Implementation of Multistream Transformers in Pytorch

Last update: Jul 26, 2022

Overview

Multistream Transformers

Implementation of Multistream Transformers in Pytorch.

This repository deviates slightly from the paper, where instead of using the skip connection across all streams, it uses attention pooling across all tokens in the same position. This has produced the best results in my experiments with number of streams greater than 2.

Install

$ pip install multistream-transformers

Usage

import torch
from multistream_transformers import MultistreamTransformer

model = MultistreamTransformer(
    num_tokens = 256,         # number of tokens
    dim = 512,                # dimension
    depth = 4,                # depth
    causal = True,            # autoregressive or not
    max_seq_len = 1024,       # maximum sequence length
    num_streams = 2           # number of streams - 1 would make it a regular transformer
)

x = torch.randint(0, 256, (2, 1024))
mask = torch.ones((2, 1024)).bool()

logits = model(x, mask = mask) # (2, 1024, 256)

Citations

@misc{burtsev2021multistream,
    title   = {Multi-Stream Transformers}, 
    author  = {Mikhail Burtsev and Anna Rumshisky},
    year    = {2021},
    eprint  = {2107.10342},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

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Implementation of Multistream Transformers in Pytorch

Related tags

Overview

Multistream Transformers

Install

Usage

Citations

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Releases(0.0.4)

0.0.4(Jul 31, 2021)

0.0.3(Jul 31, 2021)

0.0.2(Jul 30, 2021)

0.0.1(Jul 30, 2021)

Owner

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