mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-04-30 20:55:17 +00:00
5a6fd71f90
* 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
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
import os
|
|
import argparse
|
|
|
|
import torch
|
|
from torch import nn
|
|
import torch.multiprocessing as mp
|
|
import torch.distributed.rpc as rpc
|
|
from torch.optim import SGD, Adam, RMSprop, Optimizer
|
|
from colorama import Back, Style
|
|
|
|
|
|
def color_debug(text, prefix=' ', color='blue'):
|
|
color = color.upper()
|
|
print(getattr(Back, color), prefix, Style.RESET_ALL, text)
|
|
|
|
|
|
class RpcTestModel(nn.Module):
|
|
|
|
def __init__(self, stage_id, actual_stage_num, feat_num, h) -> None:
|
|
super().__init__()
|
|
self.rank = stage_id
|
|
self.is_last_rank = stage_id == actual_stage_num - 1
|
|
self.linear_name = f'linear_{stage_id}'
|
|
if stage_id == 0:
|
|
setattr(self, self.linear_name, nn.Linear(feat_num, h))
|
|
elif stage_id == actual_stage_num - 1:
|
|
setattr(self, self.linear_name, nn.Linear(h, 1))
|
|
else:
|
|
setattr(self, self.linear_name, nn.Linear(h, h))
|
|
|
|
def forward(self, x) -> torch.Tensor:
|
|
linear: nn.Module = getattr(self, self.linear_name)
|
|
out: torch.Tensor = linear(x)
|
|
|
|
if self.is_last_rank:
|
|
out = out.sum()
|
|
return out
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--epoch', type=int, default=1)
|
|
parser.add_argument('--world_size', type=int, default=2)
|
|
parser.add_argument('--num_microbatches', type=int, default=2)
|
|
parser.add_argument('--chunk', type=int, default=1)
|
|
parser.add_argument('--use_checkpoint', action='store_true')
|
|
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'RMSprop'], default='SGD')
|
|
parser.add_argument('--device', type=str, choices=['cpu', 'cuda'], default='cuda')
|
|
parser.add_argument('--master_addr', type=str, default='localhost')
|
|
parser.add_argument('--master_port', type=str, default='29020')
|
|
parser.add_argument('--num_worker_threads', type=str, default=128)
|
|
return parser.parse_args()
|
|
|
|
|
|
def run_worker(rank, args, master_func):
|
|
os.environ['MASTER_ADDR'] = args.master_addr
|
|
os.environ['MASTER_PORT'] = args.master_port
|
|
|
|
# config rpc
|
|
# if cuda is used, set_device_map is a must is configured
|
|
# for cuda is not supported in torch rpc by default
|
|
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=args.num_worker_threads)
|
|
|
|
world_size = args.world_size
|
|
for rank_idx in range(world_size):
|
|
options.set_device_map(f'work{rank_idx}', {rank: rank_idx})
|
|
|
|
rpc.init_rpc(name=f'work{rank}', rank=rank, world_size=world_size, rpc_backend_options=options)
|
|
|
|
# in rpc mode, only rank 0 is needed to be coded
|
|
if rank == 0:
|
|
master_func(args)
|
|
# barrier here
|
|
rpc.shutdown()
|
|
|
|
|
|
def rpc_run(args, master_func):
|
|
world_size = args.world_size
|
|
assert args.num_microbatches >= args.world_size, "num_microbatches cannot be fewer than world_size!"
|
|
mp.spawn(run_worker, args=(args, master_func), nprocs=world_size)
|