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Table of Contents

安装

推荐使用英伟达提供的官方镜像 nvcr.io/nvidia/pytorch:23.12-py3 来创建容器

git clone --recurse-submodules https://github.com/alibaba/Pai-Megatron-Patch.git
cd Pai-Megatron-Patch
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/

数据集和模型下载

cd /mnt
mkdir llama2-ckpts
cd llama2-ckpts
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/llama2-ckpts/Llama-2-13b-hf.tgz
tar -zxf Llama-2-13b-hf.tgz

mkdir llama2-datasets
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/llama2-datasets/wudao_llamabpe_text_document.bin
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/llama2-datasets/wudao_llamabpe_text_document.idx

wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/llama2-datasets/alpaca_zh-llama2-train.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/llama2-datasets/alpaca_zh-llama2-valid.json

Megatron-LM-Dense模型训练流程

运行hf2megatron_convertor.sh脚本，需要传入的参数列表如下

MEGATRON_PATH=$1                   # Megatron-LM的路径
SOURCE_CKPT_PATH=$2                # 原始CKPT的路径
TARGET_CKPT_PATH=$3                # 目标CKPT的路径
TP=$4                              # 模型并行度
PP=$5                              # 流水并行度
MN=$6                              # llama-7b, llama-13b, llama-30b, llama-65b, llama2-7b, llama2-13b, llama2-70b
EXTRA_VOCAB_SIZE=$7                # 词表扩充大小
mg2hf=$8                           # 是否执行mg2hf转换

运行run_pretrain_megatron_llama.sh脚本，需要传入的参数列表如下

ENV=$1                          # 运行环境: dlc, dsw
MEGATRON_PATCH_PATH=$2          # 设置Megatron Patch的代码路径
MODEL_SIZE=$3                   # 模型结构参数量级：7B, 13B
BATCH_SIZE=$4                   # 每卡训练一次迭代样本数: 4, 8
GLOBAL_BATCH_SIZE=$5            # 全局batch size
LR=$6                           # 学习率: 1e-5, 5e-5
MIN_LR=$7                       # 最小学习率: 1e-6, 5e-6
SEQ_LEN=$8                      # 序列长度
PAD_LEN=$9                      # Padding长度：100
EXTRA_VOCAB_SIZE=${10}          # 词表扩充大小
PR=${11}                        # 训练精度: fp16, bf16
TP=${12}                        # 模型并行度
PP=${13}                        # 流水并行度
AC=${14}                        # 激活检查点模式: sel, full
DO=${15}                        # 是否使用Megatron版Zero-1降显存优化器: true, false
FL=${16}                        # 是否使用Flash Attention: true, false
SP=${17}                        # 是否使用序列并行: true, false
TE=${18}                        # 是否使用Transformer Engine: true, false
SAVE_INTERVAL=${19}             # 保存ckpt的间隔
DATASET_PATH=${20}              # 训练数据集路径
PRETRAIN_CHECKPOINT_PATH=${21}  # 预训练模型路径
TRAIN_TOKENS=${22}              # 训练token数
WARMUP_TOKENS=${23}             # 预热token数
OUTPUT_BASEPATH=${24}           # 训练输出文件路径

运行run_finetune_megatron_llama_withGA.sh脚本，需要传入的参数列表如下

ENV=$1                          # 运行环境: dlc, dsw
MEGATRON_PATCH_PATH=$2          # 设置Megatron Patch的代码路径
MODEL_SIZE=$3                   # 模型结构参数量级：7B, 13B
BATCH_SIZE=$4                   # 每卡训练一次迭代样本数: 4, 8
GLOBAL_BATCH_SIZE=$5            # 全局batch size
LR=$6                           # 学习率: 1e-5, 5e-5
MIN_LR=$7                       # 最小学习率: 1e-6, 5e-6
SEQ_LEN=$8                      # 序列长度
PAD_LEN=$9                      # Padding长度：100
EXTRA_VOCAB_SIZE=${10}          # 词表扩充大小
PR=${11}                        # 训练精度: fp16, bf16
TP=${12}                        # 模型并行度
PP=${13}                        # 流水并行度
AC=${14}                        # 激活检查点模式: sel, full
DO=${15}                        # 是否使用Megatron版Zero-1降显存优化器: true, false
FL=${16}                        # 是否使用Flash Attention: true, false
SP=${17}                        # 是否使用序列并行: true, false
TE=${18}                        # 是否使用Transformer Engine: true, false
SAVE_INTERVAL=${19}             # 保存ckpt的间隔
DATASET_PATH=${20}              # 训练数据集路径
VALID_DATASET_PATH=${21}        # 验证数据集路径
PRETRAIN_CHECKPOINT_PATH=${22}  # 预训练模型路径
TRAIN_ITERS=${23}               # 训练steps数
WARMUP_ITERS=${24}              # 预热steps数
OUTPUT_BASEPATH=${25}           # 训练输出文件路径

Megatron-LM-Dense模型格式转换

cd /workspace/Pai-Megatron-Patch/toolkits/model_checkpoints_convertor/llama
sh hf2megatron_convertor.sh \
../../../     \
/mnt/llama2-ckpts/Llama-2-13b-hf     \
/mnt/llama2-ckpts/Llama-2-13b-hf-to-megatron-tp8-pp1  \
8  \
1  \
llama2-13b \
0 \
false

Megatron-LM-Dense继续预训练

cd /workspace/Pai-Megatron-Patch/examples/llama2
sh run_pretrain_megatron_llama.sh  \
dsw  \
../../ \
13B   \
1    \
8 \
1e-5   \
1e-6   \
128  \
128  \
0   \
bf16  \
8   \
1  \
sel  \
true   \
false  \
false   \
false   \
100000  \
/mnt/llama2-datasets/wudao_llamabpe_text_document   \
/mnt/llama2-ckpts/Llama-2-13b-hf-to-megatron-tp8-pp1   \
100000000   \
10000   \
/mnt/output_megatron_llama2

Megatron-LM-Dense指令微调

cd /workspace/Pai-Megatron-Patch/examples/llama2
sh run_finetune_megatron_llama_withGA.sh  \
dsw  \
../../ \
13B     \
1      \
32     \
1e-5   \
1e-6   \
128   \
128     \
0      \
bf16   \
8      \
1      \
sel    \
true   \
false  \
false  \
false \
100 \
/mnt/llama2-datasets/alpaca_zh-llama2-train.json   \
/mnt/llama2-datasets/alpaca_zh-llama2-valid.json   \
/mnt/llama2-ckpts/Llama-2-13b-hf-to-megatron-tp8-pp1   \
1000 \
10 \
/mnt/output_megatron_llama2/

Megatron-Core-Dense模型训练流程

运行hf2mcore_convertor.sh脚本，需要传入的参数列表如下

MODEL_SIZE=$1                  # 模型参数：7B/13B/70B
HG_CKPT_PATH=$2                # HF的CKPT的路径
MEGATRON_PATH=$3               # Megatron-LM的根目录
SOURCE_CKPT_PATH=$4            # 源路径
TARGET_CKPT_PATH=$5            # 目标路径
TP=$6                          # 模型并行度
PP=$7                          # 流水并行度
EXTRA_VOCAB_SIZE=$8            # 额外扩充词表大小
NUM_EXPERTS=$9                 # 专家数量
EXPERTS_TOPK=${10}             # 专家路由Topk
EP=${11}                       # 专家并行度
mg2hf=${12}                    # 是否执行mcore2hf转换

运行run_pretrain_mcore_llama.sh脚本，需要传入的参数列表如下

ENV=$1                          # 运行环境: dlc, dsw
MEGATRON_PATCH_PATH=$2          # 设置Megatron Patch的代码路径
MODEL_SIZE=$3                   # 模型结构参数量级：7B, 13B
BATCH_SIZE=$4                   # 每卡训练一次迭代样本数: 4, 8
GLOBAL_BATCH_SIZE=$5            # 全局batch size
LR=$6                           # 学习率: 1e-5, 5e-5
MIN_LR=$7                       # 最小学习率: 1e-6, 5e-6
SEQ_LEN=$8                      # 序列长度
PAD_LEN=$9                      # Padding长度：100
EXTRA_VOCAB_SIZE=${10}          # 词表扩充大小
PR=${11}                        # 训练精度: fp16, bf16
TP=${12}                        # 模型并行度
PP=${13}                        # 流水并行度
AC=${14}                        # 激活检查点模式: sel, full
DO=${15}                        # 是否使用Megatron版Zero-1降显存优化器: true, false
FL=${16}                        # 是否使用Flash Attention: true, false
SP=${17}                        # 是否使用序列并行: true, false
TE=${18}                        # 是否使用Transformer Engine: true, false
MOE=${19}                       # 是否打开MOE: true, false
SAVE_INTERVAL=${20}             # 保存ckpt的间隔
DATASET_PATH=${21}              # 训练数据集路径
PRETRAIN_CHECKPOINT_PATH=${22}  # 预训练模型路径
TRAIN_TOKENS=${23}              # 训练token数
WARMUP_TOKENS=${24}             # 预热token数
OUTPUT_BASEPATH=${25}           # 训练输出文件路径

运行run_finetune_mcore_llama_withGA.sh脚本，需要传入的参数列表如下

ENV=$1                          # 运行环境: dlc, dsw
MEGATRON_PATCH_PATH=$2          # 设置Megatron Patch的代码路径
MODEL_SIZE=$3                   # 模型结构参数量级：7B, 13B
BATCH_SIZE=$4                   # 每卡训练一次迭代样本数: 4, 8
GLOBAL_BATCH_SIZE=$5            # 全局batch size
LR=$6                           # 学习率: 1e-5, 5e-5
MIN_LR=$7                       # 最小学习率: 1e-6, 5e-6
SEQ_LEN=$8                      # 序列长度
PAD_LEN=$9                      # Padding长度：100
EXTRA_VOCAB_SIZE=${10}          # 词表扩充大小
PR=${11}                        # 训练精度: fp16, bf16
TP=${12}                        # 模型并行度
PP=${13}                        # 流水并行度
AC=${14}                        # 激活检查点模式: sel, full
DO=${15}                        # 是否使用Megatron版Zero-1降显存优化器: true, false
FL=${16}                        # 是否使用Flash Attention: true, false
SP=${17}                        # 是否使用序列并行: true, false
TE=${18}                        # 是否使用Transformer Engine: true, false
MOE=${19}                       # 是否打开MOE: true, false
SAVE_INTERVAL=${20}             # 保存ckpt的间隔
DATASET_PATH=${21}              # 训练数据集路径
VALID_DATASET_PATH=${22}        # 验证数据集路径
PRETRAIN_CHECKPOINT_PATH=${23}  # 预训练模型路径
TRAIN_ITERS=${24}               # 训练step数
WARMUP_ITERS=${25}              # 预热step数
OUTPUT_BASEPATH=${26}           # 训练输出文件路径

Megatron-Core-Dense模型格式转换

cd /workspace/Pai-Megatron-Patch/toolkits/model_checkpoints_convertor/llama \
sh hf2mcore_convertor.sh \
7B \
/mnt/llama2-ckpts/Llama-2-13b-hf \
../../../     \
/mnt/llama2-ckpts/Llama-2-13b-hf \
/mnt/llama2-ckpts/Llama-2-13b-hf-to-mcore-tp8-pp1  \
8  \
1  \
0  \
0  \
0  \
0 \
false

Megatron-Core-Dense继续预训练

cd /workspace/Pai-Megatron-Patch/examples/llama2
sh run_pretrain_mcore_llama.sh  \
dsw  \
../../ \
13B   \
1    \
8 \
1e-5   \
1e-6   \
128  \
128  \
0   \
bf16  \
8   \
1  \
sel  \
true   \
false  \
false   \
false   \
false \
100000  \
/mnt/llama2-datasets/wudao_llamabpe_text_document   \
/mnt/llama2-ckpts/Llama-2-13b-hf-to-mcore-tp8-pp1   \
100000000   \
10000   \
/mnt/output_mcore_llama2

Megatron-Core-Dense指令微调

cd /workspace/Pai-Megatron-Patch/examples/llama2
sh run_finetune_mcore_llama_withGA.sh  \
dsw  \
../../ \
13B   \
1    \
8 \
1e-5   \
1e-6   \
128  \
128  \
0   \
bf16  \
8   \
1  \
sel  \
true   \
false  \
false   \
false   \
false \
100000  \
/mnt/llama2-datasets/alpaca_zh-llama2-train.json   \
/mnt/llama2-datasets/alpaca_zh-llama2-valid.json   \
/mnt/llama2-ckpts/Llama-2-13b-hf-to-mcore-tp8-pp1   \
100000000   \
10000   \
/mnt/output_mcore_llama2

下游任务评估

Megatron-LM-Dense模型转成Huggingface格式

cd /workspace/Pai-Megatron-Patch/toolkits/model_checkpoints_convertor/llama
sh hf2megatron_convertor.sh \
../../../     \
/mnt/llama2-ckpts/Llama-2-13b-hf-to-mcore-tp8-pp1/release  \
/mnt/llama2-ckpts/Llama-2-13b-hf-megatron-to-hf    \
8  \
1  \
llama2-13b \
0 \
true

运行评估工具

cd /workspace/Pai-Megatron-Patch/LM-Evaluation-Harness-240310
accelerate launch --main_process_port 29051 -m lm_eval \
--model hf \
--model_args pretrained=/mnt/llama2-ckpts/Llama-2-13b-hf-megatron-to-hf,trust_remote_code=True \
--tasks mmlu,ceval-valid  \
--batch_size 16