ModelLink2/examples/gpt3

GPT3 \color{black}{\rm\tiny{【model}} \color{black}{\rm\tiny{contributed}} \color{black}{\rm\tiny{by}} \color{black}{\rm\tiny{Community】}}

English | English

Contents

• GPT3
• Contents
• GPT3-175B
  • Training-175B
    • Script
    • Perforfance
      • Machine performance

GPT3-175B

Training

Here is a hardware summary of pre-trianing GPT3-175B:

Hardware	Value
NPU	128 x Ascend NPUs

Script

1. Clone repository to your local server:

   git clone https://gitee.com/ascend/ModelLink.git
   git clone https://github.com/NVIDIA/Megatron-LM.git
   cd Megatron-LM
   git checkout core_r0.6.0
   cp -r megatron ../ModelLink/
   cd .. 
   cd ModelLink
   mkdir logs
   mkdir vocab_file
   mkdir dataset
   

2. Build environment

   # python3.8
   conda create -n test python=3.8
   conda activate test
   
   # install torch and torch_npu
   pip install torch-2.1.0-cp38-cp38m-manylinux2014_aarch64.whl
   pip install torch_npu-2.1.0*-cp38-cp38m-linux_aarch64.whl
   pip install apex-0.1_ascend*-cp38-cp38m-linux_aarch64.whl
   
   # modify ascend-toolkit path
   source /usr/local/Ascend/ascend-toolkit/set_env.sh 
   
   # install MindSpeed
   git clone https://gitee.com/ascend/MindSpeed.git
   cd MindSpeed
   git checkout 2b0edd2
   pip install -r requirements.txt 
   pip3 install -e .
   cd ..
   
   # install other packages
   pip install -r requirements.txt 
   

3. Prepare dataset and vocab file for pretrain 3.1 Prepare dataset

   Download the GPT raw dataset from here

   # download enwiki raw data
   # There are 41 files in total, we can just select part to make our datasets.
   cd ./dataset
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00000-of-00041.parquet
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00001-of-00041.parquet
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00002-of-00041.parquet
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00003-of-00041.parquet
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00004-of-00041.parquet
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00005-of-00041.parquet
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00006-of-00041.parquet
   wget https://huggingface.co/datasets/wikipedia/blob/main/data/20220301.en/train-00007-of-00041.parquet
   cd ..
   
   # download vocab file and merge table
   cd vocab_file
   wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
   wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt
   cd ..
   
   # process formal dataset
   python ./tools/preprocess_data.py \
       --input ./dataset/ \
       --output-prefix ./dataset/gpt_text_sentence \
       --tokenizer-type GPT2BPETokenizer \
       --vocab-file ./vocab_file/gpt2-vocab.json \
       --merge-file ./vocab_file/gpt2-merges.txt \
       --append-eod \
       --workers 4 \
       --log-interval 1000
   

   3.2 pre-training in ptd mode Config GPT3-175B PTD pre-training script: examples/gpt3/pretrain_gpt3_175B.sh

   # modify ascend-toolkit path according to your own config
   source /usr/local/Ascend/ascend-toolkit/set_env.sh 
   
   # modify config according to your own actual situation
   VOCAB_FILE="./vocab_file/gpt2-vocab.json"   # vocab file for training
   MERGE_FILE="./vocab_file/gpt2-merges.txt"   # BPE merge file for training
   DATA_PATH="./dataset/gpt_text_sentence"  # dataset path
   

   Launch GPT3-175B PTD pre-training script: examples/gpt3/pretrain_gpt3_175B.sh

   bash examples/gpt3/pretrain_gpt3_175B.sh
   

Performance

Machine performance

The performance of GPT3-175B in Ascend NPU：

device	model	tokens capacity (tokens/s/p)
NPUs	GPT3-175B	153.1