NAN after 50 epochs

I am training a self supervised learning model.

The learning rate, loss goes from learning rate = 0.00073495, loss = 310.6790 to learning rate = 0.00073412, loss = nan, in the middle of the 52th epoch

I have read earlier suggestions as well, but since my loss in not exploding could there be other issue?

Since, the error occurs after 50th epoch, and my each epoch takes upto 30min, the issue can not be reproduced immediately. I am using pytorch nightly binaries 1.6+dev

Could you post your training code here so that we could have a look, please?
Also, are you seeing this error on the CPU or GPU?

Thanks for replying. This is on GPU.

There are 4 parts to the model - frontend, classification, regression, regularizers; and corresponding optimizers. Error should be due to the scheduler, because nan value occurs on decreasing the learning rate during an epoch

Training code

        scaler = torch.cuda.amp.GradScaler()

        for e in range(self.epoch_beg, self.epoch):
            iterator = iter(dataloader)
            with trange(1, self.bpe + 1) as pbar:
                for bidx in pbar:
                    pbar.set_description("Epoch {}/{}".format(e, self.epoch))
                        batch = next(iterator)
                    except StopIteration:
                        iterator = iter(dataloader)
                        batch = next(iterator)

                    for k in batch.keys():
                        batch[k] = batch[k].cuda()

                    for worker in
                    for worker in

                    tot_loss = 0
                    losses = {}

                    with autocast():
                        h, chunk, preds, labels = self.model.forward(batch, self.alphaSG, device)
                        label = labels
                        for worker in
                            loss = worker.loss_weight * worker.loss(preds[], label[])
                            losses[] = loss
                            tot_loss += loss

                        for worker in
                            loss = worker.loss_weight * worker.loss(preds[], label[])
                            losses[] = loss
                            tot_loss += loss

                        for worker in self.reg:
                            loss = worker.loss_weight * worker.loss(preds[], label[])
                            losses[] = loss
                            tot_loss += loss


                    for _, optim in self.cls_optim.items():

                    for _, optim in self.regr_optim.items():

                    losses["total"] = tot_loss
                    self.alphaSG = 1

                    if bidx % self.log_freq == 0 or bidx >= self.bpe:
                        # decrease learning rate
                        lrs = {}
                        lrs["frontend"] = self.fe_scheduler(self.frontend_optim, bidx, e, losses["total"].item())

                        for name, scheduler in self.cls_scheduler.items():
                            lrs[name] = scheduler(self.cls_optim[name], bidx, e, losses[name].item())

                        for name, scheduler in self.regr_scheduler.items():
                            lrs[name] = scheduler(self.regr_optim[name], bidx, e, losses[name].item())

                        for k in losses.keys():
                            if k not in lrs:
                                lrs[k] = 0


Scheduler code

import math

class LR_Scheduler(object):
    """Learning Rate Scheduler
    Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}``
    Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))``
    Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9``
          :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`),
          :attr:`` base learning rate, :attr:`args.epochs` number of epochs,
        iters_per_epoch: number of iterations per epoch
    def __init__(self, mode, optim_name, base_lr, num_epochs, iters_per_epoch=0,
                 lr_step=30, warmup_epochs=0):
        self.mode = mode = optim_name
        print('Using {} LR Scheduler for {}!'.format(self.mode, optim_name)) = base_lr
        if mode == 'step':
            assert lr_step
        self.lr_step = lr_step
        self.iters_per_epoch = iters_per_epoch
        self.N = num_epochs * iters_per_epoch
        self.epoch = -1
        self.warmup_iters = warmup_epochs * iters_per_epoch

    def __call__(self, optimizer, i, epoch, loss):
        T = epoch * self.iters_per_epoch + i
        lr = * pow((1 - 1.0 * T / self.N), 0.9)
        # warm up lr schedule
        if self.warmup_iters > 0 and T < self.warmup_iters:
            lr = lr * 1.0 * T / self.warmup_iters

        self.epoch = epoch
        assert lr >= 0
        self._adjust_learning_rate(optimizer, lr)
        return lr

    def _adjust_learning_rate(self, optimizer, lr):
        if len(optimizer.param_groups) == 1:
            optimizer.param_groups[0]['lr'] = lr
            # enlarge the lr at the head
            optimizer.param_groups[0]['lr'] = lr
            for i in range(1, len(optimizer.param_groups)):
                optimizer.param_groups[i]['lr'] = lr * 10

Are you using Adam optimizer? If yes then I’d suggest you to try setting it’s eps to bigger value like 1e-6 or 1e-4 or even bigger if required, (where it’s default value is 1e-8, as mentioned here).
Because generally Adam optimizer won’t work well with autocast or half precision.

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I am using Adam with a learning rate of 0.0004

Does this help?
You can set eps as-

opimizer = torch.opim.Adam(model.parameters(), lr, eps = 1e-4)

I set eps to 1e-4 and clipped the gradients.
Tried running the model for 53 epochs, no nan as of now! Thanks!

Clipping gradient is a good idea. :+1:

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Could you share code for clipping gradient please?

here -

nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

If you want to combine GradScaler use with gradient clipping, see the example:

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