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 <noreply@anthropic.com>

1 files changed, 92 insertions, 0 deletions

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