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kv_cache_quantization
performance-kv-cache-quantizatio-quantizedcache-quantizes-the-first-token-immediate-0bd3b77d
IFQuantizedCache quantizes the first token immediately upon inference start, ignoring the attention sink principle.
THENModify the update method of QuantizedCache to defer quantization of the first token: keep the first token in full-precision (FP16) buffers until the total cache length exceeds the maximum capacity (buffer size). This prevents precision loss for the attention sink token and improves model perplexity.
Tier 170%
kv_cache_quantization
performance-kv-cache-quantizatio-quantizedcache-in-huggingface-transformers-prematu-8979957f
IFQuantizedCache in HuggingFace Transformers prematurely quantizes the first token, harming perplexity because the first token acts as an attention sink.
THENModify the QuantizedCache's `update` method to keep the first token in full-precision (FP16) buffers until the cache exceeds its maximum capacity. Only then quantize older tokens, preserving the first token if possible. This matches the behavior described in the docstring and aligns with attention-sink research.
Tier 170%
kv_cache_quantization
performance-kv-cache-quantizatio-quantized-kv-cache-quantizes-the-first-token-immed-19ccedde
IFQuantized KV cache quantizes the first token immediately, reducing model accuracy because the first token acts as an attention sink.
THENModify the update method of QuantizedCache to keep the first token (index 0) in the full-precision buffer instead of moving it to the quantized cache immediately. Only quantize tokens when the cache length exceeds the buffer capacity.
Tier 170%
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