mirror of https://github.com/vllm-project/vllm.git
134 lines
4.2 KiB
Python
134 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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This file demonstrates the example usage of remote KV cache sharing
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with LMCache.
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We will launch 2 vllm instances, and launch an additional LMCache server.
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KV cache is transferred in the following manner:
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(1) vLLM instance 1 -> LMCache server (KV cache store).
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(2) LMCache server -> vLLM instance 2 (KV cache reuse/retrieve).
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Note that lmcache needs to be installed to run this example.
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Learn more about LMCache in https://github.com/LMCache/LMCache.
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"""
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import os
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import subprocess
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import time
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from multiprocessing import Event, Process
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from lmcache.integration.vllm.utils import ENGINE_NAME
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from lmcache.v1.cache_engine import LMCacheEngineBuilder
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from vllm import LLM, SamplingParams
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from vllm.config import KVTransferConfig
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# LMCache-related environment variables
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# The port to start LMCache server
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port = 8100
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# Use experimental features in LMCache
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os.environ["LMCACHE_USE_EXPERIMENTAL"] = "True"
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# LMCache is set to use 256 tokens per chunk
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os.environ["LMCACHE_CHUNK_SIZE"] = "256"
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# Disable local CPU backend in LMCache
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os.environ["LMCACHE_LOCAL_CPU"] = "False"
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# Set local CPU memory buffer limit to 5.0 GB
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os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5.0"
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# Set the remote URL for LMCache server
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os.environ["LMCACHE_REMOTE_URL"] = f"lm://localhost:{port}"
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# Set the serializer/deserializer between vllm and LMCache server
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# `naive` indicates using raw bytes of the tensor without any compression
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os.environ["LMCACHE_REMOTE_SERDE"] = "naive"
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prompts = [
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"Hello, how are you?" * 1000,
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]
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def run_store(store_done, prompts):
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# We use GPU 0 for KV cache store process.
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
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ktc = KVTransferConfig(kv_connector="LMCacheConnectorV1", kv_role="kv_both")
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# Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
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# memory. Reduce the value if your GPU has less memory.
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llm = LLM(
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model="mistralai/Mistral-7B-Instruct-v0.2",
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kv_transfer_config=ktc,
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max_model_len=8000,
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gpu_memory_utilization=0.8,
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enforce_eager=True,
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)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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generated_text = output.outputs[0].text
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print(f"Generated text: {generated_text!r}")
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print("KV cache store is finished.")
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store_done.set()
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# Clean up lmcache backend
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LMCacheEngineBuilder.destroy(ENGINE_NAME)
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def run_retrieve(store_done, prompts, timeout=1):
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# We use GPU 1 for KV cache retrieve process.
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
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ktc = KVTransferConfig(kv_connector="LMCacheConnectorV1", kv_role="kv_both")
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# Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
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# of memory. Reduce the value if your GPU has less memory.
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llm = LLM(
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model="mistralai/Mistral-7B-Instruct-v0.2",
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kv_transfer_config=ktc,
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max_model_len=8000,
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gpu_memory_utilization=0.8,
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enforce_eager=True,
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)
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print("Waiting for KV cache store to finish...")
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store_done.wait()
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time.sleep(timeout)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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generated_text = output.outputs[0].text
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print(f"Generated text: {generated_text!r}")
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# Clean up lmcache backend
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LMCacheEngineBuilder.destroy(ENGINE_NAME)
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def run_lmcache_server(port):
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server_proc = subprocess.Popen(
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["python", "-m", "lmcache.v1.server", "localhost", str(port)]
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)
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return server_proc
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def main():
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store_done = Event()
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store_process = Process(target=run_store, args=(store_done, prompts))
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retrieve_process = Process(target=run_retrieve, args=(store_done, prompts))
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lmcache_server_process = run_lmcache_server(port)
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# Start KV cache store process
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store_process.start()
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# Start KV cache retrieve process
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retrieve_process.start()
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# Clean up the processes
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store_process.join()
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retrieve_process.terminate()
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lmcache_server_process.terminate()
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lmcache_server_process.wait()
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if __name__ == "__main__":
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main()
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