Considering the case empty request list is given to base model

src/lighteval/data.py

@@ -20,7 +20,8 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from typing import Iterator
+import math
+from typing import Iterator, Tuple
 
 import torch
 from torch.utils.data import Dataset
@@ -80,7 +81,7 @@ def init_split_limits(self, num_dataset_splits):
             )
             num_dataset_splits = 1
 
-        split_size = self.total_size // num_dataset_splits + 1
+        split_size = math.ceil(self.total_size / num_dataset_splits)
         splits_indices = [
             (ix * split_size, min((ix + 1) * split_size, self.total_size)) for ix in range(num_dataset_splits)
         ]
@@ -110,7 +111,7 @@ def get_original_order(self, new_arr: list) -> list:
 
         return original_order
 
-    def get_split_start_end(self, split_id: int) -> tuple[int, int]:
+    def get_split_start_end(self, split_id: int) -> Tuple[int, int]:
         """
         Get the start and end indices of a dataset split.
 
@@ -123,7 +124,7 @@ def get_split_start_end(self, split_id: int) -> tuple[int, int]:
         self.split_start, self.split_end = self.splits[split_id]
         return self.split_start, self.split_end
 
-    def splits_start_end_iterator(self) -> tuple[int, int]:
+    def splits_start_end_iterator(self) -> Iterator[Tuple[int, int]]:
         """
         Iterator that yields the start and end indices of each dataset split.
         Also updates the starting batch size for each split (trying to double
@@ -132,7 +133,10 @@ def splits_start_end_iterator(self) -> tuple[int, int]:
         Yields:
             tuple: A tuple containing the start and end indices of a split.
         """
-        for split_id in range(self.num_dataset_splits):
+        split_range = self.num_dataset_splits
+        if self.total_size == 0:
+            split_range = 0
+        for split_id in range(split_range):
             yield self.get_split_start_end(split_id)
 
     def __getitem__(self, index) -> Request:
@@ -247,7 +251,8 @@ def init_split_limits(self, num_dataset_splits):
                 "You cannot select the number of dataset splits for a generative evaluation at the moment. Automatically inferring."
             )
 
-        all_sorting_criterion = [self._sorting_criteria(self.sorted_data[0])[:2]]
+        if len(self.sorted_data) > 0:
+            all_sorting_criterion = [self._sorting_criteria(self.sorted_data[0])[:2]]
         splits_indices = [[0, None]]
         for ix, req in enumerate(self.sorted_data):
             current_sorting_criteria = self._sorting_criteria(req)

tests/models/test_base_model.py

@@ -0,0 +1,37 @@
+# MIT License
+
+# Copyright (c) 2024 The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from lighteval.models.base_model import BaseModel
+from lighteval.models.model_config import BaseModelConfig
+from lighteval.models.model_loader import load_model
+from lighteval.utils.utils import EnvConfig
+
+
+def test_empty_requests():
+    model_config = BaseModelConfig("hf-internal-testing/tiny-random-LlamaForCausalLM")
+    model: BaseModel = load_model(config=model_config, env_config=EnvConfig(cache_dir="."))
+
+    assert model.loglikelihood([]) == []
+    assert model.loglikelihood_single_token([]) == []
+    assert model.loglikelihood_rolling([]) == []
+    assert model.greedy_until([]) == []
+    assert model.greedy_until_multi_turn([]) == []