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<div class="section" id="build-a-smaug-model-with-python-api">
<span id="label-build-python-model"></span><h1>Build a SMAUG model with Python API<a class="headerlink" href="#build-a-smaug-model-with-python-api" title="Permalink to this headline">¶</a></h1>
<p>SMAUG’s Python frontend provides easy APIs to build DL models. The created model
is a computational graph, which is serialized into two protobuf files, one for
the model topology and the other for the parameters. These two files are inputs
to SMAUG’s C++ runtime that performs the actual simulation. In this tutorial,
we will be using the SMAUG Python APIs to build new DL models.</p>
<p>Before building a model, we need to create a <cite>Graph</cite> context in which we will
add operators.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">smaug</span> <span class="k">as</span> <span class="nn">sg</span>
<span class="k">with</span> <span class="n">sg</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"my_model"</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"SMV"</span><span class="p">)</span> <span class="k">as</span> <span class="n">graph</span><span class="p">:</span>
<span class="c1"># Any operators instantiated within the context will be added to `graph`.</span>
</pre></div>
</div>
<p>When using the <a class="reference internal" href="smaug.html#smaug.Graph" title="smaug.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">smaug.Graph</span></code></a> API to create a graph context, we need to
give a name for the model and select the backend we want to use when running the
model through the C++ runtime. A backend in SMAUG is a logical combination of
hardware blocks that implements all the SMAUG operators. Refer to
<a class="reference external" href="doxygen_html/index.html">C++ docs</a> for more details of a backend
implementation in the C++ runtime. Here, we choose the <code class="code docutils literal notranslate"><span class="pre">SMV</span></code> backend that
comes with SMAUG, which is modeled after the NVDLA architecture. SMAUG also has
another backend named <code class="code docutils literal notranslate"><span class="pre">Reference</span></code>, which is a reference implementation
without having specific performance optimizations. Also, refer to
<a class="reference internal" href="smaug.html#smaug.Graph" title="smaug.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">smaug.Graph</span></code></a> for a detailed description of the parameters.</p>
<p>Now we can start adding operators to the graph context. The following gives an
example of building a simple 3-layer model.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">smaug</span> <span class="k">as</span> <span class="nn">sg</span>
<span class="k">def</span> <span class="nf">generate_random_data</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.005</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
<span class="k">with</span> <span class="n">sg</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"my_model"</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"SMV"</span><span class="p">)</span> <span class="k">as</span> <span class="n">graph</span><span class="p">:</span>
<span class="n">input_tensor</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span>
<span class="n">data_layout</span><span class="o">=</span><span class="n">sg</span><span class="o">.</span><span class="n">NHWC</span><span class="p">,</span> <span class="n">tensor_data</span><span class="o">=</span><span class="n">generate_random_data</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
<span class="n">conv_weights</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span>
<span class="n">data_layout</span><span class="o">=</span><span class="n">sg</span><span class="o">.</span><span class="n">NHWC</span><span class="p">,</span> <span class="n">tensor_data</span><span class="o">=</span><span class="n">generate_random_data</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
<span class="n">fc_weights</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span>
<span class="n">data_layout</span><span class="o">=</span><span class="n">sg</span><span class="o">.</span><span class="n">NC</span><span class="p">,</span> <span class="n">tensor_data</span><span class="o">=</span><span class="n">generate_random_data</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6272</span><span class="p">)))</span>
<span class="c1"># Shape of act: [1, 28, 28, 1].</span>
<span class="n">act</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">input_data</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span>
<span class="c1"># After the convolution, shape of act: [1, 32, 28, 28].</span>
<span class="n">act</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span>
<span class="n">act</span><span class="p">,</span> <span class="n">conv_weights</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s2">"same"</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">)</span>
<span class="c1"># After the max pooling, shape of act: [1, 32, 14, 14].</span>
<span class="n">act</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">act</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="c1"># After the matrix multiply, shape of act: [1, 10].</span>
<span class="n">act</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">mat_mul</span><span class="p">(</span><span class="n">act</span><span class="p">,</span> <span class="n">fc_weights</span><span class="p">)</span>
</pre></div>
</div>
<p>As we create the first operator for the model, we need to first prepare an
input tensor and weight tensors that are used by the operator. Tensors are
represented by the <a class="reference internal" href="smaug.html#smaug.Tensor" title="smaug.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">smaug.Tensor</span></code></a>. In the example, we create an input
tensor <code class="code docutils literal notranslate"><span class="pre">input_tensor</span></code> using the API. Here, we specify the
<code class="code docutils literal notranslate"><span class="pre">data_layout</span></code> as <code class="code docutils literal notranslate"><span class="pre">sg.NHWC</span></code>, which stands for a 4D tensor shape with
the channel-major layout. We also specify the <code class="code docutils literal notranslate"><span class="pre">tensor_data</span></code> parameter
with a randomly generated NumPy array, with a shape of <code class="code docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">28,</span> <span class="pre">28,</span> <span class="pre">1]</span></code>.
However, the user can use the real weights extracted from a pretrained model.
Likewise, we create two weight tensors that will be used by a convolution
operator and a matrix multiply operator, respectively.</p>
<p>A <code class="xref py py-func docutils literal notranslate"><span class="pre">smaug.data_op()</span></code>, which simply forwards an input tensor to its output, is
required for any tensor that is not the output of another operator. Here,
<code class="code docutils literal notranslate"><span class="pre">act</span></code> is a reference to code:<cite>input_tensor</cite>. Then, <code class="code docutils literal notranslate"><span class="pre">act</span></code> is
fed to a convolution operator that also takes <code class="code docutils literal notranslate"><span class="pre">conv_weights</span></code> as its
filter input. With more details provided in <a class="reference internal" href="nn.html#smaug.nn.convolution" title="smaug.nn.convolution"><code class="xref py py-func docutils literal notranslate"><span class="pre">smaug.nn.convolution()</span></code></a>, it
computes a 3D convolution given the 4D input and filter tensors, and we use 1x1
strides, the <code class="code docutils literal notranslate"><span class="pre">same</span></code> padding and a ReLU activation fused with the
convolution operation. The output of it then goes through a max pooling
operator with a 2x2 filter size, which in turn fans its output into the last
matrix multiply operator. Note that since the output of the max pooling
operator is a 4D tensor while <a class="reference internal" href="nn.html#smaug.nn.mat_mul" title="smaug.nn.mat_mul"><code class="xref py py-func docutils literal notranslate"><span class="pre">smaug.nn.mat_mul()</span></code></a> expects a 2D input
tensor, SMAUG will automatically add a layout transformation operator
<a class="reference internal" href="tensor.html#smaug.tensor.reorder" title="smaug.tensor.reorder"><code class="xref py py-func docutils literal notranslate"><span class="pre">smaug.tensor.reorder()</span></code></a> in between to make the data layout format
compatible. Thus, the 4D tensor of shape <code class="code docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">32,</span> <span class="pre">14,</span> <span class="pre">14]</span></code> will be
flattened into a 2D tensor of shape <code class="code docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">6272]</span></code> before running the matrix
multiply. Similarly, SMAUG will also perform the NHWC to NCHW layout
transformation or vice versa as per the expected layout format of the backend.</p>
<p>After finishing adding operators to the model, we can now take a look at the
summary of the model using the <a class="reference internal" href="smaug.html#smaug.Graph.print_summary" title="smaug.Graph.print_summary"><code class="xref py py-func docutils literal notranslate"><span class="pre">smaug.Graph.print_summary()</span></code></a> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="o">.</span><span class="n">print_summary</span><span class="p">()</span>
</pre></div>
</div>
<p>This prints model-level information and operator-specific properties as below:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">=================================================================</span>
<span class="n">Summary</span> <span class="n">of</span> <span class="n">the</span> <span class="n">network</span><span class="p">:</span> <span class="n">my_model</span> <span class="p">(</span><span class="n">SMV</span><span class="p">)</span>
<span class="o">=================================================================</span>
<span class="n">Host</span> <span class="n">memory</span> <span class="n">access</span> <span class="n">policy</span><span class="p">:</span> <span class="n">AllDma</span><span class="o">.</span>
<span class="o">-----------------------------------------------------------------</span>
<span class="n">Name</span><span class="p">:</span> <span class="n">data</span> <span class="p">(</span><span class="n">Data</span><span class="p">)</span>
<span class="n">Parents</span><span class="p">:</span>
<span class="n">Children</span><span class="p">:</span><span class="n">conv</span>
<span class="n">Input</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">data</span><span class="o">/</span><span class="n">input0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">Output</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">data</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="o">-----------------------------------------------------------------</span>
<span class="n">Name</span><span class="p">:</span> <span class="n">data_1</span> <span class="p">(</span><span class="n">Data</span><span class="p">)</span>
<span class="n">Parents</span><span class="p">:</span>
<span class="n">Children</span><span class="p">:</span><span class="n">conv</span>
<span class="n">Input</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">data_1</span><span class="o">/</span><span class="n">input0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">Output</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">data_1</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="o">-----------------------------------------------------------------</span>
<span class="n">Name</span><span class="p">:</span> <span class="n">conv</span> <span class="p">(</span><span class="n">Convolution3d</span><span class="p">)</span>
<span class="n">Parents</span><span class="p">:</span><span class="n">data</span> <span class="n">data_1</span>
<span class="n">Children</span><span class="p">:</span><span class="n">max_pool</span>
<span class="n">Input</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">data</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">data_1</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">Output</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">conv</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">32</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="o">-----------------------------------------------------------------</span>
<span class="n">Name</span><span class="p">:</span> <span class="n">max_pool</span> <span class="p">(</span><span class="n">MaxPooling</span><span class="p">)</span>
<span class="n">Parents</span><span class="p">:</span><span class="n">conv</span>
<span class="n">Children</span><span class="p">:</span><span class="n">reorder</span>
<span class="n">Input</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">conv</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">32</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">Output</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">max_pool</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">32</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="o">-----------------------------------------------------------------</span>
<span class="n">Name</span><span class="p">:</span> <span class="n">reorder</span> <span class="p">(</span><span class="n">Reorder</span><span class="p">)</span>
<span class="n">Parents</span><span class="p">:</span><span class="n">max_pool</span>
<span class="n">Children</span><span class="p">:</span><span class="n">mat_mul</span>
<span class="n">Input</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">max_pool</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">32</span><span class="p">]</span> <span class="n">NHWC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">Output</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">reorder</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6272</span><span class="p">]</span> <span class="n">NC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="o">-----------------------------------------------------------------</span>
<span class="n">Name</span><span class="p">:</span> <span class="n">data_2</span> <span class="p">(</span><span class="n">Data</span><span class="p">)</span>
<span class="n">Parents</span><span class="p">:</span>
<span class="n">Children</span><span class="p">:</span><span class="n">mat_mul</span>
<span class="n">Input</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">data_2</span><span class="o">/</span><span class="n">input0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6272</span><span class="p">]</span> <span class="n">NC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">Output</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">data_2</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6272</span><span class="p">]</span> <span class="n">NC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="o">-----------------------------------------------------------------</span>
<span class="n">Name</span><span class="p">:</span> <span class="n">mat_mul</span> <span class="p">(</span><span class="n">InnerProduct</span><span class="p">)</span>
<span class="n">Parents</span><span class="p">:</span><span class="n">reorder</span> <span class="n">data_2</span>
<span class="n">Children</span><span class="p">:</span>
<span class="n">Input</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">reorder</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6272</span><span class="p">]</span> <span class="n">NC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">data_2</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6272</span><span class="p">]</span> <span class="n">NC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="n">Output</span> <span class="n">tensors</span><span class="p">:</span>
<span class="n">mat_mul</span><span class="o">/</span><span class="n">output0</span> <span class="n">Float16</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span> <span class="n">NC</span> <span class="n">alignment</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
<span class="o">-----------------------------------------------------------------</span>
</pre></div>
</div>
<p>Finally, we can export the model files using the
<a class="reference internal" href="smaug.html#smaug.Graph.write_graph" title="smaug.Graph.write_graph"><code class="xref py py-func docutils literal notranslate"><span class="pre">smaug.Graph.write_graph()</span></code></a> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="o">.</span><span class="n">write_graph</span><span class="p">()</span>
</pre></div>
</div>
<p>This gives us two files named <code class="code docutils literal notranslate"><span class="pre">my_model_topo.pbtxt</span></code> and
<code class="code docutils literal notranslate"><span class="pre">my_model_params.pb</span></code>, where the former stores all the model information
except for the parameters, which are stored in the latter. This separation is
helpful for us to quickly check things in the human readable topology file
while still compressing as much as possible the oftentimes large paramaters.
We can now move on to the <a class="reference external" href="doxygen_html/index.html">C++ side tutorials</a> that
explain the details of using these two files to run the model.</p>
</div>
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