Metadata-Version: 2.3
Name: sinkhorn-router-pytorch
Version: 0.0.12
Summary: Sinkhorn Router - Pytorch
Project-URL: Homepage, https://pypi.org/project/sinkhorn-router-pytorch/
Project-URL: Repository, https://github.com/lucidrains/sinkhorn-router-pytorch
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Phil Wang
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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License-File: LICENSE
Keywords: artificial intelligence,deep learning,mixture of experts,routing
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: einops>=0.8.0
Requires-Dist: einx>=0.3.0
Requires-Dist: torch>=2.0
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

## Sinkhorn Router - Pytorch (wip)

Self contained pytorch implementation of a sinkhorn based router, for mixture of experts or otherwise. Will contain both a causal and non-causal variant. The causal variant will follow the example used in Megatron

## Install

```bash
$ pip install sinkhorn-router-pytorch
```

## Usage

```python
import torch
from torch import nn
from sinkhorn_router_pytorch import SinkhornRouter

experts = nn.Parameter(torch.randn(8, 8, 512, 256)) # (experts, heads, dim [in], dim [out])

router = SinkhornRouter(
    dim = 512,
    experts = experts,
    competitive = True,
    causal = False,
)

x = torch.randn(1, 8, 1017, 512)
out = router(x) # (1, 8, 1017, 256)
```

## Citations

```bibtex
@article{Shoeybi2019MegatronLMTM,
    title   = {Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism},
    author  = {Mohammad Shoeybi and Mostofa Patwary and Raul Puri and Patrick LeGresley and Jared Casper and Bryan Catanzaro},
    journal = {ArXiv},
    year    = {2019},
    volume  = {abs/1909.08053},
    url     = {https://api.semanticscholar.org/CorpusID:202660670}
}
```

```bibtex
@article{Anthony2024BlackMambaMO,
    title   = {BlackMamba: Mixture of Experts for State-Space Models},
    author  = {Quentin Anthony and Yury Tokpanov and Paolo Glorioso and Beren Millidge},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2402.01771},
    url     = {https://api.semanticscholar.org/CorpusID:267413070}
}
```

```bibtex
@article{Csordas2023SwitchHeadAT,
    title   = {SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention},
    author  = {R'obert Csord'as and Piotr Piekos and Kazuki Irie and J{\"u}rgen Schmidhuber},
    journal = {ArXiv},
    year    = {2023},
    volume  = {abs/2312.07987},
    url     = {https://api.semanticscholar.org/CorpusID:266191825}
}
```
