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  <section id="torchdynamo-troubleshooting">
<h1>TorchDynamo Troubleshooting<a class="headerlink" href="#torchdynamo-troubleshooting" title="Permalink to this heading">¶</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/mlazos">Michael Lazos</a></p>
<p>TorchDynamo is still in active development, and many of the reasons for
graph breaks and excessive recompilation will be fixed with upcoming
support for <a class="reference external" href="https://docs.google.com/document/d/1QJB-GOnbv-9PygGlOMXwiO9K6vVNm8sNg_olixJ9koc/edit?usp=sharing">tracing dynamic tensor
shapes</a>,
more careful choices for guards and better tuned heuristics.</p>
<p>In the meantime, you may need to diagnose a particular issue and
determine if it is easy to work around with a change to your model, or
file an issue for support.</p>
<p>Also, we are actively developing debug tools, profilers, and improving our
errors/warnings. Please give us feedback if you have an issue with this
infra, or an idea for an improvement. Below is a table of the available
tools and their typical usage. For additional help see
<a class="reference external" href="#diagnosing-runtime-errors">Diagnosing Runtime Errors</a>.</p>
<table class="docutils align-default" id="id1">
<caption><span class="caption-text">Title</span><a class="headerlink" href="#id1" title="Permalink to this table">¶</a></caption>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 50%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Tool</p></th>
<th class="head"><p>Purpose</p></th>
<th class="head"><p>Usage</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Info logging</p></td>
<td><p>View summarized steps of compilation</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.log_level</span> <span class="pre">=</span> <span class="pre">logging.INFO</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>Debug logging</p></td>
<td><p>View detailed steps of compilation (print every instruction traced)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.log_level</span> <span class="pre">=</span> <span class="pre">logging.DEBUG</span></code> and
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.verbose</span> <span class="pre">=</span> <span class="pre">True</span></code></p></td>
</tr>
<tr class="row-even"><td><p>Minifier for any backend</p></td>
<td><p>Find smallest subgraph which reproduces errors for any backend</p></td>
<td><p>set environment variable <code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER=&quot;dynamo&quot;</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>Minifier for <code class="docutils literal notranslate"><span class="pre">TorchInductor</span></code></p></td>
<td><p>If the error is known to occur after <cite>AOTAutograd`</cite> find
smallest subgraph wich reproduces errors during TorchInductor lowering</p></td>
<td><p>set environment variable <code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER=&quot;aot&quot;</span></code></p></td>
</tr>
<tr class="row-even"><td><p>Dynamo accuracy minifier</p></td>
<td><p>Finds the smallest subgraph which reproduces an accuracy issue
between an eager model model and optimized model, when you
suspect the problem is in AOTAutograd</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER=&quot;dynamo&quot;</span> <span class="pre">TORCHDYNAMO_REPRO_LEVEL=4</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>Inductor accuracy minifier</p></td>
<td><p>Finds the smallest subgraph which reproduces an accuracy issue
between an eager model model and optimized model, when you
suspect the problem is in the backend (e.g., inductor).
If this doesn’t work, try the Dynamo accuracy minifier
instead.</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER=&quot;aot&quot;</span> <span class="pre">TORCHDYNAMO_REPRO_LEVEL=4</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch._dynamo.explain</span></code></p></td>
<td><p>Find graph breaks and display reasoning for them</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch._dynamo.explain(fn,</span> <span class="pre">*inputs)</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>Record/Replay</p></td>
<td><p>Record and replay frames which to reproduce errors during graph capture</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.replay_record_enabled</span> <span class="pre">=</span> <span class="pre">True</span></code></p></td>
</tr>
<tr class="row-even"><td><p>TorchDynamo function name filtering</p></td>
<td><p>Only compile functions with the given name to reduce noise when
debugging an issue</p></td>
<td><p>set environment variable <code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_DEBUG_FUNCTION=&lt;name&gt;</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>TorchInductor Debug logging</p></td>
<td><p>Print general TorchInductor debug info and generated Triton/C++ code</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch._inductor.config.debug</span> <span class="pre">=</span> <span class="pre">True</span></code></p></td>
</tr>
<tr class="row-even"><td><p>TorchInductor Tracing</p></td>
<td><p>Show time taken in each TorchInductor stage + output code and graph
visualization</p></td>
<td><p>set the environment variable TORCH_COMPILE_DEBUG=1 or
<code class="docutils literal notranslate"><span class="pre">torch._inductor.config.trace.enabled</span> <span class="pre">=</span> <span class="pre">True</span></code></p></td>
</tr>
</tbody>
</table>
<section id="diagnosing-runtime-errors">
<h2>Diagnosing Runtime Errors<a class="headerlink" href="#diagnosing-runtime-errors" title="Permalink to this heading">¶</a></h2>
<p>Below is the TorchDynamo compiler stack.</p>
<p>At a high level, the TorchDynamo stack consists of a graph capture from
Python code (TorchDynamo) and a backend compiler. In this example, the
backend compiler consists of backward graph tracing (AOTAutograd) and
graph lowering (TorchInductor)*. Errors can occur in any component of
the stack and will provide full stack traces.</p>
<p>You may use info logging
(<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.log_level</span> <span class="pre">=</span> <span class="pre">logging.INFO</span></code>) and look for
<code class="docutils literal notranslate"><span class="pre">Step</span> <span class="pre">#:</span> <span class="pre">...</span></code> outputs in order to determine in which component the
error has occurred. Logs are made at the beginning and end of each step,
so the step that an error should correspond to is the most recent logged
step whose end has not yet been logged. The steps correspond to the
following parts of the stack (according to the image above):</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Step</p></th>
<th class="head"><p>Component</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>1</p></td>
<td><p>TorchDynamo</p></td>
</tr>
<tr class="row-odd"><td><p>2</p></td>
<td><p>Compiler Backend</p></td>
</tr>
<tr class="row-even"><td><p>3</p></td>
<td><p>TorchInductor</p></td>
</tr>
</tbody>
</table>
<p>The beginning and end of AOTAutograd is currently not logged, but we
plan to add it soon.</p>
<p>If info logging is insufficient, then there are also some backend
options which can enable you to determine which component is causing the
error if you’re unable to understand the error message that is
generated. These are the following:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">&quot;eager&quot;</span></code>: only runs torchdynamo forward graph capture and then
runs the captured graph with PyTorch. This provides an indication as
to whether TorchDynamo is raising the error.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">&quot;aot_eager&quot;</span></code>: runs torchdynamo to capture a forward graph, and
then AOTAutograd to trace the backward graph without any additional
backend compiler steps. PyTorch eager will then be used to run the
forward and backward graphs. This is useful to narrow down the issue
to AOTAutograd.</p></li>
</ul>
<p>The general procedure to narrow down an issue is the following:</p>
<ol class="arabic simple">
<li><p>Run your program with the <code class="docutils literal notranslate"><span class="pre">&quot;eager&quot;</span></code> backend. If the error no longer
occurs, the issue is in the backend compiler that is being used (if
using TorchInductor, proceed to step 2. If not, see <a class="reference external" href="#minifying-backend-compiler-errors">this
section</a>). If the error still
occurs with the <code class="docutils literal notranslate"><span class="pre">&quot;eager&quot;</span></code> backend, it is an <a class="reference external" href="#torchdynamo-errors">error while running
torchdynamo</a>.</p></li>
<li><p>This step is only necessary if <code class="docutils literal notranslate"><span class="pre">TorchInductor</span></code> is used as the backend
compiler. Run the model with the <code class="docutils literal notranslate"><span class="pre">&quot;aot_eager&quot;</span></code> backend. If this
backend raises an error then the error is occurring during
AOTAutograd tracing. If the error no longer occurs with this backend,
then <a class="reference external" href="#minifying-torchinductor-errors">the error is in
TorchInductor*</a>.</p></li>
</ol>
<p>Each of these cases are analyzed in the following sections.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The TorchInductor backend consists of
both AOTAutograd tracing and the TorchInductor compiler itself. We will
disambiguate by referring to <code class="docutils literal notranslate"><span class="pre">TorchInductor</span></code> as the backend, and
TorchInductor lowering as the phase which lowers the graph traced by
AOTAutograd.</p>
</div>
<section id="torchdynamo-errors">
<h3>Torchdynamo Errors<a class="headerlink" href="#torchdynamo-errors" title="Permalink to this heading">¶</a></h3>
<p>If the error that is generated occurs with the <code class="docutils literal notranslate"><span class="pre">&quot;eager&quot;</span></code> backend, then
TorchDynamo is the most likely source of the error. Here is a sample code
which will generate an error.</p>
<div class="highlight-py 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._dynamo</span> <span class="k">as</span> <span class="nn">dynamo</span>


<span class="k">def</span> <span class="nf">test_assertion_error</span><span class="p">():</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
    <span class="n">z</span> <span class="o">=</span> <span class="p">{</span><span class="n">y</span><span class="p">:</span> <span class="mi">5</span><span class="p">}</span>
    <span class="k">return</span> <span class="n">z</span>

<span class="n">compiled_test_assertion_error</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">test_assertion_error</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;eager&quot;</span><span class="p">)</span>

<span class="n">compiled_test_assertion_error</span><span class="p">()</span>
</pre></div>
</div>
<p>Which will generate the following error:</p>
<div class="highlight-default 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">convert_frame</span><span class="p">:</span> <span class="p">[</span><span class="n">ERROR</span><span class="p">]</span> <span class="n">WON</span><span class="s1">&#39;T CONVERT test_assertion_error /scratch/mlazos/torchdynamo/../test/errors.py line 26</span>
<span class="n">due</span> <span class="n">to</span><span class="p">:</span>
<span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">):</span>
  <span class="n">File</span> <span class="s2">&quot;/scratch/mlazos/torchdynamo/torchdynamo/symbolic_convert.py&quot;</span><span class="p">,</span> <span class="n">line</span> <span class="mi">837</span><span class="p">,</span> <span class="ow">in</span> <span class="n">BUILD_MAP</span>
    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">ConstantVariable</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span>
<span class="ne">AssertionError</span>

<span class="kn">from</span> <span class="nn">user</span> <span class="n">code</span><span class="p">:</span>
   <span class="n">File</span> <span class="s2">&quot;/scratch/mlazos/torchdynamo/../test/errors.py&quot;</span><span class="p">,</span> <span class="n">line</span> <span class="mi">34</span><span class="p">,</span> <span class="ow">in</span> <span class="n">test_assertion_error</span>
    <span class="n">z</span> <span class="o">=</span> <span class="p">{</span><span class="n">y</span><span class="p">:</span> <span class="mi">5</span><span class="p">}</span>

<span class="n">Set</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">verbose</span><span class="o">=</span><span class="kc">True</span> <span class="k">for</span> <span class="n">more</span> <span class="n">information</span>
<span class="o">==========</span>
</pre></div>
</div>
<p>As the message suggests you can set
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.verbose=True</span></code> to get a full stack trace to both
the error in TorchDynamo and the user code. In addition to this flag,
you can also set the <code class="docutils literal notranslate"><span class="pre">log_level</span></code> of torchdynamo through
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.log_level</span></code>. The available levels are the
following:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">logging.DEBUG</span></code>: Print every instruction that is
encountered in addition to all below log levels.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">logging.INFO</span></code>:
Print each function that is compiled (original and modified bytecode)
and the graph that is captured in addition to all below log levels.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">logging.WARNING</span></code> (default): Print graph breaks in addition to all
below log levels.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">logging.ERROR</span></code>: Print errors only.</p></li>
</ul>
<p>If a model is sufficiently large, the logs can become overwhelming. If
an error occurs deep within a model’s Python code, it can be useful to
execute only the frame in which the error occurs to enable easier
debugging. There are two tools available to enable this:</p>
<ul class="simple">
<li><p>Setting the environment variable <code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_DEBUG_FUNCTION</span></code> to the desired function name will only run torchdynamo on functions with that name.</p></li>
<li><p>Enabling the record/replay tool (set <code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.replay_record_enabled</span> <span class="pre">=</span> <span class="pre">True</span></code>) which dumps anexecution record when an error is encountered. This record can then be replayed to run only the frame where an error occurred.</p></li>
</ul>
</section>
<section id="torchinductor-errors">
<h3>TorchInductor Errors<a class="headerlink" href="#torchinductor-errors" title="Permalink to this heading">¶</a></h3>
<p>If the error does not occur with the <code class="docutils literal notranslate"><span class="pre">&quot;eager&quot;</span></code> backend, then the
backend compiler is the source of the error (<a class="reference external" href="https://gist.github.com/mlazos/2f13681e3cc6c43b3911f336327032de%5D">example
error</a>).
There are <a class="reference external" href="https://github.com/pytorch/torchdynamo/blob/0b8aaf340dad4777a080ef24bf09623f1aa6f3dd/README.md#existing-backends">different
choices</a>
for backend compilers for TorchDynamo, with TorchInductor or nvfuser
fitting the needs of most users. This section focuses on TorchInductor
as the motivating example, but some tools will be usable with other
backend compilers.</p>
<p>Below is the portion of the stack which we are focusing on:</p>
<p>With TorchInductor as the chosen backend, AOTAutograd is used to
generate the backward graph from the forward graph captured by
torchdynamo. It is important to note that errors can occur during this
tracing and also while TorchInductor lowers the forward and backward
graphs to GPU code or C++. A model can often consist of hundreds or
thousands of FX nodes, so narrowing the exact nodes where this problem
occurred can be very difficult. Fortunately, there are tools available to
automatically minify these input graphs to the nodes which are causing
the issue. The first step is to determine whether the error occurs
during tracing of the backward graph with AOTAutograd or during
TorchInductor lowering. As mentioned above in step 2, the
<code class="docutils literal notranslate"><span class="pre">&quot;aot_eager&quot;</span></code> backend can be used to run only AOTAutograd in isolation
without lowering. If the error still occurs with this backend, this
indicates that the error is occurring during AOTAutograd tracing.</p>
<p>Here is an example:</p>
<div class="highlight-py 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._dynamo</span> <span class="k">as</span> <span class="nn">dynamo</span>

<span class="n">model</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">Sequential</span><span class="p">(</span><span class="o">*</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">Linear</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</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">5</span><span class="p">)])</span>

<span class="k">def</span> <span class="nf">test_backend_error</span><span class="p">():</span>

    <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
    <span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
    <span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">_foobar</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>  <span class="c1"># dummy function which errors</span>
    <span class="k">return</span> <span class="n">model</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>


<span class="n">compiled_test_backend_error</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">test_backend_error</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;inductor&quot;</span><span class="p">)</span>
<span class="n">compiled_test_backend_error</span><span class="p">()</span>
</pre></div>
</div>
<p>Running this should give you this error with a longer stack trace below
it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">):</span>
  <span class="n">File</span> <span class="s2">&quot;/scratch/mlazos/torchdynamo/torchinductor/graph.py&quot;</span><span class="p">,</span> <span class="n">line</span> <span class="mi">246</span><span class="p">,</span> <span class="ow">in</span> <span class="n">call_function</span>
    <span class="k">return</span> <span class="n">lowerings</span><span class="p">[</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="n">File</span> <span class="s2">&quot;/scratch/mlazos/torchdynamo/torchinductor/lowering.py&quot;</span><span class="p">,</span> <span class="n">line</span> <span class="mi">185</span><span class="p">,</span> <span class="ow">in</span> <span class="n">wrapped</span>
    <span class="k">return</span> <span class="n">decomp_fn</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="n">File</span> <span class="s2">&quot;/scratch/mlazos/torchdynamo/torchinductor/lowering.py&quot;</span><span class="p">,</span> <span class="n">line</span> <span class="mi">810</span><span class="p">,</span> <span class="ow">in</span> <span class="n">_foobar</span>
    <span class="k">assert</span> <span class="kc">False</span>
<span class="ne">AssertionError</span>
<span class="o">...</span>
</pre></div>
</div>
<p><a class="reference external" href="https://gist.github.com/mlazos/d6947854aa56d686800259a164c62100">error with full stack
trace</a></p>
<p>If you then change <code class="docutils literal notranslate"><span class="pre">torch.compile(backend=&quot;inductor&quot;)</span></code> to
<code class="docutils literal notranslate"><span class="pre">torch.compile(backend=&quot;aot_eager&quot;)</span></code>, it will run without error, because
<a class="reference external" href="https://github.com/pytorch/torchdynamo/blob/d09e50fbee388d466b5252a63045643166006f77/torchinductor/lowering.py#:~:text=%23%20This%20shouldn%27t%20be,assert%20False">the
issue</a>
is in the TorchInductor lowering process, not in AOTAutograd.</p>
</section>
<section id="minifying-torchinductor-errors">
<h3>Minifying TorchInductor Errors<a class="headerlink" href="#minifying-torchinductor-errors" title="Permalink to this heading">¶</a></h3>
<p>From here, let’s run the minifier to get a minimal repro. Setting the
environment variable <code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER=“aot”</span></code> (or setting
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.repro_after=&quot;aot&quot;</span></code> directly) will generate a
Python program which reduces the graph produced by AOTAutograd to the
smallest subgraph which reproduces the error. (See below for an example
where we minify the graph produced by torchdynamo) Running the program
with this environment variable should show nearly <a class="reference external" href="https://gist.github.com/mlazos/0458ab828aa403c779fe73c012aa5982">identical
output</a>,
with an additional line indicating where <code class="docutils literal notranslate"><span class="pre">minifier_launcher.py</span></code> has
been written to. The output directory is configurable by setting
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.base_dir</span></code> to a valid directory name. The final
step is to run the minifier and check that it runs successfully. A
successful run looks like
<a class="reference external" href="https://gist.github.com/mlazos/e6ea41ccce68a7b1b8a7a09acb1b206a">this</a>.
If the minifier runs successfully, it generates runnable python code
which reproduces the exact error. For our example this is the following
code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">tensor</span><span class="p">,</span> <span class="n">device</span>
<span class="kn">import</span> <span class="nn">torch.fx</span> <span class="k">as</span> <span class="nn">fx</span>
<span class="kn">from</span> <span class="nn">torch._dynamo.testing</span> <span class="kn">import</span> <span class="n">rand_strided</span>
<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">inf</span>
<span class="kn">from</span> <span class="nn">torch.fx.experimental.proxy_tensor</span> <span class="kn">import</span> <span class="n">make_fx</span>

<span class="c1"># torch version: 1.13.0a0+gitfddfc44</span>
<span class="c1"># torch cuda version: 11.6</span>
<span class="c1"># torch git version: fddfc4488afb207971c54ad4bf58130fdc8a4dc5</span>


<span class="c1"># CUDA Info:</span>
<span class="c1"># nvcc: NVIDIA (R) Cuda compiler driver</span>
<span class="c1"># Copyright (c) 2005-2022 NVIDIA Corporation</span>
<span class="c1"># Built on Thu_Feb_10_18:23:41_PST_2022</span>
<span class="c1"># Cuda compilation tools, release 11.6, V11.6.112</span>
<span class="c1"># Build cuda_11.6.r11.6/compiler.30978841_0</span>

<span class="c1"># GPU Hardware Info:</span>
<span class="c1"># NVIDIA A100-SXM4-40GB : 8</span>

<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="o">*</span>

<span class="k">class</span> <span class="nc">Repro</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="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">add</span><span class="p">):</span>
        <span class="n">_foobar</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">_foobar</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">add</span><span class="p">);</span>  <span class="n">add</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">return</span> <span class="p">(</span><span class="n">_foobar</span><span class="p">,)</span>

<span class="n">args</span> <span class="o">=</span> <span class="p">[((</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">),</span> <span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="s1">&#39;cpu&#39;</span><span class="p">)]</span>
<span class="n">args</span> <span class="o">=</span> <span class="p">[</span><span class="n">rand_strided</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">shape</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">device</span> <span class="ow">in</span> <span class="n">args</span><span class="p">]</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">make_fx</span><span class="p">(</span><span class="n">Repro</span><span class="p">())(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch._inductor.compile_fx</span> <span class="kn">import</span> <span class="n">compile_fx_inner</span>

<span class="n">compiled</span> <span class="o">=</span> <span class="n">compile_fx_inner</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span>
<span class="n">compiled</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">forward</span></code> method of the <code class="docutils literal notranslate"><span class="pre">Repro</span></code> module contains the exact op
which causes the issue. When filing an issue, please include any
minified repros to aid in debugging.</p>
</section>
<section id="minifying-backend-compiler-errors">
<h3>Minifying Backend Compiler Errors<a class="headerlink" href="#minifying-backend-compiler-errors" title="Permalink to this heading">¶</a></h3>
<p>With backend compilers other than TorchInductor the process for finding
the subgraph causing the error is nearly identical to the procedure in
<a class="reference external" href="#torchinductor-errors">errors in TorchInductor</a> with one important
caveat. Namely, that the minifier will now be run on the graph that is
traced by TorchDynamo, not the output graph of AOTAutograd. Let’s walk
through an example.</p>
<div class="highlight-py 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._dynamo</span> <span class="k">as</span> <span class="nn">dynamo</span>

<span class="n">model</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">Sequential</span><span class="p">(</span><span class="o">*</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">Linear</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</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">5</span><span class="p">)])</span>
<span class="c1"># toy compiler which fails if graph contains relu</span>
<span class="k">def</span> <span class="nf">toy_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">_</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</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">relu</span><span class="p">:</span>
            <span class="k">assert</span> <span class="kc">False</span>

    <span class="k">return</span> <span class="n">gm</span>


<span class="k">def</span> <span class="nf">test_backend_error</span><span class="p">():</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
    <span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
    <span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>


<span class="n">compiled_test_backend_error</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">test_backend_error</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">toy_compiler</span><span class="p">)</span>
<span class="n">compiled_test_backend_error</span><span class="p">()</span>
</pre></div>
</div>
<p>In order to run the code after TorchDynamo has traced the forward graph,
you can use the <code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER</span></code> environment variable. Running
this program with <code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER=“dynamo”</span></code> (or
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.repro_after=&quot;dynamo&quot;</span></code>) should produce <a class="reference external" href="https://gist.github.com/mlazos/244e3d5b53667e44078e194762c0c92b">this
output</a>and
the following code in <code class="docutils literal notranslate"><span class="pre">{torch._dynamo.config.base_dir}/repro.py</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The other option for TORCHDYNAMO_REPRO_AFTER are <code class="docutils literal notranslate"><span class="pre">&quot;aot&quot;</span></code>, which
will run the minifier after the backward graph has been generated.</p>
</div>
<div class="highlight-python 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._dynamo</span> <span class="k">as</span> <span class="nn">dynamo</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">device</span>
<span class="kn">import</span> <span class="nn">torch.fx</span> <span class="k">as</span> <span class="nn">fx</span>
<span class="kn">from</span> <span class="nn">torch._dynamo.testing</span> <span class="kn">import</span> <span class="n">rand_strided</span>
<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">inf</span>
<span class="kn">from</span> <span class="nn">torch._dynamo.debug_utils</span> <span class="kn">import</span> <span class="n">run_fwd_maybe_bwd</span>

<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="o">*</span>

<span class="k">class</span> <span class="nc">Repro</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="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">add</span><span class="p">):</span>
        <span class="n">relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">add</span><span class="p">);</span>  <span class="n">add</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">return</span> <span class="p">(</span><span class="n">relu</span><span class="p">,)</span>


<span class="n">mod</span> <span class="o">=</span> <span class="n">Repro</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">opt_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;None&quot;</span><span class="p">)</span>


<span class="n">args</span> <span class="o">=</span> <span class="p">[((</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">),</span> <span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)]</span>
<span class="n">args</span> <span class="o">=</span> <span class="p">[</span><span class="n">rand_strided</span><span class="p">(</span><span class="n">sh</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="n">rg</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">sh</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">dev</span><span class="p">,</span> <span class="n">rg</span><span class="p">)</span> <span class="ow">in</span> <span class="n">args</span><span class="p">]</span>


<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">enabled</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="n">ref</span> <span class="o">=</span> <span class="n">run_fwd_maybe_bwd</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span>
    <span class="n">res</span> <span class="o">=</span> <span class="n">run_fwd_maybe_bwd</span><span class="p">(</span><span class="n">opt_mod</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span>
</pre></div>
</div>
<p>The minifier successfully reduced the graph to the op that raises the
error in <code class="docutils literal notranslate"><span class="pre">toy_compiler</span></code>. The other difference from the procedure in
<a class="reference external" href="#torchinductor-errors">TorhInductor Errors</a> is that the minifier is
automatically run after encountering a backend compiler error. After a
successful run, the minifier writes <code class="docutils literal notranslate"><span class="pre">repro.py</span></code> to
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.base_dir</span></code>.</p>
</section>
</section>
<section id="performance-profiling">
<h2>Performance Profiling<a class="headerlink" href="#performance-profiling" title="Permalink to this heading">¶</a></h2>
<section id="accessing-torchdynamo-profiler">
<h3>Accessing TorchDynamo Profiler<a class="headerlink" href="#accessing-torchdynamo-profiler" title="Permalink to this heading">¶</a></h3>
<p>TorchDynamo has a builtin stats function for collecting and displaying
the time spent in each compilation phase. These stats can be accessed by
calling <code class="docutils literal notranslate"><span class="pre">torch._dynamo.utils.compile_times()</span></code> after executing
Torch._Dynamo. By default, this returns a string representation of the
compile times spent in each TorchDynamo function by name.</p>
</section>
<section id="torchinductor-debug-tracing">
<h3>TorchInductor Debug Tracing<a class="headerlink" href="#torchinductor-debug-tracing" title="Permalink to this heading">¶</a></h3>
<p>TorchInductor has a builtin stats and trace function for displaying time
spent in each compilation phase, output code, output graph visualization
and IR dump. This is a debugging tool designed to make it easier to
understand and troubleshoot the internals of TorchInductor.</p>
<p>Setting the environment variable <code class="docutils literal notranslate"><span class="pre">TORCH_COMPILE_DEBUG=1</span></code> will cause a
debug trace directory to be created and printed:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ env TORCH_COMPILE_DEBUG=1 python repro.py
torch._inductor.debug: [WARNING] model_forward_0 debug trace: /tmp/torchinductor_jansel/rh/crhwqgmbqtchqt3v3wdeeszjb352m4vbjbvdovaaeqpzi7tdjxqr.debug
</pre></div>
</div>
<p>Here is an <a class="reference external" href="https://gist.github.com/jansel/f4af078791ad681a0d4094adeb844396">example debug directory
output</a>
for the test program:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</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">Linear</span><span class="p">(</span><span class="mi">10</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">nn</span><span class="o">.</span><span class="n">LayerNorm</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">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
    <span class="p">)</span>
</pre></div>
</div>
<p>Each file in that debug trace can be enabled and disabled through
<code class="docutils literal notranslate"><span class="pre">torch._inductor.config.trace.*</span></code>. The profile and the diagram are both
disabled by default since they are expensive to generate.</p>
<p>A single node in this new debug format looks like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">buf1</span><span class="p">:</span> <span class="n">SchedulerNode</span><span class="p">(</span><span class="n">ComputedBuffer</span><span class="p">)</span>
<span class="n">buf1</span><span class="o">.</span><span class="n">writes</span> <span class="o">=</span>
    <span class="p">{</span>   <span class="n">MemoryDep</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;buf1&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">()),</span>
        <span class="n">MemoryDep</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;buf1&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">s0</span><span class="p">,))}</span>
<span class="n">buf1</span><span class="o">.</span><span class="n">unmet_dependencies</span> <span class="o">=</span> <span class="p">{</span><span class="n">MemoryDep</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;buf0&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">c0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">s0</span><span class="p">,))}</span>
<span class="n">buf1</span><span class="o">.</span><span class="n">met_dependencies</span> <span class="o">=</span> <span class="p">{</span><span class="n">MemoryDep</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;primals_2&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">c0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">s0</span><span class="p">,))}</span>
<span class="n">buf1</span><span class="o">.</span><span class="n">group</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">cuda</span><span class="p">:</span><span class="mi">0</span>
<span class="n">buf1</span><span class="o">.</span><span class="n">group</span><span class="o">.</span><span class="n">iteration</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">s0</span><span class="p">)</span>
<span class="n">buf1</span><span class="o">.</span><span class="n">sizes</span> <span class="o">=</span> <span class="p">([],</span> <span class="p">[</span><span class="n">s0</span><span class="p">])</span>
<span class="k">class</span> <span class="nc">buf1_loop_body</span><span class="p">:</span>
    <span class="n">var_ranges</span> <span class="o">=</span> <span class="p">{</span><span class="n">z0</span><span class="p">:</span> <span class="n">s0</span><span class="p">}</span>
    <span class="n">index0</span> <span class="o">=</span> <span class="n">z0</span>
    <span class="n">index1</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">def</span> <span class="nf">body</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ops</span><span class="p">):</span>
        <span class="n">get_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="s1">&#39;index0&#39;</span><span class="p">)</span>
        <span class="n">load</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;buf0&#39;</span><span class="p">,</span> <span class="n">get_index</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
        <span class="n">get_index_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="s1">&#39;index0&#39;</span><span class="p">)</span>
        <span class="n">load_1</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;primals_2&#39;</span><span class="p">,</span> <span class="n">get_index_1</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
        <span class="n">add</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">load</span><span class="p">,</span> <span class="n">load_1</span><span class="p">)</span>
        <span class="n">get_index_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="s1">&#39;index1&#39;</span><span class="p">)</span>
        <span class="n">reduction</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">reduction</span><span class="p">(</span><span class="s1">&#39;buf1&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="n">get_index_2</span><span class="p">,</span> <span class="n">add</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">reduction</span>
</pre></div>
</div>
<p>See the <a class="reference external" href="https://gist.github.com/jansel/f4af078791ad681a0d4094adeb844396">example debug directory
output</a>
for more examples.</p>
</section>
<section id="graph-breaks">
<h3>Graph Breaks<a class="headerlink" href="#graph-breaks" title="Permalink to this heading">¶</a></h3>
<p>Given a program like this:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">some_fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
    <span class="o">...</span>

<span class="n">compiled_fun</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">some_fun</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
<span class="o">...</span>
</pre></div>
</div>
<p>TorchDynamo will attempt to compile all of the torch/tensor operations
within some_fun into a single FX graph, but it may fail to capture
everything into one graph.</p>
<p>Some graph break reasons are insurmountable to TorchDynamo, and can’t be
easily fixed. - calling into a C extension other than torch is invisible
to torchdynamo, and could do arbitrary things without TorchDynamo being
able to introduce necessary <a class="reference external" href="./GuardsOverviewPt1.md">guards</a> to
ensure that the compiled program would be safe to reuse. Graph breaks
can hinder performance if the resulting fragments are small. To maximize
performance, it’s important to have as few graph breaks as possible.</p>
</section>
</section>
<section id="identifying-the-cause-of-a-graph-break">
<h2>Identifying the Cause of a Graph Break<a class="headerlink" href="#identifying-the-cause-of-a-graph-break" title="Permalink to this heading">¶</a></h2>
<p>To identify all graph breaks in a program and the associated reasons for
the breaks, <code class="docutils literal notranslate"><span class="pre">torch._dynamo.explain</span></code> can be used. This tool runs
TorchDynamo on the supplied function and aggregates the graph breaks
that are encountered. Here is an example usage:</p>
<div class="highlight-python 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._dynamo</span> <span class="k">as</span> <span class="nn">dynamo</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="nb">print</span><span class="p">(</span><span class="s2">&quot;woo&quot;</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">&lt;</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="n">explanation</span><span class="p">,</span> <span class="n">out_guards</span><span class="p">,</span> <span class="n">graphs</span><span class="p">,</span> <span class="n">ops_per_graph</span> <span class="o">=</span> <span class="n">dynamo</span><span class="o">.</span><span class="n">explain</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>
<span class="nb">print</span><span class="p">(</span><span class="n">explanation</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Dynamo produced 3 graphs, with 2 graph break and 6 ops.</span>
<span class="sd"> Break reasons:</span>
<span class="sd">1. call_function BuiltinVariable(print) [ConstantVariable(str)] {}</span>
<span class="sd">   File &quot;t2.py&quot;, line 16, in toy_example</span>
<span class="sd">    print(&quot;woo&quot;)</span>

<span class="sd">2. generic_jump</span>
<span class="sd">   File &quot;t2.py&quot;, line 17, in toy_example</span>
<span class="sd">    if b.sum() &lt; 0:</span>
<span class="sd"> &quot;&quot;&quot;</span>
</pre></div>
</div>
<p>Outputs include:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">out_guards</span></code> - a list of lists where each sublist contains the guards that must pass to ensure the traced graphs are valid.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">graphs</span></code> - a list of graph modules which were successfully traced.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ops_per_graph</span></code> - a list of lists where each sublist contains the ops that are run in the graph.</p></li>
</ul>
<p>To throw an error on the first graph break encountered, use the <code class="docutils literal notranslate"><span class="pre">nopython</span></code>
mode. This mode disables TorchDynamo’s Python fallback, and only
succeeds if the entire program is convertible into a single graph. Example
usage:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="o">...</span>

<span class="n">compiled_toy</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">toy_example</span><span class="p">,</span> <span class="n">fullgraph</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=&lt;</span><span class="n">compiler</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
<section id="excessive-recompilation">
<h3>Excessive Recompilation<a class="headerlink" href="#excessive-recompilation" title="Permalink to this heading">¶</a></h3>
<p>When TorchDynamo compiles a function (or part of one), it makes certain
assumptions about locals and globals in order to allow compiler
optimizations, and expresses these assumptions as guards that check
particular values at runtime. If any of these guards fail, Dynamo will
recompile that function (or part) up to
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.cache_size_limit</span></code> times. If your program is
hitting the cache limit, you will first need to determine which guard is
failing and what part of your program is triggering it.</p>
<p>The <a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/_dynamo/utils.py">compile profiler</a> automates the
process of setting TorchDynamo’s cache limit to 1 and running your
program under an observation-only ‘compiler’ that records the causes of
any guard failures. You should be sure to run your program for at least
as long (as many iterations) as you were running when you ran into
trouble, and the profiler will accumulate statistics over this duration.</p>
<p>If your program exhibits a bounded amount of dynamism, you may be able
to tune the TorchDynamo cache limit to allow for each variation to be
compiled and cached, but if the cache limit is too high you may find the
cost of recompilation outweighs any optimization benefits.</p>
<div class="highlight-default 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">cache_size_limit</span> <span class="o">=</span> <span class="o">&lt;</span><span class="n">your</span> <span class="n">desired</span> <span class="n">cache</span> <span class="n">limit</span><span class="o">&gt;</span>
</pre></div>
</div>
<p>Torchdynamo plans to support many common cases of dynamic tensor shapes,
such as varying batch size or sequence length. It does not plan to
support rank-dynamism. In the meantime, setting a specific cache limit
can be used in coordination with bucketing techniques to achieve an
acceptable number of recompilations for some dynamic models.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch._dynamo.utils</span> <span class="kn">import</span> <span class="n">CompileProfiler</span>

<span class="n">prof</span> <span class="o">=</span> <span class="n">CompileProfiler</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">my_model</span><span class="p">():</span>
    <span class="o">...</span>

<span class="n">profiler_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">prof</span><span class="p">)</span>
<span class="n">profiler_model</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">report</span><span class="p">())</span>
</pre></div>
</div>
</section>
</section>
<section id="accuracy-debugging">
<h2>Accuracy Debugging<a class="headerlink" href="#accuracy-debugging" title="Permalink to this heading">¶</a></h2>
<p>Accuracy issues can also be minified if you set the environment variable
<code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_LEVEL=4</span></code>, it operates with a similar git bisect
model and a full repro might be something like
<code class="docutils literal notranslate"><span class="pre">TORCHDYNAMO_REPRO_AFTER=&quot;aot&quot;</span> <span class="pre">TORCHDYNAMO_REPRO_LEVEL=4</span></code> the reason
we need this is downstream compilers will codegen code whether it’s
Triton code or the C++ backend, the numerics from those downstream
compilers can be different in subtle ways yet have dramatic impact on
your training stability. So the accuracy debugger is very useful for us
to detect bugs in our codegen or with a backend compiler.</p>
</section>
<section id="file-an-issue">
<h2>File an Issue<a class="headerlink" href="#file-an-issue" title="Permalink to this heading">¶</a></h2>
<p>If you experience problems with TorchDynamo, <a class="reference external" href="https://github.com/pytorch/torchdynamo/issues">file a GitHub
issue</a>.</p>
<p>Before filing an issue, read over the <a class="reference external" href="../README.md">README</a>,
<a class="reference external" href="./TROUBLESHOOTING.md">TROUBLESHOOTING</a>, and search for similar
issues.</p>
<p>When filing an issue, include the information about your
OS, Python&lt; PyTorch, CUDA, and Triton versions info by running:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python tools/verify_install.py
</pre></div>
</div>
<ul class="simple">
<li><p>A minimal repro script if possible, which can be generated by running
Minifier</p></li>
<li><p>A description of the error</p></li>
<li><p>The expected behavior</p></li>
<li><p>A log (set <code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.log_file</span></code> to a valid file name to
dump the logs to a file and
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.log_level</span> <span class="pre">=</span> <span class="pre">logging.DEBUG</span></code> and
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.verbose</span> <span class="pre">=</span> <span class="pre">True</span></code>)</p></li>
</ul>
</section>
</section>


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              <ul>
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<li><a class="reference internal" href="#diagnosing-runtime-errors">Diagnosing Runtime Errors</a><ul>
<li><a class="reference internal" href="#torchdynamo-errors">Torchdynamo Errors</a></li>
<li><a class="reference internal" href="#torchinductor-errors">TorchInductor Errors</a></li>
<li><a class="reference internal" href="#minifying-torchinductor-errors">Minifying TorchInductor Errors</a></li>
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<li><a class="reference internal" href="#torchinductor-debug-tracing">TorchInductor Debug Tracing</a></li>
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