File size: 8,466 Bytes
ff5b2f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | import sys
from pathlib import Path
# 获取项目根目录（train.py上级的上级）
root_path = Path(__file__).parent.parent
sys.path.append(str(root_path))
import torch
import os
import numpy as np
import torch.distributed as dist
import logging
import time
from torch.nn.parallel import DistributedDataParallel
from model.fourcastnet import FourCastNet
from onescience.datapipes.climate import ERA5Datapipe
from onescience.utils.YParams import YParams
from onescience.utils.fcn.darcy_loss import LpLoss
from apex import optimizers
def main():
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger()
## Model config init
config_file_path = os.path.join(current_path, "conf/config.yaml")
cfg = YParams(config_file_path, "model")
## Distributed config init
cfg.world_size = 1
if "WORLD_SIZE" in os.environ:
cfg.world_size = int(os.environ["WORLD_SIZE"])
world_rank = 0
local_rank = 0
if cfg.world_size > 1:
dist.init_process_group(backend="nccl", init_method="env://")
local_rank = int(os.environ["LOCAL_RANK"])
world_rank = dist.get_rank()
## DataLoader init
cfg_data = YParams(config_file_path, "datapipe")
cfg['N_in_channels'] = len(cfg_data.dataset.channels)
cfg['N_out_channels'] = len(cfg_data.dataset.channels)
datapipe = ERA5Datapipe(
dataset_dir=cfg_data.dataset.data_dir,
used_variables=cfg_data.dataset.channels,
used_years=cfg_data.dataset.train_time,
distributed=dist.is_initialized()
)
train_dataloader, train_sampler = datapipe.get_dataloader("train")
datapipe = ERA5Datapipe(
dataset_dir=cfg_data.dataset.data_dir,
used_variables=cfg_data.dataset.channels,
used_years=cfg_data.dataset.val_time,
distributed=dist.is_initialized()
)
val_dataloader, val_sampler = datapipe.get_dataloader("valid")
# Model init
model = FourCastNet().to(local_rank)
optimizer = optimizers.FusedAdam(model.parameters(), lr=cfg.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2, patience=5, mode='min')
loss_obj = LpLoss()
## Train process init
os.makedirs(cfg.checkpoint_dir, exist_ok=True)
train_loss_file = f"{cfg.checkpoint_dir}/trloss.npy"
valid_loss_file = f"{cfg.checkpoint_dir}/valoss.npy"
best_valid_loss = 1.0e6
best_loss_epoch = 0
train_losses = np.empty((0,), dtype=np.float32)
valid_losses = np.empty((0,), dtype=np.float32)
## Get model params count
if cfg.world_size == 1:
total_params = sum(p.numel() for p in model.parameters())
print("\n\n")
print("-" * 50)
print(f"📂 now params is {total_params}, {total_params / 1e6:.2f}M, {total_params / 1e9:.2f}B")
print("-" * 50, "\n")
## Load model weight if there exist well-trained model
if os.path.exists(f"{cfg.checkpoint_dir}/model_bak.pth"):
if world_rank == 0:
print("\n\n")
print("-" * 50)
print(f"✅ There has a model weight, load and continue training...")
print(f'If you want to train a new model, ensure there is no *.pth file in {cfg.checkpoint_dir}')
print("-" * 50, "\n")
ckpt = torch.load(f"{cfg.checkpoint_dir}/model_bak.pth", map_location=f'cuda:{local_rank}', weights_only=False)
model.load_state_dict(ckpt["model_state_dict"])
optimizer.load_state_dict(ckpt["optimizer_state_dict"])
scheduler.load_state_dict(ckpt["scheduler_state_dict"])
best_valid_loss = ckpt["best_valid_loss"]
best_loss_epoch = ckpt["best_loss_epoch"]
train_losses = np.load(train_loss_file)
valid_losses = np.load(valid_loss_file)
## Distributed model
if cfg.world_size > 1:
model = DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank)
world_rank == 0 and logger.info(f"start training ...")
for epoch in range(cfg.max_epoch):
if dist.is_initialized():
train_sampler.set_epoch(epoch)
val_sampler.set_epoch(epoch)
model.train()
train_loss = 0
start_time = time.time()
for j, data in enumerate(train_dataloader):
invar = data[0].to(local_rank, dtype=torch.float32)
outvar = data[1].to(local_rank, dtype=torch.float32)
invar = invar[:, :, :-1, :]
outvar = outvar[:, :, :-1, :]
outvar_pred = model(invar)
loss = loss_obj(outvar, outvar_pred)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
if world_rank == 0:
logger.info(f'Train: Epoch {epoch}-{j+1}/{len(train_dataloader)} '
f'[cost {int((time.time()-start_time) // 60):02}:{int((time.time()-start_time) % 60):02}] '
f'[{(time.time()-start_time)/(j+1): .02f}s/{cfg_data.dataloader.batch_size}batch] '
f'loss:{train_loss / (j+1): .04f}')
train_loss /= len(train_dataloader)
model.eval()
valid_loss = 0
with torch.no_grad():
start_time = time.time()
for j, data in enumerate(val_dataloader):
invar = data[0].to(local_rank, dtype=torch.float32)
outvar = data[1].to(local_rank, dtype=torch.float32)
invar = invar[:, :, :-1, :]
outvar = outvar[:, :, :-1, :]
outvar_pred = model(invar)
loss = loss_obj(outvar, outvar_pred)
if cfg.world_size > 1:
loss_tensor = loss.detach().to(local_rank) # torch.tensor(loss, device=local_rank)
dist.all_reduce(loss_tensor)
loss = loss_tensor.item() / cfg.world_size
valid_loss += loss
else:
valid_loss += loss.item()
if world_rank == 0:
logger.info(f'Valid: Epoch {epoch}-{j+1}/{len(val_dataloader)} '
f'[cost {int((time.time()-start_time) // 60):02}:{int((time.time()-start_time) % 60):02}] '
f'[{(time.time()-start_time)/(j+1): .02f}s/{cfg_data.dataloader.batch_size}batch] '
f'loss:{valid_loss / (j+1): .04f}')
valid_loss /= len(val_dataloader)
is_save_ckp = False
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_loss_epoch = epoch
world_rank == 0 and save_checkpoint(model, optimizer, scheduler, best_valid_loss, best_loss_epoch, cfg.checkpoint_dir)
is_save_ckp = True
scheduler.step(valid_loss)
if world_rank == 0:
logger.info(f"Epoch [{epoch + 1}/{cfg.max_epoch}], "
f"Train Loss: {train_loss:.4f}, "
f"Valid Loss: {valid_loss:.4f}, "
f"Best loss at Epoch: {best_loss_epoch + 1}"
+ (", saving checkpoint" if is_save_ckp else "")
)
train_losses = np.append(train_losses, train_loss)
valid_losses = np.append(valid_losses, valid_loss)
np.save(train_loss_file, train_losses)
np.save(valid_loss_file, valid_losses)
if epoch - best_loss_epoch > cfg.patience:
print(f"Loss has not decrease in {cfg.patience} epochs, stopping training...")
exit()
def save_checkpoint(model, optimizer, scheduler, best_valid_loss, best_loss_epoch, model_path):
model_to_save = model.module if hasattr(model, "module") else model
state = {"model_state_dict": model_to_save.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"scheduler_state_dict": scheduler.state_dict(),
"best_valid_loss": best_valid_loss,
"best_loss_epoch": best_loss_epoch,
}
torch.save(state, f"{model_path}/model.pth")
### the weight file saving may interrupted due to DCU queue limit, get a backup to ensure there at least has one model
os.system(f"mv {model_path}/model.pth {model_path}/model_bak.pth")
if __name__ == "__main__":
current_path = os.getcwd()
sys.path.append(current_path)
main()
|