From 8d358d9c614e8a5f85751d842dc48396c0f9a8bc Mon Sep 17 00:00:00 2001 From: Hugo Larcher Date: Thu, 11 Jul 2024 11:45:24 +0200 Subject: [PATCH] feat: Add load tests --- .github/workflows/ci_build.yaml | 2 + .github/workflows/load_test.yaml | 84 +- load_tests/Makefile | 29 +- load_tests/benchmarks/__init__.py | 0 load_tests/benchmarks/engine.py | 151 ++ load_tests/benchmarks/k6.py | 233 +++ .../templates/k6_constant_arrival_rate.js.j2 | 16 + .../templates/k6_constant_vus.js.j2 | 14 + .../templates/k6_ramping_arrival_rate.js.j2 | 20 + load_tests/benchmarks/templates/main.js.j2 | 131 ++ load_tests/benchmarks/test_k6.py | 31 + load_tests/benchmarks/utils.py | 25 + load_tests/common.js | 94 - load_tests/download_artifact.js | 98 ++ load_tests/filter.py | 26 - load_tests/load_test.py | 103 ++ load_tests/orca.py | 27 - load_tests/parse_load_test.py | 115 ++ load_tests/poetry.lock | 1561 +++++++++++++++++ load_tests/pyproject.toml | 23 + 20 files changed, 2617 insertions(+), 166 deletions(-) create mode 100644 load_tests/benchmarks/__init__.py create mode 100644 load_tests/benchmarks/engine.py create mode 100644 load_tests/benchmarks/k6.py create mode 100644 load_tests/benchmarks/templates/k6_constant_arrival_rate.js.j2 create mode 100644 load_tests/benchmarks/templates/k6_constant_vus.js.j2 create mode 100644 load_tests/benchmarks/templates/k6_ramping_arrival_rate.js.j2 create mode 100644 load_tests/benchmarks/templates/main.js.j2 create mode 100644 load_tests/benchmarks/test_k6.py create mode 100644 load_tests/benchmarks/utils.py delete mode 100644 load_tests/common.js create mode 100644 load_tests/download_artifact.js delete mode 100644 load_tests/filter.py create mode 100644 load_tests/load_test.py delete mode 100644 load_tests/orca.py create mode 100644 load_tests/parse_load_test.py create mode 100644 load_tests/poetry.lock create mode 100644 load_tests/pyproject.toml diff --git a/.github/workflows/ci_build.yaml b/.github/workflows/ci_build.yaml index d62297e4..bbfee495 100644 --- a/.github/workflows/ci_build.yaml +++ b/.github/workflows/ci_build.yaml @@ -43,3 +43,5 @@ jobs: # https://github.com/actions/runner/issues/2206 release-tests: ${{ inputs.release-tests == true }} secrets: inherit + load_tests: + uses: ./.github/workflows/load_test.yaml \ No newline at end of file diff --git a/.github/workflows/load_test.yaml b/.github/workflows/load_test.yaml index 637df472..9bc6309e 100644 --- a/.github/workflows/load_test.yaml +++ b/.github/workflows/load_test.yaml @@ -3,6 +3,7 @@ name: Nightly load test on: schedule: - cron: '0 0 * * 1-5' + workflow_call: pull_request: paths: @@ -10,33 +11,92 @@ on: branches: - 'main' +env: + AWS_DEFAULT_REGION: us-east-1 + AWS_ACCESS_KEY_ID: ${{ secrets.S3_AWS_ACCESS_KEY_ID }} + AWS_SECRET_ACCESS_KEY: ${{ secrets.S3_AWS_SECRET_ACCESS_KEY }} + jobs: load-tests: concurrency: group: ${{ github.workflow }}-${{ github.job }}-${{ github.head_ref || github.run_id }} cancel-in-progress: true - runs-on: [self-hosted, nvidia-gpu , multi-gpu, 4-a10, ci] + runs-on: [ self-hosted, nvidia-gpu , multi-gpu, 4-a10, ci ] env: DOCKER_VOLUME: /cache steps: - name: Checkout repository uses: actions/checkout@v3 - - name: Install k6 + - name: Install awscli run: | - curl https://github.com/grafana/k6/releases/download/v0.44.0/k6-v0.44.0-linux-amd64.tar.gz -L | tar xvz --strip-components 1 + sudo apt-get update + sudo apt-get install -y awscli - - name: Start starcoder + - name: Install poetry run: | - docker run --name tgi-starcoder --rm --gpus all -p 3000:80 -v /mnt/cache:/data -e HF_TOKEN=${{ secrets.HF_TOKEN }} --pull always -d ghcr.io/huggingface/text-generation-inference:latest --model-id bigcode/starcoder --num-shard 2 --max-batch-total-tokens 32768 - sleep 10 - wget --timeout 10 --retry-on-http-error --waitretry=1 --tries=240 http://localhost:3000/health + curl -sSL https://install.python-poetry.org | python3 - + export PATH="$HOME/.local/bin:$PATH" + poetry --version - - name: Run k6 + - name: Install texlive minimal run: | - ./k6 run load_tests/starcoder_load.js + sudo apt-get update + sudo apt-get install -y texlive-latex-extra texlive-fonts-recommended dvipng cm-super - - name: Stop starcoder - if: ${{ always() }} + - name: Install Go 1.21 + uses: actions/setup-go@v2 + with: + go-version: 1.21 + + - name: Install Python 3.11 + uses: actions/setup-python@v2 + with: + python-version: 3.11 + + - name: Download artifacts from previous runs + uses: actions/github-script@v6 + continue-on-error: true + env: + WORKFLOW_FILENAME: load_test.yaml + ARTIFACT_NAME: benchmark_results_csv + ARTIFACT_FILENAME: benchmark_results_csv.zip + UNZIP_DIR: /tmp/artifacts + with: + script: | + const script = require('./load_tests/download_artifact.js') + await script({github, context, core}) + + - name: Run load test run: | - docker stop tgi-starcoder || true + cd load_tests + make load-test + shell: bash + + - name: Archive test results artifacts + uses: actions/upload-artifact@v4 + with: + name: benchmark_results_plots + path: | + load_tests/output/* + + - name: Upload to S3 + run: | + aws s3 cp load_tests/output/ s3://text-generation-inference-ci/${{ github.sha }} --recursive + + - uses: actions/github-script@v6 + if: github.event_name == 'pull_request' + with: + script: | + github.rest.issues.createComment({ + issue_number: context.issue.number, + owner: context.repo.owner, + repo: context.repo.repo, + body: '🚀 Load test results are in:\n\n'+ + '## Variable length prompts\n'+ + '\n' + + '\n\n' + + '## Constant length prompts\n'+ + '\n' + + '\n' + }) diff --git a/load_tests/Makefile b/load_tests/Makefile index 9199aa3b..914e7f09 100644 --- a/load_tests/Makefile +++ b/load_tests/Makefile @@ -1,9 +1,24 @@ +.PHONY: download-dataset load-test build-k6 +download-dataset: + @if [ ! -f ./benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json ]; then \ + echo "Downloading dataset"; \ + curl -L -o benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json; \ + else \ + echo "Dataset already downloaded"; \ + fi -ShareGPT_V3_unfiltered_cleaned_split.json: - wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json +load-test: download-dataset build-k6 + poetry install && poetry run python load_test.py -prepare_share: ShareGPT_V3_unfiltered_cleaned_split.json - python filter.py - -prepare_orca: - python orca.py +build-k6: + mkdir -p /tmp/xk6 && \ + cd /tmp/xk6 && \ + git clone https://github.com/mstoykov/xk6-sse.git && \ + cd xk6-sse && \ + git checkout useSobek && \ + go install go.k6.io/xk6/cmd/xk6@latest && \ + xk6 build --with github.com/phymbert/xk6-sse=. && \ + mkdir -p ~/.local/bin/ && \ + mv k6 /tmp/k6-sse && \ + rm -rf /tmp/xk6 && \ + /tmp/k6-sse --version \ No newline at end of file diff --git a/load_tests/benchmarks/__init__.py b/load_tests/benchmarks/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/load_tests/benchmarks/engine.py b/load_tests/benchmarks/engine.py new file mode 100644 index 00000000..d3d9715f --- /dev/null +++ b/load_tests/benchmarks/engine.py @@ -0,0 +1,151 @@ +import subprocess +import threading +from typing import Dict, List + +import docker +from docker.models.containers import Container +from loguru import logger + +from benchmarks.utils import kill + + +class InferenceEngineRunner: + def __init__(self, model: str): + self.model = model + + def run(self, parameters: list[tuple]): + NotImplementedError("This method should be implemented by the subclass") + + def stop(self): + NotImplementedError("This method should be implemented by the subclass") + + +class TGIRunner(InferenceEngineRunner): + def __init__(self, model: str): + super().__init__(model) + self.process = None + self.model = model + + def run(self, parameters: list[tuple]): + params = "" + for p in parameters: + params += f"--{p[0]} {str(p[1])}" + # start a TGI subprocess with the given parameter + args = f"text-generation-launcher --port 8080 --model-id {self.model} --huggingface-hub-cache /scratch {params}" + logger.info(f"Running TGI with parameters: {args}") + self.process = subprocess.Popen(args, + stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) + for line in iter(self.process.stdout.readline, b""): + print(line.decode("utf-8")) + # wait for TGI to listen to the port + if b"Connected" in line: + break + if b"Error" in line: + raise Exception(f"Error starting TGI: {line}") + + # continue to stream the logs in a thread + def stream_logs(): + for line in iter(self.process.stdout.readline, b""): + print(line.decode("utf-8")) + + if self.process.returncode is not None: + raise Exception("Error starting TGI") + self.thread = threading.Thread(target=stream_logs) + self.thread.start() + + def stop(self): + logger.warning(f"Killing TGI with PID {self.process.pid}") + if self.process: + kill(self.process.pid) + if self.thread: + self.thread.join() + + +class TGIDockerRunner(InferenceEngineRunner): + def __init__(self, + model: str, + image: str = "ghcr.io/huggingface/text-generation-inference:latest", + volumes=None): + super().__init__(model) + if volumes is None: + volumes = [] + self.container = None + self.image = image + self.volumes = volumes + + def run(self, parameters: list[tuple]): + params = f"--model-id {self.model} --port 8080" + for p in parameters: + params += f" --{p[0]} {str(p[1])}" + logger.info(f"Running TGI with parameters: {params}") + volumes = {} + for v in self.volumes: + volumes[v[0]] = {"bind": v[1], "mode": "rw"} + self.container = run_docker(self.image, params, + "Connected", + "Error", + volumes=volumes) + + def stop(self): + if self.container: + self.container.stop() + + +class VLLMDockerRunner(InferenceEngineRunner): + def __init__(self, + model: str, + image: str = "vllm/vllm-openai:latest", + volumes=None): + super().__init__(model) + if volumes is None: + volumes = [] + self.container = None + self.image = image + self.volumes = volumes + + def run(self, parameters: list[tuple]): + parameters.append(("max-num-seqs", "256")) + params = f"--model {self.model} --tensor-parallel-size {get_num_gpus()} --port 8080" + for p in parameters: + params += f" --{p[0]} {str(p[1])}" + logger.info(f"Running VLLM with parameters: {params}") + volumes = {} + for v in self.volumes: + volumes[v[0]] = {"bind": v[1], "mode": "rw"} + self.container = run_docker(self.image, params, "Uvicorn running", + "Error ", + volumes=volumes) + + def stop(self): + if self.container: + self.container.stop() + + +def run_docker(image: str, args: str, success_sentinel: str, + error_sentinel: str, volumes=None) -> Container: + if volumes is None: + volumes = {} + client = docker.from_env() + # retrieve the GPU devices from CUDA_VISIBLE_DEVICES + devices = [f"{i}" for i in + range(get_num_gpus())] + container = client.containers.run(image, args, + detach=True, + device_requests=[ + docker.types.DeviceRequest(device_ids=devices, capabilities=[['gpu']]) + ], + volumes=volumes, + shm_size="1g", + ports={"8080/tcp": 8080}) + for line in container.logs(stream=True): + print(line.decode("utf-8"), end="") + if success_sentinel.encode("utf-8") in line: + break + if error_sentinel.encode("utf-8") in line: + container.stop() + raise Exception(f"Error starting container: {line}") + return container + + +def get_num_gpus() -> int: + return len(subprocess.run(["nvidia-smi", "-L"], capture_output=True).stdout.splitlines()) diff --git a/load_tests/benchmarks/k6.py b/load_tests/benchmarks/k6.py new file mode 100644 index 00000000..0ea50274 --- /dev/null +++ b/load_tests/benchmarks/k6.py @@ -0,0 +1,233 @@ +import json +import os +import subprocess +import tempfile +from enum import Enum +from typing import Any, Dict, List + +import numpy as np +from jinja2 import Environment, PackageLoader, select_autoescape +from loguru import logger +from transformers import LlamaTokenizerFast + +from benchmarks.utils import kill + +env = Environment( + loader=PackageLoader("benchmarks"), + autoescape=select_autoescape() +) + + +class ExecutorInputType(Enum): + CONSTANT_TOKENS = "constant_tokens" + SHAREGPT_CONVERSATIONS = "sharegpt_conversations" + + +class K6Executor: + def __init__(self, name, template_name, executor_input_type=ExecutorInputType.SHAREGPT_CONVERSATIONS): + self.template_name = template_name + self.variables = {} + self.rendered_file = None + self.name = name + self.executor_input_type = executor_input_type + if executor_input_type == ExecutorInputType.CONSTANT_TOKENS: + self.input_filename = "inputs_constant_tokens.json" + elif executor_input_type == ExecutorInputType.SHAREGPT_CONVERSATIONS: + self.input_filename = "inputs_variable_tokens.json" + + def render(self): + template = env.get_template(self.template_name) + _, path = tempfile.mkstemp("k6", "benchmark") + cwd = os.getcwd() + with open(path, "w") as f: + f.write(template.render(cwd=cwd, input_filename=self.input_filename, **self.variables)) + self.rendered_file = path + + def __str__(self): + # returns an underscore separated string of the variables for filename generation + params = "_".join([f"{k}_{v}" for k, v in sorted(self.variables.items()) if type(v) == str or type(v) == int]) + return f"{self.executor_input_type.value}_{params}" + + +class K6ConstantArrivalRateExecutor(K6Executor): + def __init__(self, pre_allocated_vus: int, rate_per_second: int, duration: str, + executor_input_type: ExecutorInputType): + super().__init__("constant_arrival_rate", "k6_constant_arrival_rate.js.j2", executor_input_type) + self.variables = { + "pre_allocated_vus": pre_allocated_vus, # it's also the max vus + "rate": rate_per_second, + "duration": duration + } + + +class K6RampingArrivalRateExecutor(K6Executor): + def __init__(self, pre_allocated_vus: int, start_rate: int, time_unit: str, stages: List[Dict[str, Any]], + executor_input_type: ExecutorInputType): + super().__init__("ramping_arrival_rate", "k6_ramping_arrival_rate.js.j2", executor_input_type) + self.variables = { + "pre_allocated_vus": pre_allocated_vus, + "start_rate": start_rate, + "time_unit": time_unit, + "stages": stages + } + + +class K6ConstantVUsExecutor(K6Executor): + def __init__(self, vus: int, duration: str, executor_input_type: ExecutorInputType): + super().__init__("constant_vus", "k6_constant_vus.js.j2", executor_input_type) + self.variables = { + "vus": vus, + "duration": duration + } + + +class K6Config: + def __init__(self, name: str, executor: K6Executor, + tokenizer=LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer"), + conversations_input_file=None, + input_num_tokens=200, + max_new_tokens=200, + extra_info=None + ): + self.executor = executor + # max_new_token will be set in k6 template + self.executor.variables["max_new_tokens"] = max_new_tokens + self.name = name + self.tokenizer = tokenizer + self.extra_info = extra_info + if conversations_input_file is None: + self.conversation_input_file = "benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json" + self.input_num_tokens = input_num_tokens + + def __str__(self): + return f"K6Config(name={self.name} executor={self.executor})" + + +class K6Benchmark: + def __init__(self, k6_config: K6Config, output_dir: str): + self.process = None + self.k6_config = k6_config + self.output_dir = output_dir + self.input_tokens_len = k6_config.input_num_tokens + self._prepare_inputs() + + def _prepare_inputs(self): + get_tokens_count = lambda txt: len(self.k6_config.tokenizer.encode(txt)) + MAX_SAMPLES = 5000 + + # create a first input file with a constant number of tokens + # check if the file already exists + if not os.path.exists("inputs_constant_tokens.json"): + logger.info(f'Preparing input file with {self.input_tokens_len} input tokens') + outputs = [] + with open(self.k6_config.conversation_input_file, "r") as f: + data = json.load(f) + for doc in data: + for conversation in doc["conversations"]: + if not conversation["from"] == "human": + continue + if get_tokens_count(conversation["value"]) < self.input_tokens_len: + continue + # encode the message + encoding = self.k6_config.tokenizer(conversation["value"], truncation=True, + max_length=self.input_tokens_len) + # find last encoded characters + span = encoding.token_to_chars(len(encoding["input_ids"]) - 1) + outputs.append( + {"message": conversation["value"][0:span.end], "num_tokens": len(encoding["input_ids"])}) + if len(outputs) >= MAX_SAMPLES: # limit the number of inputs + break + with open("inputs_constant_tokens.json", "w") as f: + f.write(json.dumps(outputs)) + + # create a second input file with a sampling of inputs + # check if the file already exists + if not os.path.exists("inputs_variable_tokens.json"): + logger.info( + f'Preparing input file by randomly sampling shareGPT conversations at "{self.k6_config.conversation_input_file}"') + outputs = [] + with open(self.k6_config.conversation_input_file, "r") as f: + data = json.load(f) + num_docs = len(data) + # generate random indexes to sample the data + indexes = np.random.choice(num_docs, 200, replace=False) + for i in indexes: + doc = data[i] + for conversation in doc["conversations"]: + if not conversation["from"] == "human": + continue + # encode the message without truncation + encoding = self.k6_config.tokenizer(conversation["value"]) + outputs.append( + {"message": conversation["value"], "num_tokens": len(encoding["input_ids"])}) + if len(outputs) >= MAX_SAMPLES: # limit the number of inputs + break + with open("inputs_variable_tokens.json", "w") as f: + f.write(json.dumps(outputs)) + + def run(self): + self.k6_config.executor.render() + args = f"/tmp/k6-sse run --out json=results.json {self.k6_config.executor.rendered_file}" + logger.info(f"Running k6 with parameters: {args}") + logger.info(f"K6Config is: {self.k6_config}") + # start a k6 subprocess + self.process = subprocess.Popen(args, + stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True) + while buffer := os.read(self.process.stdout.fileno(), + 2048): # read the output of the process, don't buffer on new lines + print(buffer.decode(), end='') + self.process.wait() + logger.info(f"K6 process finished with return code {self.process.returncode}") + logger.info(f"Writing results to {self.get_results_path()}") + self.add_config_to_summary() + self.add_config_to_results() + + def stop(self): + if self.process: + kill(self.process.pid) + + def add_config_to_summary(self): + with open("summary.json", "r") as f: + summary = json.load(f) + summary["k6_config"] = { + "name": self.k6_config.name, + "input_type": self.k6_config.executor.executor_input_type.value, + "extra_info": self.k6_config.extra_info, + **self.k6_config.executor.variables + } + # create directory if it doesn't exist + os.makedirs(self._get_output_dir(), exist_ok=True) + with open(self.get_summary_path(), "w") as f2: + json.dump(summary, f2) + + def add_config_to_results(self): + with open("results.json", "r") as f: + results = f.readlines() + # append the k6 config to the results in jsonlines format + results += "\n" + results += json.dumps({ + "name": self.k6_config.name, + "input_type": self.k6_config.executor.executor_input_type.value, + "extra_info": self.k6_config.extra_info, + **self.k6_config.executor.variables + }) + # create directory if it doesn't exist + os.makedirs(self._get_output_dir(), exist_ok=True) + with open(self.get_results_path(), "w") as f2: + f2.writelines(results) + + def _get_output_dir(self): + # check if output_dir is relative or absolute + if self.output_dir.startswith("/"): + return f"{self.output_dir}/{self.k6_config.executor.name}" + else: + return f"{os.getcwd()}/{self.output_dir}/{self.k6_config.executor.name}" + + def _get_output_path(self): + return f"{self._get_output_dir()}/{self.k6_config.name}_{self.k6_config.executor}" + + def get_results_path(self): + return f"{self._get_output_path()}.json" + + def get_summary_path(self): + return f"{self._get_output_path()}.summary.json" diff --git a/load_tests/benchmarks/templates/k6_constant_arrival_rate.js.j2 b/load_tests/benchmarks/templates/k6_constant_arrival_rate.js.j2 new file mode 100644 index 00000000..244ce6e5 --- /dev/null +++ b/load_tests/benchmarks/templates/k6_constant_arrival_rate.js.j2 @@ -0,0 +1,16 @@ +{% include 'main.js.j2' %} + +export function get_options() { + return { + scenarios: { + load_test: { + executor: 'constant-arrival-rate', + gracefulStop: '0s', + duration: '{{ duration }}', + preAllocatedVUs: {{ pre_allocated_vus }}, + rate: {{ rate }}, + timeUnit: '1s', + }, + }, + }; +} \ No newline at end of file diff --git a/load_tests/benchmarks/templates/k6_constant_vus.js.j2 b/load_tests/benchmarks/templates/k6_constant_vus.js.j2 new file mode 100644 index 00000000..ac7f5593 --- /dev/null +++ b/load_tests/benchmarks/templates/k6_constant_vus.js.j2 @@ -0,0 +1,14 @@ +{% include 'main.js.j2' %} + +export function get_options() { + return { + scenarios: { + load_test: { + executor: 'constant-vus', + gracefulStop: '0s', + duration: '{{ duration }}', + vus: {{ vus }}, + }, + }, + }; +} \ No newline at end of file diff --git a/load_tests/benchmarks/templates/k6_ramping_arrival_rate.js.j2 b/load_tests/benchmarks/templates/k6_ramping_arrival_rate.js.j2 new file mode 100644 index 00000000..0c21a55e --- /dev/null +++ b/load_tests/benchmarks/templates/k6_ramping_arrival_rate.js.j2 @@ -0,0 +1,20 @@ +{% include 'main.js.j2' %} + +export function get_options() { + return { + scenarios: { + load_test: { + executor: 'ramping-arrival-rate', + gracefulStop: '0s', + preAllocatedVUs: {{ pre_allocated_vus }}, + timeUnit: '{{ time_unit }}', + startRate: {{ start_rate }}, + stages: [ + {%- for stage in stages %} + {target: {{ stage.target }}, duration: '{{ stage.duration }}'}, + {%- endfor %} + ], + }, + }, + }; +} diff --git a/load_tests/benchmarks/templates/main.js.j2 b/load_tests/benchmarks/templates/main.js.j2 new file mode 100644 index 00000000..ffb55d14 --- /dev/null +++ b/load_tests/benchmarks/templates/main.js.j2 @@ -0,0 +1,131 @@ +import {check, fail} from 'k6'; +import sse from "k6/x/sse" +import {scenario} from 'k6/execution'; +import http from 'k6/http'; +import {Trend, Counter} from 'k6/metrics'; + +const host = "127.0.0.1:8080"; +const model_id = "Qwen/Qwen2-72B"; + +const endToEndLatency = new Trend('end_to_end_latency', true); +const requestThroughput = new Counter('request_throughput'); +const tokenThroughput = new Counter('tokens_throughput'); + +const timeToFirstToken = new Trend('time_to_first_token', true); +const interTokenLatency = new Trend('inter_token_latency', true); // is microseconds + +const tokensReceived = new Trend('tokens_received'); + +const max_new_tokens = {{ max_new_tokens }}; + +const shareGPT = JSON.parse(open("{{ cwd }}/{{ input_filename }}")) + +export function handleSummary(data) { + return { + 'summary.json': JSON.stringify(data), + }; +} + +function generate_payload(gpt, max_new_tokens) { + let input = gpt["message"]; + return { + "messages": [{"role": "user", "content": input}], + "temperature": 0, + "model": `${model_id}`, + "max_tokens": max_new_tokens, + "stream": true + }; +} + +export const options = get_options(); + +export default function run() { + const headers = {'Content-Type': 'application/json'}; + const query = shareGPT[scenario.iterationInTest % shareGPT.length]; + const payload = JSON.stringify(generate_payload(query, max_new_tokens)); + const url = `http://${host}/v1/chat/completions`; + const params = { + method: 'POST', + body: payload, + headers + } + + const startTime = Date.now(); + let firstTokenTime = null; + let lastTokenTime = null; + let tokensCount = 0; + let response = "" + + const res = sse.open(url, params, function (client) { + client.on('event', function (event) { + // console.log(event.data) + if (parseInt(event.id) === 4) { + client.close() + } + if (event.data.includes("[DONE]") || event.data === "") { + return + } + try { + const data = JSON.parse(event.data); + if (!'choices' in data) { + fail('http_200') + return; + } + const content = data['choices'][0]['delta']['content'] + if (content !== undefined) { + response += data['choices'][0]['delta']['content'] + tokensCount += 1; + } + + // Measure time to first token + if (!firstTokenTime) { + firstTokenTime = Date.now(); + timeToFirstToken.add(firstTokenTime - startTime); + } + + // Measure inter-token latency + const currentTime = Date.now(); + if (lastTokenTime) { + interTokenLatency.add((currentTime - lastTokenTime) * 1000.); + } + lastTokenTime = currentTime; + + if ('finish_reason' in data['choices'][0]) { + if (data['choices'][0]['finish_reason'] != null) { + const endTime = Date.now(); + const deltaMs = endTime - startTime; + endToEndLatency.add(deltaMs) + requestThroughput.add(1); + tokenThroughput.add(tokensCount); + tokensReceived.add(tokensCount); + } + } + } catch (e) { + // catch any errors that occur during the event processing + // increase the fail count of the 'http_200' check + check(true, { + 'http_200': (val) => false, + }) + fail('http_200') + } + }) + + client.on('error', function (e) { + console.log('An unexpected error occurred: ', e.error()) + }) + }) + + if (tokensCount === 0) { + // something went wrong with generation + fail('http_200') + } + + if (res.status >= 400 && res.status < 500) { + return; + } + + check(res, { + 'http_200': (res) => res.status === 200, + }); + +} \ No newline at end of file diff --git a/load_tests/benchmarks/test_k6.py b/load_tests/benchmarks/test_k6.py new file mode 100644 index 00000000..1c35568f --- /dev/null +++ b/load_tests/benchmarks/test_k6.py @@ -0,0 +1,31 @@ +import os +from unittest import TestCase + +from benchmarks.k6 import K6RampingArrivalRateExecutor, K6Config, K6ConstantVUsExecutor, K6Benchmark, ExecutorInputType + + +class K6RampingArrivalRateExecutorTest(TestCase): + def test_render(self): + executor = K6RampingArrivalRateExecutor( + 100, + 1, + "1s", + [ + {"target": 1, "duration": "30s"}, + {"target": 100, "duration": "30s"} + ], + ExecutorInputType.SHAREGPT_CONVERSATIONS) + executor.render() + self.assertIsNotNone(executor.rendered_file) + with open(executor.rendered_file, "r") as f: + content = f.read() + self.assertTrue("stages: [" in content) + self.assertTrue("target: 1, duration: '30s'" in content) + self.assertTrue(os.getcwd() in content) + + +class K6BenchmarkTest(TestCase): + def test_prepare_inputs(self): + executor = K6ConstantVUsExecutor(1, '1m') + config = K6Config("test", executor, input_num_tokens=500) + bench = K6Benchmark(config, "output") diff --git a/load_tests/benchmarks/utils.py b/load_tests/benchmarks/utils.py new file mode 100644 index 00000000..1dc616a8 --- /dev/null +++ b/load_tests/benchmarks/utils.py @@ -0,0 +1,25 @@ +from itertools import chain + +import psutil + + +class SweepParameter: + def __init__(self, name, start, end, step): + self.name = name + self.start = start + self.end = end + self.step = step + + def __str__(self): + return f"{self.name} from {self.start} to {self.end} in steps of {self.step}" + + def __iter__(self): + for value in chain(range(self.start, self.end, self.step), [self.end]): + yield value + + +def kill(proc_pid): + process = psutil.Process(proc_pid) + for proc in process.children(recursive=True): + proc.kill() + process.kill() diff --git a/load_tests/common.js b/load_tests/common.js deleted file mode 100644 index e0a10595..00000000 --- a/load_tests/common.js +++ /dev/null @@ -1,94 +0,0 @@ -import { check } from 'k6'; -import { scenario } from 'k6/execution'; -import http from 'k6/http'; -import { Trend, Counter } from 'k6/metrics'; - -const host = __ENV.HOST; -const model_id = __ENV.MODEL_ID; -const timePerToken = new Trend('time_per_token', true); -const tokens = new Counter('tokens'); -const new_tokens = new Counter('new_tokens'); -const input_tokens = new Counter('input_tokens'); -const max_new_tokens = 50; - -// const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json")) -const shareGPT = JSON.parse(open("small.json")) - - -export function get_options() { - return { - thresholds: { - http_req_failed: ['rate==0'], - // time_per_token: [{ - // threshold: `p(50)<${5 * reference_latency_ms}`, - // abortOnFail: true, - // delayAbortEval: '10s' - // }], - }, - scenarios: { - // single_user: { - // executor: 'constant-arrival-rate', - // duration: '60s', - // preAllocatedVUs: 1, - // rate: 20, - // timeUnit: '1s', - // }, - load_test: { - executor: 'constant-arrival-rate', - duration: '60s', - preAllocatedVUs: 100, - rate: 1, - timeUnit: '1s', - }, - // breakpoint: { - // executor: 'ramping-arrival-rate', //Assure load increase if the system slows - // preAllocatedVUs: 300, - // stages: [ - // { duration: '60s', target: 100 }, // just slowly ramp-up to a HUGE load - // ], - // }, - // throughput: { - // executor: 'shared-iterations', - // vus: 100, - // iterations: 200, - // maxDuration: '40s', - // }, - }, - }; -} - -function generate_payload(gpt, max_new_tokens) { - const input = gpt["conversations"][0]["value"]; - return { "messages": [{ "role": "user", "content": input }], "temperature": 0, "model": `${model_id}`, "max_tokens": max_new_tokens } -} - -export const options = get_options(); - -export default function run() { - const headers = { 'Content-Type': 'application/json' }; - const query = shareGPT[scenario.iterationInTest % shareGPT.length]; - const payload = JSON.stringify(generate_payload(query, max_new_tokens)); - const res = http.post(`http://${host}/v1/chat/completions`, payload, { - headers, - }); - if (res.status >= 400 && res.status < 500) { - return; - } - - - check(res, { - 'Post status is 200': (res) => res.status === 200, - }); - const duration = res.timings.duration; - - if (res.status === 200) { - const body = res.json(); - const completion_tokens = body.usage.completion_tokens; - const latency_ms_per_token = duration / completion_tokens; - timePerToken.add(latency_ms_per_token); - const prompt_tokens = body.usage.prompt_tokens; - input_tokens.add(prompt_tokens); - new_tokens.add(completion_tokens); - tokens.add(completion_tokens + prompt_tokens); - } -} diff --git a/load_tests/download_artifact.js b/load_tests/download_artifact.js new file mode 100644 index 00000000..bce79cdd --- /dev/null +++ b/load_tests/download_artifact.js @@ -0,0 +1,98 @@ +module.exports = async ({ + github, + context, + core + }) => { + const owner = context.repo.owner; + const repo = context.repo.repo; + + const workflows = await github.rest.actions.listRepoWorkflows({ + owner, + repo + }) + + const workflow = workflows.data.workflows.find(w => w.path.includes(process.env.WORKFLOW_FILENAME)); + + if (!workflow) { + core.setFailed("No workflow found"); + return; + } + + const runs = await github.rest.actions.listWorkflowRuns({ + owner, + repo, + workflow_id: workflow.id, + status: "success", + per_page: 1 + }) + + if (runs.data.total_count === 0) { + core.setFailed("No runs found"); + return; + } + + const lastRelease = await github.rest.repos.getLatestRelease({ + owner, + repo + }); + + const lastReleaseTag = lastRelease.data.tag_name; + const tagRef = `tags/${lastReleaseTag}`; + const lastReleaseCommit = await github.rest.git.getRef({ + owner, + repo, + ref: tagRef + }); + const lastReleaseSha = lastReleaseCommit.data.object.sha; + const lastReleaseRun = await github.rest.actions.listWorkflowRuns({ + owner, + repo, + workflow_id: workflow.id, + head_sha: lastReleaseSha, + status: "success", + per_page: 1 + }); + let lastReleaseArtifacts = {data: {artifacts: []}}; + if (lastReleaseRun.data.total_count > 0) { + lastReleaseArtifacts = await github.rest.actions.listWorkflowRunArtifacts({ + owner, + repo, + run_id: lastReleaseRun.data.workflow_runs[0].id + }); + } + + const lastArtifacts = await github.rest.actions.listWorkflowRunArtifacts({ + owner, + repo, + run_id: runs.data.workflow_runs[0].id + }); + + const lastReleaseArtifact = lastReleaseArtifacts.data.artifacts.find(artifact => artifact.name === process.env.ARTIFACT_NAME); + const lastArtifact = lastArtifacts.data.artifacts.find(artifact => artifact.name === process.env.ARTIFACT_NAME); + + if (lastReleaseArtifact) { + await downloadArtifact(github, owner, repo, lastReleaseArtifact, lastReleaseTag); + } else { + console.log("No release artifact found") + } + if (lastArtifact) { + await downloadArtifact(github, owner, repo, lastArtifact, lastArtifact.workflow_run.head_sha); + } else { + console.log("No last run artifact found") + } +} + +async function downloadArtifact(github, owner, repo, artifact, suffix) { + const response = await github.rest.actions.downloadArtifact({ + owner, + repo, + artifact_id: artifact.id, + archive_format: 'zip' + }); + require('fs').writeFileSync(process.env.ARTIFACT_FILENAME, Buffer.from(response.data)); + // create directory to unzip + require('fs').mkdirSync(`${process.env.UNZIP_DIR}/${artifact.workflow_run.head_sha}`, {recursive: true}); + require('child_process').execSync(`unzip -o ${process.env.ARTIFACT_FILENAME} -d ${process.env.UNZIP_DIR}/${suffix}`); + + console.log(`Artifact ${process.env.ARTIFACT_FILENAME} for ${suffix} downloaded successfully`); +} \ No newline at end of file diff --git a/load_tests/filter.py b/load_tests/filter.py deleted file mode 100644 index a00226ed..00000000 --- a/load_tests/filter.py +++ /dev/null @@ -1,26 +0,0 @@ -import json - - -def main(): - with open("./ShareGPT_V3_unfiltered_cleaned_split.json", "r") as f: - data = json.load(f) - - # Select only the first 2k conversations that start with a human. - max = 2000 - conversations = [] - for conversation in data: - conv = conversation.get("conversations") - if conv and conv[0]["from"] == "human": - # Trim the rest of the output - conversation["conversations"] = conversation["conversations"][:1] - conversations.append(conversation) - - if len(conversation) >= max: - break - - with open("./small.json", "w") as f: - data = json.dump(conversations, f, indent=4) - - -if __name__ == "__main__": - main() diff --git a/load_tests/load_test.py b/load_tests/load_test.py new file mode 100644 index 00000000..573fbe89 --- /dev/null +++ b/load_tests/load_test.py @@ -0,0 +1,103 @@ +import os +import time +import traceback + +from benchmarks.engine import TGIDockerRunner +from benchmarks.k6 import K6Config, K6Benchmark, K6ConstantArrivalRateExecutor, K6ConstantVUsExecutor, ExecutorInputType +from loguru import logger +import pandas as pd +import GPUtil + +from parse_load_test import TestType, parse_json_files, plot_metrics + + +def run_full_test(engine_name: str): + vus_concurrences = list(range(0, 1024, 40)) + vus_concurrences[0] = 1 + vus_concurrences.append(1024) + arrival_rates = list(range(0, 200, 10)) + arrival_rates[0] = 1 + arrival_rates.append(200) + for input_type in [ExecutorInputType.SHAREGPT_CONVERSATIONS, ExecutorInputType.CONSTANT_TOKENS]: + for c in arrival_rates: + logger.info(f'Running k6 with constant arrival rate for {c} req/s with input type {input_type.value}') + k6_executor = K6ConstantArrivalRateExecutor(2000, c, '60s', input_type) + k6_config = K6Config(f'{engine_name}', k6_executor, input_num_tokens=200) + benchmark = K6Benchmark(k6_config, f'results/{input_type.value}/') + benchmark.run() + for c in vus_concurrences: + logger.info(f'Running k6 with constant VUs with concurrency {c} with input type {input_type.value}') + k6_executor = K6ConstantVUsExecutor(c, '60s', input_type) + k6_config = K6Config(f'{engine_name}', k6_executor, input_num_tokens=200) + benchmark = K6Benchmark(k6_config, f'results/{input_type.value}/') + benchmark.run() + + +def merge_previous_results(csv_path: str, df: pd.DataFrame, version_id: str) -> pd.DataFrame: + if os.path.exists(csv_path): + previous_df = pd.read_csv(csv_path) + previous_df['name'] = previous_df['name'].str.replace('tgi', f'tgi_{version_id}') + df = pd.concat([previous_df, df]) + return df + + +def main(): + model = 'Qwen/Qwen2-7B' + runner = TGIDockerRunner(model) + max_concurrent_requests = 8000 + # run TGI + try: + logger.info('Running TGI') + runner.run([('max-concurrent-requests', max_concurrent_requests)]) + logger.info('TGI is running') + run_full_test('tgi') + except Exception as e: + logger.error(f'Error: {e}') + # print the stack trace + print(traceback.format_exc()) + finally: + runner.stop() + time.sleep(5) + + for input_type in [ExecutorInputType.SHAREGPT_CONVERSATIONS, ExecutorInputType.CONSTANT_TOKENS]: + for test_type in [TestType.CONSTANT_VUS, TestType.CONSTANT_ARRIVAL_RATE]: + directory = os.path.join('results', input_type.value.lower(), test_type.value.lower()) + # check if directory exists + if not os.path.exists(directory): + logger.error(f'Directory {directory} does not exist') + continue + dfs = parse_json_files(directory, test_type) + # create output directory if it does not exist + os.makedirs('output', exist_ok=True) + # save the data to a csv file + path = os.path.join(os.getcwd(), 'output', f'{input_type.value.lower()}_{test_type.value.lower()}.csv') + dfs.to_csv(path) + # check if we have previous results CSV file by listing /tmp/artifacts/ directory, + # merge them if they exist + prev_root = '/tmp/artifacts' + try: + if os.path.exists(prev_root): + directories = [item for item in os.listdir(prev_root) if + os.path.isdir(os.path.join(prev_root, item))] + for d in directories: + for f in os.listdir(f'{prev_root}/{d}'): + if f.endswith(f'{input_type.value.lower()}_{test_type.value.lower()}.csv'): + csv_path = os.path.join('/tmp/artifacts', d, f) + # only keep short commit hash + d = d[:7] + dfs = merge_previous_results(csv_path, dfs, d) + except Exception as e: + logger.error(f'Error while merging previous results, skipping: {e}') + plot_metrics(f'{model} {get_gpu_names()}', dfs, test_type, + f'output/{input_type.value.lower()}_{test_type.value.lower()}') + + +def get_gpu_names() -> str: + gpus = GPUtil.getGPUs() + if len(gpus) == 0: + return '' + return f'{len(gpus)}x{gpus[0].name if gpus else "No GPU available"}' + + +if __name__ == '__main__': + main() diff --git a/load_tests/orca.py b/load_tests/orca.py deleted file mode 100644 index e607d27c..00000000 --- a/load_tests/orca.py +++ /dev/null @@ -1,27 +0,0 @@ -import json -import datasets -import tqdm - - -def main(): - dataset = datasets.load_dataset("Open-Orca/OpenOrca", split="train") - # Select only the first 2k conversations that start with a human. - max = min(2000, len(dataset)) - conversations = [] - for item in tqdm.tqdm(dataset, total=max): - conversation = { - "conversations": [ - {"from": "human", "value": item["question"]}, - ], - "id": item["id"], - } - conversations.append(conversation) - if len(conversations) >= max: - break - - with open("./small.json", "w") as f: - data = json.dump(conversations, f, indent=4) - - -if __name__ == "__main__": - main() diff --git a/load_tests/parse_load_test.py b/load_tests/parse_load_test.py new file mode 100644 index 00000000..0535d5cc --- /dev/null +++ b/load_tests/parse_load_test.py @@ -0,0 +1,115 @@ +import json +import os +from enum import Enum +import pandas as pd +from loguru import logger + +from matplotlib import pyplot as plt +import scienceplots + +plt.style.use('science') + + +class TestType(Enum): + CONSTANT_VUS = "constant_vus" + CONSTANT_ARRIVAL_RATE = "constant_arrival_rate" + + +def parse_json_files(directory: str, test_type: TestType) -> pd.DataFrame: + metrics_to_keep = {'inter_token_latency': {'y': 'Time (ms)'}, 'end_to_end_latency': {'y': 'Time (ms)'}, + 'time_to_first_token': {'y': 'Time (ms)'}, 'tokens_throughput': {'y': 'Tokens/s'}, + 'tokens_received': {'y': 'Count'}} + df = pd.DataFrame() + for file in os.listdir(directory): + if file.endswith("summary.json"): + filepath = os.path.join(directory, file) + with open(filepath, 'r') as f: + data = json.load(f) + if test_type == TestType.CONSTANT_VUS: + entry = { + "vus": data['k6_config']['vus'], + "duration": data['k6_config']['duration'] + } + elif test_type == TestType.CONSTANT_ARRIVAL_RATE: + entry = { + 'pre_allocated_vus': data['k6_config']['pre_allocated_vus'], + 'rate': data['k6_config']['rate'], + 'duration': data['k6_config']['duration'] + } + entry['test_duration'] = data['state']['testRunDurationMs'] / 1000. + entry['requests_ok'] = data['root_group']['checks'][0]['passes'] + entry['requests_fail'] = data['root_group']['checks'][0]['fails'] + entry['dropped_iterations'] = data['metrics']['dropped_iterations']['values'][ + 'count'] if 'dropped_iterations' in data['metrics'] else 0 + # add up requests_fail and dropped_iterations to get total dropped requests + entry['dropped_requests'] = entry['requests_fail'] + entry['dropped_iterations'] + entry['error_rate'] = entry['dropped_requests'] / ( + entry['requests_ok'] + entry['dropped_requests']) * 100.0 + entry['name'] = data['k6_config']['name'] + for metric, values in sorted(data['metrics'].items()): + if metric in metrics_to_keep: + for value_key, value in values['values'].items(): + if value_key == 'p(90)' or value_key == 'count': # Only keep p(90) values if trend + entry[metric] = value + if 'tokens_throughput' in entry and 'test_duration' in entry: + entry['tokens_throughput'] = entry['tokens_throughput'] / (entry['test_duration']) + if 'inter_token_latency' in entry: + entry['inter_token_latency'] = entry['inter_token_latency'] / 1000. + df = pd.concat([df, pd.DataFrame(entry, index=[0])]) + return df + + +def plot_metrics(model_name:str, df: pd.DataFrame, test_type: TestType, save_name: str): + vus_param = '' + if test_type == TestType.CONSTANT_VUS: + vus_param = 'vus' + else: + vus_param = 'rate' + fig, axs = plt.subplots(3, 2, figsize=(15, 20)) + fig.tight_layout(pad=6.0) + fig.subplots_adjust(hspace=0.2, wspace=0.2, bottom=0.15, top=0.92) + + names = sorted(df['name'].unique()) + metrics = {'inter_token_latency': {'y': 'Time (ms)'}, 'time_to_first_token': {'y': 'Time (ms)'}, + 'end_to_end_latency': {'y': 'Time (ms)'}, 'tokens_throughput': {'y': 'Tokens/s'}, + 'requests_ok': {'y': 'Count'}, 'error_rate': {'y': 'Count'}} + titles = ['Inter Token Latency P90 (lower is better)', 'TTFT P90 (lower is better)', + 'End to End Latency P90 (lower is better)', 'Request Output Throughput P90 (higher is better)', + 'Successful requests (higher is better)', 'Error rate (lower is better)'] + labels = ['Time (ms)', 'Time (ms)', 'Time (ms)', 'Tokens/s', 'Count', '%'] + colors = ['#FF9D00', '#2F5BA1'] + # Plot each metric in its respective subplot + for ax, metric, title, label in zip(axs.flatten(), metrics, titles, labels): + for i, name in enumerate(names): + df_sorted = df[df['name'] == name].sort_values(by=vus_param) + ax.plot(df_sorted[vus_param], df_sorted[metric], marker='o', label=f"{name}", color=colors[i]) + ax.set_title(title) + ax.tick_params(axis='x', rotation=0) + ax.set_ylabel(label) + if test_type == TestType.CONSTANT_VUS: + ax.set_xlabel('VUS') + else: + ax.set_xlabel('Requests/s') + # Add grid lines for better readability + ax.grid(True, which='both', axis='y', linestyle='--', linewidth=0.5) + ax.set_axisbelow(True) # Ensure grid lines are below the bars + ax.legend(title='Engine', loc='upper right') + + # show title on top of the figure + if test_type == TestType.CONSTANT_VUS: + plt.suptitle(f'Constant VUs Load Test\n{model_name}', fontsize=16) + elif test_type == TestType.CONSTANT_ARRIVAL_RATE: + plt.suptitle(f'Constant Arrival Rate Load Test\n{model_name}', fontsize=16) + logger.info(f"Saving plot to {save_name}.png") + plt.savefig(f"{save_name}.png") + + +def main(): + for test_type in [TestType.CONSTANT_VUS, TestType.CONSTANT_ARRIVAL_RATE]: + directory = f"results/{test_type.value.lower()}" + dfs = parse_json_files(directory, test_type) + plot_metrics(dfs, test_type, test_type.value.lower()) + + +if __name__ == "__main__": + main() diff --git a/load_tests/poetry.lock b/load_tests/poetry.lock new file mode 100644 index 00000000..d0d34571 --- /dev/null +++ b/load_tests/poetry.lock @@ -0,0 +1,1561 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "certifi" +version = "2024.7.4" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, + {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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