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guhangsong d5a1d0dd13 !1329 ModelLink配套升级到megatron core 0.6.0 
Merge pull request !1329 from guhangsong/upversion
2024-06-11 07:53:57 +00:00
..
evaluate_baichuan_7B_ptd.sh !1169 整理 tasks 文件目录，对外提供 evaluation和 inference.py 2024-03-27 07:55:22 +00:00
evaluate_baichuan_13B_ptd.sh !1169 整理 tasks 文件目录，对外提供 evaluation和 inference.py 2024-03-27 07:55:22 +00:00
generate_baichuan_7b_ptd.sh !1169 整理 tasks 文件目录，对外提供 evaluation和 inference.py 2024-03-27 07:55:22 +00:00
generate_baichuan_13b_ptd.sh !1169 整理 tasks 文件目录，对外提供 evaluation和 inference.py 2024-03-27 07:55:22 +00:00
pretrain_baichuan_ptd_7B.sh !1329 ModelLink配套升级到megatron core 0.6.0 2024-06-11 07:53:57 +00:00
pretrain_baichuan_ptd_13B.sh !1329 ModelLink配套升级到megatron core 0.6.0 2024-06-11 07:53:57 +00:00
README_en.md !1329 ModelLink配套升级到megatron core 0.6.0 2024-06-11 07:53:57 +00:00
README.md !1329 ModelLink配套升级到megatron core 0.6.0 2024-06-11 07:53:57 +00:00

README_en.md

BaiChuan

简体中文 | English

Contents

Baichuan-7B

Training

Here's a hardware summary of pre-training Baichuan-7B:

Hardware Value
NPU 8 x Ascend NPUs

Script

  1. Clone the 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 model_from_hf
mkdir dataset
mkdir ckpt

  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-linux_aarch64.whl
pip install torch_npu-2.1.0.XXX-cp38-cp38m-linux_aarch64.whl
pip install apex-0.1_ascend*-cp38-cp38m-linux_aarch64.whl

# modify the path according to your own  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 pretrained weights Download the Baichuan-7B checkpoint from here

    mkdir ./model_from_hf/Baichuan-7B/
cd ./model_from_hf/Baichuan-7B/
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/config.json
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/configuration_baichuan.py
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/generation_config.json
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/handler.py
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/modeling_baichuan.py
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/pytorch_model.bin
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/special_tokens_map.json
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/tokenization_baichuan.py
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/tokenizer.model
wget https://huggingface.co/baichuan-inc/Baichuan-7B/resolve/main/tokenizer_config.json
cd ../../

  4. Weights convert

    In order to adapt to the Baichuan-7B model, the following script is used to convert the model pre-training weights. (This scenario is generally used to train open-source HuggingFace models on Megatron)

    # modify the ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh

python tools/checkpoint/convert_ckpt.py \
    --model-type GPT \
    --loader llama2_hf \
    --saver megatron \
    --target-tensor-parallel-size 8 \
    --target-pipeline-parallel-size 1 \
    --load-dir ./model_from_hf/Baichuan-7B/ \
    --save-dir ./model_weights/Baichuan-7B-v0.1-tp8-pp1/ \
    --tokenizer-model ./model_from_hf/Baichuan-7B/tokenizer.model \
    --w-pack True

    Any Megatron weights with parallel slicing strategy --> Any Megatron weights with parallel slicing strategy (This scenario is generally used to convert the trained megatron model back to the HuggingFace format)

    # Modify the ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh
python tools/checkpoint/convert_ckpt.py --model-type GPT \
    --loader megatron \
    --saver megatron \
    --save-model-type save_huggingface_llama \
    --load-dir ./model_weights/Baichuan-7B-v0.1-tp8-pp1/ \
    --target-tensor-parallel-size 1 \
    --target-pipeline-parallel-size 1 \
    --w-pack True \
    --save-dir ./model_from_hf/Baichuan-7B/   # <-- Fill in the original HF model path here, new weights will be saved in ./model_from_hf/Baichuan-7B/mg2hg/

  5. Prepare dataset

    Download the Baichuan-7B datasets from here

    # download datasets
cd ./dataset
wget https://huggingface.co/datasets/tatsu-lab/alpaca/resolve/main/data/train-00000-of-00001-a09b74b3ef9c3b56.parquet
cd ..

# process datasets          
mkdir ./dataset/Baichuan-7B/
python ./tools/preprocess_data.py \
    --input ./dataset/train-00000-of-00001-a09b74b3ef9c3b56.parquet \
    --tokenizer-name-or-path ./model_from_hf/Baichuan-7B/ \
    --output-prefix ./dataset/Baichuan-7B/alpaca \
    --workers 4 \
    --log-interval 1000 \
    --tokenizer-type PretrainedFromHF

  6. Config Baichuan-7B pre-training script : examples/baichuan/pretrain_baichuan_ptd_7B.sh

    # modify the script according to your own  ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh 

CKPT_SAVE_DIR="./ckpt/Baichuan-7B/"
DATA_PATH="./dataset/Baichuan-7B/alpaca_text_document"
TOKENIZER_MODEL="./model_from_hf/Baichuan-7B/tokenizer.model"
CKPT_LOAD_DIR="./model_weights/Baichuan-7B-v0.1-tp8-pp1/"

  7. Launch Baichuan-7B pre-training script: examples/baichuan/pretrain_baichuan_ptd_7B.sh

    bash examples/baichuan/pretrain_baichuan_ptd_7B.sh

    Note: If using multi machine training, and no data sharing configuration on the mechines, it's necessary to add the parameter --no-shared-storage. This parameter will determine whether non master nodes need to load data based on distributed parameters, and check the corresponding cache and generated data.

Performance

Machine performance

The performance of Baichuan-7B in Ascend NPU and Reference:

Device Model total Iterations throughput rate (samples/s) throughput rate (tokens/s/p) single-step time (s/step)
NPUs Baichuan-7B 1000 5.24 2685 6.1
Reference Baichuan-7B - - 2036 -

Inference

Config Baichuan-7B inference script: examples/baichuan/generate_baichuan_7b_ptd.sh

# modify the script according to your own ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh 
 
# modify script model path and tokenizer path
CHECKPOINT="./model_weights/Baichuan-7B-v0.1-tp8-pp1/"
TOKENIZER_PATH="./model_from_hf/Baichuan-7B/"

Launch Baichuan-7B inference script: examples/baichuan/generate_baichuan_7b_ptd.sh

bash examples/baichuan/generate_baichuan_7b_ptd.sh

Some inference samples are as follows:

Inference

Evaluation

We use the boolq benchmark to evaluate our model. Benchmark Download.

# config origin weight and vocab file path
CHECKPOINT=<origin-ckpt-path>
TOKENIZER_PATH=<tokenizer-path>
# config tasks and dataset path
DATA_PATH="./boolq/"
TASK="boolq"

bash ./examples/baichuan/evaluate_baichuan_7B_ptd.sh
Task Subset Model NPU OpenSource
Boolq test Baichuan 7B 0.69 0.67

Baichuan-13B

Training

Here's a hardware summary of pre-training Baichuan-13B:

Hardware Value
NPU 8 x Ascend NPUs

Script

  1. Clone the 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 model_from_hf
mkdir dataset
mkdir ckpt

  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-linux_aarch64.whl
pip install torch_npu-2.1.0.XXX-cp38-cp38m-linux_aarch64.whl
pip install apex-0.1_ascend*-cp38-cp38m-linux_aarch64.whl

# modify the path according to your own  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

    Note: If the error message "'AttributeError: 'BaichuanTokenizer' object has no attribute'sp_model'" is displayed during the script execution, run the following command to rectify the error:

    pip install transformers==4.32.0 --force

  3. Prepare pretrained weights

    Download the Baichuan-13B checkpoint from here

    mkdir ./model_from_hf/Baichuan-13B/
cd ./model_from_hf/Baichuan-13B/
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/config.json
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/configuration_baichuan.py
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/generation_config.json
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/modeling_baichuan.py
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/pytorch_model-00001-of-00003.bin
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/pytorch_model-00002-of-00003.bin
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/pytorch_model-00003-of-00003.bin
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/pytorch_model.bin.index.json
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/quantizer.py
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/special_tokens_map.json
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/tokenization_baichuan.py
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/tokenizer_config.json
wget https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/tokenizer.model
cd ../../

  4. Weights convert

    In order to adapt to the baichuan-13B model, the following script is used to convert the model pre-training weights.

    (This scenario is generally used to train open-source HuggingFace models on Megatron)

    mkdir baichuan-13B-mt

# modify the ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh

python tools/checkpoint/convert_ckpt.py \
    --model-type GPT \
    --loader llama2_hf \
    --saver megatron \
    --target-tensor-parallel-size 8 \
    --load-dir ./model_from_hf/Baichuan-13B/ \
    --save-dir ./model_weights/Baichuan-13B-Base-v0.1-tp8-pp1/ \
    --tokenizer-model ./model_from_hf/Baichuan-13B/tokenizer.model \
    --params-dtype bf16 \
    --w-pack True

    Any Megatron weights with parallel slicing strategy --> Any Megatron weights with parallel slicing strategy (This scenario is generally used to convert the trained megatron model back to the HuggingFace format)

    # Modify the ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh
python tools/checkpoint/convert_ckpt.py --model-type GPT \
    --loader megatron \
    --saver megatron \
    --save-model-type save_huggingface_llama \
    --load-dir ./model_weights/Baichuan-13B-Base-v0.1-tp8-pp1/ \
    --target-tensor-parallel-size 1 \
    --target-pipeline-parallel-size 1 \
    --w-pack True \
    --save-dir ./model_from_hf/Baichuan-13B/   # <-- Fill in the original HF model path here, new weights will be saved in ./model_from_hf/Baichuan-13B/mg2hg/

  5. Prepare dataset Download the Baichuan-13B datasets from here

    cd ./dataset/
wget https://huggingface.co/datasets/tatsu-lab/alpaca/resolve/main/data/train-00000-of-00001-a09b74b3ef9c3b56.parquet
cd ..

mkdir ./dataset/Baichuan-13B/
python ./tools/preprocess_data.py \
    --input ./dataset/train-00000-of-00001-a09b74b3ef9c3b56.parquet \
    --tokenizer-name-or-path ./model_from_hf/Baichuan-13B/ \
    --output-prefix ./dataset/Baichuan-13B/alpaca \
    --workers 4 \
    --log-interval 1000 \
    --tokenizer-type PretrainedFromHF

  6. Config Baichuan-13B pre-training script(Baichuan-13B does not support Flash Attention): examples/baichuan/pretrain_baichuan_ptd_13B.sh

    # modify the script according to your own  ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh 

CKPT_SAVE_DIR="./ckpt/Baichuan-13B/"
DATA_PATH="./dataset/Baichuan-13B/alpaca_text_document"
TOKENIZER_MODEL="./model_from_hf/Baichuan-13B/tokenizer.model"
CKPT_LOAD_DIR="./model_weights/Baichuan-13B-Base-v0.1-tp8-pp1/"

  7. Launch Baichuan-13B pre-training script: examples/baichuan/pretrain_baichuan_ptd_13B.sh

    bash examples/baichuan/pretrain_baichuan_ptd_13B.sh

    Note: If using multi machine training, and no data sharing configuration on the mechines, it's necessary to add the parameter --no-shared-storage. This parameter will determine whether non master nodes need to load data based on distributed parameters, and check the corresponding cache and generated data.

Performance

Machine performance

The performance of the Baichuan-13B in Ascend NPU and Reference:

Device Model total Iterations throughput rate (samples/s) throughput rate (tokens/s/p) single-step time (s/step)
NPUs Baichuan-13B 1000 2.37 1213 13.5
Reference Baichuan-13B - - 862 -

Inference

Config baichuan-13B inference script: examples/baichuan/generate_baichuan_13b_ptd.sh

# modify the script according to your own ascend-toolkit path
source /usr/local/Ascend/ascend-toolkit/set_env.sh 
 
# modify script model path and tokenizer path
CHECKPOINT="./model_weights/Baichuan-13B-Base-v0.1-tp8-pp1/"
TOKENIZER_PATH="./model_from_hf/Baichuan-13B/"

Launch baichuan-13B inference script: examples/baichuan/generate_baichuan_13b_ptd.sh

bash examples/baichuan/generate_baichuan_13b_ptd.sh

Some inference samples are as follows: Inference

Evaluation

We use the boolq benchmark to evaluate our model. Benchmark Download.

# config origin weight and vocab file path
CHECKPOINT=<origin-ckpt-path>
TOKENIZER_PATH=<tokenizer-path>
# config tasks and dataset path
DATA_PATH="./boolq/"
TASK="boolq"

bash ./examples/baichuan/evaluate_baichuan_13B_ptd.sh
Task Subset Model NPU OpenSource
Boolq test Baichuan 13B 0.747 0.736