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, 167 insertions, 0 deletions

diff --git a/collaborativeagents/slurm/test_vllm_70b_8b.sh b/collaborativeagents/slurm/test_vllm_70b_8b.sh
new file mode 100644
index 0000000..815f267
--- /dev/null
+++ b/collaborativeagents/slurm/test_vllm_70b_8b.sh
@@ -0,0 +1,167 @@
+#!/bin/bash
+#SBATCH --job-name=vllm_bench
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA100x4
+#SBATCH --nodes=1
+#SBATCH --gpus-per-node=2
+#SBATCH --time=02:00:00
+#SBATCH --mem=128G
+#SBATCH --output=slurm/logs/vllm_bench_70b_8b_%j.out
+#SBATCH --error=slurm/logs/vllm_bench_70b_8b_%j.err
+
+# Realistic benchmark: 70B AWQ user simulator + 8B agent
+# Tests actual conversation throughput with both models
+set -e
+
+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
+
+echo "=== Job Info ==="
+echo "Job ID: $SLURM_JOB_ID"
+echo "Node: $SLURM_NODELIST"
+date
+
+echo ""
+echo "=== GPU Info ==="
+nvidia-smi --query-gpu=index,name,memory.total,memory.free --format=csv
+
+# Download AWQ 70B model if not complete
+echo ""
+echo "=== Ensuring AWQ 70B Model is Downloaded ==="
+python -c "
+from huggingface_hub import snapshot_download
+import os
+os.environ['HF_HOME'] = '/projects/bfqt/users/yurenh2/hf_cache/huggingface'
+print('Checking/downloading hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4...')
+path = snapshot_download('hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4')
+print(f'Model ready at: {path}')
+"
+
+MODEL_70B_AWQ="hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4"
+MODEL_8B="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
+PORT_70B=8004
+PORT_8B=8003
+
+echo ""
+echo "============================================"
+echo "Starting 70B AWQ vLLM Server (GPU 0)"
+echo "============================================"
+CUDA_VISIBLE_DEVICES=0 python -m vllm.entrypoints.openai.api_server \
+    --model $MODEL_70B_AWQ \
+    --port $PORT_70B \
+    --gpu-memory-utilization 0.90 \
+    --max-model-len 4096 \
+    --disable-log-requests \
+    --quantization awq \
+    --dtype float16 &
+SERVER_70B_PID=$!
+echo "70B Server PID: $SERVER_70B_PID"
+
+echo ""
+echo "============================================"
+echo "Starting 8B vLLM Server (GPU 1)"
+echo "============================================"
+CUDA_VISIBLE_DEVICES=1 python -m vllm.entrypoints.openai.api_server \
+    --model $MODEL_8B \
+    --port $PORT_8B \
+    --gpu-memory-utilization 0.90 \
+    --max-model-len 4096 \
+    --disable-log-requests \
+    --dtype bfloat16 &
+SERVER_8B_PID=$!
+echo "8B Server PID: $SERVER_8B_PID"
+
+echo ""
+echo "Waiting for servers to start..."
+
+# Wait for 70B (may take 3-5 minutes)
+for i in $(seq 1 120); do
+    if curl -s http://localhost:$PORT_70B/health > /dev/null 2>&1; then
+        echo "70B Server ready after $((i*3)) seconds"
+        break
+    fi
+    if [ $((i % 20)) -eq 0 ]; then
+        echo "  Waiting for 70B... ($((i*3)) seconds)"
+    fi
+    sleep 3
+done
+
+# Wait for 8B
+for i in $(seq 1 60); do
+    if curl -s http://localhost:$PORT_8B/health > /dev/null 2>&1; then
+        echo "8B Server ready after $((i*2)) seconds"
+        break
+    fi
+    sleep 2
+done
+
+# Check both servers
+echo ""
+if ! curl -s http://localhost:$PORT_70B/health > /dev/null 2>&1; then
+    echo "ERROR: 70B server failed to start"
+    kill $SERVER_70B_PID $SERVER_8B_PID 2>/dev/null
+    exit 1
+fi
+echo "✓ 70B server healthy"
+
+if ! curl -s http://localhost:$PORT_8B/health > /dev/null 2>&1; then
+    echo "ERROR: 8B server failed to start"
+    kill $SERVER_70B_PID $SERVER_8B_PID 2>/dev/null
+    exit 1
+fi
+echo "✓ 8B server healthy"
+
+echo ""
+echo "=== vLLM Server Info ==="
+echo "70B model:"
+curl -s http://localhost:$PORT_70B/v1/models | python -m json.tool 2>/dev/null | head -10
+echo ""
+echo "8B model:"
+curl -s http://localhost:$PORT_8B/v1/models | python -m json.tool 2>/dev/null | head -10
+
+echo ""
+echo "============================================"
+echo "Test 1: Individual Model Throughput"
+echo "============================================"
+
+echo ""
+echo "--- 70B AWQ Sequential (10 requests) ---"
+python scripts/benchmark_inference.py --mode vllm --url http://localhost:$PORT_70B/v1 -n 10
+
+echo ""
+echo "--- 8B Sequential (20 requests) ---"
+python scripts/benchmark_inference.py --mode vllm --url http://localhost:$PORT_8B/v1 -n 20
+
+echo ""
+echo "============================================"
+echo "Test 2: Full Conversation Benchmark"
+echo "============================================"
+echo "Running 10 conversations with 70B user simulator + 8B agent..."
+python scripts/benchmark_inference.py \
+    --mode conversation \
+    --url-70b http://localhost:$PORT_70B/v1 \
+    --url-8b http://localhost:$PORT_8B/v1 \
+    -n 10
+
+# Cleanup
+echo ""
+echo "Cleaning up..."
+kill $SERVER_70B_PID $SERVER_8B_PID 2>/dev/null
+wait $SERVER_70B_PID $SERVER_8B_PID 2>/dev/null
+
+echo ""
+echo "============================================"
+echo "BENCHMARK COMPLETE!"
+echo "============================================"
+echo ""
+echo "Key metrics to compare with paper:"
+echo "  - Paper: 2000 conversations/hour on H100x8"
+echo "  - Expected A100x2 with 70B AWQ + 8B: ~100-300 conv/hr"
+echo "  - Our old code: ~20 conv/hr"
+echo ""
+echo "If throughput is good, update experiment code to use vLLM."
+echo ""
+date