KV-Cache and Efficient Inference
Transformers generate text by predicting one token at a time, using previous tokens as context. During this process, the model repeatedly applies self-attention to all tokens generated so far. Without any optimization, each new token requires recomputing attention for the entire context, which becomes increasingly expensive as the sequence grows. To address this, transformers use a key-value cache (KV-cache). The KV-cache stores the computed key and value tensors for each past token. When generating a new token, the model only needs to compute keys and values for the latest token, then append these to the cache. The attention mechanism then uses the cached keys and values from all previous tokens, rather than recalculating them from scratch. This approach significantly reduces redundant computation and speeds up the decoding process, especially for long sequences.
The memory-speed trade-off in transformer inference refers to the balance between using more memory to store cached key-value pairs (for faster inference) versus saving memory by recomputing these values (at the cost of slower inference).
Without KV-cache, the model must repeatedly recompute keys, values, and attention for the entire sequence every time it generates a new token, causing the computation cost to grow with sequence length; with KV-cache, the model stores all previously computed keys and values and only computes them for the newest token, reusing the cached history and dramatically reducing the work required at each decoding step.
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Can you explain how the KV-cache actually works during text generation?
What are some practical scenarios where using a KV-cache is especially beneficial?
Are there any strategies to manage the increased memory usage caused by the KV-cache?
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Transformers generate text by predicting one token at a time, using previous tokens as context. During this process, the model repeatedly applies self-attention to all tokens generated so far. Without any optimization, each new token requires recomputing attention for the entire context, which becomes increasingly expensive as the sequence grows. To address this, transformers use a key-value cache (KV-cache). The KV-cache stores the computed key and value tensors for each past token. When generating a new token, the model only needs to compute keys and values for the latest token, then append these to the cache. The attention mechanism then uses the cached keys and values from all previous tokens, rather than recalculating them from scratch. This approach significantly reduces redundant computation and speeds up the decoding process, especially for long sequences.
The memory-speed trade-off in transformer inference refers to the balance between using more memory to store cached key-value pairs (for faster inference) versus saving memory by recomputing these values (at the cost of slower inference).
Without KV-cache, the model must repeatedly recompute keys, values, and attention for the entire sequence every time it generates a new token, causing the computation cost to grow with sequence length; with KV-cache, the model stores all previously computed keys and values and only computes them for the newest token, reusing the cached history and dramatically reducing the work required at each decoding step.
Merci pour vos commentaires !
