Unlocking the Power of Encoder-Decoder Models with NVIDIA TensorRT-LLM
Summary
NVIDIA TensorRT-LLM has taken a significant leap forward by accelerating encoder-decoder model architectures, further expanding its capabilities for optimizing and efficiently running large language models (LLMs) across different architectures. This advancement includes support for in-flight batching, a technique that significantly improves GPU usage and throughput. This article delves into the details of this enhancement and its implications for AI applications.
The Evolution of TensorRT-LLM
NVIDIA TensorRT-LLM is an open-source library designed to optimize inference for a diverse range of model architectures. Initially, it supported decoder-only models, mixture-of-experts (MoE) models, selective state-space models (SSM), and multimodal models for vision-language and video-language applications. The recent addition of encoder-decoder model support marks a significant expansion of its capabilities, providing highly optimized inference for an even broader range of generative AI applications on NVIDIA GPUs.
Encoder-Decoder Models Explained
Encoder-decoder models are a crucial class of neural networks used in various AI applications, including natural language processing (NLP) and machine translation. These models consist of two main components: an encoder that processes input data into a continuous representation, and a decoder that generates output based on this representation. Popular encoder-decoder model families include T5, mT5, Flan-T5, BART, mBART, FairSeq NMT, UL2, and Flan-UL2.
In-Flight Batching: A Game-Changer for Encoder-Decoder Models
In-flight batching, also known as continuous batching, is a technique that allows for the efficient processing of dynamic workloads by interleaving requests to reduce latency and improve GPU usage. This is particularly beneficial for encoder-decoder models, which have different runtime patterns compared to decoder-only models. TensorRT-LLM’s support for in-flight batching includes several key extensions:
- Runtime Support for Encoder Models: This includes setting up input/output buffers and model execution for various modalities such as text, audio, or other types of data.
- Dual-Paged KV Cache Management: This feature manages the decoder’s self-attention cache and cross-attention cache computed from the encoder’s output.
- Data Passing from Encoder to Decoder: This is controlled at the LLM request level, ensuring that each request’s encoder-stage output is gathered and batched in-flight.
- Decoupled Batching Strategy: This allows for independent and asynchronous batching of requests at each stage, accommodating different sizes and compute properties of the encoder and decoder.
The Impact of In-Flight Batching
In-flight batching significantly improves the efficiency of encoder-decoder models by reducing latency and increasing throughput. By processing sequences in context and generation phases together, TensorRT-LLM can better utilize GPUs and minimize energy costs. This is particularly important for real-world LLM requests, where outputs can vary greatly in size.
Future Enhancements
Upcoming enhancements to TensorRT-LLM include FP8 quantization for encoder-decoder models, which will further improve latency and throughput. For production deployments, NVIDIA Triton Inference Server provides an ideal platform for serving these models. Enterprises looking for the fastest time to value can leverage NVIDIA NIM, part of the NVIDIA AI Enterprise software platform, which offers optimized inference on popular models from NVIDIA and its partner ecosystem.
Table: Key Features of TensorRT-LLM
| Feature | Description |
|---|---|
| Encoder-Decoder Model Support | Optimized inference for encoder-decoder models, including T5, mT5, Flan-T5, BART, mBART, FairSeq NMT, UL2, and Flan-UL2. |
| In-Flight Batching | Technique that interleaves requests to reduce latency and improve GPU usage. |
| Runtime Support for Encoder Models | Includes setup of input/output buffers and model execution for various modalities. |
| Dual-Paged KV Cache Management | Manages decoder’s self-attention cache and cross-attention cache computed from encoder’s output. |
| Data Passing from Encoder to Decoder | Controlled at LLM request level, ensuring each request’s encoder-stage output is gathered and batched in-flight. |
| Decoupled Batching Strategy | Allows for independent and asynchronous batching of requests at each stage. |
| FP8 Quantization | Upcoming enhancement for further improvements in latency and throughput. |
Table: Benefits of In-Flight Batching
| Benefit | Description |
|---|---|
| Improved GPU Usage | Better utilization of GPUs by interleaving requests. |
| Reduced Latency | Minimized latency by processing sequences in context and generation phases together. |
| Increased Throughput | Enhanced throughput by minimizing energy costs and improving efficiency. |
| Dynamic Workload Handling | Efficient processing of dynamic workloads with varying output sizes. |
Conclusion
NVIDIA TensorRT-LLM’s acceleration of encoder-decoder models with in-flight batching is a significant advancement in the field of AI. By providing highly optimized inference for a broader range of generative AI applications, TensorRT-LLM is poised to unlock new possibilities for AI developers and researchers. With its continued expansion of capabilities and upcoming enhancements, TensorRT-LLM remains at the forefront of AI innovation.