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#!/bin/bash
#SBATCH --job-name=test_rag_fix
#SBATCH --account=bfqt-delta-gpu
#SBATCH --partition=gpuH200x8
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --gres=gpu:4
#SBATCH --mem=200G
#SBATCH --time=02:00:00
#SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/test_rag_fix-%j.out
#SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/test_rag_fix-%j.err
# Small-scale test: 5 profiles, 10 sessions each
# Tests RAG fixes: extract_session accumulation, nopersonal mode
# Compare: vanilla, rag, rag_vector
cd /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents
source /u/yurenh2/miniforge3/etc/profile.d/conda.sh
conda activate eval
export HF_HOME=/projects/bfqt/users/yurenh2/hf_cache/huggingface
export PYTHONPATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/src:$PYTHONPATH"
PROFILE_PATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/data/complex_profiles_v2/profiles_200.jsonl"
AGENT_MODEL="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
USER_MODEL="meta-llama/Llama-3.1-70B-Instruct"
echo "=== RAG Fix Verification Test ==="
echo "Testing fixes:"
echo " 1. extract_session (accumulate all turns)"
echo " 2. RAG mode=nopersonal (pure dense+rerank)"
echo " 3. Explicit normalize=True"
echo ""
echo "Settings: 5 profiles, 10 sessions each, max 15 turns"
echo "User simulator: $USER_MODEL (70B)"
echo "Agent: $AGENT_MODEL (8B)"
date
nvidia-smi --query-gpu=index,name,memory.total --format=csv
# Start vLLM servers
# User simulator on GPUs 0,1 (70B, TP=2)
echo ""
echo "Starting vLLM servers..."
CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
--model $USER_MODEL \
--port 8004 --tensor-parallel-size 2 --gpu-memory-utilization 0.90 \
--max-model-len 16384 --dtype bfloat16 --download-dir $HF_HOME \
--disable-log-requests &
USER_PID=$!
# Agent on GPUs 2,3 (8B, TP=2, lower memory for embedding/reranker)
CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \
--model $AGENT_MODEL \
--port 8003 --tensor-parallel-size 2 --gpu-memory-utilization 0.45 \
--max-model-len 16384 --dtype bfloat16 \
--disable-log-requests &
AGENT_PID=$!
# Wait for servers
echo "Waiting for vLLM servers (may take 5-10 min)..."
for i in {1..200}; do
if curl -s http://localhost:8004/health > /dev/null 2>&1; then
echo "User simulator (8004) ready after $((i*5)) seconds"
break
fi
sleep 5
done
for i in {1..60}; do
if curl -s http://localhost:8003/health > /dev/null 2>&1; then
echo "Agent (8003) ready after $((i*5)) seconds"
break
fi
sleep 5
done
if ! curl -s http://localhost:8004/health > /dev/null 2>&1; then
echo "ERROR: User server not healthy"
kill $USER_PID $AGENT_PID 2>/dev/null
exit 1
fi
if ! curl -s http://localhost:8003/health > /dev/null 2>&1; then
echo "ERROR: Agent server not healthy"
kill $USER_PID $AGENT_PID 2>/dev/null
exit 1
fi
echo "Both vLLM servers ready"
sleep 5
# Test methods: vanilla (baseline), rag (fixed), rag_vector (fixed)
OUTPUT_DIR="../results/rag_fix_test_$(date +%Y%m%d_%H%M%S)"
for METHOD in vanilla rag rag_vector; do
echo ""
echo "============================================"
echo "Testing method: $METHOD"
echo "============================================"
date
START=$(date +%s)
python scripts/run_experiments.py --methods $METHOD \
--datasets math-hard --n-profiles 5 --n-sessions 10 --max-turns 15 \
--use-vllm --no-batch-processing --parallel-profiles 5 \
--output-dir $OUTPUT_DIR --profile-path $PROFILE_PATH
END=$(date +%s)
ELAPSED=$((END-START))
if [ $? -eq 0 ]; then
echo "Method $METHOD: SUCCESS (${ELAPSED}s)"
else
echo "Method $METHOD: FAILED after ${ELAPSED}s"
fi
done
echo ""
echo "============================================"
echo "RAG Fix Test Complete"
echo "============================================"
echo "Results saved to: $OUTPUT_DIR"
date
# Cleanup
pkill -f "vllm.entrypoints" 2>/dev/null || true
|
