XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models (2411.15100v2)

Abstract: The applications of LLM Agents are becoming increasingly complex and diverse, leading to a high demand for structured outputs that can be parsed into code, structured function calls, and embodied agent commands. These developments bring significant demands for structured generation in LLM inference. Context-free grammar is a flexible approach to enable structured generation via constrained decoding. However, executing context-free grammar requires going through several stack states over all tokens in vocabulary during runtime, bringing non-negligible overhead for structured generation. In this paper, we propose XGrammar, a flexible and efficient structure generation engine for LLMs. XGrammar accelerates context-free grammar execution by dividing the vocabulary into context-independent tokens that can be prechecked and context-dependent tokens that need to be interpreted during runtime. We further build transformations to expand the grammar context and reduce the number of context-independent tokens. Additionally, we build an efficient persistent stack to accelerate the context-dependent token checks. Finally, we co-design the grammar engine with LLM inference engine to overlap grammar computation with GPU executions. Evaluation results show that XGrammar can achieve up to 100x speedup over existing solutions. Combined with an LLM inference engine, it can generate near-zero overhead structure generation in end-to-end low-LLM serving.

• The paper introduces XGrammar as a novel engine for structured generation in LLMs, leveraging byte-level pushdown automata for efficient CFG execution.
• It details an adaptive token mask cache and persistent execution stack that reduce runtime overhead, achieving per-token latency improvements up to 100x.
• The system co-design facilitates seamless integration across diverse platforms, enhancing structured outputs in mobile, web, and on-device applications.

Overview of the Contour Engine for Structured Generation in LLMs

The paper "Contour: Flexible and Efficient Structured Generation Engine For LLMs" explores enhancing the capabilities of LLMs to efficiently generate structured outputs. With the increasing complexities of applications necessitating structured data such as JSON, SQL, and DSLs, the demand for efficient structured generation during LLM inference has risen substantially. Traditional decoding approaches, albeit effective in managing some of these requirements, often suffer from performance bottlenecks due to their inability to efficiently handle the intricacies of context-free grammars (CFGs). Contour is proposed as a solution to this, offering a flexible and efficient framework to cater to the needs of structured data generation.

Key Approaches

Contour leverages a byte-level pushdown automaton to effectively execute context-free grammars, addressing critical problems presented by the conventional constraints involved in runtime token checks. The distinguishing features of the Contour method include:

1. Division of Tokens: Contour introduces an innovative approach to categorize tokens into context-independent and context-dependent tokens. Context-independent tokens are precomputed and stored, significantly streamlining runtime operations.
2. Adaptive Token Mask Cache: This facilitates rapid retrieval of token validity at runtime, focusing computational resources on the relatively few context-dependent tokens that require live processing. This cache uses efficient storage formats depending on token characteristics at each automaton point.
3. Persistent Execution Stack: By organizing multiple matching stacks into a persisting data structure, Contour enhances both the speed and memory efficiency of stack operations. This structure permits efficient branching and rollback capabilities, critical for both grammar preprocessing and runtime token evaluation.
4. Context Expansion: Preprocessing is optimized through context expansion, reducing context-dependent tokens by examining the likely suffix conditions following rule completions, thereby simplifying many runtime checks.
5. System Co-Design: The engine’s design ensures that much of the grammar-related computation happens parallel to LLM operations, harmoniously balanced between GPU and CPU to minimize overhead.

Evaluation Results

Through extensive testing, Contour achieved significant performance improvements. It outperformed existing structured generation solutions by up to 100 times in per-token latency, demonstrating its prowess in efficiently managing complex CFG executions. The optimizations allowed for near-zero overhead at the end-to-end LLM serving level, achieving up to 80 times speed improvements over methods like Outlines and the native engines within libraries like llama.cpp.

Moreover, cross-platform deployment tests showcased Contour’s versatility, allowing seamless integration in environments ranging from desktop GPUs to in-browser applications using WebAssembly, without imposing significant computational burden. This makes Contour an attractive option for on-device structured generation tasks, facilitating more sophisticated applications in mobile and web platforms.

Implications and Future Directions

Contour presents a complementary approach to existing structured generation methodologies, facilitating more effective structured output without sacrificing computational efficiency. Its design allows for integration with various inference frameworks, broadening its applicability in real-world deployments. This work posits that careful co-design of grammar processing and LLM inference pathways can unlock new performance efficiencies, suggesting a path forward for expanding structured LLM applications across diverse platforms.

In future developments, further exploration could delve into novel tokenizer designs that align better with Contour's structures, or extending support to even more complex grammars and languages. Additionally, as LLMs grow, adaptive methods for handling exponentially large vocabularies while maintaining efficiency will remain critical.

The open-sourcing of Contour aligns it well with community efforts toward versatile LLM frameworks, offering researchers and practitioners the tools needed for sophisticated, structured language generation.