Route benchmark datasets through data loaders

eval_protocol/benchmarks/test_aime25.py

@@ -1,5 +1,7 @@
 from typing import Any, Dict, List, Optional
 
+from eval_protocol.common_utils import load_jsonl
+from eval_protocol.data_loader import DynamicDataLoader
 from eval_protocol.models import (
     EvaluateResult,
     EvaluationRow,
@@ -11,6 +13,7 @@
     SingleTurnRolloutProcessor,
 )
 from eval_protocol.pytest.evaluation_test import evaluation_test
+from eval_protocol.pytest.utils import parse_ep_max_rows
 
 SYSTEM_PROMPT = (
     "You are a helpful math assistant. Please reason step by step, and put your final answer within \\boxed{...}."
@@ -71,12 +74,29 @@ def aime2025_dataset_adapter(rows: List[Dict[str, Any]]) -> List[EvaluationRow]:
     return converted
 
 
+_AIME2025_DATASET_URLS: List[str] = [
+    "https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-I.jsonl",
+    "https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-II.jsonl",
+]
+
+
+def aime2025_data_generator() -> List[EvaluationRow]:
+    """Load the AIME 2025 datasets and convert them into evaluation rows."""
+    dataset_rows: List[Dict[str, Any]] = []
+    for dataset_url in _AIME2025_DATASET_URLS:
+        dataset_rows.extend(load_jsonl(dataset_url))
+
+    max_rows = parse_ep_max_rows(2)
+    if max_rows is not None:
+        dataset_rows = dataset_rows[:max_rows]
+
+    return aime2025_dataset_adapter(dataset_rows)
+
+
 @evaluation_test(
-    input_dataset=[
-        "https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-I.jsonl",
-        "https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-II.jsonl",
-    ],
-    dataset_adapter=aime2025_dataset_adapter,
+    data_loaders=DynamicDataLoader(
+        generators=[aime2025_data_generator],
+    ),
     completion_params=[
         {
             "max_tokens": 131000,

eval_protocol/benchmarks/test_tau_bench_airline.py

@@ -10,6 +10,8 @@
 from pathlib import Path
 from typing import Any, Dict, List
 
+from eval_protocol.common_utils import load_jsonl
+from eval_protocol.data_loader import DynamicDataLoader
 from eval_protocol.models import EvaluateResult, EvaluationRow, InputMetadata, Message
 from eval_protocol.pytest import evaluation_test, ExceptionHandlerConfig
 from eval_protocol.pytest.default_mcp_gym_rollout_processor import MCPGymRolloutProcessor
@@ -69,6 +71,14 @@ def _get_airline_dataset_path() -> str:
     return str(Path(__file__).parent / "data" / "airline_dataset.jsonl")
 
 
+def tau_bench_airline_data_generator() -> List[EvaluationRow]:
+    """Load and adapt the airline dataset into evaluation rows."""
+    dataset_rows: List[Dict[str, Any]] = []
+    for dataset_path in [_get_airline_dataset_path()]:
+        dataset_rows.extend(load_jsonl(dataset_path))
+    return tau_bench_airline_to_evaluation_row(dataset_rows)
+
+
 def _get_server_script_path() -> str:
     """Get the tau2 mcp server script path."""
     from eval_protocol.mcp_servers.tau2 import get_server_script_path
@@ -107,8 +117,9 @@ def tau_bench_airline_to_evaluation_row(data: List[Dict[str, Any]]) -> List[Eval
 
 
 @evaluation_test(
-    input_dataset=[_get_airline_dataset_path()],
-    dataset_adapter=tau_bench_airline_to_evaluation_row,
+    data_loaders=DynamicDataLoader(
+        generators=[tau_bench_airline_data_generator],
+    ),
     completion_params=[
         {
             "temperature": 0.8,

eval_protocol/benchmarks/test_tau_bench_retail.py

@@ -10,6 +10,8 @@
 from pathlib import Path
 from typing import Any, Dict, List
 
+from eval_protocol.common_utils import load_jsonl
+from eval_protocol.data_loader import DynamicDataLoader
 from eval_protocol.models import EvaluateResult, EvaluationRow, InputMetadata, Message
 from eval_protocol.pytest import evaluation_test, ExceptionHandlerConfig
 from eval_protocol.pytest.default_mcp_gym_rollout_processor import MCPGymRolloutProcessor
@@ -69,6 +71,14 @@ def _get_retail_dataset_path() -> str:
     return str(Path(__file__).parent / "data" / "retail_dataset.jsonl")
 
 
+def tau_bench_retail_data_generator() -> List[EvaluationRow]:
+    """Load and adapt the retail dataset into evaluation rows."""
+    dataset_rows: List[Dict[str, Any]] = []
+    for dataset_path in [_get_retail_dataset_path()]:
+        dataset_rows.extend(load_jsonl(dataset_path))
+    return tau_bench_retail_to_evaluation_row(dataset_rows)
+
+
 def tau_bench_retail_to_evaluation_row(data: List[Dict[str, Any]]) -> List[EvaluationRow]:
     """
     Convert entries from retail dataset to EvaluationRow objects.
@@ -98,8 +108,9 @@ def tau_bench_retail_to_evaluation_row(data: List[Dict[str, Any]]) -> List[Evalu
 
 
 @evaluation_test(
-    input_dataset=[_get_retail_dataset_path()],
-    dataset_adapter=tau_bench_retail_to_evaluation_row,
+    data_loaders=DynamicDataLoader(
+        generators=[tau_bench_retail_data_generator],
+    ),
     completion_params=[
         {
             "temperature": 0.8,