Make sampler length independent from consumed samples.

[jstjohn]

When resuming a training job, the pytorch lightning trainer seems to use the data loader to determine the number of total steps left in the epoch. When you kill/resume a training job partway through, the data sampler length will be reduced by consumed_samples in the current implementation, while the current step of the trainer will be restored. This results in a double impact to the number of remaining samples, and we can get into a situation where the trainer crashes out when the current_step is greater than the length of the dataloader. Leaving the length unchanged seems to fix this issue, and since it is an iterator, the stop_iteration (based on consumed_samples) does what it is supposed to do.

What does this PR do ?

Leave len(sampler) as is, not making it a function of consumed_samples. Default behavior of pytorch lightning trainer resumption seems to behave better with this configuration.

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