trainer_misc/vae_ddp_trainer.py

import math
import sys
from typing import Iterable

import torch
import torch.nn as nn

from .utils import (
    MetricLogger,
    SmoothedValue,
)


def train_one_epoch(
    model: torch.nn.Module, 
    model_dtype: str,
    data_loader: Iterable, 
    optimizer: torch.optim.Optimizer,
    optimizer_disc: torch.optim.Optimizer,
    device: torch.device, 
    epoch: int, 
    loss_scaler, 
    loss_scaler_disc,
    clip_grad: float = 0,
    log_writer=None, 
    lr_scheduler=None, 
    start_steps=None,
    lr_schedule_values=None,
    lr_schedule_values_disc=None,
    args=None,
    print_freq=20,
    iters_per_epoch=2000,
):
    # The trainer for causal video vae

    model.train()
    metric_logger = MetricLogger(delimiter="  ")

    if optimizer is not None:
        metric_logger.add_meter('lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
        metric_logger.add_meter('min_lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
    
    if optimizer_disc is not None:
        metric_logger.add_meter('disc_lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
        metric_logger.add_meter('disc_min_lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))

    header = 'Epoch: [{}]'.format(epoch)

    if model_dtype == 'bf16':
        _dtype = torch.bfloat16
    else:
        _dtype = torch.float16

    print("Start training epoch {}, {} iters per inner epoch.".format(epoch, iters_per_epoch))

    for step in metric_logger.log_every(range(iters_per_epoch), print_freq, header):
        if step >= iters_per_epoch:
            break
        
        it = start_steps + step  # global training iteration
        if lr_schedule_values is not None:
            for i, param_group in enumerate(optimizer.param_groups):
                if lr_schedule_values is not None:
                    param_group["lr"] = lr_schedule_values[it] * param_group.get("lr_scale", 1.0)

        if optimizer_disc is not None:
            for i, param_group in enumerate(optimizer_disc.param_groups):
                if lr_schedule_values_disc is not None:
                    param_group["lr"] = lr_schedule_values_disc[it] * param_group.get("lr_scale", 1.0)

        samples = next(data_loader)
    
        samples['video'] = samples['video'].to(device, non_blocking=True)

        with torch.cuda.amp.autocast(enabled=True, dtype=_dtype):
            rec_loss, gan_loss, log_loss = model(samples['video'], args.global_step, identifier=samples['identifier'])

        ###################################################################################################
        # The update of rec_loss
        if rec_loss is not None:
            loss_value = rec_loss.item()

            if not math.isfinite(loss_value):
                print("Loss is {}, stopping training".format(loss_value), force=True)
                sys.exit(1)
        
            optimizer.zero_grad()
            is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
            grad_norm = loss_scaler(rec_loss, optimizer, clip_grad=clip_grad,
                                    parameters=model.module.vae.parameters(), create_graph=is_second_order)
            
            if "scale" in loss_scaler.state_dict():
                loss_scale_value = loss_scaler.state_dict()["scale"]
            else:
                loss_scale_value = 1

            metric_logger.update(vae_loss=loss_value)
            metric_logger.update(loss_scale=loss_scale_value)

        ###################################################################################################

        # The updaet of gan_loss
        if gan_loss is not None:
            gan_loss_value = gan_loss.item()
            
            if not math.isfinite(gan_loss_value):
                print("The gan discriminator Loss is {}, stopping training".format(gan_loss_value), force=True)
                sys.exit(1)

            optimizer_disc.zero_grad()
            is_second_order = hasattr(optimizer_disc, 'is_second_order') and optimizer_disc.is_second_order
            disc_grad_norm = loss_scaler_disc(gan_loss, optimizer_disc, clip_grad=clip_grad,
                                    parameters=model.module.loss.discriminator.parameters(), create_graph=is_second_order)

            if "scale" in loss_scaler_disc.state_dict():
                disc_loss_scale_value = loss_scaler_disc.state_dict()["scale"]
            else:
                disc_loss_scale_value = 1

            metric_logger.update(disc_loss=gan_loss_value)
            metric_logger.update(disc_loss_scale=disc_loss_scale_value)
            metric_logger.update(disc_grad_norm=disc_grad_norm)

            min_lr = 10.
            max_lr = 0.
            for group in optimizer_disc.param_groups:
                min_lr = min(min_lr, group["lr"])
                max_lr = max(max_lr, group["lr"])

            metric_logger.update(disc_lr=max_lr)
            metric_logger.update(disc_min_lr=min_lr)

        torch.cuda.synchronize()
        new_log_loss = {k.split('/')[-1]:v for k, v in log_loss.items() if k not in ['total_loss']}
        metric_logger.update(**new_log_loss)

        if rec_loss is not None:
            min_lr = 10.
            max_lr = 0.
            for group in optimizer.param_groups:
                min_lr = min(min_lr, group["lr"])
                max_lr = max(max_lr, group["lr"])

            metric_logger.update(lr=max_lr)
            metric_logger.update(min_lr=min_lr)
            weight_decay_value = None
            for group in optimizer.param_groups:
                if group["weight_decay"] > 0:
                    weight_decay_value = group["weight_decay"]
            metric_logger.update(weight_decay=weight_decay_value)
            metric_logger.update(grad_norm=grad_norm)

        if log_writer is not None:
            log_writer.update(**new_log_loss, head="train/loss")
            log_writer.update(lr=max_lr, head="opt")
            log_writer.update(min_lr=min_lr, head="opt")
            log_writer.update(weight_decay=weight_decay_value, head="opt")
            log_writer.update(grad_norm=grad_norm, head="opt")

            log_writer.set_step()

        if lr_scheduler is not None:
            lr_scheduler.step_update(start_steps + step)

        args.global_step = args.global_step + 1

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}