  Transformers have emerged as the underpinning architecture for Large Language
Models (LLMs). In generative language models, the inference process involves
two primary phases: prompt processing and token generation. Token generation,
which constitutes the majority of the computational workload, primarily entails
vector-matrix multiplications and interactions with the Key-Value (KV) Cache.
This phase is constrained by memory bandwidth due to the overhead of
transferring weights and KV cache values from the memory system to the
computing units. This memory bottleneck becomes particularly pronounced in
applications that require long-context and extensive text generation, both of
which are increasingly crucial for LLMs.
  This paper introduces "Keyformer", an innovative inference-time approach, to
mitigate the challenges associated with KV cache size and memory bandwidth
utilization. Keyformer leverages the observation that approximately 90% of the
attention weight in generative inference focuses on a specific subset of
tokens, referred to as "key" tokens. Keyformer retains only the key tokens in
the KV cache by identifying these crucial tokens using a novel score function.
This approach effectively reduces both the KV cache size and memory bandwidth
usage without compromising model accuracy. We evaluate Keyformer's performance
across three foundational models: GPT-J, Cerebras-GPT, and MPT, which employ
various positional embedding algorithms. Our assessment encompasses a variety
of tasks, with a particular emphasis on summarization and conversation tasks
involving extended contexts. Keyformer's reduction of KV cache reduces
inference latency by 2.1x and improves token generation throughput by 2.4x,
while preserving the model's accuracy.
