import argparse from typing import List from cacheflow.master.frontend import Frontend from cacheflow.master.scheduler import Scheduler from cacheflow.worker.controller import Controller parser = argparse.ArgumentParser(description='CacheFlow server') parser.add_argument('--model', type=str, default='facebook/opt-125m', help='model name') parser.add_argument('--num-nodes', type=int, default=1, help='number of nodes') parser.add_argument('--num-workers', type=int, default=1, help='number of workers per node') parser.add_argument('--block-size', type=int, default=8, help='token block size') # TODO(woosuk): Add an analytical model to determine the maximum number of GPU/CPU blocks. parser.add_argument('--num-gpu-blocks', type=int, default=1024, help='number of GPU blocks (per GPU)') parser.add_argument('--num-cpu-blocks', type=int, default=256, help='number of CPU blocks (per GPU)') args = parser.parse_args() def main(): # Create a controller for each node. controllers: List[Controller] = [] for i in range(args.num_nodes): controller = Controller( node_id=i, num_workers=args.num_workers, model_name=args.model, block_size=args.block_size, num_gpu_blocks=args.num_gpu_blocks, num_cpu_blocks=args.num_cpu_blocks, ) controllers.append(controller) # Create a frontend. frontend = Frontend( model_name=args.model, block_size=args.block_size, ) # Create a scheduler. scheduler = Scheduler( frontend=frontend, controllers=controllers, block_size=args.block_size, num_gpu_blocks=args.num_gpu_blocks, num_cpu_blocks=args.num_cpu_blocks, ) # Connect the controllers. for i in range(len(controllers) - 1): controllers[i].set_next(controllers[i + 1]) controllers[-1].set_next(scheduler) test_inputs = [ 'Ion Stoica is a', 'UC Berkeley is', 'The future of cloud computing is', ] for prompt in test_inputs: frontend.query(prompt) # FIXME while True: scheduler.step() if not scheduler.pending and not scheduler.running: break if __name__ == '__main__': main()