介绍#
什么是自动前缀缓存#
自动前缀缓存（简称 APC）缓存现有查询的 KV 缓存，因此如果新查询与现有查询中的一个共享相同的前缀，新查询可以直接重用 KV 缓存，从而跳过共享部分的计算。
备注
关于 vLLM 如何实现 APC 的技术细节在下一页。
在 vLLM 中启用 APC#
在 vLLM 引擎中设置 enable_prefix_caching=True 以启用 APC。以下是一个示例：
import time
from vllm import LLM, SamplingParams
# A prompt containing a large markdown table. The table is randomly generated by GPT-4.
LONG_PROMPT = "You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as follows.\n# Table\n" + """
| ID | Name | Age | Occupation | Country | Email | Phone Number | Address |
|-----|---------------|-----|---------------|---------------|------------------------|----------------|------------------------------|
| 1 | John Doe | 29 | Engineer | USA | john.doe@example.com | 555-1234 | 123 Elm St, Springfield, IL |
| 2 | Jane Smith | 34 | Doctor | Canada | jane.smith@example.com | 555-5678 | 456 Oak St, Toronto, ON |
| 3 | Alice Johnson | 27 | Teacher | UK | alice.j@example.com | 555-8765 | 789 Pine St, London, UK |
| 4 | Bob Brown | 45 | Artist | Australia | bob.b@example.com | 555-4321 | 321 Maple St, Sydney, NSW |
| 5 | Carol White | 31 | Scientist | New Zealand | carol.w@example.com | 555-6789 | 654 Birch St, Wellington, NZ |
| 6 | Dave Green | 28 | Lawyer | Ireland | dave.g@example.com | 555-3456 | 987 Cedar St, Dublin, IE |
| 7 | Emma Black | 40 | Musician | USA | emma.b@example.com | 555-1111 | 246 Ash St, New York, NY |
| 8 | Frank Blue | 37 | Chef | Canada | frank.b@example.com | 555-2222 | 135 Spruce St, Vancouver, BC |
| 9 | Grace Yellow | 50 | Engineer | UK | grace.y@example.com | 555-3333 | 864 Fir St, Manchester, UK |
| 10 | Henry Violet | 32 | Artist | Australia | henry.v@example.com | 555-4444 | 753 Willow St, Melbourne, VIC|
| 11 | Irene Orange | 26 | Scientist | New Zealand | irene.o@example.com | 555-5555 | 912 Poplar St, Auckland, NZ |
| 12 | Jack Indigo | 38 | Teacher | Ireland | jack.i@example.com | 555-6666 | 159 Elm St, Cork, IE |
| 13 | Karen Red | 41 | Lawyer | USA | karen.r@example.com | 555-7777 | 357 Cedar St, Boston, MA |
| 14 | Leo Brown | 30 | Chef | Canada | leo.b@example.com | 555-8888 | 246 Oak St, Calgary, AB |
| 15 | Mia Green | 33 | Musician | UK | mia.g@example.com | 555-9999 | 975 Pine St, Edinburgh, UK |
| 16 | Noah Yellow | 29 | Doctor | Australia | noah.y@example.com | 555-0000 | 864 Birch St, Brisbane, QLD |
| 17 | Olivia Blue | 35 | Engineer | New Zealand | olivia.b@example.com | 555-1212 | 753 Maple St, Hamilton, NZ |
| 18 | Peter Black | 42 | Artist | Ireland | peter.b@example.com | 555-3434 | 912 Fir St, Limerick, IE |
| 19 | Quinn White | 28 | Scientist | USA | quinn.w@example.com | 555-5656 | 159 Willow St, Seattle, WA |
| 20 | Rachel Red | 31 | Teacher | Canada | rachel.r@example.com | 555-7878 | 357 Poplar St, Ottawa, ON |
| 21 | Steve Green | 44 | Lawyer | UK | steve.g@example.com | 555-9090 | 753 Elm St, Birmingham, UK |
| 22 | Tina Blue | 36 | Musician | Australia | tina.b@example.com | 555-1213 | 864 Cedar St, Perth, WA |
| 23 | Umar Black | 39 | Chef | New Zealand | umar.b@example.com | 555-3435 | 975 Spruce St, Christchurch, NZ|
| 24 | Victor Yellow | 43 | Engineer | Ireland | victor.y@example.com | 555-5657 | 246 Willow St, Galway, IE |
| 25 | Wendy Orange | 27 | Artist | USA | wendy.o@example.com | 555-7879 | 135 Elm St, Denver, CO |
| 26 | Xavier Green | 34 | Scientist | Canada | xavier.g@example.com | 555-9091 | 357 Oak St, Montreal, QC |
| 27 | Yara Red | 41 | Teacher | UK | yara.r@example.com | 555-1214 | 975 Pine St, Leeds, UK |
| 28 | Zack Blue | 30 | Lawyer | Australia | zack.b@example.com | 555-3436 | 135 Birch St, Adelaide, SA |
| 29 | Amy White | 33 | Musician | New Zealand | amy.w@example.com | 555-5658 | 159 Maple St, Wellington, NZ |
| 30 | Ben Black | 38 | Chef | Ireland | ben.b@example.com | 555-7870 | 246 Fir St, Waterford, IE |
"""
def get_generation_time(llm, sampling_params, prompts):
# time the generation
start_time = time.time()
output = llm.generate(prompts, sampling_params=sampling_params)
end_time = time.time()
# print the output and generation time
print(f"Output: {output[0].outputs[0].text}")
print(f"Generation time: {end_time - start_time} seconds.")
# set enable_prefix_caching=True to enable APC
llm = LLM(
model='lmsys/longchat-13b-16k',
enable_prefix_caching=True
)
sampling_params = SamplingParams(temperature=0, max_tokens=100)
# Querying the age of John Doe
get_generation_time(
llm,
sampling_params,
LONG_PROMPT + "Question: what is the age of John Doe? Your answer: The age of John Doe is ",
)
# Querying the age of Zack Blue
# This query will be faster since vllm avoids computing the KV cache of LONG_PROMPT again.
get_generation_time(
llm,
sampling_params,
LONG_PROMPT + "Question: what is the age of Zack Blue? Your answer: The age of Zack Blue is ",
)
示例工作负载#
我们描述了两个示例工作负载，其中APC可以提供巨大的性能优势：
长文档查询，用户重复查询同一长文档（例如软件手册或年度报告）时，使用不同的查询。在这种情况下，APC 允许 vLLM 仅处理一次长文档，所有未来的请求都可以通过重用其 KV 缓存来避免重新计算该长文档。这使得 vLLM 能够以更高的吞吐量和更低的延迟服务未来的请求。
多轮对话，用户可能在同一个聊天会话中与应用程序进行多次聊天。在这种情况下，APC 允许 vLLM 在未来的所有对话轮次中重用聊天历史的处理结果，而不是一遍又一遍地处理整个聊天历史，从而使 vLLM 能够以更高的吞吐量和更低的延迟服务于未来的请求。
限制#
一般来说，APC 不会降低 vLLM 的性能。也就是说，APC 只会减少处理查询的时间（预填充阶段），而不会减少生成新令牌的时间（解码阶段）。因此，当 vLLM 大部分时间用于生成查询的答案（例如，当答案的长度较长时），或者新查询与任何现有查询的前缀不共享（因此无法重用计算）时，APC 不会带来性能提升。