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, 102 insertions, 0 deletions

diff --git a/collaborativeagents/slurm/test_vllm_benchmark.sh b/collaborativeagents/slurm/test_vllm_benchmark.sh
new file mode 100644
index 0000000..d812b43
--- /dev/null
+++ b/collaborativeagents/slurm/test_vllm_benchmark.sh
@@ -0,0 +1,102 @@
+#!/bin/bash
+#SBATCH --job-name=vllm_bench
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA100x4
+#SBATCH --nodes=1
+#SBATCH --gpus-per-node=1
+#SBATCH --time=00:30:00
+#SBATCH --output=slurm/logs/vllm_bench_%j.out
+#SBATCH --error=slurm/logs/vllm_bench_%j.err
+
+# Benchmark vLLM vs transformers inference speed
+set -e
+
+cd /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents
+
+export HF_HOME=/projects/bfqt/users/yurenh2/hf_cache/huggingface
+export TRANSFORMERS_CACHE=/projects/bfqt/users/yurenh2/hf_cache/huggingface
+
+echo "=== Job Info ==="
+echo "Job ID: $SLURM_JOB_ID"
+echo "Node: $SLURM_NODELIST"
+echo "GPUs: $SLURM_GPUS_ON_NODE"
+date
+
+echo ""
+echo "=== GPU Info ==="
+nvidia-smi --query-gpu=index,name,memory.total,memory.free --format=csv
+
+MODEL_8B="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
+PORT=8003
+
+# ============================================
+# Test 1: Transformers baseline
+# ============================================
+echo ""
+echo "============================================"
+echo "Test 1: Transformers Baseline (10 requests)"
+echo "============================================"
+python scripts/benchmark_inference.py --mode transformers --model $MODEL_8B -n 10
+
+# ============================================
+# Test 2: vLLM server
+# ============================================
+echo ""
+echo "============================================"
+echo "Test 2: Starting vLLM Server"
+echo "============================================"
+
+# Start vLLM server
+python -m vllm.entrypoints.openai.api_server \
+    --model $MODEL_8B \
+    --port $PORT \
+    --gpu-memory-utilization 0.9 \
+    --max-model-len 4096 \
+    --disable-log-requests &
+SERVER_PID=$!
+echo "vLLM Server PID: $SERVER_PID"
+
+# Wait for server to be ready
+echo "Waiting for server to start..."
+for i in {1..60}; do
+    if curl -s http://localhost:$PORT/health > /dev/null 2>&1; then
+        echo "Server ready after $((i*5)) seconds"
+        break
+    fi
+    sleep 5
+done
+
+# Check if server is up
+if ! curl -s http://localhost:$PORT/health > /dev/null 2>&1; then
+    echo "ERROR: vLLM server failed to start"
+    kill $SERVER_PID 2>/dev/null || true
+    exit 1
+fi
+
+echo ""
+echo "============================================"
+echo "Test 2a: vLLM Sequential (20 requests)"
+echo "============================================"
+python scripts/benchmark_inference.py --mode vllm --url http://localhost:$PORT/v1 -n 20
+
+echo ""
+echo "============================================"
+echo "Test 2b: vLLM Concurrent (50 requests)"
+echo "============================================"
+python scripts/benchmark_inference.py --mode vllm --url http://localhost:$PORT/v1 -n 50 --concurrent
+
+# Cleanup
+echo ""
+echo "Cleaning up..."
+kill $SERVER_PID 2>/dev/null || true
+wait $SERVER_PID 2>/dev/null || true
+
+echo ""
+echo "============================================"
+echo "Benchmark Complete!"
+echo "============================================"
+echo ""
+echo "Target: 2000 conversations/hour (paper on H100x8)"
+echo "Our A100x4 should achieve ~500-1000 conv/hr with vLLM"
+echo ""
+date