Disaggregated prefill

LMCache v1 supports disaggregated prefill.

• The following environment variables are configured within the python script:

# LMCache-related environment variables
 # The port to start LMCache server
 port = 8100
 # Use experimental features in LMCache
 os.environ["LMCACHE_USE_EXPERIMENTAL"] = "True"
 # LMCache is set to use 256 tokens per chunk
 os.environ["LMCACHE_CHUNK_SIZE"] = "256"
 # Disable local CPU backend in LMCache
 os.environ["LMCACHE_LOCAL_CPU"] = "False"
 # Set local CPU memory buffer limit to 5.0 GB
 os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5.0"
 # Set the remote URL for LMCache server
 os.environ["LMCACHE_REMOTE_URL"] = f"lm://localhost:{port}"
 # Set the serializer/deserializer between vllm and LMCache server
 # `naive` indicates using raw bytes of the tensor without any compression
 os.environ["LMCACHE_REMOTE_SERDE"] = "naive"

"""
This file demonstrates the example usage of disaggregated prefilling
with LMCache.
We will launch 2 vllm instances (GPU 0 for prefill and GPU 1 for decode),
and launch an additional LMCache server.
KV cache is transferred in the following manner:
VLLM prefill node -> LMCache server -> VLLM decode node.

Note that `pip install lmcache` is needed to run this example.
Learn more about LMCache in https://github.com/LMCache/LMCache.
"""
import os
import subprocess
import time
from multiprocessing import Event, Process

from lmcache.experimental.cache_engine import LMCacheEngineBuilder
from lmcache.integration.vllm.utils import ENGINE_NAME

from vllm import LLM, SamplingParams
from vllm.config import KVTransferConfig

# LMCache-related environment variables
# The port to start LMCache server
port = 8100
# Use experimental features in LMCache
os.environ["LMCACHE_USE_EXPERIMENTAL"] = "True"
# LMCache is set to use 256 tokens per chunk
os.environ["LMCACHE_CHUNK_SIZE"] = "256"
# Disable local CPU backend in LMCache
os.environ["LMCACHE_LOCAL_CPU"] = "False"
# Set local CPU memory buffer limit to 5.0 GB
os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5.0"
# Set the remote URL for LMCache server
os.environ["LMCACHE_REMOTE_URL"] = f"lm://localhost:{port}"
# Set the serializer/deserializer between vllm and LMCache server
# `naive` indicates using raw bytes of the tensor without any compression
os.environ["LMCACHE_REMOTE_SERDE"] = "naive"


def run_prefill(prefill_done, prompts):
    # We use GPU 0 for prefill node.
    os.environ["CUDA_VISIBLE_DEVICES"] = "0"

    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)

    ktc = KVTransferConfig.from_cli(
        '{"kv_connector":"LMCacheConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2}'
    )
    # Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
    # memory. Reduce the value if your GPU has less memory.
    llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.2",
            kv_transfer_config=ktc,
            max_model_len=8000,
            gpu_memory_utilization=0.8,
            enforce_eager=True)

    #llm.generate(prompts, sampling_params)
    outputs = llm.generate(prompts, sampling_params)
    for output in outputs:
        generated_text = output.outputs[0].text
        print(f"Generated text: {generated_text!r}")
    print("Prefill node is finished.")
    prefill_done.set()

    # Clean up lmcache backend
    LMCacheEngineBuilder.destroy(ENGINE_NAME)


def run_decode(prefill_done, prompts, timeout=1):
    # We use GPU 1 for decode node.
    os.environ["CUDA_VISIBLE_DEVICES"] = "1"

    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)

    ktc = KVTransferConfig.from_cli(
        '{"kv_connector":"LMCacheConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2}'
    )
    # Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
    # of memory. Reduce the value if your GPU has less memory.
    llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.2",
            kv_transfer_config=ktc,
            max_model_len=8000,
            gpu_memory_utilization=0.8,
            enforce_eager=True)

    print("Waiting for prefill node to finish...")
    prefill_done.wait()
    time.sleep(timeout)

    outputs = llm.generate(prompts, sampling_params)
    for output in outputs:
        generated_text = output.outputs[0].text
        print(f"Generated text: {generated_text!r}")

    # Clean up lmcache backend
    LMCacheEngineBuilder.destroy(ENGINE_NAME)


def run_lmcache_server(port):
    server_proc = subprocess.Popen([
        "python", "-m", "lmcache.experimental.server", "localhost",
        str(port)
    ])
    return server_proc


if __name__ == "__main__":

    prompts = [
        "Hello, how are you?" * 1000,
    ]

    prefill_done = Event()
    prefill_process = Process(target=run_prefill, args=(prefill_done, prompts))
    decode_process = Process(target=run_decode, args=(prefill_done, prompts))
    lmcache_server_process = run_lmcache_server(port)

    # Start prefill node
    prefill_process.start()

    # Start decode node
    decode_process.start()

    # Clean up the processes
    decode_process.join()
    prefill_process.terminate()
    lmcache_server_process.terminate()
    lmcache_server_process.wait()

Save the file as disaggregated_prefill_lmcache.py and run it with the following command:

$ python disaggregated_prefill_lmcache.py