dist_inference_hybrid_runner_w_euler_coordinator.py

import argparse
import torch.autograd.profiler as profiler
from utils.dist_args_utils import *
from utils.dist_inference_utils import *
from comm.hybrid_comm_utils import init_hybrid_inference_communicators_with_coordinator
from task_datasets.inference_data import DummyRequestProcessor
from pipeline_parallel.dist_pp_utils import *
from coordinator.euler.coordinate_client import CoordinatorHybridInferenceClient


def main():
    parser = argparse.ArgumentParser(description='Inference Runner')
    add_device_arguments(parser)
    add_torch_distributed_hybrid_inference_w_euler_coordinator_arguments(parser)
    add_hybrid_inference_arguments(parser)
    add_inference_details_arguments(parser)
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--overwrite-request-args', type=lambda x: (str(x).lower() == 'true'),
                        default=False, metavar='S',
                        help='whether overwrite_request_args')
    parser.add_argument('--profiling', type=str, default='tidy_profiling', metavar='S',
                        help='enable which profiling? default: tidy mode')
    parser.add_argument('--trace-postfix', type=str, default='default', metavar='S',
                        help='postfix of the tracing file name.')
    args = parser.parse_args()
    torch.manual_seed(args.seed)
    if args.use_cuda:
        assert (torch.cuda.is_available())
        device = torch.device('cuda', args.cuda_id)
    else:
        device = torch.device('cpu')

    coord_client = CoordinatorHybridInferenceClient(args)
    prime_ip, rank, port = coord_client.notify_inference_join()
    print("<====Coordinator assigned prime-IP:", prime_ip, " and my assigned rank", rank, "====>")

    init_hybrid_inference_communicators_with_coordinator(args, prime_ip, rank, port=port)

    tokenizer = get_tokenizer(args)

    request_processor = DummyRequestProcessor(tokenizer)
    request_processor.set_arguments(args)

    pipe = get_pp_inference_module(args, device, rank=rank)

    if args.profiling == 'no-profiling':
        avg_iter_time = distributed_hybrid_inference_foo_iter(args, pipe, device, request_processor)
    else:
        prefix = './trace_json/inference'
        trace_file = prefix + get_hybrid_inference_arguments_str(args, rank=rank) + '_' + args.profiling + '_' + \
                     args.trace_postfix + '.json'
        if args.profiling == 'tidy_profiling':
            # distributed_inference_mask_iter(args, pipe, device, request_processor)
            avg_iter_time = distributed_hybrid_inference_foo_iter(args, pipe, device, request_processor)
            pipe.export_profiling_result(filename=trace_file)
        elif args.profiling == 'pytorch_profiling':
            with profiler.profile(profile_memory=True, use_cuda=args.use_cuda) as prof:
                avg_iter_time = distributed_hybrid_inference_foo_iter(args, pipe, device, request_processor)
            print(prof.key_averages().table())
            prof.export_chrome_trace(trace_file)
        else:
            print("No recognized profiler?")
            assert False
    train_finish_msg = str(rank) + '#' + str(round(avg_iter_time, 3))
    coord_client.notify_inference_finish(message=train_finish_msg)


if __name__ == '__main__':
    main()