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hourglass-transformer-pytorch
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hourglass-transformer-pytorch

Implementation of Hourglass Transformer, in Pytorch, from Google and OpenAI

by Phil Wang

0.0.6 (see all)License:MIT
pypi i hourglass-transformer-pytorch
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Hourglass Transformer - Pytorch

Implementation of Hourglass Transformer, in Pytorch.

Install

$ pip install hourglass-transformer-pytorch

Usage

import torch
from hourglass_transformer_pytorch import HourglassTransformerLM

model = HourglassTransformerLM(
    num_tokens = 256,               # number of tokens
    dim = 512,                      # feature dimension
    max_seq_len = 1024,             # maximum sequence length
    heads = 8,                      # attention heads
    dim_head = 64,                  # dimension per attention head
    shorten_factor = 2,             # shortening factor
    depth = (4, 2, 4),              # tuple of 3, standing for pre-transformer-layers, valley-transformer-layers (after downsample), post-transformer-layers (after upsample) - the valley transformer layers can be yet another nested tuple, in which case it will shorten again recursively
)

x = torch.randint(0, 256, (1, 1024))
logits = model(x) # (1, 1024, 256)

For something more sophisticated, two hourglasses, with one nested within the other

import torch
from hourglass_transformer_pytorch import HourglassTransformerLM

model = HourglassTransformerLM(
    num_tokens = 256,
    dim = 512,
    max_seq_len = 1024,
    shorten_factor = (2, 4),     # 2x for first hour glass, 4x for second
    depth = (4, (2, 1, 2), 3),   # 4@1 -> 2@2 -> 1@4 -> 2@2 -> 3@1
)

x = torch.randint(0, 256, (1, 1024))
logits = model(x)

Funnel Transformer would be approximately

import torch
from hourglass_transformer_pytorch import HourglassTransformerLM

model = HourglassTransformerLM(
    num_tokens = 20000,
    dim = 512,
    max_seq_len = 1024,
    causal = False,
    attn_resampling = False,
    shorten_factor = 2,
    depth = (2, (2, (2, 2, 2), 2), 2)
)

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

For images, instead of average pool and repeat for the down and upsampling functions, they found that linear projections worked a lot better. You can use this by setting updown_sample_type = 'linear'

import torch
from hourglass_transformer_pytorch import HourglassTransformer

model = HourglassTransformer(
    dim = 512,
    shorten_factor = 2,
    depth = (4, 2, 4),
    updown_sample_type = 'linear'
)

img_tokens = torch.randn(1, 1024, 512)
model(img_tokens) # (1, 1024, 512)

Although results were not presented in the paper, you can also use the Hourglass Transformer in this repository non-autoregressively.

import torch
from hourglass_transformer_pytorch import HourglassTransformerLM

model = HourglassTransformerLM(
    num_tokens = 20000,
    dim = 512,
    max_seq_len = 1024,
    shorten_factor = 2,
    depth = (4, 2, 4),
    causal = False          # set this to False
)

x = torch.randint(0, 256, (1, 1024))
mask = torch.ones((1, 1024)).bool()

logits = model(x, mask = mask) # (1, 1024, 20000)

Enwik8 autoregressive example

$ python train.py

Todo

  • work with non-autoregressive, accounting for masking
  • account for masking for attention resampling
  • account for shift padding when naive downsampling

Citations

@misc{nawrot2021hierarchical,
    title   = {Hierarchical Transformers Are More Efficient Language Models}, 
    author  = {Piotr Nawrot and Szymon Tworkowski and Michał Tyrolski and Łukasz Kaiser and Yuhuai Wu and Christian Szegedy and Henryk Michalewski},
    year    = {2021},
    eprint  = {2110.13711},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
VersionTagPublished
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0.0.5
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0.0.4
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0.0.3
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