[feature]Support tau2 bench

ais_bench/benchmark/cli/config_manager.py

@@ -12,8 +12,8 @@
 from ais_bench.benchmark.cli.utils import fill_model_path_if_datasets_need, fill_test_range_use_num_prompts
 
 class CustomConfigChecker:
-    MODEL_REQUIRED_FIELDS = ['type', 'abbr', 'attr']
-    DATASET_REQUIRED_FIELDS = ['type', 'abbr', 'reader_cfg', 'infer_cfg', 'eval_cfg']
+    MODEL_REQUIRED_FIELDS = ['abbr']
+    DATASET_REQUIRED_FIELDS = ['abbr']
     SUMMARIZER_REQUIRED_FIELDS = ['attr']
 
     def __init__(self, config, file_path):
@@ -106,6 +106,8 @@ def load_config(self, workflow):
 
     def _fill_dataset_configs(self):
         for dataset_cfg in self.cfg["datasets"]:
+            if dataset_cfg.get("infer_cfg", None) is None:
+                continue
             fill_test_range_use_num_prompts(self.cfg["cli_args"].get("num_prompts"), dataset_cfg)
             fill_model_path_if_datasets_need(self.cfg["models"][0], dataset_cfg)
             retriever_cfg = dataset_cfg["infer_cfg"]["retriever"]

ais_bench/benchmark/cli/utils.py

@@ -16,6 +16,8 @@
 logger = AISLogger()
 
 def get_config_type(obj) -> str:
+    if obj is None:
+        return None
     if isinstance(obj, str):
         return obj
     return f"{obj.__module__}.{obj.__name__}"

ais_bench/benchmark/cli/workers.py

@@ -15,6 +15,7 @@
 from ais_bench.benchmark.partitioners import NaivePartitioner
 from ais_bench.benchmark.runners import LocalRunner
 from ais_bench.benchmark.tasks import OpenICLEvalTask, OpenICLApiInferTask, OpenICLInferTask
+from ais_bench.benchmark.tasks.base import EmptyTask
 from ais_bench.benchmark.summarizers import DefaultSummarizer, DefaultPerfSummarizer
 from ais_bench.benchmark.calculators import DefaultPerfMetricCalculator
 from ais_bench.benchmark.cli.utils import fill_model_path_if_datasets_need
@@ -26,6 +27,7 @@
 class BaseWorker(ABC):
     def __init__(self, args) -> None:
         self.args = args
+        self.skip = False
 
     @abstractmethod
     def update_cfg(self, cfg: ConfigDict) -> None:
@@ -39,21 +41,29 @@ def do_work(self, cfg: ConfigDict):
 
 
 class Infer(BaseWorker):
-    def update_cfg(self, cfg: ConfigDict) -> None:
+    def update_cfg(self, cfg: ConfigDict) -> ConfigDict:
         def get_task_type() -> str:
             if cfg["models"][0]["attr"] == "service":
                 return get_config_type(OpenICLApiInferTask)
             else:
                 return get_config_type(OpenICLInferTask)
 
+        custom_infer = cfg.get("infer")
+        custom_task = None
+        if custom_infer:
+            custom_task = custom_infer["runner"]["task"].get("type")
+            if custom_task == EmptyTask:
+                self.skip = True
+                return cfg
+
         new_cfg = dict(
             infer=dict(
-                partitioner=dict(type=get_config_type(NaivePartitioner)),
+                partitioner=  dict(type=get_config_type(NaivePartitioner)),
                 runner=dict(
                     max_num_workers=self.args.max_num_workers,
                     max_workers_per_gpu=self.args.max_workers_per_gpu,
                     debug=self.args.debug,
-                    task=dict(type=get_task_type()),
+                    task=dict(type=get_config_type(custom_task) if custom_task else get_task_type()),
                     type=get_config_type(LocalRunner),
                 ),
             ),
@@ -66,6 +76,9 @@ def get_task_type() -> str:
         return cfg
 
     def do_work(self, cfg: ConfigDict):
+        if self.skip:
+            logger.info("EmptyTask is selected, skip inference.")
+            return
         partitioner = PARTITIONERS.build(cfg.infer.partitioner)
         logger.info("Starting inference tasks...")
         tasks = partitioner(cfg)
@@ -118,7 +131,7 @@ def __init__(self, args) -> None:
         super().__init__(args)
         self.judge_model_type = None
 
-    def update_cfg(self, cfg: ConfigDict) -> None:
+    def update_cfg(self, cfg: ConfigDict) -> ConfigDict:
         for dataset_cfg in cfg["datasets"]:
             judge_infer_cfg = dataset_cfg.get("judge_infer_cfg")
             if judge_infer_cfg:
@@ -258,20 +271,28 @@ def _result_post_process(self, tasks, cfg: ConfigDict):
 
 
 class Eval(BaseWorker):
-    def update_cfg(self, cfg: ConfigDict) -> None:
+    def update_cfg(self, cfg: ConfigDict) -> ConfigDict:
+        custom_eval = cfg.get("eval")
+        custom_task = None
+        if custom_eval:
+            custom_task = custom_eval["runner"]["task"].get("type")
+            if custom_task == EmptyTask:
+                self.skip = True
+                return cfg
+
         new_cfg = dict(
             eval=dict(
                 partitioner=dict(type=get_config_type(NaivePartitioner)),
                 runner=dict(
                     max_num_workers=self.args.max_num_workers,
+                    max_workers_per_gpu=self.args.max_workers_per_gpu,
                     debug=self.args.debug,
-                    task=dict(type=get_config_type(OpenICLEvalTask)),
+                    task=dict(type=get_config_type(custom_task) if custom_task else get_config_type(OpenICLEvalTask)),
+                    type=get_config_type(LocalRunner),
                 ),
             ),
         )
 
-        new_cfg["eval"]["runner"]["type"] = get_config_type(LocalRunner)
-        new_cfg["eval"]["runner"]["max_workers_per_gpu"] = self.args.max_workers_per_gpu
         cfg.merge_from_dict(new_cfg)
         if cfg.cli_args.dump_eval_details:
             cfg.eval.runner.task.dump_details = True
@@ -283,6 +304,9 @@ def update_cfg(self, cfg: ConfigDict) -> None:
         return cfg
 
     def do_work(self, cfg: ConfigDict):
+        if self.skip:
+            logger.info("EmptyTask is selected, skip evaluation.")
+            return
         partitioner = PARTITIONERS.build(cfg.eval.partitioner)
         logger.info("Starting evaluation tasks...")
         self._cfg_pre_process(cfg)

ais_bench/benchmark/partitioners/base.py

@@ -111,8 +111,8 @@ def _check_task_cfg(self, tasks):
         filtered_tasks = []
         for task in tasks:
             mode = task.get("cli_args", {}).get("mode")
-            dataset_type = task["datasets"][0][0]["type"]
-            model_type = task["models"][0]["type"]
+            dataset_type = task["datasets"][0][0].get("type", None)
+            model_type = task["models"][0].get("type", None)
             if mode not in ["perf", "perf_viz"] and dataset_type in ONLY_PERF_DATASETS:
                 self.logger.warning(
                     f"'{dataset_type}' can only be used for performance evaluation, "

ais_bench/benchmark/partitioners/naive.py

@@ -23,7 +23,7 @@ class NaivePartitioner(BasePartitioner):
     """
 
     def __init__(self,
-                 out_dir: str,
+                 out_dir: str = '',
                  n: int = 1,
                  keep_keys: Optional[List[str]] = None):
         super().__init__(out_dir=out_dir, keep_keys=keep_keys)
@@ -33,7 +33,7 @@ def partition(self,
                   model_dataset_combinations: List[Dict[str,
                                                         List[ConfigDict]]],
                   work_dir: str,
-                  out_dir: str,
+                  out_dir: str = '',
                   add_cfg: Dict = {}) -> List[Dict]:
         """Partition model-dataset pairs into tasks. Each task is defined as a
         dict and will run independently as a unit. Its structure is as

ais_bench/benchmark/registry.py

@@ -53,7 +53,7 @@ def register_module(
 
 PARTITIONERS = Registry('partitioner', locations=get_locations('partitioners'))
 RUNNERS = Registry('runner', locations=get_locations('runners'))
-TASKS = Registry('task', locations=get_locations('tasks'))
+TASKS = Registry('task', locations=get_locations('tasks') + get_locations('tasks.custom_tasks'))
 MODELS = Registry('model', locations=get_locations('models'))
 # TODO: LOAD_DATASET -> DATASETS
 LOAD_DATASET = Registry('load_dataset', locations=get_locations('datasets'))

ais_bench/benchmark/tasks/base.py

@@ -134,6 +134,14 @@ def get_output_paths(self, file_extension: str = "json") -> List[str]:
         return output_paths
 
 
+class EmptyTask(BaseTask):
+    def run(self):
+        pass
+
+    def get_command(self, cfg_path, template) -> str:
+        return ""
+
+
 class TaskStateManager:
     def __init__(self, tmp_path: str, task_name: str, is_debug: bool, refresh_interval: int = 0.5):
         self.logger = AISLogger()