Compress and Decompress the KV cache

The compress interface is defined as the following:

compress(instance_id: str, method: str, location: str, tokens: list[int]) -> event_id: str, num_tokens: int
decompress(instance_id: str, method: str, location: str, tokens: list[int]) -> event_id: str, num_tokens: int

These 2 functions compresses/decompresses the KV cache chunks specified by tokens using the given method in the storage location. The controller returns an event_id and the number of tokens scheduled for compression or decompression.

Example usage:

First, we need a yaml file example.yaml to properly configure the lmcache instance:

chunk_size: 256
local_cpu: True
max_local_cpu_size: 5

# cache controller configurations
enable_controller: True
lmcache_instance_id: "lmcache_default_instance"
controller_url: "localhost:9001"
lmcache_worker_port: 8001
distributed_url: "localhost:8005"

Second, we need to start the vllm/lmcache instance at port 8000:

CUDA_VISIBLE_DEVICES=0 LMCACHE_CONFIG_FILE=example.yaml vllm serve meta-llama/Llama-3.1-8B-Instruct --max-model-len 4096  --gpu-memory-utilization 0.8 --port 8000 --kv-transfer-config '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

Third, we need to start the lmcache controller at port 9000 and the monitor at port 9001:

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

Then we can send a request to vllm to see if it works properly:

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
  }'

Now we send a request to 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."
  }'

We should be able to see token ids in response:

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

After all, we issue a compress request:

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

The controller responds with a message similar to:

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

This indicates that 12 tokens are being compressed. The event_id can be used to query the status of the operation.

Once the kv cache is compressed, we can use cachegen to decompress

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

The controller responds with a message similar to:

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

This indicates that 12 tokens are being decompressed. The event_id can be used to query the status of the operation.