Datasets:

thanhdath
/

mats-sql-bundle

Never edit code on gf-henry. Always edit on the dev machine, then sync:
# On dev machine (not gf-henry):
bash /home/datht/mats-sql-tist/scripts/sync_code.sh

Directory Layout on gf-henry

/home/datht/
├── mats-sql-tist/               ← synced code (never edit directly)
│   ├── data -> /home/datht/mats/data    (symlink)
│   ├── alignment-handbook/
│   ├── utils/
│   ├── db_content_retrieval/
│   ├── scripts/
│   │   ├── setup_env_gf_henry.sh
│   │   ├── sync_code.sh
│   │   ├── build_all_bm25_indexes.py
│   │   ├── train_bird.sh         ← alignment-handbook train commands
│   │   └── evaluate_bird.sh
│   ├── prepare_sft_datasets.py
│   └── evaluate_end2end.py
│
├── mats/
│   ├── alignment-handbook/output/   ← Qwen SQL-writer trained models
│   │   ├── Qwen-2.5-Coder-1.5B-SQL-Writer/
│   │   ├── Qwen-2.5-Coder-3B-SQL-Writer/
│   │   └── ...
│   ├── schema_insight/output/
│   │   └── grpo_schema_bird-Qwen-Coder-0.5B-phase2/checkpoint-6200/  ← schema agent ckpt
│   └── data/                        ← all datasets + BM25 indexes
│       ├── bird/dev/  (11 dbs + BM25 indexes)
│       ├── bird/train/  (69 dbs + BM25 indexes)
│       ├── spider/  (169 dbs + BM25 indexes)
│       └── sft_data_collections/   (Spider-DK, Dr.Spider, domain, ...)
│
└── huggingface/                     ← trained MATS agent models
    ├── llama-3b-bird-planner-fft/        (6G) Planner SFT
    ├── llama-3b-bird-validator-fft/      (6G) Validator SFT
    ├── llama-3b-bird-fixed-fft/          (6G) Fixer SFT
    ├── orpo-llama-3b-iter-3-bird-planner-no-filter-seed107/  (6.1G) Planner ORPO
    └── Meta-Llama-3.1-8B-Instruct/       (30G) (available if needed)

Setup (one-time)

# 1. Setup conda env (installs torch, transformers, trl, vllm, pyserini, etc.)
cd /home/datht/mats-sql-tist
bash scripts/setup_env_gf_henry.sh

# 2. Install alignment-handbook (custom ORPO)
source /home/datht/anaconda3/etc/profile.d/conda.sh
conda activate mats
cd /home/datht/mats-sql-tist/alignment-handbook
pip install -e .

Base LLaMA Models (for retraining)

The recipe YAMLs reference /home/datht/huggingface/meta-llama/Llama-3.2-{1B,3B}-Instruct. Download them from HuggingFace if not already present:

conda activate mats
python -c "
from huggingface_hub import snapshot_download
# LLaMA 3.2 3B (Planner, Selector)
snapshot_download('meta-llama/Llama-3.2-3B-Instruct',
                  local_dir='/home/datht/huggingface/meta-llama/Llama-3.2-3B-Instruct')
# LLaMA 3.2 1B (Validator, Fixer)
snapshot_download('meta-llama/Llama-3.2-1B-Instruct',
                  local_dir='/home/datht/huggingface/meta-llama/Llama-3.2-1B-Instruct')
"

Running Experiments

Step 1: Start BM25 API (for schema retrieval during SFT data build)

conda activate mats
cd /home/datht/mats-sql-tist
python db_content_retrieval/lsh_api.py --port 8005 &

Step 2: Build SFT training data (with CHESS-style DDL + BIRD CSV descriptions)

conda activate mats
cd /home/datht/mats-sql-tist
# BIRD dev
python prepare_sft_datasets.py

Step 3: SFT Training (alignment-handbook)

conda activate mats
cd /home/datht/mats-sql-tist/alignment-handbook
export PYTHONPATH=src/
# BIRD - planner (LLaMA 3.2 3B)
ACCELERATE_LOG_LEVEL=info accelerate launch \
    --config_file recipes/accelerate_configs/multi_gpu.yaml \
    --num_processes 1 \
    scripts/run_sft.py recipes/llama-3b-bird/planner-fft.yaml

# BIRD - validator + fixer (LLaMA 3.2 1B)
ACCELERATE_LOG_LEVEL=info accelerate launch \
    --config_file recipes/accelerate_configs/multi_gpu.yaml \
    --num_processes 1 \
    scripts/run_sft.py recipes/llama-3b-bird/validator-fixer-fft.yaml

Step 4: ORPO Training (after SFT)

conda activate mats
cd /home/datht/mats-sql-tist/alignment-handbook
export PYTHONPATH=src/
# See alignment-handbook/scripts/train_bird.sh for full commands
bash scripts/train_bird.sh

Step 5: Evaluation (BIRD dev)

conda activate mats
cd /home/datht/mats-sql-tist

# Serve agents via vLLM (on TITAN RTX, 24GB)
CUDA_VISIBLE_DEVICES=1 vllm serve /home/datht/huggingface/orpo-llama-3b-iter-3-bird-planner-no-filter-seed107 \
    --host 0.0.0.0 --port 8003 --served-model-name planner \
    --dtype bfloat16 --max-model-len 4096 --gpu-memory-utilization 0.9 &

CUDA_VISIBLE_DEVICES=0 vllm serve /home/datht/huggingface/llama-3b-bird-validator-fft \
    --host 0.0.0.0 --port 8004 --served-model-name validator \
    --dtype bfloat16 --max-model-len 4096 --gpu-memory-utilization 0.8 &

CUDA_VISIBLE_DEVICES=0 vllm serve /home/datht/huggingface/llama-3b-bird-fixed-fft \
    --host 0.0.0.0 --port 8005 --served-model-name fixed \
    --dtype bfloat16 --max-model-len 4096 --gpu-memory-utilization 0.8 &

# Run evaluation
python evaluate_end2end.py \
    --input_file data/full_value_matching_sft_bird_062024_with_evidence_dev_text2sql.json \
    --output_file output/bird_dev_results.jsonl \
    --model-name llama \
    --api_host http://localhost:8003 \
    --n_processes 8

Syncing Code Updates from Dev Machine

After editing code on the dev machine:

# On DEV machine:
bash /home/datht/mats-sql-tist/scripts/sync_code.sh gf-henry

# On gf-henry (if alignment-handbook changed):
cd /home/datht/mats-sql-tist/alignment-handbook && pip install -e .

Notes

vLLM: TITAN RTX (24GB, GPU 1) is preferred for planner/selection, RTX 2080 Ti (11GB, GPU 0) for smaller validator/fixer models
BM25 indexes: already built, stored in data/*/db_contents_index/
BIRD CSV descriptions: automatically loaded during prepare_sft_datasets.py via utils/bird_csv_utils.py
Schema format: CHESS-style DDL with inline -- Column Description | Value Description (see utils/db_utils.py)