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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in C++</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2019 NVIDIA Corporation. All Rights Reserved.
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<p><img alt="61658ccbd0a844cfa5e10b33ec92ca0f" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png" /></p>
<section id="Torch-TensorRT-Getting-Started---LeNet">
<h1>Torch-TensorRT Getting Started - LeNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---LeNet" title="Permalink to this headline">¶</a></h1>
<section id="Overview">
<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
<p>In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the
best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch’s JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new
world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.</p>
<p>When deploying on NVIDIA GPUs TensorRT, NVIDIA’s Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA’s Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that
perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.</p>
<p>Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch.</p>
<section id="Learning-objectives">
<h3>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h3>
<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network.</p>
</section>
</section>
<section id="Content">
<h2>Content<a class="headerlink" href="#Content" title="Permalink to this headline">¶</a></h2>
<ol class="arabic simple">
<li><p><a class="reference external" href="#1">Requirements</a></p></li>
<li><p><a class="reference external" href="#2">Creating TorchScript modules</a></p></li>
<li><p><a class="reference external" href="#3">Compiling with Torch-TensorRT</a></p></li>
</ol>
<p>## 1. Requirements</p>
<p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this notebook.</p>
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</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
!nvidia-smi
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Thu Feb 10 22:01:27 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.39.01 Driver Version: 510.39.01 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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|===============================+======================+======================|
| 0 NVIDIA GeForce ... On | 00000000:09:00.0 Off | N/A |
| 0% 42C P8 20W / 320W | 0MiB / 10240MiB | 0% Default |
| | | N/A |
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+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
</pre></div></div>
</div>
<p>## 2. Creating TorchScript modules</p>
<p>Here we create two submodules for a feature extractor and a classifier and stitch them together in a single LeNet module. In this case this is overkill but modules give us granular control over our program including where we decide to optimize and where we don’t. It is also the unit that the TorchScript compiler operates on. So you can decide to only convert/optimize the feature extractor and leave the classifier in standard PyTorch or you can convert the whole thing. When compiling your module
to TorchScript, there are two paths: Tracing and Scripting.</p>
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</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
from torch import nn
import torch.nn.functional as F
class LeNetFeatExtractor(nn.Module):
def __init__(self):
super(LeNetFeatExtractor, self).__init__()
self.conv1 = nn.Conv2d(1, 128, 3)
self.conv2 = nn.Conv2d(128, 16, 3)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
return x
class LeNetClassifier(nn.Module):
def __init__(self):
super(LeNetClassifier, self).__init__()
self.fc1 = nn.Linear(16 * 6 * 6, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = torch.flatten(x,1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.feat = LeNetFeatExtractor()
self.classifer = LeNetClassifier()
def forward(self, x):
x = self.feat(x)
x = self.classifer(x)
return x
<br/></pre></div>
</div>
</div>
<p>Let us define a helper function to benchmark a model.</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import time
import numpy as np
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):
input_data = torch.randn(input_shape)
input_data = input_data.to("cuda")
if dtype=='fp16':
input_data = input_data.half()
print("Warm up ...")
with torch.no_grad():
for _ in range(nwarmup):
features = model(input_data)
torch.cuda.synchronize()
print("Start timing ...")
timings = []
with torch.no_grad():
for i in range(1, nruns+1):
start_time = time.time()
features = model(input_data)
torch.cuda.synchronize()
end_time = time.time()
timings.append(end_time - start_time)
if i%100==0:
print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))
print("Input shape:", input_data.size())
print("Output features size:", features.size())
print('Average batch time: %.2f ms'%(np.mean(timings)*1000))
</pre></div>
</div>
</div>
<section id="PyTorch-model">
<h3>PyTorch model<a class="headerlink" href="#PyTorch-model" title="Permalink to this headline">¶</a></h3>
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</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>model = LeNet()
model.to("cuda").eval()
</pre></div>
</div>
</div>
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<div class="highlight"><pre>
LeNet(
(feat): LeNetFeatExtractor(
(conv1): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1))
(conv2): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1))
)
(classifer): LeNetClassifier(
(fc1): Linear(in_features=576, out_features=120, bias=True)
(fc2): Linear(in_features=120, out_features=84, bias=True)
(fc3): Linear(in_features=84, out_features=10, bias=True)
)
)
</pre></div></div>
</div>
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</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(model)
</pre></div>
</div>
</div>
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<div class="prompt empty docutils container">
</div>
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<div class="highlight"><pre>
Warm up ...
Start timing ...
Iteration 100/1000, ave batch time 5.56 ms
Iteration 200/1000, ave batch time 5.56 ms
Iteration 300/1000, ave batch time 5.56 ms
Iteration 400/1000, ave batch time 5.56 ms
Iteration 500/1000, ave batch time 5.56 ms
Iteration 600/1000, ave batch time 5.56 ms
Iteration 700/1000, ave batch time 5.56 ms
Iteration 800/1000, ave batch time 5.56 ms
Iteration 900/1000, ave batch time 5.56 ms
Iteration 1000/1000, ave batch time 5.56 ms
Input shape: torch.Size([1024, 1, 32, 32])
Output features size: torch.Size([1024, 10])
Average batch time: 5.56 ms
</pre></div></div>
</div>
<p>When compiling your module to TorchScript, there are two paths: Tracing and Scripting.</p>
</section>
<section id="Tracing">
<h3>Tracing<a class="headerlink" href="#Tracing" title="Permalink to this headline">¶</a></h3>
<p>Tracing follows the path of execution when the module is called and records what happens. This recording is what the TorchScript IR will describe. To trace an instance of our LeNet module, we can call torch.jit.trace with an example input.</p>
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traced_model
</pre></div>
</div>
</div>
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<div class="highlight"><pre>
LeNet(
original_name=LeNet
(feat): LeNetFeatExtractor(
original_name=LeNetFeatExtractor
(conv1): Conv2d(original_name=Conv2d)
(conv2): Conv2d(original_name=Conv2d)
)
(classifer): LeNetClassifier(
original_name=LeNetClassifier
(fc1): Linear(original_name=Linear)
(fc2): Linear(original_name=Linear)
(fc3): Linear(original_name=Linear)
)
)
</pre></div></div>
</div>
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</div>
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</pre></div>
</div>
</div>
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</div>
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<div class="highlight"><pre>
Warm up ...
Start timing ...
Iteration 100/1000, ave batch time 5.56 ms
Iteration 200/1000, ave batch time 5.56 ms
Iteration 300/1000, ave batch time 5.56 ms
Iteration 400/1000, ave batch time 5.56 ms
Iteration 500/1000, ave batch time 5.56 ms
Iteration 600/1000, ave batch time 5.56 ms
Iteration 700/1000, ave batch time 5.56 ms
Iteration 800/1000, ave batch time 5.56 ms
Iteration 900/1000, ave batch time 5.56 ms
Iteration 1000/1000, ave batch time 5.56 ms
Input shape: torch.Size([1024, 1, 32, 32])
Output features size: torch.Size([1024, 10])
Average batch time: 5.56 ms
</pre></div></div>
</div>
</section>
<section id="Scripting">
<h3>Scripting<a class="headerlink" href="#Scripting" title="Permalink to this headline">¶</a></h3>
<p>Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing simply follows the execution of your module, it cannot pick up control flow for instance, it will only follow the code path that a particular input triggers. By working from the Python code, the compiler can include these components. We can run the script compiler on our LeNet module by calling torch.jit.script.</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>model = LeNet().to("cuda").eval()
script_model = torch.jit.script(model)
<br/></pre></div>
</div>
</div>
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</pre></div>
</div>
</div>
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