Rissanen Data Analysis

Rissanen Data Analysis (RDA) is a method to determine what capabilities are helpful to solve a dataset, as described our ICML paper. This repo includes a simple tutorial on how to run RDA on any dataset with any model of your choice ( or view on GitHub). In our tutorial, we show how to run RDA on a GLUE dataset (MRPC) by training BERT models, and you'll just need to change a few lines of code to run RDA on another dataset with another model (trained via PyTorch, Tensorflow, or anything else). The tutorial is our recommended way to learn about how to use RDA with your own dataset and models. We've also converted the tutorial to a Python script to run RDA via command line (instructions here). The rest of the repo includes the code and instructions to reproduce our results, as well as our precomputed results that we used to produce our paper's plots (overview here).

RDA Demo Script

Here, we describe how to use rda.py to run our tutorial RDA code from command line to evaluate the Minimum Description Length (MDL) of MRPC using BERT. Please see our notebook tutorial () for a detailed explanation of the code in the script and where to modify the code to run with different datasets and models.

To run rda.py, please create a fresh virtual environment (we used Anaconda 3 with Python 3.7). Then, install PyTorch via the instructions here and then the below dependencies (not required for RDA in general, just the demo):

# Install HuggingFace datasets to load GLUE datasets / MRPC
pip install datasets
# Install a version of HuggingFace transformers, modified to return test loss after model training
pip install git+https://github.com/ethanjperez/transformers_rda.git

Download the rda.py file into your current working directory:

wget https://raw.githubusercontent.com/ethanjperez/rda/main/rda.py

Then, evaluate the MDL of MRPC:

python rda.py --label_range 2 --data_dir data/rda/mrpc --training_args "--model_name_or_path bert-base-cased --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 32 --learning_rate 2e-5 --num_train_epochs 3 --output_dir checkpoint/mrpc --train_file TRAIN_FILE --validation_file VALIDATION_FILE --test_file TEST_FILE --overwrite_output_dir"

You can see a detailed description of the possible command line arguments with:

python rda.py -h

The descriptions are useful if you would like to run on another task (e.g., regression instead of classification) or want to change the settings for RDA (e.g., number of blocks, minimum/maximum number of examples used, model training hyperparameters, etc.).

Reproducing Our Results

Below, we will step through the procedure we used to produce our results. To see how we train a model on dataset, please skim through the README to see the bash snippet and python script we use to train that model. To see how we compute minimum description length after training our models, please see scripts/plot_results.py.

Code Overview:

film/: Code for training FiLM models on CLEVR (modified from the FiLM codebase)
transformers/: Code for training other, transformer-based models (modified from HuggingFace Transformers)
scripts/: All other code for reproducing our results, including code for formatting data (including adding subquestions/subanswers, adding explanations/rationales, ablating data, etc.), training FastText models, tuning model temperature, evaluating MDL / plotting results, and computing other statistics we report
gender_words_list.csv: The list of gendered words that we used for gender bias evaluation, originally from these two papers

Initial Setup

Clone this repo, then step into the directory and set its path to be BASE_DIR, your main working directory:

git clone https://github.com/ethanjperez/rda.git
cd rda
export BASE_DIR=$PWD

Create folders for storing data and model checkpoints/results:

mkdir $BASE_DIR/data  # or symlink to another location that can hold large files
mkdir $BASE_DIR/checkpoint  # or symlink to another location that can hold large files
mkdir $BASE_DIR/checkpoint/rda  # subdirectory where we'll save most results

Installing Dependencies

To run experiments on CLEVR, please jump to RDA on CLEVR. Otherwise, continue below. Setup a Python 3.7+ virtual environment. We installed Anaconda 3 and created a Python 3.7 conda environment:

conda create -y -n rda python=3.7
conda activate rda

Next, install PyTorch (instructions here). We used PyTorch 1.4 with CUDA 10.1, installing with the below command:

conda install -y pytorch=1.4 torchvision cudatoolkit=10.1 -c pytorch

Other versions for PyTorch/CUDA may also be compatible, and CPU-only installation is fine if you'd just like to reproduce our plots without training your own models. Then, install the remaining dependencies:

pip install -r requirements_transformers.txt
python -m spacy download en_core_web_lg
cd $BASE_DIR/transformers
pip install --editable .

[Optional] If you'd like to train FastText models, then install FastText like so:

cd $BASE_DIR
git clone https://github.com/facebookresearch/fastText
cd fastText
pip install .

and download the pretrained FastText vectors that we used:

mkdir $BASE_DIR/data/fastText
cd $BASE_DIR/data/fastText
wget https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip .
unzip crawl-300d-2M.vec.zip
rm crawl-300d-2M.vec.zip

[Optional] If your GPU supports floating point 16 training, you can train transformer models faster by installing NVIDIA/apex (instructions here), e.g.:

cd $BASE_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
git reset --hard de6378f5da  # Optional step. We installed an older version of apex that was compatible with our PyTorch/CUDA versions above.
export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5"  # optional, but helps to install apex in a way that is compatible with many different GPU types
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--deprecated_fused_adam" ./

If you run into installation errors with the above, look for your error in the NVIDIA/apex repo issues, or just skip apex installation for now, as it's not required to run our code.

Reproducing our plots from cached training results

You can compute MDL and reproduce all of our plots by downloading the results our of training runs (skip this step if you'd like to train your own models):

# Function to google drive from terminal
function gdrive_download () {
  CONFIRM=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')
  wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2
  rm -rf /tmp/cookies.txt
}

# Download RDA results
cd $BASE_DIR/checkpoint
gdrive_download 1sWcjvOdNg_TEV4jWyY6lX2ToOomiEv9h rda_results.tar.gz
tar -xvzf rda_results.tar.gz
rmdir rda
mv rda_results rda
rm rda_results.tar.gz

If you run into issues when downloading from Google Drive with the gdrive_download command or when extracting from the downloaded tar.gz file, just download directly from Google Drive here.

Then, you can plot our results on CLEVR using:

python $BASE_DIR/scripts/plot_results.py --exp clevr

Likewise, plot our results for HotpotQA with --exp hotpot and for e-SNLI with --exp esnli. To plot GLUE/SNLI/ANLI results, set --exp to the ablation type (in order of plot appearance in our paper): pos, gender, shuffle, content, causal, logical, gender_frequency_controlled, length.

RDA on e-SNLI

Download and format (e-)SNLI for model training (with explanations or rationales or neither):

cd $BASE_DIR/data
mkdir e-SNLI
cd e-SNLI
for SPLIT in "dev" "test" "train_1" "train_2"; do
    wget https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/esnli_$SPLIT.csv
done
python $BASE_DIR/scripts/esnli.format_data.py

The above script will save data to different folders $BASE_DIR/data/$TN where the data in Task Name TN is described below:

TN (Task Name)	Type of Input Data
esnli.input-raw	Normal Input (Premise+Hypothesis)
esnli.input-markedonly	Rationale
esnli.input-markedmasked	Input with Rationale Words Masked
esnli.input-markedunmasked	Input with Non-Rationale Words Masked
esnli.input-marked	Input + Rationale
esnli.input-explanation	Explanation
esnli.input-raw,explanation	Input + Explanation
esnli.input-marked,explanation	Input + Rationale + Explanation

Here is how you can train the FastText classifier on the above data (runs on CPU, requires ~40GB CPU memory):

for PBN in 0 1 2 3 4 5 6 7; do
for TN in "esnli.input-raw" "esnli.input-markedonly" "esnli.input-markedmasked" "esnli.input-markedunmasked" "esnli.input-marked" "esnli.input-raw,explanation" "esnli.input-explanation"; do
    python $BASE_DIR/scripts/train_fasttext_classifier.py --task_name $TN --prequential_block_no $PBN --autotune_duration 7200 --seeds 12 20 21 22 23
done
done

The above will train five FastText models with different random seeds to evaluate codelengths (loss) for each trainin data block (numbered by PBN or prequential block number). It will also tune the softamx temperature of FastText models automatically (via grid search, choosing the best tmeperature based on validation loss). The script above chooses hyperparameters using FastText's autotune functionality and then use the chosen hyperparameters from the first random seed to train four additional models using different random seeds (for model training and data orderings for online/prequential coding).

Here is how you can train the transformer-based models we used (runs on 1 GPU, requires ~30GB CPU memory max):

FOL=-1  # floating-point optimization level ("-1" for fp32, "2" for fp16 via apex)
cd $BASE_DIR/transformers
for MN in "distilroberta-base" "distilgpt2" "roberta-base" "gpt2" "facebook/bart-base" "albert-base-v2" "roberta-large" "roberta-base.from_scratch" "roberta-large.from_scratch"; do
for TN in "esnli.input-raw" "esnli.input-markedonly" "esnli.input-markedmasked" "esnli.input-markedunmasked" "esnli.input-marked" "esnli.input-raw,explanation" "esnli.input-explanation"; do
    python online_coding.py --mn $MN --tn $TN --max_pbn 7 --fp16_opt_level $FOL --cpus 1  # NB: Increase --cpus if you have more available, for faster data loading and preprocessing
done
done

The above will train five models with different random seeds for each model class (roberta-base, gpt2, etc.) to evaluate codelengths (loss) for each block of the training data. It will run a hyperparameter sweep for a given model class and then use the chosen hyperparameters from the first random seed to train four additional models using different random seeds (for model training and data orderings for online/prequential coding). In practice, you'll probably want to parallelize calls to online_coding.py for different model classes and tasks Running the above will take a while on a single GPU, so you'll probably want to change the for loop as needed (e.g., just use one random seed or one task) or parallelize the above training runs by running each as a separate job on a cluster. The GPU batch size has also been tuned for a 48GB GPU, so if you have less memory, please reduce the maximum GPU batch size (i.e., that can fit in a single forward pass). You can do so by changing the mn2max_tbs variable at the top of $BASE_DIR/scripts/rda_utils.py, from which we load the maximum training batch sizes for different models. The code will accumulate gradients such that you will train with the same effective batch size, regardless of your GPU batch size.

The below command will tune temperatures (parallelized across CPU cores) for all transformer-based models trained above:

python $BASE_DIR/scripts/tune_temperature_parallelized.py --group esnli

Finally, compute MDL using the codelengths obtained from above and save plots to $BASE_DIR/scripts/plots with:

python scripts/plot_results.py --exp esnli

Note 1: The above script will also tune the softmax temperatures for any models that have not had temperature tuned already via tune_temperature_parallelized.py (though tune_temperature_parallelized.py is parallelized and thus faster).

Note 2: If you downloaded our results earlier, you'll want to delete them before running scripts/plot_results.py, as the script loads in the cached results if they are available. You can delete the cached results like so:

rm $BASE_DIR/checkpoint/*pbn2seed2hpstr2stats.json $BASE_DIR/checkpoint/*/*pbn2seed2hpstr2stats.json

To change the models used in the ensemble (or just use a single model), use the --model_names flag, e.g.:

python scripts/plot_results.py --exp esnli --model_names distilroberta-base distilgpt2  # ensemble these two models

To compute the results over different (e.g., fewer) random seeds:

python scripts/plot_results.py --exp esnli --seeds 12 20 21

RDA on GLUE/ANLI/SNLI

For SNLI, we use the downloaded and formatted data from the e-SNLI section above (saved to $BASE_DIR/data/esnli.input-raw).

For GLUE, download its datasets using the GLUE data download script:

cd $BASE_DIR/data
wget https://raw.githubusercontent.com/nyu-mll/jiant/093c556dc513583770ccad4b3f3daad6f37a7bda/scripts/download_glue_data.py
python download_glue_data.py --data_dir .
rm download_glue_data.py
for TN in "CoLA" "MNLI" "MRPC" "QNLI" "QQP" "RTE" "SNLI" "SST-2" "STS-B" "WNLI"; do
    mv $TN $(echo "$TN" | tr '[:upper:]' '[:lower:]')  # lowercase data directory names
done

For ANLI, download the data for each round (1-3) as follows:

cd $BASE_DIR/data
wget https://dl.fbaipublicfiles.com/anli/anli_v1.0.zip
unzip anli_v1.0.zip
for ROUND in "1" "2" "3"; do
    mv anli_v1.0/R$ROUND anli.round$ROUND
done
# Clean up unnecessary files
rm -r anli_v1.0
rm anli_v1.0.zip

For simplicity, we will first show how to run RDA to evaluate the importance of word order on CoLA. The procedure for evaluating the importance of different features on other GLUE/SNLI/ANLI datasets is the similar (details later).

Create the ablated versions of the original data that we test in our paper (masking different POS words, logical words, etc.):

cd $BASE_DIR/scripts
TN="cola"
for SPLIT in "train" "dev" "test"; do
    python dataset_ablations.py --task $TN --split $SPLIT --shard_no $SHARD_NO --num_shards $NUM_SHARDS
done

You'll now have ~20 different ablated versions of the original dataset. Here, we'll show how to train models on the original vs. word-shuffled text (used for our word order experiments). Just use a different task name (TN) for other dataset ablations, matching the name of the ablation's directory. For example, to train of CoLA with (1) input nouns masked use TN=cola.mask_noun and (2) the same fraction of input words use TN=cola.mask_noun_fraction.

You can train FastText models on the above data in the same way as for e-SNLI above (just updating TN, which points to the task and training data located at data/$TN):

for PBN in 0 1 2 3 4 5 6 7; do
for TN in "cola" "cola.shuffle"; do
    python $BASE_DIR/scripts/train_fasttext_classifier.py --task_name $TN --prequential_block_no $PBN --autotune_duration 7200
done
done

Similarly, train transformer-based models with:

cd $BASE_DIR/transformers
for MN in "distilroberta-base" "distilgpt2" "roberta-base" "gpt2" "facebook/bart-base" "albert-base-v2" "roberta-large" "roberta-base.from_scratch" "roberta-large.from_scratch"; do
for TN in "cola" "cola.shuffle"; do
    python online_coding.py --mn $MN --tn $TN --max_pbn 7 --cpus 1  # NB: Increase --cpus if you have more available, for faster data loading and preprocessing
done
done

And tune their temperature with:

python $BASE_DIR/scripts/tune_temperature_parallelized.py --group cola.shuffle

Finally, compute MDL using the codelengths obtained from above and save plots to $BASE_DIR/scripts/plots with:

python scripts/plot_results.py --exp shuffle --task_types cola

As before, you can choose which random seeds you use for evaluation with the --seeds flag (e.g., --seeds 12). You can plot results for multiple tasks by listing them to --task_types (e.g., --task_types cola snli anli.round1). You'll need to use our cached training results (as described earlier) or train your own models on these tasks. Below is a table that lists the task names (TN) you'll need to train models on, to plot the results for our different ablation experiments, where $TASK is dataset (cola, snli, anli.round, etc.):

Experiment Type (--exp)	Training Task Names (TN) Required
shuffle	$TASK $TASK.shuffle (example for cola above)
pos	$TASK $TASK.noun $TASK.noun_fraction $TASK.verb $TASK.verb_fraction $TASK.adj $TASK.adj_fraction $TASK.adv $TASK.adv_fraction $TASK.pos $TASK.pos_fraction
gender gender_frequency_controlled	$TASK $TASK.mask_male_words $TASK.mask_male_words_fraction $TASK.mask_female_words $TASK.mask_female_words_fraction
content	$TASK $TASK.content_pos_words $TASK.content_pos_words_fraction
causal	$TASK $TASK.causal_words $TASK.causal_words_fraction
logical	$TASK $TASK.logical_words $TASK.logical_words_fraction
length	$TASK $TASK.length

$TASK indicates the normal dataset (no words removed), located at $BASE_DIR/data/$TASK. $TASK.$ABLATION indicates that same dataset with words of type $ABLATION removed (e.g., nouns removed for cola.mask_noun). $TASK.$ABLATION_fraction (e.g., cola.mask_noun_fraction) indicates a dataset $TASK where input words are masked uniformly at random, with the same frequency as the corresponding ablation $TASK.$ABLATION (e.g., cola.mask_noun). For mnli and snli ablation runs (with or without random masking), you'll also need to prefix the task name TN with nli and postfix with v2 to match our setup and cached training results. For example, to train on mnli with nouns masked, set TN=nli.mnli.mask_noun.v2.

RDA on HotpotQA

Download the HotpotQA data, with/without subanswers from different decomposition methods as follows (located on Google drive here):

# Function to google drive from terminal
function gdrive_download () {
  CONFIRM=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')
  wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2
  rm -rf /tmp/cookies.txt
}

# Download
cd $BASE_DIR/data
gdrive_download 1QFMJSH5fB_OPH4xoDhvjyAWoUsLQIImw hotpot.with_subanswers.tar.gz
tar -xvzf hotpot.with_subanswers.tar.gz
mv hotpot.with_subanswers/* .
rmdir hotpot.with_subanswers
rm hotpot.with_subanswers.tar.gz

Again, if you run into issues when downloading from Google Drive with the gdrive_download command or when extracting from the downloaded tar.gz file, just download directly from Google Drive (here). The above will save data to different folders $BASE_DIR/data/$TN where the data in TN is described below:

TN	Decomposition Method
hotpot.context-long.num_subas-0.shuffled-1	No Decomposition
hotpot.context-long.subqs-21979200.num_subas-2.shuffled-1	Pseudo-Decomposition
hotpot.context-long.subqs-20604919.num_subas-2.shuffled-1	Seq2Seq
hotpot.context-long.subqs-20639223.num_subas-2.shuffled-1	ONUS
hotpot.context-long.subqs-7.num_subas-2.shuffled-1	DLM
hotpot.context-long.num_subas-2.shuffled-1	Oracle

Then, you can sweep over hyperparameters for pretrained Longformer BASE model on HotpotQA (with subanswers from the above decomposition methods):

FOL=-1  # floating-point optimization level ("-1" for fp32, "2" for fp16 via apex)
cd $BASE_DIR/transformers
NTE=6  # number of training epochs
BS=32  # effective batch size
MN="allenai/longformer-base-4096"  # use "allenai/longformer-base-4096.from_scratch" to train the model from random initialization
MAX_TBS=4  # maximum GPU batch size during training (set for a 48GB GPU, reduce to 2 or 1 as appropriate for your GPU)
for TN in "hotpot.context-long.num_subas-0.shuffled-1" "hotpot.context-long.subqs-21979200.num_subas-2.shuffled-1" "hotpot.context-long.subqs-20604919.num_subas-2.shuffled-1" "hotpot.context-long.subqs-20639223.num_subas-2.shuffled-1" "hotpot.context-long.subqs-7.num_subas-2.shuffled-1" "hotpot.context-long.num_subas-2.shuffled-1"; do
for PBN in 0 1 2 3 4 5 6 7; do
for SEED in 12; do
for LR in 3e-5 5e-5 1e-4; do
    GAS=$((((BS-1)/MAX_TBS)+1))
    TBS=$((BS/GAS))
    OUTPUT_DIR="checkpoint/rda/tn-$TN.mn-$MN.bs-$BS.lr-$LR.nte-$NTE.seed-$SEED.pbn-$PBN"
    CPUS=4  # Number of CPU available on your system (e.g., for data loading/processing)
    mkdir -p $OUTPUT_DIR
    python examples/question-answering/run_squad.py --model_type longformer --model_name_or_path $MN --data_dir data/$TN --do_train --do_eval --dev_file dev1.json --test_file dev2.json --fp16_opt_level $FOL --output_dir $OUTPUT_DIR --per_gpu_train_batch_size $TBS --per_gpu_eval_batch_size $((2*TBS)) --gradient_accumulation_steps $GAS --learning_rate $LR --max_seq_length 4096 --doc_stride 1024 --seed $SEED --max_grad_norm inf --adam_epsilon 1e-6 --weight_decay 0.01 --warmup_proportion 0.06 --num_train_epochs $NTE --threads $CPUS --logging_steps 1 --prequential_block_no $PBN --early_stopping --evaluate_loss --overwrite_output_dir
done
done
done
done

The above requires ~100GB of CPU memory max. To train additional random seeds using the best hyperparameters from the above sweep, simply set LR=0 (e.g., replace for LR in 3e-5 5e-5 1e-4; do with for LR in 0; done) and sweep over the SEED variable with a for loop (e.g., replace for SEED in 12; do with for SEED in 20 21 22 23; do). Finally, compute MDL and plot/save results:

python $BASE_DIR/scripts/plot_results.py --exp hotpot

As before, you can control which random seeds you use for evaluation with the --seeds flag and, e.g., just evaluate on one random seed:

python $BASE_DIR/scripts/plot_results.py --exp hotpot --seeds 12

Answering Subquestions

Here, you can see how to answer subquestions (from ONUS) with a pretrained SQuAD (as we did) and add the subanswers (paragraph markings) to HotpotQA input paragraphs. These instructions are useful if you have your own subquestions that you'd like to test. First, setup the Unsupervised Question Decomposition repo, cloning the repo into $BASE_DIR/, e.g.:

git clone
cd UnsupervisedDecomposition
export MAIN_DIR=$PWD
# Setup conda environment and download data and pretrained models according the instructions in https://github.com/facebookresearch/UnsupervisedDecomposition

Next, activate the virtual environment for that repo and then answer subquestions for HotpotQA training set as follows:

MODEL_DIR=$MAIN_DIR/XLM/dumped/umt.dev1.pseudo_decomp.replace_entity_by_type/20639223  # ONUS decompositions path. To use other decompositions, point this path to a folder containing other decompositions
SPLIT="train"  # use "dev" to answer subquestions for dev set (not necessary for RDA experiments)
ST=0.0
LP=1.0
BEAM=5
SEED=0
MODEL_NO="$(echo $MODEL_DIR | rev | cut -d/ -f1 | rev)"
DATA_FOLDER=data/hotpot.umt.all.model=$MODEL_NO.st=$ST.beam=$BEAM.lp=$LP.seed=$SEED

cd $MAIN_DIR/pytorch-transformers

# Convert Sub-Qs to SQUAD format
if [ $SPLIT == "dev" ]; then SUBQS_SPLIT="valid"; else SUBQS_SPLIT="$SPLIT"; fi
python umt_gen_subqs_to_squad_format.py --model_dir $MODEL_DIR --data_folder all --sample_temperature $ST --beam $BEAM --length_penalty $LP --seed $SEED --split $SUBQS_SPLIT --new_data_format

for NUM_PARAGRAPHS in 1 3; do
    python examples/run_squad.py --model_type roberta --model_name_or_path roberta-large --train_file $DATA_FOLDER/train.json --predict_file $DATA_FOLDER/$SPLIT.json --do_eval --do_lower_case --version_2_with_negative --output_dir checkpoint/roberta_large.hotpot_easy_and_squad.num_paragraphs=$NUM_PARAGRAPHS --per_gpu_train_batch_size 64 --per_gpu_eval_batch_size 32 --learning_rate 1.5e-5 --max_query_length 234 --max_seq_length 512 --doc_stride 50 --num_shards 1 --seed 0 --max_grad_norm inf --adam_epsilon 1e-6 --adam_beta_2 0.98 --weight_decay 0.01 --warmup_proportion 0.06 --num_train_epochs 2 --write_dir $DATA_FOLDER/roberta_predict.np=$NUM_PARAGRAPHS --no_answer_file
done

# Ensemble sub-answer predictions
python ensemble_answers_by_confidence_script.py --seeds_list 1 3 --no_answer_file --split $SPLIT --preds_file1 data/hotpot.umt.all.model=$MODEL_NO.st=$ST.beam=$BEAM.lp=$LP.seed=$SEED/roberta_predict.np={}/nbest_predictions_$SPLIT.json

# Add sub-questions and sub-answers to QA input
python add_umt_subqs_subas_to_q_squad_format_new.py --subqs_dir data/hotpot.umt.all.model=$MODEL_NO.st=$ST.beam=$BEAM.lp=$LP.seed=$SEED --splits $SPLIT --num_shards 1 --model_dir $MODEL_DIR --sample_temperature $ST --beam $BEAM --length_penalty $LP --seed $SEED --use_easy --use_squad "--atype sentence-1-center --subq_model roberta-large-np=1-3 --use_q --use_suba --use_subq"

# Find paragraphs containing sub-answers
python $BASE_DIR/scripts/hotpot.eval_supporting_fact_retrieval.py --model_no $MODEL_NO --num_paragraphs_str "1-3" --split $SPLIT

# Generate a SQuAD-formatted version of the data (and add 0-2 subanswers)
python $BASE_DIR/scripts/hotpot.format_data.py --model_no $MODEL_NO --split $SPLIT

Distilled Language Model Decompositions

Here, we include details on how to train a Distilled Language Model (DLM). You'll need to include your own language model decompositions for training data, where each input example is formatted on a new line and each output is on a new line (with the same line number as the corresponding input). Put training inputs in a single directory, DATA_DIR, containing a file with name train.source that contains training inputs and train.target that contains training outputs/targets. Likewise, do the same for validation examples (val.source and val.target) and test examples (test.source and test.target).

To train DLM, clone my version of HuggingFace Transformers 4.0 (located here), e.g.:

cd $BASE_DIR
git clone https://github.com/ethanjperez/distilled-lm.git
cd distilled-lm

The repo is HuggingFace Transformers 4.0 with a few bug fixes to get sequence-to-sequence training with T5 to work. Next, follow that repo's instructions to install it along with the necessary dependencies (e.g., inside a new conda environment). Now, you can train a Distilled Language Model like so (be sure to update the DATA_DIR variable):

cd $BASE_DIR/distilled-lm/examples/seq2seq
MSL=142  # max seq len
CPUS=10  # number of CPUs available
EB=4  # evaluation number of beams (for beam search
MAX_TBS=16  # max training batch size that will fit on GPU (currently set for a 48GB GPU, decrease for GPUs with less memory)
BS=64  # effective training batch size (after gradient accumulation)
GAS=$((((BS-1)/MAX_TBS)+1))  # gradient accumulation steps
TBS=$((BS/GAS))  # GPU batch size during training (per forward pass)
OUTPUT_DIR="$BASE_DIR/checkpoint/dlm"
mkdir -p $OUTPUT_DIR
DATA_DIR="/path/to/decompositions"  # TODO: Set the data directory containing decompositions you'd like to train on
# Train DLM model
python finetune.py --data_dir=$DATA_DIR --learning_rate=1e-4 --train_batch_size=$TBS --eval_batch_size=$((2*TBS/EB)) --output_dir=$OUTPUT_DIR --max_source_length=$MSL --max_target_length=$MSL --n_val=-1 --do_train --do_predict --model_name_or_path "t5-3b" --adafactor --gpus 1 --gradient_accumulation_steps $GAS --num_workers $CPUS --val_check_interval=1.0 --warmup_steps 0 --max_grad_norm inf --task translation --val_metric bleu --early_stopping_patience 1 --eval_beams $EB --label_smoothing 0.1 --num_train_epochs 3

Then, set the QUESTIONS_PATH variable to a file containing (line-by-line) the questions you'd like to decompose. Also set the GENERATION_OUTPUT_PATH variable to the filepath where you'd like to save question decompositions. You can then generate decompositions with the trained DLM as follows:

QUESTIONS_PATH="/path/to/questions/to/be/decomposed.txt"
GENERATION_OUTPUT_PATH="/path/to/decomposed/questions.txt"
python run_eval.py "$OUTPUT_DIR/best_tfmr" "$QUESTIONS_PATH" "$GENERATION_OUTPUT_PATH" --reference_path "$QUESTIONS_PATH" --task translation --bs 8 --num_beams 4 --length_penalty 0.6 --early_stopping true

We then apply some postprocessing (to remove excess punctuation) and lowercasing (to match how the ) to generated question decompositions:

python $BASE_DIR/scripts/hotpot.postprocess_dlm_decompositions.py --gen_path $GENERATION_OUTPUT_PATH

Then, you can follow the instructions from earlier/above to answer subquestions and add subanswers to input paragraphs.

RDA on CLEVR

Install CUDA to run on GPU. We used CUDA 9.2 with GCC 6.3.0, but other versions should work as well. Then, setup a Python 3.7+ virtual environment. We installed Anaconda 3 and created a Python 3.7 conda environment:

conda create -y -n film python=3.7
conda activate film
# Install PyTorch (instructions here: https://pytorch.org/). We used the below command:
conda install -y pytorch=0.4.1 torchvision=0.2.1 cuda92 -c pytorch
pip install -r requirements_film.txt

Next, you need to download and unpack the CLEVR dataset into $BASE_DIR/film/data:

cd $BASE_DIR/film
wget https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip -O data/CLEVR_v1.0.zip
unzip data/CLEVR_v1.0.zip -d data
rm data/CLEVR_v1.0.zip

Extract ResNet-101 features for the CLEVR train and val images with the following commands:

python scripts/extract_features.py \
  --input_image_dir data/CLEVR_v1.0/images/train \
  --output_h5_file data/train_features.h5

python scripts/extract_features.py \
  --input_image_dir data/CLEVR_v1.0/images/val \
  --output_h5_file data/val_features.h5

Next, find the three subsets of CLEVR questions that we use in our paper and append answers to subquestions to the CLEVR question:

cd $BASE_DIR/data
mkdir CLEVR_1.0_templates
cd CLEVR_1.0_templates
for QTYPE in "comparison" "compare_integer" "same_relate"; do
    wget https://raw.githubusercontent.com/facebookresearch/clevr-dataset-gen/master/question_generation/CLEVR_1.0_templates/$QTYPE.json
done
python $BASE_DIR/scripts/clevr.format_data.py  # Takes ~5 minutes

Then, preprocess the question files generated above:

cd $BASE_DIR/film
for num_sas in 0 1 2; do
for QTYPE in "comparison" "compare_integer" "same_relate"; do
    questions_dirname="questions.$QTYPE.num_sas-$num_sas"
    if [[ "$questions_dirname" != "questions.same_relate.num_sas-2" ]]; then
        mkdir -p "data/CLEVR_v1.0/$questions_dirname"
        sh scripts/preprocess.sh $questions_dirname
    fi
done
done

As shown below, you can then train models on CLEVR (all 3 question families we tested, with various numbers of subanswers, 5 random seeds):

cd $BASE_DIR/film
MN="FiLM"
BS=64
LR="3e-4"
NTE=20
for SEED in 12 20 21 22 23; do
for TN in "clevr.comparison.num_sas-$SAS" "clevr.compare_integer.num_sas-$SAS" "clevr.same_relate.num_sas-$SAS"; do
for SAS in 0 1 2; do
for PBN in 0 1 2 3 4 5 6 7; do
    if [[ "$TN" != "clevr.same_relate.num_sas-2" ]]; then
        OUTPUT_DIR="exp/rda/tn-$TN.mn-$MN.bs-$BS.lr-$LR.nte-$NTE.seed-$SEED.pbn-$PBN"
        mkdir -p $OUTPUT_DIR
        sh scripts/train/film_prequential.sh $TN $MN $BS $LR $NTE $SEED $PBN
    fi
done
done
done
done

Each call to scripts/train/num_train_samples.modified_questions.prequential.sh trains a single FiLM model for a particular random seed (SEED), task (with name TN, i.e., the question type), number of subanswers (SAS), and prequential block number (PBN indicates training on all blocks of data up to and including block number PBN). Running the above will take a while on a single GPU, so you'll probably want to change the for loop as needed (e.g., just use one random seed or one task) or parallelize the above training runs by running each as a separate job on a cluster.

Finally, compute MDL and save plots to $BASE_DIR/scripts/plots with:

python $BASE_DIR/scripts/plot_results.py --exp clevr

If you just want to compute MDL for one question type (TN above), just run:

TN="clevr-compare_integer"  # in {clevr-compare_integer,clevr-comparison,clevr-same_relate}, or add multiple with one space separating each
python scripts/plot_results.py --exp clevr --task_types $TN

Other Scripts

To compute H(y) (the label entropy baseline) for CLEVR, run:

python $BASE_DIR/scripts/clevr.compute_entropy.py

and for GLUE/(e)SNLI/ANLI, run:

python $BASE_DIR/scripts/glue.compute_entropy.py

To compute statistics about the male/female gendered word list (e.g., POS distribution), run:

python $BASE_DIR/scripts/gender_bias.word_stats.py

To compute the frequency of male/female gendered words for each class label, run:

TN="cola" # Set to the task name you want, something in {"cola", "mrpc", "sts-b", "rte", "wnli", "qnli", "qqp", "sst-2", "anli.round1", "anli.round2", "anli.round3"}
python $BASE_DIR/scripts/gender_bias.word_frequency_by_label.py --task $TN  # runs on training set by default

Bibtex Citation

@inproceedings{perez2021rissanen,
  author = {Ethan Perez and Douwe Kiela and Kyunghyun Cho},
  title = {Rissanen Data Analysis: Examining Dataset Characteristics via Description Length},
  booktitle={ICML},
  year = {2021},
  url = {https://arxiv.org/abs/2103.03872}
}

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gender_words_list.csv		gender_words_list.csv
rda.ipynb		rda.ipynb
rda.png		rda.png
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Distilled Language Model Decompositions

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