KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913348821450752 |
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| author | Cho, Minsik Rastegari, Mohammad Naik, Devang |
| author_facet | Cho, Minsik Rastegari, Mohammad Naik, Devang |
| contents | Large Language Model or LLM inference has two phases, the prompt (or prefill) phase to output the first token and the extension (or decoding) phase to the generate subsequent tokens. In this work, we propose an efficient parallelization scheme, KV-Runahead to accelerate the prompt phase. The key observation is that the extension phase generates tokens faster than the prompt phase because of key-value cache (KV-cache). Hence, KV-Runahead parallelizes the prompt phase by orchestrating multiple processes to populate the KV-cache and minimizes the time-to-first-token (TTFT). Dual-purposing the KV-cache scheme has two main benefits. First, since KV-cache is designed to leverage the causal attention map, we minimize computation and computation automatically. Second, since it already exists for the extension phase, KV-Runahead is easy to implement. We further propose context-level load-balancing to handle uneven KV-cache generation (due to the causal attention) and to optimize TTFT. Compared with an existing parallelization scheme such as tensor or sequential parallelization where keys and values are locally generated and exchanged via all-gather collectives, our experimental results demonstrate that KV-Runahead can offer over 1.4x and 1.6x speedups for Llama 7B and Falcon 7B respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05329 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation Cho, Minsik Rastegari, Mohammad Naik, Devang Distributed, Parallel, and Cluster Computing Artificial Intelligence Computation and Language Large Language Model or LLM inference has two phases, the prompt (or prefill) phase to output the first token and the extension (or decoding) phase to the generate subsequent tokens. In this work, we propose an efficient parallelization scheme, KV-Runahead to accelerate the prompt phase. The key observation is that the extension phase generates tokens faster than the prompt phase because of key-value cache (KV-cache). Hence, KV-Runahead parallelizes the prompt phase by orchestrating multiple processes to populate the KV-cache and minimizes the time-to-first-token (TTFT). Dual-purposing the KV-cache scheme has two main benefits. First, since KV-cache is designed to leverage the causal attention map, we minimize computation and computation automatically. Second, since it already exists for the extension phase, KV-Runahead is easy to implement. We further propose context-level load-balancing to handle uneven KV-cache generation (due to the causal attention) and to optimize TTFT. Compared with an existing parallelization scheme such as tensor or sequential parallelization where keys and values are locally generated and exchanged via all-gather collectives, our experimental results demonstrate that KV-Runahead can offer over 1.4x and 1.6x speedups for Llama 7B and Falcon 7B respectively. |
| title | KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2405.05329 |