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NLP2024-tutorial-3

NLP2024 チュートリアル３: 作って学ぶ日本語大規模言語モデル - 環境構築手順と実験ソースコード
NLP2024 Tutorial 3: Practicing how to build a Japanese large-scale language model - Environment construction and experimental source codes

  • Tutorial Video


https://www.youtube.com/watch?v=eiP2KUOi570

Index

環境構築手順

Environment Construction

For Ubuntu

前提条件 / Prerequisites

  • Hardwares
    • CPU Intel 64bit, RAM >=32GB (>=64GB recommended), Free Disk Space >=200GB
    • GPU RAM >=8GB (>=16GB recommended), Compute Capabilty >=7.0 (>=8.0 recommended)
      • Compute Capability 8.0未満ではbfloat16を使用することができない / Cannot use bfloat16 with Compute Capability below 8.0
      • Compute CapabiltyはHPCシステムズ社のこちらの一覧表を参照 / Compute Capabilty can be checked in this table.
  • Softwares
    • Ubuntu 22.04がクリーンインストールされた状態を想定 / Assuming a clean installation of Ubuntu 22.04
    • 環境構築を行うユーザにsudo権限が付与されていること / The sudo privileges have been granted to the user who will be building the environment.

gcc-12 installation steps

sudo apt update
sudo apt upgrade
sudo apt install make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev git
sudo apt install gcc-12 g++-12
sudo ln -s -f /usr/bin/gcc-12 /usr/bin/gcc
sudo ln -s -f /usr/bin/g++-12 /usr/bin/g++

nvidia-driver-535 installation steps

nvidia-smiが実行できたら既にnvidia-driverがインストールされている。
If you can run nvidia-smi, nvidia-driver is already installed.

nvidia-smi

nvidia-driver-525未満がインストールされていたら下記で一旦削除。525以上がインストールされていたら以降はスキップしてCUDAのインストールに進む。
If the installed nvidia-driver version is lower than 525, remove it by following the steps below. If the nvidia-driver version is 525 or higher is installed, skip the rest and proceed to install CUDA.

sudo apt-get --purge remove nvidia-*
sudo apt-get --purge remove cuda-*

nvidia-driverをインストールして再起動。
Install nvidia-driver and reboot.

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt install nvidia-driver-535
sudo reboot

再起動したらログインしてnvidia-smiが動作するか確認。
After restarting, login and check if nvidia-smi works.

nvidia-smi

nvidia-driverが自動更新されて動作しなくなることがあるので、nano等のエディタで設定ファイルのUnattended-Upgradeの値を"0"に変更しておく。
Since nvidia-driver may be updated automatically and stop working, change the value of Unattended-Upgrade in the configuration file to "0" using an editor such as nano.

sudo nano /etc/apt/apt.conf.d/20auto-upgrades
APT::Periodic::Update-Package-Lists "1";
APT::Periodic::Unattended-Upgrade "0";

CUDA 12.1 installation steps

公式サイトにあるrunfileでのインストール手順を実行。
Execute the installation procedure using the runfile on the official website.

wget https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda_12.1.1_530.30.02_linux.run
sudo sh cuda_12.1.1_530.30.02_linux.run

既存のドライバを削除することを推奨されるがContinueを選択。
Although it is recommended to remove the existing driver, select Continue.

│ Existing package manager installation of the driver found. It is strongly    │
│ recommended that you remove this before continuing.                          │
│ Abort                                                                        │
│ Continue                                                                     │

End User License Agreementについて確認したらacceptを入力。
After confirming the End User License Agreement, enter accept.

Do you accept the above EULA? (accept/decline/quit):
accept

セットアップオプションを次のように設定してInstallを実行。
Set the setup options as follows and run Install.

│ CUDA Installer                                                               │
│ - [ ] Driver                                                                 │
│      [ ] 530.30.02                                                           │
│ + [X] CUDA Toolkit 12.1                                                      │
│   [ ] CUDA Demo Suite 12.1                                                   │
│   [ ] CUDA Documentation 12.1                                                │
│ - [ ] Kernel Objects                                                         │
│      [ ] nvidia-fs                                                           │
│   Options                                                                    │
│   Install                                                                    │

インストールが終わったらnvccを実行できるか確認。
Once the installation is complete, check if you can run nvcc.

/usr/local/cuda/bin/nvcc -V

For WSL2

前提条件 / Prerequisites

  • Harwares
  • Softwares
    • Windows11 22H2 or later (Windows10 22H2でも動作可能 / can also operate on Windows10 22H2)
    • WSL2上でUbuntu 22.04がクリーンインストールされた状態を想定 / Assuming a clean installation of Ubuntu 22.04 on WSL2
    • 環境構築を行うユーザにAdministrator権限が付与されていること / The user who will be building the environment must be granted Administrator privileges

Windows側でNVIDIA Driverをインストール / Install NVIDIA Driver on Windows side

NVIDIAのドライバーダウンロードページから使用する製品とOSを選択し、ダウンロードタイプは製品ブランチ/Studioを指定して、探すを押下。
Select the product and OS you are using from the NVIDIA driver download page, specify the Product Branch / Studio as the download type, and press Search.

nvidia-driver-download-setting-en
nvidia-driver-download-setting-en

ダウンロードしたファイルを実行してドライバをインストール。
Run the downloaded file to install the driver.

WSL2でUbuntu 22.04をインストール/ Install Ubuntu 22.04 with WSL2

管理者権限でPowerShellを起動する / Start PowerShell with administrator privileges

  • Windowsボタンを右クリックしてターミナル（管理者）を選択するとPowerShellが起動する / Right-click the Windows button and select Terminal (Administrator) to start PowerShell

WSL2の更新 / Update WSL2

  • PowerShellで次を実行して利用可能なLinuxディストリビューションのリストを表示 / View a list of available Linux distributions by running the following in PowerShell
wsl --set-default-version 2
wsl --update

WSL2上でのUbuntu 22.04のインストール / Installing Ubuntu 22.04 on WSL2

下記を実行してユーザ設定を行います。 / Execute the following to configure the user settings.

wsl --install -d Ubuntu-22.04

引き続きUbuntu側でnvidia-smiの動作確認を行います。 / Continue to check the operation of nvidia-smi on the Ubuntu side.

nvidia-smi

Ubuntu側でのCUDAのインストール / Installing CUDA on Ubuntu side

WSL2上のUbuntuで、Ubuntu編のgcc等のインストール、および、CUDA 12.1のインストールを実施します。
On Ubuntu on WSL2, perform the steps described in the Ubuntu edition for gcc-12 installation steps and CUDA 12.1 installation steps.

Windowsターミナルのインストール / Installing Windows Terminal

以降の作業と実験の作業性をよくするためWindowsターミナルの利用を推奨します。 / We recommend using Windows Terminal to improve the workability of subsequent work and experiments.
Microsoft Storeからインストールできます。 / The Windows Terminal can be installed from Microsoft Store.

For macOS

前提条件/ Prerequisites

  • Hardwares
    • CPU Apple M1 or later, RAM >=16GB (>=32GB recommended), Free Disk Space >=200GB
  • Softwares
    • macOS 13 or later

Installing Command Line Tools

Command Line Toolsをインストールしていない場合はコンソールアプリで下記を実行。 / If you do not have Command Line Tools installed, run the following in the console app.

xcode-select --install

Installing Python3.10.11 and PATH setting

python.orgからpython 3.10.11 macOS 64-bit universal2 installerをダウンロードして実行。 / Download python 3.10.11 macOS 64-bit universal2 installer from python.org and run it.

実験ソースコード

Experimental Source Codes

Software Installation

CUDAの動作確認 / Checking the operation of CUDA

  • Ubuntu / WSL2
/usr/local/cuda/bin/nvcc -V

環境変数LD_LIBRARY_PATHにCUDAのパスを追加 / Add CUDA path to environment variable LD_LIBRARY_PATH

  • Ubuntu / WSL2
echo 'export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-12.1/lib64"' >> ~/.bashrc
source ~/.bashrc

python3でvenvが使える状態かの確認 / Check if venv is usable in python3

python3 -V
python3 -m venv venv
source venv/bin/activate
deactivate
rm -r venv

pyenv環境の構築 / Building a pyenv environment

pyenv未導入の場合 / If pyenv is not installed

curl https://pyenv.run | bash

pyenv導入済みの場合 / If pyenv has been installed

cd ~/.pyenv/plugins/python-build/../.. && git pull && cd -

pyenvのパス追加 / Add pyenv to the PATH

  • Ubuntu / WSL2
    • ~/.bashrc（zshの場合は ~/.zshrc）に追加 / Add to ~/.bashrc (~/.zshrc for zsh)
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc
echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc
echo 'eval "$(pyenv init --path)"' >> ~/.bashrc
source ~/.bashrc
  • macOS
    • ~/.bash_profile（zshの場合は ~/.zshrc）に追加 / Add to ~/.bash_profile (~/.zshrc for zsh)
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bash_profile
echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bash_profile
echo 'eval "$(pyenv init --path)"' >> ~/.bash_profile
source ~/.bash_profile

pyenvでPython 3.10.13をインストール / Install Python 3.10.13 with pyenv

pyenv install 3.10.13

実験ディレクトリとvenv環境の作成・有効化・バージョン確認 / Creation, activation, and version confirmation of experiment directory and venv environment

mkdir my-llm
cd my-llm
pyenv local 3.10.13
python -m venv venv
source venv/bin/activate
which python
python -V
pip -V

PyTorchのインストールと動作確認 / Installing PyTorch and checking its operation

pip install torch
  • Ubuntu / WSL2
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
import torch
torch.cuda.is_available()
torch.cuda.device_count()
torch.cuda.get_device_name()
  • macOS
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
import torch
torch.backends.mps.is_available()

BERTでTransformersの動作確認 / Check the operation of Transformers with BERT

pip install transformers fugashi unidic-lite
  • Ubuntu / WSL2
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline
model_name = "cl-tohoku/bert-large-japanese-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model = model.to("cuda:0")
mlm = pipeline("fill-mask", model=model, tokenizer=tokenizer, device="cuda:0")
mlm("語りえぬものについては、[MASK]しなければならない。")[:2]
  • macOS
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline
model_name = "cl-tohoku/bert-large-japanese-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model = model.to("mps")
mlm = pipeline("fill-mask", model=model, tokenizer=tokenizer, device="mps")
mlm("語りえぬものについては、[MASK]しなければならない。")[:2]

Inference and Evaluation

text-generation

pip install accelerate safetensors bitsandbytes

1.3B

  • FP32
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name = "llm-jp/llm-jp-1.3b-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto", pad_token_id=tokenizer.pad_token_id)
print(pipe("語りえぬものについては、", max_length=128))
  • FP16
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name = "llm-jp/llm-jp-1.3b-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto", pad_token_id=tokenizer.pad_token_id)
print(pipe("語りえぬものについては、", max_length=128))
  • BF16 - Ubuntu / WSL2
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name = "llm-jp/llm-jp-1.3b-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto", pad_token_id=tokenizer.pad_token_id)
print(pipe("語りえぬものについては、", max_length=128))

13B

  • FP16
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name = "llm-jp/llm-jp-13b-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto", pad_token_id=tokenizer.pad_token_id)
print(pipe("語りえぬものについては、", max_length=128))
  • 4bit - Ubuntu / WSL2
    • Pythonの対話モードで下記を実行 / Run the following in Python interactive mode
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
model_name = "llm-jp/llm-jp-13b-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto", quantization_config=quantization_config)

llm-jp-eval

Installation

  • venv環境に入っている場合はいったん抜ける / If you are in a venv environment, exit it once.
deactivate
  • llm-jp-evalのcloneとvenv環境の作成・有効化 / After cloning llm-jp-eval, create and enable venv.
git clone https://github.com/llm-jp/llm-jp-eval.git
cd llm-jp-eval
cp configs/config_template.yaml configs/config.yaml
python -m venv venv
source venv/bin/activate
pip install -e .
wandb disabled

jasterのビルドとディレクトリ構成の確認 / Building jaster and checking the directory structure

python scripts/preprocess_dataset.py --dataset-name all --output-dir jaster/
ls jaster/
ls jaster/1.2.0/
ls jaster/1.2.0/evaluation

dataset_dirの設定 / Setting dataset_dir

  • configs/config.yamlをエディタで開き、上で確認したdev/までのパスをdataset_dirの値を次のようにセットする / Open configs/config.yaml in an editor and set the path to dev/ confirmed above and the value of dataset_dir as follows.
dataset_dir: "jaster/1.2.0/evaluation/dev"

精度評価 / Accuracy evaluation

JNLI devセット全件の評価 / Evaluation of all JNLI dev sets
  • FP32
python scripts/evaluate_llm.py torch_dtype=fp32 \
  target_dataset="[jnli]" \
  metainfo.max_num_samples=-1 \
  wandb.run_name=llm-jp-1.3b-v1.0_fp32_dev-jnli \
  model.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0 \
  tokenizer.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0
  • FP16
python scripts/evaluate_llm.py torch_dtype=fp16 \
  target_dataset="[jnli]" \
  metainfo.max_num_samples=-1 \
  wandb.run_name=llm-jp-1.3b-v1.0_fp16_dev-jnli \
  model.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0 \
  tokenizer.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0
  • BF16 - Ubuntu / WSL2
python scripts/evaluate_llm.py torch_dtype=bf16 \
  target_dataset="[jnli]" \
  metainfo.max_num_samples=-1 \
  wandb.run_name=llm-jp-1.3b-v1.0_bf16_dev-jnli \
  model.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0 \
  tokenizer.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0
jaster全データセット先頭100件の評価 / Evaluate the first 100 results for each of all datasets in jaster
  • FP32
python scripts/evaluate_llm.py torch_dtype=fp32 \
  wandb.run_name=llm-jp-1.3b-v1.0_fp32_dev-all \
  model.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0 \
  tokenizer.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0
  • FP16
python scripts/evaluate_llm.py torch_dtype=fp16 \
  wandb.run_name=llm-jp-1.3b-v1.0_fp16_dev-all \
  model.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0 \
  tokenizer.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0
  • BF16 - Ubuntu / WSL2
python scripts/evaluate_llm.py torch_dtype=bf16 \
  wandb.run_name=llm-jp-1.3b-v1.0_bf16_dev-all \
  model.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0 \
  tokenizer.pretrained_model_name_or_path=llm-jp/llm-jp-1.3b-v1.0

Supervised Fine-tuning

Installation

  • llm-jp-eval等のvenv環境に入っている場合はいったん抜ける / If you are in a venv environment such as llm-jp-eval, exit it once.
deactivate
cd ..
  • llm-jp-sftのcloneとvenv環境の作成・有効化 / After cloning llm-jp-sft, create and enable venv.
git clone https://github.com/llm-jp/llm-jp-sft.git
cd llm-jp-sft
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
wandb disabled
  • macOSでは pip uninstall bitsandbytes を行っておく / On macOS, run pip uninstall bitsandbytes

jasterの参照

  • llm-jp-evalのjasterディレクトリへのsymbolic linkを作成しておく / Create a symbolic link to the jaster directory of llm-jp-eval
ln -s ../llm-jp-eval/jaster .

Ichikara-instruction公開データのプロンプト化 / Converting Ichikara-instruction public data to prompt format

import json
import random
import sys

if __name__ == "__main__":
    inst = "以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。"
    records = []
    for f in sys.argv[1:]:
        with open(f, "r", encoding="utf8") as fin:
            for r in json.load(fin):
                records.append({
                    "ID": r["ID"],
                    "text": f'{inst}\n\n### 指示:\n{r["text"]}\n\n### 応答:\n{r["output"]}',
                })
    random.shuffle(records)
    dev_len = len(records) // 10
    dev, train = records[:dev_len], records[dev_len:]
    json.dump(train, sys.stdout, indent=1, ensure_ascii=False)
    json.dump(dev, sys.stderr, indent=1, ensure_ascii=False)
  • 公開データの変換と出力の確認 / Convert published data and check output
python convert_ichikara.py Distribution20231115/*.json \
  > jaster/1.2.0/tuning/train/ichikara.json \
  2> jaster/1.2.0/tuning/dev/ichikara.json
head -n 5 jaster/1.2.0/tuning/dev/ichikara.json

LoRA SFT BF16 - Ubuntu / WSL2

python train.py \
  --num_train_epochs 1 \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 16 \
  --learning_rate 2e-5 \
  --warmup_ratio 0.1 \
  --lr_scheduler_type cosine \
  --bf16 \
  --max_seq_length 2048 \
  --gradient_checkpointing \
  --data_files `ls jaster/1.2.0/tuning/train/*.json` \
  --use_peft \
  --model_name_or_path llm-jp/llm-jp-1.3b-v1.0 \
  --output_dir results/llm-jp-1.3b-lora-jaster_ichikara-v1.0

LoRA SFT 4bit - Ubuntu / WSL2

python train.py \
  --num_train_epochs 1 \
  --per_device_train_batch_size 8 \
  --gradient_accumulation_steps 8 \
  --learning_rate 2e-5 \
  --warmup_ratio 0.1 \
  --lr_scheduler_type cosine \
  --bf16 \
  --load_in_4bit True \
  --max_seq_length 2048 \
  --gradient_checkpointing \
  --data_files `ls jaster/1.2.0/tuning/train/*.json` \
  --use_peft \
  --model_name_or_path llm-jp/llm-jp-1.3b-v1.0 \
  --output_dir results/llm-jp-1.3b-lora-jaster_ichikara-v1.0

Full Parameter SFT

  • 8GPU構成向けのconfigs/accelerate_config_zero3.yamlの作成
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
deepspeed_config:
  zero_stage: 3
  offload_optimizer_device: none
  offload_param_device: cpu
  zero3_init_flag: true
  zero3_save_16bit_model: true
  • 13Bモデルの8GPUフルパラメータSFT
accelerate launch --config_file configs/accelerate_config_zero3.yaml \
  train.py \
  --model_name_or_path llm-jp/llm-jp-13b-v1.0 \
  --tokenizer_name_or_path llm-jp/llm-jp-13b-v1.0 \
  --num_train_epochs 1 \
  --per_device_train_batch_size 1 --gradient_accumulation_steps 32 \
  --learning_rate 1e-4 --warmup_ratio 0.1 --lr_scheduler_type cosine \
  --bf16 \
  --max_seq_length 2048 \
  --gradient_checkpointing \
  --data_files `ls jaster/1.2.0/tuning/train/*.json` \
  --output_dir results/llm-jp-13b-full-jaster_ichikara-v1.0

Direct Preference Optimization

Clone the repository

  • llm-jp-sft等の環境に入っている場合はいったん抜ける / If you are in an environment such as llm-jp-sft, exit it once.
deactivate
cd ..
  • Clone llm-jp-dpo
git clone https://github.com/llm-jp/llm-jp-dpo.git
cd llm-jp-dpo

Ubuntu / WSL2

  • Installing libraries with poetry
poetry install
poetry shell
wandb disabled
  • Creating accelerate_config/single_gpu.yaml
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: 'NO'
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 1
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
  • AccelerateでDPO学習プロセスを起動 / Launch the DPO training process with Accelerate
accelerate launch --config_file accelerate_configs/single_gpu.yaml train.py --model llm-jp/llm-jp-1.3b-v1.0 --per-device-train-batch-size 4 --per-device-eval-batch-size 8

macOS (パフォーマンスに難があるため改良中です / Performance needs improvement)

  • ライブラリのインストールはpoetryではなくpipで行う / Install libraries using pip instead of poetry
python -m venv venv
source venv/bin/activate
pip install torch==2.2.0 transformers==4.37.2 trl==0.7.10 peft==0.8.2 datasets==2.16.1 accelerate==0.26.1 wandb
wandb disabled
  • エディタでtrain.pyを開きmain()の先頭にtorch.dynamoのエラー対策を追加 / Open train.py in the editor and add a workaround to avoid errors in torch.dynamo at the beginning of main()
def main():
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
  • 同様にAutoModelForCausalLM.from_pretrained()のtorch_dtypeをfloat16に変更 / Similarly, change torch_dtype of AutoModelForCausalLM.from_pretrained() to float16
        torch_dtype=torch.float16, # bfloat16,
  • 同様にTrainingArguments()からbf16の指定をコメントアウト / Similarly, comment out the bf16 specification from TrainingArguments()
        # bf16=True,
  • PythonでDPO学習プロセスを起動 / Launch DPO learning process with Python
python train.py --model llm-jp/llm-jp-1.3b-v1.0 --per-device-train-batch-size 4 --per-device-eval-batch-size 8

Pretraining

環境構築 / Environment construction

  • llm-jp-dpo等の環境に入っている場合はいったん抜ける / If you are in an environment such as llm-jp-dpo, exit it once.
deactivate
cd ..
  • CUDA 11.8向けにMegatron-DeepSpeed環境を構築する / Build a Megatron-DeepSpeed ​​environment for CUDA 11.8
echo 'export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-11.8/lib64"' >> ~/.bashrc
source ~/.bashrc
git clone https://github.com/microsoft/Megatron-DeepSpeed
cd Megatron-DeepSpeed
mkdir -p tmp
python3 -m venv venv
source venv/bin/activate
pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118
pip install "pip>=23.1" "setuptools>=65.5.0" wheel pybind11 six regex nltk numpy deepspeed==0.12.2 einops tensorboard transformers sentencepiece "protobuf<3.21.0"

Installing apex

  • Installation - From Source - Linux の手順をpip>=23.1の前提で進める / Execute Installation - From Source - Linux with assuming pip>=23.1
  • エラーにハマりやすいので以下の点に注意して作業を行う / It's easy to get stuck in comiplation errors, so pay attention to the following points.
    • 本来10分ほどかかるはずのビルド処理がすぐに終わる場合は*.soのコンパイルがスキップされている / If the build process, which should normally take about 10 minutes, finishes immediately, the *.so compilation is being skipped.
    • 次のファイルががなければビルド失敗 / Build fails if the following files are missing
      • build/lib.linux-x86_64-cpython-310/apex_C.cpython-310-x86_64-linux-gnu.so
git clone https://github.com/NVIDIA/apex -b 23.08 ; cd apex
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
cd ..

Installing FlashAttention2

  • ninjaのバージョンが 1.11.1 か確認する / Check if ninja version is 1.11.1
ninja --version
  • バージョン上限を指定してflash-attnをインストール / Install flash-attn with specified version limit
pip install "flash-attn<2.4.0" --no-build-isolation

トークナイザの準備 / Preparing the tokenizer

  • llm-jp-tokenizer v2.1 SentencePieceモデルファイルのダウンロード / llm-jp-tokenizer v2.1 SentencePiece model file download
curl -O -L https://github.com/llm-jp/llm-jp-tokenizer/raw/main/models/ver2.1/code10k_en20k_ja30k.ver2.1.model

事前学習データの準備 / Preparation of pre-training data

  • 次の内容でdownload_mc4_ja.pyを作成 / Create download_mc4_ja.py with the following content
import json
import sys
from datasets import load_dataset

if __name__ == "__main__":
    dataset = load_dataset('mc4', 'ja', split='train', streaming=True)
    limit = int(sys.argv[1])
    count = 0
    for doc in dataset:
        json.dump(doc, sys.stdout, ensure_ascii=False)
        print()
        count += 1
        if count == limit:
            break
  • download_mc4_ja.pyを実行してmC4の日本語パートから先頭1万件をmc4-ja-10k.jsonlに保存 / Execute download_mc4_ja.py and save the first 10,000 items from the Japanese part of mC4 to mc4-ja-10k.jsonl
python download_mc4_ja.py 10000 > mc4-ja-10k.jsonl
  • データセットをビルドして作成されたファイルを確認 / Build the dataset and check the created files
python tools/preprocess_data.py \
  --input ./mc4-ja-10k.jsonl \
  --output-prefix dataset/mc4-ja-10k \
  --tokenizer-model ./code10k_en20k_ja30k.ver2.1.model \
  --append-eod \
  --tokenizer-type SentencePieceTokenizer \
  --dataset-impl mmap --workers 8
ls -l dataset/mc4-ja-10k*

事前学習スクリプトの準備 / Preparing the pretraining script

  • サンプルのpretrain_llama2_distributed.shをコピー / Copy the sample pretrain_llama2_distributed.sh
cp examples_deepspeed/pretrain_llama2_distributed.sh .
chmod +x pretrain_llama2_distributed.sh
  • ./pretrain_llama2_distributed.shを編集して次の行を変更 / Edit ./pretrain_llama2_distributed.sh and change the following line
    • <の行を>の行の内容に置き換える / Replace the < line with the contents of the > line
< DATASET_1="./tmp/data/bookcorpus_train_1m_text_sentence"
> DATASET_1="./dataset/mc4-ja-10k_text_document"

< TOKENIZER_PATH=./tmp/tokenizer.model # official llama tokenizer.model
> TOKENIZER_PATH=./code10k_en20k_ja30k.ver2.1.model
> export NCCL_IB_GID_INDEX=3
> export NCCL_IB_TC=106

<        --tokenizer-type GPTSentencePieceTokenizer \
>        --tokenizer-type SentencePieceTokenizer \
  • pretrain_gpt.pyの最後から2行目のデフォルトトークナイザの指定をコメントアウト / Comment out the default tokenizer specification on the second to last line of pretrain_gpt.py
             # args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},

事前学習の実行 / Execute pretraining

./pretrain_llama2_distributed.sh

以上 / That's all.