a class="headerlink" href="#common-pitfalls-in-transform-authoring" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p>Nondeterministic <code class="docutils literal notranslate"><span class="pre">set</span></code> iteration order. In Python, the <code class="docutils literal notranslate"><span class="pre">set</span></code> datatype is
unordered. Using <code class="docutils literal notranslate"><span class="pre">set</span></code> to contain collections of objects like <code class="docutils literal notranslate"><span class="pre">Node</span></code>s,
for example, can cause unexpected nondeterminism. An example is iterating
over a set of <code class="docutils literal notranslate"><span class="pre">Node</span></code>s to insert them into a <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. Because the
<code class="docutils literal notranslate"><span class="pre">set</span></code> data type is unordered, the ordering of the operations in the output
program will be nondeterministic and can change across program invocations.
The recommended alternative is to use a <code class="docutils literal notranslate"><span class="pre">dict</span></code> data type, which is
<a class="reference external" href="https://mail.python.org/pipermail/python-dev/2017-December/151283.html">insertion ordered</a>
as of Python 3.7 (and as of cPython 3.6). A <code class="docutils literal notranslate"><span class="pre">dict</span></code> can be used equivalently
to a set by storing values to be deduplicated in the keys of the <code class="docutils literal notranslate"><span class="pre">dict</span></code>.</p></li>
</ul>
</section>
<section id="checking-correctness-of-modules">
<h3>Checking Correctness of Modules<a class="headerlink" href="#checking-correctness-of-modules" title="Permalink to this heading">¶</a></h3>
<p>Because the output of most deep learning modules consists of floating
point <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> instances, checking for equivalence between
the results of two <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a> is not as straightforward
as doing a simple equality check. To motivate this, let’s use an
example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">m</span> <span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="n">gm</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">symbolic_trace</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="c1"># Imagine we're doing some transforms here</span>
<span class="c1"># <...></span>
<span class="n">gm</span><span class="o">.</span><span class="n">recompile</span><span class="p">()</span>
<span class="k">return</span> <span class="n">gm</span>
<span class="n">resnet18</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">()</span>
<span class="n">transformed_resnet18</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">resnet18</span><span class="p">)</span>
<span class="n">input_image</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">resnet18</span><span class="p">(</span><span class="n">input_image</span><span class="p">)</span> <span class="o">==</span> <span class="n">transformed_resnet18</span><span class="p">(</span><span class="n">input_image</span><span class="p">)</span>
<span class="sd">"""</span>
<span class="sd">RuntimeError: Boolean value of Tensor with more than one value is ambiguous</span>
<span class="sd">"""</span>
</pre></div>
</div>
<p>Here, we’ve tried to check equality of the values of two deep learning
models with the <code class="docutils literal notranslate"><span class="pre">==</span></code> equality operator. However, this is not well-
defined both due to the issue of that operator returning a tensor
and not a bool, but also because comparison of floating point values
should use a margin of error (or epsilon) to account for the
non-commutativity of floating point operations (see
<a class="reference external" href="https://floating-point-gui.de/errors/comparison/">here</a> for more
details). We can use <a class="reference internal" href="generated/torch.allclose.html#torch.allclose" title="torch.allclose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.allclose()</span></code></a> instead, which will give
us an approximate comparison taking into account a relative and
absolute tolerance threshold:</p>