236 lines
8.3 KiB
Bash
236 lines
8.3 KiB
Bash
#!/bin/bash
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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set -e
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IMAGE_REPO=${IMAGE_REPO:-"opea"}
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IMAGE_TAG=${IMAGE_TAG:-"latest"}
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echo "REGISTRY=IMAGE_REPO=${IMAGE_REPO}"
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echo "TAG=IMAGE_TAG=${IMAGE_TAG}"
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export REGISTRY=${IMAGE_REPO}
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export TAG=${IMAGE_TAG}
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WORKPATH=$(dirname "$PWD")
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LOG_PATH="$WORKPATH/tests"
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ip_address=$(hostname -I | awk '{print $1}')
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function build_docker_images() {
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opea_branch=${opea_branch:-"main"}
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# If the opea_branch isn't main, replace the git clone branch in Dockerfile.
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if [[ "${opea_branch}" != "main" ]]; then
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cd $WORKPATH
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OLD_STRING="RUN git clone --depth 1 https://github.com/opea-project/GenAIComps.git"
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NEW_STRING="RUN git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git"
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find . -type f -name "Dockerfile*" | while read -r file; do
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echo "Processing file: $file"
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sed -i "s|$OLD_STRING|$NEW_STRING|g" "$file"
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done
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fi
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cd $WORKPATH/docker_image_build
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git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git
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git clone --depth 1 --branch v0.6.4.post2+Gaudi-1.19.0 https://github.com/HabanaAI/vllm-fork.git
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sed -i 's/triton/triton==3.1.0/g' vllm-fork/requirements-hpu.txt
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echo "Build all the images with --no-cache, check docker_image_build.log for details..."
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service_list="chatqna-without-rerank chatqna-ui dataprep retriever vllm-gaudi nginx"
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docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
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docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
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docker pull ghcr.io/huggingface/tei-gaudi:1.5.0
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docker images && sleep 1s
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}
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function start_services() {
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cd $WORKPATH/docker_compose/intel/hpu/gaudi
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export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
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export LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
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export NUM_CARDS=1
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export INDEX_NAME="rag-redis"
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export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
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# Start Docker Containers
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docker compose -f compose_without_rerank.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
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n=0
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until [[ "$n" -ge 160 ]]; do
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docker logs vllm-gaudi-server > vllm_service_start.log
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if grep -q "Warmup finished" vllm_service_start.log; then
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break
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fi
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sleep 5s
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n=$((n+1))
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done
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}
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function validate_service() {
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local URL="$1"
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local EXPECTED_RESULT="$2"
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local SERVICE_NAME="$3"
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local DOCKER_NAME="$4"
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local INPUT_DATA="$5"
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if [[ $SERVICE_NAME == *"dataprep_upload_file"* ]]; then
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cd $LOG_PATH
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'files=@./dataprep_file.txt' -H 'Content-Type: multipart/form-data' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_upload_link"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'link_list=["https://www.ces.tech/"]' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_get"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_del"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d '{"file_path": "all"}' -H 'Content-Type: application/json' "$URL")
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else
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
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fi
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HTTP_STATUS=$(echo $HTTP_RESPONSE | tr -d '\n' | sed -e 's/.*HTTPSTATUS://')
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RESPONSE_BODY=$(echo $HTTP_RESPONSE | sed -e 's/HTTPSTATUS\:.*//g')
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docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
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# check response status
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if [ "$HTTP_STATUS" -ne "200" ]; then
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echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
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exit 1
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else
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echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
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fi
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# check response body
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if [[ "$RESPONSE_BODY" != *"$EXPECTED_RESULT"* ]]; then
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echo "[ $SERVICE_NAME ] Content does not match the expected result: $RESPONSE_BODY"
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exit 1
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else
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echo "[ $SERVICE_NAME ] Content is as expected."
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fi
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sleep 1s
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}
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function validate_microservices() {
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# Check if the microservices are running correctly.
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# tei for embedding service
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validate_service \
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"${ip_address}:8090/embed" \
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"[[" \
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"tei-embedding" \
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"tei-embedding-gaudi-server" \
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'{"inputs":"What is Deep Learning?"}'
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sleep 1m # retrieval can't curl as expected, try to wait for more time
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# test /v1/dataprep/ingest upload file
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echo "Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to analyze various levels of abstract data representations. It enables computers to identify patterns and make decisions with minimal human intervention by learning from large amounts of data." > $LOG_PATH/dataprep_file.txt
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/ingest" \
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"Data preparation succeeded" \
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"dataprep_upload_file" \
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"dataprep-redis-server"
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# test /v1/dataprep/ingest upload link
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/ingest" \
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"Data preparation succeeded" \
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"dataprep_upload_link" \
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"dataprep-redis-server"
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# test /v1/dataprep/get
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/get" \
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'{"name":' \
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"dataprep_get" \
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"dataprep-redis-server"
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# test /v1/dataprep/delete
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/delete" \
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'{"status":true}' \
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"dataprep_del" \
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"dataprep-redis-server"
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# retrieval microservice
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test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
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validate_service \
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"${ip_address}:7000/v1/retrieval" \
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"retrieved_docs" \
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"retrieval-microservice" \
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"retriever-redis-server" \
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"{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
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# vllm for llm service
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validate_service \
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"${ip_address}:8007/v1/chat/completions" \
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"content" \
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"vllm-llm" \
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"vllm-gaudi-server" \
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'{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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validate_service \
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"${ip_address}:8888/v1/chatqna" \
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"data: " \
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"chatqna-megaservice" \
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"chatqna-gaudi-backend-server" \
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'{"messages": "What is the revenue of Nike in 2023?"}'
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}
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function validate_frontend() {
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cd $WORKPATH/ui/svelte
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local conda_env_name="OPEA_e2e"
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export PATH=${HOME}/miniforge3/bin/:$PATH
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if conda info --envs | grep -q "$conda_env_name"; then
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echo "$conda_env_name exist!"
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else
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conda create -n ${conda_env_name} python=3.12 -y
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fi
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source activate ${conda_env_name}
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sed -i "s/localhost/$ip_address/g" playwright.config.ts
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conda install -c conda-forge nodejs=22.6.0 -y
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npm install && npm ci && npx playwright install --with-deps
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node -v && npm -v && pip list
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exit_status=0
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npx playwright test || exit_status=$?
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if [ $exit_status -ne 0 ]; then
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echo "[TEST INFO]: ---------frontend test failed---------"
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exit $exit_status
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else
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echo "[TEST INFO]: ---------frontend test passed---------"
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fi
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}
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function stop_docker() {
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cd $WORKPATH/docker_compose/intel/hpu/gaudi
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docker compose -f compose_without_rerank.yaml down
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}
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function main() {
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stop_docker
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if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
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start_time=$(date +%s)
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start_services
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end_time=$(date +%s)
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duration=$((end_time-start_time))
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echo "Mega service start duration is $duration s"
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if [ "${mode}" == "perf" ]; then
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python3 $WORKPATH/tests/chatqna_benchmark.py
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elif [ "${mode}" == "" ]; then
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validate_microservices
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validate_megaservice
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validate_frontend
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fi
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stop_docker
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echo y | docker system prune
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}
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main
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