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    <li>Profiling the Python Code</li>
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  <p>This tutorial introduces techniques we use to profile and tune the
CPU performance of PaddlePaddle.  We will use Python packages
<code class="docutils literal"><span class="pre">cProfile</span></code> and <code class="docutils literal"><span class="pre">yep</span></code>, and Google&#8217;s <code class="docutils literal"><span class="pre">perftools</span></code>.</p>
<p>Profiling is the process that reveals performance bottlenecks,
which could be very different from what&#8217;s in the developers&#8217; mind.
Performance tuning is done to fix these bottlenecks. Performance optimization
repeats the steps of profiling and tuning alternatively.</p>
<p>PaddlePaddle users program AI applications by calling the Python API, which calls
into <code class="docutils literal"><span class="pre">libpaddle.so.</span></code> written in C++.  In this tutorial, we focus on
the profiling and tuning of</p>
<ol class="simple">
<li>the Python code and</li>
<li>the mixture of Python and C++ code.</li>
</ol>
<div class="section" id="profiling-the-python-code">
<span id="profiling-the-python-code"></span><h1>Profiling the Python Code<a class="headerlink" href="#profiling-the-python-code" title="Permalink to this headline">¶</a></h1>
<div class="section" id="generate-the-performance-profiling-file">
<span id="generate-the-performance-profiling-file"></span><h2>Generate the Performance Profiling File<a class="headerlink" href="#generate-the-performance-profiling-file" title="Permalink to this headline">¶</a></h2>
<p>We can use Python standard
package, <a class="reference external" href="https://docs.python.org/2/library/profile.html"><code class="docutils literal"><span class="pre">cProfile</span></code></a>,
to generate Python profiling file.  For example:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>python -m cProfile -o profile.out main.py
</pre></div>
</div>
<p>where <code class="docutils literal"><span class="pre">main.py</span></code> is the program we are going to profile, <code class="docutils literal"><span class="pre">-o</span></code> specifies
the output file.  Without <code class="docutils literal"><span class="pre">-o</span></code>, <code class="docutils literal"><span class="pre">cProfile</span></code> would outputs to standard
output.</p>
</div>
<div class="section" id="look-into-the-profiling-file">
<span id="look-into-the-profiling-file"></span><h2>Look into the Profiling File<a class="headerlink" href="#look-into-the-profiling-file" title="Permalink to this headline">¶</a></h2>
<p><code class="docutils literal"><span class="pre">cProfile</span></code> generates <code class="docutils literal"><span class="pre">profile.out</span></code> after <code class="docutils literal"><span class="pre">main.py</span></code> completes. We can
use <a class="reference external" href="https://github.com/ymichael/cprofilev"><code class="docutils literal"><span class="pre">cprofilev</span></code></a> to look into
the details:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>cprofilev -a <span class="m">0</span>.0.0.0 -p <span class="m">3214</span> -f profile.out main.py
</pre></div>
</div>
<p>where <code class="docutils literal"><span class="pre">-a</span></code> specifies the HTTP IP, <code class="docutils literal"><span class="pre">-p</span></code> specifies the port, <code class="docutils literal"><span class="pre">-f</span></code>
specifies the profiling file, and <code class="docutils literal"><span class="pre">main.py</span></code> is the source file.</p>
<p>Open the Web browser and points to the local IP and the specifies
port, we will see the output like the following:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span>   <span class="n">ncalls</span>  <span class="n">tottime</span>  <span class="n">percall</span>  <span class="n">cumtime</span>  <span class="n">percall</span> <span class="n">filename</span><span class="p">:</span><span class="n">lineno</span><span class="p">(</span><span class="n">function</span><span class="p">)</span>
        <span class="mi">1</span>    <span class="mf">0.284</span>    <span class="mf">0.284</span>   <span class="mf">29.514</span>   <span class="mf">29.514</span> <span class="n">main</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">1</span><span class="p">(</span><span class="o">&lt;</span><span class="n">module</span><span class="o">&gt;</span><span class="p">)</span>
     <span class="mi">4696</span>    <span class="mf">0.128</span>    <span class="mf">0.000</span>   <span class="mf">15.748</span>    <span class="mf">0.003</span> <span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">yuyang</span><span class="o">/</span><span class="n">perf_test</span><span class="o">/.</span><span class="n">env</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python2</span><span class="o">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">paddle</span><span class="o">/</span><span class="n">fluid</span><span class="o">/</span><span class="n">executor</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">20</span><span class="p">(</span><span class="n">run</span><span class="p">)</span>
     <span class="mi">4696</span>   <span class="mf">12.040</span>    <span class="mf">0.003</span>   <span class="mf">12.040</span>    <span class="mf">0.003</span> <span class="p">{</span><span class="n">built</span><span class="o">-</span><span class="ow">in</span> <span class="n">method</span> <span class="n">run</span><span class="p">}</span>
        <span class="mi">1</span>    <span class="mf">0.144</span>    <span class="mf">0.144</span>    <span class="mf">6.534</span>    <span class="mf">6.534</span> <span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">yuyang</span><span class="o">/</span><span class="n">perf_test</span><span class="o">/.</span><span class="n">env</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python2</span><span class="o">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">paddle</span><span class="o">/</span><span class="n">v2</span><span class="o">/</span><span class="fm">__init__</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">14</span><span class="p">(</span><span class="o">&lt;</span><span class="n">module</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
<p>where each line corresponds to Python function, and the meaning of
each column is as follows:</p>
<p>| column | meaning |
| &#8212; | &#8212; |
| ncalls | the number of calls into a function |
| tottime | the total execution time of the function, not including the execution time of other functions called by the function |
| percall | tottime divided by ncalls |
| cumtime | the total execution time of the function, including the execution time of other functions being called |
| percall | cumtime divided by ncalls |
| filename:lineno(function) | where the function is defined |</p>
</div>
<div class="section" id="identify-performance-bottlenecks">
<span id="identify-performance-bottlenecks"></span><h2>Identify Performance Bottlenecks<a class="headerlink" href="#identify-performance-bottlenecks" title="Permalink to this headline">¶</a></h2>
<p>Usually, <code class="docutils literal"><span class="pre">tottime</span></code> and the related <code class="docutils literal"><span class="pre">percall</span></code> time is what we want to
focus on. We can sort above profiling file by tottime:</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>     4696   12.040    0.003   12.040    0.003 {built-in method run}
   300005    0.874    0.000    1.681    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
   107991    0.676    0.000    1.519    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:219(__init__)
     4697    0.626    0.000    2.291    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp)
        1    0.618    0.618    0.618    0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/__init__.py:1(&lt;module&gt;)
</pre></div>
</div>
<p>We can see that the most time-consuming function is the <code class="docutils literal"><span class="pre">built-in</span> <span class="pre">method</span> <span class="pre">run</span></code>, which is a C++ function in <code class="docutils literal"><span class="pre">libpaddle.so</span></code>.  We will
explain how to profile C++ code in the next section.  At this
moment, let&#8217;s look into the third function <code class="docutils literal"><span class="pre">sync_with_cpp</span></code>, which is a
Python function.  We can click it to understand more about it:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Called</span> <span class="n">By</span><span class="p">:</span>

   <span class="n">Ordered</span> <span class="n">by</span><span class="p">:</span> <span class="n">internal</span> <span class="n">time</span>
   <span class="n">List</span> <span class="n">reduced</span> <span class="kn">from</span> <span class="mi">4497</span> <span class="n">to</span> <span class="mi">2</span> <span class="n">due</span> <span class="n">to</span> <span class="n">restriction</span> <span class="o">&lt;</span><span class="s1">&#39;sync_with_cpp&#39;</span><span class="o">&gt;</span>

<span class="n">Function</span>                                                                                                 <span class="n">was</span> <span class="n">called</span> <span class="n">by</span><span class="o">...</span>
                                                                                                             <span class="n">ncalls</span>  <span class="n">tottime</span>  <span class="n">cumtime</span>
<span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">yuyang</span><span class="o">/</span><span class="n">perf_test</span><span class="o">/.</span><span class="n">env</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python2</span><span class="o">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">paddle</span><span class="o">/</span><span class="n">fluid</span><span class="o">/</span><span class="n">framework</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">428</span><span class="p">(</span><span class="n">sync_with_cpp</span><span class="p">)</span>  <span class="o">&lt;-</span>    <span class="mi">4697</span>    <span class="mf">0.626</span>    <span class="mf">2.291</span>  <span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">yuyang</span><span class="o">/</span><span class="n">perf_test</span><span class="o">/.</span><span class="n">env</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python2</span><span class="o">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">paddle</span><span class="o">/</span><span class="n">fluid</span><span class="o">/</span><span class="n">framework</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">562</span><span class="p">(</span><span class="n">sync_with_cpp</span><span class="p">)</span>
<span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">yuyang</span><span class="o">/</span><span class="n">perf_test</span><span class="o">/.</span><span class="n">env</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python2</span><span class="o">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">paddle</span><span class="o">/</span><span class="n">fluid</span><span class="o">/</span><span class="n">framework</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">562</span><span class="p">(</span><span class="n">sync_with_cpp</span><span class="p">)</span>  <span class="o">&lt;-</span>    <span class="mi">4696</span>    <span class="mf">0.019</span>    <span class="mf">2.316</span>  <span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">yuyang</span><span class="o">/</span><span class="n">perf_test</span><span class="o">/.</span><span class="n">env</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python2</span><span class="o">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">paddle</span><span class="o">/</span><span class="n">fluid</span><span class="o">/</span><span class="n">framework</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">487</span><span class="p">(</span><span class="n">clone</span><span class="p">)</span>
                                                                                                                  <span class="mi">1</span>    <span class="mf">0.000</span>    <span class="mf">0.001</span>  <span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">yuyang</span><span class="o">/</span><span class="n">perf_test</span><span class="o">/.</span><span class="n">env</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python2</span><span class="o">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">paddle</span><span class="o">/</span><span class="n">fluid</span><span class="o">/</span><span class="n">framework</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">534</span><span class="p">(</span><span class="n">append_backward</span><span class="p">)</span>


<span class="n">Called</span><span class="p">:</span>

   <span class="n">Ordered</span> <span class="n">by</span><span class="p">:</span> <span class="n">internal</span> <span class="n">time</span>
   <span class="n">List</span> <span class="n">reduced</span> <span class="kn">from</span> <span class="mi">4497</span> <span class="n">to</span> <span class="mi">2</span> <span class="n">due</span> <span class="n">to</span> <span class="n">restriction</span> <span class="o">&lt;</span><span class="s1">&#39;sync_with_cpp&#39;</span><span class="o">&gt;</span>
</pre></div>
</div>
<p>The lists of the callers of <code class="docutils literal"><span class="pre">sync_with_cpp</span></code> might help us understand
how to improve the function definition.</p>
</div>
</div>
<div class="section" id="profiling-python-and-c-code">
<span id="profiling-python-and-c-code"></span><h1>Profiling Python and C++ Code<a class="headerlink" href="#profiling-python-and-c-code" title="Permalink to this headline">¶</a></h1>
<div class="section" id="generate-the-profiling-file">
<span id="generate-the-profiling-file"></span><h2>Generate the Profiling File<a class="headerlink" href="#generate-the-profiling-file" title="Permalink to this headline">¶</a></h2>
<p>To profile a mixture of Python and C++ code, we can use a Python
package, <code class="docutils literal"><span class="pre">yep</span></code>, that can work with Google&#8217;s <code class="docutils literal"><span class="pre">perftools</span></code>, which is a
commonly-used profiler for C/C++ code.</p>
<p>In Ubuntu systems, we can install <code class="docutils literal"><span class="pre">yep</span></code> and <code class="docutils literal"><span class="pre">perftools</span></code> by running the
following commands:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>apt update
apt install libgoogle-perftools-dev
pip install yep
</pre></div>
</div>
<p>Then we can run the following command</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>python -m yep -v main.py
</pre></div>
</div>
<p>to generate the profiling file.  The default filename is
<code class="docutils literal"><span class="pre">main.py.prof</span></code>.</p>
<p>Please be aware of the <code class="docutils literal"><span class="pre">-v</span></code> command line option, which prints the
analysis results after generating the profiling file.  By examining the
the print result, we&#8217;d know that if we stripped debug
information from <code class="docutils literal"><span class="pre">libpaddle.so</span></code> at build time.  The following hints
help make sure that the analysis results are readable:</p>
<ol class="simple">
<li>Use GCC command line option <code class="docutils literal"><span class="pre">-g</span></code> when building <code class="docutils literal"><span class="pre">libpaddle.so</span></code> so to
include the debug information.  The standard building system of
PaddlePaddle is CMake, so you might want to set
<code class="docutils literal"><span class="pre">CMAKE_BUILD_TYPE=RelWithDebInfo</span></code>.</li>
<li>Use GCC command line option <code class="docutils literal"><span class="pre">-O2</span></code> or <code class="docutils literal"><span class="pre">-O3</span></code> to generate optimized
binary code. It doesn&#8217;t make sense to profile <code class="docutils literal"><span class="pre">libpaddle.so</span></code>
without optimization, because it would anyway run slowly.</li>
<li>Profiling the single-threaded binary file before the
multi-threading version, because the latter often generates tangled
profiling analysis result.  You might want to set environment
variable <code class="docutils literal"><span class="pre">OMP_NUM_THREADS=1</span></code> to prevents OpenMP from automatically
starting multiple threads.</li>
</ol>
</div>
<div class="section" id="examining-the-profiling-file">
<span id="examining-the-profiling-file"></span><h2>Examining the Profiling File<a class="headerlink" href="#examining-the-profiling-file" title="Permalink to this headline">¶</a></h2>
<p>The tool we used to examine the profiling file generated by
<code class="docutils literal"><span class="pre">perftools</span></code> is <a class="reference external" href="https://github.com/google/pprof"><code class="docutils literal"><span class="pre">pprof</span></code></a>, which
provides a Web-based GUI like <code class="docutils literal"><span class="pre">cprofilev</span></code>.</p>
<p>We can rely on the standard Go toolchain to retrieve the source code
of <code class="docutils literal"><span class="pre">pprof</span></code> and build it:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>go get github.com/google/pprof
</pre></div>
</div>
<p>Then we can use it to profile <code class="docutils literal"><span class="pre">main.py.prof</span></code> generated in the previous
section:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>pprof -http<span class="o">=</span><span class="m">0</span>.0.0.0:3213 <span class="sb">`</span>which python<span class="sb">`</span>  ./main.py.prof
</pre></div>
</div>
<p>Where <code class="docutils literal"><span class="pre">-http</span></code> specifies the IP and port of the HTTP service.
Directing our Web browser to the service, we would see something like
the following:</p>
<p><img alt="result" src="../../_images/pprof_1.png" /></p>
</div>
<div class="section" id="identifying-the-performance-bottlenecks">
<span id="identifying-the-performance-bottlenecks"></span><h2>Identifying the Performance Bottlenecks<a class="headerlink" href="#identifying-the-performance-bottlenecks" title="Permalink to this headline">¶</a></h2>
<p>Similar to how we work with <code class="docutils literal"><span class="pre">cprofilev</span></code>, we&#8217;d focus on <code class="docutils literal"><span class="pre">tottime</span></code> and
<code class="docutils literal"><span class="pre">cumtime</span></code>.</p>
<p><img alt="kernel_perf" src="../../_images/pprof_2.png" /></p>
<p>We can see that the execution time of multiplication and the computing
of the gradient of multiplication takes 2% to 4% of the total running
time, and <code class="docutils literal"><span class="pre">MomentumOp</span></code> takes about 17%. Obviously, we&#8217;d want to
optimize <code class="docutils literal"><span class="pre">MomentumOp</span></code>.</p>
<p><code class="docutils literal"><span class="pre">pprof</span></code> would mark performance critical parts of the program in
red. It&#8217;s a good idea to follow the hints.</p>
</div>
</div>


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