○ TensorRT is an SDK for high-performance deep learning inference and with TensorRT 8.0, you can import models trained using Quantization Aware Training (QAT)…
Overview
The article discusses how to achieve FP32 accuracy for INT8 inference using Quantization Aware Training (QAT) with NVIDIA TensorRT. It covers the benefits of model quantization, the methods of quantization, and provides a detailed walkthrough of implementing QAT in TensorRT.
What You'll Learn
1
How to implement Quantization Aware Training with TensorRT
2
Why using INT8 precision can improve inference performance
3
When to choose QAT over Post-Training Quantization
Prerequisites & Requirements
- Understanding of deep learning concepts and model training
- Familiarity with NVIDIA TensorRT and PyTorch(optional)
Key Questions Answered
What is Quantization Aware Training and how does it work?
Quantization Aware Training (QAT) is a technique that incorporates quantization error into the training phase, allowing the model to adapt to quantized weights and activations. This is achieved by adding fake-quantization operations during training, which simulate the effects of quantization without altering the underlying floating-point computations.
How does TensorRT handle INT8 models?
TensorRT supports INT8 models using two processing modes: one that utilizes the TensorRT tensor dynamic-range API for optimization and another that processes floating-point ONNX networks with explicit quantization rules. This allows for optimized inference latency while maintaining model accuracy.
What are the differences between Post-Training Quantization and Quantization Aware Training?
Post-Training Quantization (PTQ) is performed after training a high-precision model and is generally simpler and faster. In contrast, Quantization Aware Training (QAT) incorporates quantization during the training process, often leading to better accuracy, especially for models sensitive to quantization effects.
What are the benefits of model quantization?
Model quantization reduces the precision of model parameters and activations, which can lead to faster inference times, lower memory usage, and reduced power consumption. This is particularly beneficial for deployment in resource-constrained environments where performance and efficiency are critical.
Key Statistics & Figures
Top1 accuracy of EfficientNet B0
77.4%
This is the baseline floating-point accuracy before quantization.
PTQ Top1 accuracy of EfficientNet B0
33.9%
This is the accuracy achieved after applying Post-Training Quantization.
QAT Top1 accuracy of EfficientNet B0
76.8%
This is the accuracy achieved after applying Quantization Aware Training.
Technologies & Tools
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Backend
Nvidia Tensorrt
Used for optimizing and deploying deep learning models with quantization.
Backend
Pytorch
Used for model training and implementing Quantization Aware Training.
Key Actionable Insights
1Implementing Quantization Aware Training can significantly enhance model accuracy during inference.By incorporating quantization into the training process, models can better adapt to the reduced precision, which is crucial for maintaining performance in applications requiring real-time responses.
2Utilizing TensorRT's INT8 processing modes can optimize inference latency.Choosing the appropriate processing mode based on the model's requirements can lead to substantial performance improvements, especially in environments where computational resources are limited.
3Carefully selecting the quantization method is essential for achieving desired accuracy.Understanding the trade-offs between Post-Training Quantization and Quantization Aware Training allows developers to make informed decisions based on their specific use cases and model characteristics.
Common Pitfalls
1
Relying solely on Post-Training Quantization may lead to significant accuracy loss.
PTQ is simpler but may not achieve the desired accuracy for all models, particularly those sensitive to quantization. It's crucial to evaluate if QAT is necessary for maintaining model performance.
Related Concepts
Model Quantization Techniques
Deep Learning Model Optimization
Nvidia Tensorrt Capabilities
Quantization Effects On Model Accuracy