Mistral Spelled Out: Prefill and Chunking : Part 9

Summary

The video explains prefilling and chunking techniques used to optimize performance when prompting large language models. Rather than generating tokens one-by-one or caching the entire prompt, prefilling uses 'chunks' - splitting the prompt into segments the size of the attention window. Each chunk is cached and referenced to provide context when processing subsequent chunks. This balances loading time, memory usage and context for optimal performance. These techniques, along with others like mixture of experts models, aim to fully leverage the capabilities of large language models.

Takeaways

• 😀 The goal is to optimize model performance when using long prompts by prefilling and chunking the prompt
• 👌 Prefilling allows caching the entire prompt in the key-value cache, but this may crash with very long prompts
• 💡 Chunking splits the prompt into chunks the size of the sliding window attention length
• 📝 The key-value cache is prefilled with the first chunk before processing the next chunk
• 🔀 When processing a new chunk, contents from the cache are combined with the new chunk to provide more context
• 🔁 This cycle repeats - cache gets updated and used with each new chunk for better context
• ⚖️ Chunking balances loading the full prompt vs loading tokens one-by-one
• 🚀 Utilizing prefilling and chunking improves performance compared to no caching or full prompt caching
• 🎯 The goal is optimal performance in generating tokens conditioned on the prompt
• 📈 Additional techniques like mixture of experts further improve performance

Q & A

Why do we need prefill and chunking?

We need prefill and chunking to optimize performance when generating tokens from a long prompt. Loading the entire long prompt into the KV cache may crash it, while generating tokens one by one does not utilize the GPU optimally. Prefill and chunking strike a balance.

How does prefill work?

In prefill, we first calculate the attention matrix for the first chunk of tokens from the prompt. Then we fill the KV cache with the output of this operation before moving to the next chunk.

What is the chunk size used in chunking?

The chunk size used is the same as the sliding window size in the attention mechanism, usually around 3 tokens.

How are the key and query matrices populated when chunking?

The query matrix gets the current chunk. The key matrix gets the current chunk concatenated with contents from the KV cache to provide more context.

Why bring KV cache contents along with the current chunk for key matrix?

This provides more context to the current tokens in relation to previous tokens. For example, the token 'you' needs the context of previous tokens to understand its meaning.

What happens as we move from chunk to chunk?

The KV cache gets populated with the attention output of the previous chunk. So later chunks have access to representations of earlier chunks.

How does chunking balance prompt token generation?

By using KV cache for key matrix and current chunk for query matrix. So it utilizes the prompt better than token-by-token generation but does not overload cache like full prompt prefill.

What techniques optimize Mystal performance?

Techniques like KV cache, mixture of experts layers, prefill and chunking optimize Mystal performance for long sequence tasks like prompting.

Does chunking reduce compute compared to full prompt prefill?

Yes, chunking requires less compute per inference compared to calculating attention on the full prompt in one go for prefill.

Why is prompt optimization important?

Prompting is used heavily in AI systems today to get desired outputs from LLMs. Optimizing prompt handling improves real-world performance, latency and cost.