Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/lucidrains/memorizing-transformers-pytorch

Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch
https://github.com/lucidrains/memorizing-transformers-pytorch

approximate-nearest-neighbors artificial-intelligence attention-mechanism deep-learning memory retrieval transformers

Last synced: 18 days ago
JSON representation

Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch

Awesome Lists containing this project

README 

        



## Memorizing Transformers - Pytorch



Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch



This repository deviates from the paper slightly, using a hybrid attention across attention logits local and distant (rather than the sigmoid gate setup). It also uses cosine similarity attention (with learned temperature) for the KNN attention layer.



## Install



```bash

$ pip install memorizing-transformers-pytorch

```



## Usage



```python

import torch

from memorizing_transformers_pytorch import MemorizingTransformer



model = MemorizingTransformer(

    num_tokens = 20000,                 # number of tokens

    dim = 512,                          # dimension

    dim_head = 64,                      # dimension per attention head

    depth = 8,                          # number of layers

    memorizing_layers = (4, 5),         # which layers to have ANN memories

    max_knn_memories = 64000,           # maximum ANN memories to keep (once it hits this capacity, it will be reset for now, due to limitations in faiss' ability to remove entries)

    num_retrieved_memories = 32,        # number of ANN memories to retrieve

    clear_memories_on_sos_token_id = 1, # clear passed in ANN memories automatically for batch indices which contain this specified SOS token id - otherwise, you can also manually iterate through the ANN memories and clear the indices before the next iteration

)



data = torch.randint(0, 20000, (2, 1024)) # mock data



knn_memories = model.create_knn_memories(batch_size = 2) # create collection of KNN memories with the correct batch size (2 in example)



logits = model(data, knn_memories = knn_memories) # (1, 1024, 20000)

```



You can make the KNN memories read-only by setting `add_knn_memory` on forward to `False`



ex.



```python

logits = model(data, knn_memories = knn_memories, add_knn_memory = False) # knn memories will not be updated

```



With Transformer-XL memories (only the memories that will be discarded will be added to the KNN memory)



```python

import torch

from memorizing_transformers_pytorch import MemorizingTransformer



model = MemorizingTransformer(

    num_tokens = 20000,

    dim = 512,

    depth = 8,

    memorizing_layers = (4, 5),

    max_knn_memories = 64000,

    num_retrieved_memories = 32,

    clear_memories_on_sos_token_id = 1,

    xl_memory_layers = (2, 3, 4, 5),      # xl memory layers - (https://arxiv.org/abs/2007.03356 shows you do not need XL memory on all layers, just the latter ones) - if a KNNAttention layer ends up using XL memories, only the XL memories that will be discarded will be added to long term memory

    xl_max_memories = 512,                # number of xl memories to keep

    shift_knn_memories_down = 1,          # let a layer look at the KNN memories this number of layers above

    shift_xl_memories_down = 1,           # let a layer look at the XL memories this number of layers above, shown to enhance receptive field in ernie-doc paper

)



data = torch.randint(0, 20000, (2, 1024)) # mock data



xl_memories = None



with model.knn_memories_context(batch_size = 2) as knn_memories:

    logits1, xl_memories = model(data, knn_memories = knn_memories, xl_memories = xl_memories)

    logits2, xl_memories = model(data, knn_memories = knn_memories, xl_memories = xl_memories)

    logits3, xl_memories = model(data, knn_memories = knn_memories, xl_memories = xl_memories)



    # ... and so on

```



## KNN Memory



This repository contains a wrapper around Faiss that can automatically store and retrieve key / values



```python

import torch

from memorizing_transformers_pytorch import KNNMemory



memory = KNNMemory(

    dim = 64,                   # dimension of key / values

    max_memories = 64000,       # maximum number of memories to keep (will throw out the oldest memories for now if it overfills)

    num_indices = 2             # this should be equivalent to batch dimension, as each batch keeps track of its own memories, expiring when it sees a new document

)



memory.add(torch.randn(2, 512, 2, 64))  # (batch, seq, key | value, feature dim)

memory.add(torch.randn(2, 512, 2, 64))



memory.clear([0]) # clear batch 0, if it saw an 



memory.add(torch.randn(2, 512, 2, 64))

memory.add(torch.randn(2, 512, 2, 64))



key_values, mask = memory.search(torch.randn(2, 512, 64), topk = 32)

```



## Training



Enwik8 training



```bash

$ python train.py

```



## Todo



- [x] switch to ivfhnsw and just remember all memories

- [x] enwik8 demo

- [x] validation for enwik8

- [x] solve gradient accumulation problem by offering some way to scope reads and writes to knn memories with another indices array

- [ ] setup text generation with memories

- [ ] figure out how to deal with memories efficiently once capacity has been hit

- [ ] try to speed up reading and writing to knn memories collection with multiprocessing



## Citations



```bibtex

@article{wu2022memorizing,

  title   = {Memorizing transformers},

  author  = {Wu, Yuhuai and Rabe, Markus N and Hutchins, DeLesley and Szegedy, Christian},

  journal = {arXiv preprint arXiv:2203.08913},

  year    = {2022}

}

```



```bibtex

@article{Shazeer2019FastTD,

  title   = {Fast Transformer Decoding: One Write-Head is All You Need},

  author  = {Noam M. Shazeer},

  journal = {ArXiv},

  year    = {2019},

  volume  = {abs/1911.02150}

}

```



```bibtex

@Article{AlphaFold2021,

  author  = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A A and Ballard, Andrew J and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},

  journal = {Nature},

  title   = {Highly accurate protein structure prediction with {AlphaFold}},

  year    = {2021},

  doi     = {10.1038/s41586-021-03819-2},

  note    = {(Accelerated article preview)},

}

```



```bibtex

@inproceedings{Rae2020DoTN,

  title   = {Do Transformers Need Deep Long-Range Memory?},

  author  = {Jack W. Rae and Ali Razavi},

  booktitle = {ACL},

  year    = {2020}

}

```



```bibtex

@misc{ding2021erniedoc,

  title   = {ERNIE-Doc: A Retrospective Long-Document Modeling Transformer},

  author  = {Siyu Ding and Junyuan Shang and Shuohuan Wang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang},

  year    = {2021},

  eprint  = {2012.15688},

  archivePrefix = {arXiv},

  primaryClass = {cs.CL}

}

```



```bibtex

@misc{henry2020querykey,

    title   = {Query-Key Normalization for Transformers},

    author  = {Alex Henry and Prudhvi Raj Dachapally and Shubham Pawar and Yuxuan Chen},

    year    = {2020},

    eprint  = {2010.04245},

    archivePrefix = {arXiv},

    primaryClass = {cs.CL}

}

```



*Memory is Attention through Time* - Alex Graves