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<section id="dla">
<span id="using-dla"></span><h1>DLA<a class="headerlink" href="#dla" title="Permalink to this headline">¶</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">DLA</span></code> NVIDIA Deep Learning Accelerator is a fixed-function accelerator engine targeted for deep learning operations. DLA is designed to do full hardware acceleration of convolutional neural networks. DLA supports various layers such as convolution, deconvolution, fully-connected, activation, pooling, batch normalization, etc. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> supports compilation of TorchScript Module and deployment pipeline on the DLA hardware available on NVIDIA embedded platforms.</p>
<p>NOTE: DLA supports fp16 and int8 precision only.</p>
<p>Using DLA with torchtrtc</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc<span class="w"> </span><span class="o">[</span>input_file_path<span class="o">]</span><span class="w"> </span><span class="o">[</span>output_file_path<span class="o">]</span><span class="w"> </span><span class="o">[</span>input_shapes...<span class="o">]</span><span class="w"> </span>-p<span class="w"> </span>f16<span class="w"> </span>-d<span class="w"> </span>dla<span class="w"> </span><span class="o">{</span>OPTIONS<span class="o">}</span>
</pre></div>
</div>
<p>Using DLA in a C++ application</p>
<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="kt">int64_t</span><span class="o">>></span><span class="w"> </span><span class="n">input_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{{</span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">}};</span>
<span class="k">auto</span><span class="w"> </span><span class="n">compile_spec</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="p">({</span><span class="n">input_shape</span><span class="p">});</span>
<span class="cp"># Set a precision. DLA supports fp16 or int8 only</span>
<span class="n">compile_spec</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kF16</span><span class="p">};</span>
<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">device_type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">DeviceType</span><span class="o">::</span><span class="n">kDLA</span><span class="p">;</span>
<span class="cp"># Make sure the gpu id is set to Xavier id for DLA</span>
<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">gpu_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
<span class="cp"># Set the DLA core id</span>
<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">dla_core</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
<span class="cp"># If a layer fails to run on DLA it will fallback to GPU</span>
<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">allow_gpu_fallback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span>
</pre></div>
</div>
<p>Using DLA in a python application</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">compile_spec</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"inputs"</span><span class="p">:</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="o">.</span><span class="n">shape</span><span class="p">)],</span>
<span class="s2">"device"</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Device</span><span class="p">(</span><span class="s2">"dla:0"</span><span class="p">,</span> <span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="s2">"enalbed_precisions"</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">},</span>
<span class="p">}</span>
<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scripted_model</span><span class="p">,</span> <span class="n">compile_spec</span><span class="p">)</span>
</pre></div>
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