7cbfc10943
Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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This file demonstrates the example usage of disaggregated prefilling
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with LMCache.
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We will launch 2 vllm instances (GPU 0 for prefill and GPU 1 for decode),
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and launch an additional LMCache server.
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KV cache is transferred in the following manner:
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vLLM prefill node -> LMCache server -> vLLM decode node.
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Note that `pip install lmcache` is needed 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.experimental.cache_engine import LMCacheEngineBuilder
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from lmcache.integration.vllm.utils import ENGINE_NAME
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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_prefill(prefill_done, prompts):
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# We use GPU 0 for prefill node.
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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=1)
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ktc = KVTransferConfig.from_cli(
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'{"kv_connector":"LMCacheConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2}'
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)
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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(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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#llm.generate(prompts, sampling_params)
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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("Prefill node is finished.")
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prefill_done.set()
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# Clean up lmcache backend
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LMCacheEngineBuilder.destroy(ENGINE_NAME)
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def run_decode(prefill_done, prompts, timeout=1):
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# We use GPU 1 for decode node.
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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.from_cli(
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'{"kv_connector":"LMCacheConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2}'
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)
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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(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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print("Waiting for prefill node to finish...")
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prefill_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.experimental.server", "localhost",
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str(port)
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])
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return server_proc
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def main():
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prefill_done = Event()
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prefill_process = Process(target=run_prefill, args=(prefill_done, prompts))
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decode_process = Process(target=run_decode, args=(prefill_done, prompts))
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lmcache_server_process = run_lmcache_server(port)
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# Start prefill node
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prefill_process.start()
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# Start decode node
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decode_process.start()
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# Clean up the processes
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decode_process.join()
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prefill_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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