Retrieval-Based Speculative Decoding: A Novel Approach to Accelerate Language Model Generation

Основні поняття

REST, a novel algorithm, leverages retrieval from a datastore to generate draft tokens, enabling significant speedup in language model generation without requiring additional training.

Анотація

The paper introduces Retrieval-Based Speculative Decoding (REST), a novel approach to accelerate the inference of large language models (LLMs).
Key highlights:

REST replaces the parametric draft model used in previous speculative decoding methods with a non-parametric retrieval datastore, allowing it to be easily integrated with any LLM.
During inference, REST first retrieves relevant continuation candidates from the datastore based on the current context, then constructs a Trie to select the most probable draft tokens.
The draft tokens are verified by the LLM using a carefully designed attention mask, ensuring efficient computation on shared prefixes across different draft sequences.
Experiments on the HumanEval and MT-Bench benchmarks show that REST can achieve 1.62x to 2.36x speedup over standard autoregressive decoding and speculative decoding, without compromising the quality of the generated output.
The effectiveness of REST is influenced by the size and quality of the retrieval datastore, opening up opportunities for further enhancements by using larger or more specialized datastores.

Статистика

REST achieves a speedup of 1.62x to 2.36x on language model generation compared to standard autoregressive decoding and speculative decoding.
The average time for retrieval, including Trie construction, is less than 1 ms, which is negligible compared to the overall generation time.

Цитати

None

Ключові висновки, отримані з

REST

by Zhenyu He,Ze... о arxiv.org 04-05-2024

https://arxiv.org/pdf/2311.08252.pdf

Глибші Запити

How can the retrieval datastore be further optimized to improve the quality and coverage of the draft tokens, potentially leading to even greater speedups?

To optimize the retrieval datastore for improved quality and coverage of draft tokens, several strategies can be implemented:

Enhanced Retrieval Models: Utilize advanced dense retrieval models that can better capture the nuances of the input context and retrieve more relevant continuation candidates. Models like dense retrievers with improved embeddings and retrieval mechanisms can enhance the quality of retrieved tokens.

Contextual Embeddings: Incorporate contextual embeddings in the retrieval process to capture the context more effectively. By considering the context surrounding the input tokens, the retrieval process can be more accurate and comprehensive.

Fine-tuning Datastore: Continuously update and fine-tune the datastore with new data to ensure it remains relevant and up-to-date. Regularly adding new content can improve the coverage of potential draft tokens.

Diverse Data Sources: Expand the sources of data used to construct the datastore. Incorporating a diverse range of text or code corpora can provide a broader set of potential draft tokens, enhancing the coverage and quality of retrieved tokens.

Multi-step Retrieval: Implement a multi-step retrieval process where the initial retrieval is refined through subsequent retrievals. This iterative approach can help filter out irrelevant tokens and focus on the most suitable draft tokens.

By implementing these optimization strategies, the retrieval datastore can be fine-tuned to provide higher-quality and more comprehensive draft tokens, leading to even greater speedups in language model inference.

How can the limitations of the current retrieval-based approach be addressed, and how can it be extended to handle more complex generation tasks that require a deeper understanding of context?

The current retrieval-based approach has limitations in handling complex generation tasks that require a deeper understanding of context. To address these limitations and extend the approach for more sophisticated tasks, the following steps can be taken:

Contextual Understanding: Enhance the retrieval process by incorporating contextual understanding mechanisms. This can involve leveraging pre-trained contextual embeddings or contextualized representations to better capture the nuances of the input context.

Hierarchical Retrieval: Implement a hierarchical retrieval system where the initial retrieval is followed by a more detailed and context-aware retrieval process. This multi-level approach can help in capturing complex context dependencies for generating accurate draft tokens.

Domain-specific Datastores: Develop domain-specific datastores tailored to the specific requirements of complex generation tasks. By curating datastores with specialized content relevant to the task at hand, the retrieval process can be optimized for more intricate contexts.

Advanced Attention Mechanisms: Integrate advanced attention mechanisms, such as hierarchical or multi-head attention, to enable the model to focus on different aspects of the input context simultaneously. This can enhance the model's ability to understand and generate complex sequences.

Interactive Generation: Explore interactive generation techniques where the model can interact with the user to clarify ambiguous contexts or seek additional information. This interactive approach can help in handling complex tasks that require a deeper understanding of context.

By addressing these limitations and incorporating advanced techniques for contextual understanding and attention mechanisms, the retrieval-based approach can be extended to handle more complex generation tasks effectively.

Could REST be combined with other acceleration techniques, such as model pruning or quantization, to achieve even greater efficiency gains in language model inference?

REST can indeed be combined with other acceleration techniques like model pruning or quantization to achieve enhanced efficiency gains in language model inference. Here's how the combination can be beneficial:

Model Pruning: By integrating REST with model pruning techniques, redundant or less important parts of the language model can be removed, leading to a more compact and efficient model. REST can then operate on the pruned model, further speeding up the inference process without compromising accuracy.

Quantization: Quantizing the language model to lower precision can reduce memory and computational requirements. REST can be applied to the quantized model, leveraging the benefits of both techniques to achieve faster inference speeds while maintaining performance.

Hybrid Approaches: Combining REST with a hybrid approach that includes elements of both model pruning and quantization can result in a highly optimized and efficient language model. REST can work in conjunction with these techniques to maximize speedups and minimize resource utilization.

Dynamic Adaptation: Implementing dynamic adaptation mechanisms that adjust the model's architecture based on the retrieval results from REST can further optimize the inference process. This adaptive approach can tailor the model's structure to the specific requirements of each generation task.

By combining REST with model pruning, quantization, or hybrid approaches, language model inference can be significantly accelerated while ensuring efficient resource utilization and maintaining high performance levels. This integration of techniques can lead to substantial efficiency gains in language model inference.