Cluster-Branch-Train-Merge (c-BTM)

Code for the paper Scaling Expert Language Models with Unsupervised Domain Discovery

This repository is a fork of metaseq.

Citation

If you use this code, please consider citing our work:

@article{cbtm,
 author = {Suchin Gururangan and Margaret Li and Mike Lewis and Weijia Shi and Tim Althoff and Noah A. Smith and Luke Zettlemoyer},
 title = {Scaling Expert Language Models with Unsupervised Domain Discovery},
 journal={arXiv preprint arXiv:2303.14177},
 year = {2023}
}

Set up environment

Clone this repository

git clone https://github.com/kernelmachine/cbtm.git
cd cbtm

Create a new conda env (recommended)

conda create -n cbtm python=3.9
conda activate cbtm

Alternatively, we supply an environment.yml file; which can be used to create a conda environment with python 3.9 and a variety of dependencies. This will take a few minutes, and may not work for all versions of conda.

conda env create -n cbtm -f environment.yml
conda activate cbtm

Install PyTorch

We tested this code with torch compiled with Cuda 11.3.

pip3 install torch==1.10.1+cu113  -f https://download.pytorch.org/whl/cu113/torch_stable.html

Install Megatron

Make sure you have a GPU and CUDA visible for this step.

cd ..
git clone --branch fairseq_v2 https://github.com/ngoyal2707/Megatron-LM.git
cd Megatron-LM
pip3 install six regex numpy
pip3 install -e .

Install fairscale

cd ..
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .

Install balanced-kmeans

cd ..
git clone https://github.com/kernelmachine/balanced-kmeans.git
cd balanced-kmeans
pip3 install -e .

(Optional) Install Apex

Apex may not be compatible with all GPUs. In particular, if you're seeing that CUDA doesn't support your model during the forward pass, you might want to try uninstalling Apex and trying again.

cd ..
git clone https://github.com/NVIDIA/apex.git
cd apex
git checkout e2083df5eb96643c61613b9df48dd4eea6b07690

Depending on your hardware, you may need to comment out lines 101-107 in setup.py before running the next pip install.

pip3 install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--deprecated_fused_adam" --global-option="--xentropy" --global-option="--fast_multihead_attn" ./

Install c-BTM library

Build the c-BTM library.

cd ../cbtm
pip3 install -e .

Fix setuptools version

There is a known compatibility problem between the above torch version and setuptools>=59.6.0, so we roll this package back:

pip3 install setuptools==59.5.0

c-BTM Training and Evaluation

Step 0: Set up data, models, and directories

We'll use the following environment variables in this tutorial, for simplicity. You can set these to whatever you want.

export CBTM_DIR=~/cbtm; 
export DATA_DIR=${CBTM_DIR}/data;
export SERIALIZATION_DIR=${CBTM_DIR}/experiments;
export KMEANS_DIR=${CBTM_DIR}/clusterers;
export CLUSTERS_DIR=${CBTM_DIR}/clusters;
export VOCAB_DIR=$CBTM_DIR/vocab
export PRETRAINED_MODELS_DIR=${CBTM_DIR}/pretrained_models;
mkdir -p ${CBTM_DIR} ${DATA_DIR} ${SERIALIZATION_DIR} ${KMEANS_DIR} ${CLUSTERS_DIR} ${VOCAB_DIR} ${PRETRAINED_MODELS_DIR};

Configure cbtm_constants.py

Next, the constants necessary to make this repo work are at metaseq/cbtm_constants.py. Modify these to suit your local environment.

Make sure the variables in metaseq/cbtm_constants.py are consistent with the paths you set as environment variables above.

Download vocab files and seed models

We use the GPT-2 vocabulary:

wget -O $VOCAB_DIR/gpt2-vocab.json http://s3.wasabisys.com/c4-example/vocab/gpt2-vocab.json
wget -O $VOCAB_DIR/gpt2-merges.txt http://s3.wasabisys.com/c4-example/vocab/gpt2-merges.txt

Download the OPT-1.3B and OPT-6.7B checkpoints, which we use as our seed models. If you don't plan to use the 6.7B model, you can skip the last 2 wgets, which will take a while to complete:

mkdir -p ${PRETRAINED_MODELS_DIR}/opt/1.3b/
mkdir -p ${PRETRAINED_MODELS_DIR}/opt/6.7b/
wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/1.3B/sharded_for_ddp.tgz | tar xvz  --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/1.3b/
wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_0.tgz | tar xvz  --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/6.7b/
wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_1.tgz | tar xvz --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/6.7b/

Download data

We provide some sample C4 data to get you started. Our model only expects (sharded) line-separated jsonl files, split into train and validation data. If you'd like to train on your own data, just follow the overall data layout in the example.

mkdir -p ${DATA_DIR}/c4_example/
wget -qO- http://s3.wasabisys.com/c4-example/c4_example.tar.gz | tar xvz -C ${DATA_DIR}/

This example dataset is a single shard of C4 and a small sample of data from the validation dataset.

You can download the full C4 dataset from Huggingface datasets at the following link: https://huggingface.co/datasets/c4. Keep in mind that the dataset is very large, and comes as json.gz files. Our code expects raw jsonl files in the structure from the example directory, so make sure you have enough space (in total, it's about 1.4 terabytes of data uncompressed).

Metaseq expects the data to be in this format:

{"text": "this is a document", "id": 0}
{"text": "this is another document", "id": 1}

Step 1: Train Clusterer

This command trains a balanced k-means clusterer on a single shard of the C4 training data. Here we use k=8, and give as an argument a folder which contains a file called C4.jsonl, as described above.

Make sure you have access to a GPU here, to speed up training!

NUM_CLUSTERS=8;
DATASET=c4_example;
python -m metaseq.scripts.train_clusterer \
--data-dir ${DATA_DIR}/${DATASET}/train/00000 \
--num-clusters ${NUM_CLUSTERS} \
--balanced \
--output-dir ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/

This will create tfidf.pkl (a tf-idf embedder) and kmeans.pkl (a kmeans clusterer) pickle files at ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/.

Step 2: Cluster data

This code uses your trained clusterer to cluster the dataset's documents. This is substantially faster if you can parallelize it as slurm jobs.

We will do this automatically for you via submitit, just provide your slurm account and partition (either as a flag to the program, or in metaseq/cbtm_constants.py)

If you don't have access to slurm, you can cluster your data locally with the flag --run local, but it might take some time!

DATASET=c4_example;
NUM_CLUSTERS=8;

# Cluster train data
python -m metaseq.scripts.cluster \
--job-dir ${CBTM_DIR}/cluster_logs \
--data-dir ${DATA_DIR}/${DATASET} \
--path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \
--num-clusters ${NUM_CLUSTERS} \
--output-prefix ${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \
--split train \
--run slurm; 

# Cluster validation data
python -m metaseq.scripts.cluster \
--job-dir ${CBTM_DIR}/cluster_logs \
--data-dir ${DATA_DIR}/${DATASET} \
--path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \
--num-clusters ${NUM_CLUSTERS} \
--output-prefix ${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \
--split valid/C4_small \
--run slurm;

Logs for these clustering jobs appear in ${CBTM_DIR}/cluster_logs.

After these jobs complete, open files in ${CLUSTERS_DIR}, e.g., ${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/train/00000/C4.jsonl. You should see lines like the following:

{"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|0","cluster":5}
{"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|1","cluster":3}
{"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|2","cluster":2}

The field sp_id indicates a line (i.e., document) within a file, and the field cluster indicates its predicted cluster.

Step 3: Train Models

Now we'll use the clustered data to train experts. You'll need at least 4 GPUs simultaneously to train each model.

This tutorial uses our train_cbtm script, which interfaces with SLURM. By default, training jobs will request 8 CPUs per GPU and 48G of memory. These resource parameters can be customized in the train_cbtm.sh script.

We have also provided an example sbatch script, if desired, in metaseq/scripts/example_sbatch.sh. You may need to edit this example sbatch command to include any additional slurm arguments you might need to get it working on your system.

Train experts

The following command will train 8 expert models with 4 GPUs each for 50 steps (increase to 10000 steps to replicate our paper).

DATASET=c4_example;
NUM_CLUSTERS=8;
NUM_GPUS_PER_EXPERT=4;

if [[ "$NUM_GPUS_PER_EXPERT" -lt 8 ]]; then
    NUM_NODES=1
    NUM_GPUS_PER_NODE=$NUM_GPUS
else 
    NUM_NODES=$((NUM_GPUS/8))
    NUM_GPUS_PER_NODE=8
fi
python -m metaseq.scripts.train_cbtm \
   --model-size 1.3b \
   --run slurm   \
   --path-to-clusters-dir $CLUSTERS_DIR/${DATASET}/$NUM_CLUSTERS/ \
   --num-clusters $NUM_CLUSTERS  \
   --num-nodes $NUM_NODES \
   --num-gpus $NUM_GPUS_PER_NODE \
   --data-name ${DATASET}  \
   --path-to-data $DATA_DIR/${DATASET} \
   --learning-rate 2e-4 \
   --max-steps 50 \
   --valid-subset valid/C4_small \
   --train-subset train

To train on a specific cluster(s), you can add the flag --train-cluster 1,3,5

To debug locally, change the run flag to --run local.

This command will output checkpoints and logs to ${SERIALIZATION_DIR}/8_clusters/.

Dense training

The following command will train a dense model with 4 GPUs for 50 steps (increase to 10000 steps to replicate our paper).

DATASET=c4_example;
NUM_GPUS_PER_EXPERT=4;

if [[ "$NUM_GPUS_PER_EXPERT" -lt 8 ]]; then
    NUM_NODES=1
    NUM_GPUS_PER_NODE=$NUM_GPUS
else 
    NUM_NODES=$((NUM_GPUS/8))
    NUM_GPUS_PER_NODE=8
fi
python -m metaseq.scripts.train_cbtm \
   --num-clusters 1 \
   --model-size 1.3b \
   --run slurm \
   --num-nodes $NUM_NODES \
   --num-gpus $NUM_GPUS_PER_NODE \
   --data-name ${DATASET}  \
   --path-to-data $DATA_DIR/$DATASET  \
   --learning-rate 2e-4 \
   --max-steps 50 \
   --valid-subset valid/C4_small \
   --train-subset train

To debug locally, change the run flag to --run local.

This command will output checkpoints to ${SERIALIZATION_DIR}/1_clusters/.

Evaluation

To evaluate your models, first consolidate your shards into a single checkpoint file.

The following script depends on the gnu-parallel package. You may need to modify the last line of metaseq/scripts /consolidate_fsdp_shards.sh to point to your gnu-parallel path.

NUM_CLUSTERS=8;
NUM_GPUS_PER_EXPERT=4;
bash metaseq/scripts/consolidate_fsdp_shards.sh ${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/ "*ngpu${NUM_GPUS_PER_EXPERT}"

This will create a consolidated.pt checkpoint in each model's folder.

Now the checkpoints are ready for eval. To launch on slurm:

export NUM_CLUSTERS=8;
# we want as many GPUs as we have clusters
export NUM_GPUS=${NUM_CLUSTERS};
export DATASET=c4_example;
export EVAL_DIR=${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/eval

mkdir -p ${EVAL_DIR};

# get model checkpoints
CONSOLIDATED_MODEL_PATHS=;
# this function gets all model checkpoint directories and sorts them by the cluster ID
# modify the folder pattern to match the directories names of your models, if you need.
FOLDER_PATTERN="cbtm\.c4_example\.*ngpu4"
mapfile -t MODEL_FOLDERS < <(find ${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/ -type d -name $FOLDER_PATTERN  -name "*\.cluster*" -printf "%f|%p\n" | sort -t "|" -k1,1 -t "|" -k2,2 | cut -d "|" -f 2)
for folder in "${MODEL_FOLDERS[@]}"; do
    # check if there are any consolidated.pt files in the model folder 
    if test -f "${folder}/consolidated.pt"; then
        CONSOLIDATED_MODEL_PATHS+="${folder}/consolidated.pt ";
    fi;
done
# function to join checkpoints into comma separated string
function join { local IFS=","; echo "$*"; }

# these model paths should be ordered by cluster ID!
JOINED_MODEL_PATHS=$(join ${CONSOLIDATED_MODEL_PATHS[@]})

python -m metaseq_cli.eval_cbtm \
    --data-dir ${DATA_DIR}/${DATASET} \
    --data-subset valid/C4_small \
    --path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \
    --model-paths $(join ${CONSOLIDATED_MODEL_PATHS[@]}) \
    --job-dir ${EVAL_DIR} \
    --temperature 0.1 \
    --max-valid-steps 200 \
    --ensemble-type clustering \
    --submitit

You can check out logs for your slurm job at ${EVAL_DIR}.

To launch locally, remove the flag --submitit in the command above. Make sure you have $NUM_CLUSTERS GPUs visible though!

This will output perplexity results to ${EVAL_DIR}/result.json.

Use the same command as above to evaluate your dense models, just change the environment variable NUM_CLUSTERS=1.

On our machine we get ppl: 17.86 for the 8-cluster model from this tutorial, and ppl: 18.56 for the 1-cluster model. (Note that these perplexities are only for reproducibility purposes; increase the step size to 10000 to replicate our runs from the paper).

MoE baseline training via sparse upcycling

See our fairseq fork for instructions and code to train the sparse upcycling MoE baseline.

Open-sourced pretrained models

Downloading clusterers and embedders

To get a pretrained clusterer and embedder from the paper, you can run:

# c4 or s2orc
export DATASET=c4
# 2, 4, 8, 16, 32, 64, 128
export NUM_CLUSTERS=4
mkdir -p ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/
wget -O ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/tfidf.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/${DATASET}/${NUM_CLUSTERS}/tfidf.pkl
wget -O ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/kmeans.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/${DATASET}/${NUM_CLUSTERS}/kmeans.pkl

Downloading language models

To access the language models we trained in our paper, you can run the following command:

# c4 or s2orc
export DATASET=c4
# opt1.3b or opt6.7b
export MODEL_ARCH=opt1.3b
# 4, 8, 16, 32, 64, 128, 256, 512, 1024
export GPUS_PER_EXPERT=4
# 2, 4, 8, 16, 32, 64, 128
export NUM_CLUSTERS=64
# anything from 0 to $NUM_CLUSTERS-1
export CLUSTER_NUMBER=28
FOLDER=${DATASET}.${MODEL_ARCH}.${NUM_CLUSTERS}_clusters.cluster${CLUSTER_NUMBER}.ngpu${GPUS_PER_EXPERT}/
mkdir -p ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER
wget -O ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/$DATASET/${MODEL_ARCH}/${NUM_CLUSTERS}_clusters/ngpu${GPUS_PER_EXPERT}/${CLUSTER_NUMBER}/consolidated.pt

To get all n experts for an n-cluster c-BTM model, you can do a for loop:

# c4 or s2orc
export DATASET=c4
# opt1.3b or opt6.7b
export MODEL_ARCH=opt1.3b
# 4, 8, 16, 32, 64, 128, 256, 512, 1024
export GPUS_PER_EXPERT=4
# 2, 4, 8, 16, 32, 64, 128
export NUM_CLUSTERS=4
for CLUSTER_NUMBER in $(seq 0 $((NUM_CLUSTERS-1))); do 
    FOLDER=${DATASET}.${MODEL_ARCH}.${NUM_CLUSTERS}_clusters.cluster${CLUSTER_NUMBER}.ngpu${GPUS_PER_EXPERT}/
    mkdir -p ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER
    wget -O ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/$DATASET/${MODEL_ARCH}/${NUM_CLUSTERS}_clusters/ngpu${GPUS_PER_EXPERT}/${CLUSTER_NUMBER}/consolidated.pt
done

All models for which we show results in the core results section of the paper can be downloaded here. The highest budget (168B token) 1.3B parameter models have ${NUM_CLUSTERS} * $GPUS_PER_EXPERT = 1024, resulting in (${NUM_CLUSTERS}, $GPUS_PER_EXPERT) = (1, 1024), (2, 512) ... (128, 8). Settings for smaller compute budget models can be found with a proportionally small product. E.g., for 42B token 1.3B parameter models, we have ${NUM_CLUSTERS} * $GPUS_PER_EXPERT = 256, resulting in (${NUM_CLUSTERS}, $GPUS_PER_EXPERT) = (1, 256), (2, 128) ... (64, 4).

These models can be evaluated using the command given above, under "Cluster BTM Training and Evaluation". However, to continue training any of these experts using model parallel, you will need to reshard them, check the metaseq library to learn more on how to do this.

See this README for details on rate-limiting when downloading models.

Converting from metaseq to Huggingface

We have provided a script to convert all metaseq models to Huggingface transformers compatible checkpoints.

First, you have to install huggingface transformers from source, and make sure you consolidate your metaseq checkpoint using the consolidate_fsdp_shards.sh first (see above).

Then run:

PATH_TO_TRANSFORMERS=/path/to/installed/transformers/library/
# path to directory containing consolidated.pt checkpoint
INPUT_DIR=/path/to/metaseq/model/dir
OUTPUT_DIR=my_huggingface_model

bash metaseq/scripts/convert_hf.sh $PATH_TO_TRANSFORMERS $INPUT_DIR $OUTPUT_DIR

This will output a checkpoint in $OUTPUT_DIR you can use in any huggingface transformers pipeline.

Name		Name	Last commit message	Last commit date
Latest commit History 79 Commits
downstream_tasks		downstream_tasks
metaseq		metaseq
metaseq_cli		metaseq_cli
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
environment.yml		environment.yml
pyproject.toml		pyproject.toml
setup.py		setup.py

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Cluster-Branch-Train-Merge (c-BTM)

Citation

Set up environment

Clone this repository

Create a new conda env (recommended)

Install PyTorch

Install Megatron

Install fairscale

Install balanced-kmeans

(Optional) Install Apex

Install c-BTM library

Fix setuptools version

c-BTM Training and Evaluation

Step 0: Set up data, models, and directories

Configure cbtm_constants.py

Download vocab files and seed models

Download data

Step 1: Train Clusterer

Step 2: Cluster data

Step 3: Train Models

Train experts

Dense training

Evaluation

MoE baseline training via sparse upcycling

Open-sourced pretrained models

Downloading clusterers and embedders

Downloading language models

Converting from metaseq to Huggingface

About

Releases

Packages

Contributors 4

Languages

License

kernelmachine/cbtm

Folders and files

Latest commit

History

Repository files navigation

Cluster-Branch-Train-Merge (c-BTM)

Citation

Set up environment

Clone this repository

Create a new conda env (recommended)

Install PyTorch

Install Megatron

Install fairscale

Install balanced-kmeans

(Optional) Install Apex

Install c-BTM library

Fix setuptools version

c-BTM Training and Evaluation

Step 0: Set up data, models, and directories

Configure cbtm_constants.py

Download vocab files and seed models

Download data

Step 1: Train Clusterer

Step 2: Cluster data

Step 3: Train Models

Train experts

Dense training

Evaluation

MoE baseline training via sparse upcycling

Open-sourced pretrained models

Downloading clusterers and embedders

Downloading language models

Converting from metaseq to Huggingface

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Contributors 4

Languages

Packages