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# Data
import pandas as pd
import numpy as np
import os
import gc
import random
from tqdm import tqdm
# Torch
import torch
from torch.utils.data import DataLoader
from torch.nn.parallel import DistributedDataParallel as DDP
class Trainer:
def __init__(
self,
model: torch.nn.Module,
datasets: DataLoader,
optimizers: torch.optim.Optimizer,
save_every: int,
save_checkpoint_path: str,
load_checkpoint_path: str,
config
) -> None:
self.config = config
self.local_rank = int(os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK', '0'))
self.global_rank = int(os.environ["RANK"])
self.model = model.to(self.local_rank)
# data
self.train_data = datasets[0]
self.valid_data = datasets[1]
# optimizers
self.opt_ae = optimizers[0]
self.opt_disc = optimizers[1]
# mixed precision
self.scaler = torch.cuda.amp.GradScaler()
# checkpoint
self.save_every = save_every
self.epochs_run = 1
self.last_batch_idx = -1
self.save_checkpoint_path = save_checkpoint_path
if os.path.exists(load_checkpoint_path):
print("Loading checkpoint")
self._load_checkpoint(load_checkpoint_path)
self.model = DDP(self.model, find_unused_parameters=True)
def _load_checkpoint(self, checkpoint_path):
loc = f"cuda:{self.local_rank}"
ckpt_dict = torch.load(checkpoint_path, map_location=loc)
self.model.load_state_dict(ckpt_dict["MODEL_STATE"])
self.opt_ae.load_state_dict(ckpt_dict["optimizer"][0])
self.opt_disc.load_state_dict(ckpt_dict["optimizer"][1])
self.scaler.load_state_dict(ckpt_dict["scaler"])
self.last_batch_idx = ckpt_dict["last_batch_idx"] if 'last_batch_idx' in ckpt_dict else -1
self.epochs_run = ckpt_dict["EPOCHS_RUN"] + 1 if self.last_batch_idx == -1 else ckpt_dict["EPOCHS_RUN"]
# load RNG states each time the model and states are loaded from checkpoint
if 'rng' in ckpt_dict:
rng = ckpt_dict['rng']
for key, value in rng.items():
if key =='torch_state':
torch.set_rng_state(value.cpu())
elif key =='cuda_state':
torch.cuda.set_rng_state(value.cpu())
elif key =='numpy_state':
np.random.set_state(value)
elif key =='python_state':
random.setstate(value)
else:
print('unrecognized state')
print(f"Resuming training from checkpoint at Epoch {self.epochs_run}")
def _save_checkpoint(self, epoch, config, last_idx):
# save RNG states each time the model and states are saved
out_dict = dict()
out_dict['torch_state'] = torch.get_rng_state()
out_dict['cuda_state'] = torch.cuda.get_rng_state()
if np:
out_dict['numpy_state'] = np.random.get_state()
if random:
out_dict['python_state'] = random.getstate()
checkpoint = {
"MODEL_STATE": self.model.module.state_dict(),
"EPOCHS_RUN": epoch,
"optimizer": [self.opt_ae.state_dict(), self.opt_disc.state_dict()],
"scaler": self.scaler.state_dict(),
"hparams": vars(config),
"last_batch_idx": last_idx,
"rng": out_dict
}
if last_idx == -1:
filename = f'VQGAN_{epoch}.pt'
else:
filename = f'VQGAN_{last_idx}_{epoch}.pt'
torch.save(checkpoint, os.path.join(self.save_checkpoint_path, filename))
print(f"Epoch {epoch} | Training checkpoint saved at {os.path.join(self.save_checkpoint_path, filename)}.")
def train(self, max_epochs: int):
for epoch in range(self.epochs_run, max_epochs+1):
self._run_epoch(epoch)
if self.global_rank == 0:
self._save_checkpoint(epoch, self.config, last_idx=-1)
def _run_epoch(self, epoch):
# b_sz = len(next(iter(self.train_data))[0])
print(f"[GPU{self.global_rank}] Epoch {epoch} | Batchsize: {self.config.model.batch_size} | Steps: {len(self.train_data)} | LastIdx: {self.last_batch_idx}")
self.train_data.sampler.set_epoch(epoch)
# self.valid_data.sampler.set_epoch(epoch)
# training data
train_losses = pd.DataFrame()
for step, data in enumerate(tqdm(self.train_data)):
# skip batches
if step <= self.last_batch_idx:
continue
self.model.train()
x_train = data['data'].to(self.local_rank)
global_step = step * epoch
recon_loss, aeloss, perceptual_loss, g_image_loss, image_gan_feat_loss, commitment_loss, perplexity = self._run_batch(global_step, x_train)
# track loss
if self.global_rank == 0:
df = pd.DataFrame({
'recon_loss': [recon_loss.detach().cpu().item()],
'aeloss': [aeloss.detach().cpu().item()],
'perceptual_loss': [perceptual_loss.detach().cpu().item()],
'g_image_loss': [g_image_loss.detach().cpu().item()],
'image_gan_feat_loss': [image_gan_feat_loss.detach().cpu().item()],
'perplexity': [perplexity.detach().cpu().item()],
'commitment_loss': [commitment_loss.detach().cpu().item()]
})
train_losses = pd.concat([train_losses, df], axis=0)
print(f"[Training] recon_loss={recon_loss.item()}, aeloss={aeloss.item()}, perceptual_loss={perceptual_loss.item()}, g_image_loss={g_image_loss.item()}, image_gan_feat_loss={image_gan_feat_loss.item()}, perplexity={perplexity.item()}, commitment_loss={commitment_loss.item()}")
# checkpoint
if self.global_rank == 0 and step % self.save_every == 0 and step != 0:
self._save_checkpoint(epoch, self.config, step)
# WARN: due to job limit time - save loss for each iter
train_losses.to_csv(os.path.join(self.save_checkpoint_path, f'training_loss_{step}_epoch{epoch}.csv'), index=False)
train_losses = pd.Series()
# TODO: use a properly validation split
# validation data
# val_losses = pd.DataFrame()
# with torch.no_grad():
# for step, data in enumerate(self.valid_data):
# self.model.eval()
# x_valid = data['data'].to(self.local_rank)
# global_step = step * epoch
# recon_loss, _, vq_output, perceptual_loss = self.model.forward(global_step, x_valid, gpu_id=self.local_rank)
# # clear GPU memory
# torch.cuda.empty_cache()
# gc.collect()
# if self.local_rank == 0:
# df = pd.DataFrame({
# 'recon_loss': [recon_loss.detach().cpu().item()],
# 'perceptual_loss': [perceptual_loss.detach().cpu().item()],
# 'perplexity': [vq_output['perplexity'].detach().cpu().item()],
# 'commitment_loss': [vq_output['commitment_loss'].detach().cpu().item()]
# })
# val_losses = pd.concat([val_losses, df], axis=0)
# print(f"[Validation] recon_loss={recon_loss.item()}, perceptual_loss={perceptual_loss.item()}, perplexity={vq_output['perplexity'].item()}, commitment_loss={vq_output['commitment_loss'].item()}")
self.last_batch_idx = -1
# save logs
if self.global_rank == 0:
train_losses.to_csv(os.path.join(self.save_checkpoint_path, f'training_loss_epoch{epoch}.csv'), index=False)
# val_losses.to_csv(os.path.join(self.save_checkpoint_path, f'validation_losses_epoch{epoch}.csv'), index=False)
def _run_batch(self, global_step, x):
# autoencoder optimization
self.opt_ae.zero_grad()
recon_loss, _, vq_output, aeloss, perceptual_loss, gan_feat_loss, others = self.model.forward(global_step, x, optimizer_idx=0, gpu_id=self.local_rank)
commitment_loss = vq_output['commitment_loss']
loss_ae = recon_loss + commitment_loss + aeloss + perceptual_loss + gan_feat_loss
loss_ae.backward()
self.opt_ae.step()
# disc optimization
self.opt_disc.zero_grad()
loss_disc = self.model.forward(global_step, x, optimizer_idx=1, gpu_id=self.local_rank)
loss_disc.backward()
self.opt_disc.step()
g_image_loss, image_gan_feat_loss, commitment_loss, perplexity = others[0], others[1], others[2], others[3]
return recon_loss, aeloss, perceptual_loss, g_image_loss, image_gan_feat_loss, commitment_loss, perplexity
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