Google colab notebook for finetuning.
https://colab.research.google.com/drive/1jwFks82BLyy8x3oxyKpiNdlL1PfKSQwW?usp=sharing
Google colab notebook for generating text corpus.
https://colab.research.google.com/drive/1Hsp2508TXMR0ihYOLjKYOzWm9byqg9ue
You can try writing with the model here https://porfirevich.ru and with Telegram chat bot @PorfBot
You can try poetry with Telegram chat bot @NeuroPoetBot
Your perplexity will be different, depending on the tokenizer, the vocab and the dataset. The better your tokenizer the worse your perplexity, actually.
Values in the table are perplexity on the validation set.
Huge dataset
| GPT-2 | Small, 124M. BS 64 | Medium, 355M. BS 32 |
|---|---|---|
| Unfreeze 0, LR 24e-4 | 80 epoch, 85-90 | 80 epoch, 81-85 |
| Unfreeze 0, LR 3e-4 | 80 epoch, 75-76 | 100 epoch, 64-65 |
| Unfreeze 0, LR 6e-5 | 80 epoch, 73-73.5 | 40 epoch, 63-63.5 |
| Unfreeze 1, LR 3e-4 | 118 epoch, 51-52 | 142 epoch, 42.3-43.7 |
| Unfreeze 1, LR 6e-5 | 80 epoch, 49-49.5 | 40 epoch, 41.-41.6 |
| Unfreeze 2, LR 3e-4 | 70 epoch, 45.5 | 68 epoch, 37.2-38.6 |
| Unfreeze 2, LR 6e-5 | 200 epoch, 41.18-42.19 | 87 epoch, 35.4-35.9 |
| Unfreeze 7, LR 3e-4 | 90 epoch, 35.3 - 35.9 | 163 epoch, 28.6-29.6 |
| Unfreeze 7, LR 6e-5 | 88 epoch, 32.6-33. | 90 epoch, 27.2-27.5 |
| Unfreeze -1 (all), LR 6e-5 | 160 epoch, 30.5-30.9 | 163 epoch, 23.8-24.15 |
Classics dataset. It's only 500Mb and GPT-2 overfits it pretty fast.
| GPT-2 | Small, 124M | Medium, 355M |
|---|---|---|
| Unfreeze -1 (all) | 28 epoch, 26.22 | 7 epoch, 20.9722 |
Poetry dataset
| GPT-2 | Small, 124M | Medium, 355M |
|---|---|---|
| Unfreeze -1 (all) | 25 epoch, 26.22 | 7 epoch, 48.36 |
Pelevin dataset
| GPT-2 | Small, 124M | Medium, 355M |
|---|---|---|
| Unfreeze -1 (all) | 5 epoch, 44.55 | 3 epoch, 33.38 |
I've trained the model using gradual unfreezing with '--unfreeze_level' parameter. The sequence was 0,1,2,7,-1 (as in the table with results). When loss don't improve for a day I switch to next value (like from 2 to 7). You can find my exact scripts in tpu/schedule_small.txt and tpu/schedule_medium.txt.
The model that isn't fine-tuned on any author is here
pip install awscli
aws s3 sync --no-sign-request s3://models.dobro.ai/gpt2/ru/unfreeze_all gpt2Folders with s_ prefix contain Small (124M) model, m_ - for Medium (355M) model.
To understand how to generate text you should start by looking at rest.py.
Also, you can download all fine-tuned models.
aws s3 sync --no-sign-request s3://models.dobro.ai/gpt2/ru allThe one with which you can play on the site is located in the Pelevin folder.
Download the models (intructions above), choose the model and put it in your output folder. Use validation set and be careful with overfitting. On small dataset it will overfit very fast - 3-7 epoch. Follow instructions below, except you don't need to train you tokenization dictionary, because you already have one.
I'd suggest that if you don't have a bunch of GPU's you should consider renting a Google TPU. On my Nvidia Titan RTX an epoch takes 70 minutes and the same epoch takes 12.5 minutes on TPU v3-8. I've used fp16 on GPU, but I can't use bfloat16 on TPU, because it's training poorly on bfloat16 at the moment (it could have been 8 minutes if implemented properly).
You can ask for access to Google's TensorFlow Research Cloud and use TPUs for free for one month.
In the process, I've switched tokenization library from SentencePiece to YTTM. YTTM is better (10% smaller files) and much faster. If you for some reason want to use SentencePiece then the code is here, just change the tokenizer in the command line.
First, the GPT-2 model will learn Russian on a huge dataset (230 GB), and then it will learn good Russian on the Russian classical literature (500 MB). I use progressive layer unfreezing to use transfer training. Validation set is the correspondence between Leo Tolstoy with young Mahatma Gandhi.
Main link
For finetuning first second Dostoyevskiy Tolstoy Pushkin Bulgakov Gogol Pelevin
sudo xargs -a apt.txt apt install
conda env create -f environment.ymlFollow instructions here https://github.com/google/sentencepiece
Use corpus/corpus.ipynb on your dataset.
Or in google colab: https://colab.research.google.com/drive/1Hsp2508TXMR0ihYOLjKYOzWm9byqg9ue
You can skip this step if you want only to finetune the model with the existing vocab.
yttm bpe --data ./corpus/tmp/russian_corpus_for_vocab.txt --model bpe/yt.model --vocab_size 50257 --coverage 0.9999
# SentencePiece
spm_train --input=./corpus/tmp/russian_corpus_for_vocab.txt --model_prefix=bpe/m50 --vocab_size=50257 --user_defined_symbols='<|n|>'5.6. If you want to use Google TPU, go here https://github.com/mgrankin/ru_transformers/tree/master/tpu
Mixed precision training with opt_level O2 gives the exact same loss but much faster and with less memory. The downside - APEX with O2 doesnt work with DataParallel yet, see NVIDIA/apex#227
wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run -O cuda.run
chmod +x cuda.run
sudo ./cuda.runexport CUDA_HOME=/usr/local/cuda-10.2
git clone https://github.com/NVIDIA/apex
cd apex
# fix setup.py if complains for version mismatch
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./cd ru_transformers
conda activate gpt
export TRAIN_FILE=./data/classic
# GPT-2 124M, final perplexity ?
export CUDA_VISIBLE_DEVICES=1
export MODEL_SIZE=gpt2
export OUTPUT=output_yt/s
export BS=8
export LR=5e-5
# GPT-2 355M, final perplexity 18.99?
export CUDA_VISIBLE_DEVICES=2
export MODEL_SIZE=gpt2-medium
export OUTPUT=output_yt/m
export BS=3
export LR=3e-5
# GPT-2 774M, final perplexity 21.09?
export CUDA_VISIBLE_DEVICES=3
export MODEL_SIZE=gpt2-large
export OUTPUT=output_yt/l
export BS=1
export LR=1e-5
# training script
# You shouldn't use --model_name_or_path=$MODEL_SIZE if you want to start with pre-trained Russian GPT-2. If you set --model_name_or_path=gpt2 you'll start with English GPT-2. For Russian GPT-2 you should download the model, put it in the output dir and use --model_name_or_path=$OUTPUT.
# This step will download an English GPT-2 to the $OUTPUT and start training it.
# If you want to start from Russian GPT-2 then skip this step. Instead download the Russian GPT-2, put it to $OUTPUT manually.
python run_lm_finetuning.py \
--output_dir=$OUTPUT \
--model_type=gpt2 \
--model_name_or_path=$MODEL_SIZE \
--do_train \
--train_data_file=$TRAIN_FILE \
--per_gpu_train_batch_size $BS \
--save_steps=10000 \
--logging_steps=1 \
--fp16 \
--fp16_opt_level O2 \
--warmup_samples 16000 \
--learning_rate $LR \
--tokenizer_class YTEncoder \
--tokenizer_name bpe/yt.model \
--do_eval \
--evaluate_during_training \
--eval_steps 1000 \
--eval_data_file=./data/classic/valid \
--unfreeze_level 0
# My dataset is 230Gb and it doesn't fit in RAM, so each epoch is a random sample from it. That is why the loop.
while true
do
python run_lm_finetuning.py \
--output_dir=$OUTPUT \
--model_type=gpt2 \
--model_name_or_path=$OUTPUT \
--do_train \
--train_data_file=$TRAIN_FILE \
--per_gpu_train_batch_size $BS \
--save_steps=10000 \
--logging_steps=10 \
--fp16 \
--fp16_opt_level O2 \
--warmup_samples 16000 \
--learning_rate $LR \
--overwrite_output_dir \
--tokenizer_class YTEncoder \
--tokenizer_name bpe/yt.model \
--do_eval \
--evaluate_during_training \
--eval_steps 1000 \
--eval_data_file=./data/classic/valid \
--save_total_limit 30 \
--num_train_epochs 10.0 \
--unfreeze_level 0
sleep 1
done
# with decay
python run_lm_finetuning.py \
--output_dir=$OUTPUT \
--model_type=gpt2 \
--model_name_or_path=$OUTPUT \
--do_train \
--train_data_file=$TRAIN_FILE \
--per_gpu_train_batch_size $BS \
--save_steps=10000 \
--logging_steps=10 \
--fp16 \
--fp16_opt_level O2 \
--warmup_samples 16000 \
--learning_rate $LR \
--overwrite_output_dir \
--tokenizer_class YTEncoder \
--tokenizer_name bpe/yt.model \
--do_eval \
--evaluate_during_training \
--eval_steps 1000 \
--eval_data_file=./data/classic/valid \
--save_total_limit 30 \
--num_train_epochs 3.0 \
--unfreeze_level 0 \
--lr_decay
# and then repeat with unfreeze_level 1,2,3...aws s3 cp output_s/config.json s3://models.dobro.ai/gpt2/ru/small/
aws s3 cp output_s/encoder.model s3://models.dobro.ai/gpt2/ru/small/
aws s3 cp output_s/pytorch_model.bin s3://models.dobro.ai/gpt2/ru/small/git clone https://github.com/mgrankin/ru_transformers.git
cd ru_transformers
mkdir logs
aws s3 sync --no-sign-request s3://models.dobro.ai/gpt2/ru gpt2
cp -R gpt2/pelevin/m_checkpoint-3365357 gpt2/medium
cp -R gpt2/poetry/m_checkpoint-3397989 gpt2/medium/poetry
conda env create -f environment.yml
conda activate gpt
uvicorn rest:app --reload --host 0.0.0.0
# crontab DEVICE="cuda:1"
# @reboot /bin/bash -c "cd ru_transformers; git pull; source ~/.bashrc; conda activate gpt; DEVICE="cuda:1" uvicorn rest:app --reload --host 0.0.0.0"
evaluate_model.py - to evaluate your model using input file or prompt.
text_processing.py - to process your dataset.
to_token_convertor.py - to convert your string to tokens. In case if you curious.