bartuer/d-bert

Distilling BERT using natural language generation.

D-BERT

This repository provides implementations of our original BERT distillation technique, Distilling Task-Specific Knowledge from BERT into Simple Neural Networks, and our more recent text generation-based method, Natural Language Generation for Effective Knowledge Distillation. The latter is more effective than the former, albeit at the cost of computational efficiency, requiring multiple GPUs to fine-tune Transformer-XL or GPT-2 for constructing the transfer set. Thus, we will henceforth refer to them as d-lite and d-heavy, respectively.

The codebase admittedly is in a messy state; we plan to continue refactoring it. If you desire just the data from our second paper, you may download that here.

Transfer Set Construction

Our first task is to construct a transfer set. The two papers differ for this step only.

Instructions for d-lite

1. Install the dependencies using pip install -r requirements.txt.

2. Build the transfer set by running python -m dbert.distill.run.augment_data --dataset_file (the TSV dataset file) > (output file) or python -m dbert.distill.run.augment_paired_data --task (the task) --dataset_file (the TSV dataset file) > (output file).

These follow the GLUE datasets' formats.

Instructions for d-heavy

1. Install the dependencies using pip install -r requirements.txt.

At the time of the experiments, transformers was still pytorch_pretrained_bert, with no support for GPT-2 345M, so we had to add that manually. We provide the configuration file in confs/345m-config.json.

2. Build a cache dataset using python -m dbert.generate.cache_datasets --data-dir (directory) --output-file (cache file).

The data directory should contain train.tsv, dev.tsv, and test.tsv, as in GLUE. For sentence-pair datasets, append --dataset-type pair-sentence.

3. Fine-tune Transformer-XL using python -m dbert.generate.finetune_transfoxl --save (checkpoint file) --cache-file (the cache file) --train-batch-size (# that'll fit).

You can also use generate.finetune_gpt for fine-tuning GPT-2. In our paper, we used a batch size of 48, which might be too much for your system to handle. You can probably reduce it without much change in the final quality.

4. Build a prefix sampler for the transformer-dataset pair with `python -m dbert.generate.build_sampler --cache-file (the cache file) --save (the prefix sampler output file).

For GPT-2, add --model-type gpt2.

5. Sample from the Transformer-XL using python -m dbert.generate.sample_transfoxl --prefix-file (the prefix sampler) > (output file).

For sentence-pair sampling, append --paired.

Teacher Fine-tuning

Next, fine-tune the teacher, e.g., large BERT.

1. Run python -m dbert.finetune.classifier --config confs/*-ft.json --learning_rate 4e-5 --workspace (output workspace directory)

See confs/sst2-ft.json for an example of the configuration. You need to modify data_dir and --model_file appropriately, or specify them as command-line options (e.g., --data_dir).

2. To export the logits of the transfer set file, run python -m dbert.finetune.classifier --config confs/*_export.json --no_train --do_test_only --data_dir (the data directory) --export (logits file)

See scripts/export_sst.sh for an example.

Student Distillation

Finally, we can distill the exported logits into the student model.

1. Join the logits to the original TSV using python -m dbert.distill.run.join_logits.

2. Download the word vectors from here.

3. Distill and train a BiLSTM model using python -m dbert.run.distill_birnn --config confs/birnn_sst2.json.