mit-han-lab/streaming-llm
Streaming Llm
This project is a long context inference engine and optimizer designed to process infinite text streams using large language models without memory growth or performance degradation. It serves as a system for maintaining constant memory usage during the generation of text from arbitrarily long input sequences.
The implementation utilizes a rolling key-value cache manager and attention sink mechanisms to stabilize the attention process during continuous stream processing. By retaining initial tokens and employing a sliding window of key-value pairs, the system enables constant-time inference and prevents memory crashes when handling large volumes of text.
The framework covers capabilities for efficient cache management, including selective key-value eviction and fixed-window attention. These mechanisms allow for real-time text streaming and deployment in environments where memory consumption must remain fixed regardless of the sequence length.
Features
- Rolling KV Caches - Maintains a fixed-size sliding window of key-value pairs by discarding oldest tokens as new ones arrive.
- Streaming Attention Kernels - Implements attention masks that retain sink tokens to stabilize memory and attention during continuous streaming.
- Infinite Sequence Processing - Handles input sequences of any length without needing to restart the system via a rolling cache of attention sinks.
- Constant-Memory Deployments - Enables running large language models in production where memory consumption must remain fixed regardless of sequence length.
- LLM Performance Optimization Libraries - Increases resource efficiency and execution speed for LLMs processing infinite text streams.
- Long Context Processing - Generates text from extremely long inputs while keeping memory usage constant and avoiding performance degradation.
- KV Cache Management - Optimizes memory used for attention mechanisms to enable scalable inference on hardware with limited RAM.
- KV Cache Eviction - Implements token removal from the memory buffer to keep sequence length constant while preserving critical sink tokens.
- Selective KV Eviction - Removes specific non-sink tokens from the cache to maintain a constant sequence length during inference.
- Constant-Time Inference Engines - Provides stable time and memory complexity for token generation by capping the active attention window.
- LLM-Based Text Processing - Uses large language models to process and maintain context over massive volumes of text.
- LLM Stream Processing - Handles and concatenates real-time token streams from language models to optimize latency and throughput.
- Linear-Time Sequence Models - Processes arbitrarily long sequences with linear computational efficiency and constant memory usage per token.
- Out-of-Core Processing - Processes continuous streams of text that exceed standard memory limits using a rolling cache mechanism.
- Real-Time Text Streaming - Integrates LLMs into workflows requiring low-latency processing of arbitrary length input streams.
- Inference Frameworks - Enables efficient streaming with attention sink techniques.
