Implementation of a Transformer using ReLA (Rectified Linear Attention)

Related tags

Deep Learningrela-transformer
Overview

ReLA (Rectified Linear Attention) Transformer

Implementation of a Transformer using ReLA (Rectified Linear Attention). It will also contain an attempt to combine the feedforward into the ReLA layer as memory key / values, as proposed in All Attention, suggestion made by Charles Foster.

Install

$ pip install rela-transformer

Usage

import torch
from rela_transformer.rela_transformer import ReLATransformer

model = ReLATransformer(
    num_tokens = 20000,
    dim = 512,
    depth = 8,
    max_seq_len = 1024,
    dim_head = 64,
    heads = 8,
    causal = True
)

x = torch.randint(0, 20000, (1, 1024))
logits = model(x) # (1, 1024, 20000)

Enwik8

$ python train.py

Citations

@misc{zhang2021sparse,
    title   = {Sparse Attention with Linear Units},
    author  = {Biao Zhang and Ivan Titov and Rico Sennrich},
    year    = {2021},
    eprint  = {2104.07012},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}
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Comments
  • LayerNorm/GatedRMS inconsistency

    LayerNorm/GatedRMS inconsistency

    Hi! looking through pipeline it seems there are some inconsistencies with normalisation

    # ReLA
input to GRMSNorm
# att code
output: Linear(inner_dim, dim) + GRMSNorm
# next in FF module 
input to LayerNorm

    here we have problem with double norm since we have last layer GRMSNorm in att and first layer LayerNorm in FF.

    looking at the paper it seems that in ReLA GRMSNorm is applied to result of mult(attn, v) before output projection not after projection like in this code. I also confused about usage of LayerNorm in FF should it be GRMSNorm instead? not clear from the paper as well

    opened by inspirit 6
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Phil Wang
Working with Attention. It's all we need
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