#!/bin/bash # This script build the CPU docker image and run the offline inference inside the container. # It serves a sanity check for compilation and basic model usage. set -ex # allow to bind to different cores CORE_RANGE=${CORE_RANGE:-48-95} NUMA_NODE=${NUMA_NODE:-1} # Setup cleanup remove_docker_container() { set -e; docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || true; docker image rm cpu-test-"$BUILDKITE_BUILD_NUMBER" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 || true; } trap remove_docker_container EXIT remove_docker_container # Try building the docker image numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f docker/Dockerfile.cpu . numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f docker/Dockerfile.cpu . # Run the image, setting --shm-size=4g for tensor parallel. docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER" docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 function cpu_tests() { set -e export NUMA_NODE=$2 export BUILDKITE_BUILD_NUMBER=$3 # offline inference docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" bash -c " set -e python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" # Run basic model test docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c " set -e pytest -v -s tests/kernels/test_cache.py -m cpu_model pytest -v -s tests/kernels/test_mla_decode_cpu.py -m cpu_model pytest -v -s tests/models/decoder_only/language -m cpu_model pytest -v -s tests/models/embedding/language -m cpu_model pytest -v -s tests/models/encoder_decoder/language -m cpu_model pytest -v -s tests/models/decoder_only/audio_language -m cpu_model pytest -v -s tests/models/decoder_only/vision_language -m cpu_model" # Run compressed-tensor test docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c " set -e pytest -s -v \ tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \ tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynamic_per_token" # Run AWQ test docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c " set -e pytest -s -v \ tests/quantization/test_ipex_quant.py" # Run chunked-prefill and prefix-cache test docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c " set -e pytest -s -v -k cpu_model \ tests/basic_correctness/test_chunked_prefill.py" # online serving docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c " set -e export VLLM_CPU_KVCACHE_SPACE=10 export VLLM_CPU_OMP_THREADS_BIND=$1 python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half & timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1 python3 benchmarks/benchmark_serving.py \ --backend vllm \ --dataset-name random \ --model facebook/opt-125m \ --num-prompts 20 \ --endpoint /v1/completions \ --tokenizer facebook/opt-125m" # Run multi-lora tests docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c " set -e pytest -s -v \ tests/lora/test_qwen2vl.py" } # All of CPU tests are expected to be finished less than 40 mins. export -f cpu_tests timeout 40m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE $BUILDKITE_BUILD_NUMBER"