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.
pip install pytorch-fast-elmoFastElmo should have the same behavior as AllenNLP's ELMo.
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.
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:
# 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.txtCredits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
