lmcache--lmcache/examples/cache_controller/pin

LMCache Pin/Persistence

This is an example to demonstrate how to pin/persist a request's KV cache in an LMCacheEngine externally.

Prerequisites

Your server should have at least 1 GPU.

This will use port 8000 for 1 vllm and port 8001 for LMCache. The controller occupies ports 9000 and 9001.

Steps

1. Start the vllm engine at port 8000:

CUDA_VISIBLE_DEVICES=0 LMCACHE_CONFIG_FILE=example.yaml vllm serve meta-llama/Llama-3.1-8B-Instruct --gpu-memory-utilization 0.8 --port 8000 --kv-transfer-config '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

2. Start the lmcache controller at port 9000 and the monitor at port 9001:

lmcache_controller --host localhost --port 9000 --monitor-port 9001

3. Send a request to vllm engine:

curl -X POST http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "prompt": "Explain the significance of KV cache in language models.",
    "max_tokens": 10
  }'

4. Tokenize the prompt:

curl -X POST http://localhost:8000/tokenize \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "prompt": "Explain the significance of KV cache in language models."
  }'

You should be able to see the returned token ids as:

{"count":12,"max_model_len":4096,"tokens":[128000,849,21435,279,26431,315,85748,6636,304,4221,4211,13],"token_strs":null}

5. Pin a request's KV cache in the system:

curl -X POST http://localhost:9000/pin \
  -H "Content-Type: application/json" \
  -d '{
    "tokens": [128000, 849, 21435, 279, 26431, 315, 85748, 6636, 304, 4221, 4211, 13],
    "instance_id": "lmcache_default_instance",
    "location": "LocalCPUBackend"
  }'

You should be able to see a return message indicating the number of tokens' KV cache that has been successfully pinned in the system:

{"event_id": "xxx", "num_tokens": 12}