Mastering TensorRT: Deploy Deep Learning Models Efficiently

TensorRT Overview

TensorRT is an NVIDIA inference engine that helps engineers deploy trained deep‑learning models to production environments such as data‑center servers, cloud instances, embedded devices, robots, or vehicles. Development can be done in Python or C++, and its performance surpasses other frameworks (e.g., MNN, TNN, Tengine) by leveraging CUDA and cuDNN.

Typical application scenarios include autonomous driving, robotics, and video surveillance, where limited compute resources on edge devices require model acceleration.

Deep Learning Deployment Workflow

The overall deployment pipeline starts with model design, training, and performance testing on the server side. After meeting accuracy targets, large models are quantized and pruned to reduce size and compute cost. The model is then converted to an intermediate ONNX file, whose parameters are verified against the original PyTorch model. ONNX is subsequently transformed by TensorRT into an engine file, and inference results are validated against the PyTorch baseline before deployment.

Registering Custom Operators for ONNX

When an operator is not supported by ONNX, a symbolic method must be added to PyTorch to export the custom op correctly.

class _DCNv2(Function):
    @staticmethod
    def symbolic(g, input, offset_mask, weight, bias, stride, padding, dilation, deformable_groups):
        return g.op("Plugin", input, offset_mask, weight, bias, name_s="DCNv2", infos=json.dumps({
            "dilation": dilation,
            "padding": padding,
            "stride": stride,
            "deformable_groups": deformable_groups
        }))
    @staticmethod
    def forward(ctx, input, offset_mask, weight, bias, stride, padding, dilation, deformable_groups):
        # implementation code
        pass
    @staticmethod
    @once_differentiable
    def backward(ctx, grad_output):
        # implementation code
        pass

Creating TensorRT Plugins

To enable TensorRT to recognize the custom operator, developers subclass the TensorRT plugin interfaces.

class DCNv2PluginDynamic : public IPluginV2DynamicExt {
    // Define inputs, outputs, inference implementation, serialization, etc.
};

class DCNv2PluginDynamicCreator : public IPluginCreator {
    // Handles plugin creation and deserialization.
};

TensorRT Inference Process

Deformable Convolution (DCN) and TensorRT Implementation

Standard convolution struggles with objects of varying scale, pose, or deformation. Deformable convolution (DCN) predicts offset values for sampling locations, improving feature extraction for irregular targets. Since DCN is not natively supported by PyTorch or TensorRT, a custom plugin must be registered.

Two main technical routes exist for integrating DCN into TensorRT:

Route 1: Directly add the custom operator to TensorRT’s library or generate a serialized plugin file. This is quick but requires recompilation when TensorRT versions change.

Route 2: Build a flexible plugin that can be loaded at runtime, offering higher adaptability at the cost of more development effort.