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import subprocess
import numpy as np
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm import tqdm
from misc_downscaling_functionality import *
from models import *
from loss_functions import *
sys.path.append("..")
class DDPTrainer:
"""
Main class for training models using DDP
"""
def __init__(
self,
model,
rank,
train_loader,
val_loader,
loss_function,
save_path,
learning_rate,
sampler,
weight_decay,
test_loader=None,
weights_path=None,
tune_film=False,
):
self.rank = rank
self.model = model
self.sampler = sampler
self.tune_film = tune_film
self.train_loader = train_loader
self.val_loader = val_loader
self.save_path = save_path
self.loss_function = loss_function
self.best_loss = 1000
self.test_loader = test_loader
self.model = self.model.to(rank)
self.model = DDP(self.model, device_ids=[rank], find_unused_parameters=True)
if self.model.module.decoder == "vit":
self.opt = torch.optim.AdamW(
model.parameters(),
lr=learning_rate,
betas=(0.9, 0.95),
weight_decay=1e-5,
)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(self.opt, 891 * 80)
else:
self.opt = torch.optim.Adam(
model.parameters(), lr=learning_rate, weight_decay=weight_decay
)
self.losses = []
self.train_losses = []
self.maes = []
def _unravel_to_numpy(self, x):
return x.view(-1).detach().cpu().numpy()
def eval_epoch(self, fix_sigma, epoch):
self.model.eval()
mae = []
lf = []
lf_unnorm = []
with torch.no_grad():
for count, task in enumerate(self.val_loader):
out = self.model(task, film_index=0)
prev_step = None
mae.append(
np.nanmean(
np.abs(
task["y_target"].detach().cpu()
- out[..., : task["y_target"].shape[-1]].detach().cpu()
)
)
)
l = (
self.loss_function(
task["y_target"], out, prev_step, fix_sigma=fix_sigma
)
.detach()
.item()
)
lf.append(l)
try:
ic = self.train_loader.dataset.unnorm_base_context(
task["y_context"][:, :-11, ...]
).permute(0, 3, 2, 1)
unnorm_pred = self.train_loader.dataset.unnorm_pred(out)
unnorm_target = self.train_loader.dataset.unnorm_pred(
task["y_target"]
)
unnorm_pred = unnorm_pred + ic
unnorm_target = unnorm_target + ic
lu = (
self.loss_function(
unnorm_target,
unnorm_pred,
prev_step,
fix_sigma=fix_sigma,
expand=True,
)
.detach()
.cpu()
.numpy()
)
lf_unnorm.append(lu)
except:
pass
if self.test_loader is not None:
forecasts = []
targets = []
stations = []
for count, task in enumerate(self.test_loader):
out = self.model(task, film_index=0)
forecasts.append(unnormalise_hadisd_tas(out.detach().cpu().numpy()))
targets.append(
unnormalise_hadisd_tas(task["y_target"].detach().cpu().numpy())
)
stations.append(
task["downscaling"]["x_target"].detach().cpu().numpy() * 360
)
# Save the test outputs
np.save(
self.save_path + f"forecast_{self.rank}_{self.epoch}.npy",
np.concatenate(forecasts, axis=0),
)
np.save(
self.save_path + f"targets_{self.rank}_{self.epoch}.npy",
np.concatenate(targets, axis=0),
)
np.save(
self.save_path + f"stations_{self.rank}_{self.epoch}.npy",
np.concatenate(stations, axis=0),
)
log_loss = np.nanmean(np.array(lf))
if log_loss < self.best_loss:
np.save(
self.save_path + "unnorm_preds.npy",
self.train_loader.dataset.unnorm_pred(out).detach().cpu().numpy(),
)
np.save(
self.save_path + "unnorm_targets.npy",
self.train_loader.dataset.unnorm_pred(task["y_target"])
.detach()
.cpu()
.numpy(),
)
log_loss_unnorm = np.nanmean(np.stack(lf_unnorm), axis=0)
if np.logical_and(self.rank == 0, self.epoch % 5 == 0):
np.save(self.save_path + "preds_eval.npy", out.cpu().numpy())
np.save(
self.save_path + "y_target_eval.npy", task["y_target"].cpu().numpy()
)
return log_loss, log_loss_unnorm
def train(self, n_epochs=100):
torch.cuda.set_device(self.rank)
subprocess.run(["cp", "reproduce_training.sh", f"{self.save_path}"])
train_loss = []
ll = []
fix_sigma = False
prev_step = None
self.epoch = 0
epoch_loss, log_loss_unnorm = self.eval_epoch(fix_sigma, 0)
train_loss = np.mean(train_loss)
for epoch in range(n_epochs):
self.epoch = epoch
self.sampler.set_epoch(epoch)
self.model.train()
train_loss = []
with tqdm(self.train_loader, unit="batch") as tepoch:
for count, task in enumerate(tepoch):
out = self.model(task, film_index=0)
loss = self.loss_function(
task["y_target"], out, prev_step, fix_sigma=fix_sigma
)
loss.backward()
tepoch.set_postfix(loss=loss.item())
prev_step = out
self.opt.step()
self.opt.zero_grad()
train_loss.append(loss.item())
try:
if self.model.module.decoder == "vit":
if epoch > 20:
self.scheduler.step()
except:
pass
epoch_loss, log_loss_unnorm = self.eval_epoch(fix_sigma, epoch)
train_loss = np.mean(train_loss)
ll.append(log_loss_unnorm)
self.losses.append(epoch_loss)
self.train_losses.append(train_loss)
np.save(
self.save_path + "losses_{}.npy".format(self.rank),
np.array(self.losses),
)
np.save(
self.save_path + "train_losses_{}.npy".format(self.rank),
np.array(self.train_losses),
)
np.save(self.save_path + "rmse_{}.npy".format(self.rank), np.array(ll))
if epoch_loss < self.best_loss:
self.best_loss = epoch_loss
if self.rank == 0:
if self.model.module.decoder == "vit":
torch.save(
{
"epoch": epoch,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.opt.state_dict(),
"scheduler_state_dict": self.scheduler.state_dict(),
"loss": epoch_loss,
},
self.save_path + "epoch_{}".format(epoch),
)
else:
torch.save(
{
"epoch": epoch,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.opt.state_dict(),
"loss": epoch_loss,
},
self.save_path + "epoch_{}".format(epoch),
)
try:
np.save(
self.save_path + "preds_train.npy".format(epoch),
out.detach().cpu().numpy(),
)
np.save(
self.save_path + "y_target_train.npy".format(epoch),
task["y_target"].detach().cpu().numpy(),
)
except:
pass
class DDPTrainerE2E:
"""
Training class for E2E
"""
def __init__(
self,
model,
rank,
train_loader,
val_loader,
loss_function,
save_path,
learning_rate,
sampler,
weight_decay,
hadisd_variable_name,
test_loader=None,
weights_path=None,
tune_film=False,
):
self.rank = rank
self.model = model
self.sampler = sampler
self.tune_film = tune_film
self.train_loader = train_loader
self.val_loader = val_loader
self.save_path = save_path
self.loss_function = loss_function
self.best_loss = 1000
self.test_loader = test_loader
self.hadisd_variable_name = hadisd_variable_name
self.model = self.model.to(rank)
self.model = DDP(self.model, device_ids=[rank], find_unused_parameters=True)
if self.model.module.decoder == "vit":
self.opt = torch.optim.AdamW(
model.parameters(),
lr=learning_rate,
betas=(0.9, 0.95),
weight_decay=1e-5,
)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(self.opt, 891 * 80)
else:
self.opt = torch.optim.Adam(
model.parameters(), lr=learning_rate, weight_decay=weight_decay
)
self.losses = []
self.train_losses = []
self.maes = []
def _unravel_to_numpy(self, x):
return x.view(-1).detach().cpu().numpy()
def eval_epoch(self, fix_sigma, epoch):
self.model.eval()
mae = []
lf = []
lf_unnorm = []
with torch.no_grad():
forecasts = []
targets = []
stations = []
indices = []
for count, task in tqdm(enumerate(self.val_loader)):
out = self.model(task, film_index=0)
forecasts.append(
unnormalise_hadisd_var(
out.detach().cpu().numpy(), self.hadisd_variable_name
)
)
targets.append(
unnormalise_hadisd_var(
task["y_target"].detach().cpu().numpy(),
self.hadisd_variable_name,
)
)
stations.append(
task["downscaling"]["x_target"].detach().cpu().numpy() * 360
)
indices.append(task["index"])
prev_step = None
mae.append(
np.nanmean(
np.abs(
task["y_target"].detach().cpu()
- out[..., : task["y_target"].shape[-1]].detach().cpu()
)
)
)
l = (
self.loss_function(
task["y_target"], out, prev_step, fix_sigma=fix_sigma
)
.detach()
.item()
)
lf.append(l)
try:
ic = self.train_loader.dataset.unnorm_base_context(
task["y_context"][:, :-11, ...]
).permute(0, 3, 2, 1)
unnorm_pred = self.train_loader.dataset.unnorm_pred(out)
unnorm_target = self.train_loader.dataset.unnorm_pred(
task["y_target"]
)
unnorm_pred = unnorm_pred + ic
unnorm_target = unnorm_target + ic
lu = (
self.loss_function(
unnorm_target,
unnorm_pred,
prev_step,
fix_sigma=fix_sigma,
expand=True,
)
.detach()
.cpu()
.numpy()
)
lf_unnorm.append(lu)
except:
pass
# Save the test outputs
np.save(
self.save_path + f"val_forecast_{self.rank}_{self.epoch}.npy",
np.concatenate(forecasts, axis=0),
)
np.save(
self.save_path + f"val_targets_{self.rank}_{self.epoch}.npy",
np.concatenate(targets, axis=0),
)
np.save(
self.save_path + f"val_stations_{self.rank}_{self.epoch}.npy",
np.concatenate(stations, axis=0),
)
np.save(
self.save_path + f"val_indices_{self.rank}_{self.epoch}.npy",
np.concatenate(indices, axis=0),
)
if self.test_loader is not None:
forecasts = []
targets = []
stations = []
indices = []
for count, task in enumerate(self.test_loader):
out = self.model(task, film_index=0)
forecasts.append(
unnormalise_hadisd_var(
out.detach().cpu().numpy(), self.hadisd_variable_name
)
)
targets.append(
unnormalise_hadisd_var(
task["y_target"].detach().cpu().numpy(),
self.hadisd_variable_name,
)
)
stations.append(
task["downscaling"]["x_target"].detach().cpu().numpy() * 360
)
indices.append(task["index"])
np.save(
self.save_path + f"test_forecast_{self.rank}_{self.epoch}.npy",
np.concatenate(forecasts, axis=0),
)
np.save(
self.save_path + f"test_targets_{self.rank}_{self.epoch}.npy",
np.concatenate(targets, axis=0),
)
np.save(
self.save_path + f"test_stations_{self.rank}_{self.epoch}.npy",
np.concatenate(stations, axis=0),
)
np.save(
self.save_path + f"test_indices_{self.rank}_{self.epoch}.npy",
np.concatenate(indices, axis=0),
)
log_loss = np.nanmean(np.array(lf))
if log_loss < self.best_loss:
np.save(
self.save_path + "unnorm_preds.npy",
self.train_loader.dataset.unnorm_pred(out).detach().cpu().numpy(),
)
np.save(
self.save_path + "unnorm_targets.npy",
self.train_loader.dataset.unnorm_pred(task["y_target"])
.detach()
.cpu()
.numpy(),
)
log_loss_unnorm = np.nanmean(np.stack(lf_unnorm), axis=0)
if np.logical_and(self.rank == 0, self.epoch % 5 == 0):
np.save(self.save_path + "preds_eval.npy", out.cpu().numpy())
np.save(
self.save_path + "y_target_eval.npy", task["y_target"].cpu().numpy()
)
return log_loss, log_loss_unnorm
def train(self, n_epochs=100):
torch.cuda.set_device(self.rank)
subprocess.run(["cp", "reproduce_training.sh", f"{self.save_path}"])
train_loss = []
ll = []
fix_sigma = False
prev_step = None
self.epoch = 0
epoch_loss, log_loss_unnorm = self.eval_epoch(fix_sigma, 0)
train_loss = np.mean(train_loss)
for epoch in range(n_epochs):
self.epoch = epoch
self.sampler.set_epoch(epoch)
epoch_loss, log_loss_unnorm = self.eval_epoch(fix_sigma, epoch)
train_loss = np.mean(train_loss)
ll.append(log_loss_unnorm)
self.losses.append(epoch_loss)
self.train_losses.append(train_loss)
np.save(
self.save_path + "losses_{}.npy".format(self.rank),
np.array(self.losses),
)
np.save(
self.save_path + "train_losses_{}.npy".format(self.rank),
np.array(self.train_losses),
)
np.save(self.save_path + "rmse_{}.npy".format(self.rank), np.array(ll))
if epoch_loss < self.best_loss:
self.best_loss = epoch_loss
if self.rank == 0:
if self.model.module.decoder == "vit":
torch.save(
{
"epoch": epoch,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.opt.state_dict(),
"scheduler_state_dict": self.scheduler.state_dict(),
"loss": epoch_loss,
},
self.save_path + "epoch_{}".format(epoch),
)
else:
torch.save(
{
"epoch": epoch,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.opt.state_dict(),
"loss": epoch_loss,
},
self.save_path + "epoch_{}".format(epoch),
)
try:
np.save(
self.save_path + "preds_train.npy".format(epoch),
out.detach().cpu().numpy(),
)
np.save(
self.save_path + "y_target_train.npy".format(epoch),
task["y_target"].detach().cpu().numpy(),
)
except:
pass
self.model.train()
train_loss = []
with tqdm(self.train_loader, unit="batch") as tepoch:
for count, task in tqdm(enumerate(tepoch)):
out = self.model(task, film_index=0)
loss = self.loss_function(
task["y_target"], out, prev_step, fix_sigma=fix_sigma
)
loss.backward()
tepoch.set_postfix(loss=loss.item())
prev_step = out
self.opt.step()
self.opt.zero_grad()
train_loss.append(loss.item())
if self.model.module.decoder == "vit":
if epoch > 20:
self.scheduler.step()
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