wehub-resource-sync
107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
"""
|
|
This file demonstrates the example usage of CPU offloading
|
|
with LMCache in vLLM v1.
|
|
|
|
Note that `lmcache` is needed to run this example.
|
|
Requirements:
|
|
https://docs.lmcache.ai/getting_started/installation.html#prerequisites
|
|
Learn more about LMCache environment setup, please refer to:
|
|
https://docs.lmcache.ai/getting_started/installation.html
|
|
"""
|
|
|
|
import contextlib
|
|
import os
|
|
import time
|
|
from dataclasses import asdict
|
|
|
|
from lmcache.integration.vllm.utils import ENGINE_NAME
|
|
from lmcache.v1.cache_engine import LMCacheEngineBuilder
|
|
|
|
from vllm import LLM, SamplingParams
|
|
from vllm.config import KVTransferConfig
|
|
from vllm.engine.arg_utils import EngineArgs
|
|
|
|
|
|
def setup_environment_variables():
|
|
# LMCache-related environment variables
|
|
# LMCache is set to use 256 tokens per chunk
|
|
os.environ["LMCACHE_CHUNK_SIZE"] = "256"
|
|
# Enable local CPU backend in LMCache
|
|
os.environ["LMCACHE_LOCAL_CPU"] = "True"
|
|
# Set local CPU memory limit to 5.0 GB
|
|
os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5.0"
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def build_llm_with_lmcache(model: str):
|
|
ktc = KVTransferConfig(
|
|
kv_connector="LMCacheConnectorV1",
|
|
kv_role="kv_both",
|
|
)
|
|
# Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
|
|
# memory. Reduce the value if your GPU has less memory.
|
|
# Note: LMCache supports chunked prefill (see vLLM#14505, LMCache#392).
|
|
llm_args = EngineArgs(
|
|
model=model,
|
|
kv_transfer_config=ktc,
|
|
max_model_len=8000,
|
|
gpu_memory_utilization=0.8,
|
|
)
|
|
|
|
llm = LLM(**asdict(llm_args))
|
|
try:
|
|
yield llm
|
|
finally:
|
|
# Clean up lmcache backend
|
|
LMCacheEngineBuilder.destroy(ENGINE_NAME)
|
|
|
|
|
|
def print_output(
|
|
llm: LLM,
|
|
prompt: list[str],
|
|
sampling_params: SamplingParams,
|
|
req_str: str,
|
|
):
|
|
# Should be able to see logs like the following:
|
|
# `LMCache INFO: Storing KV cache for 6006 out of 6006 tokens for request 0`
|
|
# This indicates that the KV cache has been stored in LMCache.
|
|
start = time.time()
|
|
outputs = llm.generate(prompt, sampling_params)
|
|
print("-" * 50)
|
|
for output in outputs:
|
|
generated_text = output.outputs[0].text
|
|
print(f"Generated text: {generated_text!r}")
|
|
print(f"Generation took {time.time() - start:.2f} seconds, {req_str} request done.")
|
|
print("-" * 50)
|
|
|
|
|
|
def main():
|
|
model = "meta-llama/Meta-Llama-3.1-8B-Instruct"
|
|
setup_environment_variables()
|
|
with build_llm_with_lmcache(model) as llm:
|
|
# This example script runs two requests with a shared prefix.
|
|
# Define the shared prompt and specific prompts
|
|
shared_prompt = "Hello, how are you?" * 1000
|
|
first_prompt = [
|
|
shared_prompt + "Hello, my name is",
|
|
]
|
|
second_prompt = [
|
|
shared_prompt + "Tell me a very long story",
|
|
]
|
|
|
|
sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
|
|
|
|
# Print the first output
|
|
print_output(llm, first_prompt, sampling_params, "first")
|
|
|
|
time.sleep(1)
|
|
|
|
# print the second output
|
|
print_output(llm, second_prompt, sampling_params, "second")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|