File size: 3,479 Bytes
f0d6538 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | #!/bin/bash
set -ex
CONDA_PYTHON=/mlx/users/jiashuo.fan/miniconda3/envs/abbie/bin/python
CONDA_TORCHRUN=/mlx/users/jiashuo.fan/miniconda3/envs/abbie/bin/torchrun
export DISABLE_ZB=1
export USE_DETERMINISTIC_BACKWARD=0
export TORCH_NCCL_ENABLE_MONITORING=0
export WANDB_MODE=disabled
export WANDB_DISABLED=true
export UCX_ERROR_SIGNALS=""
export NCCL_IB_DISABLE=0
export UCX_TLS=rc,sm,self
# pip3 install byted-thoth-arpeggio==0.2.0c6
# git clone -b feat/qwen3-vl-lbh --single-branch git@code.byted.org:Thoth/Abbie.git
# unzip /mlx/users/jiashuo.fan/playground/Abbie-feat_qwen3-vl-lbh-sft.zip -d /opt/tiger/Abbie
cd /opt/tiger/Abbie
# conda activate
DATA_PATHS='[
["/mnt/hdfs/byte_tt_data_cu_vagcp/haogeng.liu/new_policy7w_v2_reformat","100%"]
]'
DATA_PATHS=$(echo "$DATA_PATHS" | tr -d ' \n\t')
# hdfs dfs -get hdfs://harunava/home/byte_tt_data_cu_vagcp/libohan.1024/models/TiViLa-Qwen3-VL-8B-Instruct-Compliance /home/tiger/.cache/
MODEL_PATH=/home/tiger/.cache/TiViLa-Qwen3-VL-8B-Instruct-Compliance
PROJNAME=qwen3_vl_cpt
EXPNAME=new_policy7w_v2_reformat
OUTDIR=/mnt/bn/bohanzhainas1/jiashuo/exp/new_policy7w_v2_reformat
# ls /mnt/bn/bohanzhainas1/jiashuo/viz
MASTER_PORT=$($CONDA_PYTHON - <<'EOF'
import socket
s = socket.socket()
s.bind(('', 0))
print(s.getsockname()[1])
s.close()
EOF
)
echo "Using port: $MASTER_PORT"
LOG_DIR=/mlx/users/jiashuo.fan/playground/.claude/run_$(date +%Y%m%d_%H%M%S)
mkdir -p "$LOG_DIR"
echo "Logs: $LOG_DIR"
$CONDA_TORCHRUN \
--nproc_per_node=8 \
--master_port=$MASTER_PORT \
--log-dir="$LOG_DIR" \
tivila_trainer.py \
model.pretrained_path=${MODEL_PATH} \
optim.lr="1e-5" \
optim.visual_lr="1e-5" \
optim.lr_warmup_steps_ratio=0.03 \
trainer.output_path=${OUTDIR} \
trainer.project_name=${PROJNAME} \
trainer.experiment_name=${EXPNAME} \
trainer.log_interval=10 \
trainer.checkpoint_interval=850 \
trainer.checkpoint_hf_model=true \
data.patterns=${DATA_PATHS} \
data.transform_extra_kwargs.image_max_pixels=16777216 \
data.transform_extra_kwargs.video_max_pixels=307200 \
data.transform_extra_kwargs.video_max_frames=32 \
data.max_seq_len=8192 \
data.is_continuous_batch=true \
data.num_training_steps=99999999 \
data.chunks_per_step=4 \
model.recompute_attn=true \
model.activation_offloading=true \
model.visual_activation_offloading=true
# torchrun \
# --nproc_per_node=${ARNOLD_WORKER_GPU} \
# --nnodes=${ARNOLD_WORKER_NUM} \
# --node_rank=${ARNOLD_ID} \
# --master_addr=${METIS_WORKER_0_HOST} \
# --master_port=${METIS_WORKER_0_PORT} \
# tivila_trainer.py \
# model.pretrained_path=${MODEL_PATH} \
# optim.lr="1e-5" \
# optim.visual_lr="1e-5" \
# optim.lr_warmup_steps_ratio=0.03 \
# trainer.output_path=${OUTDIR} \
# trainer.project_name=${PROJNAME} \
# trainer.experiment_name=${EXPNAME} \
# trainer.log_interval=10 \
# trainer.checkpoint_interval=850 \
# trainer.checkpoint_hf_model=true \
# data.patterns=${DATA_PATHS} \
# data.transform_extra_kwargs.image_max_pixels=16777216 \
# data.transform_extra_kwargs.video_max_pixels=307200 \
# data.transform_extra_kwargs.video_max_frames=32 \
# data.max_seq_len=8192 \
# data.is_continuous_batch=true \
# data.num_training_steps=99999999 \
# data.chunks_per_step=4 \
# model.recompute_attn=true \
# model.activation_offloading=false \
# model.visual_activation_offloading=true |