semiring_torch
Run your pytorch code on any semiring with a single line of code! Semiring_torch is built on top of autoray.
Warning: this is a proof of concept. Expect bugs and missing features.
Example
By using the logarithmic semiring, you can easily write numerically stable code. In the following example, we compute a matrix product in log-space.
| Regular torch | semiring_torch |
|---|---|
import torch
x1 = torch.tensor([[0.1, 0.6], [0.1, 0.4]])
x2 = torch.tensor([[0.5, 0.3], [0.2, 0.1]])
x1 = x1.log()
x2 = x2.log()
result = x1[:, :, None] + x2[None, :, :]
result = torch.logsumexp(result, dim=1)
result = result.exp()
|
from autoray import numpy as torch
from semiring_torch import logarithmic_semiring
with logarithmic_semiring:
x1 = torch.tensor([[0.1, 0.6], [0.1, 0.4]])
x2 = torch.tensor([[0.5, 0.3], [0.2, 0.1]])
result = x1 @ x2
|
Supported semirings
Currently only the logarithmic semiring is supported, but more semirings can be added easily.
1 maintainer
Metadata
1 maintainer
Release files for semiring-torch 0.0.1
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| semiring_torch-0.0.1.tar.gz | 21.4 kB | Without provenance | Details |
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| semiring_torch-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 49.6 kB
Release files / semiring_torch-0.0.1.tar.gz
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| Tags | Python 3 |
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