Continuous Batching for LLM Inference
Continuous batching is an iteration-based scheduling method, notably used in the Orca system, where the composition of a request batch is dynamically adjusted between computational steps. This flexibility allows for new input sequences to be added or completed sequences to be removed from the batch during any iteration. This adjustment can occur even if the processing for the entire batch is not yet finished, distinguishing it from static methods.
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References
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Continuous Batching for LLM Inference
In a common architecture for language model inference, the initial processing of a user's prompt (prefilling) and the subsequent token-by-token generation of the response (decoding) are treated as distinct computational stages, even though they execute on the same hardware. What is the primary analytical reason for this architectural separation?
Optimizing Inference Throughput
Trade-offs in a Staged Inference Architecture
Learn After
Iteration in Continuous Batching
General Process of Continuous Batching
Example of Interleaving Prefilling and Decoding in Continuous Batching
Overhead of Dynamic Batch Reorganization in Continuous Batching
Memory Fragmentation in LLM Inference
Prefilling-Prioritized Strategy in Continuous Batching
Simple Iteration-level Scheduling
Priority-Based Scheduling in LLM Inference
Custom Priority Policies in LLM Scheduling
Disaggregation of Prefilling and Decoding using Pipelined Engines
Comparison of Continuous (Prefilling-Prioritized) vs. Standard (Decoding-Prioritized) Batching
LLM Inference Scheduling Strategy
An LLM inference server is processing a batch of three long-running requests. In the middle of this process, after several computational steps have already been completed for the initial batch, a new, short request arrives. How would a system implementing continuous batching most likely handle this new request in the next computational step?
An LLM inference system is designed to maximize hardware utilization. Which of the following operational descriptions best illustrates the core principle of continuous batching, distinguishing it from a static batching approach?