From dc801c07cf38b0c495686463e6ca6f871a64440e Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Tue, 27 Jan 2026 09:57:37 -0600 Subject: Add collaborativeagents module and update gitignore - Add collaborativeagents subproject with adapters, agents, and evaluation modules - Update .gitignore to exclude large binary files (.whl, .tar), wandb logs, and results Co-Authored-By: Claude Opus 4.5 --- .../scripts/fullscale_method.sbatch | 92 ++++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 collaborativeagents/scripts/fullscale_method.sbatch (limited to 'collaborativeagents/scripts/fullscale_method.sbatch') diff --git a/collaborativeagents/scripts/fullscale_method.sbatch b/collaborativeagents/scripts/fullscale_method.sbatch new file mode 100644 index 0000000..6847f4e --- /dev/null +++ b/collaborativeagents/scripts/fullscale_method.sbatch @@ -0,0 +1,92 @@ +#!/bin/bash +#SBATCH --job-name=fs_%x +#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=30:00:00 +#SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/fs_%x-%j.out +#SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/fs_%x-%j.err + +# Usage: sbatch --job-name=vanilla fullscale_method.sbatch vanilla +METHOD=$1 + +cd /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents +source /u/yurenh2/miniforge3/etc/profile.d/conda.sh +conda activate eval +export HF_HOME=/projects/bfqt/users/yurenh2/hf_cache/huggingface +export PYTHONPATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/src:$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" +# Full-precision 70B for user simulator (H200 143GB/GPU can handle it with TP=2) +USER_MODEL="meta-llama/Llama-3.1-70B-Instruct" + +# vLLM memory and parallel workers for methods needing preference extractor +# These methods need GPU memory for embedding/reranker/extractor models on GPUs 2,3 +if [[ "$METHOD" == "all_memory" || "$METHOD" == "rag" || "$METHOD" == "rag_vector" ]]; then + AGENT_MEM=0.40 # Leave 60% free for embedding/reranker/extractor + PARALLEL_PROFILES=30 # With CUDA_VISIBLE_DEVICES=2,3, extractor uses correct GPUs +else + AGENT_MEM=0.90 + PARALLEL_PROFILES=50 +fi + +echo "=== Starting vLLM servers ===" +echo "Method: $METHOD" +echo "User simulator: $USER_MODEL (70B full-precision)" +echo "Agent: $AGENT_MODEL (8B)" +echo "Agent memory: $AGENT_MEM" +date + +# User simulator on GPUs 0,1 (70B full-precision, ~70GB/GPU with TP=2) +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 & + +# Agent on GPUs 2,3 +CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \ + --model $AGENT_MODEL \ + --port 8003 --tensor-parallel-size 2 --gpu-memory-utilization $AGENT_MEM \ + --max-model-len 16384 --dtype bfloat16 & + +# Wait for 70B model to load (takes 9-12 minutes) +echo "Waiting for vLLM servers to be ready (this may take 10-15 minutes for 70B)..." +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 $((i*5)) seconds" + break + fi + sleep 5 +done +echo "Both vLLM servers ready" +sleep 10 + +# Batch processing only for vanilla +if [[ "$METHOD" == "vanilla" ]]; then + EXTRA_ARGS="--use-batch-processing --batch-size 100" +else + EXTRA_ARGS="--no-batch-processing" +fi + +echo "Parallel profiles: $PARALLEL_PROFILES" + +# Run experiment with CUDA_VISIBLE_DEVICES=2,3 so preference extractor/embedding/reranker +# use GPUs 2,3 (which have more headroom) instead of GPUs 0,1 (saturated by 70B model) +CUDA_VISIBLE_DEVICES=2,3 python scripts/run_experiments.py --methods $METHOD \ + --datasets math-hard --n-profiles 200 --n-sessions 30 --max-turns 15 \ + --use-vllm $EXTRA_ARGS --parallel-profiles $PARALLEL_PROFILES \ + --output-dir ../results/fullscale --profile-path $PROFILE_PATH + +pkill -f "vllm.entrypoints" 2>/dev/null || true -- cgit v1.2.3