Efficient LLM Inference

Early Exit Mechanisms

Early exit strategies aim to reduce the computational cost of inference by allowing the model to stop processing once a sufficiently confident prediction has been made, rather than always executing the full network depth.

Patience-based Early Exit

Patience-based early exit monitors the model's predictions at intermediate layers. If the prediction remains stable for a certain number of consecutive layers (the "patience" threshold), the model exits early, returning the current prediction. This avoids unnecessary computation in later layers when the outcome is unlikely to change, speeding up inference, especially for easier inputs.

Confident Adaptive Language Modeling (CALM)

CALM is a specific early exit technique where intermediate layers of an LLM can predict the final token and an associated confidence score. If the confidence score at an early layer exceeds a predefined threshold, the generation process for that token terminates early. This adaptively adjusts the computation performed per token based on the difficulty or certainty of the prediction, significantly speeding up inference on average.

Parallel Inference on LLMs

Parallel inference techniques aim to generate multiple tokens or explore multiple possibilities simultaneously, overcoming the sequential bottleneck of standard autoregressive decoding.

Speculative Sampling (or Decoding)

Speculative sampling uses a smaller, faster "draft" model to generate a sequence of candidate tokens quickly. Then, the larger, more powerful "verifier" model processes these candidates in parallel to check their validity. Accepted tokens are kept, and the process repeats from the first rejected token. This significantly speeds up inference when the draft model's predictions align well with the verifier model, achieving faster generation with potentially identical output distribution to the original large model.

Lookahead Decoding

Lookahead decoding is another parallel decoding strategy. It aims to improve upon speculative decoding by generating multiple candidate continuations (n-grams) in parallel using the main LLM itself, verified in a single forward pass. It identifies verifiable n-grams efficiently using a trie-based structure (Ja-Lookahead) or other branching mechanisms, allowing the model to generate multiple tokens per step while maintaining the output distribution.

Optimized Attention Algorithms

Attention mechanisms are computationally intensive. Optimizing their implementation, particularly around memory access and computation patterns during inference, is crucial for efficiency.

Efficient Memory Management with PagedAttention

PagedAttention tackles memory inefficiency in LLM inference caused by fragmentation and over-reservation of the Key-Value (KV) cache. It applies concepts from operating systems' virtual memory and paging to manage the KV cache. Memory is allocated in non-contiguous fixed-size blocks ("pages"), allowing for flexible sharing of memory between requests (e.g., in beam search or parallel sampling) and minimizing wasted space. This enables much larger batch sizes and longer sequence handling within the same memory footprint, significantly boosting throughput.

Flash-Decoding for Long-Context Inference

Flash-Decoding adapts the principles of FlashAttention (optimized I/O between GPU memory levels) specifically for the autoregressive decoding phase of LLMs, which is often memory-bandwidth bound, especially for long sequences. By optimizing how attention keys and values are loaded and processed for the single query token generated at each step, Flash-Decoding significantly reduces memory access overhead and speeds up inference for models handling very long contexts.