GPT - gMLP
This repository will attempt to crack long context autoregressive language modeling (GPT) using variations of gMLPs. Specifically, it will contain a variant that does gMLP for local sliding windows. The hope is to be able to stretch a single GPU to be able to train context lengths of 4096 and above efficiently and well.
GPT is technically a misnomer now, since there will be no attention (transformer) at all contained in the architecture.
Install
$ pip install g-mlp-gpt
Usage
import torch
from g_mlp_gpt import gMLPGPT
model = gMLPGPT(
num_tokens = 20000,
dim = 512,
depth = 4,
seq_len = 1024,
window_size = (128, 256, 512, 1024) # window sizes for each depth
)
x = torch.randint(0, 20000, (1, 1000))
logits = model(x) # (1, 1000, 20000)
16k context length
import torch
from g_mlp_gpt import gMLPGPT
model = gMLPGPT(
num_tokens = 20000,
dim = 512,
seq_len = 16384,
depth = 8,
reversible = True,
window = (128, 128, 256, 512, 1024, 1024, 2048, 2048, 4096, 4096, 8192, 8192),
axial = (1, 1, 1, 1, 1, 1, 2, 2, 4, 4, 8, 8)
).cuda()
x = torch.randint(0, 20000, (1, 16384)).cuda()
logits = model(x) # (1, 16384, 20000)
Citations
@misc{liu2021pay,
title = {Pay Attention to MLPs},
author = {Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le},
year = {2021},
eprint = {2105.08050},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
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