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add readme (#10)

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* remove submodule

* add mPLUG MiniGPT4

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---------

Co-authored-by: Yuliang Liu <34134635+Yuliang-Liu@users.noreply.github.com>
This commit is contained in:
lz
2023-06-01 09:57:03 +08:00
committed by GitHub
parent 64f7eb334d
commit 3213a65d96
275 changed files with 16059 additions and 6 deletions
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72
models/mPLUG_Owl/scripts/train_it.sh Normal file
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#!/bin/bash
DIR=`pwd`
DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'`
if [ $MASTER_ADDR ];then
echo $MASTER_ADDR
echo $MASTER_PORT
echo $WORLD_SIZE
echo $RANK
else
MASTER_ADDR=127.0.0.1
MASTER_PORT=2$(($RANDOM % 10))$(($RANDOM % 10))15
WORLD_SIZE=1
RANK=0
fi
DISTRIBUTED_ARGS="--nproc_per_node 8 \
--nnodes ${WORLD_SIZE} \
--node_rank ${RANK} \
--master_addr ${MASTER_ADDR} \
--master_port ${MASTER_PORT}"
EXP_NAME=sft_v0.1
SAVE_NAME=sft_v0.1_ft_grad_ckpt
SAVE_PATH="./output/${SAVE_NAME}/"
max_length=2048
micro_batch_size=4
global_batch_size=256
gradient_accumulation_steps=1
# train_iters = total_data * train_epochs // global_batch_size
# 361481 * 3 / 256 = 4236
train_epochs=3
train_iters=4236
lr_warmup_iters=50
eval_iter=50
eval_interval=50
save_interval=500
mkdir -p ${SAVE_PATH}
options=" \
--pretrained-ckpt MAGAer13/mplug-owl-llama-7b-pt \
--seq-length ${max_length} \
--micro-batch-size ${micro_batch_size} \
--num-training-steps ${train_iters} \
--train-epochs ${train_epochs} \
--num-warmup-steps ${lr_warmup_iters} \
--gradient-accumulation-steps ${gradient_accumulation_steps} \
--lr 2e-5 \
--min-lr 1e-6 \
--eval-iters ${eval_iter} \
--save-interval ${save_interval} \
--save-path ${SAVE_PATH} \
--clip-grad 1.0 \
--weight-decay 0.0001 \
--adam-beta1 0.9 \
--adam-beta2 0.999 \
--num-workers 32 \
--use-lora \
--gradient-checkpointing \
--bf16"
multimodal_options=" \
--mm-config configs/v0.yaml
"
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pipeline/train.py $@ ${options} ${multimodal_options} 2>&1 | tee ${SAVE_PATH}/train.log

72
models/mPLUG_Owl/scripts/train_it_wo_lora.sh Normal file
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#!/bin/bash
DIR=`pwd`
DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'`
if [ $MASTER_ADDR ];then
echo $MASTER_ADDR
echo $MASTER_PORT
echo $WORLD_SIZE
echo $RANK
else
MASTER_ADDR=127.0.0.1
MASTER_PORT=2$(($RANDOM % 10))$(($RANDOM % 10))15
WORLD_SIZE=1
RANK=0
fi
DISTRIBUTED_ARGS="--nproc_per_node 8 \
--nnodes ${WORLD_SIZE} \
--node_rank ${RANK} \
--master_addr ${MASTER_ADDR} \
--master_port ${MASTER_PORT}"
EXP_NAME=sft_v0.1
SAVE_NAME=sft_v0.1_ft_grad_ckpt
SAVE_PATH="./output/${SAVE_NAME}/"
max_length=2048
micro_batch_size=1
global_batch_size=256
gradient_accumulation_steps=4
# train_iters = total_data * train_epochs // global_batch_size
# 361481 * 3 / 256 = 4236
train_epochs=3
train_iters=4236
lr_warmup_iters=50
lr_decay_iters=`expr $train_iters - $lr_warmup_iters`
eval_iter=50
eval_interval=50
save_interval=500
mkdir -p ${SAVE_PATH}
options=" \
--pretrained-ckpt MAGAer13/mplug-owl-llama-7b-pt \
--seq-length ${max_length} \
--micro-batch-size ${micro_batch_size} \
--train-epochs ${train_epochs} \
--num-warmup-steps ${lr_warmup_iters} \
--num-training-steps ${train_iters} \
--gradient-accumulation-steps ${gradient_accumulation_steps} \
--lr 1e-5 \
--min-lr 1e-6 \
--eval-iters ${eval_iter} \
--save-interval ${save_interval} \
--save-path ${SAVE_PATH} \
--clip-grad 1.0 \
--weight-decay 0.0001 \
--adam-beta1 0.9 \
--adam-beta2 0.999 \
--num-workers 32 \
--gradient-checkpointing \
--bf16"
multimodal_options=" \
--mm-config configs/v0.yaml
"
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pipeline/train.py $@ ${options} ${multimodal_options} 2>&1 | tee ${SAVE_PATH}/train.log