Efficient Nearest Neighbor Language Models

This is implementation of the paper:

Efficient Nearest Neighbor Language Models
Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
EMNLP 2021

This repo implements several techniques to speed up the evaluation of non-parametric, nearest neighbor language models. Specifically, we improve the efficiency along three axes: adaptive retrieval, datastore prunning, and dimension reduction.

Updates

October 23, 2021

The original knnlm repo and our original code use faiss CPU to perform retrieval, and most of the faiss benchmarks are performed on a CPU environment. However, recently I was playing with faiss-gpu and found that the gpu faiss could reduce the evaluation latency significantly, at least in the WikiText-103 and Law-MT datasets. I would like to report the faiss-gpu results here for readers of interest to have a better understanding. The faiss-gpu here uses a 40GB A100 GPU (the LM is on the same GPU, i.e. we only need one GPU) and the faiss-cpu uses 32 CPU cores.

We note that GPU memory is typically scarce resource, and both WikiText-103 and Law-MT here are not in a very large scale compared to other language modeling training corpus nowadays. Given that kNN-LM produces a vector for every single token, the resulted faiss index may not be able to fit into the GPU memory easily for larger datasets, which could prohibit using kNN-LM+faiss-gpu. See here for limitations of faiss-gpu.

WikiText-103:

Method	ppl	tokens/s
NLM	18.66	5559
kNN-LM (faiss-cpu)	16.65	281
kNN-LM (faiss-gpu)	16.65	3204
efficient kNN-LM (faiss-cpu)	16.67	2015
efficient kNN-LM (faiss-gpu)	16.67	4528

Law-MT:

Method	ppl	tokens/s
NLM	106.56	38.2K
kNN-LM (faiss-cpu)	12.64	1230
kNN-LM (faiss-gpu)	12.32	5781
efficient kNN-LM (faiss-cpu)	12.29	6037
efficient kNN-LM (faiss-gpu)	12.03	9214

Install Dependencies

This repository is largly based on the knnlm repo which is a fork of Fairseq (commit da544b). Please use the exact commit page to determine software requirements for using this code.

git clone [email protected]:jxhe/efficient-knnlm.git

cd efficient-knnlm
pip install --editable .

# install faiss gpu + cpu
conda install -c pytorch faiss-gpu

# install fass cpu only
# conda install -c pytorch faiss-cpu

Hardware

Experiments for this paper were conducted on machines that contain 32 CPUs, 100GB of RAM, and one NVIDIA 3090 24GB GPU. Saving the Wikitext-103 datastore requires 200GB of disk space. Note that the number of CPUs has a great impact on the speed.

Running Efficient kNNLM

Preparation

Data

We share Fairseq's instructions on how to prepare the data here.

mkdir -p datasets/wikitext-103
cp examples/language_model/wikitext-103/prepare-wikitext-103.sh datasets/wikitext-103

cd datasets/wikitext-103
bash prepare-wikitext-103.sh
cd ../..

TEXT=datasets/wikitext-103
python preprocess.py \
    --only-source \
    --trainpref $TEXT/wiki.train.tokens \
    --validpref $TEXT/wiki.valid.tokens \
    --testpref $TEXT/wiki.test.tokens \
    --destdir data-bin/wikitext-103 \
    --workers 20

Download the language model checkpoint pretrained on WikiText-103

# the model checkpoint link is from the knnlm repo
wget https://nlp.stanford.edu/projects/knnlm/wt103_checkpoint_best.pt -P knnlm_ckpt

Save the datastore

mkdir -p dstore

python eval_lm.py data-bin/wikitext-103 \
    --path knnlm_ckpt/checkpoint_best.pt \
    --sample-break-mode none --max-tokens 3072 \
    --softmax-batch 1024 --gen-subset train \
    --context-window 1536 --tokens-per-sample 1536 \
    --dstore-mmap dstore/dstore --knn-keytype 'last_ffn_input' \
    --dstore-size 103225485 --model-overrides "{'knn_keytype': 'last_ffn_input'}" \
    --save-knnlm-dstore --fp16 --dstore-fp16

Dimension Reduction

# the script applies PCA of dimension 512 by default 
# the PCA hyperparameter can be tuned in this script
# set pca=0 to revert back to the vanilla version
bash ef_knnlm/build_faiss.sh

The faiss index is saved into dstore. Try it out:

bash ef_knnlm/utils_cmd/eval_knnlm.sh \
    -d wikitext-103 \
    -s valid \
    -p dstore/dstore_size103225485_embed1024_fp16 \
    -i dstore/knn.103225485.pca512.m64.index \
    -n 103225485 \
    # -g "True" \     # enable this to use GPU faiss

You should already observe a speedup.

Adaptive Retrieval

prepare heldout data to train the retrieval adaptor

# this randomly selects 90% of validation data as the training data to 
# train the retrieval adaptor
bash ef_knnlm/adaptive_retrieval/prepare_heldout.sh wikitext-103

prepare features

bash ef_knnlm/adaptive_retrieval/prepare_feature_pipeline.sh

train

bash ef_knnlm/adaptive_retrieval/train_ar.sh

It saves the retrieval adaptor checkpoints into checkpoint/wikitext-103-valid

evaluation

# the cutoff ratio in adaptive retrieval
# by default we cut off half of the retrieval
cutoff=50

# please change this to the .pt file path observed from the last step
ar_ckpt=xxx

# this hyperparameter needs to be changed if 
# the datastore sizes change (e.g. datastore pruning)
size=103225485

dstore_prefix=dstore/dstore_size${size}_embed1024_fp16
index_file=dstore/knn.${size}.pca512.m64.index

bash ef_knnlm/utils_cmd/eval_knnlm.sh \
    -d wikitext-103 \
    -s test \
    -p ${dstore_prefix} \
    -i ${index_file} \
    -c knnlm_ckpt/wt103_checkpoint_best.pt \
    -n ${size} \
    -f datasets/wikitext-103 \
    -a ctxt,freq,lm_ent,lm_max,fert \
    -u ${cutoff} \
    -h ${ar_ckpt} \
    # -w "True"  # read datastore weights file, required for greedy-merged datastore
    # -g "True" # enable this to use GPU faiss

Datastore Pruning

precompute all the retrieval results for every record in the datastore:

# It is possible to parallel this operation by change 
# "--start-point" and "--num" arguments so that the training
# data would be splitted into multiple smaller ones. In this case
# the retrieval results would be saved into multiple files
bash ef_knnlm/dstore_compression/save_retrieval_results.sh

The retrieval results are saved into dstore/greedy_merge, other datastore pruning algorithms may be played around using these pre-computed results.

greedy merging

# perform greedy merging to yield a new smaller datastore, 
# and build faiss index from the new datastore
bash ef_knnlm/dstore_compression/greedy_merge.sh

The pruned datastore and index are saved into dstore/greedy_merging, replace the previousdstore_prefix/index_file with the new ones to use the pruned the datastore. The option -w "True"needs to be passed to eval_knnlm.sh to read the generated datastore weights file from greedy merging.

Reference

@inproceedings{he2021eff,
title={Efficient Nearest Neighbor Language Models},
author={Junxian He and Graham Neubig and Taylor Berg-Kirkpatrick},
booktitle={Proceedings of EMNLP},
year={2021}
}