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import torch as t
def split_batch(obj, n_samples, split_size):
n_passes = (n_samples + split_size - 1) // split_size
if isinstance(obj, t.Tensor):
return t.split(obj, split_size, dim=0)
elif isinstance(obj, list):
return list(zip(*[t.split(item, split_size, dim=0) for item in obj]))
elif obj is None:
return [None] * n_passes
else:
raise TypeError('Unknown input type')
# Break total_length into hops/windows of size n_ctx separated by hop_length
def get_starts(total_length, n_ctx, hop_length):
starts = []
for start in range(0, total_length - n_ctx + hop_length, hop_length):
if start + n_ctx >= total_length:
# Last hop could be smaller, we make it n_ctx to maximise context
start = total_length - n_ctx
starts.append(start)
return starts
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