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lucidrains
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Description

Fully featured implementation of Routing Transformer

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# 15,928
Python
pytorch
golang
swagger
103 commits

Routing Transformer

PyPI version

A fully featured implementation of Routing Transformer. The paper proposes using k-nearest neighbors to route similar queries / keys into the same cluster for attention.

Open In Colab 131k tokens

Install

$ pip install routing_transformer

Usage

A simple language model

import torch
from routing_transformer import RoutingTransformerLM

model = RoutingTransformerLM( num_tokens = 20000, dim = 512, heads = 8, depth = 12, max_seq_len = 8192, causal = True, # auto-regressive or not emb_dim = 128, # embedding factorization, from Albert weight_tie = False, # weight tie layers, from Albert tie_embedding = False, # multiply final embeddings with token weights for logits dim_head = 64, # be able to fix the dimension of each head, making it independent of the embedding dimension and the number of heads attn_dropout = 0.1, # dropout after attention attn_layer_dropout = 0., # dropout after self attention layer ff_dropout = 0.1, # feedforward dropout layer_dropout = 0., # layer dropout window_size = 128, # target window size of each cluster n_local_attn_heads = 4, # number of local attention heads reversible = True, # reversible networks for memory savings, from Reformer paper ff_chunks = 10, # feed forward chunking, from Reformer paper ff_glu = True, # use GLU variant in feedforward pkm_layers = (4, 7), # specify layers to use product key memory. paper shows 1 or 2 modules near the middle of the transformer is best pkm_num_keys = 128, # defaults to 128, but can be increased to 256 or 512 as memory allows moe_layers = (3, 6), # specify which layers to use mixture of experts moe_num_experts = 4, # number of experts in the mixture of experts layer, defaults to 4. increase for adding more parameters to model moe_loss_coef = 1e-2, # the weight for the auxiliary loss in mixture of experts to keep expert usage balanced num_mem_kv = 8, # number of memory key/values to append to each cluster of each head, from the 'All-Attention' paper. defaults to 1 in the causal case for unshared QK to work use_scale_norm = False, # use scale norm, simplified normalization from 'Transformers without Tears' paper use_rezero = False # use Rezero with no normalization ).cuda()

x = torch.randint(0, 20000, (1, 8192)).long().cuda() input_mask = torch.ones_like(x).bool().cuda()

y, aux_loss = model(x, input_mask = input_mask) # (1, 8192, 20000) aux_loss.backward() # add auxiliary loss to main loss before backprop

A simple transformer

import torch
from routing_transformer import RoutingTransformer

model = RoutingTransformer( dim = 512, heads = 8, depth = 12, max_seq_len = 8192, window_size = 128, n_local_attn_heads = 4 ).cuda()

x = torch.randn(1, 8192, 512).cuda() input_mask = torch.ones(1, 8192).bool().cuda()

y, aux_loss = model(x, input_mask = input_mask) # (1, 8192, 512) aux_loss.backward() # add auxiliary loss to main loss before backprop

Encoder Decoder

To use a full encoder, decoder, simply import the

RoutingTransformerEncDec
class. Save for the
dim
keyword, all other keywords will be either prepended with
enc_
or
dec_
for the encoder and decoder
RoutingTransformerLM
class respectively.
import torch
from routing_transformer import RoutingTransformerEncDec

model = RoutingTransformerEncDec( dim=512, enc_num_tokens = 20000, enc_depth = 4, enc_heads = 8, enc_max_seq_len = 4096, enc_window_size = 128, dec_num_tokens = 20000, dec_depth = 4, dec_heads = 8, dec_max_seq_len = 4096, dec_window_size = 128, dec_reversible = True ).cuda()

src = torch.randint(0, 20000, (1, 4096)).cuda() tgt = torch.randint(0, 20000, (1, 4096)).cuda() src_mask = torch.ones_like(src).bool().cuda() tgt_mask = torch.ones_like(tgt).bool().cuda()

loss, aux_loss = model(src, tgt, enc_input_mask = src_mask, dec_input_mask = tgt_mask, return_loss = True, randomly_truncate_sequence = True) loss.backward() aux_loss.backward()

do your training, then to sample up to 2048 tokens based on the source sequence

src = torch.randint(0, 20000, (1, 4096)).cuda() start_tokens = torch.ones(1, 1).long().cuda() # assume starting token is 1

sample = model.generate(src, start_tokens, seq_len = 2048, eos_token = 2) # (1, <= 2048, 20000)

Product Key Memory

To see the benefits of using PKM, the learning rate of the values must be set higher than the rest of the parameters. (Recommended to be

1e-2
)

You can follow the instructions here to set it correctly https://github.com/lucidrains/product-key-memory#learning-rates

Kmeans Hyperparameters

  1. kmeans_ema_decay = {defaults to 0.999}

This is the exponential moving average decay for updating the k-means. The lower this is, the faster the means will adjust, but at the cost of stability.

  1. commitment_factor = {defaults to 1e-4}

The weight of the auxiliary loss that encourages tokens to get closer (commit) to the k-mean centroids that were chosen for them.

Updating kmeans manually

The following instructions will allow you to update the kmeans manually. By default the kmeans are updated automatically on every backward pass.

import torch
from routing_transformer import RoutingTransformerLM, AutoregressiveWrapper

model = RoutingTransformerLM( num_tokens = 20000, dim = 1024, heads = 8, depth = 6, window_size = 256, max_seq_len = 8192, causal = True, _register_kmeans_update = False # set to False to disable auto-updating )

model = AutoregressiveWrapper(model)

x = torch.randint(0, 20000, (1, 8192)) loss = model(x, return_loss = True) loss.backward()

update kmeans with this call

model.update_kmeans()

Issues

This architecture has trouble generalizing to shorter sequence lengths when decoding tokens from 1 -> maximum sequence length. The simplest and surest solution is to randomly truncate the sequence during training. This helps the network and the kmeans generalize to variable number of tokens, at the cost of prolonged training.

If you are priming the network with the full sequence length at start, then you will not face this problem, and you can skip this training procedure.

import torch
from routing_transformer import RoutingTransformerLM, AutoregressiveWrapper

model = RoutingTransformerLM( num_tokens = 20000, dim = 1024, heads = 8, depth = 12, window_size = 256, max_seq_len = 8192, causal = True )

model = AutoregressiveWrapper(model)

x = torch.randint(0, 20000, (1, 8192)) loss = model(x, return_loss = True, randomly_truncate_sequence = True) # (1, 8192, 20000)

Appreciation

Special thanks to Aran Komatsuzaki for bootstrapping the initial implementation in Pytorch that evolved into this library.

Citation

@misc{roy*2020efficient,
    title   = {Efficient Content-Based Sparse Attention with Routing Transformers},
    author  = {Aurko Roy* and Mohammad Taghi Saffar* and David Grangier and Ashish Vaswani},
    year    = {2020},
    url     = {https://arxiv.org/pdf/2003.05997.pdf}
}
@misc{shazeer2020glu,
    title   = {GLU Variants Improve Transformer},
    author  = {Noam Shazeer},
    year    = {2020},
    url     = {https://arxiv.org/abs/2002.05202}    
}
@inproceedings{kitaev2020reformer,
    title       = {Reformer: The Efficient Transformer},
    author      = {Nikita Kitaev and Lukasz Kaiser and Anselm Levskaya},
    booktitle   = {International Conference on Learning Representations},
    year        = {2020},
    url         = {https://openreview.net/forum?id=rkgNKkHtvB}
}
@inproceedings{fan2020reducing,
    title     ={Reducing Transformer Depth on Demand with Structured Dropout},
    author    ={Angela Fan and Edouard Grave and Armand Joulin},
    booktitle ={International Conference on Learning Representations},
    year      ={2020},
    url       ={https://openreview.net/forum?id=SylO2yStDr}
}
@misc{lan2019albert,
    title       = {ALBERT: A Lite BERT for Self-supervised Learning of Language Representations},
    author      = {Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut},
    year        = {2019},
    url         = {https://arxiv.org/abs/1909.11942}
}
@misc{lample2019large,
    title   = {Large Memory Layers with Product Keys},
    author  = {Guillaume Lample and Alexandre Sablayrolles and Marc'Aurelio Ranzato and Ludovic Denoyer and Hervé Jégou},
    year    = {2019},
    eprint  = {1907.05242},
    archivePrefix = {arXiv}
}
@article{DBLP:journals/corr/abs-1907-01470,
    author    = {Sainbayar Sukhbaatar and
               Edouard Grave and
               Guillaume Lample and
               Herv{\'{e}} J{\'{e}}gou and
               Armand Joulin},
    title     = {Augmenting Self-attention with Persistent Memory},
    journal   = {CoRR},
    volume    = {abs/1907.01470},
    year      = {2019},
    url       = {http://arxiv.org/abs/1907.01470}
}
@misc{bhojanapalli2020lowrank,
    title   = {Low-Rank Bottleneck in Multi-head Attention Models},
    author  = {Srinadh Bhojanapalli and Chulhee Yun and Ankit Singh Rawat and Sashank J. Reddi and Sanjiv Kumar},
    year    = {2020},
    eprint  = {2002.07028}
}
@article{1910.05895,
    author  = {Toan Q. Nguyen and Julian Salazar},
    title   = {Transformers without Tears: Improving the Normalization of Self-Attention},
    year    = {2019},
    eprint  = {arXiv:1910.05895},
    doi     = {10.5281/zenodo.3525484},
}
@misc{bachlechner2020rezero,
    title   = {ReZero is All You Need: Fast Convergence at Large Depth},
    author  = {Thomas Bachlechner and Bodhisattwa Prasad Majumder and Huanru Henry Mao and Garrison W. Cottrell and Julian McAuley},
    year    = {2020},
    url     = {https://arxiv.org/abs/2003.04887}
}
@misc{vaswani2017attention,
    title   = {Attention Is All You Need},
    author  = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
    year    = {2017},
    eprint  = {1706.03762},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

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