101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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This file demonstrates the example usage of cpu offloading
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with LMCache.
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Note that `lmcache` is needed to run this example.
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Requirements: Linux, Python: 3.10 or higher, CUDA: 12.1
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Learn more about LMCache environment setup, please refer to:
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https://docs.lmcache.ai/getting_started/installation.html
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"""
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import contextlib
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import os
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import time
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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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def setup_environment_variables():
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# LMCache-related environment variables
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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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# Enable local CPU backend in LMCache
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os.environ["LMCACHE_LOCAL_CPU"] = "True"
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# Set local CPU memory limit to 5.0 GB
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os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5.0"
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@contextlib.contextmanager
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def build_llm_with_lmcache():
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ktc = KVTransferConfig.from_cli(
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'{"kv_connector":"LMCacheConnector", "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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# Note: LMCache supports chunked prefill (see vLLM#14505, LMCache#392).
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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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enable_chunked_prefill=True,
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gpu_memory_utilization=0.8)
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try:
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yield llm
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finally:
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# Clean up lmcache backend
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LMCacheEngineBuilder.destroy(ENGINE_NAME)
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def print_output(
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llm: LLM,
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prompt: list[str],
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sampling_params: SamplingParams,
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req_str: str,
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):
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start = time.time()
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outputs = llm.generate(prompt, sampling_params)
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print("-" * 50)
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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(f"Generation took {time.time() - start:.2f} seconds, "
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f"{req_str} request done.")
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print("-" * 50)
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def main():
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setup_environment_variables()
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with build_llm_with_lmcache() as llm:
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# This example script runs two requests with a shared prefix.
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# Define the shared prompt and specific prompts
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shared_prompt = "Hello, how are you?" * 1000
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first_prompt = [
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shared_prompt + "Hello, my name is",
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]
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second_prompt = [
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shared_prompt + "Tell me a very long story",
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]
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sampling_params = SamplingParams(temperature=0,
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top_p=0.95,
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max_tokens=10)
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# Print the first output
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print_output(llm, first_prompt, sampling_params, "first")
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time.sleep(1)
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# print the second output
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print_output(llm, second_prompt, sampling_params, "second")
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if __name__ == "__main__":
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main()
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