Space Complexity of Sliding Window Attention
In sliding window attention, the space complexity of the Key-Value (KV) cache is reduced by storing keys and values for only a fixed-size window of recent tokens (), rather than the entire sequence. This approach results in a constant memory footprint with respect to the sequence length, defined by the formula , where is the number of layers, is the number of attention heads, and is the head dimension.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Formula for Fixed-Size Window Memory
Window-based Cache as an Example of Fixed-Size Memory
Space Complexity of Sliding Window Attention
Window Size (n_c)
A language model is designed to process extremely long sequences of text, and its developers are concerned about computational resources. They are considering two approaches for the attention mechanism: one that considers all previous tokens in the sequence, and another that only considers a fixed-size window of the 100 most recent tokens. What is the fundamental trade-off between these two approaches?
Applying Sliding Window Attention
In an attention mechanism that uses a fixed-size sliding window, the amount of memory required to store the keys and values for the attention calculation increases as the input sequence gets longer.
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Post-Incident Review: Memory Design for Long-Running Customer Support Chats
Diagnosing Long-Range Failures in a Segment-Processed LLM with Dual Memory
Choosing a Memory Architecture for Long-Context Enterprise Summarization
Postmortem: Long-Document QA Failures Under Fixed-Window vs Compressive Memory
Selecting and Justifying a Long-Context Memory Design for a Regulated Audit Assistant
Incident Triage: Long-Running Agent Workflow with Windowed vs Compressive Memory
Space Complexity of Sliding Window Attention
Optimizing Memory for Long-Document Processing
An auto-regressive language model is generating a long text, one token at a time. To manage memory, it employs a key-value caching strategy where it only stores the keys and values for the most recent 2048 tokens. How will the memory allocated for this cache change as the model generates the 5000th token and continues beyond it?
Comparing KV Cache Memory Growth
Learn After
A large language model is configured to process text by only storing and considering the keys and values of the most recent 512 tokens when calculating attention for each new token. As the model processes a document that grows from 1,000 tokens to 100,000 tokens in length, how will the memory required for this key-value storage be affected?
Chatbot Memory Optimization
Comparing Memory Usage of Attention Mechanisms