Is it possible to train model with batches that have unequal lenght during an epoch? I am new to pytorch.
If you take a look at the dataloader documentation, you'll see a drop_last parameter, which explains that sometimes when the dataset size is not divisible by the batch size, then you get a last batch of different size. So basically the answer is yes, it is possible, it happens often and it does not affect (too much) the training of a neural network.
However you must a bit careful, some pytorch layers deal poorly with very small batch sizes. For example if you happen to have Batchnorm layers, and if you get a batch of size 1, you'll get errors due to the fact that batchnorm at some point divides by len(batch)-1. More generally, training a network that has batchnorms generally require batches of significant sizes, say at least 16 (literature generally aims for 32 or 64). So if you happen to have variable size batches, take the time to check whether your layers have requirement in terms of batch size for optimal training and convergence. But except in particular cases, your network will train anyway, no worries.
As for how to make your batches with custom sizes, I suggest you look at and take inspiration from the pytorch implementation of dataloader and sampler. You may want to implement something similar to BatchSampler and use the batch_sampler argument of Dataloader