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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="torchdynamo-deeper-dive">
<h1>TorchDynamo Deeper Dive<a class="headerlink" href="#torchdynamo-deeper-dive" title="Permalink to this heading">¶</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/jansel">Jason Ansel</a></p>
<section id="what-is-a-guard">
<h2>What is a guard?<a class="headerlink" href="#what-is-a-guard" title="Permalink to this heading">¶</a></h2>
<p>TorchDynamo operates just-in-time and specializes graphs based on
dynamic properties. For example, the first graph above has the following
guards:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">GUARDS</span><span class="p">:</span>
<span class="o">-</span> <span class="n">local</span> <span class="s1">'a'</span> <span class="n">TENSOR_MATCH</span>
<span class="o">-</span> <span class="n">local</span> <span class="s1">'b'</span> <span class="n">TENSOR_MATCH</span>
<span class="o">-</span> <span class="k">global</span> <span class="s1">'torch'</span> <span class="n">FUNCTION_MATCH</span>
</pre></div>
</div>
<p>If any of those guards fail, the graph will be recaptured and
recompiled. The interesting guard type there is <code class="docutils literal notranslate"><span class="pre">TENSOR_MATCH</span></code>, which
checks the following <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> properties:</p>
<ul class="simple">
<li><p>Python class of the tensor (tensor subclassing, etc)</p></li>
<li><p>dtype</p></li>
<li><p>device</p></li>
<li><p>requires_grad</p></li>
<li><p>dispatch_key (with thread-local includes/excludes applied)</p></li>
<li><p>ndim</p></li>
<li><p>sizes* (optional)</p></li>
<li><p>strides* (optional)</p></li>
</ul>
<p>For sizes/strides you can disable this specialization by setting the
following parameter:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">dynamic_shapes</span> <span class="o">=</span> <span class="kc">True</span>
</pre></div>
</div>
<p>The full specialization mode allows the backend compiler to assume an
entirely static graph. Unfortunately, most backends require this.
Operators which return dynamic shapes will trigger a graph break when
not in dynamic shape mode.</p>
</section>
<section id="what-is-dynamo-doing">
<h2>What is Dynamo doing?<a class="headerlink" href="#what-is-dynamo-doing" title="Permalink to this heading">¶</a></h2>
<p>If you want to understand better what TorchDynamo is doing, you can set:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch._dynamo.config</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">log_level</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">INFO</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">output_code</span> <span class="o">=</span> <span class="kc">True</span>
</pre></div>
</div>
<p>This code triggers useful (but spammy) printouts.</p>
<p>For example, the printouts for the first graph in the <code class="docutils literal notranslate"><span class="pre">toy_example</span></code>
are:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">__compiled_fn_0</span> <span class="o"><</span><span class="n">eval_with_key</span><span class="o">></span><span class="mf">.1</span>
<span class="n">opcode</span> <span class="n">name</span> <span class="n">target</span> <span class="n">args</span> <span class="n">kwargs</span>
<span class="o">-------------</span> <span class="o">-------</span> <span class="o">------------------------------------------------------</span> <span class="o">----------------</span> <span class="o">--------</span>
<span class="n">placeholder</span> <span class="n">a</span> <span class="n">a</span> <span class="p">()</span> <span class="p">{}</span>
<span class="n">placeholder</span> <span class="n">b</span> <span class="n">b</span> <span class="p">()</span> <span class="p">{}</span>
<span class="n">call_function</span> <span class="n">abs_1</span> <span class="o"><</span><span class="n">built</span><span class="o">-</span><span class="ow">in</span> <span class="n">method</span> <span class="nb">abs</span> <span class="n">of</span> <span class="nb">type</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x7f9ca082f8a0</span><span class="o">></span> <span class="p">(</span><span class="n">a</span><span class="p">,)</span> <span class="p">{}</span>
<span class="n">call_function</span> <span class="n">add</span> <span class="o"><</span><span class="n">built</span><span class="o">-</span><span class="ow">in</span> <span class="n">function</span> <span class="n">add</span><span class="o">></span> <span class="p">(</span><span class="n">abs_1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="p">{}</span>
<span class="n">call_function</span> <span class="n">truediv</span> <span class="o"><</span><span class="n">built</span><span class="o">-</span><span class="ow">in</span> <span class="n">function</span> <span class="n">truediv</span><span class="o">></span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">add</span><span class="p">)</span> <span class="p">{}</span>
<span class="n">call_method</span> <span class="n">sum_1</span> <span class="nb">sum</span> <span class="p">(</span><span class="n">b</span><span class="p">,)</span> <span class="p">{}</span>
<span class="n">call_function</span> <span class="n">lt</span> <span class="o"><</span><span class="n">built</span><span class="o">-</span><span class="ow">in</span> <span class="n">function</span> <span class="n">lt</span><span class="o">></span> <span class="p">(</span><span class="n">sum_1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="p">{}</span>
<span class="n">output</span> <span class="n">output</span> <span class="n">output</span> <span class="p">((</span><span class="n">truediv</span><span class="p">,</span> <span class="n">lt</span><span class="p">),)</span> <span class="p">{}</span>
<span class="n">ORIGINAL</span> <span class="n">BYTECODE</span> <span class="n">toy_example</span> <span class="n">example</span><span class="o">.</span><span class="n">py</span> <span class="mi">9</span>
<span class="mi">10</span> <span class="mi">0</span> <span class="n">LOAD_FAST</span> <span class="mi">0</span> <span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="mi">2</span> <span class="n">LOAD_GLOBAL</span> <span class="mi">0</span> <span class="p">(</span><span class="n">torch</span><span class="p">)</span>
<span class="mi">4</span> <span class="n">LOAD_METHOD</span> <span class="mi">1</span> <span class="p">(</span><span class="nb">abs</span><span class="p">)</span>
<span class="mi">6</span> <span class="n">LOAD_FAST</span> <span class="mi">0</span> <span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="mi">8</span> <span class="n">CALL_METHOD</span> <span class="mi">1</span>
<span class="mi">10</span> <span class="n">LOAD_CONST</span> <span class="mi">1</span> <span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="mi">12</span> <span class="n">BINARY_ADD</span>
<span class="mi">14</span> <span class="n">BINARY_TRUE_DIVIDE</span>
<span class="mi">16</span> <span class="n">STORE_FAST</span> <span class="mi">2</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="mi">11</span> <span class="mi">18</span> <span class="n">LOAD_FAST</span> <span class="mi">1</span> <span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="mi">20</span> <span class="n">LOAD_METHOD</span> <span class="mi">2</span> <span class="p">(</span><span class="nb">sum</span><span class="p">)</span>
<span class="mi">22</span> <span class="n">CALL_METHOD</span> <span class="mi">0</span>
<span class="mi">24</span> <span class="n">LOAD_CONST</span> <span class="mi">2</span> <span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="mi">26</span> <span class="n">COMPARE_OP</span> <span class="mi">0</span> <span class="p">(</span><span class="o"><</span><span class="p">)</span>
<span class="mi">28</span> <span class="n">POP_JUMP_IF_FALSE</span> <span class="mi">38</span>
<span class="mi">12</span> <span class="mi">30</span> <span class="n">LOAD_FAST</span> <span class="mi">1</span> <span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="mi">32</span> <span class="n">LOAD_CONST</span> <span class="mi">3</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="mi">34</span> <span class="n">BINARY_MULTIPLY</span>
<span class="mi">36</span> <span class="n">STORE_FAST</span> <span class="mi">1</span> <span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="mi">13</span> <span class="o">>></span> <span class="mi">38</span> <span class="n">LOAD_FAST</span> <span class="mi">2</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="mi">40</span> <span class="n">LOAD_FAST</span> <span class="mi">1</span> <span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="mi">42</span> <span class="n">BINARY_MULTIPLY</span>
<span class="mi">44</span> <span class="n">RETURN_VALUE</span>
<span class="n">MODIFIED</span> <span class="n">BYTECODE</span>
<span class="mi">9</span> <span class="mi">0</span> <span class="n">LOAD_GLOBAL</span> <span class="mi">3</span> <span class="p">(</span><span class="n">__compiled_fn_0</span><span class="p">)</span>
<span class="mi">2</span> <span class="n">LOAD_FAST</span> <span class="mi">0</span> <span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="mi">4</span> <span class="n">LOAD_FAST</span> <span class="mi">1</span> <span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="mi">6</span> <span class="n">CALL_FUNCTION</span> <span class="mi">2</span>
<span class="mi">8</span> <span class="n">UNPACK_SEQUENCE</span> <span class="mi">2</span>
<span class="mi">10</span> <span class="n">STORE_FAST</span> <span class="mi">2</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="mi">12</span> <span class="n">POP_JUMP_IF_FALSE</span> <span class="mi">24</span>
<span class="mi">14</span> <span class="n">LOAD_GLOBAL</span> <span class="mi">4</span> <span class="p">(</span><span class="n">__resume_at_30_1</span><span class="p">)</span>
<span class="mi">16</span> <span class="n">LOAD_FAST</span> <span class="mi">1</span> <span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="mi">18</span> <span class="n">LOAD_FAST</span> <span class="mi">2</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="mi">20</span> <span class="n">CALL_FUNCTION</span> <span class="mi">2</span>
<span class="mi">22</span> <span class="n">RETURN_VALUE</span>
<span class="o">>></span> <span class="mi">24</span> <span class="n">LOAD_GLOBAL</span> <span class="mi">5</span> <span class="p">(</span><span class="n">__resume_at_38_2</span><span class="p">)</span>
<span class="mi">26</span> <span class="n">LOAD_FAST</span> <span class="mi">1</span> <span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="mi">28</span> <span class="n">LOAD_FAST</span> <span class="mi">2</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="mi">30</span> <span class="n">CALL_FUNCTION</span> <span class="mi">2</span>
<span class="mi">32</span> <span class="n">RETURN_VALUE</span>
<span class="n">GUARDS</span><span class="p">:</span>
<span class="o">-</span> <span class="n">local</span> <span class="s1">'a'</span> <span class="n">TENSOR_MATCH</span>
<span class="o">-</span> <span class="n">local</span> <span class="s1">'b'</span> <span class="n">TENSOR_MATCH</span>
<span class="o">-</span> <span class="k">global</span> <span class="s1">'torch'</span> <span class="n">FUNCTION_MATCH</span>
</pre></div>
</div>
<p>At the top you can see the FX graph.
Next, you see the original bytecode of the function, followed by the
modified bytecode generated by TorchDynamo. Finally, you see the guards
which we covered above.</p>
<p>In the modified bytecode, <code class="docutils literal notranslate"><span class="pre">__compiled_fn_0</span></code> is the return value of
<code class="docutils literal notranslate"><span class="pre">my_compiler()</span></code> (the compiled graph). <code class="docutils literal notranslate"><span class="pre">__resume_at_30_1</span></code> and
<code class="docutils literal notranslate"><span class="pre">__resume_at_38_2</span></code> are both generated continuation functions that pick
up execution after a graph break (at bytecode offsets 30 and 38). Each
of these functions take the form:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">__resume_at_</span><span class="o"><</span><span class="n">offset</span><span class="o">></span><span class="p">:</span>
<span class="o">...</span> <span class="n">restore</span> <span class="n">stack</span> <span class="n">state</span> <span class="k">if</span> <span class="n">needed</span> <span class="o">...</span>
<span class="n">JUMP_ABSOLUTE</span> <span class="o"><</span><span class="n">offset</span><span class="o">></span> <span class="n">into</span> <span class="n">toy_example</span>
<span class="o">...</span> <span class="n">original</span> <span class="n">bytecode</span> <span class="n">of</span> <span class="n">toy_example</span> <span class="o">...</span>
</pre></div>
</div>
<p>By generating this <cite>resume_at</cite> function, we force the remainder of the
function to be executed in a new Python frame which recursively
triggers TorchDynamo to restart its capture once execution reaches that
point for the first time.</p>
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