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#!/bin/bash
#SBATCH --job-name=test_vllm_a100
#SBATCH --account=bfqt-delta-gpu
#SBATCH --partition=gpuA100x4
#SBATCH --gres=gpu:4
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --mem=128G
#SBATCH --time=00:30:00
#SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/test_vllm_a100-%j.out
#SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/test_vllm_a100-%j.err
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
export PYTHONPATH="${PWD}:${PWD}/scripts:${PWD}/../src:${PYTHONPATH}"
MODEL_8B="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
echo "=== vLLM Speed Test (A100) ==="
date
nvidia-smi --query-gpu=index,name,memory.used,memory.total --format=csv
pkill -f "vllm.entrypoints" 2>/dev/null || true
sleep 2
echo ""
echo "=== Test 1: ContextualAdapter with vLLM (2 servers) ==="
CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
--model $MODEL_8B --port 8004 --tensor-parallel-size 2 \
--gpu-memory-utilization 0.90 --max-model-len 8192 \
--disable-log-requests --dtype bfloat16 &
CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \
--model $MODEL_8B --port 8003 --tensor-parallel-size 2 \
--gpu-memory-utilization 0.90 --max-model-len 8192 \
--disable-log-requests --dtype bfloat16 &
for i in $(seq 1 120); do
u=$(curl -s http://localhost:8004/health > /dev/null 2>&1 && echo 1 || echo 0)
a=$(curl -s http://localhost:8003/health > /dev/null 2>&1 && echo 1 || echo 0)
if [ "$u" = "1" ] && [ "$a" = "1" ]; then
echo "Both servers ready after $((i*2))s"; break
fi
sleep 2
done
python -c "
import time
import sys
sys.path.insert(0, '.')
from adapters.contextual_adapter import ContextualAdapter
print('Testing ContextualAdapter with vLLM...')
adapter = ContextualAdapter(vllm_url='http://localhost:8003/v1')
adapter.initialize()
adapter.start_session('test_user')
# Warm up
adapter.generate_response('Hello')
# Benchmark
n_requests = 20
start = time.time()
for i in range(n_requests):
resp = adapter.generate_response(f'Solve: What is {i*7} + {i*3}? Give a brief answer.')
elapsed = time.time() - start
print(f'ContextualAdapter (vLLM): {n_requests} requests in {elapsed:.2f}s')
print(f'Throughput: {n_requests/elapsed:.2f} req/s = {n_requests/elapsed*3600:.0f} requests/hr')
print(f'Estimated sessions/hr (5 turns/session): {n_requests/elapsed*3600/5:.0f}')
"
pkill -f "vllm.entrypoints" 2>/dev/null || true
sleep 5
echo ""
echo "=== Test 2: PersonalizedLLMAdapter (vLLM user + transformers) ==="
CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
--model $MODEL_8B --port 8004 --tensor-parallel-size 2 \
--gpu-memory-utilization 0.90 --max-model-len 8192 \
--disable-log-requests --dtype bfloat16 &
for i in $(seq 1 120); do
if curl -s http://localhost:8004/health > /dev/null 2>&1; then
echo "Server ready after $((i*2))s"; break
fi
sleep 2
done
CUDA_VISIBLE_DEVICES=2,3 python -c "
import time
import sys
sys.path.insert(0, '.')
from adapters.personalized_llm_adapter import create_baseline_adapter
print('Testing PersonalizedLLMAdapter (all_memory)...')
adapter = create_baseline_adapter('all_memory')
adapter.initialize()
adapter.start_session('test_user')
# Warm up
adapter.generate_response('Hello')
# Benchmark
n_requests = 10
start = time.time()
for i in range(n_requests):
resp = adapter.generate_response(f'Solve: What is {i*7} + {i*3}? Give a brief answer.')
elapsed = time.time() - start
print(f'PersonalizedLLMAdapter (transformers): {n_requests} requests in {elapsed:.2f}s')
print(f'Throughput: {n_requests/elapsed:.2f} req/s = {n_requests/elapsed*3600:.0f} requests/hr')
print(f'Estimated sessions/hr (5 turns/session): {n_requests/elapsed*3600/5:.0f}')
"
pkill -f "vllm.entrypoints" 2>/dev/null || true
echo ""
echo "=== Test Complete ==="
date
|
