7cbfc10943
Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
116 lines
4.0 KiB
Python
116 lines
4.0 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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We will launch 2 vllm instances (GPU 0 for prefill and GPU 1 for decode),
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and then transfer the KV cache between them.
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"""
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import os
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import time
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from multiprocessing import Event, Process
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from vllm import LLM, SamplingParams
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from vllm.config import KVTransferConfig
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def run_prefill(prefill_done):
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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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# The prefill node receives two requests, while the decode node receives
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# three requests. So the decode node will only receive the KV Cache for
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# requests 1 and 3. The decode node will use the KV Cache of requests 1
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# and 3 and do prefilling on request 2.
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prompts = [
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"Hello, my name is",
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"Hi, your name is",
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# The decode node will actually "prefill" this request.
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"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=1)
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# Using PyNcclConnector to transmit KV caches between vLLM instances.
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# This instance is the prefill node (kv_producer, rank 0).
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# The number of parallel instances for KV cache transfer is set to 2,
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# as required for PyNcclConnector.
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ktc = KVTransferConfig.from_cli(
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'{"kv_connector":"PyNcclConnector","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 A6000 GPU with 40GB
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# memory. You may need to adjust the value to fit your GPU.
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llm = LLM(model="meta-llama/Meta-Llama-3.1-8B-Instruct",
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kv_transfer_config=ktc,
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max_model_len=2000,
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gpu_memory_utilization=0.8)
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llm.generate(prompts, sampling_params)
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print("Prefill node is finished.")
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prefill_done.set()
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# To keep the prefill node running in case the decode node is not done;
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# otherwise, the script might exit prematurely, causing incomplete decoding.
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try:
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while True:
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time.sleep(1)
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except KeyboardInterrupt:
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print("Script stopped by user.")
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def run_decode(prefill_done):
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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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prompts = [
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"Hello, my name is",
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"Hi, your name is",
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"Tell me a very long story",
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]
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sampling_params = SamplingParams(temperature=0, top_p=0.95)
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# Using PyNcclConnector to transmit KV caches between vLLM instances.
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# This instance is the decode node (kv_consumer, rank 1).
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# The number of parallel instances for KV cache transfer is set to 2,
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# as required for PyNcclConnector.
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ktc = KVTransferConfig.from_cli(
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'{"kv_connector":"PyNcclConnector","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 A6000 GPU with 40GB
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# memory. You may need to adjust the value to fit your GPU.
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llm = LLM(model="meta-llama/Meta-Llama-3.1-8B-Instruct",
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kv_transfer_config=ktc,
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max_model_len=2000,
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gpu_memory_utilization=0.8)
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# Wait for the producer to start the pipe
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print("Waiting for prefill node to finish...")
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prefill_done.wait()
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# At this point when the prefill_done is set, the kv-cache should have been
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# transferred to this decode node, so we can start decoding.
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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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, ))
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decode_process = Process(target=run_decode, args=(prefill_done, ))
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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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# Terminate the prefill node when decode is finished
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decode_process.join()
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prefill_process.terminate()
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if __name__ == "__main__":
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main()
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