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<section id="using-torch-tensorrt-directly-from-pytorch">
<span id="use-from-pytorch"></span><h1>Using Torch-TensorRT Directly From PyTorch<a class="headerlink" href="#using-torch-tensorrt-directly-from-pytorch" title="Permalink to this headline">¶</a></h1>
<p>You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature
is very similar to the compilation workflow described in <a class="reference internal" href="../getting_started/getting_started_with_python_api.html#getting-started-with-python-api"><span class="std std-ref">Using Torch-TensorRT in Python</span></a></p>
<p>Start by loading <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> into your application.</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_tensorrt</span>
</pre></div>
</div>
<p>Then given a TorchScript module, you can compile it with TensorRT using the <code class="docutils literal notranslate"><span class="pre">torch._C._jit_to_backend("tensorrt",</span> <span class="pre">...)</span></code> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
<p>Unlike the <code class="docutils literal notranslate"><span class="pre">compile</span></code> API in Torch-TensorRT which assumes you are trying to compile the <code class="docutils literal notranslate"><span class="pre">forward</span></code> function of a module
or the <code class="docutils literal notranslate"><span class="pre">convert_method_to_trt_engine</span></code> which converts a specified function to a TensorRT engine, the backend API
will take a dictionary which maps names of functions to compile to Compilation Spec objects which wrap the same
sort of dictionary you would provide to <code class="docutils literal notranslate"><span class="pre">compile</span></code>. For more information on the compile spec dictionary take a look
at the documentation for the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">TensorRTCompileSpec</span></code> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"forward"</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">TensorRTCompileSpec</span><span class="p">(</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="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">])],</span>
<span class="s2">"enabled_precisions"</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</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="s2">"refit"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s2">"debug"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s2">"device"</span><span class="p">:</span> <span class="p">{</span>
<span class="s2">"device_type"</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">,</span>
<span class="s2">"gpu_id"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">"dla_core"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">"allow_gpu_fallback"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="p">},</span>
<span class="s2">"capability"</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
<span class="s2">"num_avg_timing_iters"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Now to compile with Torch-TensorRT, provide the target module objects and the spec dictionary to <code class="docutils literal notranslate"><span class="pre">torch._C._jit_to_backend("tensorrt",</span> <span class="pre">...)</span></code></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">trt_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_to_backend</span><span class="p">(</span><span class="s2">"tensorrt"</span><span class="p">,</span> <span class="n">script_model</span><span class="p">,</span> <span class="n">spec</span><span class="p">)</span>
</pre></div>
</div>
<p>To run explicitly call the function of the method you want to run (vs. how you can just call on the module itself in standard PyTorch)</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">)</span><span class="o">.</span><span class="n">to</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="nb">print</span><span class="p">(</span><span class="n">trt_model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
</pre></div>
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