This is re-implementation of Google BERT model [paper] in Pytorch. I was strongly inspired by Hugging Face's code and I referred a lot to their codes, but I tried to make my codes more pythonic and pytorchic style. Actually, the number of lines is less than a half of HF's.
(It is still not so heavily tested - let me know when you find some bugs.)
Python > 3.6, fire, tqdm, tensorboardx, tensorflow (for loading checkpoint file)
This contains 9 python files.
tokenization.py
: Tokenizers adopted from the original Google BERT's codecheckpoint.py
: Functions to load a model from tensorflow's checkpoint filemodels.py
: Model classes for a general transformeroptim.py
: A custom optimizer (BertAdam class) adopted from Hugging Face's codetrain.py
: A helper class for training and evaluationutils.py
: Several utility functionspretrain.py
: An example code for pre-training transformerclassify.py
: An example code for fine-tuning using pre-trained transformerDownload pretrained model BERT-Base, Uncased and GLUE Benchmark Datasets before fine-tuning.
export GLUE_DIR=/path/to/glue
export BERT_PRETRAIN=/path/to/pretrain
export SAVE_DIR=/path/to/save
python classify.py \ --task mrpc \ --mode train \ --train_cfg config/train_mrpc.json \ --model_cfg config/bert_base.json \ --data_file $GLUE_DIR/MRPC/train.tsv \ --pretrain_file $BERT_PRETRAIN/bert_model.ckpt \ --vocab $BERT_PRETRAIN/vocab.txt \ --save_dir $SAVE_DIR \ --max_len 128
Output :
cuda (8 GPUs) Iter (loss=0.308): 100%|██████████████████████████████████████████████| 115/115 [01:19<00:00, 2.07it/s] Epoch 1/3 : Average Loss 0.547 Iter (loss=0.303): 100%|██████████████████████████████████████████████| 115/115 [00:50<00:00, 2.30it/s] Epoch 2/3 : Average Loss 0.248 Iter (loss=0.044): 100%|██████████████████████████████████████████████| 115/115 [00:50<00:00, 2.33it/s] Epoch 3/3 : Average Loss 0.068
### Evaluation of the trained Classifier
export GLUE_DIR=/path/to/glue export BERT_PRETRAIN=/path/to/pretrain export SAVE_DIR=/path/to/save
python classify.py \ --task mrpc \ --mode eval \ --train_cfg config/train_mrpc.json \ --model_cfg config/bert_base.json \ --data_file $GLUE_DIR/MRPC/dev.tsv \ --model_file $SAVE_DIR/model_steps_345.pt \ --vocab $BERT_PRETRAIN/vocab.txt \ --max_len 128
Output :
cuda (8 GPUs) Iter(acc=0.792): 100%|████████████████████████████████████████████████| 13/13 [00:27<00:00, 2.01it/s] Accuracy: 0.843137264251709
[Google BERT original repo](https://github.com/google-research/bert) also reported 84.5%.
### Pre-training Transformer
Input file format :
1. One sentence per line. These should ideally be actual sentences, not entire paragraphs or arbitrary spans of text. (Because we use the sentence boundaries for the "next sentence prediction" task).
2. Blank lines between documents. Document boundaries are needed so that the "next sentence prediction" task doesn't span between documents.
Document 1 sentence 1 Document 1 sentence 2 ... Document 1 sentence 45
Document 2 sentence 1 Document 2 sentence 2 ... Document 2 sentence 24
Usage :
export DATA_FILE=/path/to/corpus export BERT_PRETRAIN=/path/to/pretrain export SAVE_DIR=/path/to/save
python pretrain.py \ --train_cfg config/pretrain.json \ --model_cfg config/bert_base.json \ --data_file $DATA_FILE \ --vocab $BERT_PRETRAIN/vocab.txt \ --save_dir $SAVE_DIR \ --max_len 512 \ --max_pred 20 \ --mask_prob 0.15
Output (with Toronto Book Corpus):
cuda (8 GPUs) Iter (loss=5.837): : 30089it [18:09:54, 2.17s/it] Epoch 1/25 : Average Loss 13.928 Iter (loss=3.276): : 30091it [18:13:48, 2.18s/it] Epoch 2/25 : Average Loss 5.549 Iter (loss=4.163): : 7380it [4:29:38, 2.19s/it] ...
Training Curve (1 epoch ~ 30k steps ~ 18 hours):
Loss for Masked LM vs Iteration steps
<img src="https://user-images.githubusercontent.com/32828768/50011629-9a0e5380-ff8a-11e8-87ab-18cd22453561.png">
Loss for Next Sentence Prediction vs Iteration steps
<img src="https://user-images.githubusercontent.com/32828768/50011633-9c70ad80-ff8a-11e8-8670-8baaebb6e51a.png">