Sync from v0.13

examples/others/lmcache/kv_cache_sharing_lmcache_v1.py

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