https://github.com/huntzhan/pytorch-fast-elmo

A Fast ELMo Implementation. (NOT MAINTAIN ANYMORE)
https://github.com/huntzhan/pytorch-fast-elmo

Last synced: 3 months ago
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A Fast ELMo Implementation. (NOT MAINTAIN ANYMORE)

• Host: GitHub
• URL: https://github.com/huntzhan/pytorch-fast-elmo
• Owner: huntzhan
• License: mit
• Created: 2019-01-02T10:07:22.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-26T20:50:47.000Z (almost 2 years ago)
• Last Synced: 2024-08-04T14:45:50.717Z (3 months ago)
• Language: Python
• Homepage:
• Size: 485 KB
• Stars: 38
• Watchers: 3
• Forks: 9
• Open Issues: 7
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

Awesome Lists containing this project

README

        
=================

pytorch-fast-elmo

=================

.. image:: https://img.shields.io/pypi/v/pytorch_fast_elmo.svg

        :target: https://pypi.python.org/pypi/pytorch_fast_elmo

.. image:: https://img.shields.io/travis/cnt-dev/pytorch-fast-elmo.svg

        :target: https://travis-ci.org/cnt-dev/pytorch-fast-elmo

.. image:: https://img.shields.io/badge/License-MIT-yellow.svg

        :target: https://travis-ci.org/cnt-dev/pytorch-fast-elmo

Introduction

------------

A fast ELMo implementation with features:

- **Lower execution overhead.** The core components are reimplemented in Libtorch in order to reduce the Python execution overhead (**45%** speedup).

- **A more flexible design.** By redesigning the workflow, the user could extend or change the ELMo behavior easily.

Benchmark

---------

Hardware:

- CPU: i7-7800X

- GPU: 1080Ti

Options:

- Batch size: 32

- Warm up iterations: 20

- Test iterations: 1000

- Word length: [1, 20]

- Sentence length: [1, 30]

- Random seed: 10000

+--------------------------------------+------------------------+------------------------+

| Item                                 | Mean Of Durations (ms) | cumtime(synchronize)%  |

+======================================+========================+========================+

| Fast ELMo (CUDA, no synchronize)     | **31**                 | N/A                    |

+--------------------------------------+------------------------+------------------------+

| AllenNLP ELMo (CUDA, no synchronize) | 56                     | N/A                    |

+--------------------------------------+------------------------+------------------------+

| Fast ELMo (CUDA, synchronize)        | 47                     | **26.13%**             |

+--------------------------------------+------------------------+------------------------+

| AllenNLP ELMo (CUDA, synchronize)    | 57                     | 0.02%                  |

+--------------------------------------+------------------------+------------------------+

| Fast ELMo (CPU)                      | 1277                   | N/A                    |

+--------------------------------------+------------------------+------------------------+

| AllenNLP ELMo (CPU)                  | 1453                   | N/A                    |

+--------------------------------------+------------------------+------------------------+

Usage

-----

Please install **torch==1.0.0** first. Then, simply run this command to install.

.. code-block:: bash

    pip install pytorch-fast-elmo

``FastElmo`` should have the same behavior as AllenNLP's ``ELMo``.

.. code-block:: python

    from pytorch_fast_elmo import FastElmo, batch_to_char_ids

    options_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_options.json'

    weight_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5'

    elmo = FastElmo(options_file, weight_file)

    sentences = [['First', 'sentence', '.'], ['Another', '.']]

    character_ids = batch_to_ids(sentences)

    embeddings = elmo(character_ids)

Use ``FastElmoWordEmbedding`` if you have disabled ``char_cnn`` in ``bilm-tf``, or have exported the Char CNN representation to a weight file.

.. code-block:: python

    from pytorch_fast_elmo import FastElmoWordEmbedding, load_and_build_vocab2id, batch_to_word_ids

    options_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_options.json'

    weight_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5'

    vocab_file = '/path/to/vocab.txt'

    embedding_file = '/path/to/cached_elmo_embedding.hdf5'

    elmo = FastElmoWordEmbedding(

            options_file,

            weight_file,

            # Could be omitted if the embedding weight is in `weight_file`.

            word_embedding_weight_file=embedding_file,

    )

    vocab2id = load_and_build_vocab2id(vocab_file)

    sentences = [['First', 'sentence', '.'], ['Another', '.']]

    word_ids = batch_to_word_ids(sentences, vocab2id)

    embeddings = elmo(word_ids)

CLI commands:

.. code-block:: bash

    # Cache the Char CNN representation.

    fast-elmo cache-char-cnn ./vocab.txt ./options.json ./lm_weights.hdf5 ./lm_embd.hdf5

    # Export word embedding.

    fast-elmo export-word-embd ./vocab.txt ./no-char-cnn.hdf5 ./embd.txt

Credits

-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage