Awesome

Torch-Struct: Structured Prediction Library

A library of tested, GPU implementations of core structured prediction algorithms for deep learning applications.

• HMM / LinearChain-CRF
• HSMM / SemiMarkov-CRF
• Dependency Tree-CRF
• PCFG Binary Tree-CRF
• ...

Designed to be used as efficient batched layers in other PyTorch code.

Tutorial paper describing methodology.

Getting Started

!pip install -qU git+https://github.com/harvardnlp/pytorch-struct
# Optional CUDA kernels for FastLogSemiring
!pip install -qU git+https://github.com/harvardnlp/genbmm
# For plotting.
!pip install -q matplotlib

import torch
from torch_struct import DependencyCRF, LinearChainCRF
import matplotlib.pyplot as plt
def show(x): plt.imshow(x.detach())

# Make some data.
vals = torch.zeros(2, 10, 10) + 1e-5
vals[:, :5, :5] = torch.rand(5)
vals[:, 5:, 5:] = torch.rand(5) 
dist = DependencyCRF(vals.log())
show(dist.log_potentials[0])

# Compute marginals
show(dist.marginals[0])

# Compute argmax
show(dist.argmax.detach()[0])

# Compute scoring and enumeration (forward / inside)
log_partition = dist.partition
max_score = dist.log_prob(dist.argmax)

# Compute samples 
show(dist.sample((1,)).detach()[0, 0])

# Padding/Masking built into library.
dist = DependencyCRF(vals, lengths=torch.tensor([10, 7]))
show(dist.marginals[0])
plt.show()
show(dist.marginals[1])

# Many other structured prediction approaches
chain = torch.zeros(2, 10, 10, 10) + 1e-5
chain[:, :, :, :] = vals.unsqueeze(-1).exp()
chain[:, :, :, :] += torch.eye(10, 10).view(1, 1, 10, 10) 
chain[:, 0, :, 0] = 1
chain[:, -1,9, :] = 1
chain = chain.log()

dist = LinearChainCRF(chain)
show(dist.marginals.detach()[0].sum(-1))

Library

Full docs: http://nlp.seas.harvard.edu/pytorch-struct/

Current distributions implemented:

• LinearChainCRF
• SemiMarkovCRF
• DependencyCRF
• NonProjectiveDependencyCRF
• TreeCRF
• NeuralPCFG / NeuralHMM

Each distribution includes:

• Argmax, sampling, entropy, partition, masking, log_probs, k-max

Extensions:

• Integration with torchtext, pytorch-transformers, dgl
• Adapters for generative structured models (CFG / HMM / HSMM)
• Common tree structured parameterizations TreeLSTM / SpanLSTM

Low-level API:

Everything implemented through semiring dynamic programming.

• Log Marginals
• Max and MAP computation
• Sampling through specialized backprop
• Entropy and first-order semirings.

Examples

• BERT <a href="https://github.com/harvardnlp/pytorch-struct/blob/master/notebooks/BertTagger.ipynb">Part-of-Speech</a>
• BERT <a href="https://github.com/harvardnlp/pytorch-struct/blob/master/notebooks/BertDependencies.ipynb">Dependency Parsing</a>
• <a href="https://github.com/harvardnlp/pytorch-struct/blob/master/notebooks/Unsupervised_CFG.ipynb">Unsupervised Learning</a>
• <a href="https://github.com/harvardnlp/pytorch-struct/blob/master/examples/tree.py">Structured VAE </a>

Citation

@misc{alex2020torchstruct,
    title={Torch-Struct: Deep Structured Prediction Library},
    author={Alexander M. Rush},
    year={2020},
    eprint={2002.00876},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

This work was partially supported by NSF grant IIS-1901030.