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<section id="guards-overview">
<h1>Guards Overview<a class="headerlink" href="#guards-overview" title="Permalink to this heading">¶</a></h1>
<p>From a UX perspective, TorchDynamo is very easy to use. The user invokes
<code class="docutils literal notranslate"><span class="pre">torchdynamo.optimize</span></code> as an annotation:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torchdynamo</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">my_compiler</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">fn_foo</span><span class="p">(</span><span class="n">bar</span><span class="p">):</span>
</pre></div>
</div>
<p>Where a complete example looks like this:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">_dynamo</span> <span class="k">as</span> <span class="n">torchdynamo</span>
<span class="k">def</span> <span class="nf">my_compiler</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">example_inputs</span><span class="p">:</span> <span class="n">List</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="nb">print</span><span class="p">(</span><span class="s2">"my_compiler() called with FX 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">print_tabular</span><span class="p">()</span>
<span class="k">return</span> <span class="n">gm</span><span class="o">.</span><span class="n">forward</span> <span class="c1"># return a python callable</span>
<span class="nd">@torchdynamo</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">my_compiler</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">toy_example</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">a</span> <span class="o">/</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">b</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">toy_example</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
</pre></div>
</div>
<p>This allows TorchDynamo to capture the interpreted Python frames, grab
any and all relevant information, and speed things up wherever it can.
The speedup comes from a few places, and can be rather dependent on the
backend (<cite>my_compiler</cite> in the example above) provided, but the one speedup
that is important in this section is <strong>caching</strong>. Caching itself is not
a direct speedup but a critical enablement that prevents
recompilation. We dig a hole with dynamo, and caching allows us to get
out. It enables us to hold perf
neutrality while then enabling backends - the true source of our
speedups.</p>
<p>With even a pass-through no-op backend provided:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">my_compiler</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">example_inputs</span><span class="p">:</span> <span class="n">List</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="k">return</span> <span class="n">gm</span><span class="o">.</span><span class="n">forward</span>
</pre></div>
</div>
<p>We can see TorchDynamo speeding up Python execution even on
regular Python, not just PyTorch.</p>
<section id="caching-and-guards-overview">
<h2>Caching and Guards Overview<a class="headerlink" href="#caching-and-guards-overview" title="Permalink to this heading">¶</a></h2>
<p>TorchDynamo operates through caching transformed (by TorchDynamo) user
bytecode. When TorchDynamo receives a frame for evaluation, it checks if the
<strong>objects referenced in the frame have changed</strong> in certain ways, and if
not, TorchDynamo reads the previously transformed user bytecode to evaluate it.
In this section, we will focus on how we can identify whether or not the
<strong>objects referenced in the frame have changed</strong>. This is a critical
piece of functionality in TorchDynamo, because it drives the entire
invalidation lifecycle. This functionality is called <strong>guards</strong>.</p>
<p>At a very high level, the flow can be summarized like this:</p>
<ol class="arabic simple">
<li><p>TorchDynamo receives a Python frame.</p></li>
<li><p>It converts the frame (1) passing it through instruction
translation.</p></li>
<li><p>For the objects captured in (2), TorchDynamo creates tracking objects that
are:
* tracked on an output graph, which is an internal specialization
of a <cite>torch.fx.Tracer</cite>
* guards</p></li>
<li><p>TorchDynamo processes the guard objects created in (3), turning them into a
generated Python function, <cite>check_fn</cite>, associated with a piece of code.</p></li>
<li><p>The <cite>check_fn</cite> is evaluated whenever we encounter this code a
subsequent time - if a <cite>check_fn</cite> passes and evaluates to <cite>True</cite>, TorchDynamo
identifies the code in the cache and the code encountered here as same, and
can be safely used. If it fails and evaluates to <cite>False</cite>, TorchDynamo
identifies the code in the cache as not valid, and can be thrown out in
favor of a new entry, through recompilation or a graph break.</p></li>
</ol>
</section>
<section id="python-frame-evaluation-and-pep-523">
<h2>Python Frame Evaluation and PEP 523<a class="headerlink" href="#python-frame-evaluation-and-pep-523" title="Permalink to this heading">¶</a></h2>
<p>The functionality of TorchDynamo is based on
<a class="reference external" href="https://peps.python.org/pep-0523/">PEP 523</a>.</p>
<p>TorchDynamo installs a frame evaluation function on Python by using
<cite>_PyInterpreterState_SetEvalFrameFunc</cite>. TorchDynamo has a hook where
Python can hand control back to us during evaluation.</p>
<p>The function we have installed is <code class="docutils literal notranslate"><span class="pre">convert_frame</span></code> or
<code class="docutils literal notranslate"><span class="pre">convert_frame_assert</span></code> in the <code class="docutils literal notranslate"><span class="pre">nopython=True</span></code> case, but glossing
over that nuance for now, let’s take a look at <code class="docutils literal notranslate"><span class="pre">convert_frame_assert</span></code>,
as <code class="docutils literal notranslate"><span class="pre">convert_frame</span></code> proxies to it.</p>
<p>We can find it on <a class="reference external" href="https://github.com/pytorch/torchdynamo/blob/main/torchdynamo/convert_frame.py#L200">line 20 of convert_frame.py</a>,
with a signature as follows:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">convert_frame_assert</span><span class="p">(</span><span class="n">compiler_fn</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">one_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
</pre></div>
</div>
<p>This function wraps the entry point of where Python invokes TorchDynamo
with a frame:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">_convert_frame_assert</span><span class="p">(</span><span class="n">frame</span><span class="p">:</span> <span class="n">types</span><span class="o">.</span><span class="n">FrameType</span><span class="p">,</span> <span class="n">cache_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
</pre></div>
</div>
<p>Here is what this function does:</p>
<ol class="arabic">
<li><p>Checks if it has seen this <code class="docutils literal notranslate"><span class="pre">code</span></code>(see: f_code <a class="reference external" href="https://docs.python.org/3/library/inspect.html">here</a>) before and exits
early if it did.</p></li>
<li><p>Checks if the code is an unsupported case.</p></li>
<li><p>Checks if the <code class="docutils literal notranslate"><span class="pre">cache_size</span></code> (second arg above) crosses the limit
defined in the config, <code class="docutils literal notranslate"><span class="pre">cache_size_limit</span></code>. If it has, the function
drops the frame and logs warnings. This helps to avoid constant
recompilation of a frame as it generally means that the frame is hot
in an unexpected way and caching it produces needless overhead,
as it is likely to get evicted the next time it is encountered.</p></li>
<li><p>Passes the frame, alongside a function that creates an
<code class="docutils literal notranslate"><span class="pre">InstructionTranslator</span></code> through bytecode
transformation, via <code class="docutils literal notranslate"><span class="pre">transform_code_object</span></code>. A few crucial things
happen under the hood here:</p>
<ol class="arabic">
<li><p>New code is produced through <code class="docutils literal notranslate"><span class="pre">transform_code_object</span></code>.</p></li>
<li><p>An FX tracer named <code class="docutils literal notranslate"><span class="pre">output</span></code> is produced through
<code class="docutils literal notranslate"><span class="pre">InstructionTranslator</span></code>.</p>
<p>This can be a bit confusing,
as <code class="docutils literal notranslate"><span class="pre">InstructionTranslator</span></code> is not an <cite>fx</cite> tracer, but its stored
in a variable named tracer, and its output*<strong>is</strong><em>an `fx`tracer.</em></p>
</li>
<li><p>The function produces guards and stores them on <code class="docutils literal notranslate"><span class="pre">output</span></code> above.</p></li>
<li><p>The function produces <code class="docutils literal notranslate"><span class="pre">output_instructions</span></code> and stores them on
<code class="docutils literal notranslate"><span class="pre">output</span></code> above.</p></li>
<li><p>The function maps the newly produced transformed code to the initial code it
read off the frame. This mapping is worth remembering, we will
refer to it much later on below where we cover guard failures.</p></li>
</ol>
</li>
<li><p>Using the transformed code from 4.1 and the guards from 4.3,
the function produces a <cite>GuardedCode</cite>.</p></li>
</ol>
<p>Now that we have learned about frame evaluation, let’s review
<code class="docutils literal notranslate"><span class="pre">InstructionTranslator</span></code>, and see how it turns the frame we handed
it over into TorchDynamo internal types.</p>
</section>
<section id="instructiontranslator">
<h2>InstructionTranslator<a class="headerlink" href="#instructiontranslator" title="Permalink to this heading">¶</a></h2>
<p><cite>InstructionTranslator</cite> does a lot! We won’t cover the details of
everything it does, but most importantly for this document, it produces
a mapping of <code class="docutils literal notranslate"><span class="pre">symbolic_locals</span></code> which maintains a mapping from the
frame’s <code class="docutils literal notranslate"><span class="pre">f_locals</span></code> to TorchDynamo internal Variable objects (more on these
in a moment. <code class="docutils literal notranslate"><span class="pre">symbolic_locals</span></code> is filled via traversing the frame’s
locals:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">symbolic_locals</span> <span class="o">=</span> <span class="n">collections</span><span class="o">.</span><span class="n">OrderedDict</span><span class="p">(</span>
<span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">VariableBuilder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LocalSource</span><span class="p">(</span><span class="n">k</span><span class="p">))(</span><span class="n">f_locals</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">vars</span>
<span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">f_locals</span>
<span class="p">)</span>
</pre></div>
</div>
<p>The important component here is the invocation of a call
into <code class="docutils literal notranslate"><span class="pre">VariableBuilder</span></code>. <code class="docutils literal notranslate"><span class="pre">VariableBuilder</span></code>’s call implementation
proxies into a function called <code class="docutils literal notranslate"><span class="pre">_wrap</span></code>, which in turn both constructs
instances of <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code> and calls <code class="docutils literal notranslate"><span class="pre">make_guards</span></code> on them. More
on that later.</p>
<p>This mapping, in turn, is critical as each Variable has associated
guards, which are then passed to <code class="docutils literal notranslate"><span class="pre">self.output</span></code>, the instance of
<code class="docutils literal notranslate"><span class="pre">OutputGraph</span></code>, an fx tracer, mentioned in 4.2 of the section above. If
you recall, this <code class="docutils literal notranslate"><span class="pre">OutputGraph</span></code>, stored in a variable called <code class="docutils literal notranslate"><span class="pre">output</span></code>
is where our guards are stored before being passed on to become
<code class="docutils literal notranslate"><span class="pre">GuardedCode</span></code></p>
<p>How does <code class="docutils literal notranslate"><span class="pre">InstructionTranslator</span></code> do this? At the heart of it, there is
a loop that is pumped, which drives a function <code class="docutils literal notranslate"><span class="pre">step</span></code>.</p>
<p><code class="docutils literal notranslate"><span class="pre">step</span></code> is just that - a single processing step, taking exactly one
instruction and doing <em>something</em> with it.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>These are real instructions processed by TorchDynamo’s
<code class="docutils literal notranslate"><span class="pre">transform_code_object</span></code>, and it is pretty cool.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This section purposely skips the details of
<a class="reference external" href="https://docs.python.org/3/library/dis.html">dis.get_instructions</a>.</p>
</div>
<p>For the example above, here is a snippet of a what a few
<code class="docutils literal notranslate"><span class="pre">Instruction</span></code>'s may look like:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">Instruction</span><span class="p">(</span><span class="n">opcode</span><span class="o">=</span><span class="mi">124</span><span class="p">,</span> <span class="n">opname</span><span class="o">=</span><span class="s1">'LOAD_FAST'</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">argval</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">starts_line</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">is_jump_target</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">Instruction</span><span class="p">(</span><span class="n">opcode</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">opname</span><span class="o">=</span><span class="s1">'LOAD_CONST'</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">argval</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">34</span><span class="p">,</span> <span class="n">starts_line</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">is_jump_target</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">Instruction</span><span class="p">(</span><span class="n">opcode</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">opname</span><span class="o">=</span><span class="s1">'BINARY_MULTIPLY'</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">argval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">36</span><span class="p">,</span> <span class="n">starts_line</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">is_jump_target</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
</pre></div>
</div>
<p>This is the core functionality of this function. Take a look at the <code class="docutils literal notranslate"><span class="pre">opname</span></code>,
and then take a look at this little snippet from inside <code class="docutils literal notranslate"><span class="pre">step</span></code>;</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inst</span><span class="o">.</span><span class="n">opname</span><span class="p">):</span>
<span class="n">unimplemented</span><span class="p">(</span><span class="sa">f</span><span class="s2">"missing: </span><span class="si">{</span><span class="n">inst</span><span class="o">.</span><span class="n">opname</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inst</span><span class="o">.</span><span class="n">opname</span><span class="p">)(</span><span class="n">inst</span><span class="p">)</span>
</pre></div>
</div>
<p>As we can see, the function checks if the current class, the
<code class="docutils literal notranslate"><span class="pre">InstructionTranslator</span></code> has an attribute set matching the operator name
(for example, <code class="docutils literal notranslate"><span class="pre">LOAD_CONST</span></code>). If it does, the function invokes it, passing the
whole instruction object in. If it does not, the function drops the frame as
unimplemented.</p>
<p>For the <code class="docutils literal notranslate"><span class="pre">LOAD_CONST</span></code> example, we can see that we do indeed support it,
with a relatively straightforward definition:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">LOAD_CONST</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inst</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">ConstantVariable</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">inst</span><span class="o">.</span><span class="n">argval</span><span class="p">))</span>
</pre></div>
</div>
<p>We can see that this function creates a new instance of the class
<code class="docutils literal notranslate"><span class="pre">ConstantVariable</span></code> , with a value, in our example case, -1, and then
pushes it onto the stack.</p>
<p>There are dozens of such methods - see <code class="docutils literal notranslate"><span class="pre">symbolic_convert.py</span></code> for all of
them. Generally, we implement as many matching methods to Python
bytecode instructions as possible.</p>
<p>Across both the logic downstream of <code class="docutils literal notranslate"><span class="pre">step</span></code> and the logic from invoking
<code class="docutils literal notranslate"><span class="pre">VariableBuilder</span></code> - we now have a lot of <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>s and of
course, we’ve spoken about creating guards quiet a bit. Let’s dig into
what Variables are, and get a little closer to understanding guards.</p>
</section>
<section id="variables">
<h2>Variables<a class="headerlink" href="#variables" title="Permalink to this heading">¶</a></h2>
<p>A <code class="docutils literal notranslate"><span class="pre">ConstantVariable</span></code> is an instance of<code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>.
<code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code> represents a tracked Python local or stack value.</p>
<p>When it comes to representing an object inside TorchDynamo, a
<code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code> does exactly what it says - it tracks a given variable.
It is an extremely flexible class, but there are a few points to keep in
mind:</p>
<ul class="simple">
<li><p>It manages the <code class="docutils literal notranslate"><span class="pre">guard</span></code> relationship around the underlying object
through:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">make_guard</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">replace_guards</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">add_guard(s)</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">propagate</span></code> - <code class="docutils literal notranslate"><span class="pre">propagate(*vars:</span> <span class="pre">List[List["VariableTracker"]])</span></code> -
Perhaps the most important of all, in that it combines guards from
all the provided <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code> instances passed in. It visits
the guards and combines the guards from these onto itself.</p></li>
</ul>
</li>
<li><p>It acts as a proxy on behalf of the underlying object, implementing
methods for the rest of TorchDynamo to get information about the
tracked object:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">call_method</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">call_function</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">python_type</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">as_proxy</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">is/as_python_proxy</span></code></p></li>
</ul>
</li>
<li><p>It stores the variable <code class="docutils literal notranslate"><span class="pre">source</span></code> of type <code class="docutils literal notranslate"><span class="pre">Source</span></code>, from
<code class="docutils literal notranslate"><span class="pre">torchdynamo/source.py</span></code>. This source type is a relatively self
contained class that helps us organize and bookkeep where the original
source came from, and helps provide convenience methods for things
like getting the name, and importantly for us, producing guards.</p></li>
</ul>
<p>And this class (<code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>) is built around subclassing,
somewhere between a full Abstract Base Class and fully fleshed out class
- it leaves many methods raising <code class="docutils literal notranslate"><span class="pre">NotImplementedError</span></code> - with reliance on
subclasses. See <code class="docutils literal notranslate"><span class="pre">torchdynamo/variables/</span></code> for all subclasses to fulfill
contracts and custom behaviors.</p>
<p>Knowing what we know now, we can see an example of how an instruction
from <code class="docutils literal notranslate"><span class="pre">dis</span></code>, <code class="docutils literal notranslate"><span class="pre">BUILD_TUPLE</span></code>:</p>
<blockquote>
<div><p><code class="docutils literal notranslate"><span class="pre">BUILD_TUPLE(count)</span></code> Creates a tuple consuming count items from the
stack, and pushes the resulting tuple onto the stack.</p>
</div></blockquote>
<p>In our case, our signature will be a <em>little</em> different due to the way
we create <code class="docutils literal notranslate"><span class="pre">Instruction</span></code> objects, but the gist of it will be the same.
Instead of passing in <code class="docutils literal notranslate"><span class="pre">count</span></code>, we pass in an object with a little
extra bookkeeping, and of course, we deal with turning regular old
python objects into TorchDynamo notions:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">BUILD_TUPLE</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inst</span><span class="p">):</span>
<span class="n">items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">popn</span><span class="p">(</span><span class="n">inst</span><span class="o">.</span><span class="n">argval</span><span class="p">)</span>
<span class="n">options</span> <span class="o">=</span> <span class="n">VariableTracker</span><span class="o">.</span><span class="n">propagate</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">TupleVariable</span><span class="p">(</span><span class="n">items</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">))</span>
</pre></div>
</div>
<p>Here is what this code does:</p>
<ol class="arabic simple">
<li><p>The function reads <code class="docutils literal notranslate"><span class="pre">argval</span></code>, which in this case, is
analogous to <code class="docutils literal notranslate"><span class="pre">counts</span></code> in the pydoc for the equivalent instruction.</p></li>
<li><p>The function <code class="docutils literal notranslate"><span class="pre">popn</span></code> the items, in this case, the signature is
<code class="docutils literal notranslate"><span class="pre">def</span>  <span class="pre">popn(self,</span> <span class="pre">n:</span> <span class="pre">int)</span> <span class="pre">-></span> <span class="pre">List[TensorVariable]:</span></code> this hints at an
underlying contract - we are returning <code class="docutils literal notranslate"><span class="pre">TensorVariables</span></code>. If we
take a closer look at <code class="docutils literal notranslate"><span class="pre">sybmolic_convert.py</span></code> and
<code class="docutils literal notranslate"><span class="pre">InstructionTranslatorBase</span></code>/<code class="docutils literal notranslate"><span class="pre">InstructionTranslator</span></code>we see that
the only thing pushed onto and popped from our stack are
<code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>s.</p></li>
</ol>
<ol class="arabic simple" start="3">
<li><p>The function calls <code class="docutils literal notranslate"><span class="pre">VariableTracker.propagate</span></code>. This
takes the guards from every single item popped off the stack in 2,
and recursively traverses it and combines all the guards into
<code class="docutils literal notranslate"><span class="pre">options</span></code>: <code class="docutils literal notranslate"><span class="pre">py</span>  <span class="pre">return</span> <span class="pre">{</span>      <span class="pre">"guards":</span> <span class="pre">guards,</span>  <span class="pre">}</span></code></p></li>
<li><p>The function then makes a new instance of a <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>,
<code class="docutils literal notranslate"><span class="pre">TupleVariable</span></code>out of the <code class="docutils literal notranslate"><span class="pre">items</span></code> and <code class="docutils literal notranslate"><span class="pre">options</span></code>. This then
allows us to install all the appropriate guards from the <code class="docutils literal notranslate"><span class="pre">items</span></code>
that make up the new <code class="docutils literal notranslate"><span class="pre">TupleVariable</span></code></p></li>
</ol>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Where did the first guards come from? Propagation
is a good technique, but we need something created before it can be
propagated. <code class="docutils literal notranslate"><span class="pre">VariableBuilder</span></code> calls
<code class="docutils literal notranslate"><span class="pre">make_guards</span></code> as it creates <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code> instances, from
<code class="docutils literal notranslate"><span class="pre">f_locals</span></code>. This in turn calls into the <code class="docutils literal notranslate"><span class="pre">source</span></code>, to have it create
guards.</p>
</div>
<p>After all this, bytecode translation is done and we are one step closer
to producing <code class="docutils literal notranslate"><span class="pre">GuardedCode</span></code>. We now understand how locals become
<code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>s, how instructions are handled, and where guards
are called on for creation. Before we can go into seeing how code and
guards are combined into a GuardedCode object, we need to dig a little
bit into those <code class="docutils literal notranslate"><span class="pre">make_guard</span></code> and <code class="docutils literal notranslate"><span class="pre">source.make_guard</span></code> calls above. We
can then understand, what was going on when we made guards
alongside, and on, <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code> instances.</p>
</section>
<section id="making-guards">
<h2>Making Guards<a class="headerlink" href="#making-guards" title="Permalink to this heading">¶</a></h2>
<p>Guards are just Python objects, of the class <code class="docutils literal notranslate"><span class="pre">Guard</span></code>. Let’s look at them
in more detail.</p>
<p>Looking at the definition of the dataclass (and therefore, ctor
signature), we see that it has a name, a source, and a create function.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span>
<span class="k">class</span> <span class="nc">Guard</span><span class="p">:</span>
<span class="n">name</span><span class="p">:</span> <span class="nb">str</span>
<span class="n">source</span><span class="p">:</span> <span class="n">GuardSource</span>
<span class="n">create_fn</span><span class="p">:</span> <span class="n">Callable</span>
</pre></div>
</div>
<p>The name should be the name of the variable.</p>
<p>The source here is an enum indicating what <em>kind</em> of source the guard
belongs to.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Not to be confused with <code class="docutils literal notranslate"><span class="pre">Source</span></code> and the other types
in <code class="docutils literal notranslate"><span class="pre">source.py</span></code>, as stored on <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">create_fn</span></code> provides the main functionality to transition from a simple
dataclass to actually producing valid Python code to be invoked for
knowing whether or not things have changed in between invocations, and
whether we can safely read from the code cache or not.</p>
<p>The most common code paths for getting an instance of a guard are
through <code class="docutils literal notranslate"><span class="pre">make_guards</span></code> on <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>.
<code class="docutils literal notranslate"><span class="pre">make_guards</span></code>->``source.make_guard``->``return Guard(self.name(), self.guard_source(), fn)``</p>
<p>Or, in a concrete example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">...</span>
<span class="k">elif</span> <span class="n">istype</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">range</span><span class="p">):</span>
<span class="n">guards</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_guards</span><span class="p">(</span><span class="n">GuardBuilder</span><span class="o">.</span><span class="n">EQUALS_MATCH</span><span class="p">)</span>
<span class="k">return</span> <span class="n">RangeVariable</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">guards</span><span class="o">=</span><span class="n">guards</span><span class="p">)</span>
</pre></div>
</div>
<p>Since <code class="docutils literal notranslate"><span class="pre">source</span></code> was set at the construction time of this
<code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>, all that was needed here was to provide the <code class="docutils literal notranslate"><span class="pre">fn</span></code>,
<code class="docutils literal notranslate"><span class="pre">GuardBuilder.EQUALS_MATCH</span></code> to the <code class="docutils literal notranslate"><span class="pre">create_fn</span></code> field.</p>
<p>This <code class="docutils literal notranslate"><span class="pre">create_fn</span></code> must be a method on <code class="docutils literal notranslate"><span class="pre">GuardBuilder</span></code>. The reason for
this becomes apparent in our next step. Once we have all the guards
created for a frame, we move on to <code class="docutils literal notranslate"><span class="pre">CheckFunctionManager</span></code> and
<code class="docutils literal notranslate"><span class="pre">compile_check_fn</span></code>.</p>
<p>Before the <code class="docutils literal notranslate"><span class="pre">convert_frame</span></code> function can produce a <code class="docutils literal notranslate"><span class="pre">GuardedCode</span></code>,
it needs to run the <code class="docutils literal notranslate"><span class="pre">CheckFunctionManager</span></code>, with all the guards, to
produce a <code class="docutils literal notranslate"><span class="pre">check_fn</span></code> which will then, in turn get passed in alongside
the code into <code class="docutils literal notranslate"><span class="pre">GuardedCode</span></code>. This is the same <code class="docutils literal notranslate"><span class="pre">check_fn</span></code> that we store in our
cache entry, and the same one we run to know whether or not to retrieve
the code stored alongside. For reference, here is that code:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">static</span><span class="w"> </span><span class="n">CacheEntry</span><span class="w"> </span><span class="o">*</span><span class="nf">create_cache_entry</span><span class="p">(</span><span class="n">CacheEntry</span><span class="w"> </span><span class="o">*</span><span class="n">next</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="n">PyObject</span><span class="w"> </span><span class="o">*</span><span class="n">guarded_code</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w"> </span><span class="n">CacheEntry</span><span class="w"> </span><span class="o">*</span><span class="n">e</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">CacheEntry</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="n">malloc</span><span class="p">(</span><span class="k">sizeof</span><span class="p">(</span><span class="n">CacheEntry</span><span class="p">));</span><span class="w"></span>
<span class="w"> </span><span class="n">DEBUG_NULL_CHECK</span><span class="p">(</span><span class="n">e</span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="n">e</span><span class="o">-></span><span class="n">check_fn</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PyObject_GetAttrString</span><span class="p">(</span><span class="n">guarded_code</span><span class="p">,</span><span class="w"> </span><span class="s">"check_fn"</span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="n">NULL_CHECK</span><span class="p">(</span><span class="n">e</span><span class="o">-></span><span class="n">check_fn</span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="n">e</span><span class="o">-></span><span class="n">code</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">PyCodeObject</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="n">PyObject_GetAttrString</span><span class="p">(</span><span class="n">guarded_code</span><span class="p">,</span><span class="w"> </span><span class="s">"code"</span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="n">NULL_CHECK</span><span class="p">(</span><span class="n">e</span><span class="o">-></span><span class="n">code</span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="n">e</span><span class="o">-></span><span class="n">next</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">next</span><span class="p">;</span><span class="w"></span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">e</span><span class="p">;</span><span class="w"></span>
<span class="p">}</span><span class="w"></span>
</pre></div>
</div>
<p>We now know how a <code class="docutils literal notranslate"><span class="pre">check_fn</span></code> function is used, and who makes it, and
what it is composed of, but what we do not yet know is how. How does a
list of <code class="docutils literal notranslate"><span class="pre">Guard</span></code> objects become a function we can run later on?</p>
<p>First, we iterate these guards:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">guard</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">guards</span> <span class="ow">or</span> <span class="p">[],</span> <span class="n">key</span><span class="o">=</span><span class="n">Guard</span><span class="o">.</span><span class="n">sort_key</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">config</span><span class="o">.</span><span class="n">guard_nn_modules</span> <span class="ow">and</span> <span class="n">guard</span><span class="o">.</span><span class="n">is_nn_module</span><span class="p">():</span>
<span class="k">continue</span>
<span class="n">guard</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">local_builder</span><span class="p">,</span> <span class="n">global_builder</span><span class="p">)</span>
</pre></div>
</div>
<p>Calling <code class="docutils literal notranslate"><span class="pre">guard.create</span></code> runs that <code class="docutils literal notranslate"><span class="pre">create_fn</span></code> we set on the <code class="docutils literal notranslate"><span class="pre">Guard</span></code>
class above (don’t confuse it with the <code class="docutils literal notranslate"><span class="pre">check_fn</span></code> we are working on
producing, the names are similar, so it can get a little confusing). In
our example above, our <code class="docutils literal notranslate"><span class="pre">create_fn</span></code> is <code class="docutils literal notranslate"><span class="pre">GuardBuilder.EQUALS_MATCH</span></code>.
So we are now invoking it, passing in the <code class="docutils literal notranslate"><span class="pre">self</span></code>, the guard itself,
in.</p>
<p>The signature is: <code class="docutils literal notranslate"><span class="pre">def</span> <span class="pre">EQUALS_MATCH(self,</span> <span class="pre">guard:</span> <span class="pre">Guard):</span></code></p>
<p>And internally to that function, we can use the <code class="docutils literal notranslate"><span class="pre">name</span></code> on the guard to
get back our original object, querying it for data and type information,
which in turn gets us to the most important bit: appending code.</p>
<p>At its simplest, <code class="docutils literal notranslate"><span class="pre">EQUALS_MATCH</span></code> appends just one line of code:
<code class="docutils literal notranslate"><span class="pre">self.code.append(f"{ref}</span> <span class="pre">==</span> <span class="pre">{val!r}")</span></code>. Where <code class="docutils literal notranslate"><span class="pre">ref</span></code> is the name of
the variable, and <code class="docutils literal notranslate"><span class="pre">val</span></code> is the value. It might produce code like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span> <span class="o">==</span> <span class="mi">2</span>
</pre></div>
</div>
<p>This is a basic example. But if we append a few other kinds of <code class="docutils literal notranslate"><span class="pre">GuardBuilder</span></code>
functions and then combine them all with
<code class="docutils literal notranslate"><span class="pre">and</span></code> in between each statement (as we do), we might get something
like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">___guarded_code</span><span class="o">.</span><span class="n">valid</span> <span class="ow">and</span> <span class="n">___check_type_id</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">94367738391392</span><span class="p">)</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">___check_tensors</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Here is what this code performs:</p>
<ol class="arabic simple">
<li><p>A check for <code class="docutils literal notranslate"><span class="pre">.valid</span></code></p></li>
<li><p>A type ID check</p></li>
<li><p>A value check</p></li>
<li><p>A tensor check</p></li>
</ol>
<p>This becomes the heart of the code our <code class="docutils literal notranslate"><span class="pre">check_fn</span></code>, which in turn
is evaluated the <strong>next</strong> time we encounter this code. It
will then check:</p>
<ol class="arabic simple">
<li><p>Is this code still valid?</p></li>
<li><p>If (1), Does <code class="docutils literal notranslate"><span class="pre">y</span></code> still have a type of <code class="docutils literal notranslate"><span class="pre">94367738391392</span></code>?</p></li>
<li><p>If (2), is <code class="docutils literal notranslate"><span class="pre">y</span></code> still 2?</p></li>
<li><p>If (3), let’s check on if tensor <code class="docutils literal notranslate"><span class="pre">x</span></code> changed in some specific ways.</p></li>
</ol>
<p>If all of these are still true, then we can use the code cached
alongside this <code class="docutils literal notranslate"><span class="pre">check_fn</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For a deeper dive for how and where this happens
you can read <code class="docutils literal notranslate"><span class="pre">static</span> <span class="pre">PyCodeObject</span> <span class="pre">*lookup(CacheEntry</span> <span class="pre">*e,</span> <span class="pre">PyObject</span> <span class="pre">*f_locals)</span> <span class="pre">{</span></code> of
<code class="docutils literal notranslate"><span class="pre">_eval_frame.c</span></code>.</p>
</div>
<p>If not, then, we can move on to recompiling the code anew, and storing
that in the cache alongside this code, and a whole new <code class="docutils literal notranslate"><span class="pre">check_fn</span></code>,
again to be checked on yet another subsequent frame.</p>
<p>There are lots of other such functions on <code class="docutils literal notranslate"><span class="pre">GuardBuilder</span></code> which get
coalesced into, at times massive, strings which then get evaluated as
Python code and stored into <code class="docutils literal notranslate"><span class="pre">check_fn</span></code>. The example above
illustrates of a simple case. To understand this functionality better, read
the other functions on <code class="docutils literal notranslate"><span class="pre">GuardBuilder</span></code>, or better yet, dump the <code class="docutils literal notranslate"><span class="pre">code</span></code> variable
in <code class="docutils literal notranslate"><span class="pre">compile_check_fn</span></code> to see what is getting produced,
especially on larger, real models.</p>
</section>
<section id="summary">
<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this heading">¶</a></h2>
<p>In this section, we have reviewed:</p>
<ul class="simple">
<li><p>The role of <code class="docutils literal notranslate"><span class="pre">.valid</span></code> and invalidation around weak references (and potentially soon to be NN Moduleinvalidations).</p></li>
<li><p>How the C++ side of guard functions (<code class="docutils literal notranslate"><span class="pre">___check_type_id</span></code>, <code class="docutils literal notranslate"><span class="pre">___check_tensors</span></code>, etc) operate</p></li>
<li><p>What happens when guards fail.</p></li>
<li><p>What happens if we produce invalid guard code.</p></li>
</ul>
<p>We covered how user provided code wrapped in a TorchDynamo context
goes on to get traced and tracked internally, organized into <code class="docutils literal notranslate"><span class="pre">VariableTracker</span></code>s
<code class="docutils literal notranslate"><span class="pre">Source</span></code>s and subsequently <code class="docutils literal notranslate"><span class="pre">Guard</span></code>s, and how those <code class="docutils literal notranslate"><span class="pre">Guards</span></code> in
turn guide cache entry selection and invalidation when handing Python
code.</p>
</section>
</section>
</article>
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