| |
| import os |
| from contextlib import nullcontext |
| from typing import List, Union |
|
|
| from evalscope.constants import EvalBackend, EvalType |
| from evalscope.run import TaskConfig, run_task |
| from evalscope.summarizer import Summarizer |
|
|
| from swift.utils import append_to_jsonl, get_logger |
| from .. import MediaResource |
| from ..argument import EvalArguments |
| from ..base import SwiftPipeline |
| from ..infer import run_deploy |
|
|
| logger = get_logger() |
|
|
|
|
| class SwiftEval(SwiftPipeline): |
| args_class = EvalArguments |
| args: args_class |
|
|
| def run(self): |
| args = self.args |
| eval_report = {} |
| deploy_context = nullcontext() if args.eval_url else run_deploy(args, return_url=True) |
| with deploy_context as base_url: |
| base_url = args.eval_url or base_url |
| url = f"{base_url.rstrip('/')}/chat/completions" |
|
|
| task_cfg = self.get_task_cfg(args.eval_dataset, args.eval_backend, url) |
| result = self.get_task_result(task_cfg) |
| eval_report[args.eval_backend] = result |
|
|
| eval_report.update({ |
| 'time': args.time, |
| 'model': args.model, |
| 'adapters': args.adapters, |
| 'result_path': args.result_path, |
| 'eval_output_dir': args.eval_output_dir, |
| 'eval_limit': args.eval_limit |
| }) |
|
|
| if args.result_jsonl: |
| append_to_jsonl(args.result_jsonl, eval_report) |
| logger.info(f'The eval result have been saved to result_jsonl: `{args.result_jsonl}`.') |
| return eval_report |
|
|
| def get_task_result(self, task_cfg: TaskConfig): |
| run_task(task_cfg=task_cfg) |
| reports = Summarizer.get_report_from_cfg(task_cfg=task_cfg) |
| result = {} |
| if task_cfg.eval_backend == EvalBackend.OPEN_COMPASS: |
| for report in reports: |
| if report[self.args.model_suffix] != '-': |
| result[report['dataset']] = {report['metric']: report[self.args.model_suffix]} |
| elif task_cfg.eval_backend == EvalBackend.VLM_EVAL_KIT: |
| for report in reports: |
| splited_key = next(iter(report)).rsplit('_', 2) |
| if len(splited_key) == 3: |
| _, dataset, metric = splited_key |
| else: |
| dataset, metric = '-', '-' |
| result[dataset] = {metric: list(report.values())[0]} |
| else: |
| result = reports |
| return result |
|
|
| def get_task_cfg(self, dataset: List[str], eval_backend: str, url: str): |
| assert eval_backend in {EvalBackend.NATIVE, EvalBackend.OPEN_COMPASS, EvalBackend.VLM_EVAL_KIT} |
| if eval_backend == EvalBackend.OPEN_COMPASS: |
| if self.args.local_dataset: |
| if os.path.exists('data'): |
| if not os.path.exists(os.path.join('data', 'CMB')): |
| raise RuntimeError('Opencompass need a `data` folder in your work dir(' |
| 'which will be created automatically by swift eval), ' |
| 'but a local path named `data` already exists, ' |
| 'please consider moving the dir to another location.') |
| else: |
| local_dir = MediaResource.download( |
| 'https://modelscope.cn/datasets/' |
| 'opencompass/OpenCompassDataComplete/' |
| 'resolve/master/OpenCompassData-complete-20240207.zip', 'OpenCompassData') |
| os.symlink(os.path.join(local_dir, 'data'), 'data') |
|
|
| task_cfg = self.get_opencompass_task_cfg(dataset, url) |
| elif eval_backend == EvalBackend.VLM_EVAL_KIT: |
| task_cfg = self.get_vlmeval_task_cfg(dataset, url) |
| else: |
| task_cfg = self.get_native_task_cfg(dataset, url) |
| return task_cfg |
|
|
| def get_native_task_cfg(self, dataset: List[str], url: str): |
| args = self.args |
| work_dir = os.path.join(args.eval_output_dir, 'native') |
| return TaskConfig( |
| model=args.model_suffix, |
| eval_type=EvalType.SERVICE, |
| api_url=url, |
| api_key=args.api_key or 'EMPTY', |
| datasets=dataset, |
| work_dir=work_dir, |
| limit=args.eval_limit, |
| eval_batch_size=args.eval_num_proc, |
| dataset_args=args.dataset_args, |
| generation_config=args.eval_generation_config, |
| **args.extra_eval_args) |
|
|
| def get_opencompass_task_cfg(self, dataset: List[str], url: str): |
| args = self.args |
| work_dir = os.path.join(args.eval_output_dir, 'opencompass') |
| return TaskConfig( |
| eval_backend=EvalBackend.OPEN_COMPASS, |
| eval_config={ |
| 'datasets': |
| dataset, |
| 'batch_size': |
| args.eval_num_proc, |
| 'work_dir': |
| work_dir, |
| 'models': [{ |
| 'path': args.model_suffix, |
| 'openai_api_base': url, |
| 'key': args.api_key or 'EMPTY', |
| 'is_chat': args.use_chat_template |
| }], |
| 'limit': |
| args.eval_limit |
| }, |
| work_dir=work_dir) |
|
|
| def get_vlmeval_task_cfg(self, dataset: List[str], url: str): |
| args = self.args |
| work_dir = os.path.join(args.eval_output_dir, 'vlmeval') |
| return TaskConfig( |
| eval_backend=EvalBackend.VLM_EVAL_KIT, |
| eval_config={ |
| 'data': |
| dataset, |
| 'model': [{ |
| 'type': args.model_suffix, |
| 'name': 'CustomAPIModel', |
| 'api_base': url, |
| 'key': args.api_key or 'EMPTY', |
| **args.eval_generation_config |
| }], |
| 'nproc': |
| args.eval_num_proc, |
| 'limit': |
| args.eval_limit |
| }, |
| work_dir=work_dir) |
|
|
|
|
| def eval_main(args: Union[List[str], EvalArguments, None] = None): |
| return SwiftEval(args).main() |
|
|
