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# mypy: allow-untyped-defs
# Owner(s): ["oncall: distributed"]
import os
import shutil
import traceback
import torch
import torch.distributed as dist
import torch.distributed.checkpoint as dcp
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
from torch.distributed.checkpoint.state_dict import (
_patch_model_state_dict,
_patch_optimizer_state_dict,
)
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.tensor.device_mesh import init_device_mesh
DEVICE = "cuda"
NUM_EPOCHS = 1000
SAVE_PERIOD = 10
FAULT_PERIOD = 25
CHECKPOINT_DIR = f"~/{os.environ.get('LOGNAME', '')}/checkpoint"
class InjectedException(Exception):
pass
class Model(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.net1 = nn.Linear(8, 32)
self.net2 = nn.Linear(32, 128)
self.net3 = nn.Linear(128, 64)
self.net4 = nn.Linear(64, 8)
self.net5 = nn.Linear(8, 1)
def forward(self, x):
x = F.relu(self.net1(x))
x = F.relu(self.net2(x))
x = F.relu(self.net3(x))
x = F.relu(self.net4(x))
x = F.sigmoid(self.net5(x))
return x
def _init_model(rank, world_size):
device_mesh = init_device_mesh(DEVICE, (world_size,))
# Create a dummy model and wrap it in FSDP
model = Model().cuda()
device_mesh = init_device_mesh(DEVICE, (world_size,))
model = FSDP(model, device_mesh=device_mesh, use_orig_params=True)
optim = torch.optim.Adam(model.parameters(), lr=0.0001)
_patch_model_state_dict(model)
_patch_optimizer_state_dict(model, optimizers=optim)
return model, optim
def _print(msg):
if dist.get_rank() == 0:
print(msg)
def _input():
x = torch.rand(128, 8, device="cuda")
y = torch.zeros(128, 1, device="cuda")
y[torch.sum(x, dim=1) >= 4] = 1.0
return x, y
def run(rank, world_size):
# Set up world pg
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
dist.init_process_group("cpu:gloo,cuda:nccl", rank=rank, world_size=world_size)
torch.cuda.set_device(rank)
model, optim = _init_model(rank, world_size)
state_dict = {"model": model, "optim": optim}
loss_calc = torch.nn.BCELoss()
f = None
for epoch in range(NUM_EPOCHS):
try:
torch.manual_seed(epoch)
x, y = _input()
loss = loss_calc(model(x), y)
_print(f"{epoch=} {loss=}")
loss.backward()
optim.step()
optim.zero_grad()
if epoch % SAVE_PERIOD == 0:
if f is not None:
f.result()
f = dcp.state_dict_saver.async_save(
state_dict, checkpoint_id=CHECKPOINT_DIR
)
if FAULT_PERIOD > 0 and epoch % FAULT_PERIOD == 0:
raise InjectedException("Fault injection!")
except InjectedException as e:
dist.barrier()
_print("Trainer encountered exception:")
traceback.print_tb(e.__traceback__)
_print("Reloading model from last checkpoint!")
if f is not None:
f.result()
dcp.load(state_dict)
if __name__ == "__main__":
world_size = torch.cuda.device_count()
print(f"Running an example of Async Checkpointing on {world_size} devices.")
shutil.rmtree(CHECKPOINT_DIR, ignore_errors=True)
mp.spawn(
run,
args=(world_size,),
nprocs=world_size,
join=True,
)
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