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Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers

This is the code for our paper:

[Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers] Yimeng Wu*, Peyman Passban*, Mehdi Rezagholizadeh, Qun Liu (*These authors contributied equally) Proceedings of EMNLP. 2020.

Requirements

  • Python >=3.6; Pytorch==1.3.0
  • Install packages:
pip install -r requirements.opt.txt

Data Preparation

  • The original data consists of parallel source (src) and target(tgt) tokenized data.
    • src-train.txt
    • tgt-train.txt
    • src-val.txt
    • tgt-val.txt
  • First the tokenized train & valid data are processed into three .pt files
    • prefix.train.num_of_features.pt
    • prefix.valid.num_of_features.pt
    • prefix.vocab.pt
  • Key Features:
    • data_dir: directory for the raw data
    • train_src: the name of the training source data
    • train_tgt: the name of the training target data
    • valid_src: the name of the valid source data
    • valid_tgt: the name of the valid target data
    • save_data: Output file for the prepared data (with prefix)
    • src_vocab: Path to an existing source vocabulary. Format: one word per line.
    • tgt_vocab: Path to an existing target vocabulary. Format: one word per line.
    • share_vocab: Share source and target vocabulary
    • src_seq_length: Maximum source sequence length
    • tgt_seq_length: Maximum target sequence length
    • save_data: save path with prefix
python preprocess.py -data_dir <original_data_dir> -train_src <train_source_data_name> -train_tgt <train_target_data_name> -valid_src <valid_source_data_name> 
-valid_tgt <valid_target_data_name> -save_data <save_path_with_prefix> -src_vocab <soruce_vocab_file_name> -tgt_vocab <target_vocab_file_name> 
-share_vocab 
-src_seq_length <the maximum length of the source language> -tgt_seq_length <the maximum length of the target language>

Example

We upload our 200k ende datasets under data/.The command below will process these tokenized train & valid data into three .pt files: data.train.num_of_features.pt, data.valid.num_of_features.pt, data.vocab.pt.

python preprocess.py
    -data_dir data/200k_vocab_15k/raw_200k_data
    -train_src train_200k.spm.en
    -train_tgt train_200k.spm.de
    -valid_src newstest2013-src.spm.en
    -valid_tgt newstest2013-ref.spm.de
    -save_data: data/200k_vocab_15k/processed
    -src_vocab: ende_200k.vocab
    -tgt_vocab: ende_200k.vocab
    -share_vocab: 'true'
    -src_seq_length: 300
    -tgt_seq_length: 300

Training with or without distillation

  • Four training modes are supported for transformer models: NO-KD, RKD, PKD, CKD (regular_comb, cross_comb, overlap_comb, skip_middle)

    • RKD: loss = alpha * soft_loss + (1-alpha)*hard_loss
    • PKD: loss = alpha * soft_loss + delta * hard_loss + beta * MSE_enc(H^S, H^T) (alpha, beta and delta should sum to 1)
    • Comb_model:
      • Regular_comb: [1,2,3] -> 1, [4,5,6]->2
      • Overlap_comb: [1,2,3,4] -> 1, [3,4,5,6] ->2
      • Skip_middle: [1,2] ->1, [5,6] ->2
      • Cross_comb: [1,3] ->1, [4,6] -> 2
  • They code only support to train 12-layer teacher and 4-layer student now.

  • Key Features:

    • data: Path to the pre-processed .pt files with prefix
    • config: config file path (model configs are defined here)
    • teacher: If KD is applied then this is the path to the teacher model's state_dict.
    • logit_distillation: Whether to do KD on the output logits. If yes then the soft loss willbe added to the loss function. Default to False.
    • enc_distillation_mode: Whether to do the intermediate-layer KD on encoder side. If yes then the internal_loss will be added to the loss function. Default to False.
      • Possible choices: None, skip (PKD), regular_comb, cross_comb, overlap_comb, skip_middle
    • tensorboard: Whether to use tensorboard during training. Default to False.
    • tensorboard_log_dir: Log directory for Tensorboard. This is also the name of the run.
    • world_size: how many gpus are used.
    • save_model: Path to the saved model. Model filename (the model will be saved as <save_model>_N.pt where N is the number of steps.
    • val_result_file: Output validation results to a file under this path. (Used to choose the top best models during inference)
    • alpha: the fixed loss weight before softloss.
    • beta: the fixed loss weight before internal_loss. Used when trainable_loss_weight is False.
  • Side Features:

    • dec_distillation_mode: Whether to do the intermediate-layer KD on decoder side. If yes then the internal_loss will be added to the loss function. Default to False.
      • Possible choices: None, skip (PKD), regular_comb, cross_comb, overlap_comb, skip_middle
    • attn_distillation_mode: Do which distillation on attention matrix (only support for PKD now)
      • Possible choices: only_self, only_context, both_self_context
    • theta: the fixed weight before attn_loss
  • Before training, make sure you export CUDA_VISIBLE_DEVICES=0,1,2,3. For example, If you want to use GPU id 1 and 3 of your OS, you will need to export CUDA_VISIBLE_DEVICES=1,3.

Example

Here is an example on how to use regular_comb on processed en -> de dataset. The command below will save the student models in save_model (or train_url) with filename transformer_N.pt (N=step number) and a val_result file to record the validation result. Vocab File is expected to be present in .vocab.pt.

Step 1: Train a transformer_base teacher

python train.py 
    -config config/distillation/transformer-base.yml
    -world_size 4
    -data data/200k_vocab_15k/processed
    -save_model <Save_path_with_prefix>
    -val_result_file <Path to the saved validation result file> 

Step 2: Train the student

python train.py 
    -data data/200k_vocab_15k/processed
    -save_model <Save_path_with_prefix>
    -logit_distillation
    -config config/distillation/transformer-2layer.yml
    -world_size 4
    -enc_distillation_mode regular_comb
    -teacher <Path to the teacher model>
    -val_result_file <Path to the saved validation result file> 
    -alpha 0.2
    -beta 0.7

Inference

  • Key Features:
    • train_dir: Path to model directory where model is stored.
    • data_dir: Path to data directory where data is stored.
    • src: Source sequence to decode (one line per sequence); multiple separated with spaces.
    • tgt: True target sequence.
    • best_n_ckpts: avg best n checkpoints based upon loss.
    • gpu: Device to run on.
    • max_length: Maximum prediction length.
    • val_result_file: Output validation results to a file with this name.
    • model_prefix: Model prefix used at the time of training.
    • vocab_model: Name of vocab model file for detokenization in data_dir.
python translate.py \
    -train_dir <Path to the model> \
    -data_dir data/200k_vocab_15k/raw_200k_data \
    -src newstest2014-src.spm.en \
    -tgt newstest2014-ref.de \
    -best_n_ckpts 3 \
    -gpu 0 \
    -max_length 600 \
    -val_result_file val_result.yml \
    -model_prefix transformer \
    -vocab_model ende_vocab.model \

Acknowledgement

This repo is based on (https://github.com/OpenNMT/OpenNMT-py) [Open-NMT-PyTorch]

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Pytorch implementation for our EMNLP paper Why Skip If You Can Combine: A Simple Knowledge DistillationTechnique for Intermediate Layers

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