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[Torch-TensorRT Quickstart] Compiling TorchScript Modules with
<code class="docutils literal notranslate">
<span class="pre">
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</span>
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<span id="getting-started">
</span>
<h1 id="tutorials-getting-started-with-cpp-api--page-root">
Getting Started with C++
<a class="headerlink" href="#tutorials-getting-started-with-cpp-api--page-root" title="Permalink to this headline">
¶
</a>
</h1>
<p>
If you haven’t already, acquire a tarball of the library by following the instructions in
<a class="reference internal" href="installation.html#installation">
<span class="std std-ref">
Installation
</span>
</a>
</p>
<h2 id="using-torch-tensorrt-in-c">
Using Torch-TensorRT in C++
<a class="headerlink" href="#using-torch-tensorrt-in-c" title="Permalink to this headline">
¶
</a>
</h2>
<p>
Torch-TensorRT C++ API accepts TorchScript modules (generated either from
<code class="docutils literal notranslate">
<span class="pre">
torch.jit.script
</span>
</code>
or
<code class="docutils literal notranslate">
<span class="pre">
torch.jit.trace
</span>
</code>
) as an input and returns
a Torchscript module (optimized using TensorRT). This requires users to use Pytorch (in python) to generate torchscript modules beforehand.
Please refer to
<a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html">
Creating TorchScript modules in Python
</a>
section to generate torchscript graphs.
</p>
<span id="torch-tensorrt-quickstart">
</span>
<h3 id="torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
[Torch-TensorRT Quickstart] Compiling TorchScript Modules with
<code class="docutils literal notranslate">
<span class="pre">
torchtrtc
</span>
</code>
<a class="headerlink" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc" title="Permalink to this headline">
¶
</a>
</h3>
<p>
An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
<code class="docutils literal notranslate">
<span class="pre">
torchtrtc
</span>
</code>
, which supports almost all features of the compiler from the command line including post training quantization
(given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
precision and input size. This new TorchScript file can be loaded into Python (note: you need to
<code class="docutils literal notranslate">
<span class="pre">
import
</span>
<span class="pre">
torch_tensorrt
</span>
</code>
before loading
these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).
</p>
<div class="highlight-shell notranslate">
<div class="highlight">
<pre><span></span>❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts <span class="s2">"(1,1,32,32)"</span>
❯ python3
Python <span class="m">3</span>.6.9 <span class="o">(</span>default, Apr <span class="m">18</span> <span class="m">2020</span>, <span class="m">01</span>:56:04<span class="o">)</span>
<span class="o">[</span>GCC <span class="m">8</span>.4.0<span class="o">]</span> on linux
Type <span class="s2">"help"</span>, <span class="s2">"copyright"</span>, <span class="s2">"credits"</span> or <span class="s2">"license"</span> <span class="k">for</span> more information.
>>> import torch
>>> import torch_tensorrt
>>> <span class="nv">ts_model</span> <span class="o">=</span> torch.jit.load<span class="o">(</span>“trt_lenet_scripted.ts”<span class="o">)</span>
>>> ts_model<span class="o">(</span>torch.randn<span class="o">((</span><span class="m">1</span>,1,32,32<span class="o">))</span>.to<span class="o">(</span>“cuda”<span class="o">)</span>.half<span class="o">())</span>
</pre>
</div>
</div>
<p>
You can learn more about
<code class="docutils literal notranslate">
<span class="pre">
torchtrtc
</span>
</code>
usage here:
<a class="reference internal" href="torchtrtc.html#torchtrtc">
<span class="std std-ref">
torchtrtc
</span>
</a>
</p>
<span id="ts-in-cc">
</span>
<h3 id="working-with-torchscript-in-c">
Working with TorchScript in C++
<a class="headerlink" href="#working-with-torchscript-in-c" title="Permalink to this headline">
¶
</a>
</h3>
<p>
If we are developing an application to deploy with C++, we can save either our traced or scripted module using
<code class="docutils literal notranslate">
<span class="pre">
torch.jit.save
</span>
</code>
which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.
</p>
<div class="highlight-python notranslate">
<div class="highlight">
<pre><span></span><span class="n">torch_script_module</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"lenet.jit.pt"</span><span class="p">)</span>
</pre>
</div>
</div>
<p>
From here we can now load our TorchScript module in C++
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="cp">#include</span> <span class="cpf"><torch/script.h> // One-stop header.</span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf"><iostream></span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf"><memory></span><span class="cp"></span>
<span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
<span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
<span class="k">try</span> <span class="p">{</span>
<span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
<span class="k">module</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">load</span><span class="p">(</span><span class="s">"<PATH TO SAVED TS MOD>"</span><span class="p">);</span>
<span class="p">}</span>
<span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
<span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o"><<</span> <span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
<span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
<span class="p">}</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o"><<</span> <span class="s">"ok</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
</pre>
</div>
</div>
<p>
You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
have access to the same powerful tensor library that backs PyTorch. (For more information:
<a class="reference external" href="https://pytorch.org/cppdocs/">
https://pytorch.org/cppdocs/
</a>
).
For instance we can do inference with our LeNet module like this:
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</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">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">});</span>
<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
</pre>
</div>
</div>
<p>
and to run on the GPU:
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</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">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
</pre>
</div>
</div>
<p>
As you can see it is pretty similar to the Python API. When you call the
<code class="docutils literal notranslate">
<span class="pre">
forward
</span>
</code>
method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.
</p>
<span id="compile-cpp">
</span>
<h3 id="compiling-with-torch-tensorrt-in-c">
Compiling with Torch-TensorRT in C++
<a class="headerlink" href="#compiling-with-torch-tensorrt-in-c" title="Permalink to this headline">
¶
</a>
</h3>
<p>
We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.
</p>
<p>
With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
<span class="p">...</span>
<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
<span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
<span class="k">auto</span> <span class="n">in</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">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">});</span>
<span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">></span><span class="p">{{</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()}});</span>
<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
</pre>
</div>
</div>
<p>
Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).
</p>
<p>
We can also set settings like operating precision to run in FP16.
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
<span class="p">...</span>
<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
<span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
<span class="k">auto</span> <span class="n">in</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">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">input_sizes</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="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">></span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
<span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span> <span class="n">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
<span class="n">info</span><span class="p">.</span><span class="n">enable_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">info</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
</pre>
</div>
</div>
<p>
And now we are running the module in FP16 precision. You can then save the module to load later.
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="n">trt_mod</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="s">"<PATH TO SAVED TRT/TS MOD>"</span><span class="p">)</span>
</pre>
</div>
</div>
<p>
Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against
<code class="docutils literal notranslate">
<span class="pre">
libtorchtrt.so
</span>
</code>
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
<span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
<span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
<span class="k">try</span> <span class="p">{</span>
<span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
<span class="k">module</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">load</span><span class="p">(</span><span class="s">"<PATH TO SAVED TRT/TS MOD>"</span><span class="p">);</span>
<span class="p">}</span>
<span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
<span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o"><<</span> <span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
<span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
<span class="p">}</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</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">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o"><<</span> <span class="s">"ok</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
<span class="p">}</span>
</pre>
</div>
</div>
<p>
If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the
<code class="docutils literal notranslate">
<span class="pre">
ConvertGraphToTRTEngine
</span>
</code>
API.
</p>
<div class="highlight-c++ notranslate">
<div class="highlight">
<pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
<span class="p">...</span>
<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
<span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
<span class="k">auto</span> <span class="n">in</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">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">input_sizes</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="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">></span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
<span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span> <span class="n">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
<span class="n">info</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ConvertGraphToTRTEngine</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="s">"forward"</span><span class="p">,</span> <span class="n">info</span><span class="p">);</span>
<span class="n">std</span><span class="o">::</span><span class="n">ofstream</span> <span class="n">out</span><span class="p">(</span><span class="s">"/tmp/engine_converted_from_jit.trt"</span><span class="p">);</span>
<span class="n">out</span> <span class="o"><<</span> <span class="n">engine</span><span class="p">;</span>
<span class="n">out</span><span class="p">.</span><span class="n">close</span><span class="p">();</span>
</pre>
</div>
</div>
<span id="id1">
</span>
<h3 id="under-the-hood">
Under The Hood
<a class="headerlink" href="#under-the-hood" title="Permalink to this headline">
¶
</a>
</h3>
<p>
When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:
</p>
<div class="highlight-none notranslate">
<div class="highlight">
<pre><span></span>graph(%input.2 : Tensor):
%2 : Float(84, 10) = prim::Constant[value=<Tensor>]()
%3 : Float(120, 84) = prim::Constant[value=<Tensor>]()
%4 : Float(576, 120) = prim::Constant[value=<Tensor>]()
%5 : int = prim::Constant[value=-1]() # x.py:25:0
%6 : int[] = prim::Constant[value=annotate(List[int], [])]()
%7 : int[] = prim::Constant[value=[2, 2]]()
%8 : int[] = prim::Constant[value=[0, 0]]()
%9 : int[] = prim::Constant[value=[1, 1]]()
%10 : bool = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
%11 : int = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
%12 : bool = prim::Constant[value=0]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
%self.classifer.fc3.bias : Float(10) = prim::Constant[value= 0.0464 0.0383 0.0678 0.0932 0.1045 -0.0805 -0.0435 -0.0818 0.0208 -0.0358 [ CUDAFloatType{10} ]]()
%self.classifer.fc2.bias : Float(84) = prim::Constant[value=<Tensor>]()
%self.classifer.fc1.bias : Float(120) = prim::Constant[value=<Tensor>]()
%self.feat.conv2.weight : Float(16, 6, 3, 3) = prim::Constant[value=<Tensor>]()
%self.feat.conv2.bias : Float(16) = prim::Constant[value=<Tensor>]()
%self.feat.conv1.weight : Float(6, 1, 3, 3) = prim::Constant[value=<Tensor>]()
%self.feat.conv1.bias : Float(6) = prim::Constant[value= 0.0530 -0.1691 0.2802 0.1502 0.1056 -0.1549 [ CUDAFloatType{6} ]]()
%input0.4 : Tensor = aten::_convolution(%input.2, %self.feat.conv1.weight, %self.feat.conv1.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
%input0.5 : Tensor = aten::relu(%input0.4) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
%input1.2 : Tensor = aten::max_pool2d(%input0.5, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
%input0.6 : Tensor = aten::_convolution(%input1.2, %self.feat.conv2.weight, %self.feat.conv2.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
%input2.1 : Tensor = aten::relu(%input0.6) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
%x.1 : Tensor = aten::max_pool2d(%input2.1, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
%input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0
%27 : Tensor = aten::matmul(%input.1, %4)
%28 : Tensor = trt::const(%self.classifer.fc1.bias)
%29 : Tensor = aten::add_(%28, %27, %11)
%input0.2 : Tensor = aten::relu(%29) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
%31 : Tensor = aten::matmul(%input0.2, %3)
%32 : Tensor = trt::const(%self.classifer.fc2.bias)
%33 : Tensor = aten::add_(%32, %31, %11)
%input1.1 : Tensor = aten::relu(%33) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
%35 : Tensor = aten::matmul(%input1.1, %2)
%36 : Tensor = trt::const(%self.classifer.fc3.bias)
%37 : Tensor = aten::add_(%36, %35, %11)
return (%37)
(CompileGraph)
</pre>
</div>
</div>
<p>
The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.
</p>
<p>
When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
by the Torch-TensorRT runtime.
</p>
<p>
Here is the graph that you get back after compilation is complete:
</p>
<div class="highlight-none notranslate">
<div class="highlight">
<pre><span></span>graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
%1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
%3 : Tensor[] = prim::ListConstruct(%input_0)
%4 : Tensor[] = trt::execute_engine(%3, %1)
%5 : Tensor = prim::ListUnpack(%4)
return (%5)
</pre>
</div>
</div>
<p>
You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.
</p>
<span id="unsupported-ops">
</span>