Accelerating BERT Deployment on NVIDIA GPU

Table of Contents

1. Introduction
2. Understanding GPU Serving on Media's GPU
3. Optimizing GPU Utilization on Media's GPU
4. The Key Features of Triton Inference Server
   • Dynamic Batching Scheduler
   • Concurrent Model Execution
   • Model Control and Configuration
   • Model Metrics and Monitoring
   • Profiling and Performance Analysis
5. Optimizing Word Implementations on GPUs
   • Faster Transformer 2.0
   • TensorRT Optimization
   • Algorithm-Level Acceleration
6. Deploying Models Efficiently with Triton Inference Server
   • Model Export and File Structure
   • Launching Triton Inference Server
   • Using the Profiling Tool
7. Case Study: Optimizing Inference on Media's Work Solution
   • Optimization Challenges
   • Implementing Fast Transformer and TensorRT
   • Performance Results and Comparisons
8. Scaling Out Deployment with Multiple GPUs
   • Load Balancing with Triton Inference Server
9. Conclusion

Introduction

In this article, we will dive into the topic of GPU serving on Media's GPU and explore ways to optimize GPU utilization and improve the performance of inference systems. We will cover the key features of Triton Inference Server, a powerful tool for deploying models efficiently on GPUs. Additionally, we will discuss the optimization techniques and best practices for implementing word transformation models on GPUs. We will also provide a step-by-step guide on how to deploy models with Triton Inference Server and highlight a case study on optimizing inference on Media's work solution. Finally, we will explore strategies for scaling out the deployment using multiple GPUs. Let's get started!

Understanding GPU Serving on Media's GPU

In recent years, GPU serving has become widely adopted for online systems in China, especially in the media industry. This is due to the increasing demand for real-time video processing and the need to fully utilize GPUs while maintaining low latency. However, the original word implementation on GPUs is not very efficient and does not leverage the latest software and hardware features of the GPU, such as tensor cores. To address this issue, Media has developed optimizations for inference using TensorRT and handwritten CUDA code. With Triton Inference Server (formerly known as TensorRT Inference Server), they have created an efficient deployment solution for serving models on GPUs.

Optimizing GPU Utilization on Media's GPU

To fully leverage the power of GPUs, it is important to optimize their utilization. This can be done through methods such as batching and concurrent model execution. Batching involves combining multiple tasks into a single batch to increase efficiency and reduce overhead. Concurrent model execution allows multiple models to run concurrently on the GPU, reducing idle time and maximizing throughput. These optimization techniques can significantly improve GPU utilization and the overall performance of inference systems.

The Key Features of Triton Inference Server

Triton Inference Server offers a range of powerful features that enable efficient deployment of models on GPUs. Some of the key features include:

Dynamic Batching Scheduler

The dynamic batching scheduler helps group different requests into a single batch, even when the batch sizes are different. This optimizes the execution of inference tasks and improves throughput. By dynamically batching requests, the server can efficiently utilize the GPU resources and minimize latency.

Concurrent Model Execution

Concurrent model execution allows multiple models to run concurrently on the GPU, utilizing the full power of the GPU and reducing idle time. Triton manages multiple execution contexts and schedules the execution of models on different streams. This feature enables efficient Parallel execution and faster inference times.

Model Control and Configuration

Triton provides comprehensive control and configuration options for managing models. The model repository can be easily managed using file systems, and the server allows for loading and unloading models at runtime. Model configuration files can be used to customize the deployment of models, including platform selection, input and output configurations, version control, and more.

Model Metrics and Monitoring

Triton offers a range of metrics and monitoring capabilities that allow You to monitor the performance of your models in real-time. You can monitor metrics such as latency, throughput, and resource utilization at different levels of granularity and frequency. This helps you optimize and fine-tune your deployment for optimal performance.

Profiling and Performance Analysis

The server provides a profiling tool that allows you to generate and analyze detailed profiling data for your models. This tool helps you identify performance bottlenecks and optimize your deployment. The profiling results can be exported and analyzed using post-processing software, providing insights into the composition of inference time and resource utilization.

Optimizing Word Implementations on GPUs

Optimizing word implementations on GPUs is crucial for achieving efficient and high-performance inference. There are several techniques and frameworks available for optimizing word models on GPUs, such as the Faster Transformer and TensorRT.

Faster Transformer 2.0

Faster Transformer is an open-source project developed by Media that focuses on accelerating the transformer encoder. It provides an optimized implementation of the transformer encoder in CUDA, making it ideal for GPU inference. Faster Transformer 2.0 introduces optimizations such as kernel Fusion and layer pruning to improve efficiency and reduce memory footprint. It offers a high-performance solution for word inference on GPUs.

TensorRT Optimization

TensorRT is a deep learning inference optimizer and runtime library developed by NVIDIA. Media has optimized word models using TensorRT, taking AdVantage of its ability to optimize model execution for specific GPU platforms. TensorRT performs layer fusion, memory optimization, and kernel selection to maximize inference performance and reduce memory footprint. It also supports lower precision computations, such as FP16, to further improve performance without significant accuracy loss.

Algorithm-Level Acceleration

In addition to framework-level optimizations, algorithm-level acceleration techniques can further improve inference performance. Techniques such as pruning, factorization, quantization, and knowledge distillation can help reduce model complexity and make it more suitable for deployment on GPUs. These techniques provide a way to optimize your models for faster and more efficient inference, without sacrificing accuracy.

Deploying Models Efficiently with Triton Inference Server

Triton Inference Server provides a powerful and efficient solution for deploying models on GPUs. Here are the steps to deploy models using Triton Inference Server:

1. Build the source code and generate the TensorFlow custom OP library.
2. Export the trained models and save them in the required file structure.
3. Launch Triton Inference Server using Docker images.
4. Use the profiling tool to generate fake data and analyze the performance of the deployment.

These steps ensure that your models are deployed efficiently and in line with the best practices recommended by Triton Inference Server. Detailed instructions for each step can be found in the documentation and resources provided by Media.

Case Study: Optimizing Inference on Media's Work Solution

In this case study, we will explore the optimization techniques used in Media's work solution. The solution is built on top of the Faster Transformer and TensorRT optimizations. We will analyze the performance of the solution and compare it with the original word implementation.

We started by running the inference on CPUs, which provided a baseline performance. Then, we optimized the implementation using the Faster Transformer and TensorRT. We observed significant improvements in throughput and latency, making the inference more efficient and faster.

We also explored techniques such as dynamic batching and concurrent model execution to further improve the performance. These techniques helped reduce idle time and maximize GPU utilization. The results showed a significant boost in throughput and reduced latency.

Overall, the case study demonstrates the effectiveness of the optimization techniques employed in Media's work solution. By leveraging frameworks like Faster Transformer and TensorRT, and applying optimization techniques at the algorithm-level, Media achieved efficient and high-performance inference on GPUs.

Scaling Out Deployment with Multiple GPUs

To handle high-Scale deployments, Media's solution supports scaling out using multiple GPUs. By load balancing the models across multiple GPUs, they can achieve higher throughput and handle larger workloads. Media's solution leverages the capabilities of Triton Inference Server to manage and schedule the execution of models across multiple GPUs, ensuring efficient utilization of resources.

The load balancing feature, combined with Triton Inference Server's support for Kubernetes and cooper flow, enables the deployment of models on tens or hundreds of GPUs. This scalability makes it possible to serve a large number of inference requests in real-time, ensuring high availability and fast response times.

Conclusion

In this article, we have explored the topic of GPU serving on Media's GPU and discussed various optimization techniques and strategies for deploying models efficiently. We covered the key features of Triton Inference Server, such as dynamic batching, concurrent model execution, and profiling tools. We also discussed the optimization techniques used in Media's work solution, including the Faster Transformer and TensorRT optimizations. Additionally, we highlighted a case study that demonstrated the performance improvements achieved through these optimizations. Lastly, we touched upon scaling out deployment using multiple GPUs.

By following these best practices and utilizing the powerful features of Triton Inference Server, you can achieve high-performance GPU serving and efficiently deploy word models for various applications. The optimizations discussed in this article enable you to fully leverage the capabilities of Media's GPU and provide fast and efficient inference capabilities for your applications.