Handle float limit_val_batches
State: closed | Pull request: yes
Labels: name: NLP

name: CI

# What does this PR do ?

1) When a float `limit_val_batches` is passed by the user, the PR ensures that the `limit_val_batches` is cast to an equivalent int value and also makes sure the cast `limit_val_batches` is a multiple of num of microbatches as required by PTL >= 2.0.

2) Calls `self._reconfigure_val_batches()` after the setup of datasets, so that the original value of limit_val_batches is used to build the dataset and not the reconfigured value.

3) Returns the` len(dataloader)` in terms of number micro batches instead of num of global batches. This is required for 2 reasons: 

- a) Since `limit_val_batches` is reconfigured to be in terms of number of micro batches, if the `len(dataloader)` is in num of global batches then we can run into situations where `len(dataloader) < num_micro_batches` and this can lead to one of the ranks hitting `StopIteration` in the midst of completing a global batch, leading to a hang if a different rank is waiting for the output of the first rank in case of PP.

- b) Another reason being ideally, the `len(dataloader)` should be returned in terms of the granularity of the batch size in which we fetch the data from the `dataloader_iter`. Since in megatron models a micro batch is fetched each time `next(dataloader_iter)` is called, the len(dataloader) should be returned in the same metric. Also, PTL's progress bar is such that it increments the epoch number after the num of batches extracted hit `len(dataloader)`. So if `len(dataloader)` is x in terms of global batches, then we incorrectly increment the epoch after x micro batches are extracted even though, the dataloader still has microbatches and is not empty. This can be very misleading to the end users.

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LGTM

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