File size: 1,664 Bytes
b72357c | 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 | #!/usr/bin/env bash
# 最小化的数据增强测试脚本，用于调试collator问题
set -euo pipefail
# 激活conda环境
source /home/v-meiszhang/miniconda3/etc/profile.d/conda.sh
conda activate group
# 参考run_group_layout_sft.sh的配置
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")"/../../.. && pwd)"
LF_SRC="$ROOT_DIR/src/train_sft/src"
export PYTHONPATH="$LF_SRC${PYTHONPATH:+:$PYTHONPATH}"
# 使用现有的全量微调配置
YAML="src/train_sft/examples/train_full/group_layout_qwen2_5_full_sft.yaml"
# 设置数据集目录
export FT_DATASET_DIR="${FT_DATASET_DIR:-$ROOT_DIR/datas}"
# 生成时间戳用于输出目录
RUN_ID="debug_aug_$(date +%Y%m%d_%H%M%S)"
# 极简配置，仅测试collator
EXTRA_ARGS=(
output_dir="$ROOT_DIR/saves/debug_augmentation_$RUN_ID"
dataset_dir="$ROOT_DIR/src/train_sft/data"
max_samples=1
num_train_epochs=1
per_device_train_batch_size=1
gradient_accumulation_steps=1
logging_steps=1
save_steps=1000
eval_steps=1000
warmup_steps=0
max_steps=1
enable_data_augmentation=true
augmentation_ratio=1.0
augmentation_types="random_group_shuffle"
overwrite_output_dir=true
report_to="none"
evaluation_strategy="no"
save_strategy="no"
lr_scheduler_type="constant"
learning_rate=1e-5
)
# 使用简单的命令格式，避免复杂的torchrun
CMD=(python -m llamafactory.cli train "$YAML" "${EXTRA_ARGS[@]}")
echo "🔍 开始数据增强调试测试"
echo "配置文件: $YAML"
echo "输出目录: $ROOT_DIR/saves/debug_augmentation_$RUN_ID"
echo "执行命令: ${CMD[*]}"
echo ""
# 设置环境变量以减少输出噪音
export FORCE_TORCHRUN=0
# 执行命令
"${CMD[@]}"
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