scripts/test_small_training.sh

#!/bin/bash

echo "==========================================="
echo "Testing GLEN on The Vault dataset (Small)"
echo "==========================================="

# Set memory monitoring parameters
GPU_MEMORY_THRESHOLD=0.85
GPU_CHECK_INTERVAL=50

echo "Resource Protection enabled:"
echo "- Memory threshold: ${GPU_MEMORY_THRESHOLD} (85%)"  
echo "- Check interval: ${GPU_CHECK_INTERVAL} steps"
echo ""

# Ensure data preprocessing is done
echo "Checking data preprocessing..."
if [ ! -f "data/the_vault/DOC_VAULT_train.tsv" ] || [ ! -f "data/the_vault/GTQ_VAULT_dev.tsv" ]; then
    echo "Running data preprocessing..."
    python scripts/preprocess_vault_dataset.py --input_dir the_vault_dataset/ --output_dir data/the_vault/ --sample_size 1000 --create_test_set
    if [ $? -ne 0 ]; then
        echo "Error: Data preprocessing failed!"
        exit 1
    fi
else
    echo "Data already preprocessed."
fi

# Phase 1 Training
echo ""
echo "=== Phase 1 Training (Document ID Assignment) ==="

# Check if CUDA is available
if command -v nvidia-smi &> /dev/null; then
    export CUDA_VISIBLE_DEVICES="0"
    echo "Using GPU for training"
    BATCH_SIZE=8
    EVAL_BATCH_SIZE=4
    ACCUM_STEPS=2
else
    echo "No GPU detected, using CPU with reduced batch sizes"
    BATCH_SIZE=2
    EVAL_BATCH_SIZE=1
    ACCUM_STEPS=8
fi

python examples/glen_phase1/train_glen.py \
    --output_dir logs/test_glen_vault/GLEN_P1_test \
    --model_name_or_path t5-base \
    --query_type gtq_doc \
    --per_device_train_batch_size $BATCH_SIZE \
    --per_device_eval_batch_size $EVAL_BATCH_SIZE \
    --gradient_accumulation_steps $ACCUM_STEPS \
    --dropout_rate 0.1 \
    --Rdrop 0.15 \
    --aug_query True \
    --aug_query_type corrupted_query \
    --input_dropout 1 \
    --id_class t5_bm25_truncate_3 \
    --dataset_name the_vault \
    --test100 1 \
    --tree 1 \
    --pretrain_decoder True \
    --max_input_length 128 \
    --val_check_interval 1.0 \
    --tie_word_embeddings True \
    --decoder_input doc_rep \
    --max_output_length 5 \
    --num_return_sequences 5 \
    --logging_steps 100 \
    --overwrite_output_dir \
    --wandb_tag glen_vault_test_p1 \
    --do_eval True \
    --num_train_epochs 1 \
    --save_steps 1000 \
    --save_strategy steps \
    --evaluation_strategy steps \
    --eval_steps 1000 \
    --seed 42 \
    --gpu_memory_threshold $GPU_MEMORY_THRESHOLD \
    --gpu_check_interval $GPU_CHECK_INTERVAL \
    --fp16 True \
    --dataloader_num_workers 0 \
    --dataloader_pin_memory False

if [ $? -ne 0 ]; then
    echo "Error: Phase 1 training failed!"
    exit 1
fi

echo "✅ Phase 1 training completed successfully!"

# Check if Phase 1 checkpoint exists
PHASE1_CKPT="logs/test_glen_vault/GLEN_P1_test"
if [ ! -d "$PHASE1_CKPT" ]; then
    echo "Error: Phase 1 checkpoint not found at $PHASE1_CKPT"
    exit 1
fi

# Check for model files
model_files=("pytorch_model.bin" "model.safetensors")
found_model=false
for file in "${model_files[@]}"; do
    if [ -f "$PHASE1_CKPT/$file" ]; then
        found_model=true
        echo "📁 Found Phase 1 model: $file"
        break
    fi
done

if [ "$found_model" = false ]; then
    echo "Error: No model files found in Phase 1 checkpoint"
    exit 1
fi

echo ""
echo "=== Phase 2 Training (Ranking-based Refinement) ==="

# Adjust batch sizes for Phase 2
if command -v nvidia-smi &> /dev/null; then
    BATCH_SIZE=4
    EVAL_BATCH_SIZE=2
    ACCUM_STEPS=4
else
    BATCH_SIZE=1
    EVAL_BATCH_SIZE=1
    ACCUM_STEPS=16
fi

python examples/glen_phase2/train_glen.py \
    --output_dir logs/test_glen_vault/GLEN_P2_test \
    --model_name_or_path $PHASE1_CKPT \
    --per_device_train_batch_size $BATCH_SIZE \
    --per_device_eval_batch_size $EVAL_BATCH_SIZE \
    --gradient_accumulation_steps $ACCUM_STEPS \
    --dropout_rate 0.1 \
    --warmup_ratio 0.1 \
    --id_class t5_bm25_truncate_3 \
    --dataset_name the_vault \
    --tree 1 \
    --q_max_len 32 \
    --p_max_len 128 \
    --negative_passage_type self \
    --positive_passage_no_shuffle True \
    --tie_word_embeddings True \
    --num_return_sequences 5 \
    --logging_steps 100 \
    --overwrite_output_dir \
    --wandb_tag glen_vault_test_p2 \
    --do_eval True \
    --num_train_epochs 1 \
    --save_steps 1000 \
    --save_strategy steps \
    --evaluation_strategy steps \
    --eval_steps 1000 \
    --seed 42 \
    --gpu_memory_threshold $GPU_MEMORY_THRESHOLD \
    --gpu_check_interval $GPU_CHECK_INTERVAL \
    --fp16 True \
    --dataloader_num_workers 0 \
    --dataloader_pin_memory False

if [ $? -ne 0 ]; then
    echo "Error: Phase 2 training failed!"
    exit 1
fi

echo "✅ Phase 2 training completed successfully!"

# Validate Phase 2 checkpoint
PHASE2_CKPT="logs/test_glen_vault/GLEN_P2_test"
if [ ! -d "$PHASE2_CKPT" ]; then
    echo "Error: Phase 2 checkpoint not found at $PHASE2_CKPT"
    exit 1
fi

# Check for checkpoint subdirectories or model files
checkpoint_dir=$(find "$PHASE2_CKPT" -maxdepth 1 -type d -name "checkpoint-*" | sort -V | tail -n 1)
if [ -n "$checkpoint_dir" ]; then
    echo "📁 Found Phase 2 checkpoint: $(basename $checkpoint_dir)"
    if [ ! -f "$checkpoint_dir/model.safetensors" ] && [ ! -f "$checkpoint_dir/pytorch_model.bin" ]; then
        echo "Error: No model files in checkpoint directory"
        exit 1
    fi
else
    # Check for model files in root
    found_model=false
    for file in "${model_files[@]}"; do
        if [ -f "$PHASE2_CKPT/$file" ]; then
            found_model=true
            echo "📁 Found Phase 2 model: $file"
            break
        fi
    done
    if [ "$found_model" = false ]; then
        echo "Error: No model files found in Phase 2 checkpoint"
        exit 1
    fi
fi

echo ""
echo "=== Document ID Generation ==="

python examples/glen_phase2/makeid_glen.py \
    --model_name_or_path $PHASE2_CKPT \
    --infer_dir $PHASE2_CKPT \
    --dataset_name the_vault \
    --docid_file_name GLEN_P2_test_docids \
    --per_device_eval_batch_size 1 \
    --max_input_length 128 \
    --num_return_sequences 10 \
    --dataloader_num_workers 0 \
    --dataloader_pin_memory False

if [ $? -ne 0 ]; then
    echo "Error: Document ID generation failed!"
    exit 1
fi

# Validate docid file was created
docid_file="logs/test_glen_vault/GLEN_P2_test_docids.tsv"
if [ ! -f "$docid_file" ]; then
    echo "Error: Document ID file not created: $docid_file"
    exit 1
fi

line_count=$(wc -l < "$docid_file")
echo "✅ Document ID generation completed! Generated $line_count document IDs"

echo ""
echo "=== Query Inference ==="

# First, ensure we have test queries
if [ ! -f "data/the_vault/GTQ_VAULT_dev.tsv" ]; then
    echo "Error: Test queries file not found. Please run preprocessing with --create_test_set flag"
    exit 1
fi

python examples/glen_phase2/evaluate_glen.py \
    --model_name_or_path $PHASE2_CKPT \
    --infer_dir $PHASE2_CKPT \
    --dataset_name the_vault \
    --docid_file_name GLEN_P2_test_docids \
    --per_device_eval_batch_size 1 \
    --q_max_len 32 \
    --num_return_sequences 5 \
    --logs_dir logs/test_glen_vault \
    --test100 1 \
    --dataloader_num_workers 0 \
    --dataloader_pin_memory False

if [ $? -ne 0 ]; then
    echo "Error: Query inference failed!"
    exit 1
fi

echo "✅ Query inference completed successfully!"

echo ""
echo "==========================================="
echo "🎉 TESTING COMPLETED SUCCESSFULLY! 🎉"
echo "==========================================="
echo ""
echo "📊 Summary:"
echo "  ✅ Phase 1 Training (Document ID Assignment)"
echo "  ✅ Phase 2 Training (Ranking-based Refinement)"  
echo "  ✅ Document ID Generation ($line_count IDs)"
echo "  ✅ Query Inference & Evaluation"
echo ""
echo "📁 Results saved in: logs/test_glen_vault/"
echo "📁 Document IDs: $docid_file"
echo ""
echo "🛡️ Resource Protection Summary:"
echo "  - Memory threshold: ${GPU_MEMORY_THRESHOLD} (85%)"
echo "  - Check interval: ${GPU_CHECK_INTERVAL} steps"
echo "  - FP16 training enabled"
echo "  - Optimized batch sizes for current hardware"
echo ""
echo "🚀 Testing completed! The model is ready for full training."