BERT-EMD

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BERT-EMD 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# BERT-EMD
This repository contains an implementation with PyTorch of model presented in the paper "BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance" in EMNLP 2020.
The figure below illustrates a high-level view of the model's architecture.
![BERT-EMD Model](https://raw.githubusercontent.com/lxk00/BERT-EMD/master/BERT-EMD-model.png "BERT-EMD")
For more details about the techniques of BERT-EMD, refer to our [paper](https://arxiv.org/abs/2010.06133).

### Installation

Run command below to install the environment (using python3).

```
pip install -r requirements.txt 
```

### Data and Pre-train Model Prepare

1. Get GLUE data:
```
python download_glue_data.py --data_dir glue_data --tasks all
```
[BaiduYun](https://pan.baidu.com/s/1tl83ER8N5FfN6m9m9u0zyA?pwd=1234 ) for alternative

2. Get BERT-Base offical model from [here](https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-12_H-768_A-12.zip), download and unzip to directory  `./model/bert_base_uncased`. Convert tf model to pytorch model:
``` sh
cd bert_finetune
python convert_bert_original_tf_checkpoint_to_pytorch.py \
--tf_checkpoint_path ../model/bert_base_uncased \
--bert_config_file ../model/bert_base_uncased/bert_config.json \
--pytorch_dump_path ../model/pytorch_bert_base_uncased
``` 
Or you can download the pytorch version directly from [huggingface](https://huggingface.co/bert-base-uncased#) and download to `../model/pytorch_bert_base_uncased`.

3. Get finetune teacher model, take task MRPC for example (working dir: `./bert_finetune`):
``` sh
export MODEL_PATH=../model/pytorch_bert_base_uncased/
export TASK_NAME=MRPC
python run_glue.py \
  --model_type bert \
  --model_name_or_path $MODEL_PATH \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --do_lower_case \
  --data_dir ../data/glue_data/$TASK_NAME/ \
  --max_seq_length 128 \
  --per_gpu_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 4.0 \
  --save_steps 2000 \
  --output_dir ../model/$TASK_NAME/teacher/ \
  --evaluate_during_training \
  --overwrite_output_dir
```
4. Get the pretrained general distillation TinyBERT v2 student model: [4-layer](https://drive.google.com/open?id=1PhI73thKoLU2iliasJmlQXBav3v33-8z) and [6-layer](https://drive.google.com/open?id=1r2bmEsQe4jUBrzJknnNaBJQDgiRKmQjF).
Unzip to directory  `model/student/layer4` and  `model/student/layer6` respectively. (This link may be temporarily unavailable, for alternative you can download from here [BaiduYun](https://pan.baidu.com/s/1JdjT95k-G7cI1gxnxDwRCw?pwd=1234)).

5. Distill student model, take 4-layer student model for example:
``` sh
cd ../bert_emd
export TASK_NAME=MRPC
python emd_task_distill.py  \
--data_dir ../data/glue_data/$TASK_NAME/ \
--teacher_model ../model/$TASK_NAME/teacher/ \
--student_model ../model/student/layer4/ \
--task_name $TASK_NAME \
--output_dir ../model/$TASK_NAME/student/ \
--beta 0.01 --theta 1
```

### update 2021/08/06
We replace the layer weight update method with division by addition. In our experiments, this normalization method is better than softmax on some datasets. Wight can be in range from 1e-3 to 1e+3

### update 2022/06/01

We add the hyperparameters for best-performing models as bellow and fixed some bugs.

#### Hyperparameters configurations for best-performing models

|Layer Num|Task|alpha|beta|T_emd|T|Learning Rate|
| --- | --- | --- | --- | --- | --- | --- |
|4|CoLA|1|0.001|5|1|2.00E-05|
|4|MNLI|1|0.005|1|3|5.00E-05|
|4|MRPC|1|0.001|10|1|2.00E-05|
|4|QQP|1|0.005|1|3|2.00E-05|
|4|QNLI|1|0.005|1|3|2.00E-05|
|4|RTE|1|0.005|1|1|2.00E-05|
|4|SST-2|1|0.001|1|1|2.00E-05|
|4|STS-b|1|0.005|1|1|3.00E-05|
|6|CoLA|1|0.001|1|7|2.00E-05|
|6|MNLI|1|0.005|1|1|5.00E-05|
|6|MRPC|1|0.005|1|1|2.00E-05|
|6|QQP|1|0.005|1|1|2.00E-05|
|6|QNLI|1|0.001|1|1|5.00E-05|
|6|RTE|1|0.005|1|1|2.00E-05|
|6|SST-2|1|0.001|1|1|2.00E-05|
|6|STS-b|1|0.005|1|1|3.00E-05|

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