mirror of
https://github.com/hpcaitech/ColossalAI.git
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f1e1836218
* support p2p communication with any type of object | pass test * reconstruct pipeline schedule with p2p_v2.py(support communication with List[Any]) | pass test * [engin/schedule] use p2p_v2 to recontruct pipeline_schedule * [pipeline/rpc] implement a demo for PP with cuda rpc framework * [pipeline/rpc] support interleaving | fix checkpoint bug | change logic when dispatch data in work_list to ensure steady 1F1B * [pipeline/rpc] implement distributed optimizer | test with assert_close * [pipeline/rpc] implement distributed optimizer | test with assert_close * [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy * [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy * [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy * [pipeline/pipleline_process_group] finish PipelineProcessGroup to manage local abd global rank in TP,DP and PP * [pipeline/pipleline_process_group] remove comment * [pipeline/pipleline_process_group] remove comment * [pipeline/pipleline_process_group] skip process group test * [pipeline/pipleline_process_group] remove test named function
101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
import os
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import argparse
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import warnings
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import torch
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from torch import nn
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import torch.multiprocessing as mp
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import torch.distributed.rpc as rpc
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from torch.optim import SGD, Adam, RMSprop, Optimizer
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from torch._C._distributed_rpc import _is_current_rpc_agent_set
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from colorama import Back, Style
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rpc_is_initialized = _is_current_rpc_agent_set
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def color_debug(text, prefix=' ', color='blue'):
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color = color.upper()
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print(getattr(Back, color), prefix, Style.RESET_ALL, text)
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class RpcTestModel(nn.Module):
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def __init__(self, stage_id, actual_stage_num, feat_num, h) -> None:
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super().__init__()
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self.rank = stage_id
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self.is_last_rank = stage_id == actual_stage_num - 1
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self.linear_name = f'linear_{stage_id}'
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if stage_id == 0:
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setattr(self, self.linear_name, nn.Linear(feat_num, h))
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elif stage_id == actual_stage_num - 1:
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setattr(self, self.linear_name, nn.Linear(h, 1))
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else:
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setattr(self, self.linear_name, nn.Linear(h, h))
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def forward(self, x) -> torch.Tensor:
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linear: nn.Module = getattr(self, self.linear_name)
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out: torch.Tensor = linear(x)
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if self.is_last_rank:
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out = out.sum()
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return out
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--epoch', type=int, default=1)
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parser.add_argument('--world_size', type=int, default=2)
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parser.add_argument('--num_microbatches', type=int, default=2)
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parser.add_argument('--chunk', type=int, default=1)
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parser.add_argument('--use_checkpoint', action='store_true')
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parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'RMSprop'], default='SGD')
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parser.add_argument('--device', type=str, choices=['cpu', 'cuda'], default='cuda')
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parser.add_argument('--master_addr', type=str, default='localhost')
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parser.add_argument('--master_port', type=str, default='29020')
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parser.add_argument('--num_worker_threads', type=str, default=128)
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return parser.parse_args()
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def pg_parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--world_size', type=int, default=4)
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parser.add_argument('--dp_degree', type=int, default=2)
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parser.add_argument('--tp_degree', type=int, default=1)
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parser.add_argument('--chunk', type=int, default=1)
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parser.add_argument('--num_worker_threads', type=str, default=128)
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parser.add_argument('--device', type=str, choices=['cpu', 'cuda'], default='cuda')
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parser.add_argument('--master_addr', type=str, default='localhost')
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parser.add_argument('--master_port', type=str, default='29020')
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return parser.parse_args()
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def run_worker(rank, args, master_func):
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os.environ['MASTER_ADDR'] = args.master_addr
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os.environ['MASTER_PORT'] = args.master_port
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# config rpc
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# if cuda is used, set_device_map is a must is configured
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# for cuda is not supported in torch rpc by default
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options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=args.num_worker_threads)
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world_size = args.world_size
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for rank_idx in range(world_size):
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options.set_device_map(f'work{rank_idx}', {rank: rank_idx})
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rpc.init_rpc(name=f'work{rank}', rank=rank, world_size=world_size, rpc_backend_options=options)
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# in rpc mode, only rank 0 is needed to be coded
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if rank == 0:
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master_func(args)
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# barrier here
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if rpc_is_initialized():
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rpc.shutdown()
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else:
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warnings.warn("RPC has not been initialized")
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def rpc_run(args, master_func):
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world_size = args.world_size
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assert args.num_microbatches >= args.world_size, "num_microbatches cannot be fewer than world_size!"
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mp.spawn(run_worker, args=(args, master_func), nprocs=world_size)
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