# A library of vectorized implementations of core structured prediction algorithms

## Pytorch-Struct

A library of tested, GPU implementations of core structured prediction algorithms for deep learning applications. (or an implementation of Inside-Outside and Forward-Backward Algorithms Are Just Backprop")

## Getting Started

```
pip install .
```

```
import torch_struct
import torch
batch, N = 10, 100
scores = torch.rand(N, 100, 100, requires_grad=True)
# Tree marginals
marginals = torch.deptree(scores)
# Tree Argmax
argmax = torch.deptree(scores, seminring=torch_struct.MaxSemiring)
max_score = torch.mul(argmax, scores)
# Tree Counts
ones = torch.ones(N, 100, 100)
ntrees = torch.deptree(ones, semiring=torch_struct.StdSemiring)
# Tree Sample
sample = torch.deptree(scores, seminring=torch_struct.SampledSemiring)
# Tree Partition
v, _ = torch.deptree_inside(scores)
# Tree Max
v, _ = torch.deptree_inside(scores, semiring=torch_struct.MaxSemiring)
```

## Library

Current algorithms implemented:

- Linear Chain (CRF / HMM)
- Semi-Markov (CRF / HSMM)
- Dependency Parsing (Projective and Non-Projective)
- CKY (CFG)

Design Strategy:

- Minimal implementatations. Most are 10 lines.
- Batched for GPU.
- Code can be ported to other backends

Semirings:

- Log Marginals
- Max and MAP computation
- Sampling through specialized backprop

Example: https://github.com/harvardnlp/pytorch-struct/blob/master/notebooks/Examples.ipynb

# Applications

Application Example (to come):

- Structured Attention
- EM training
- Stuctured VAE
- Posterior Regularization

## GitHub

### Comments

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