pytorch_beam_search

KO | EN

gitlite — search

#javascript #hacktoberfest #typescript #llm #android #rust #deep-learning #linux #nodejs #awesome #cli #vue #pytorch #ios

pytorch_beam_search

(★ 61)

A lightweight implementation of Beam Search for sequence models in PyTorch.

pytorch_beam_search Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# PyTorch Beam Search

This library implements fully vectorized Beam Search, Greedy Search and Sampling for sequence models written in PyTorch.
This is specially useful for tasks in Natural Language Processing, but can also be used for anything that requires
generating a sequence from a sequence model.

## Usage

### A GPT-like character-level language model

```python    
from pytorch_beam_search import autoregressive

# Create vocabulary and examples
# Tokenize the way you need

corpus = list("abcdefghijklmnopqrstwxyz ")
# len(corpus) == 25
# An Index object represents a mapping from the vocabulary
# to integers (indices) to feed into the models
index = autoregressive.Index(corpus)
n_gram_size = 17  # 16 with an offset of 1 
n_grams = [corpus[i:n_gram_size + i] for i in range(len(corpus))[:-n_gram_size + 1]]

# Create tensor

X = index.text2tensor(n_grams)
# X.shape == (n_examples, len_examples) == (25 - 17 + 1 = 9, 17)

# Create and train the model

model = autoregressive.TransformerEncoder(index)  # just a PyTorch model
model.fit(X)  # basic method included

# Generate new predictions

new_examples = [list("new first"), list("new second")]
X_new = index.text2tensor(new_examples)
loss, error_rate = model.evaluate(X_new)  # basic method included
predictions, log_probabilities = autoregressive.beam_search(model, X_new)
# every element in predictions is the list of candidates for each example
output = [index.tensor2text(p) for p in predictions]
output
```

### A Transformer character sequence-to-sequence model

```python
from pytorch_beam_search import seq2seq

# Create vocabularies
# Tokenize the way you need

source = [list("abcdefghijkl"), list("mnopqrstwxyz")]
target = [list("ABCDEFGHIJKL"), list("MNOPQRSTWXYZ")]
# An Index object represents a mapping from the vocabulary
# to integers (indices) to feed into the models
source_index = seq2seq.Index(source)
target_index = seq2seq.Index(target)

# Create tensors

X = source_index.text2tensor(source)
Y = target_index.text2tensor(target)
# X.shape == (n_source_examples, len_source_examples) == (2, 11)
# Y.shape == (n_target_examples, len_target_examples) == (2, 12)

# Create and train the model

model = seq2seq.Transformer(source_index, target_index)  # just a PyTorch model
model.fit(X, Y, epochs=100)  # basic method included

# Generate new predictions

new_source = [list("new first in"), list("new second in")]
new_target = [list("new first out"), list("new second out")]
X_new = source_index.text2tensor(new_source)
Y_new = target_index.text2tensor(new_target)
loss, error_rate = model.evaluate(X_new, Y_new)  # basic method included
predictions, log_probabilities = seq2seq.beam_search(model, X_new)
output = [target_index.tensor2text(p) for p in predictions]
output
```

## Features

### Algorithms

- The **greedy_search** function implements Greedy Search, which simply picks the most likely token at every step. This
  is the fastest and simplest algorithm, but can work well if the model is properly trained.
- The **sample** function implements sampling from a sequence model, using the learned distribution at every step to
  build the output token by token. This is very useful to inspect what the model learned.
- The **beam_search** function implements Beam Search, a form of pruned Breadth-First Search that expands a fixed number
  of the best candidates at every step. This is the slowest algorithm, but usually outperforms Greedy Search.

### Models

- The **autoregressive** module implements the search algorithms and some architectures for unsupervised models that
  learn to predict the next token in a sequence.
    - **LSTM** is a simple baseline/sanity check.
    - **TransformerEncoder** is
      a [GPT](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
      -like model for state-of-the-art performance.
- The **seq2seq** module implements the search algorithms and some architectures for supervised encoder-decoder models
  that learn how to map sequences to sequences.
    - **LSTM** is a sequence-to-sequence unidirectional LSTM model similar to the one
      in [Cho et al., 2014](https://arxiv.org/pdf/1406.1078.pdf), useful as a simple baseline/sanity check.
    - **ReversingLSTM** is a sequence-to-sequence unidirectional LSTM model that reverses the order of the tokens in the
      input, similar to the one in [Sutskever et al., 2014](https://arxiv.org/pdf/1409.3215.pdf). A bit more complex
      than LSTM but gives better performance.
    - **Transformer** is a standard [Transformer](https://arxiv.org/pdf/1706.03762.pdf) model for state-of-the-art
      performance.

## Installation

```shell
pip install pytorch_beam_search
```

## Contribute

- [Issue Tracker](https://github.com/jarobyte91/pytorch_beam_search/issues)
- [Pull Requests](https://github.com/jarobyte91/pytorch_beam_search/pulls)

## License

The project is licensed under the MIT License.

pytorch_beam_search

pytorch_beam_search Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

pytorch_beam_search

pytorch_beam_search Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README