Description:

localsfmx 0.0.4

LocalSoftmax
Local Softmax parallelize the softmax computation by splitting the tensor into smaller sub-tensors and applying the softmax function on each of these smaller tensors independently. In other words, we want to compute a "local" softmax on each chunk of the tensor, instead of on the entire tensor.
Appreciation

Lucidrains
Agorians

Install
pip install local-sftmx
Usage
import torch
from local_sfmx import local_softmax

tensor = torch.rand(10, 5)
result = local_softmax(tensor, 2)
print(result)

Algorithm
function LocalSoftmax(tensor, num_chunks):
split tensors into num_chunks smaller tensors
for each smaller tensor:
apply standard softmax
concatenate the results
return concatenated tensor
License
MIT

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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