wehub-resource-sync
153 lines
4.7 KiB
Python
153 lines
4.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Standard
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import argparse
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import contextlib
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import os
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import time
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# Third Party
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from vllm import LLM, SamplingParams
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from vllm.config import KVTransferConfig
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# First Party
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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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def setup_environment_variables(vllm_version: str, use_disk: bool = False):
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# LMCache-related environment variables
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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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if use_disk:
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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 the maximum size of the local CPU buffer size to 5GB
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os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5"
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# Enable local disk backend in LMCache
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os.environ["LMCACHE_LOCAL_DISK"] = "file://local_disk/"
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# Set the maximum size of the local disk size to 10GB
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os.environ["LMCACHE_MAX_LOCAL_DISK_SIZE"] = "10"
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else:
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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 the maximum size of the local CPU size to 5GB
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os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5"
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if vllm_version == "v0":
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os.environ["VLLM_USE_V1"] = "0"
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@contextlib.contextmanager
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def build_llm_with_lmcache(lmcache_connector: str, model: str, vllm_version: str):
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ktc = KVTransferConfig(
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kv_connector=lmcache_connector,
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kv_role="kv_both",
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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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# Note: LMCache supports chunked prefill (see vLLM#14505, LMCache#392).
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#
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# Pass kwargs directly to LLM() instead of routing through
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# EngineArgs + asdict(). asdict() emits None for every unset EngineArgs
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# field, and vLLM >= 0.20 / pydantic v2 rejects None for
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# CompilationConfig fields like cudagraph_capture_sizes (list) and
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# pass_config.fuse_minimax_qk_norm (bool). See issue #3438.
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llm_kwargs = {
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"model": model,
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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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}
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if vllm_version == "v0":
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llm_kwargs["enable_chunked_prefill"] = True # Only in v0
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llm = LLM(**llm_kwargs)
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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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# Should be able to see logs like the following:
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# `LMCache INFO: Storing KV cache for 6006 out of 6006 tokens for request 0`
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# This indicates that the KV cache has been stored in LMCache.
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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, {req_str} request done.")
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print("-" * 50)
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-v",
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"--version",
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choices=["v0", "v1"],
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default="v1",
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help=(
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"Specify vLLM version (default: v1). "
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"v0 requires vLLM <= 0.10.x; vLLM 0.11.0+ removed the V0 engine."
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),
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)
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parser.add_argument(
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"-d",
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"--use-disk",
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action="store_true",
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help="Specify whether to use disk as backend (default: False)",
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)
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return parser.parse_args()
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def main():
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args = parse_args()
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if args.version == "v0":
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lmcache_connector = "LMCacheConnector"
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model = "mistralai/Mistral-7B-Instruct-v0.2"
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else:
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lmcache_connector = "LMCacheConnectorV1"
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model = "mistralai/Mistral-7B-Instruct-v0.2"
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setup_environment_variables(args.version, args.use_disk)
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with build_llm_with_lmcache(lmcache_connector, model, args.version) 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, top_p=0.95, 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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