Lookahead decoding is a novel technique that improves the speed and efficiency of autoregressive decoding in large language models (LLMs) like GPT-4 and LLaMA. It eliminates the need for preliminary models and reduces the number of decoding steps by utilizing parallel processing. The technique has been shown to significantly decrease latency in LLM applications like chatbots and personal assistants. The researchers developed an implementation to make lookahead decoding compatible with huggingface/transformers.
Lookahead Decoding: A Parallel Decoding Algorithm to Accelerate LLM Inference
Large language models (LLMs) like GPT-4 and LLaMA are revolutionizing modern applications, but their inference is slow and difficult to optimize. This is because autoregressive decoding, the basis of LLM inference, is time-consuming. The delay in LLM response depends on the length of the answer, as each decoding step produces only one token at a time. This poses a challenge for practical LLM applications that require instant responses, such as chatbots and personal assistants.
However, there are solutions to speed up autoregressive decoding. Speculative decoding methods like Medusa and OSD use a “guess-and-verify” strategy, where a preliminary model predicts several possible tokens in the future, and the original LLM checks these predictions in parallel. These methods can reduce latency by taking advantage of situations where fewer decoding steps are needed. But they have limitations, such as the upper bound on speedup and the need for a reliable preliminary model.
A new study introduces lookahead decoding, a novel technique that addresses these challenges. Lookahead decoding leverages the ability of LLMs to produce multiple orthogonal n-grams simultaneously. It adapts the traditional Jacobi iteration method for parallel decoding, treating autoregressive decoding as the solution to nonlinear equations. Lookahead decoding has the following notable features:
- No need for a preliminary model, speeding up the process.
- Reduces the total number of decoding steps by a factor of log(FLOPs) for each stage.
The researchers demonstrate that lookahead decoding significantly reduces latency by 1.5x-2.3x with minimal increase in computational burden. It enables a tradeoff between processing and reduced latency, although the benefits diminish over time.
The implementation of lookahead decoding is compatible with huggingface/transformers. Users can enhance its efficiency with a few lines of code.
How Lookahead Decoding Works
Lookahead decoding capitalizes on Jacobi Decoding’s ability to generate parallel n-grams. Each new token is decoded using values from previous iterations, creating many n-grams. Lookahead decoding gathers and caches these n-grams based on their trajectories. It simultaneously checks promising n-grams from the cache while performing parallel decoding using Jacobi iterations for future tokens.
Lookahead decoding splits each phase into two parallel branches: the lookahead branch and the verification branch. The lookahead branch maintains a constant-sized window to generate n-grams from the Jacobi iteration trajectory. The verification branch selects and checks promising n-grams.
By combining the lookahead and verification branches into a single pass, lookahead decoding takes advantage of the GPU’s parallel processing capacity while minimizing associated overheads.
Benefits and Applications
The study tested lookahead decoding on different models and benchmarks, demonstrating its effectiveness:
- MT-Bench LLaMA: Lookahead decoding achieved a speedup of around 1.5x in many model configurations.
- HumanEval’s CodeLLaMA: Lookahead decoding reduced CodeLLaMA’s latency by over two times, thanks to easily guessable N-grams in the code.
- Instructional CodeLLaMA for GSM8K: Lookahead decoding reduced latency by 1.8, benefiting GSM8K’s mathematical challenges.
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